Maintenance method, device, electronic equipment and readable storage medium for CNC machine tools

Abstract

The present application discloses a maintenance method, device, electronic device and readable storage medium for a numerically controlled machine tool, and relates to the technical field of numerically controlled machine tools, including: obtaining a first part having a fault in the numerically controlled machine tool and first sensor data associated with the first part when a fault is detected in the operation of the numerically controlled machine tool; determining a potential fault part associated with the first part when the first part has a fault according to the first sensor data and a preset potential prediction model; inputting the first sensor data and the acquired potential fault data of the potential fault part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential fault part; and using the first maintenance parameter and the second maintenance parameter as a maintenance plan for the numerically controlled machine tool when it is determined that the first maintenance parameter and the second maintenance parameter meet preset normal conditions. The present application solves the technical problem of low maintenance efficiency of numerically controlled machine tools.

Description

Technical Field

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

[0002] As high-precision and high-efficiency processing equipment, the stable operation of CNC machine tools is crucial to ensure product quality and production efficiency. However, with the continuous development of CNC machine tool technology and the increasing application fields, its complexity and integration have also increased, making the fault detection and maintenance of CNC machine tools a very challenging task.

[0003] At present, the fault detection and repair of CNC machine tools mainly rely on technicians to conduct intuitive observation, manual troubleshooting, and judgment based on long-term work experience. This traditional fault repair method has many limitations. For example, even if a fault is detected in the operation of the CNC machine tool, there are many reasons for the fault. Even if the fault is known, it is difficult to determine the corresponding maintenance plan. This requires not only solid professional knowledge and rich practical experience, but also a deep understanding of the overall structure and operating principle of the machine tool. Only then can the maintenance personnel analyze the corresponding maintenance plan, which is not only time-consuming and laborious, but also there may be a maintenance plan that cannot deal with the fault, which leads to low maintenance efficiency of CNC machine tools.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present application is to provide a maintenance method, device, electronic device and readable storage medium for a CNC machine tool, aiming to solve the technical problem of low maintenance efficiency of CNC machine tools.

[0006] To achieve the above-mentioned object, the present application provides a maintenance method for a CNC machine tool, the method comprising: when a fault is detected in the CNC machine tool during operation, obtaining a first part having a fault in the CNC machine tool and first sensor data associated with the first part;

[0007] Determine, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part fails;

[0008] Input the first sensing data and the acquired potential failure data of the potential failure part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential failure part;

[0009] When it is determined that the first maintenance parameter and the second maintenance parameter meet the preset normal conditions, the first maintenance parameter and the second maintenance parameter are used as the maintenance plan for the CNC machine tool; the preset normal conditions indicate that there is no abnormality in the processing operation status of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter.

[0010] In a feasible embodiment, the step of determining, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part is faulty comprises:

[0011] Inputting the first sensing data into a preset potential prediction model, determining abnormal data from the first sensing data, and first coordinate data of the first part in the CNC machine tool;

[0012] Determining an abnormality coefficient of the first part according to a gap between the abnormal data and preset standard data;

[0013] determining a fault impact area centered on the first coordinate data according to the abnormal coefficient and the first coordinate data, wherein the abnormal coefficient is positively correlated with the fault impact area;

[0014] A target machine tool part in the fault-affected area of ​​the numerically controlled machine tool is used as a potential faulty part of the first part.

[0015] In a feasible embodiment, the potential fault data includes an abnormal coefficient of the first part and second sensor data of the potential fault part;

[0016] The step of inputting the first sensing data and the acquired potential failure data of the potential failure part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential failure part comprises:

[0017] Inputting the first sensor data into the preset large model, and determining a first maintenance parameter matching the first sensor data in a machine tool maintenance database corresponding to the preset large model;

[0018] The potential fault data is input into a preset large model, an abnormal coefficient and second sensor data are determined from the potential fault data, and a second maintenance parameter matching the abnormal coefficient and the second sensor data is determined in a machine tool maintenance database corresponding to the preset large model.

[0019] In a feasible embodiment, the processing operation state includes a first processing operation state and a second processing operation state, and before the step of using the first maintenance parameter and the second maintenance parameter as the maintenance plan of the CNC machine tool when determining that the first maintenance parameter and the second maintenance parameter meet the preset normal condition, the step further includes:

[0020] Calling a preset simulation model, replacing a first target simulation parameter in the preset simulation model that matches the parameter type of the first maintenance parameter with the first maintenance parameter, and simulating a first machining operation state of the numerically controlled machine tool corresponding to the first maintenance parameter in the preset simulation model;

[0021] If there is no abnormality in the first processing operation state, determining that the first maintenance parameter meets the preset normal condition;

[0022] Calling a preset simulation model, replacing a second target simulation parameter in the preset simulation model that matches the parameter type of the second maintenance parameter with the second maintenance parameter, and simulating a second machining operation state of the CNC machine tool corresponding to the second maintenance parameter in the preset simulation model;

[0023] If there is no abnormality in the second processing operation state, it is determined that the second maintenance parameter meets the preset normal condition.

[0024] In a feasible embodiment, after the steps of calling the preset simulation model, replacing the first target simulation parameter matching the parameter type of the first maintenance parameter in the preset simulation model with the first maintenance parameter, and simulating the first machining operation state of the numerically controlled machine tool corresponding to the first maintenance parameter in the preset simulation model, the method further includes:

[0025] calling a preset generalized model to predict at least one debugging parameter for simulating debugging of the CNC machine tool when simulating a first machining operation state of the CNC machine tool by using the first maintenance parameter;

[0026] Determine at least one debugging combination according to each of the debugging parameters, wherein the debugging combination includes at least one debugging parameter;

[0027] The first maintenance parameter is adjusted according to each of the debugging combinations to obtain generalized maintenance parameters corresponding to each of the debugging combinations;

[0028] Calling the preset simulation model to determine a target generalized maintenance parameter that meets a preset generalization condition among the generalized maintenance parameters;

[0029] updating the first maintenance parameter according to the target generalized maintenance parameter;

[0030] The preset generalization condition includes that the third machining operation state of the simulated numerical control machine tool corresponding to the target generalized maintenance parameter does not have an abnormality.

[0031] In a feasible embodiment, after the step of simulating the second machining operation state of the numerically controlled machine tool corresponding to the second maintenance parameter in the preset simulation model, the method further includes:

[0032] When it is determined that the first maintenance parameter satisfies the preset normal condition and the second maintenance parameter does not satisfy the preset normal condition, the first maintenance parameter is used as a maintenance plan for the CNC machine tool.

[0033] In a feasible embodiment, after determining that the first maintenance parameter and the second maintenance parameter meet the preset normal condition, taking the first maintenance parameter and the second maintenance parameter as the maintenance plan of the numerical control machine tool, the method further includes:

[0034] When the numerically controlled machine tool is in a non-operating state, periodically acquiring third sensor data of the numerically controlled machine tool in the non-operating state;

[0035] Inputting the third sensor data into a simulation module of the CNC machine tool, and simulating a third machining operation state of the CNC machine tool in the simulation module based on the third sensor data;

[0036] When it is detected that the third processing operation state is abnormal, an alarm prompt is output.

