A numerical control machine tool tool life prediction method, device and electronic equipment

Abstract

A numerical control machine tool tool life prediction method and device and electronic equipment, relate to numerical control machine tool detection field. Method includes: in response to the life detection operation of the user for the target tool, the tool type corresponding to the target tool is acquired;In the prediction model database, the first life prediction model corresponding to the tool type is acquired, and the prediction model database is used for storing the corresponding relationship between the tool type and the first life prediction model;Through the first life prediction model, the operation of life detection of the target tool is carried out, and the predicted life of the target tool is obtained;If it is confirmed that the predicted life is less than the preset predicted life, the replacement information is sent to the user, and the replacement information is used to prompt the user to replace the target tool. The technical scheme of the present application solves the problem that it is difficult to accurately predict the actual life of the cutting device only by relying on the working experience of the manufacturer or the recommendation of the equipment supplier.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of numerical control machine tool detection, and particularly relates to a numerical control machine tool tool life prediction method and device and electronic equipment. BACKGROUND

[0002] In the field of mechanical manufacturing, due to the wide use of numerical control machine tools and the development of science and technology, the automation level of the mechanical production workshop has been further improved. At the same time, in the workshop, cutting devices such as tools have become an important machining factor, which is directly related to the normal operation and production efficiency of the workshop. Therefore, in actual work, in order to master the working state of cutting, the life of the cutting device is often predicted so as to replace the cutting device in time when the service life of the cutting device reaches a certain degree.

[0003] At present, the service life prediction of the cutting device depends on the actual operation experience of the production enterprise and the recommendation of the equipment manufacturer. However, in practice, there are many factors affecting the durability of the cutting machine. It is difficult to consider all potential influencing factors by relying only on the work experience of the production plant or the recommendation of the equipment supplier, so it is difficult to accurately predict the actual life of the cutting device.

[0004] Therefore, there is an urgent need for a numerical control machine tool tool life prediction method, device and electronic equipment. SUMMARY

[0005] The present application provides a numerical control machine tool tool life prediction method, device and electronic equipment, which solves the problem that it is difficult to accurately predict the actual life of the cutting device by relying only on the work experience of the production plant or the recommendation of the equipment supplier.

[0006] In a first aspect of the present application, a numerical control machine tool tool life prediction method is provided, the method comprising: in response to a user's life detection operation on a target tool, acquiring a tool type corresponding to the target tool; in a prediction model database, acquiring a first life prediction model corresponding to the tool type, the prediction model database being used to store the correspondence between the tool type and the first life prediction model; performing a life detection operation on the target tool by the first life prediction model to obtain a predicted life of the target tool; and if the predicted life is less than a preset predicted life, sending replacement information to the user, the replacement information being used to prompt the user to replace the target tool.

[0007] By adopting the above technical solution, the tools in the numerical control machine tool are classified, and a corresponding life prediction model is trained for each tool type. The remaining life of a target tool of a certain type can be predicted through the life prediction model corresponding to the tool type, so that the tool can be replaced in time in actual production, and the tool is kept in the best performance state at all times.

[0008] Optionally, the tool type corresponding to the target tool is acquired, specifically including: acquiring a feature picture corresponding to the target tool through a camera, the feature picture including a plurality of features corresponding to the target tool; the plurality of features including a target feature, the target feature being any one of the plurality of features; acquiring a plurality of preset features corresponding to the tool type; the plurality of preset features including a target preset feature; the target preset feature being any one of the plurality of preset features; determining whether a similarity value of the target feature and the target preset feature is greater than a preset similarity value; if the similarity value is greater than the preset similarity value, determining that the tool type is the tool type corresponding to the target tool.

[0009] By adopting the above technical solution, the target tool is photographed by the camera, and a plurality of target features in the picture are extracted by preprocessing, so that the tool type of the target tool can be distinguished according to the target features, and the server can select a corresponding life prediction model according to the tool type of the target tool.

[0010] Optionally, before acquiring the first life prediction model corresponding to the tool type in the prediction model database, the prediction model database is constructed, specifically including: acquiring a plurality of tools corresponding to the tool type and a plurality of residual lives corresponding to the plurality of tools, one tool corresponding to one residual life; acquiring a plurality of core features corresponding to the plurality of tools respectively, the plurality of core features being necessary features for determining the residual life of the tool; constructing the first life prediction model according to the plurality of core features and the plurality of residual lives; and saving the corresponding relationship between the first life prediction model and the tool type in the prediction model database.

