Nylon slicing control method

Abstract

The invention relates to a chinlon slicing control method, and the method comprises the steps: carrying out the segmented screening of all sample data through different cutter information and different chinlon component information, so as to obtain the corresponding sample data when different cutters carry out the slicing of chinlon with different components. A plurality of second sample databases are obtained after screening, and the cutter operation information and the slicing state information in each second sample database are calculated and analyzed to obtain the temperature correlation degree when the cutter slices chinlon with different components; according to the method, the abrasion degree of different cutters during slicing of chinlon with different components at different temperatures can be judged, the abrasion degree is mainly reflected through the slicing size on the basis of the safe slicing temperature condition, and the larger the slicing size deviation is, the more serious the cutter abrasion is proved. Therefore, different slicing size predicted change curves can be obtained along with the temperature change when different cutters slice chinlon with different components, and the opportunity of cutter maintenance or replacement can be deduced.

Description

Technical Field

[0001] This application relates to the technical field of nylon chips, and particularly to a control method for nylon chips. Background Art

[0002] Nylon chips are the sheet-shaped granulation products obtained by the granulation method due to the low melt strength in nylon production. Nylon is the trade name of polyamide fiber, also known as Nylon. The English name is Polyamide (abbreviated as PA), and its basic constituent is an aliphatic polyamide connected by an amide bond -[NHCO]-. It is an important synthetic fiber.

[0003] Currently, in the existing nylon chip production process, manufacturers often use different types of cutting tools for slicing different materials of nylon according to economic benefits. In the actual use process, as the service life of the cutting tool increases, its wear also increases, which will reduce the success rate and efficiency of nylon slicing. However, at present, the replacement or maintenance of the cutting tool often requires the observation or experience judgment of the staff, and its accuracy is relatively low, and it is impossible to accurately judge the replacement or maintenance time of the cutting tool. Summary of the Invention

[0004] Based on this, in view of the problem that the replacement or maintenance of the cutting tool in the traditional nylon slicing process often requires the observation or experience judgment of the staff, and its accuracy is relatively low, and it is impossible to accurately judge the replacement or maintenance time of the cutting tool, a control method for nylon chips is provided.

[0005] This application provides a control method for nylon chips, including: Obtain a plurality of sample data, where the sample data includes cutting tool information, nylon composition information, and slicing data; Based on different cutting tool information, screen each sample data to obtain a plurality of sample data corresponding to different cutting tool information, and store the plurality of sample data corresponding to different cutting tool information into the first sample database corresponding to different cutting tool information; Based on different nylon composition information, screen the sample data in each sample database to obtain a plurality of sample data corresponding to different nylon composition information, and store the plurality of sample data corresponding to different nylon composition information into the second sample database corresponding to different nylon composition information; Obtain the slicing data of each sample data in each second sample database, where the slicing data includes cutting tool operation information and slicing status information; Calculate the wear degree of different cutting tools in different working scenarios based on the cutting tool operation information and slicing status information in each slicing data; Construct and train a tool wear model using each sample data, each tool information, each nylon component information, each slice data, each tool operation information, each slice status information, and the wear degree of different tools in different working scenarios as training data; Obtain the sample data to be measured; Analyze the sample data to be measured to obtain the tool information to be measured, the nylon component information to be measured, the slice data to be measured, and the preset slice size range in the sample data to be measured; Input the tool information to be measured, the nylon component information to be measured, the slice data to be measured, and the preset slice size range into the tool wear model, and start the tool wear model. The tool wear model outputs the predicted replacement time of the tool.

[0006] Further, screening each sample data based on different tool information to obtain multiple sample data corresponding to different tool information, and storing the multiple sample data corresponding to different tool information into the first sample database corresponding to different tool information, including: Select a type of tool information; Index all the sample data with this type of tool information to obtain all the sample data with the same tool information as this tool information; Create a first sample database; Store the copies of all the obtained sample data into the first sample database; Return to select a type of tool information until each type of tool information has been selected once.