[0037] In addition, to achieve the above-mentioned purpose, the present application provides a maintenance device for a CNC machine tool, the maintenance device for the CNC machine tool comprising:

[0038] A monitoring module, for acquiring a first part having a fault in the CNC machine tool and first sensor data associated with the first part when a fault is detected in the CNC machine tool during operation;

[0039] A potential prediction module, used for determining, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part is faulty;

[0040] A maintenance parameter determination module, used for inputting the first sensing data and the acquired potential failure data of the potential failure part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential failure part;

[0041] A simulation module is used to use the first maintenance parameter and the second maintenance parameter as a maintenance plan for the CNC machine tool when it is determined that the first maintenance parameter and the second maintenance parameter meet preset normal conditions; the preset normal conditions indicate that there is no abnormality in the processing operation state of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter.

[0042] In addition, to achieve the above-mentioned purpose, the present application also provides an electronic device, which includes: a memory, a processor, and a program of the maintenance method of the CNC machine tool stored in the memory and executable on the processor. When the program of the maintenance method of the CNC machine tool is executed by the processor, the steps of the maintenance method of the CNC machine tool as described above can be implemented.

[0043] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, on which is stored a program for implementing the maintenance method of a CNC machine tool. When the program of the maintenance method of a CNC machine tool is executed by a processor, the steps of the maintenance method of a CNC machine tool as described above are implemented.

[0044] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned maintenance method for CNC machine tools when executed by a processor.

[0045] One or more technical solutions proposed in this application have at least the following technical effects: when the CNC machine tool is detected to have a fault during operation, the application can obtain the first faulty part in the CNC machine tool, and obtain the first sensor data associated with the first part, so as to monitor the operation process of the CNC machine tool. Then, through the first sensor data and the preset potential prediction model, the potential faulty part associated with the first part when the first part has a fault is determined, so that the part that is not currently faulty but may have a fault due to the fault of the first part can be determined, and then the first sensor data and the potential fault data of the potential faulty part are input into the preset large model, so that the first maintenance parameter corresponding to the first part and the second maintenance parameter of the potential faulty part can be output through the preset large model, so as to deal with the fault of the first part and the potential fault of the potential faulty part, so as to facilitate the comprehensive maintenance of the CNC machine tool.

[0046] And when the first maintenance parameter and the second maintenance parameter meet the preset normal conditions, the first maintenance parameter and the second maintenance parameter will be used as the maintenance plan for the CNC machine tool. Since the preset normal conditions indicate that there is no abnormality in the processing and operation state of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter, the first maintenance parameter and the second maintenance parameter in the maintenance plan can enable the CNC machine tool to operate normally, thus ensuring the accuracy of the first maintenance parameter and the second maintenance parameter. Therefore, in the present application, there is no need to manually observe the fault of the CNC machine tool, nor is there any need to manually determine the maintenance plan corresponding to the fault. An accurate and comprehensive maintenance plan can be determined, and the CNC machine tool can be repaired quickly and effectively, thereby improving the maintenance efficiency of the CNC machine tool. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description, are used to explain the principles of the present application.

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 A schematic diagram of a process flow of an embodiment of a maintenance method for a CNC machine tool of the present application;

[0050] Figure 2 A flowchart of another embodiment of the maintenance method of a CNC machine tool of the present application;

[0051] Figure 3 This is a flow chart of another embodiment of the maintenance method of a CNC machine tool of the present application;

[0052] Figure 4 This is a schematic diagram of the device structure of the maintenance device for a CNC machine tool according to an embodiment of the present application;

[0053] Figure 5 It is a schematic diagram of the equipment structure of the hardware operating environment involved in the maintenance method of a CNC machine tool in the embodiment of the present application.

[0054] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0057] The main solution of the embodiment of the present application is: by configuring the sensor on the CNC machine tool in advance, the operation process of the CNC machine tool is detected. When the operation process is detected to have a fault, the first part with a fault in the CNC machine tool and the corresponding first sensor data are obtained. The potential fault part can be determined through the first sensor data and the preset potential prediction model, and the first maintenance parameter corresponding to the first part and the second maintenance parameter of the potential fault part can be predicted through the preset large model, and the first maintenance parameter and the second maintenance parameter are verified respectively. For example, the processing operation state of the CNC machine tool is simulated according to the first maintenance parameter, and when there is no abnormality in the processing operation state, the first maintenance parameter is determined to meet the preset normal condition. The processing operation state of the CNC machine tool is simulated according to the second maintenance parameter, and when there is no abnormality in the processing operation state, the second maintenance parameter is determined to meet the preset normal condition. When the first maintenance parameter and the second maintenance parameter meet the preset normal condition, the first maintenance parameter and the second maintenance parameter are used as the maintenance plan of the CNC machine tool.

[0058] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above functions.

[0059] Based on this, the present application embodiment provides a maintenance method for a CNC machine tool, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of a maintenance method for a CNC machine tool of the present application.

[0060] In this embodiment, the maintenance method of the CNC machine tool includes steps S10 to S40:

[0061] Step S10, when it is detected that the CNC machine tool fails during operation, obtaining a first part having a fault in the CNC machine tool and first sensor data associated with the first part;

[0062] It should be noted that this embodiment is applied to CNC machine tools, and the operation process of the CNC machine tools can be detected by intelligent sensing devices. The intelligent sensing devices can be composed of multiple sensors installed on the CNC machine tools and / or multiple sensors installed in the space where the CNC machine tools are located, so as to monitor the operation process of the CNC machine tools, for example, temperature and humidity sensors, speed sensors, pressure sensors, liquid level sensors, etc. The first part is a machine tool part with a fault in the CNC machine tool. The machine tool part is a part on the body of the CNC machine tool. For example, the machine tool part can be a tool on the CNC machine tool, or a firmware such as a screw that fixes the tool on the CNC machine tool. The first sensing data includes the sensor data of the first part monitored, and can also include all sensor data corresponding to the CNC machine tool, etc. The sensor data of the first part can include the coordinate data of the first part in the CNC machine tool, and can also include the identification of the first part, etc.