[0011] By the above technical solution, the plurality of core features corresponding to different residual lives in the same tool type can be used to train the life prediction model corresponding to the same tool type, and the corresponding relationship between the life prediction model and the tool type is saved in the prediction model database, so that the server can select a corresponding life prediction model according to the tool type of the target tool according to the corresponding relationship.

[0012] Optionally, the residual life is a residual cutting amount that can be performed by the tool; the cutting amount is an amount of material that can be removed from a workpiece by the tool in a cutting operation according to a preset parameter group; the preset parameter group including a cutting speed, a cutting acceleration, a cutting depth, and a cutting width; and the core feature including a tool material feature and a workpiece material feature.

[0013] By adopting the technical scheme, the remaining life of the tool is quantified as the remaining cutting amount that the tool can perform, and the remaining life of the target tool is determined by the life prediction model, so as to facilitate the life prediction model to predict the remaining life of the target tool according to the core features.

[0014] Optionally, after the operation of detecting the life of the target tool by the first life prediction model, the method further comprises: replacing the target tool with a specified tool in response to a replacement instruction sent by a user; the replacement instruction comprises an automatic replacement instruction and a manual replacement instruction.

[0015] By adopting the technical scheme, the server can automatically replace the target tool with a predicted life less than a preset predicted life, and the target tool on some important numerical control machine tools can also be manually replaced by the user.

[0016] Optionally, after the operation of detecting the life of the target tool by the first life prediction model, the method further comprises: performing an actual life test on the target tool to obtain an actual life of the target tool; determining whether a difference between the actual life and the predicted life is greater than a preset difference; if the difference between the actual life and the predicted life is greater than the preset difference, sending correction information to the user, and adjusting the first life prediction model to a second life prediction model according to the correction information, and updating the corresponding relationship between the second life prediction model and the tool type to the prediction model database.

[0017] By adopting the technical scheme, when the life prediction model is inaccurate, the server can retrain the life prediction model according to the actual life of the target tool and the plurality of features of the target tool to correct the error.

[0018] Optionally, the tool type comprises a milling cutter tool, a turning tool, and a drilling tool.

[0019] By adopting the technical scheme, a plurality of types of tools are set in advance, for example, the tools are divided into types including a milling cutter tool, a turning tool, and a drilling tool, so as to facilitate the server to classify the target tool to be identified.

[0020] In a second aspect of the present application, a numerical control machine tool life prediction device is provided, the device comprising an acquisition module and a processing module, wherein,

[0021] The acquisition module is configured to acquire a tool type corresponding to the target tool in response to a user's life detection operation on the target tool; and acquire a first life prediction model corresponding to the tool type in a prediction model database, the prediction model database being configured to store a correspondence between a tool type and a first life prediction model.

[0022] The processing module is configured to obtain a predicted life of the target tool by performing a life detection operation on the target tool through the first life prediction model; and replace the target tool to complete the test on the target tool if it is confirmed that the predicted life is less than a preset predicted life.

[0023] Optionally, the acquisition module is configured to acquire the tool type corresponding to the target tool, and specifically includes: acquiring a feature picture corresponding to the target tool through a camera, the feature picture including a plurality of features corresponding to the target tool; the plurality of features including a target feature, the target feature being any one of the plurality of features; acquiring a plurality of preset features corresponding to the tool type; the plurality of preset features including a target preset feature, the target preset feature being any one of the plurality of preset features; judging whether a similarity value of the target feature and the target preset feature is greater than a preset similarity value; and judging that the tool type is the tool type corresponding to the target tool if the similarity value is greater than the preset similarity value.

[0024] Optionally, the acquisition module is configured to construct the prediction model database before acquiring the first life prediction model corresponding to the tool type in the prediction model database, and specifically includes: acquiring a plurality of tools corresponding to the tool type and a plurality of residual lives corresponding to the plurality of tools, one tool corresponding to one residual life; acquiring a plurality of core features corresponding to the plurality of tools respectively, the plurality of core features being necessary features for judging the residual life of the tool; constructing the first life prediction model according to the plurality of core features and the plurality of residual lives; and saving the correspondence between the first life prediction model and the tool type in the prediction model database.

