Load monitoring method and device and electronic equipment

Abstract

The invention provides a load monitoring method and device and electronic equipment, and relates to the technical field of numerical control machine tools, and the method comprises the steps: detecting K pieces of load data of a target tool in the process of using the target tool for cutting machining; calculating data deviation among the K pieces of load data; detecting the working state of the target cutter based on the data deviation and a preset monitoring threshold range; wherein the preset monitoring threshold range is determined based on M pieces of load sample data of the target tool, and each piece of load sample data is determined based on the data deviation between N pieces of load data collected by the target tool at N moments. According to the embodiment of the invention, in the cutting process by using the target cutter, the load data deviation is monitored, and the monitoring threshold range is determined through the deviation of the load sample data, so that the data mutation of the cutter in the normal working state does not cause the misrecognition of the cutter, and the accuracy of cutter recognition can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of numerically controlled machine tools, and in particular to a load monitoring method, device and electronic equipment. Background Art

[0002] In turning, the blade tool gradually wears out as the number of processed products increases, thus affecting the product size. Workers usually need to adjust the tool offset to correct the product size. In the process of adjusting the tool offset, the monitoring load data often changes suddenly. When the tool status monitoring device detects the sudden change data, it is easy to mistakenly identify the current processing as an abnormal tool, resulting in poor reliability of the monitoring device. Summary of the invention

[0003] The embodiments of the present application provide a load monitoring method, device and electronic device, which can solve the problem of misidentification of a tool by a monitoring device when adjusting a tool offset.

[0004] In a first aspect, an embodiment of the present application provides a load monitoring method, the method comprising:

[0005] In a process of using a target tool to perform cutting processing, detecting K load data of the target tool;

[0006] Calculating data deviations between the K load data;

[0007] Based on the data deviation and a preset monitoring threshold range, detecting the working state of the target tool;

[0008] Wherein, the preset monitoring threshold range is determined based on M load sample data of the target tool, wherein each load sample data is determined based on the data deviation between N load data collected from the target tool at N moments, and K, M and N are all integers greater than 1.

[0009] Optionally, before detecting the K load data of the target tool, the method further includes:

[0010] Acquire M sample data sequences collected during the cutting process of the target tool, each of the sample data sequences including the N load data collected at the N moments;

[0011] Determine N data deviations between the N load data in each of the sample data sequences;

[0012] Calculate the average value of the N data deviations corresponding to each of the sample data sequences;

[0013] The monitoring threshold range is determined based on the M average values ​​corresponding to the M sample data sequences, and the monitoring threshold of the monitoring device is set to the monitoring threshold range.

[0014] Optionally, the determining N data deviations between the N load data in each of the sample data sequences comprises:

[0015] Determining minimum load data among the N load data in each of the sample data sequences;

[0016] The difference between the N load data in each of the sample data sequences and the minimum load data is calculated to obtain the N data deviations.

[0017] Optionally, determining the monitoring threshold range based on M average values ​​corresponding to the M sample data sequences includes:

[0018] Obtaining the upper quartile and the lower quartile of the M average values;

[0019] Determine the interquartile range of the M mean values ​​based on the upper quartile and the lower quartile;

[0020] Based on the upper quartile, the lower quartile and the interquartile range, an upper monitoring threshold and a lower monitoring threshold of the monitoring threshold range are calculated.

[0021] Optionally, the calculating, based on the upper quartile, the lower quartile and the interquartile range, an upper monitoring threshold and a lower monitoring threshold of the monitoring threshold range includes:

[0022] The upper monitoring threshold and the lower monitoring threshold of the monitoring threshold range are determined according to the following formula:

[0023] The calculation formula of the upper monitoring threshold P1 is: P1 = q3 + 3 × iqr;

[0024] The calculation formula of the lower monitoring threshold P2 is: P2 = q1-3 × iqr;

[0025] Among them, P1 represents the upper monitoring threshold, P2 represents the lower monitoring threshold, q3 represents the upper quartile, and q1 represents the lower quartile; the interquartile range iqr=q3-q1, and when q1-3×iqr<0, P2 is initialized to 0.

