Control method and device of drilling and tapping machine and electronic equipment

Abstract

The invention provides a control method and device of a drilling and tapping machine and electronic equipment, and relates to the field of data processing. The method comprises the steps that temperature data and deformation data which are sent by a nano-film sensor and aim at a processing object are obtained, and the nano-film sensor is located in a processing area corresponding to the processing object; according to the temperature data and the deformation data, a temperature distribution diagram of the machining area is determined; based on the temperature distribution diagram, surrounding type cooling equipment of the drilling and tapping machine is controlled to cool the machined object in real time; determining a first thread image of a thread corresponding to the processing object according to the deformation data; acquiring a second thread image which is sent by image acquisition equipment of the drilling and tapping machine and aims at a thread corresponding to the processing object; and based on the first thread image and the second thread image, adopting a preset support vector machine model to generate a quality result of a thread corresponding to the processing object. According to the technical scheme, quality detection can be conveniently conducted on the threads machined by the drilling and tapping machine.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a control method, device and electronic equipment for a drilling and tapping machine. Background Art

[0002] At present, drilling and tapping machines are an important mechanical processing equipment, widely used in the field of industrial manufacturing, used to process various specifications of through holes, blind holes and internal threads on workpieces. The processing of this equipment will generate a lot of heat, and an effective cooling system is needed to control the temperature to avoid tool wear and thermal deformation of the workpiece due to temperature rise, which will affect the quality of thread processing.

[0003] At present, the existing drilling and tapping machines have a low degree of intelligence in the cooling process, usually using a fixed spray coolant mode, which cannot be adaptively adjusted according to the real-time processing status, resulting in unsatisfactory cooling effect. Among them, the unsatisfactory cooling process easily leads to temperature accumulation in the processing area, affecting the thread shape and accuracy, and bringing adverse effects to the subsequent thread quality inspection.

[0004] Therefore, a control method, device and electronic equipment for a drilling and tapping machine are urgently needed. Summary of the invention

[0005] The present application provides a control method, device and electronic equipment for a drilling and tapping machine, which are convenient for quality inspection of threads processed by the drilling and tapping machine.

[0006] In the first aspect of the present application, a control method for a drilling and tapping machine is provided, the method comprising: obtaining temperature data and deformation data for a processing object sent by a nanofilm sensor, the nanofilm sensor being located in a processing area corresponding to the processing object; determining a temperature distribution map of the processing area based on the temperature data and the deformation data; based on the temperature distribution map, controlling a surround cooling device of the drilling and tapping machine to cool the processing object in real time; determining a first thread image of a thread corresponding to the processing object based on the deformation data; obtaining a second thread image of a thread corresponding to the processing object sent by an image acquisition device of the drilling and tapping machine; and based on the first thread image and the second thread image, using a preset support vector machine model to generate a quality result of the thread corresponding to the processing object.

[0007] By adopting the above technical solutions, the nano film sensor can monitor the temperature and deformation data of the processing area in real time, accurately reflect the temperature changes during the processing, and avoid the temperature estimation error in the traditional method. The temperature distribution map can help to more accurately understand the temperature distribution of each part during the processing, avoiding the inefficiency of the global cooling solution. According to the real-time temperature distribution, the system can accurately adjust the cooling strategy to reduce the problems caused by excessive or insufficient cooling. The real-time adjustment of the surround cooling device can adaptively change according to the temperature distribution of the processing area, avoiding the deformation of the workpiece or excessive wear of the tool caused by uneven temperature. Excessive heat may cause thermal expansion or deformation of the workpiece or thread, thereby affecting the accuracy and quality of the thread. By accurately controlling the temperature and preventing overheating, the negative impact of this thermal expansion on thread processing can be effectively reduced. Real-time cooling control minimizes temperature fluctuations during processing, ensures processing stability, and improves the processing accuracy and consistency of the thread. Using deformation data, the system can timely detect whether deformation occurs during thread processing, and analyze it through the first thread image to provide a basis for subsequent quality assessment. This method is more efficient and accurate than traditional manual measurement. The second thread image is acquired through the image acquisition device of the drilling and tapping machine, providing an actual finished image of the thread, which can be compared with the first thread image to find any minor quality problems. The support vector machine model is used to compare and analyze the first thread image and the second thread image, which can automatically identify the quality problems of the thread. After training, the support vector machine model can efficiently and accurately evaluate the quality of the thread, and even identify minor defects that cannot be found by traditional detection methods. Model-based quality assessment can provide real-time feedback, and adjust the processing parameters or cooling strategies in time when quality problems are found, thereby achieving closed-loop control and ensuring processing quality. Therefore, it is convenient to perform quality inspection on threads processed by drilling and tapping machines.

[0008] Optionally, the method of acquiring the temperature data and deformation data for the processing object sent by the nanofilm sensor specifically includes: receiving the original temperature data sent by the thermosensitive nanofilm sensor, the thermosensitive nanofilm sensor is used to detect in real time the local overheating phenomenon caused by heat accumulation and / or cutting friction of the processing object during the processing process; receiving the original deformation data sent by the piezoelectric nanofilm sensor, the piezoelectric nanofilm sensor is used to detect the thread shape through the deformation of the film surface; performing data processing on the original temperature data and the original deformation data to obtain the temperature data and the deformation data, and the data processing includes denoising, filtering and normalization.

[0009] By adopting the above technical solutions, the thermosensitive nano film sensor can monitor the temperature change of the processing object in real time, especially during the processing, due to the local overheating phenomenon caused by cutting friction and heat accumulation. The traditional thread quality detection often relies on later manual inspection or inaccurate mechanical measurement. The use of piezoelectric sensors improves the accuracy and real-time performance of monitoring. Remove irrelevant data caused by sensor noise or environmental interference to ensure that the data is cleaner and more accurate. Remove high-frequency and unnecessary fluctuations through filtering technology to make the data smoother and avoid misjudgment caused by data jitter. Normalize data from different sources and scales to ensure that all types of data are processed under the same standard, so that temperature data and deformation data can be better compared and analyzed, avoiding the deviation of the results caused by different data scales. Through real-time processing of temperature and deformation data, the system can instantly identify abnormalities in the processing process. Traditional detection methods have delays and it is difficult to provide real-time feedback. Real-time data processing based on nano film sensors can detect problems at the early stage of the problem, issue warnings in advance, and help operators adjust processing parameters or deal with problems in time to avoid serious quality defects or equipment damage.