[0063] Exemplarily, the operation process of the CNC machine tool can be monitored by multiple sensors installed on the CNC machine tool and sensors in the space where the CNC machine tool is located. When the CNC machine tool is detected to have a fault, the first part of the CNC machine tool with a fault and the first sensor data associated with the first part are obtained. Specifically, when the CNC machine tool is monitored to be in operation, when the sensor data corresponding to any machine tool part in the CNC machine tool meets the preset abnormal condition, it means that the machine tool part is abnormal, and the machine tool part can be used as the first part. The preset abnormal condition includes that the sensor data of the monitored machine tool part is not within the corresponding standard sensor data range. Each machine tool part has its own corresponding standard sensor data range. When the CNC machine tool is running, if the sensor data corresponding to each machine tool part is within the corresponding standard sensor data range, it means that the CNC machine tool will not be unable to operate normally due to the abnormality of the machine tool part. When the sensor data corresponding to any machine tool part is not within the corresponding standard sensor data range during operation, it means that the CNC machine tool may operate abnormally. The sensor data can be a sensor value. The standard sensor data range refers to the range of normal sensor values ​​corresponding to the machine tool part. For any machine tool part, multiple types of sensor data corresponding to the machine tool part may be detected, and each type of sensor data has its own corresponding standard sensor data range. Multiple types of sensor data refer to data collected by different types of sensors. For example, speed sensor data collected by a speed sensor is one type of sensor data, and temperature sensor data collected by a temperature sensor is another type of sensor data. It can be understood that any machine tool part may correspond to multiple standard sensor data ranges.

[0064] Step S20, determining, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part is faulty;

[0065] It should be noted that the preset potential prediction model is used to predict the potential faulty parts of the CNC machine tool caused by the fault of the first part. The potential faulty parts are machine tool parts that have not been detected to be abnormal at present but may have faults in the future due to the fault of the first part. There can be one or more potential faulty parts. This embodiment predicts the potential faulty parts associated with the first part, so as to facilitate the subsequent comprehensive maintenance of the CNC machine tool.

[0066] Exemplarily, the first sensor data is input into a preset potential prediction model, and a potential faulty part associated with the first part is output when the first part is faulty. After the potential faulty part is determined, potential fault data corresponding to the potential faulty part can be obtained, and the potential fault data includes the second sensor data corresponding to the potential faulty part.

[0067] Step S30, inputting the first sensing data and the acquired potential failure data of the potential failure part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential failure part;

[0068] It should be noted that the preset large model refers to a large language model with powerful natural language processing capabilities and a wide knowledge base. The preset large model can understand complex natural language instructions and generate high-quality answers or text content. The preset large model can be a GPT-4 model, the first maintenance parameter can include one or more first sub-parameters, the second maintenance parameter can include one or more second sub-parameters, each first sub-parameter in the first maintenance parameter can be used to indicate that the CNC machine tool resumes normal operation, and each second sub-parameter in the second maintenance parameter can be used to indicate that the CNC machine tool maintains a normal operating state. The first maintenance parameter can carry the maintenance order of the maintenance operation corresponding to each first sub-parameter, so as to indicate that the CNC machine tool controls the CNC machine tool to resume normal operation according to the maintenance order of the maintenance operation corresponding to each first sub-parameter, and the second maintenance parameter can also carry the maintenance order corresponding to the maintenance operation corresponding to each second sub-parameter, so as to indicate that the CNC machine tool maintains normal operation according to the maintenance order of the maintenance operation corresponding to each second sub-parameter. The maintenance order corresponding to each first sub-parameter can be the same or different, and the maintenance order of the maintenance operation corresponding to each first sub-parameter can also be not limited. This embodiment does not make specific limitations on this. The maintenance order corresponding to each second sub-parameter may be the same or different, and the maintenance order of the maintenance operations corresponding to each second sub-parameter may not be limited. This embodiment does not make specific limitations on this. For example, when the first sub-parameter is: the oil volume in the oil tank should be A, the maintenance operation corresponding to the first sub-parameter is: receiving the oil volume until the oil volume in the CNC machine tool is A. When there are multiple first sub-parameters, the corresponding maintenance order can be marked after the maintenance operation corresponding to the first sub-parameter. In other embodiments, the first sub-parameter can also be tool feed speed, tool temperature, cutting depth, cutting width, tool radius, cutting force or precision, etc. This embodiment does not make specific limitations on this.

[0069] Exemplarily, the first sensor data is input into the preset large model, and the first maintenance parameter matching the first sensor data is output; the potential fault data is input into the preset large model, and the second maintenance parameter matching the potential fault data is output. Specifically, the first sensor data can be input into the preset question and answer template of the preset large model to search for the corresponding first maintenance parameter in the machine tool maintenance database of the preset large model; the potential fault data can be input into the preset question and answer template of the preset large model to search for the corresponding second maintenance parameter in the machine tool maintenance database of the preset grinding class. The preset question and answer template is used to instruct the preset large model to search for data corresponding to the content filled in the preset question and answer template (for example, the first maintenance parameter and / or the second maintenance parameter) in the corresponding machine tool maintenance database. The content filled in the preset question and answer template can be the first sensor data or the potential fault data.

[0070] In a feasible embodiment, step S30 further includes steps S31 to S32:

[0071] Step S31, inputting the first sensor data into the preset large model, and determining a first maintenance parameter matching the first sensor data in a machine tool maintenance database corresponding to the preset large model;

[0072] It should be noted that the first sensor data may include sensor data corresponding to the monitored first part, and may also include data of all corresponding sensors in the CNC machine tool. After the first sensor data is input into the preset question-answer template of the preset large model, the preset large model may search for the first maintenance parameter matching the first sensor data in the machine tool maintenance database. The machine tool maintenance database stores the corresponding maintenance plan when each machine tool part in the CNC machine tool fails, and the maintenance plan includes the corresponding maintenance parameters. The preset large model can search for the first maintenance parameter related to the first sensor data by indexing in the machine tool maintenance database. The first maintenance parameter can be used to correct the abnormal data in the first sensor data, which is the sensor data of the abnormality of the monitored first part. The first maintenance parameter can also be used to correct the influence data related to the abnormal data in the first sensor data, which is the data causing the abnormal data corresponding to the first part. The influence data can also be a kind of sensor data, and the influence data is the data within the standard sensing range of the first part. Based on the preset large model and the machine tool maintenance database of the preset large model, the abnormal data and the influence data in the first sensor data can be identified, and then the corresponding first maintenance parameter can be determined.

[0073] Step S32, input the potential fault data into a preset large model, determine the abnormal coefficient and the second sensor data from the potential fault data, and determine a second maintenance parameter matching the abnormal coefficient and the second sensor data in a machine tool maintenance database corresponding to the preset large model.

[0074] It should be noted that the potential fault data includes an abnormal coefficient and second sensor data corresponding to the potential fault part, and the second sensor data is the sensor data of the monitored potential fault part. The abnormal coefficient is characterized by the severity of the fault of the first part, and the abnormal coefficient is positively correlated with the severity. The abnormal coefficient can carry the identification of the first part, and the identification of the first part can be the name and purpose of the first part. The machine tool maintenance database also stores the maintenance plan corresponding to the possible future failure of other machine tool parts caused by the failure of any machine tool part, and the maintenance plan may include the second maintenance parameter. The abnormal coefficient can be used to determine the adjustment degree of the value of the parameter corresponding to the potential fault part. The smaller the abnormal coefficient, the smaller the adjustment degree, and the larger the abnormal coefficient, the greater the adjustment degree. The second maintenance parameter is used to maintain the normal operation of the potential fault part in the CNC machine tool. The second maintenance parameter includes the target value that can make the potential fault part operate normally. It can be understood that each second sub-parameter in the second maintenance parameter is the target value for controlling the normal operation of the CNC machine tool. It can be understood that when the second maintenance parameter is input to the CNC machine tool, the CNC machine tool will operate according to the second maintenance parameter.