[0026] Optionally, before detecting the K load data of the target tool, the method further includes:

[0027] Receive target input from the user;

[0028] In response to the target input, the monitoring data of the monitoring device is switched from the current first data to the data deviation of the load data, and the data monitoring range of the monitoring device is switched from the current first monitoring range to the monitoring threshold range.

[0029] Optionally, K=N.

[0030] In a second aspect, an embodiment of the present application provides a load monitoring device, the device comprising:

[0031] A first detection module is used to detect K load data of the target tool during cutting processing using the target tool;

[0032] A first calculation module, used for calculating the data deviation between the K load data;

[0033] A second detection module, configured to detect the working state of the target tool based on the data deviation and a preset monitoring threshold range;

[0034] Wherein, the preset monitoring threshold range is determined based on M load sample data of the target tool, wherein each load sample data is determined based on the data deviation between N load data collected from the target tool at N moments, and K, M and N are all integers greater than 1.

[0035] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0036] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0037] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0038] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the method described in the first aspect.

[0039] In the embodiment of the present application, during the process of using the target tool for cutting processing, the data deviation between the load data is monitored, and the monitoring threshold range is determined by the deviation of the load sample data. When the load data suddenly changes, the deviation of the load data is monitored to see whether it is within the monitoring threshold range. The data mutation under normal use will not lead to misidentification of the tool, which can improve the accuracy of identifying the tool working status. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 It is one of the flow charts of a load monitoring method provided in an embodiment of the present application;

[0042] Figure 2 This is the second flow chart of a load monitoring method provided in an embodiment of the present application;

[0043] Figure 3 is a structural schematic diagram of a load monitoring device provided in an embodiment of the present application;

[0044] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0046] The terms "first", "second", etc. in the specification and claims of this 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 interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0047] The load monitoring method provided in the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0048] See also Figure 1 , Figure 1 A flow chart of a load monitoring method provided in this application, the load monitoring method comprising:

[0049] Step 101, in the process of using a target tool for cutting, detecting K load data of the target tool;

[0050] Step 102: Calculate the data deviation between the K load data;

[0051] Step 103: detecting the working state of the target tool based on the data deviation and a preset monitoring threshold range;

[0052] Wherein, the preset monitoring threshold range is determined based on M load sample data of the target tool, wherein each load sample data is determined based on the data deviation between N load data collected from the target tool at N moments, and K, M and N are all integers greater than 1.

[0053] During the cutting process using the target tool, the load data of the target tool can be monitored, and the load data can be determined by the current data. Adjusting the tool offset is an adjustment of the tool position during the machining process. When the offset value of the target tool needs to be adjusted, the load data monitored before and after the adjustment may mutate. For example, when the target tool is cutting at a first offset value, a first load data sequence is detected. After the offset value of the target tool is adjusted, a second load data sequence is detected, and the second load data sequence mutates relative to the first load data sequence.

[0054] After obtaining K load data of the target tool, the data deviation between the K load data is calculated based on the K load data, that is, the data change between the K load data. When K is 2, the data deviation can be the difference between two load data; when the K load data include more than three load data, K deviation values ​​of each load data in the K load data relative to the target data can be obtained, and the target data can be the minimum value, median value or maximum value of the K load data. After obtaining the K deviation values, the average value of the K deviation values ​​can be calculated, and the average value is used as the data deviation of the above K load data.

[0055] In the case where the offset value of the target tool is adjusted during the cutting process, the K load data include the load data before and after the tool offset adjustment. Of course, the K load data may also include data in other working scenarios, which is not limited here.