[0010] Optionally, determining the temperature distribution map of the processing area based on the temperature data and the deformation data specifically includes: determining the temperature distribution data of each layer of thread corresponding to the processing area based on the temperature data; extracting the electrical signal data of the surface changes of the film for analysis based on the deformation data to generate a three-dimensional graph; adding the temperature distribution data to the three-dimensional graph to obtain the temperature distribution map of the processing area.

[0011] By adopting the above technical solution, the temperature status of different thread layers during the processing can be more accurately understood by refining the temperature distribution of each layer of the thread. Since thread processing is a layer-by-layer process, the temperature change of each layer will affect the quality and shape of the thread, so it is crucial to accurately capture the temperature change of each layer. This fine-grained temperature data helps to more accurately control the temperature during the processing and prevent thread deformation or quality problems caused by local overheating. Deformation data is usually used to monitor the shape change of the thread, while temperature data is used to control the impact of thermal effects on thread processing. Through this local temperature analysis, more accurate cooling control of the processing area can be performed. The quality of the thread is not only affected by the processing temperature, but also by the deformation during the processing. By combining temperature data with deformation data, the processing status can be fully evaluated and potential problems can be found. For example, excessive temperature may cause metal expansion, while deformation data can help identify whether the slight deformation of the thread is caused by abnormal temperature. By integrating temperature distribution data and deformation data in real time to generate a three-dimensional graph, the system can promptly detect abnormalities in the processing and automatically adjust the processing strategy. This closed-loop control can optimize the processing process and improve thread quality and processing efficiency.

[0012] Optionally, based on the temperature distribution map, the surrounding cooling device of the drilling and tapping machine is controlled to cool down the processing object in real time, specifically including: determining a first area and a second area according to the temperature distribution map, the temperature average corresponding to the first area is higher than the temperature average corresponding to the second area, the first area and the second area are any two areas among the multiple areas included in the processing area; determining a first coolant nozzle corresponding to the first area and a second coolant nozzle corresponding to the second area, one area corresponds to one coolant nozzle, and the coolant nozzle is used to spray atomized coolant to the area; sending control instructions to the first coolant nozzle and the second coolant nozzle respectively, so as to control the first coolant nozzle to spray atomized coolant to the first area at a first shooting speed, and to control the second coolant nozzle to spray atomized coolant to the second area at a second shooting speed, the first shooting speed being higher than the second shooting speed.

[0013] By adopting the above technical solution, by analyzing the temperature distribution map, the areas with higher temperatures and lower temperatures in the processing area can be determined. This regional division based on actual temperature data can achieve more accurate temperature control and ensure that the cooling requirements of different temperature zones are met. By selecting coolant nozzles with different shooting rates to accurately control the cooling effect, the cooling system can adjust the cooling requirements of different areas more intelligently and individually. The system can respond to temperature changes in the processing process in real time by automatically sensing and adjusting the temperature, so that the cooling equipment no longer relies on manual settings, enhancing the intelligence and automation level of the system. According to the real-time changes in temperature during the processing process, the system can dynamically adjust the cooling strategy to ensure the accuracy of the cooling process. This highly adaptable cooling control can effectively reduce the impact of temperature fluctuations on processing accuracy and improve the accuracy of thread processing. By reasonably controlling the cooling rate and distribution, the thermal stress of the workpiece caused by excessive or uneven cooling can be avoided, thereby reducing rework and scrap and improving production efficiency. Local overheating will accelerate tool wear and may even cause equipment failure. By accurately controlling the temperature and reducing unnecessary thermal damage, the service life of the drilling and tapping machine and its components can be extended and failures caused by overheating can be reduced. Controlling the firing rate and injection volume to avoid excessive coolant consumption caused by over-cooling helps save coolant and reduce system maintenance requirements.

[0014] Optionally, determining the first thread image of the thread corresponding to the processing object based on the deformation data specifically includes: obtaining time series data when the drilling and tapping machine processes the processing object within a preset time period; performing data enhancement on the three-dimensional graphics, combining the time series data, and obtaining a three-dimensional image through 3D modeling; and extracting the first thread image of the thread corresponding to the processing object based on the three-dimensional image.

[0015] By adopting the above technical solution, by acquiring the time series data when the drilling and tapping machine processes the processing object within a preset time period, the dynamic changes of the thread morphology during the processing process can be captured. This time series data can reflect the gradual changes in the thread shape during the processing process, thereby avoiding focusing only on static thread images and improving the timeliness and accuracy of quality control. This dynamic tracking method makes quality control no longer rely on images at a single moment, but is based on data from the entire processing process, thereby more comprehensively evaluating the processing quality. Data enhancement of three-dimensional graphics and combining time series data to generate 3D images can make the final thread image closer to the actual situation during the processing process. Data enhancement technology improves the visibility and detail of the image by expanding the sample data set and improving the image quality, so that it can more accurately reflect the slight deformations that occur during the processing process. For high-precision processing, any slight deformation may affect the final quality, and three-dimensional modeling can help workers better monitor and correct these deformations. Combining time series data to update three-dimensional graphics in real time can provide detailed dynamic changes in the thread processing process, help to promptly discover problems and take corresponding adjustment measures. The method of generating thread images based on time series data and three-dimensional images can realize automated thread quality assessment and reduce manual intervention. Traditional quality assessment methods usually require manual inspection and analysis of thread shape and quality, while automated image generation and analysis can greatly improve the efficiency and consistency of assessment and reduce human errors.

[0016] Optionally, based on the first thread image and the second thread image, a preset support vector machine model is used to generate the quality result of the thread corresponding to the processing object, specifically including: fusing the first thread image and the second thread image to obtain a fused thread image; inputting the fused thread image into the preset support vector machine model to obtain a hyperspace coordinate; judging the positional relationship between the hyperspace coordinate and the hyperplane corresponding to the preset support vector machine model; based on the positional relationship, calculating a first probability value and a second probability value, the first probability value is used to represent the probability value corresponding to the hyperspace coordinate on the first side of the hyperplane, and the second probability value is used to represent the probability value corresponding to the hyperspace coordinate on the second side of the hyperplane, the first side and the second side being two opposite sides of the hyperplane; if it is determined that the hyperspace coordinate is located on the first side and the first probability value is greater than or equal to a preset threshold, then it is determined that the quality result indicates normal; if it is determined that the hyperspace coordinate is located on the second side and the second probability value is greater than or equal to the preset threshold, then it is determined that the quality result indicates abnormal.