[0025] Optionally, the processing module is configured to, after performing the life detection operation on the target tool through the first life prediction model, the method further includes: replacing the target tool with a specified tool in response to a replacement instruction sent by a user; the replacement instruction including an automatic replacement instruction and a manual replacement instruction.

[0026] Optionally, the processing module is configured to, after performing the life detection operation on the target tool through the first life prediction model, the method further includes: performing an actual life test on the target tool to acquire an actual life of the target tool; judging whether a difference between the actual life and the predicted life is greater than a preset difference value; and sending correction information to the user if it is judged that the difference between the actual life and the predicted life is greater than the preset difference value, the user adjusting the first life prediction model to a second life prediction model according to the correction information, and updating the correspondence between the second life prediction model and the tool type to the prediction model database.

[0027] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of the above aspects.

[0028] In a fourth aspect of the present application, a computer-readable storage medium is provided, the computer-readable storage medium stores a computer program, and the computer program is configured to enable a processor to perform the method of any one of the above aspects.

[0029] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0030] 1. By adopting the above technical solution, by classifying the tools in the numerical control machine tool and training a corresponding life prediction model for each tool type, the residual life of a target tool of a certain type can be predicted through the life prediction model corresponding to the tool type, thereby ensuring that the tool can be replaced in time in actual production and ensuring that the tool is in the best performance state in actual production at all times.

[0031] 2. By the plurality of core features corresponding to different residual lives in the same tool type, a life prediction model corresponding to the same tool type can be trained, and by saving the corresponding relationship between the life prediction model and the tool type in the prediction model database, the server can select a corresponding life prediction model value according to the tool type of the target tool according to the corresponding relationship.

[0032] 3. When the life prediction model is inaccurate, the server can retrain the life prediction model by the plurality of features of the target tool and the actual life of the target tool to correct the error. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of a numerical control machine tool tool life prediction method provided by the embodiments of the present application.

[0034] Figure 2 is a structural schematic diagram of a numerical control machine tool tool life prediction device provided by the embodiments of the present application.

[0035] Figure 3 is a structural schematic diagram of an electronic device provided by the embodiments of the present application.

[0036] REFERENCE SIGNS: 21, acquisition module; 22, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0037] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in combination with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0038] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to be limiting to the present application. As used in the specification, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or" as used herein refer to and encompass any and all possible combinations of one or more of the associated listed items.

[0039] Hereinafter, the terms "first", "second" are only for the purpose of description, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise specified.

[0040] In order for those skilled in the art to better understand the technical solutions of the present application, the present application will be further described in combination with the drawings.

[0041] In the field of mechanical manufacturing, due to the wide use of numerical control machine tools and the development of science and technology, the automation level of mechanical production workshops has been further improved. At the same time, in the workshop, cutting devices such as cutters have become an important machining factor, which is directly related to the normal operation and production efficiency of the workshop. Therefore, in actual work, in order to grasp the working state of cutting, the service life of the cutting device is often predicted, so that the cutting device can be replaced in time when the service life of the cutting device reaches a certain degree.

[0042] At present, the service life prediction of cutting device depends on the actual operation experience of production enterprises and the recommendation of equipment manufacturers. However, in practice, there are many factors affecting the durability of the cutting machine, and it is difficult to consider all potential influencing factors by relying only on the work experience of the production plant or the recommendation of the equipment supplier, so it is difficult to accurately predict the actual life of the cutting device.

[0043] Therefore, there is an urgent need for a numerical control machine tool cutter life prediction method, device and electronic equipment.

[0044] Please refer toFigure 1 Fig. 9 shows a flowchart of a method for predicting the tool life of a numerical control machine tool according to an embodiment of the present application. The method is applied to a server, and the flowchart mainly includes the following steps: S101-S104.

[0045] In step S101, the tool type corresponding to the target tool is obtained in response to a user's tool life detection operation on the target tool.