[0075] Exemplarily, the first sensor data is input into the preset question and answer template of the preset large model to search for the first maintenance parameter corresponding to the first sensor data in the machine tool maintenance database of the preset large model; the potential fault data is input into the preset question and answer template of the preset large model to determine the second maintenance parameter corresponding to the abnormal coefficient and the second sensor data in the machine tool maintenance database. Specifically, the data storage module corresponding to the first part can be searched in the machine tool maintenance database according to the identifier of the first part carried by the abnormal coefficient, and the second maintenance parameter can be determined in the data storage module through the preset large model, the coefficient value of the abnormal coefficient and the second sensor data. The data storage module can store parameters corresponding to all potential fault parts of the first part under different abnormal coefficients. It can be understood that the preset large model can predict the second maintenance parameter corresponding to the abnormal coefficient and the second sensor data based on the data stored in the data storage module.

[0076] Step S40, when it is determined that the first maintenance parameter and the second maintenance parameter meet the preset normal conditions, the first maintenance parameter and the second maintenance parameter are used as the maintenance plan for the CNC machine tool; the preset normal conditions indicate that there is no abnormality in the processing operation status of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter.

[0077] It should be noted that, in order to ensure the accuracy of the first maintenance parameter and the second maintenance parameter, the first maintenance parameter and the second maintenance parameter will also be verified in this embodiment. For example, the processing operation state of the CNC machine tool can be simulated based on the first maintenance parameter, or the processing operation state of the CNC machine tool can be simulated based on the second maintenance parameter to determine whether there is an abnormality in the processing operation state of the CNC machine tool, and avoid using the wrong first maintenance parameter and second maintenance parameter to repair the CNC machine tool. The processing operation state refers to the state in which the CNC machine tool performs a processing task, and the processing task refers to the task of processing a workpiece. It is convenient to ensure the accuracy and safety of the maintenance of the CNC machine tool. The maintenance plan may include the first maintenance parameter and the second maintenance parameter, and the maintenance plan may also include only the first maintenance parameter. The preset normal condition includes that there is no abnormality in the processing operation state simulated by the first maintenance parameter and / or the second maintenance parameter. The processing operation state may include the first processing operation state and the second processing operation state. The first processing operation state is characterized as the processing operation state of the CNC machine tool under the first maintenance parameter, and the second processing operation state is characterized as the processing operation state of the CNC machine tool under the second maintenance parameter.

[0078] Exemplarily, a first processing operation state of a CNC machine tool is simulated based on a first maintenance parameter, and a second processing operation state of the CNC machine tool is simulated based on a second maintenance parameter. When there is no abnormality in the first processing operation state and the second processing operation state, it means that the first maintenance parameter and the second maintenance parameter meet the preset normal conditions. The first maintenance parameter and the second maintenance parameter can be used as a maintenance plan for the CNC machine tool, and then the first maintenance parameter and the second maintenance parameter can be output to the operation control system of the CNC machine tool so that the CNC machine tool can operate normally.

[0079] In the case of monitoring that a CNC machine tool has a fault during operation, the first faulty part in the CNC machine tool can be obtained, and the first sensor data associated with the first part can be obtained to monitor the operation process of the CNC machine tool. Then, through the first sensor data and the preset potential prediction model, the potential faulty part associated with the first part when the first part has a fault can be determined, so that the part that is not currently faulty but may have a fault due to the fault of the first part can be determined, and then the first sensor data and the potential fault data of the potential faulty part are input into the preset large model, so that the first maintenance parameter corresponding to the first part and the second maintenance parameter of the potential faulty part can be output through the preset large model, so as to deal with the fault of the first part and the potential fault of the potential faulty part, so as to facilitate the comprehensive maintenance of the CNC machine tool.

[0080] And when the first maintenance parameter and the second maintenance parameter meet the preset normal conditions, the first maintenance parameter and the second maintenance parameter will be used as the maintenance plan of the CNC machine tool. Since the preset normal conditions indicate that there is no abnormality in the processing and operation state of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter, the first maintenance parameter and the second maintenance parameter in the maintenance plan can enable the CNC machine tool to operate normally, thereby ensuring the accuracy of the first maintenance parameter and the second maintenance parameter. Therefore, in this embodiment, there is no need to manually observe the fault of the CNC machine tool, nor is there any need to manually determine the maintenance plan corresponding to the fault. An accurate and comprehensive maintenance plan can be determined, and the CNC machine tool can be quickly and effectively repaired, thereby improving the maintenance efficiency of the CNC machine tool.

[0081] In a feasible embodiment, before step S40, steps S41 to S44 are also included:

[0082] Step S41, calling a preset simulation model, replacing a first target simulation parameter in the preset simulation model that matches the parameter type of the first maintenance parameter with the first maintenance parameter, and simulating a first machining operation state of the CNC machine tool corresponding to the first maintenance parameter in the preset simulation model;

[0083] Step S42, if there is no abnormality in the first processing operation state, determining that the first maintenance parameter meets a preset normal condition;

[0084] It should be noted that the preset simulation model is used to simulate the processing operation state of the CNC machine tool. The preset simulation model can be obtained based on the training of the neural network model. The preset simulation model is configured with simulation parameters that can enable the CNC machine tool to operate normally and simulate the processing operation state of the CNC machine tool. The preset simulation model can simulate the operation of the CNC machine tool based on the simulation parameters.

[0085] The first maintenance parameter includes one or more first sub-parameters, and the parameter type of the first maintenance parameter includes the first sub-type of each first sub-parameter. The first target simulation parameter may include one or more first simulation sub-parameters. The first sub-type refers to the type to which the first sub-parameter belongs. The first sub-type may be determined based on the parameter name of the first sub-parameter. For example, when the first sub-parameter is: the tool cutting speed is A, then the first sub-type of the first sub-parameter is the tool cutting speed. The first simulation sub-parameter of the same type as the first sub-type may be found in the preset simulation model, and the first simulation sub-parameter may be replaced with the first sub-parameter, thereby simulating the first processing operation state of the CNC machine tool based on the first maintenance parameter. The preset simulation model may output the first state result of the first processing operation state, and the first state result is used to describe whether the first processing operation state is normal. For example, when the first processing operation state is abnormal, the first state result is abnormal, and when the first processing operation state is normal, the first processing operation state is normal. When the first processing operation state is normal, it indicates that the first maintenance parameter meets the preset normal condition. The first simulation sub-parameter is a simulation parameter of the same type as the first sub-type.