[0007] Further, screening the sample data in each sample database based on different nylon component information to obtain multiple sample data corresponding to different nylon component information, and storing the multiple sample data corresponding to different nylon component information into the second sample database corresponding to different nylon component information, including: Select a type of nylon component information in a first sample database; Index each sample data in this first sample database with this nylon component information to obtain all the sample data with the same nylon component information as this nylon component information; Create a second sample database; Store the copies of all the obtained sample data into the second sample database; Return to select a type of nylon component information in a first sample database until each type of nylon component information in each first sample database has been selected once.

[0008] Further, obtaining the sliced data of each sample data in each second sample database, where the sliced data includes tool operation information and slicing status information, includes: Select the sliced data of a sample data in a second sample database; Analyze the sliced data to obtain the tool operation information and slicing status information in the sliced data; Return the sliced data of a sample data in a selected second sample database until the sliced data of each sample data in each second sample database has been selected once.

[0009] Further, calculating the wear degrees of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data includes: Select a sliced data; Obtain the tool operation information and slicing status information in the sliced data; Construct a tool operation temperature-time curve based on the tool operation information; Construct a slicing size-time curve based on the slicing status information; Synchronously fuse the obtained tool operation temperature-time coordinate system and slicing qualification rate-time coordinate system based on the time dimension to obtain a fusion curve; Return the selected sliced data until each sliced data has been selected once.

[0010] Further, calculating the wear degrees of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data further includes: Select a second sample database; Obtain all the fusion curves in the second sample database; Obtain a preset slicing size range; Cut each fusion curve based on the preset slicing size range of each fusion curve to obtain the curves within the preset slicing size range in each fusion curve, and define the cut curves as initial target curves; Return the selected second sample database until each second sample database has been selected once.

[0011] Further, calculating the wear degrees of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data further includes: Select an initial target curve in a second sample database; Analyze the initial target curve to obtain the working temperature range of the tool in the initial target curve; Divide the working temperature range of the tool into multiple first temperature intervals; Define the initial target curve corresponding to each first temperature interval as the subordinate curve of this first temperature interval; Return an initial target curve in the selected one of the second sample databases until each initial target curve in each second sample database has been selected once.

[0012] Further, calculating the wear degrees of different tools in different working scenarios based on the tool running information and the slice status information in each slice data further includes: Select a first temperature interval of a tool information; Analyze the first temperature interval of the tool information to obtain all the subordinate curves corresponding to the first temperature interval of the tool information; Copy the subordinate curves to a coordinate system; Select a time point in the coordinate system; Analyze a time point in the coordinate system to obtain all the slice size values of the time point in the coordinate system; Calculate the average value of all the slice size values and define this average value as the first target value; Return the selection of a time point in the coordinate system until each time point in the coordinate system has been selected once; Obtain multiple first target values; Connect the multiple first target values with a smooth curve based on the chronological order to obtain an intermediate target curve; Return the selection of a first temperature interval of a tool information until each first temperature interval of each tool information.

[0013] Further, calculating the wear degrees of different tools in different working scenarios based on the tool running information and the slice status information in each slice data further includes: Select a tool information; Obtain all the intermediate target curves in the tool information; Smoothly connect all the intermediate target curves based on the chronological order to obtain a final target curve; Return the selection of a tool information until each tool information has been selected once.

[0014] Further, the preset slice size range includes: a first preset slice size and a second preset slice size, and the second preset slice size is greater than the first preset slice size.

[0015] The present application relates to a method for controlling nylon chips. By segmentally screening all sample data based on different tool information and different nylon composition information, corresponding sample data when different tools slice nylons with different compositions can be obtained. Based on the above screening, multiple second sample databases are obtained. By calculating and analyzing the tool running information and slicing status information in each second sample database, the correlation degree of the temperature when the tool slices nylons with different compositions can be obtained, thereby helping to judge the wear degree of different tools when slicing nylons with different compositions at different temperatures. Based on the safe slicing temperature condition, the wear degree is mainly reflected by the slicing size. The larger the deviation of the slicing size, the more serious the tool wear. Therefore, the expected change curves of different slicing sizes can be obtained when different tools slice nylons with different compositions with the change of temperature, thereby helping to infer the timing of tool repair or replacement. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of the nylon chip control method provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

[0018] As Figure 1 shown, in an embodiment of the present application, the nylon chip control method includes the following S001 to S009: S001, obtaining a plurality of sample data, where the sample data includes tool information, nylon composition information, and slicing data.