[0017] By adopting the above technical solution, the first thread image and the second thread image are fused to combine data from different sources to avoid information that may be missed by a single image. The first image and the second image may capture different features of the processed object at different angles or time points. After data fusion, the true form of the thread can be more fully expressed. By fusing image data, not only can the accuracy of quality assessment be improved, but also the deviation caused by the single image perspective or shooting conditions can be reduced. By calculating the first probability value and the second probability value, the support vector machine can assign a probability value to each classification result. Unlike traditional hard classification, the probability method provides quantification of the uncertainty of the classification result, which can more clearly show the confidence of the model in a certain category and help make more appropriate decisions. When the first probability value or the second probability value is greater than the preset threshold, the quality status of the thread can be relatively determined. If the probability value is large enough, it means that the classification is more reliable, which helps to avoid misjudgment or missed judgment and ensure accurate identification of thread quality. The support vector machine model can distinguish between abnormal threads and normal threads through hyperplanes, and automatically calculate the probability value of abnormalities based on the relative position of the hyperspace coordinates and the hyperplane. This method does not rely on manual inspection, is more automated and accurate, and can promptly detect any quality problems that occur during the processing process to avoid defective products. By setting the threshold, the sensitivity of the classification can be flexibly controlled. Through algorithmic processing, the results of each evaluation can be kept consistent, and the evaluation standards are fixed, which provides strong quality assurance for large-scale production and ensures that each thread meets the standards.

[0018] Optionally, the method also includes: obtaining training data, the training data including sample data, and normal quality data and abnormal quality data corresponding to the sample data; inputting the sample data, and the normal quality data and abnormal quality data corresponding to the sample data into an initial model for training; setting a kernel function and model parameters for the initial model, and optimizing the initial model through an optimization algorithm to obtain the preset support vector machine model, the model parameters including regularization parameters and kernel parameters.

[0019] By adopting the above technical solutions, after preliminary training, the support vector machine can form a basic decision boundary or hyperplane to distinguish data points with normal quality and abnormal quality. The construction of the initial model lays the foundation for subsequent optimization. Inputting sample data for preliminary training can enable the model to quickly understand the basic structure and quality judgment rules of the data, and prepare for further optimization. Setting a suitable regularization parameter value can balance the complexity of the model with the degree of fit of the training data, thereby improving the accuracy of the model. Adjusting the kernel parameters can change the sensitivity of the model to local features, thereby improving its adaptability to different data patterns, so that the model can accurately capture important patterns in the data. By training and optimizing the support vector machine model, the thread quality in the processing process can be automatically judged and evaluated without manual intervention. The automation of this process makes the production process more efficient, while reducing the subjectivity and errors of manual inspection. Automated quality assessment can provide real-time feedback on the quality status of the thread, helping production equipment to adjust process parameters in a timely manner to ensure that the quality of each thread meets the standard. By quickly and accurately detecting the quality of the thread, defective products in production can be avoided, the qualified rate of products can be improved, and production costs can be reduced.

[0020] In a second aspect of the present application, a control device for a drilling and tapping machine is provided, the control device comprising an acquisition module and a processing module, wherein the acquisition module is used to acquire temperature data and deformation data for a processing object sent by a nanofilm sensor, and the nanofilm sensor is located in a processing area corresponding to the processing object; the processing module is used to determine a temperature distribution map of the processing area based on the temperature data and the deformation data; the processing module is also used to control the surround cooling device of the drilling and tapping machine to cool the processing object in real time based on the temperature distribution map; the processing module is also used to determine a first thread image of the thread corresponding to the processing object based on the deformation data; the acquisition module is also used to acquire a second thread image of the thread corresponding to the processing object sent by an image acquisition device of the drilling and tapping machine; the processing module is also used to generate a quality result of the thread corresponding to the processing object using a preset support vector machine model based on the first thread image and the second thread image.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: Nanofilm sensors can monitor the temperature and deformation data of the processing area in real time, accurately reflect the temperature changes during the processing, and avoid the temperature estimation errors in traditional methods. The temperature distribution map can help to more accurately understand the temperature distribution of each part during the processing, avoiding the inefficiency of the global cooling solution. According to the real-time temperature distribution, the system can accurately adjust the cooling strategy to reduce the problems caused by excessive or insufficient cooling. The real-time adjustment of the surround cooling device can adaptively change according to the temperature distribution of the processing area, avoiding the deformation of the workpiece or excessive wear of the tool caused by uneven temperature. Excessive heat may cause thermal expansion or deformation of the workpiece or thread, which in turn affects the accuracy and quality of the thread. By accurately controlling the temperature and preventing overheating, the negative impact of this thermal expansion on thread processing can be effectively reduced. Real-time cooling control minimizes temperature fluctuations during processing, ensures processing stability, and improves the processing accuracy and consistency of the thread. Using deformation data, the system can timely detect whether deformation occurs during thread processing, and analyze it through the first thread image to provide a basis for subsequent quality assessment. This method is more efficient and accurate than traditional manual measurement. The second thread image is acquired through the image acquisition device of the drilling and tapping machine, providing an actual finished image of the thread, which can be compared with the first thread image to find any minor quality problems. The support vector machine model is used to compare and analyze the first thread image and the second thread image, which can automatically identify the quality problems of the thread. After training, the support vector machine model can efficiently and accurately evaluate the quality of the thread, and even identify minor defects that cannot be found by traditional detection methods. Model-based quality assessment can provide real-time feedback, and adjust the processing parameters or cooling strategies in time when quality problems are found, thereby achieving closed-loop control and ensuring processing quality. Therefore, it is convenient to perform quality inspection on threads processed by drilling and tapping machines. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a control method for a drilling and tapping machine provided in an embodiment of the present application.

[0025] Figure 2 Another flow chart of a control method for a drilling and tapping machine provided in an embodiment of the present application.

[0026] Figure 3 A module schematic diagram of a control device for a drilling and tapping machine provided in an embodiment of the present application.

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. DETAILED DESCRIPTION

[0029] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0030] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0031] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0032] As a key mechanical processing equipment, drilling and tapping machines are widely used in industrial production to process through holes, blind holes and internal threads of various specifications. However, during the processing, a large amount of heat is generated. If the temperature cannot be effectively controlled, it will cause tool wear and thermal deformation of the workpiece, thus affecting the processing quality of the thread.