[0056] When the K load data include the load data before and after adjusting the bias value, the average load data before adjusting the bias value and the average load data after adjusting the bias value can also be obtained, and the difference between the two average load data can be used as the above-mentioned deviation data. This is just an example and does not constitute a limitation on the deviation data.

[0057] Before obtaining the load data of the target tool, M load sample data of the target tool during the cutting process can be collected. Each load sample data includes N load data collected at N times for the target tool, and N data deviations between the N load data are calculated. Among them, the N data deviations can be determined based on the difference between each load data and the target load data therein, and the target load data can be the minimum value, maximum value or median value of the N load data. The mean of the N data deviations in each load sample is obtained, and the M deviation means corresponding to the data of the M load samples are obtained to form a mean sequence, and the interquartile range of the mean sequence is calculated, and the upper and lower monitoring thresholds of the above-mentioned monitoring threshold range are calculated based on the interquartile range.

[0058] In addition, the maximum data deviation and the minimum data deviation in each load sample data may be obtained, and the upper and lower monitoring thresholds of the monitoring threshold range may be determined based on the minimum data deviation and the maximum data deviation in the M load sample data.

[0059] After monitoring the K load data of the target tool during the machining process, it is determined whether the data deviation between the K load data is within the preset monitoring threshold range, so as to determine whether the target tool is in a normal working state, and an abnormal prompt can be output when the data deviation exceeds the monitoring threshold range.

[0060] The specific values ​​of K, M, and N can be determined according to the actual application scenario or accuracy requirements, and are not specifically limited here. In one implementation, the number of the K load data can be equal to the number of data in each load sample data, that is, K=N.

[0061] In the process of adjusting the tool offset, if the load data is monitored directly, the current tool is easily identified as an abnormal tool due to the sudden change in the load data. In the present application, the data deviation (also referred to as data change) between the load data is monitored, and the monitoring threshold range is determined by the deviation of the load sample data. In the above-mentioned scenario of adjusting the tool offset, when the load data suddenly changes, the deviation of the load data is monitored to see whether it is within the monitoring threshold range. The sudden change of the load data under normal working conditions will not affect the tool identification, and the above-mentioned method can normally detect abnormal conditions such as broken tools, which can improve the accuracy of tool identification.

[0062] Optionally, before detecting the K load data of the target tool, the method further includes:

[0063] Acquire M sample data sequences collected during the machining process of the target tool, each of the sample data sequences including the N load data collected at the N moments;

[0064] Determine N data deviations between the N load data in each of the sample data sequences;

[0065] Calculate the average value of the N data deviations corresponding to each of the sample data sequences;

[0066] The monitoring threshold range is determined based on the M average values ​​corresponding to the M sample data sequences, and the monitoring threshold of the monitoring device is set to the monitoring threshold range.

[0067] In the process of cutting with the target tool (including normal use scenarios or tool offset adjustment process), M sample data sequences are collected, wherein each sample data sequence includes N load data, and each load data is collected at a different time. For example, in the process of repeating the processing M times with the target tool, N load data are collected as sample data at different N sampling points each time during the processing.

[0068] Obtain target load data from N load data, the target load data may be the minimum value, median value, average value or maximum value of the N load data, and calculate the difference between each load data and the target load data as N data deviations. When the difference is a negative number, the absolute value of the difference may be obtained as the data deviation. The N data deviations are averaged to obtain M average values ​​corresponding to the M sample data.

[0069] After obtaining the M average values, the interquartile range of the M average values ​​can be obtained, and the upper and lower monitoring thresholds can be calculated based on the interquartile range. In addition, the upper and lower monitoring thresholds can also be determined based on the obtained M average values ​​and an empirical calculation formula or an empirical deviation range.

[0070] Through the above method, the monitoring threshold range is determined based on the data deviation between each sample data sequence in the M sample data sequences. Since the collected data is large, the impact of errors in individual data on the data sequence can be reduced, and the accuracy of determining the monitoring threshold range can be improved, thereby improving the accuracy of data monitoring.