[0019] Specifically, the tool information includes information such as tool model, material, and hardness. The nylon composition information includes the molecular weight of the nylon, additive ratio, crystallinity, etc. The slicing data includes tool rotation speed (unit: rpm), working temperature (unit: °C), and slicing size information, etc.

[0020] S002, screening each sample data based on different tool information to obtain multiple sample data corresponding to different tool information, and storing the multiple sample data corresponding to different tool information into the first sample database corresponding to different tool information.

[0021] S003. Screen the sample data in each sample database based on different polyamide component information to obtain multiple sample data corresponding to different polyamide component information, and store the multiple sample data corresponding to different polyamide component information into the second sample database corresponding to different polyamide component information.

[0022] S004. Obtain the sliced data of each sample data in each second sample database, where the sliced data includes tool operation information and slicing status information.

[0023] S005. Calculate the wear degree of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data.

[0024] Specifically, different tools in different working scenarios refer to the scenarios of different tool working temperatures when different tools slice polyamides with different polyamide component information.

[0025] S006. Construct and train a tool wear model using each sample data, each tool information, each polyamide component information, each sliced data, each tool operation information, each slicing status information, and the wear degree of different tools in different working scenarios as training data.

[0026] S007. Obtain the sample data to be measured.

[0027] S008. Analyze the sample data to be measured to obtain the tool information to be measured, the polyamide component information to be measured, the sliced data to be measured, and the preset sliced size range to be measured.

[0028] S009. Input the tool information to be measured, the polyamide component information to be measured, the sliced data to be measured, and the preset sliced size range into the tool wear model, start the tool wear model, and the tool wear model outputs the predicted replacement time of the tool.

[0029] In this embodiment, all sample data are screened in a segmented manner based on different tool information and different nylon composition information to obtain the corresponding sample data when different tools slice different components of nylon. Based on the above screening, multiple second sample databases are obtained. By calculating and analyzing the tool running information and slicing status information in each second sample database, the correlation degree of the temperature when the tool slices different components of nylon is obtained, so as to help judge the wear degree of different tools when slicing different components of nylon at different temperatures. Based on the safe slicing temperature condition, the wear degree is mainly reflected by the slicing size. The larger the deviation of the slicing size, the more serious the tool wear. Therefore, it is possible to obtain the expected change curves of different slicing sizes when different tools slice different components of nylon with the change of temperature, so as to help infer the timing of tool maintenance or replacement.

[0030] In one embodiment of the present application, screening each sample data based on different tool information to screen and obtain multiple sample data corresponding to different tool information, and storing the multiple sample data corresponding to different tool information into the first sample database corresponding to different tool information, including the following S002a to S002e: S002a, select a kind of tool information.

[0031] S002b, using this kind of tool information as an index, retrieve all sample data to obtain all sample data with the same tool information as this tool information.

[0032] S002c, create a first sample database.

[0033] S002d, store the copies of all obtained sample data into the first sample database.

[0034] S002e, return to select a kind of tool information until each kind of tool information has been selected once.

[0035] Specifically, the name of the first sample database is the name of the tool; further, by counting the nylon composition information in all sample data in the first sample database, all types of nylon composition information existing in the first sample database are obtained.

[0036] Screening the sample data in each sample database based on different nylon composition information to screen and obtain multiple sample data corresponding to different nylon composition information, and storing the multiple sample data corresponding to different nylon composition information into the second sample database corresponding to different nylon composition information, including the following S003a to S003e: S003a, select a kind of nylon composition information in a first sample database.

[0037] S003b, retrieve each sample data in the first sample database using the nylon component information as an index to obtain all sample data with nylon component information consistent with the nylon component information.

[0038] S003c, create a second sample database.

[0039] S003d, store copies of all the obtained sample data in the second sample database.

[0040] S003e, return a nylon component information selected from one of the first sample databases until each nylon component information in each first sample database has been selected once.

[0041] Specifically, the name of the first sample database is the name of the nylon component information; the first sample database contains the second sample database.

[0042] In this embodiment, first, copies of sample data with the same tool information in the sample data are stored in the same first sample database through the tool information in the sample data, and then, for all sample data in each first sample database, sample data with the same nylon component information is stored in a second sample database based on the nylon component information to achieve multi-level distributed screening.