[0046] The user can select a target tool and send an instruction to the server to detect the tool life of the target tool. In response to the instruction sent by the user, the server detects the tool life of the target tool. The user can also set a preset time period for the target tool. When the actual use time of the target tool exceeds the target time, the server automatically detects the tool life of the target tool. The preset time period can be set according to different types of tools. The higher the quality of the tool, the longer the preset time period. When the server detects the tool life of the target tool, the tool type of the target tool is obtained first. The user can pre-set different tool types and save them in the server. For example, the numerical control machine tool can be classified into milling tools, turning tools, and drilling tools according to the use purpose. The milling tools can be further classified into ceramic blade milling tools, cubic boron nitride blade milling tools, and diamond composite blade milling tools according to the material characteristics of the tools. It should be noted that the classification of tools can be set according to actual application. The above classification is only an example provided by the present embodiment, and the classification of tools is not limited by the present embodiment.

[0047] In one possible implementation, step S101 further includes: obtaining a feature picture corresponding to the target tool through a camera, the feature picture including a plurality of features corresponding to the target tool; the plurality of features including a target feature, the target feature being any one of the plurality of features; obtaining a plurality of preset features corresponding to the tool type; the plurality of preset features including a target preset feature, the target preset feature being any one of the plurality of preset features; determining whether a similarity value between the target feature and the target preset feature is greater than a preset similarity value; and if the similarity value is greater than the preset similarity value, determining that the tool type is the tool type corresponding to the target tool.

[0048] Specifically, the server can identify the tool type of the target tool through a plurality of target features in a feature picture of the target tool captured by the camera. The server acquires a plurality of target features of the target tool type in the feature picture, and respectively performs similarity calculation on the plurality of target features and a plurality of preset features. One tool type corresponds to a plurality of preset features. If, during the similarity calculation, for each target feature in the feature picture of the target tool, a preset feature can be found in the plurality of preset features of a certain tool type, and the similarity value between the target feature in the feature picture of the target tool and the preset feature is greater than a preset similarity value, it is determined that the tool type is the tool type of the target tool.

[0049] For example, assuming that the plurality of target features of the target tool in the feature picture are target feature A, target feature B, and target feature C, the preset features of the tool type A are preset feature A, preset feature B, preset feature C, preset feature D, and preset feature E, and the similarity calculation is performed on target feature A, target feature B, and target feature C and each preset feature in the tool type A. Assuming that the similarity value between target feature A and preset feature E is greater than the preset similarity value, the similarity value between target feature B and preset feature D is greater than the preset similarity value, and the similarity value between target feature B and preset feature C is greater than the preset similarity value, it is indicated that for each target feature in the feature picture of the target tool, a preset feature can be found in the plurality of preset features of the tool type A, and the similarity value between the target feature in the feature picture of the target tool and the preset feature is greater than the preset similarity value. It is indicated that the target tool is the tool of the tool type A. In this embodiment, the preset similarity value can be set according to the actual situation of each feature, and the setting of the preset similarity value is not limited in this embodiment.

[0050] In step S102, a first life prediction model corresponding to the tool type is acquired from the prediction model database. The prediction model database is used to store the correspondence between the tool type and the first life prediction model.

[0051] Specifically, the prediction model database is used to store the correspondence between a plurality of tool types and a plurality of life prediction models. The tool type and the life prediction model correspond to each other.

[0052] In one possible implementation, before step S102, the method further includes constructing the prediction model database: acquiring a plurality of tools corresponding to the tool type and a plurality of residual lives corresponding to the plurality of tools. One tool corresponds to one residual life. Acquiring a plurality of core features corresponding to each of the plurality of tools. The plurality of core features are necessary features for judging the residual life of the tool. Constructing the first life prediction model according to the plurality of core features and the plurality of residual lives. Saving the correspondence between the first life prediction model and the tool type in the prediction model database.

[0053] Specifically, the life prediction model corresponding to the same type of tool can be trained by a large number of tools of the same type with different residual life. Therefore, when training the life prediction model, the residual life of the tool can be segmented, and the residual life is the residual cutting amount of the tool; the cutting amount is the amount of material that the tool can remove from the workpiece according to the preset parameter set in the cutting operation; the preset parameter set includes cutting speed, cutting acceleration, cutting depth and cutting width, for example, the segmentation time can be set as one day, then a plurality of tools of the same tool type with one day of residual life are collected (that is, the tool can remove one day of material from the workpiece according to the preset parameter set, and after one day, the tool can remove less than one day of material from the workpiece according to the preset parameter set), a plurality of tools of the same tool type with two days of residual life are collected, a plurality of tools of the same tool type with three days of residual life are collected, and so on. Then, a plurality of core features of the plurality of tools of the same tool type with one day, two days and three days of residual life are extracted, respectively, and the plurality of core features corresponding to the tool of the same tool type with one day of residual life are input into the life prediction model, the plurality of core features corresponding to the tool of the same tool type with two days of residual life are input into the life prediction model, and the plurality of core features corresponding to the tool of the same tool type with three days of residual life are input into the life prediction model, so as to train the life prediction model corresponding to the tool type. Then the trained life prediction model and the corresponding relationship of the tool type are saved in the prediction model database. In the embodiment, the segmentation time can also be set as three days, five days, one week, etc. The actual needs of production should be set, and the segmentation time is not limited in the embodiment.