[0033] At present, the cooling system of most drilling and tapping machines has a low degree of intelligence. They usually use a fixed mode to spray coolant, which cannot be dynamically adjusted according to the real-time processing status, resulting in unsatisfactory cooling effect. Uneven or insufficient cooling will cause temperature accumulation in the processing area, which will affect the shape and accuracy of the thread, and further affect the subsequent thread quality inspection.

[0034] In order to solve the above technical problems, the present application provides a control method for a drilling and tapping machine, referring to Figure 1 , Figure 1 A flow chart of a control method for a drilling and tapping machine provided in an embodiment of the present application. The control method is applied to a server and includes steps S110 to S160, which are as follows: S110, acquiring temperature data and deformation data of the processing object sent by the nano film sensor, where the nano film sensor is located in a processing area corresponding to the processing object.

[0035] Specifically, the server is the core part of data processing. It is responsible for receiving data from sensors and processing, analyzing, storing or transmitting them. The role of the server is to centrally manage and analyze information in order to make decisions or guide subsequent operations. The server needs to receive data transmitted from sensors in real time. In this system, the data types include temperature data and deformation data, which will provide key information about the processing object, such as workpiece, material or processing area.

[0036] Nanofilm sensors are highly sensitive sensors made of tiny nanomaterials. Due to their small size and fast response, they are able to detect tiny physical changes and convert them into electronic signals. Nanofilm sensors can measure the temperature of the processing area, which is crucial to controlling the processing process. Temperature changes directly affect tool life, workpiece quality, and processing accuracy. For example, during drilling and tapping, if the temperature is too high, it may cause material deformation or loss of thread accuracy. Nanofilm sensors can also measure material deformation. These deformations are usually caused by external forces or stresses and are related to factors such as friction and pressure during the processing process. Deformation data can reflect physical changes in materials, such as slight bending or deformation of the workpiece during processing, which is very important for controlling processing accuracy. The processing object refers to the material or workpiece processed on equipment such as drilling and tapping machines. During the drilling and tapping process, the processing object may experience complex mechanical forces and temperature changes, resulting in changes in shape, size, and other physical properties. The processing area refers to the part of the workpiece that is being processed, such as the location of the hole, the processing area of ​​the thread, etc. Sensors are placed in these areas to monitor temperature and deformation to ensure the accuracy of the processing process.

[0037] For example, suppose a worker is using a drilling and tapping machine to process a metal workpiece, and the surface of the workpiece will generate a lot of friction and heat during the drilling and tapping process. In order to ensure the accuracy and processing quality of the thread, nanofilm sensors are pre-installed in the processing area. These sensors can monitor the temperature of the processing area in real time. For example, suppose the sensor detects that the temperature rises to a certain threshold, indicating that the area is overheating, which may cause the tool to wear too quickly or the workpiece to deform. Similarly, the sensor will monitor the deformation of the processing area. If a part of the workpiece bends slightly due to excessive cutting force, the deformation data can capture this change and help adjust the tool or processing conditions to avoid thread accuracy problems. These sensors transmit temperature and deformation data to the server in real time. The server analyzes this data and may trigger automatic adjustment of the cooling system to avoid overheating, or adjust the processing parameters to compensate for deformation to ensure that the thread processing quality meets the requirements.

[0038] In a possible implementation, the temperature data and deformation data for the processing object sent by the nano film sensor are obtained, specifically including: receiving the original temperature data sent by the thermistor nano film sensor, the thermistor nano film sensor is used to detect in real time the local overheating phenomenon caused by heat accumulation and / or cutting friction of the processing object during the processing; receiving the original deformation data sent by the piezoelectric nano film sensor, the piezoelectric nano film sensor is used to detect the thread shape through the deformation of the film surface; performing data processing on the original temperature data and the original deformation data to obtain temperature data and deformation data, the data processing including denoising, filtering and normalization.

[0039] Specifically, the thermosensitive nanofilm sensor is used to monitor the temperature changes generated during the processing in real time, especially the local overheating caused by heat accumulation or cutting friction. Nanofilm sensors can accurately measure temperature changes and respond quickly due to their small structure and high sensitivity. During the processing, the drilling and tapping machine may cause the temperature of the local area to rise due to factors such as friction and cutting. The thermosensitive nanofilm sensor detects these temperature changes and converts them into electrical signals to generate raw temperature data. This data can provide real-time temperature information of the processing area. For example, during the thread processing process, the temperature of the workpiece and the tool may rise due to excessive friction, resulting in a decrease in processing accuracy. Piezoelectric nanofilm sensors sense the tiny deformations generated during the processing process by detecting the deformation of the film surface. The working principle of the piezoelectric sensor is that when the film surface is deformed, the piezoelectric material generates an electrical signal. In this way, the sensor can detect the deformation changes caused by the action of forces such as cutting force or friction during the processing. During the drilling and tapping process, the processing of the thread may cause a slight deformation on the workpiece surface, especially the threaded part. If the deformation is too large, it may affect the accuracy of the thread. Piezoelectric nanofilm sensors detect these deformations and convert them into electrical signals to form raw deformation data. For example, during the tapping process, if the threaded part is bent or deformed due to uneven cutting force or tool wear, the piezoelectric sensor will record this change. When drilling and tapping machines are processing threads, piezoelectric nanofilm sensors can monitor the slight deformation of the threads in real time. If deformation is found in a certain area that exceeds the preset threshold, the system can automatically adjust the cutting parameters or immediately start the cooling system to prevent further expansion of the deformation.

[0040] The data collected by the sensor is usually accompanied by some interference or noise, which may come from changes in the external environment or errors in the sensor itself. De-noising refers to filtering out these useless signals through a certain algorithm to ensure that the final temperature and deformation data are more accurate and reliable. Filtering refers to removing high-frequency noise or unnecessary fluctuations in the data through specific algorithms, such as low-pass filtering and high-pass filtering, so that the data is smoother and short-term, irregular fluctuations are avoided from interfering with the analysis results. Normalization is to convert data from different sources to the same scale for unified analysis. The values ​​of temperature and deformation data may vary greatly. Normalization can convert them to the same scale to avoid a certain type of data having too much impact on the analysis results due to large values. In the process of drilling and tapping machine processing, temperature data may have different magnitudes due to different sensor positions, while deformation data may have large fluctuations. After denoising, filtering and normalization, these data can be standardized to ensure that subsequent analysis will not cause system errors due to abnormal fluctuations in certain data.

[0041] S120, determining a temperature distribution map of the processing area according to the temperature data and the deformation data.