[0043] In an embodiment of the present application, obtaining slice data of each sample data in each second sample database, where the slice data includes tool running information and slice status information, includes the following S004a to S004c: S004a, select slice data of a sample data from one of the second sample databases.

[0044] S004b, parse the slice data to obtain the tool running information and slice status information in the slice data.

[0045] S004c, return the slice data of a sample data selected from one of the second sample databases until the slice data of each sample data in each second sample database has been selected once.

[0046] Specifically, the tool running information includes tool rotation speed (unit: rpm) and tool working temperature (unit: °C), and the slice status information includes slice size information.

[0047] In this embodiment, by traversing each sample data in the second sample database, the tool running information and slice status information of each sample data are extracted, and all the extracted tool running information and slice status information are converted into a unified time series data structure.

[0048] In one embodiment of the present application, calculating the wear degrees of different tools in different working scenarios based on the tool running information and the slice status information in each slice data includes the following S015 to S065: S015, select a slice data; S025, obtain the tool running information and the slice status information in this slice data; S035, construct a tool running temperature-time curve based on the tool running information; S045, construct a slice size-time curve based on the slice status information; S055, synchronously fuse the obtained tool running temperature-time coordinate system and the slice qualification rate-time coordinate system based on the time dimension to obtain a fusion curve; S065, return the selected slice data until each slice data has been selected once.

[0049] Specifically, the slice status information is obtained by a camera photographing a conveyor belt on which slices are placed. The camera acquires an image of the conveyor belt, and through processing such as analyzing the features of the obtained image and establishing a coordinate system, the size of the slices recognized in the image is obtained. In the situation discussed in the present application, the slices are evenly spread on the surface of the conveyor belt and move at a constant speed with the conveyor belt. Therefore, the size differences of the slices recognized in the same image are small, and their sizes are defined as the same.

[0050] In this embodiment, the tool running temperature-time curve established based on the time dimension and the slice size-time curve established based on the time dimension are fused into one coordinate system in the time dimension, thereby facilitating the analysis of data at the same moment.

[0051] In one embodiment of the present application, calculating the wear degrees of different tools in different working scenarios based on the tool running information and the slice status information in each slice data further includes the following S075 to S125: S075, select a second sample database; S085, obtain all the fusion curves in this second sample database; S095, obtain a preset slice size range; Specifically, the preset slice size range includes: a first preset slice size and a second preset slice size. The second preset slice size is greater than the first preset slice size. More specifically, it means that the shape of the first preset slice is the same as the shape of the second preset slice. For example, if the shape of the first preset slice is circular, then the shape of the corresponding second preset slice is also circular.

[0052] S105, that the second preset slice size is greater than the first preset slice size means that the second preset slice is obtained by proportionally enlarging the first preset slice by a certain size, and the enlargement ratio is greater than 1.

[0053] S115, Cut each fusion curve based on the preset slice size range of each fusion curve to obtain the curves within the preset slice size range in each fusion curve, and define the cut curves as the initial target curves; S125, Return to select one second sample database until each second sample database has been selected once.

[0054] In this embodiment, by cutting all the fusion curves in the same second sample database within the preset slice size range, and this preset slice size range is larger than the size requirements in conventional production to increase the range of the selected database, thereby improving the robustness of the tool wear model.

[0055] In an embodiment of the present application, calculating the wear degrees of different tools in different working scenarios based on the tool running information and slice status information in each slice data further includes the following S135 to S175: S135, Select an initial target curve from one second sample database; S145, Analyze the initial target curve to obtain the working temperature range of the tool in the initial target curve; S155, Divide the working temperature range of the tool into multiple first temperature intervals; S165, Define the initial target curve corresponding to each first temperature interval as the subordinate curve of this first temperature interval; S175, Return to select an initial target curve from one second sample database until each initial target curve in each second sample database has been selected once.