[0054] In step S103, the life detection of the target tool is performed by the first life prediction model, and the predicted life of the target tool is obtained.

[0055] Specifically, the plurality of features of the target tool corresponding to the tool type are identified by the life prediction model to determine the predicted life of the target tool. When the life prediction model performs the life detection operation, the plurality of target features of the target tool in the feature picture can be obtained by the camera, and the plurality of features are input into the life prediction model to perform the life detection operation. The life prediction model can determine the number of features of the target tool that meet the core features.

[0056] For example, it can be set that if the number of features satisfying the core feature of a target tool exceeds 6, then the predicted lifespan of the target tool is determined to be the remaining lifespan corresponding to that core feature. When the number of features satisfying the core feature of a target tool exceeds 6 in different remaining lifespans, for example, by comparison, tool A satisfies 7 core features corresponding to a remaining lifespan of one day, and 8 core features corresponding to a remaining lifespan of two days. Then, the remaining lifespan with the higher number of satisfied features is taken as the predicted lifespan of the tool, i.e., tool A's predicted lifespan is two days remaining. If tool A satisfies 7 core features corresponding to a remaining lifespan of one day, and also 7 core features corresponding to a remaining lifespan of two days, then the prediction lifespan can be determined by the average similarity between each feature and the core feature; that is, the one with the larger average similarity is taken as tool A's predicted lifespan. It should be noted that this embodiment does not limit the number of features of the target tool that meet the core characteristics. The number of features can be set according to the tool material characteristics of the target tool. The more expensive the tool material of the target tool, the more features need to be met.

[0057] Step S104: If it is confirmed that the predicted lifespan is less than the preset predicted lifespan, a replacement message is sent to the user. The replacement message is used to prompt the user to replace the target tool.

[0058] Specifically, the server determines whether the predicted lifespan of the target tool, based on the lifespan prediction model, is less than the preset predicted lifespan. If it is less than the preset predicted lifespan, the server sends a replacement message to the user, who can then replace the target tool according to the received message. If the lifespan is greater than the preset predicted lifespan, the server can continue to use the tool and sends a tool inspection report to the user, which includes the lifespan prediction result of the target tool.

[0059] In one possible implementation, step S104 further includes: in response to a replacement instruction sent by the user, replacing the target tool with a specified tool; the replacement instruction includes an automatic replacement instruction and a manual replacement instruction.

[0060] Specifically, after receiving the replacement information, the user can send a replacement command to the server to select the replacement method for the target tool. The replacement method can include multiple options; for example, the user can manually perform the replacement, or the server can perform the replacement, changing the target tool to a tool specified by the user.

[0061] In a possible implementation, after step S104, the method further comprises: performing an actual life test on the target tool to obtain an actual life of the target tool; determining whether a difference between the actual life and the predicted life is greater than a preset difference; and if the difference between the actual life and the predicted life is greater than the preset difference, sending correction information to a user, wherein the user adjusts the first life prediction model to a second life prediction model according to the correction information, and updates the correspondence between the second life prediction model and the tool type to the prediction model database.

[0062] Specifically, after the server completes the life detection operation, the target tool that is replaced can be tested, and through the test, the actual life of the target tool can be known. The server determines whether a difference between the actual life and the predicted life is greater than a preset difference. If the difference is greater than the preset difference, it indicates that the life prediction model is inaccurate. The server can retrain the life prediction model according to the plurality of features of the target tool and the actual life of the target tool, to correct the error, and save the corrected life prediction model and the correspondence between the tool type of the target tool in the prediction model database. It should be noted that the life prediction model in the prediction model database can be updated in real time. The historical data of all target tools that have undergone the life detection operation are saved and used to train or correct the life prediction model corresponding to the tool type.