[0042] Specifically, first, the sensor collects temperature data at different points in the processing area. This data can be collected continuously or at specific time intervals, and the sensor can monitor different locations in the entire processing area. Piezoelectric sensors obtain processing-related data by detecting surface deformation. These data help understand which areas may produce significant deformations due to high temperatures, further affecting the processing accuracy of the threads. Based on the temperature data, the temperature changes in different parts of the processing area can be determined. Combined with the deformation data, it is possible to analyze which high-temperature areas may cause larger deformations. The server generates a temperature distribution map by analyzing and fusing this data. This distribution map shows the temperature of each processing area, and may combine these temperature data with deformation data to show which areas have too high temperatures and which areas may be affected by greater deformations.

[0043] In one possible implementation, a temperature distribution map of the processing area is determined based on temperature data and deformation data, specifically including: determining the temperature distribution data of each layer of thread corresponding to the processing area based on the temperature data; extracting electrical signal data of changes on the film surface for analysis based on the deformation data to generate a three-dimensional graph; and adding the temperature distribution data to the three-dimensional graph to obtain a temperature distribution map of the processing area.

[0044] Specifically, thread processing is divided into multiple levels or stages. Each layer of thread may be affected by different heat, because factors such as the contact mode between the tool and the workpiece and the cutting rate may cause different temperatures in different layers. Assume that during the processing, the temperature of the first layer of thread is higher due to the large cutting force, while the second layer of thread may be less affected by heat and has a lower temperature. Through the temperature sensor, the temperature data of each layer of thread can be obtained, which can help determine which layers of thread are prone to deformation problems caused by overheating. Using these deformation data, the slight deformation of the workpiece surface at different points can be analyzed, and then a three-dimensional graph can be generated. The three-dimensional graph is obtained by processing and converting these deformation data according to spatial coordinates, and can clearly show the changes of the surface during the processing in three-dimensional space. Assume that during the processing, a layer of thread has obvious deformation due to overheating. At this time, the deformation sensor will record this data, and after conversion, a three-dimensional graph can be formed to show the surface deformation of the thread area. After obtaining the three-dimensional graph of the surface deformation, the temperature distribution data can be fused into the three-dimensional graph. When the temperature distribution data is added to the three-dimensional deformation graph, a comprehensive temperature distribution graph will eventually be obtained. This image can more clearly show the temperature changes in the processing area and combine it with the surface deformation to help workers better understand the heat accumulation in specific processing areas and its impact on thread shape and quality.

[0045] S130, based on the temperature distribution diagram, controlling the surrounding cooling device of the drilling and tapping machine to cool the processing object in real time.

[0046] Specifically, the drilling and tapping machine is equipped with a cooling system for spraying coolant to reduce the temperature of the workpiece. The surround cooling device provided in the embodiment of the present application has a multi-point spraying function, which can perform all-round cooling around the processing area. In this way, the temperature of different areas can be controlled more evenly and accurately. Using the temperature distribution map, the cooling device can intelligently adjust the position and intensity of the sprayed coolant according to the temperature difference in different areas, so as to quickly cool down the area with more heat accumulation, avoid local overheating affecting the quality of the thread or causing the deformation of the workpiece. The control of the cooling device needs to be carried out in real time to ensure that the temperature changes during the processing can be quickly responded to. Through the feedback of the automation system and the temperature sensor, the cooling device can adjust the cooling intensity and the spraying mode in real time according to the temperature distribution map of the current workpiece. Real-time cooling avoids the hysteresis problem existing in traditional cooling methods. During the workpiece processing, the temperature change is dynamic, so real-time temperature monitoring and response are crucial, which can effectively prevent overheating, protect the workpiece and tools, and improve the processing quality.

[0047] In a possible implementation, based on a temperature distribution map, the surround cooling device of the drilling and tapping machine is controlled to cool the processing object in real time, specifically including: determining a first area and a second area according to the temperature distribution map, the temperature average corresponding to the first area is higher than the temperature average corresponding to the second area, the first area and the second area are any two areas of the multiple areas included in the processing area; determining a first coolant nozzle corresponding to the first area and a second coolant nozzle corresponding to the second area, one area corresponds to one coolant nozzle, and the coolant nozzle is used to spray atomized coolant into the area; sending control instructions to the first coolant nozzle and the second coolant nozzle respectively, so as to control the first coolant nozzle to spray atomized coolant into the first area at a first shooting speed, and to control the second coolant nozzle to spray atomized coolant into the second area at a second shooting speed, and the first shooting speed is higher than the second shooting speed.

[0048] Specifically, the temperature of different areas will be marked on the temperature distribution map. For example, the first area may be a part of the workpiece with a higher temperature, which may be due to the accumulation of heat caused by friction during the cutting process; while the temperature of the second area is lower, which may be due to the relatively smooth cutting process in this area. Based on this map, the server can automatically identify two areas, one with higher temperature and the other with lower temperature. During the processing, the temperature of the upper thread area of ​​the workpiece rises due to continuous cutting and friction, while the temperature of the lower thread area is lower. The server identifies the temperature difference between the two areas through temperature distribution map analysis. The coolant nozzle is a device used to spray coolant on the drilling and tapping machine. Each area corresponds to a coolant nozzle, and according to the temperature distribution map, the server matches different areas with corresponding nozzles. For example, the first coolant nozzle corresponding to the first area will be responsible for cooling the area, while the second coolant nozzle corresponding to the second area will be responsible for cooling the lower temperature area. According to the temperature distribution map, the server will set different shooting speeds to adjust the cooling effect. The first area requires stronger cooling, so the first shooting speed is higher; while the second area requires weaker cooling, so the second shooting speed is lower. By controlling the spray rate of the coolant nozzle, the spray intensity of the coolant can be precisely adjusted, thereby achieving more efficient cooling. The control server automatically adjusts the spray rate of each nozzle based on real-time data feedback, so that each area can get appropriate cooling.

[0049] For example, if the temperature of the upper threaded area of ​​the workpiece is 60°C, which requires strong cooling, the first coolant nozzle sprays coolant at a higher rate, such as 100L / h. The temperature of the lower threaded area is 45°C, which is more suitable, so the server adjusts the spray rate of the second coolant nozzle to a lower value, such as 50L / h, to avoid overcooling.

[0050] S140: Determine a first thread image corresponding to the thread of the processing object according to the deformation data.

[0051] Specifically, the deformation data can be optimized through data processing technology. Then, through graphics processing algorithms, such as 3D modeling and image reconstruction, these data are converted into images of the threads. By analyzing the distribution of surface deformation, the shape, depth, direction and other characteristics of the threads can be inferred. The generated image is a 3D model or 2D image of the threads on the workpiece surface.