[0056] Calculating the wear degrees of different tools in different working scenarios based on the tool running information and slice status information in each slice data further includes the following S185 to S275: S185, Select a first temperature interval of one tool information; S195, Analyze the first temperature interval of the tool information to obtain all the subordinate curves corresponding to this first temperature interval of the tool information; S205, Copy the subordinate curves to a coordinate system; S215, Select a time point in this coordinate system; S225, Analyze a time point in the coordinate system to obtain all the slice size values of a time point in the coordinate system; S235, Calculate the average value of all the slice size values, and define this average value as the first target value; S245, Return the selection of a time point in the coordinate system until each time point in the coordinate system has been selected once; S255, Obtain multiple first target values; S265, Connect multiple first target values with a smooth curve based on the chronological order to obtain an intermediate target curve; S275, Return the selection of a first temperature range of a tool information until each first temperature range of each tool information.

[0057] In this embodiment, for each initial target curve in each second sample database, the working temperature range of the tool is evenly divided to obtain multiple first temperature ranges, and the curve corresponding to each temperature range is defined as the subordinate curve of this temperature range. Then, all the subordinate curves corresponding to each first temperature range of each tool information are copied into a coordinate system, and multiple coordinate systems are obtained; by calculating each time point in each coordinate system, the first target value of each time point is obtained. Then, multiple first target values are connected with a smooth curve based on the chronological order to obtain the intermediate target curve of each coordinate system.

[0058] In an embodiment of the present application, calculating the wear degree of different tools in different working scenarios based on the tool running information and the slice status information in each slice data further includes the following S285 to S315: S285, Select a tool information.

[0059] S295, Obtain all the intermediate target curves in this tool information.

[0060] S305, Smoothly connect all the intermediate target curves based on the chronological order to obtain a final target curve.

[0061] S315, Return the selection of a tool information until each tool information has been selected once.

[0062] In this embodiment, by placing the intermediate target curves in each coordinate system into the same coordinate system and smoothly connecting multiple intermediate target curves in this coordinate system based on the time dimension, the obtained curve is the final target curve. Similarly, a final target curve is obtained for each different nylon component information corresponding to different tool information.

[0063] Further, when the sample data to be measured is obtained, the to-be-measured data is parsed to obtain the to-be-measured tool information, to-be-measured nylon component information, to-be-measured slice data, and a preset slice size range in the to-be-measured sample data, and the obtained to-be-measured tool information, to-be-measured nylon component information, to-be-measured slice data, and preset slice size range are input into the tool wear model, and then the final target curve generated by the to-be-measured sample data based on the to-be-measured preset slice size range is obtained. In the final target curve, the time node for tool replacement or maintenance corresponds to the to-be-measured preset slice size range.

[0064] Further, when the slice temperature of the tool changes continuously, the corresponding final target curve will also change accordingly, which will further cause the final target curve to change, and then affect the time node corresponding to the to-be-measured preset slice size range in the final target curve.

[0065] The technical features of the above-described embodiments can be combined arbitrarily, and there is no limitation on the execution order of each method step. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0066] The above-described embodiments only represent several implementation manners of the present application, and the description is relatively specific and detailed, but it cannot be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for controlling nylon chips, characterized in that, The nylon chip control method includes: Obtaining a plurality of sample data, where the sample data includes tool information, nylon composition information, and chip data; Screening each sample data based on different tool information to obtain a plurality of sample data corresponding to different tool information, and storing the plurality of sample data corresponding to different tool information into a first sample database corresponding to different tool information; Screening the sample data in each sample database based on different nylon composition information to obtain a plurality of sample data corresponding to different nylon composition information, and storing the plurality of sample data corresponding to different nylon composition information into a second sample database corresponding to different nylon composition information; Obtaining the chip data of each sample data in each second sample database, where the chip data includes tool operation information and chip status information; Calculating the wear degree of different tools in different working scenarios based on the tool operation information and chip status information in each chip data; Constructing and training a tool wear model using each sample data, each tool information, each nylon composition information, each chip data, each tool operation information, each chip status information, and the wear degree of different tools in different working scenarios as training data; Obtaining sample data to be measured; Parsing the sample data to be measured to obtain the to-be-measured tool information, to-be-measured nylon composition information, to-be-measured chip data, and to-be-measured preset chip size range in the sample data to be measured; Inputting the to-be-measured tool information, to-be-measured nylon composition information, to-be-measured chip data, and preset chip size range into the tool wear model and starting the tool wear model, where the tool wear model outputs the predicted replacement time of the tool.