[0063] The application can achieve the following beneficial effects:

[0064] 1. By adopting the above technical solution, the tools in the numerical control machine tool are classified, and a corresponding life prediction model is trained for each tool type. The residual life of a target tool of a certain type can be predicted through the life prediction model corresponding to the tool type, so that the tool can be replaced in time in actual production, and the tool is kept in the best performance state in actual production at all times.

[0065] 2. By using the plurality of core features corresponding to different residual lives in the same tool type, the life prediction model corresponding to the same tool type can be trained, and the correspondence between the life prediction model and the tool type is saved in the prediction model database, so that the server can select the corresponding life prediction model value according to the tool type of the target tool according to the correspondence.

[0066] 3. When the life prediction model is inaccurate, the server can retrain the life prediction model according to the plurality of features of the target tool and the actual life of the target tool to correct the error.

[0067] Please refer to Figure 2It shows a numerical control machine tool tool life prediction device provided by one embodiment of the application, the device is a server, the device includes an acquisition module 21 and a processing module 22, wherein,

[0068] The acquisition module 21 is configured to acquire a tool type corresponding to a target tool in response to a user's life detection operation on the target tool; and acquire a first life prediction model corresponding to the tool type in a prediction model database, the prediction model database being configured to store a correspondence between a tool type and a first life prediction model.

[0069] The processing module 22 is configured to obtain a predicted life of the target tool by performing a life detection operation on the target tool through the first life prediction model; and replace the target tool to complete the test on the target tool if it is confirmed that the predicted life is less than a preset predicted life.

[0070] In a possible implementation, the acquisition module 21 is configured to acquire the tool type corresponding to the target tool, specifically including: acquiring a feature picture corresponding to the target tool through a camera, the feature picture including a plurality of features corresponding to the target tool; the plurality of features including a target feature, the target feature being any one of the plurality of features; acquiring a plurality of preset features corresponding to the tool type; the plurality of preset features including a target preset feature, the target preset feature being any one of the plurality of preset features; judging whether a similarity value of the target feature and the target preset feature is greater than a preset similarity value; and judging that the tool type is the tool type corresponding to the target tool if the similarity value is greater than the preset similarity value.

[0071] In a possible implementation, the acquisition module 21 is configured to construct the prediction model database before acquiring the first life prediction model corresponding to the tool type in the prediction model database, specifically including: acquiring a plurality of tools corresponding to the tool type and a plurality of residual lives corresponding to the plurality of tools, one tool corresponding to one residual life; acquiring a plurality of core features corresponding to the plurality of tools respectively, the plurality of core features being necessary features for judging the residual life of the tool; constructing the first life prediction model according to the plurality of core features and the plurality of residual lives; and saving the correspondence between the first life prediction model and the tool type in the prediction model database.

[0072] In a possible implementation, the processing module 22 is configured to, after performing the life detection operation on the target tool through the first life prediction model, the method further includes: replacing the target tool with a specified tool in response to a replacement instruction sent by a user; the replacement instruction including an automatic replacement instruction and a manual replacement instruction.

[0073] In a possible implementation, the processing module 22 is configured to, after the operation of performing the life detection on the target tool bit by the first life prediction model, the method further includes: performing an actual life test on the target tool bit to obtain an actual life of the target tool bit; determining whether a difference between the actual life and the predicted life is greater than a preset difference; and if the difference between the actual life and the predicted life is greater than the preset difference, sending correction information to a user, and adjusting the first life prediction model to a second life prediction model according to the correction information, and updating a correspondence between the second life prediction model and the tool bit type to the prediction model database.

[0074] The application further discloses an electronic device, including a processor, a memory, a user interface and a network interface, the memory is used for storing instructions, the user interface and the network interface are used for communicating with other devices, and the processor is used for executing the instructions stored in the memory to enable the electronic device to perform the method of any one of the above.

[0075] It should be noted that: the apparatus provided in the above embodiments is only used as an example to divide the above functional modules to achieve its functions, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0076] The application further discloses an electronic device. Referring to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiments of the application. The electronic device 300 can include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0077] The communication bus 302 is used to realize the connection and communication between the components.

[0078] The user interface 303 can include a display screen (Display) and a camera (Camera), and the optional user interface 303 can further include a standard wired interface and a wireless interface.