[0071] Optionally, the determining N data deviations between the N load data in each of the sample data sequences comprises:

[0072] Determining minimum load data among the N load data in each of the sample data sequences;

[0073] The difference between the N load data in each of the sample data sequences and the minimum load data is calculated to obtain the N data deviations.

[0074] When determining N data deviations, the minimum value can be determined among the N load data in each sample data sequence, and the N data deviations are obtained by subtracting the minimum load data from the N load data, thereby calculating the average value of the data deviations of each sample data sequence based on the N data deviations.

[0075] By using the above method, the data change amount of each sample data sequence is obtained, which can improve the efficiency of data deviation acquisition.

[0076] Optionally, determining the monitoring threshold range based on M average values ​​corresponding to the M sample data sequences includes:

[0077] Obtaining the upper quartile and the lower quartile of the M average values;

[0078] Determine the interquartile range of the M mean values ​​based on the upper quartile and the lower quartile;

[0079] Based on the upper quartile, the lower quartile and the interquartile range, an upper monitoring threshold and a lower monitoring threshold of the monitoring threshold range are calculated.

[0080] After obtaining M average values, the interquartile range is calculated for the average value sequence formed by the M average values. When calculating the interquartile range, the upper quartile and the lower quartile of the M average values ​​can be determined first, where the upper quartile and the lower quartile respectively indicate that 75% of the data in the data sequence is less than the upper quartile and 25% of the data is less than the lower quartile. The difference between the upper quartile and the lower quartile is obtained to obtain the interquartile range of the M average values.

[0081] Among them, the upper monitoring threshold can be determined by adding several multiples of the interquartile range (determined according to empirical values, such as 2 times or 3 times) to the upper quartile; the lower monitoring threshold can be determined by subtracting several multiples of the interquartile range (determined according to empirical values, such as 2 times or 3 times) from the lower quartile.

[0082] Optionally, the calculating, based on the upper quartile, the lower quartile and the interquartile range, an upper monitoring threshold and a lower monitoring threshold of the monitoring threshold range includes:

[0083] The upper and lower monitoring thresholds of the monitoring threshold range are described according to the following formula:

[0084] The calculation formula of the upper monitoring threshold P1 is: P1 = q3 + 3 × iqr;

[0085] The calculation formula of the lower monitoring threshold P2 is: P2 = q1-3 × iqr;

[0086] Among them, P1 represents the upper monitoring threshold, P2 represents the lower monitoring threshold, q3 represents the upper quartile, and q1 represents the lower quartile; the interquartile range iqr=q3-q1, and when q1-3×iqr<0, P2 is initialized to 0.

[0087] The upper and lower monitoring thresholds of the monitoring threshold range can be calculated by the above formula, and the above upper and lower monitoring thresholds are set as initial monitoring thresholds. In actual use, the initial monitoring threshold can be adjusted to a suitable position according to actual usage.

[0088] The upper and lower monitoring thresholds of the monitoring threshold range are calculated in the above manner, so that the load deviation data of the target tool under normal use during the cutting process can fall within the above monitoring threshold range, while the load deviation data under abnormal conditions such as tool breakage does not fall within the above monitoring threshold range, thereby avoiding misidentification under conditions such as tool offset adjustment, and being able to identify abnormal conditions such as tool breakage.

[0089] Optionally, before detecting the K load data of the target tool, the method further includes:

[0090] Receive target input from the user;

[0091] In response to the target input, the monitoring data of the monitoring device is switched from the current first data to the data deviation of the load data, and the data monitoring range of the monitoring device is switched from the current first monitoring range to the monitoring threshold range.