2. The nylon chip control method according to claim 1, characterized in that The step of screening each sample data based on different tool information to obtain a plurality of sample data corresponding to different tool information, and storing the plurality of sample data corresponding to different tool information into a first sample database corresponding to different tool information includes: Selecting a kind of tool information; Indexing all the sample data with this kind of tool information to obtain all the sample data with the same tool information as this kind of tool information; Creating a first sample database; Storing the copies of all the obtained sample data into the first sample database; Returning to the step of selecting a kind of tool information until each kind of tool information has been selected once.

3. The nylon chip control method according to claim 2, wherein The step of screening the sample data in each sample database based on different nylon composition information to obtain a plurality of sample data corresponding to different nylon composition information, and storing the plurality of sample data corresponding to different nylon composition information into a second sample database corresponding to different nylon composition information includes: Selecting a kind of nylon composition information in a first sample database; Indexing each sample data in this first sample database with this nylon composition information to obtain all the sample data with the same nylon composition information as this nylon composition information; Creating a second sample database; Storing the copies of all the obtained sample data into the second sample database; Return the polyamide component information in the selected first sample database until each polyamide component information in each first sample database has been selected once.

4. The nylon chip control method according to claim 3, characterized in that, Obtain the sliced data of each sample data in each second sample database, where the sliced data includes tool operation information and slicing status information, including: Select the sliced data of a sample data in a second sample database; Parse the sliced data to obtain the tool operation information and slicing status information in the sliced data; Return the sliced data of a sample data in the selected second sample database until the sliced data of each sample data in each second sample database has been selected once.

5. The nylon chip control method according to claim 4, characterized in that, Calculate the wear degrees of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data, including: Select a sliced data; Obtain the tool operation information and slicing status information in the sliced data; Construct a tool operation temperature-time curve based on the tool operation information; Construct a slicing size-time curve based on the slicing status information; Synchronously fuse the obtained tool operation temperature-time coordinate system and slicing pass rate-time coordinate system based on the time dimension to obtain a fusion curve; Return the selected sliced data until each sliced data has been selected once.

6. The nylon chip control method according to claim 5, wherein, Calculating the wear degrees of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data further includes: Select a second sample database; Obtain all the fusion curves in the second sample database; Obtain the preset slicing size range; Cut each fusion curve based on the preset slicing size range of each fusion curve to obtain the curves within the preset slicing size range in each fusion curve, and define the cut curves as initial target curves; Return the selected second sample database until each second sample database has been selected once.

7. The nylon chip control method according to claim 6, characterized in that, Calculating the wear degrees of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data further includes: Select an initial target curve in a second sample database; Parse the initial target curve to obtain the working temperature range of the tool in the initial target curve; Divide the working temperature range of the tool into multiple first temperature intervals; Define the initial target curve corresponding to each first temperature interval as the subordinate curve of the first temperature interval; Return the selected initial target curve in a second sample database until each initial target curve in each second sample database has been selected once.

8. The polyamide chip control method according to claim 7, wherein, Calculating the wear degrees of different tools in different working scenarios based on the tool operation information and slicing status information in each sliced data further includes: Select a first temperature interval of a tool information; Parse the first temperature interval of the tool information to obtain all the subordinate curves corresponding to the first temperature interval of the tool information; Copy the subordinate curves to a coordinate system; Select a time point in the coordinate system; Analyze a time point in the coordinate system to obtain all slice size values of a time point in the coordinate system; Calculate the average value of all slice size values and define this average value as the first target value; Return the selection of a time point in the coordinate system until each time point in the coordinate system has been selected once; Obtain multiple first target values; Connect the multiple first target values with a smooth curve based on the chronological order to obtain an intermediate target curve; Return the selection of a first temperature range of a tool information until each first temperature range of each tool information.

9. The nylon chip control method according to claim 8, wherein The calculating the wear degrees of different tools in different working scenarios based on the tool running information and the slice status information in each slice data further includes: Select a tool information; Obtain all the intermediate target curves in the tool information; Smoothly connect all the intermediate target curves based on the chronological order to obtain a final target curve; Return the selection of a tool information until each tool information has been selected once.

10. The nylon chip control method according to claim 9, characterized in that, The preset slice size range includes: a first preset slice size and a second preset slice size, and the second preset slice size is greater than the first preset slice size.

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