[0079] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0080] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0081] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can also be at least one storage device located away from the aforementioned processor 301. Referring to Figure 3 The memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program for predicting the life of a tool of a numerical control machine tool.

[0082] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 301 can be used to invoke an application program for predicting the tool life of a numerical control machine tool stored in the memory 305, and when executed by one or more processors 301, the electronic device 300 performs the method described in one or more of the above embodiments. It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the application is not limited by the order of the actions described, because according to the application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the application.

[0083] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0084] In the several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner for actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical or other forms.

[0085] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0086] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware, or in the form of a software functional unit.

[0087] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk and various media that can store program codes.

[0088] The above described are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and the practical true disclosure.

[0089] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not recorded in the present disclosure.

Claims

1. A method for predicting the tool life of a CNC machine tool, characterized in that, The method includes: responding to a user's life detection operation on a target tool, obtaining the tool type corresponding to the target tool; obtaining a first life prediction model corresponding to the tool type in a prediction model database, the prediction model database being used to store the correspondence between the tool type and the first life prediction model; performing a life detection operation on the target tool using the first life prediction model to obtain the predicted life of the target tool; if it is confirmed that the predicted life is less than a preset predicted life, sending a replacement message to the user, the replacement message being used to prompt the user to replace the target tool; wherein, before obtaining the first life prediction model corresponding to the tool type in the prediction model database, constructing the prediction model database specifically includes: obtaining multiple tools corresponding to the tool type and multiple remaining lifespans corresponding to the multiple tools, one tool corresponding to one remaining lifespan; obtaining multiple core features corresponding to each of the multiple tools, the multiple core features being necessary features for determining the remaining lifespan corresponding to the tool; constructing a prediction model database based on the multiple core features and the multiple remaining lifespans. The method includes: a first life prediction model; storing the correspondence between the first life prediction model and the tool type in the prediction model database; wherein, after the operation of performing life detection on the target tool using the first life prediction model, the method further includes: performing an actual life test on the target tool to obtain the actual life of the target tool; determining whether the difference between the actual life and the predicted life is greater than a preset difference; if the difference between the actual life and the predicted life is greater than the preset difference, sending correction information to the user, and the user adjusting the first life prediction model to a second life prediction model according to the correction information, and updating the correspondence between the second life prediction model and the tool type in the prediction model database; wherein, the remaining life is the remaining cutting amount that the tool can perform; the cutting amount is the amount of material that the tool can remove from the workpiece according to a preset parameter set during the cutting operation; the preset parameter set includes cutting speed, cutting acceleration, cutting depth, and cutting width; the core features include tool material features and workpiece material features.

2. The method according to claim 1, characterized in that, The step of obtaining the tool type corresponding to the target tool specifically includes: acquiring a feature image corresponding to the target tool through a camera, the feature image including multiple features corresponding to the target tool; the multiple features including a target feature, the target feature being any one of the multiple features; acquiring multiple preset features corresponding to the tool type; the multiple preset features including a target preset feature; the target preset feature being any one of the multiple preset features; determining whether the similarity value between the target feature and the target preset feature is greater than a preset similarity value; if the similarity value is greater than the preset similarity value, then determining that the tool type is the tool type corresponding to the target tool.

3. The method according to claim 1, characterized in that, After performing a life detection operation on the target tool using the first life prediction model, the method further includes: replacing the target tool with a specified tool in response to a replacement command sent by the user; the replacement command includes an automatic replacement command and a manual replacement command.

4. The method according to claim 1, characterized in that, The types of cutting tools include milling cutters, turning tools, and drilling tools.

5. The method according to any one of claims 1 to 4, characterized in that, It also includes a life prediction device, which includes an acquisition module (21) and a processing module (22). The acquisition module (21) is used to acquire the tool type corresponding to the target tool in response to the user's life detection operation for the target tool; acquire the first life prediction model corresponding to the tool type in the prediction model database, and the prediction model database is used to store the correspondence between the tool type and the first life prediction model; the processing module (22) is used to perform a life detection operation on the target tool through the first life prediction model to obtain the predicted life of the target tool; if it is confirmed that the predicted life is less than the preset predicted life, the target tool is replaced to complete the test of the target tool.

6. An electronic device, characterized in that, The device includes a processor (301), a communication bus (302), a user interface (303), a network interface (304), and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 4.

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