[0052] In a possible implementation, a first thread image of a thread corresponding to a processing object is determined based on deformation data, specifically comprising: acquiring time series data when a drilling and tapping machine processes the processing object within a preset time period; performing data enhancement on three-dimensional graphics, combining the time series data, and obtaining a three-dimensional image through 3D modeling; and extracting a first thread image of a thread corresponding to the processing object based on the three-dimensional image.

[0053] Specifically, time series data refers to various types of data in the processing process that are continuously recorded within a specific time period. In drilling and tapping machines, these data include parameters such as tool position, cutting force, processing speed, temperature, pressure, etc. These data are a dynamic reflection of the processing process and help capture possible changes and trends in the processing process.

[0054] For example, suppose that a drilling and tapping machine continuously records information such as temperature changes and cutting forces during processing. For example, during the tapping process, the motion trajectory of the tool and the temperature changes at the processing position are recorded in real time as time series data. Data enhancement is a technique that generates more samples by transforming and processing the original data. In the field of image processing, data enhancement increases the diversity of data by rotating, scaling, translating, adding noise, etc., helping to improve the robustness of the model. In this scenario, the enhanced data is the processing of three-dimensional graphics based on time series data, with the aim of improving the accuracy of the generated three-dimensional image. By combining time series data, the changes in the thread during processing can be better captured and a more accurate three-dimensional image can be generated. For example, an increase in temperature may cause slight changes in the shape of the thread, and these changes can be combined with time series data and three-dimensional graphics to obtain a more accurate representation of the processing status.

[0055] S150, obtaining a second thread image corresponding to the thread of the processing object sent by the image acquisition device of the drilling and tapping machine.

[0056] Specifically, the image acquisition device of the drilling and tapping machine refers to an imaging device installed on the drilling and tapping machine, and the embodiment of the present application is preferably a high-resolution camera or a three-dimensional scanning device. The device can capture every detail of the thread processing process in real time to ensure that each processing step is within the specified accuracy range. Among them, the second thread image is obtained by the image acquisition device after the processing is completed, which is used to observe and evaluate the actual processing of the thread. Compared with the first thread image, the second image can provide more intuitive and realistic thread surface and shape information.

[0057] S160: Based on the first thread image and the second thread image, a preset support vector machine model is used to generate a quality result of the thread corresponding to the processing object.

[0058] Specifically, a support vector machine is a machine learning model for classification and regression analysis. In an embodiment of the present application, a support vector machine model is used to classify thread images and determine whether the processing quality of the thread is normal or abnormal. When using a support vector machine for quality assessment, the model generates a hyperspace coordinate based on the input image data, and then determines the position of these coordinates relative to a preset hyperplane to obtain a quality judgment. In this way, the support vector machine can effectively process and analyze high-dimensional data, such as image data. By inputting the first thread image and the second thread image into the support vector machine model, the server will judge the quality of the thread according to the model and generate corresponding quality results. The quality results usually include information such as whether the thread meets the standard, whether there are defects, and whether it is within a predetermined tolerance range.

[0059] In a possible implementation, based on the first thread image and the second thread image, a preset support vector machine model is used to generate a quality result of the thread corresponding to the processing object, specifically including: fusing the first thread image and the second thread image to obtain a fused thread image; inputting the fused thread image into the preset support vector machine model to obtain a hyperspace coordinate; judging the positional relationship between the hyperspace coordinate and the hyperplane corresponding to the preset support vector machine model; based on the positional relationship, calculating a first probability value and a second probability value, the first probability value is used to represent the probability value corresponding to the hyperspace coordinate on the first side of the hyperplane, and the second probability value is used to represent the probability value corresponding to the hyperspace coordinate on the second side of the hyperplane, the first side and the second side being two opposite sides of the hyperplane; if it is determined that the hyperspace coordinate is located on the first side, and the first probability value is greater than or equal to the preset threshold, then it is determined that the quality result indicates normal; if it is determined that the hyperspace coordinate is located on the second side, and the second probability value is greater than or equal to the preset threshold, then it is determined that the quality result indicates abnormal.

[0060] Specifically, data fusion refers to combining data from different sources to provide more comprehensive information for subsequent analysis. Among them, the first thread image and the second thread image represent the thread morphology at different stages or different perspectives during the processing process. Fusion of thread images refers to combining these two images together to generate an image containing more information through image stitching and feature fusion. The fused image will contain all the important features in the first and second images, and can more comprehensively display the shape and surface quality of the thread. After inputting the data, the support vector machine model distinguishes different categories of samples by finding a hyperplane in high-dimensional space. This hyperplane separates images with normal and abnormal thread quality. In the support vector machine, the input image data is mapped to a high-dimensional space, and each data point in the space has a coordinate, which is called the hyperspace coordinate. This coordinate represents the position of the image feature in the high-dimensional space. In this way, the support vector machine can process high-dimensional feature data and determine which category area the data point is in. Based on the positional relationship, the support vector machine can determine which category the sample belongs to. For example, some thread images may be mapped to one side of the hyperplane, while others may be mapped to the other side. The first probability value and the second probability value are used to represent the probability that the hyperspace coordinate is on both sides of the hyperplane. The first probability value represents the probability that the hyperspace coordinate is on the first side of the hyperplane, which represents the probability that the thread quality is normal. The second probability value represents the probability that the hyperspace coordinate is on the second side of the hyperplane, which represents the probability that the thread quality is abnormal. These probability values ​​are calculated from the output of the support vector machine model and are used to quantify the likelihood of thread quality.

[0061] In one possible implementation, refer to Figure 2 , Figure 2 Another flow chart of a control method for a drilling and tapping machine provided in an embodiment of the present application specifically includes steps S210 to S230, and the above steps are as follows: S210, obtaining training data, the training data including sample data, and normal quality data and abnormal quality data corresponding to the sample data; S220, inputting the sample data, and the normal quality data and abnormal quality data corresponding to the sample data into the initial model for training; S230, setting a kernel function and model parameters for the initial model, and optimizing the initial model through an optimization algorithm to obtain a preset support vector machine model, and the model parameters include regularization parameters and kernel parameters.