[0086] Exemplarily, a preset simulation model is called, and for each first sub-parameter in the first maintenance parameter, a first simulation sub-parameter that is the same as the first sub-type of the first sub-parameter is determined from the preset simulation model, and the first simulation sub-parameter is replaced with the first sub-parameter, so as to simulate the first processing operation state of the CNC machine tool based on the first maintenance parameter.

[0087] Step S43, calling a preset simulation model, replacing a second target simulation parameter in the preset simulation model that matches the parameter type of the second maintenance parameter with the second maintenance parameter, and simulating a second machining operation state of the CNC machine tool corresponding to the second maintenance parameter in the preset simulation model;

[0088] Step S44: if there is no abnormality in the second processing operation state, it is determined that the second maintenance parameter meets the preset normal condition.

[0089] It should be noted that the second maintenance parameter includes one or more second sub-parameters, the parameter type of the second maintenance parameter includes the second sub-type of each second sub-parameter, the second target simulation parameter may include one or more second simulation sub-parameters, the second sub-type refers to the type to which the second sub-parameter belongs, and the second sub-type may be determined based on the parameter name of the second sub-parameter. For example, when the second sub-parameter is: the tool cutting speed is A, then the second sub-type of the second sub-parameter is the tool cutting speed. The second simulation sub-parameter of the same type as the second sub-type may be found in the preset simulation model, and the second simulation sub-parameter may be replaced with the second sub-parameter, thereby simulating the second machining operation state of the CNC machine tool based on the second maintenance parameter. The preset simulation model may output the second state result of the second machining operation state, and the second state result is used to describe whether the second machining operation state is normal. For example, when the second machining operation state is abnormal, the second state result is abnormal, and when the second machining operation state is normal, the second machining operation state is normal. When the second machining operation state is normal, it indicates that the second maintenance parameter meets the preset normal condition. The second simulation sub-parameter is a simulation parameter of the same type as the second sub-type.

[0090] Exemplarily, a preset simulation model is called, and for every second sub-parameter in the second maintenance parameter, a second simulation sub-parameter of the same type as the second sub-type of the second sub-parameter is determined from the preset simulation model, and the second simulation sub-parameter is replaced with the second sub-parameter, so as to simulate the second processing operation state of the CNC machine tool based on the second maintenance parameter.

[0091] The training process of the preset simulation model may include: determining a first model to be trained, which may be a neural network model, etc.; obtaining a first training sample, which includes a maintenance parameter sample and a state result label; inputting the maintenance parameter sample into the first model to be trained, obtaining a training state result, calculating the gap between the training state result and the state result label, obtaining a first training gap, judging whether the first training gap is less than or equal to a preset loss threshold, and if the first training gap is less than or equal to the preset loss threshold, determining that the first model to be trained is successfully trained, and obtaining a preset simulation model; if the first training gap is greater than the preset loss threshold, determining that the first model to be trained has failed to be trained, re-obtaining the first training sample, and iteratively training the first model to be trained until the first training gap is less than or equal to the preset loss threshold. The preset loss threshold may be determined based on actual conditions.

[0092] In a feasible embodiment, after step S41, steps S411 to S415 are also included:

[0093] Step S411, calling a preset generalized model to predict at least one debugging parameter for simulating debugging of the CNC machine tool when simulating a first machining operation state of the CNC machine tool by using the first maintenance parameter;

[0094] Step S412, determining at least one debugging combination according to each of the debugging parameters, wherein the debugging combination includes at least one debugging parameter;

[0095] Step S413, adjusting the first maintenance parameter according to each of the debugging combinations to obtain generalized maintenance parameters corresponding to each of the debugging combinations;

[0096] Step S414, calling the preset simulation model to determine a target generalized maintenance parameter that meets a preset generalization condition among the generalized maintenance parameters;

[0097] Step S415, updating the first maintenance parameter according to the target generalized maintenance parameter;

[0098] The preset generalization condition includes that the third machining operation state of the simulated numerical control machine tool corresponding to the target generalized maintenance parameter does not have an abnormality.

[0099] It should be noted that the preset generalized model can be used to update the first maintenance parameter so that the updated first maintenance parameter can support the normal operation of the CNC machine tool. The debugging parameter refers to the process of simulating the debugging of the CNC machine tool when the first maintenance parameter simulates the first processing operation state of the CNC machine tool, and the debugging parameter in the simulation debugging process can be obtained. The debugging parameter refers to the adjustment value in the simulation debugging process. Since the first maintenance parameter may include one or more first sub-parameters, one or more first sub-parameters may be debugged during the simulation debugging process. When the first maintenance parameter includes multiple first sub-parameters, each first sub-parameter may have its own corresponding debugging parameter, and the debugging combination is the debugging parameter corresponding to one or more first sub-parameters. For example, when the first maintenance parameter includes n first sub-parameters, the n first sub-parameters may have their own corresponding debugging parameters, but in the actual debugging process, some first sub-parameters may not need to be debugged, so multiple debugging combinations can be determined according to the debugging parameters corresponding to each first sub-parameter. For example, the debugging combination may be composed of debugging parameters corresponding to m first sub-parameters. n and m are positive integers, and m is less than n.

[0100] For each debugging combination, the first sub-parameter corresponding to the first maintenance parameter is debugged based on the debugging parameters in the debugging combination to obtain a generalized maintenance parameter. The preset generalization condition includes that the third processing operation state of the CNC machine tool simulated corresponding to the target generalized maintenance parameter does not have an abnormality, and the third processing operation state refers to the processing operation state of the CNC machine tool simulated based on the generalized maintenance parameter. Each generalized maintenance parameter can be input into a preset simulation model respectively, and the processing operation state of the CNC machine tool corresponding to the generalized maintenance parameter is simulated by the preset simulation model, and the third state result is output. When the third state result is normal, it means that the third processing operation state corresponding to the generalized maintenance parameter does not have an abnormality, and the generalized maintenance parameter meets the preset generalization condition; when the third state result is abnormal, it means that the third processing operation state corresponding to the generalized maintenance parameter has an abnormality, and the generalized maintenance parameter does not meet the preset generalization condition.

[0101] A target generalized maintenance parameter that meets the preset generalization condition can be determined from each generalized maintenance parameter. If there are multiple target generalized maintenance parameters, any target generalized maintenance parameter can be selected as the first maintenance parameter to update the first maintenance parameter. This embodiment updates the first maintenance parameter by presetting the generalized model, so that the parameters that can support the CNC machine tool to resume normal operation can be re-determined. This makes it easier to quickly restore the CNC machine tool to normal operation. The debugging combination can also include multiple debugging parameters. The number of debugging parameters included in different debugging combinations can be different or the same. The debugging combinations are not completely the same. Different debugging combinations can also include debugging parameters of the same first sub-parameter or debugging parameters of different first sub-parameters.