[0092] When the target tool is in the process of cutting, there is often a need to adjust the tool offset or other adjustments. The working scene of the target tool can be switched according to the specific application scenario. For example, when the tool offset needs to be adjusted during the cutting process of the target tool, the working scene of the target tool is first switched from the current first working scene to the second working scene, that is, the monitoring data of the monitoring device is switched from the current first data to the load data deviation, and the data monitoring range of the monitoring device is switched from the current first monitoring range to the monitoring threshold range. Among them, in the first working scene, the working state of the tool is identified by monitoring whether the first data is in the first monitoring range. The first data and the first monitoring range can be determined according to the current specific application scenario, which is not limited here; after switching to the second working scene, the monitoring range is set to the above-mentioned monitoring threshold range, and the load data deviation is used as the monitoring data to identify the working state of the tool.

[0093] When switching to the first working scene again, the operation can be performed again to switch the monitoring data and the monitoring range.

[0094] In this way, different monitoring data and monitoring ranges can be used for monitoring in different working scenarios, thereby improving the recognition accuracy of the working status of the target tool in different working scenarios.

[0095] Optionally, K=N.

[0096] In this embodiment, when the target tool is load-monitored, the number of load data collected during the monitoring process is equal to the number of load data in each load sample, that is, K = N. The difference between each load data and the minimum load data among the K load data collected during the monitoring process is obtained to obtain K differences, and the average value of the K differences is calculated as the data deviation between the K load data.

[0097] During monitoring, it is determined whether the average value of the K differences is within a preset monitoring threshold range. Since the number of load data collected during monitoring is equal to the number of load sample data, the detection accuracy of the working state of the target tool can be improved, thereby improving the recognition accuracy.

[0098] To facilitate understanding of the above embodiments, examples are given below in combination with specific application scenarios.

[0099] See also Figure 2 , the load monitoring method comprises the following steps:

[0100] Step 201: During the cutting process, the target tool collects training samples and obtains the minimum value of each training sample. Assume that the load of the training sample is represented by a matrix d, then d is Among them, m represents the number of training samples, n represents the number of sampling points of each sample, and d ij It represents the load value collected by the i-th sample at the j-th sampling point.

[0101] In the above matrix, get the minimum value of each row in matrix d, that is, sequence d i The minimum value d i_min , use d _min Represents the minimum value sequence, that is, d _min For:[d 1_min , d 2_min , …, d i_min , …, d m_min ];

[0102] Step 202: Calculate the difference between each data in each training sample and the minimum value, that is, dd _min , and get the new matrix d r for:

[0103] Step 203: Use the above data to determine the tool monitoring threshold.

[0104] Compute the matrix d row by row r The mean of the sequence d r_mean :[d r_mean_1 ,d r_mean_2 ,…d r_mean_i ,…,d r_mean_m ];

[0105] Calculate the sequence d r_mean The upper and lower quartiles of are represented by q3 and q1 respectively, and the interquartile range iqr = q3-q1 is obtained. Then the initial monitoring thresholds include:

[0106] Upper monitoring threshold: q3+3×iqr;

[0107] Lower monitoring threshold: q1-3×iqr. When q1-3×iqr<0, the lower monitoring threshold is initialized to 0.

[0108] Step 204: Set the upper and lower monitoring thresholds calculated in the above steps as initialization thresholds, and monitor whether the load deviation data of the target tool is within the above monitoring threshold range.

[0109] When the target tool is monitored using the above monitoring threshold range, n load data of the target tool are collected, the minimum value among the n load data is obtained, and the difference of each data in the n load data relative to the minimum value is calculated to obtain n data differences. The average value of the n data differences is calculated, and the average value is used as monitoring data to determine whether the monitoring data is within the above monitoring threshold range.

[0110] It should be noted that in actual use, the initialization threshold can be adjusted according to actual use conditions to adjust the monitoring threshold to an appropriate position.

[0111] The embodiment of the present application can effectively solve the problem of false alarms caused by tool offset adjustment by identifying tool abnormalities through machining load. For status monitoring of different types of lathes, there is no need to adjust monitoring parameters, and good results can be achieved; false alarms caused by tool offset adjustment can be avoided, and tool abnormalities such as tool breakage can also be well identified.