[0062] Specifically, the sample data is the input feature representing the thread image or related processing data. The sample data contains various information that affects the thread quality, such as thread morphology, depth, tooth shape, etc. Each sample data has a label indicating whether the thread quality corresponding to the sample is "normal" or "abnormal". This enables the model to learn how to distinguish between normal and abnormal threads during the training process. The sample data and the corresponding quality labels will be input into an initial model for model training. The initial model refers to an unoptimized model, which can be a basic support vector machine model. The purpose of training is to let the model learn to predict labels based on the input data. In this way, the model can classify the thread quality based on the features of the sample data. The kernel function is a key element in the support vector machine, which is used to map the input data to a high-dimensional space so that it is easier to find a hyperplane for classification in a higher dimension. Kernel functions include linear kernels, radial basis function kernels, etc. The regularization parameter is used to control the degree of fit of the model to the training data. A larger regularization parameter value will make the model fit the training data more closely, and a smaller regularization parameter value will allow more errors. The kernel parameter controls the influence range of the kernel function, which in turn affects the complexity and fitting ability of the model. Optimization algorithms are used to adjust the parameters of the model, such as gradient descent, to make it perform better on the training data. By adjusting the kernel function and regularization parameters, the optimization algorithm enables the model to have higher accuracy and generalization ability on new data. The optimized model is called the preset support vector machine model, which is more accurate than the initial model in predicting thread quality.

[0063] The present application also provides a control device for a drilling and tapping machine, referring to Figure 3 , Figure 3 A module schematic diagram of a control device for a drilling and tapping machine provided in an embodiment of the present application. The control device is a server, and the server includes an acquisition module 31 and a processing module 32, wherein the acquisition module 31 acquires temperature data and deformation data for a processing object sent by a nanofilm sensor, and the nanofilm sensor is located in a processing area corresponding to the processing object; the processing module 32 determines a temperature distribution map of the processing area based on the temperature data and deformation data; the processing module 32 controls the surrounding cooling device of the drilling and tapping machine to cool the processing object in real time based on the temperature distribution map; the processing module 32 determines a first thread image of a thread corresponding to the processing object based on the deformation data; the acquisition module 31 acquires a second thread image of a thread corresponding to the processing object sent by an image acquisition device of the drilling and tapping machine; the processing module 32 generates a quality result of the thread corresponding to the processing object using a preset support vector machine model based on the first thread image and the second thread image.

[0064] In a possible embodiment, the acquisition module 31 acquires the temperature data and deformation data for the processing object sent by the nanofilm sensor, specifically including: the acquisition module 31 receives the original temperature data sent by the thermistor nanofilm sensor, and the thermistor nanofilm sensor is used to detect in real time the local overheating phenomenon caused by heat accumulation and / or cutting friction of the processing object during the processing process; the acquisition module 31 receives the original deformation data sent by the piezoelectric nanofilm sensor, and the piezoelectric nanofilm sensor is used to detect the thread shape through the deformation of the film surface; the processing module 32 processes the original temperature data and the original deformation data to obtain temperature data and deformation data, and the data processing includes denoising, filtering and normalization.

[0065] In a possible implementation, the processing module 32 determines the temperature distribution map of the processing area based on the temperature data and the deformation data, specifically including: the processing module 32 determines the temperature distribution data of each layer of thread corresponding to the processing area based on the temperature data; the processing module 32 extracts the electrical signal data of the surface changes of the film based on the deformation data for analysis to generate a three-dimensional graph; the processing module 32 adds the temperature distribution data to the three-dimensional graph to obtain the temperature distribution map of the processing area.

[0066] In a possible implementation, the processing module 32 controls the surround cooling device of the drilling and tapping machine to cool the processing object in real time based on the temperature distribution map, specifically including: the processing module 32 determines the first area and the second area according to the temperature distribution map, the temperature average corresponding to the first area is higher than the temperature average corresponding to the second area, and the first area and the second area are any two areas of the multiple areas included in the processing area; the processing module 32 determines the first coolant nozzle corresponding to the first area and the second coolant nozzle corresponding to the second area, one area corresponds to one coolant nozzle, and the coolant nozzle is used to spray atomized coolant into the area; the processing module 32 sends control instructions to the first coolant nozzle and the second coolant nozzle respectively, so as to control the first coolant nozzle to spray the atomized coolant into the first area at a first shooting speed, and to control the second coolant nozzle to spray the atomized coolant into the second area at a second shooting speed, and the first shooting speed is higher than the second shooting speed.

[0067] In a possible implementation, the processing module 32 determines the first thread image of the thread corresponding to the processing object based on the deformation data, specifically including: the acquisition module 31 acquires the time series data when the drilling and tapping machine processes the processing object within a preset time period; the processing module 32 performs data enhancement on the three-dimensional graphics, combines the time series data, and obtains the three-dimensional image through 3D modeling; the processing module 32 extracts the first thread image of the thread corresponding to the processing object based on the three-dimensional image.

[0068] In a possible implementation, the processing module 32 generates a quality result of the thread corresponding to the processing object based on the first thread image and the second thread image using a preset support vector machine model, specifically including: the processing module 32 performs data fusion on the first thread image and the second thread image to obtain a fused thread image; the processing module 32 inputs the fused thread image into the preset support vector machine model to obtain a hyperspace coordinate; the processing module 32 determines the positional relationship between the hyperspace coordinate and the hyperplane corresponding to the preset support vector machine model; the processing module 32 calculates a first probability value and a second probability value based on the positional relationship, the first probability value is used to represent the probability value corresponding to the hyperspace coordinate on the first side of the hyperplane, and the second probability value is used to represent the probability value corresponding to the hyperspace coordinate on the second side of the hyperplane, the first side and the second side being two opposite sides of the hyperplane; if the processing module 32 determines that the hyperspace coordinate is located on the first side and the first probability value is greater than or equal to the preset threshold, then it is determined that the quality result indicates normal; if the processing module 32 determines that the hyperspace coordinate is located on the second side and the second probability value is greater than or equal to the preset threshold, then it is determined that the quality result indicates abnormal.

[0069] In one possible implementation, the acquisition module 31 acquires training data, which includes sample data, and normal quality data and abnormal quality data corresponding to the sample data; the processing module 32 inputs the sample data, and the normal quality data and abnormal quality data corresponding to the sample data into the initial model for training; the processing module 32 sets the kernel function and model parameters for the initial model, and optimizes the initial model through an optimization algorithm to obtain a preset support vector machine model, and the model parameters include regularization parameters and kernel parameters.

[0070] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0071] The present application also provides an electronic device, referring to Figure 4 , Figure 4 The electronic device may include: at least one processor 41 , at least one network interface 44 , a user interface 43 , a memory 45 , and at least one communication bus 42 .