[0102] Exemplarily, a preset generalized model is called to predict at least one debugging parameter for simulating debugging of the CNC machine tool when the first processing operation state of the CNC machine tool is simulated by the first maintenance parameter; at least one debugging combination is determined according to each debugging parameter, and the debugging combination includes at least one debugging parameter; the first maintenance parameter is adjusted according to each debugging combination to obtain the generalized maintenance parameters corresponding to each debugging combination; the generalized maintenance parameters are input into the preset simulation model respectively, and the preset simulation model is used to simulate the processing operation state of the CNC machine tool under the generalized maintenance parameters, and the target generalized maintenance parameters that meet the preset generalization conditions are screened out; if there are multiple target generalized maintenance parameters, any target generalized maintenance parameter is selected as the first maintenance parameter to update the first maintenance parameter.

[0103] In other embodiments, if the generalized maintenance parameters corresponding to the preset generalized model do not meet the preset generalized conditions, the first maintenance parameter can be manually debugged until the debugged first maintenance parameter can maintain the normal operation of the CNC machine tool.

[0104] The training process of the preset generalization model may include determining a second model to be trained, which may be a neural network model, etc.; obtaining a second training sample, which includes a parameter sample to be debugged and a target generalization parameter label; inputting the parameter sample to be debugged into the second model to be trained to obtain a training state result, calculating the gap between the training state result and the target generalization parameter label to obtain a second training gap, and judging whether the second training gap is less than or equal to a preset loss threshold. If the second training gap is less than or equal to the preset loss threshold, it is determined that the training of the second model to be trained is successful, and the preset generalization model is obtained; if the second training gap is greater than the preset loss threshold, it is determined that the training of the second model to be trained has failed, the second training sample is re-obtained, and the second model to be trained is iteratively trained until the second training gap is less than or equal to the preset loss threshold.

[0105] In a feasible embodiment, after step S44, step S441 is also included: when it is determined that the first maintenance parameter meets the preset normal condition and the second maintenance parameter does not meet the preset normal condition, the first maintenance parameter is used as the maintenance plan of the CNC machine tool.

[0106] It should be noted that when the second maintenance parameter does not meet the preset normal conditions, it means that there is an abnormality when simulating the second processing operation state of the CNC machine tool based on the second maintenance parameter. Therefore, the second maintenance parameter cannot be used to maintain the CNC machine tool. Therefore, the first maintenance parameter that meets the preset normal conditions can be used as a maintenance plan, and the first maintenance parameter can be used to maintain the CNC machine tool so that the CNC machine tool can resume normal operation.

[0107] Exemplarily, when it is determined that the first maintenance parameter satisfies the preset normal conditions and the second maintenance parameter does not satisfy the preset normal conditions, the first maintenance parameter is used as a maintenance plan for the CNC machine tool. The first maintenance parameter can be input into the operation control module of the CNC machine tool to restore the CNC machine tool to normal operation. The second maintenance parameter is not input into the operation module of the CNC machine tool to avoid causing failures in the CNC machine tool. Since the second maintenance parameter is used to deal with possible failures of potential faulty parts in the future, when the second processing operation state corresponding to the second maintenance parameter is abnormal, the second maintenance parameter can be discarded, and the first maintenance parameter needs to be input into the CNC machine tool to ensure that the CNC machine tool quickly returns to normal operation.

[0108] Further, see Figure 2 In a feasible embodiment, step S20 further includes steps S21 to S24:

[0109] Step S21, inputting the first sensing data into a preset potential prediction model, determining abnormal data from the first sensing data, and first coordinate data of the first part in the CNC machine tool;

[0110] Step S22, determining the abnormality coefficient of the first part according to the difference between the abnormal data and the preset standard data;

[0111] Step S23, determining a fault impact area centered on the first coordinate data according to the abnormal coefficient and the first coordinate data, wherein the abnormal coefficient is positively correlated with the fault impact area;

[0112] Step S24, taking the target machine tool part in the fault-affected area of ​​the numerically controlled machine tool as a potential faulty part of the first part.

[0113] It should be noted that the preset potential prediction model is used to predict potential faulty parts related to the first part. Abnormal data refers to abnormal sensor data in the first sensor data, and the first coordinate data can be characterized as the position of the first part in the CNC machine tool. The preset standard data refers to the standard value that supports the normal operation of the CNC machine tool. The preset standard data can be determined based on the standard sensor data range corresponding to the abnormal data in the first part. For example, the preset standard data can be the minimum value or maximum value in the standard sensor data range corresponding to the abnormal data, or it can be the average value of the maximum value and the minimum value in the standard sensor data range. This embodiment does not make specific limitations on this.

[0114] The abnormal coefficient represents the severity of the failure of the first part. The gap between the abnormal data and the corresponding preset standard data is the difference between the abnormal value in the abnormal data and the standard value in the corresponding preset standard data. The abnormal coefficient can be the ratio of the gap to the preset standard data.

[0115] The fault impact area is an area in the CNC machine tool that may be affected by the fault of the first part. The fault impact area may be an area within a sphere with the first coordinate data of the first part as the center and a preset impact value as the radius. The preset impact value may be determined based on a preset radius and an abnormality coefficient. The preset impact value may be the product of the preset radius and the abnormality coefficient. The preset radius may be determined based on actual conditions.

[0116] The target machine tool parts in the fault-affected area can be used as potential fault parts of the first part, and there can be one or more potential fault parts. Since the first part may affect the operation of other parts after it fails, the potential fault parts can be predicted by a preset potential prediction model to further maintain the potential fault parts, thereby reducing the probability of failure of the CNC machine tool.

[0117] Exemplarily, the first sensing data is input into a preset potential prediction model, abnormal data and first coordinate data of the first part are determined from the first sensing data, the difference between the abnormal value of the abnormal data and the standard value of the preset standard data is calculated, the difference is an absolute difference, the ratio of the difference to the standard value of the preset standard data is used as an abnormal coefficient, a sphere with a preset impact value as the radius and the first coordinate data as the center is determined, the area within the sphere is used as the fault impact area, and the target machine tool part in the CNC machine tool that belongs to the fault impact area is used as a potential fault part. The larger the abnormal coefficient, the larger the area corresponding to the fault impact area, and the smaller the abnormal coefficient, the smaller the area corresponding to the fault impact area.

[0118] The training process of the preset potential prediction model may include determining a third model to be trained, which may be a neural network model, etc.; obtaining a third training sample, which includes a sensor data sample and a potential fault label; inputting the sensor data sample into the third model to be trained, obtaining a training state result, calculating the gap between the training state result and the potential fault label, obtaining a third training gap, and judging whether the third training gap is less than or equal to a preset loss threshold. If the third training gap is less than or equal to the preset loss threshold, determining that the third model to be trained is successfully trained and obtaining the preset potential prediction model; if the third training gap is greater than the preset loss threshold, determining that the third model to be trained has failed to be trained, re-obtaining the third training sample, and iteratively training the third model to be trained until the third training gap is less than or equal to the preset loss threshold.