[0112] The load monitoring method provided in the embodiment of the present application can be executed by a load monitoring device. In the embodiment of the present application, a method for performing load monitoring by a load monitoring device is taken as an example to illustrate the load monitoring device provided in the embodiment of the present application.

[0113] See also Figure 3 , Figure 3 : is a structural schematic diagram of a load monitoring device provided by the present application, the load monitoring device comprising:

[0114] The first detection module 301 is used to detect K load data of the target tool during the process of using the target tool for cutting processing;

[0115] A first calculation module 302, used to calculate the data deviation between the K load data;

[0116] A second detection module 303 is used to detect the working state of the target tool based on the data deviation and a preset monitoring threshold range;

[0117] Wherein, the preset monitoring threshold range is determined based on M load sample data of the target tool, wherein each load sample data is determined based on the data deviation between N load data collected from the target tool at N moments, and K, M and N are all integers greater than 1.

[0118] Optionally, the device further comprises:

[0119] An acquisition module, used for acquiring M sample data sequences collected from the target tool during the cutting process, each of the sample data sequences comprising the N load data collected at the N moments;

[0120] A first determination module, configured to determine N data deviations between the N load data in each of the sample data sequences;

[0121] A second calculation module, used for calculating the average value of the N data deviations corresponding to each of the sample data sequences;

[0122] The second determination module is used to determine the monitoring threshold range based on the M average values ​​corresponding to the M sample data sequences, and set the monitoring threshold of the monitoring device to the monitoring threshold range.

[0123] Optionally, the first determining module includes:

[0124] A first determining submodule, configured to determine minimum load data among the N load data in each of the sample data sequences;

[0125] The first calculation submodule is used to calculate the difference between the N load data in each of the sample data sequences and the minimum load data to obtain the N data deviations.

[0126] Optionally, the second determining module includes:

[0127] An acquisition submodule, used to acquire the upper quartile and the lower quartile of the M average values;

[0128] A second determination submodule, configured to determine the interquartile range of the M mean values ​​based on the upper quartile and the lower quartile;

[0129] The second calculation submodule is used to calculate the upper monitoring threshold and the lower monitoring threshold of the monitoring threshold range based on the upper quartile, the lower quartile and the interquartile range.

[0130] Optionally, the second calculation submodule is specifically configured to calculate an upper monitoring threshold and a lower monitoring threshold of the monitoring threshold range according to the following formula:

[0131] The calculation formula of the upper monitoring threshold P1 is: P1 = q3 + 3 × iqr;

[0132] The calculation formula of the lower monitoring threshold P2 is: P2 = q1-3 × iqr;

[0133] Among them, P1 represents the upper monitoring threshold, P2 represents the lower monitoring threshold, q3 represents the upper quartile, and q1 represents the lower quartile; the interquartile range iqr=q3-q1, and when q1-3×iqr<0, P2 is initialized to 0.

[0134] Optionally, the device further comprises:

[0135] A receiving module, used for receiving a target input from a user;

[0136] The switching module is used to switch the monitoring data of the monitoring device from the current first data to the data deviation of the load data in response to the target input, and switch the data monitoring range of the monitoring device from the current first monitoring range to the monitoring threshold range.

[0137] Optionally, K=N.

[0138] In the present application, the data deviation between the load data is monitored, and the monitoring threshold range is determined by the deviation of the load sample data. When the load data changes suddenly, the change in the load data is monitored to see whether it is within the monitoring threshold range. The data mutation of the tool under normal working conditions will not lead to misidentification, and the above method can normally detect abnormal conditions such as tool breakage, which can improve the accuracy of tool identification.

[0139] The load monitoring device in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC) or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0140] The load monitoring device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0141] The load monitoring device provided in the embodiment of the present application can achieve Figure 1 to Figure 2 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0142] Alternatively, if Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401 and a memory 402, wherein the memory 402 stores programs or instructions that can be executed on the processor 401, and when the program or instructions are executed by the processor 401, the various steps of the above-mentioned load monitoring method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not described here.