[0072] The communication bus 42 is used to realize the connection and communication between these components.

[0073] The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0074] The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0075] Among them, the processor 41 may include one or more processing cores. The processor 41 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 45, and calling data stored in the memory 45. Optionally, the processor 41 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 41 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 41, and it can be implemented separately through a chip.

[0076] Among them, the memory 45 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 45 may also be optionally at least one storage device located away from the aforementioned processor 41. As Figure 4As shown, the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a control method for a drilling and tapping machine.

[0077] exist Figure 4 In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application program stored in the memory 45 for a control method of a drilling and tapping machine. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0078] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0079] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0080] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0085] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A control method for a drilling and tapping machine, characterized in that: The method comprises: Acquiring temperature data and deformation data of a processing object sent by a nano film sensor, wherein the nano film sensor is located in a processing area corresponding to the processing object; Determining a temperature distribution map of the processing area according to the temperature data and the deformation data; Based on the temperature distribution diagram, controlling the surrounding cooling device of the drilling and tapping machine to cool the processing object in real time; Determining a first thread image corresponding to the thread of the processing object according to the deformation data; Acquire a second thread image corresponding to the thread of the processing object sent by an image acquisition device of the drilling and tapping machine; Based on the first thread image and the second thread image, a preset support vector machine model is used to generate a quality result of the thread corresponding to the processing object.

2. The control method of the drilling and tapping machine according to claim 1, characterized in that: The step of obtaining the temperature data and deformation data of the processing object sent by the nano film sensor specifically includes: Receiving raw temperature data sent by a thermosensitive nanofilm sensor, wherein the thermosensitive nanofilm sensor is used to detect in real time the local overheating phenomenon caused by heat accumulation and / or cutting friction of the processing object during the processing; Receiving original deformation data sent by a piezoelectric nanofilm sensor, wherein the piezoelectric nanofilm sensor is used to detect the shape of the thread through the deformation of the film surface; The original temperature data and the original deformation data are processed to obtain the temperature data and the deformation data, wherein the data processing includes denoising, filtering and normalization processing.

3. The control method of the drilling and tapping machine according to claim 1, characterized in that: Determining the temperature distribution map of the processing area according to the temperature data and the deformation data specifically includes: Determine temperature distribution data of each thread layer corresponding to the processing area according to the temperature data; Extracting electrical signal data of the film surface change according to the deformation data for analysis to generate a three-dimensional graph; The temperature distribution data is added to the three-dimensional graph to obtain a temperature distribution graph of the processing area.

4. The control method of the drilling and tapping machine according to claim 1, characterized in that: Based on the temperature distribution diagram, controlling the surrounding cooling device of the drilling and tapping machine to cool the processing object in real time specifically includes: Determine a first region and a second region according to the temperature distribution diagram, wherein a temperature average corresponding to the first region is higher than a temperature average corresponding to the second region, and the first region and the second region are any two regions among a plurality of regions included in the processing region; Determine a first coolant nozzle corresponding to the first area and a second coolant nozzle corresponding to the second area, one area corresponds to one coolant nozzle, and the coolant nozzle is used to spray atomized coolant to the area; Control instructions are sent to the first coolant nozzle and the second coolant nozzle respectively to control the first coolant nozzle to spray atomized coolant to the first area at a first shooting speed, and to control the second coolant nozzle to spray atomized coolant to the second area at a second shooting speed, wherein the first shooting speed is higher than the second shooting speed.

5. The control method of the drilling and tapping machine according to claim 3, characterized in that: Determining a first thread image corresponding to the thread of the processing object according to the deformation data specifically includes: Acquire time series data when the drilling and tapping machine processes the processing object within a preset time period; Performing data enhancement on the three-dimensional graphics, combining the time series data, and obtaining a three-dimensional image through 3D modeling; A first thread image corresponding to the thread of the processing object is extracted based on the three-dimensional image.

6. The control method of the drilling and tapping machine according to claim 1, characterized in that: The method of generating a quality result of the thread corresponding to the processing object by using a preset support vector machine model based on the first thread image and the second thread image specifically includes: fusing the first thread image and the second thread image to obtain a fused thread image; Inputting the fused thread image into the preset support vector machine model to obtain hyperspace coordinates; Determining the positional relationship between the hyperspace coordinates and the hyperplane corresponding to the preset support vector machine model; Based on the positional relationship, a first probability value and a second probability value are calculated, wherein the first probability value is used to represent the probability value of the hyperspace coordinate corresponding to the first side of the hyperplane, and the second probability value is used to represent the probability value of the hyperspace coordinate corresponding to the second side of the hyperplane, wherein the first side and the second side are two opposite sides of the hyperplane; If it is determined that the hyperspace coordinate is located at the first side, and the first probability value is greater than or equal to a preset threshold, then determining that the quality result indicates normal; If it is determined that the hyperspace coordinate is located on the second side, and the second probability value is greater than or equal to the preset threshold, it is determined that the quality result indicates an abnormality.

7. The control method of the drilling and tapping machine according to claim 1, characterized in that: The method further comprises: Acquire training data, where the training data includes sample data, and normal quality data and abnormal quality data corresponding to the sample data; Inputting the sample data, and the normal quality data and abnormal quality data corresponding to the sample data into the initial model for training; A kernel function and model parameters are set for the initial model, and the initial model is optimized by an optimization algorithm to obtain the preset support vector machine model, wherein the model parameters include a regularization parameter and a kernel parameter.

8. A control device for a drilling and tapping machine, characterized in that: The control device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire temperature data and deformation data for a processing object sent by a nano film sensor, wherein the nano film sensor is located in a processing area corresponding to the processing object; The processing module (32) is used to determine a temperature distribution diagram of the processing area according to the temperature data and the deformation data; The processing module (32) is further used to control the surrounding cooling device of the drilling and tapping machine to cool the processing object in real time based on the temperature distribution diagram; The processing module (32) is further used to determine a first thread image corresponding to the thread of the processing object according to the deformation data; The acquisition module (31) is further used to acquire a second thread image corresponding to the thread of the processing object sent by the image acquisition device of the drilling and tapping machine; The processing module (32) is further used to generate a quality result of the thread corresponding to the processing object using a preset support vector machine model based on the first thread image and the second thread image.

9. An electronic device, characterized in that: The electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), wherein the memory (45) is used to store instructions, the user interface (43) and the network interface (44) are both used to communicate with other devices, and the processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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