[0119] Further, see Figure 3 In another feasible embodiment, after step S40, steps X10 to X30 are also included:

[0120] Step X10, when the CNC machine tool is in a non-operating state, periodically acquiring third sensor data of the CNC machine tool in the non-operating state;

[0121] Step X20, inputting the third sensor data into a simulation module of the CNC machine tool, and simulating a third machining operation state of the CNC machine tool in the simulation module based on the third sensor data;

[0122] Step X30, when it is detected that the third processing operation state is abnormal, an alarm prompt is output.

[0123] It should be noted that the third sensor data is the corresponding sensor data of the CNC machine tool in a non-operating state, and the third sensor data may include temperature and humidity data, oil volume data of the CNC machine tool, etc., and may also include the tool cutting speed and cutting depth of the CNC machine tool before stopping operation. This embodiment does not make specific limitations on this.

[0124] The simulation module is a module in the control system of the CNC machine tool for simulating the operating state of the CNC machine tool. After the third sensor data is input into the simulation module, the third processing operating state of the CNC machine tool can be simulated in the simulation module based on the third sensor data. The third processing operating state refers to the operating state of the CNC machine tool simulated based on the third sensor data. When the simulation fails, it means that there is an abnormality when simulating the operating state of the CNC machine tool based on the third sensor data, indicating that the third sensor data is abnormal, and an alarm prompt can be output to prompt that the current CNC machine tool has an abnormality, so as to prompt the maintenance personnel of the CNC machine tool to repair the CNC machine tool in time to ensure that the CNC machine tool can operate normally and stably when it is turned on.

[0125] In this embodiment, the third sensor data of the CNC machine tool in a non-operating state can also be periodically monitored to periodically detect whether the CNC machine tool is abnormal, so as to monitor whether the CNC machine tool can operate normally when the CNC machine tool is in a non-operating state, and solve the problem of high monitoring cost caused by monitoring. The non-operating state refers to the state in which the CNC machine tool is idle and does not process the workpiece.

[0126] Exemplarily, when the CNC machine tool is in a non-operating state, the third sensor data of the CNC machine tool in the non-operating state is periodically acquired, and the third sensor data is input into a simulation module of the CNC machine tool. In the simulation module, the third processing operation state of the CNC machine tool is simulated based on the third sensor data. When the simulation module simulates an interruption or an abnormality, an alarm prompt is output. When the simulation module simulates normally, a prompt that the CNC machine tool can operate normally can be output.

[0127] This application also provides a maintenance device for CNC machine tools, please refer to Figure 4 , the maintenance equipment of CNC machine tools includes:

[0128] The monitoring module 10 is used to obtain a first part having a fault in the CNC machine tool and first sensor data associated with the first part when a fault is detected in the CNC machine tool during operation;

[0129] A potential prediction module 20, configured to determine, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part is faulty;

[0130] A maintenance parameter determination module 30, for inputting the first sensing data and the acquired potential fault data of the potential fault part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential fault part;

[0131] The simulation module 40 is used to use the first maintenance parameter and the second maintenance parameter as the maintenance plan of the CNC machine tool when it is determined that the first maintenance parameter and the second maintenance parameter meet the preset normal conditions; the preset normal conditions indicate that there is no abnormality in the processing operation status of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter.

[0132] The maintenance device of the CNC machine tool provided in the present application adopts the maintenance method of the CNC machine tool in the above-mentioned embodiment, aiming to solve the technical problem of low maintenance efficiency of the CNC machine tool. Compared with the prior art, the beneficial effects of the maintenance method of the CNC machine tool provided in the embodiment of the present application are the same as the beneficial effects of the maintenance method of the CNC machine tool provided in the above-mentioned embodiment, and the other technical features in the maintenance device of the CNC machine tool are the same as the features disclosed in the above-mentioned embodiment method, which are not repeated here. The embodiment of the present application provides an electronic device, which can be a playback device, and the electronic device includes: at least one processor; and a memory connected to the communication with the at least one processor; wherein the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that at least one processor can execute the maintenance method of the CNC machine tool in the above-mentioned embodiment.

[0133] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0134] like Figure 5As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In RAM 1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0135] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 1009, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0136] The electronic device provided by the present application adopts the maintenance method of the CNC machine tool in the above-mentioned embodiment 1 to solve the technical problem of low maintenance efficiency of the CNC machine tool. Compared with the prior art, the beneficial effect of the product flow data distribution provided by the embodiment of the present application is the same as the beneficial effect of the maintenance method of the CNC machine tool provided by the above-mentioned embodiment, and the other technical features in the maintenance device of the CNC machine tool are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here. It should be understood that the various parts of the present disclosure can be implemented by hardware, software, firmware or a combination thereof. In the description of the above-mentioned embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples. The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims. The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, and the computer-readable program instructions are used to execute the maintenance method of the CNC machine tool in the above-mentioned embodiment 1. The computer-readable storage medium provided in the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, equipment or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable EPROM (Electrical Programmable Read Only Memory) or flash memory, an optical fiber, a portable compact disk CD-ROM (compact disc read-only memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution device, device or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency, radio frequency), etc., or any suitable combination of the above. The above-mentioned computer-readable storage medium may be contained in an electronic device; it may also exist alone without being assembled into an electronic device.The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device: when detecting that the CNC machine tool fails during operation, obtains a first part with a fault in the CNC machine tool and first sensor data associated with the first part; determines a potential faulty part associated with the first part when the first part fails based on the first sensor data and a preset potential prediction model; inputs the first sensor data and the acquired potential faulty data of the potential faulty part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential faulty part; when determining that the first maintenance parameter and the second maintenance parameter meet preset normal conditions, uses the first maintenance parameter and the second maintenance parameter as a maintenance plan for the CNC machine tool; the preset normal conditions indicate that there is no abnormality in the processing operation state of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter.

[0137] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN (local area network) or a WAN (Wide Area Network), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0138] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based device that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] The modules involved in the embodiments described in the present disclosure may be implemented by software or by hardware. The name of the module does not constitute a limitation on the unit itself under certain circumstances. The computer-readable storage medium provided in the present application stores computer-readable program instructions for executing the above-mentioned maintenance method for CNC machine tools, aiming to solve the technical problem of low maintenance efficiency of CNC machine tools. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the embodiments of the present application are the same as the beneficial effects of the maintenance method for CNC machine tools provided in the above-mentioned embodiments, which will not be repeated here.

[0140] The present application also provides a computer program product, including a computer program, which implements the steps of the maintenance method of the CNC machine tool as described above when the computer program is executed by a processor. The computer program product provided by the present application is intended to solve the technical problem of low maintenance efficiency of CNC machine tools. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as the beneficial effects of the maintenance method of the CNC machine tool provided by the above embodiment, which will not be repeated here. The above is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, are similarly included in the patent processing scope of the present application.