[0143] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0144] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned load monitoring method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0145] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0146] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned load monitoring method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0147] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0148] An embodiment of the present application also provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned load monitoring method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0149] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0150] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0151] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A load monitoring method, characterized in that: The method comprises: In a process of using a target tool to perform cutting processing, detecting K load data of the target tool; Calculating data deviations between the K load data; Based on the data deviation and a preset monitoring threshold range, detecting the working state of the target tool; Wherein, the preset monitoring threshold range is determined based on M load sample data of the target tool, wherein each load sample data is determined based on the data deviation between N load data collected from the target tool at N moments, and K, M and N are all integers greater than 1.

2. The method according to claim 1, characterized in that Before detecting the K load data of the target tool, the method further includes: Acquire M sample data sequences collected during the cutting process of the target tool, each of the sample data sequences including the N load data collected at the N moments; Determine N data deviations between the N load data in each of the sample data sequences; Calculate the average value of the N data deviations corresponding to each of the sample data sequences; The monitoring threshold range is determined based on the M average values ​​corresponding to the M sample data sequences, and the monitoring threshold of the monitoring device is set to the monitoring threshold range.

3. The method according to claim 2, characterized in that The determining of N data deviations between the N load data in each of the sample data sequences comprises: Determining minimum load data among the N load data in each of the sample data sequences; The difference between the N load data in each of the sample data sequences and the minimum load data is calculated to obtain the N data deviations.

4. The method according to claim 2, characterized in that: The determining the monitoring threshold range based on the M average values ​​corresponding to the M sample data sequences includes: Obtaining the upper quartile and the lower quartile of the M average values; Determine the interquartile range of the M mean values ​​based on the upper quartile and the lower quartile; Based on the upper quartile, the lower quartile and the interquartile range, an upper monitoring threshold and a lower monitoring threshold of the monitoring threshold range are calculated.

5. The method according to claim 4, characterized in that The calculating, based on the upper quartile, the lower quartile and the interquartile range, an upper monitoring threshold and a lower monitoring threshold of the monitoring threshold range comprises: The upper monitoring threshold and the lower monitoring threshold of the monitoring threshold range are determined according to the following formula: The calculation formula of the upper monitoring threshold P1 is: P1 = q3 + 3 × iqr; The calculation formula of the lower monitoring threshold P2 is: P2 = q1-3 × iqr; Among them, P1 represents the upper monitoring threshold, P2 represents the lower monitoring threshold, q3 represents the upper quartile, and q1 represents the lower quartile; the interquartile range iqr=q3-q1, and when q1-3×iqr<0, P2 is initialized to 0.

6. The method according to any one of claims 1 to 5, characterized in that Before detecting the K load data of the target tool, the method further includes: Receive target input from the user; In response to the target input, the monitoring data of the monitoring device is switched from the current first data to the data deviation of the load data, and the data monitoring range of the monitoring device is switched from the current first monitoring range to the monitoring threshold range.

7. The method according to any one of claims 1 to 5, characterized in that K=N.

8. A load monitoring device, characterized in that: The device comprises: A first detection module is used to detect K load data of the target tool during cutting processing using the target tool; A first calculation module, used for calculating the data deviation between the K load data; A second detection module, configured to detect the working state of the target tool based on the data deviation and a preset monitoring threshold range; Wherein, the preset monitoring threshold range is determined based on M load sample data of the target tool, wherein each load sample data is determined based on the data deviation between N load data collected from the target tool at N moments, and K, M and N are all integers greater than 1.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the load monitoring method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or an instruction, and when the program or the instruction is executed by the processor, the steps of the load monitoring method according to any one of claims 1 to 7 are implemented.

11. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the load monitoring method according to any one of claims 1 to 7.

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