Claims

1. A maintenance method for a CNC machine tool, characterized in that: The method includes: When a fault is detected in the CNC machine tool during operation, obtaining a first part having a fault in the CNC machine tool and first sensor data associated with the first part; Determine, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part fails; Input the first sensing data and the acquired potential failure data of the potential failure part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential failure part; In the case where it is determined that the first maintenance parameter and the second maintenance parameter meet a preset normal condition, the first maintenance parameter and the second maintenance parameter are used as a maintenance plan for the CNC machine tool; the preset normal condition indicates that there is no abnormality in the processing operation state of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter; The step of determining, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part is faulty comprises: Inputting the first sensing data into a preset potential prediction model, determining abnormal data from the first sensing data, and first coordinate data of the first part in the CNC machine tool; Determining an abnormality coefficient of the first part according to a gap between the abnormal data and preset standard data; determining a fault impact area centered on the first coordinate data according to the abnormal coefficient and the first coordinate data, wherein the abnormal coefficient is positively correlated with the fault impact area; taking a target machine tool part in the fault-affected area of ​​the numerically controlled machine tool as a potential faulty part of the first part; The abnormality coefficient represents the severity of the fault of the first part. The fault affected area is the area in the CNC machine tool affected by the fault of the first part. The fault affected area is an area within a sphere with a radius centered on the first coordinate data of the first part and a preset impact value. The preset impact value is the product of the preset radius and the abnormality coefficient.

2. The method according to claim 1, characterized in that The potential fault data includes an abnormal coefficient of the first part and second sensor data of the potential fault part; The step of inputting the first sensing data and the acquired potential failure data of the potential failure part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential failure part comprises: Inputting the first sensor data into the preset large model, and determining a first maintenance parameter matching the first sensor data in a machine tool maintenance database corresponding to the preset large model; The potential fault data is input into a preset large model, an abnormal coefficient and second sensor data are determined from the potential fault data, and a second maintenance parameter matching the abnormal coefficient and the second sensor data is determined in a machine tool maintenance database corresponding to the preset large model.

3. The method according to claim 1, characterized in that The processing operation state includes a first processing operation state and a second processing operation state. Before the step of using the first maintenance parameter and the second maintenance parameter as the maintenance plan of the numerical control machine tool when it is determined that the first maintenance parameter and the second maintenance parameter meet the preset normal condition, the method further includes: Calling a preset simulation model, replacing a first target simulation parameter in the preset simulation model that matches the parameter type of the first maintenance parameter with the first maintenance parameter, and simulating a first machining operation state of the numerically controlled machine tool corresponding to the first maintenance parameter in the preset simulation model; If there is no abnormality in the first processing operation state, determining that the first maintenance parameter meets the preset normal condition; Calling a preset simulation model, replacing a second target simulation parameter in the preset simulation model that matches the parameter type of the second maintenance parameter with the second maintenance parameter, and simulating a second machining operation state of the numerically controlled machine tool corresponding to the second maintenance parameter in the preset simulation model; If there is no abnormality in the second processing operation state, it is determined that the second maintenance parameter meets the preset normal condition.

4. The method according to claim 3, characterized in that After the steps of calling the preset simulation model, replacing the first target simulation parameter matching the parameter type of the first maintenance parameter in the preset simulation model with the first maintenance parameter, and simulating the first machining operation state of the numerically controlled machine tool corresponding to the first maintenance parameter in the preset simulation model, the method further includes: calling a preset generalized model to predict at least one debugging parameter for simulating debugging of the CNC machine tool when simulating a first machining operation state of the CNC machine tool by using the first maintenance parameter; Determine at least one debugging combination according to each of the debugging parameters, wherein the debugging combination includes at least one debugging parameter; The first maintenance parameter is adjusted according to each of the debugging combinations to obtain generalized maintenance parameters corresponding to each of the debugging combinations; Calling the preset simulation model to determine a target generalized maintenance parameter that meets a preset generalization condition among the generalized maintenance parameters; updating the first maintenance parameter according to the target generalized maintenance parameter; The preset generalization condition includes that the third machining operation state of the simulated numerical control machine tool corresponding to the target generalized maintenance parameter does not have an abnormality.

5. The method according to claim 3, characterized in that After the step of simulating the second machining operation state of the numerically controlled machine tool corresponding to the second maintenance parameter in the preset simulation model, the method further includes: When it is determined that the first maintenance parameter satisfies a preset normal condition and the second maintenance parameter does not satisfy the preset normal condition, the first maintenance parameter is used as a maintenance plan for the CNC machine tool.

6. The method according to claim 1, characterized in that After determining that the first maintenance parameter and the second maintenance parameter meet the preset normal condition, taking the first maintenance parameter and the second maintenance parameter as the maintenance plan of the numerically controlled machine tool, the method further includes: When the numerically controlled machine tool is in a non-operating state, periodically acquiring third sensor data of the numerically controlled machine tool in the non-operating state; Inputting the third sensor data into a simulation module of the CNC machine tool, and simulating a third machining operation state of the CNC machine tool in the simulation module based on the third sensor data; When it is detected that the third processing operation state is abnormal, an alarm prompt is output.

7. A maintenance device for a CNC machine tool, characterized in that: The device comprises: A monitoring module, for acquiring a first part having a fault in the CNC machine tool and first sensor data associated with the first part when a fault is detected in the CNC machine tool during operation; A potential prediction module, used for determining, based on the first sensing data and a preset potential prediction model, a potential faulty part associated with the first part when the first part is faulty; A maintenance parameter determination module, used for inputting the first sensing data and the acquired potential failure data of the potential failure part into a preset large model to obtain a first maintenance parameter of the first part and a second maintenance parameter of the potential failure part; a simulation module, configured to use the first maintenance parameter and the second maintenance parameter as a maintenance plan for the CNC machine tool when it is determined that the first maintenance parameter and the second maintenance parameter meet a preset normal condition; the preset normal condition indicates that there is no abnormality in the processing operation state of the CNC machine tool simulated by the first maintenance parameter and / or the second maintenance parameter; A potential prediction module is used to input the first sensor data into a preset potential prediction model, determine abnormal data and first coordinate data of the first part in the CNC machine tool from the first sensor data; determine the abnormal coefficient of the first part according to the gap between the abnormal data and the preset standard data; determine the fault impact area centered on the first coordinate data according to the abnormal coefficient and the first coordinate data, and the abnormal coefficient is positively correlated with the fault impact area; use the target machine tool part in the CNC machine tool that belongs to the fault impact area as the potential fault part of the first part; the abnormal coefficient represents the severity of the fault of the first part, the fault impact area is the area in the CNC machine tool that is affected by the fault of the first part, and the fault impact area is the area within a sphere with a preset impact value as the radius centered on the first coordinate data of the first part, and the preset impact value is the product of the preset radius and the abnormal coefficient.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the maintenance method of the CNC machine tool described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that: The readable storage medium is a computer-readable storage medium, on which is stored a program for implementing a maintenance method for a CNC machine tool. The program for implementing the maintenance method for a CNC machine tool is executed by a processor to implement the steps of the maintenance method for a CNC machine tool as described in any one of claims 1 to 6.

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