Method and system for optimizing segmented heat treatment process of auger bit based on temperature field simulation

Abstract

The invention discloses a temperature field simulation-based auger bit subsection heat treatment process optimization method and system. The method comprises the steps of constructing an auger bit three-dimensional temperature field simulation model based on auger bit attributes; gradient temperature distribution characteristics are obtained, and the auger bit is divided into a head strengthening section, a transition uniform temperature section and a tail slow cooling section in the axial direction; based on historical heat treatment data, calculating a temperature-stress evolution curve of each segment through transient thermal-structure coupling analysis, and constructing an ideal temperature change model of the auger bit; performing heat treatment processing on each section of the auger bit; based on the stress data of each segment of the auger bit, the error between the stress data and the temperature-stress evolution curve of each segment is calculated and fed back to the control system; and dynamically adjusting induction heating parameters, and performing closed-loop control on the temperature of each section. The method has the advantages that through axial multi-section partition temperature control, dynamic feedback adjustment and simulation model prediction, accurate matching of the temperature-stress field in the heat treatment process of the auger bit is achieved, and the bottleneck of obdurability imbalance of a traditional process is broken through.

Description

Technical Field

[0001] The invention relates to process optimization, and in particular to a method and system for optimizing a drill segmented heat treatment process based on temperature field simulation. Background Art

[0002] As a key tool for oil and gas drilling, the drill bit has been subjected to high pressure, impact and abrasion in complex underground formations for a long time, and its performance is directly related to drilling efficiency and mining costs. Traditional heat treatment processes usually adopt the conventional path of quenching + tempering to improve the surface hardness through martensitic phase transformation, but there is a tendency for microcracks caused by quenching stress concentration, and it is difficult to optimize the toughness of the core simultaneously.

[0003] With the large-scale development of deep wells, ultra-deep wells and shale gas horizontal wells, the working temperature of the drill bit has risen from 200℃ to over 400℃. The drill teeth treated by conventional processes have problems such as decreased high-temperature red hardness and peeling of the wear-resistant layer. The industry has tried to introduce dual-frequency induction heating and gradient temperature control technology to improve the distribution of the hardened layer, and explored composite strengthening methods such as boronizing and physical vapor deposition, but there are defects such as poor process stability and excessive energy consumption. Summary of the invention

[0004] In order to improve the existing heat treatment process optimization method and system of Zhiluo drill, a segmented heat treatment process optimization method and system of Zhiluo drill based on temperature field simulation is provided. This method realizes the precise matching of temperature-stress field in the heat treatment process of Zhiluo drill through axial multi-segment zone temperature control, dynamic feedback adjustment, simulation model prediction and real-time closed-loop control, thus breaking through the bottleneck of strength-toughness imbalance of traditional processes.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] The optimization method of segmented heat treatment process of Zhiluo drill based on temperature field simulation includes:

[0007] Based on the geometric parameters, material parameters and phase change kinetic parameters of the drill, a three-dimensional temperature field simulation model of the drill including the nonlinear properties of the material is established;

[0008] Based on the distributed thermocouple array, the gradient temperature distribution characteristics are obtained, and the drill bit is divided into the head strengthening section, the transition temperature section and the tail slow cooling section along the axial direction;

[0009] Based on historical heat treatment data, the temperature-stress evolution curve of each segment is calculated through transient thermal-structural coupling analysis, the model is trained, and the ideal temperature change model of each segment during the heat treatment process of the drill is constructed;

[0010] Based on the ideal temperature change model, each section of the drill bit is heat treated, and the temperature data of each section is collected in real time through the distributed thermocouple array monitoring system.

[0011] Based on the obtained stress data of each segment of the drill under the temperature data of each segment, the error between the stress data and the temperature-stress evolution curve of each segment is calculated and fed back to the control system;

[0012] The induction heating parameters are dynamically adjusted based on the real-time temperature field data based on the error size, and the temperature of each segment is closed-loop controlled.

[0013] Preferably, the establishment of a three-dimensional temperature field simulation model of a drill including nonlinear properties of the material based on the drill geometric parameters, material parameters and phase change kinetic parameters specifically includes:

[0014] Based on the three-dimensional structure of the drill, a parametric model is generated using the Carl coordinate system through CAD software;

[0015] Obtain material thermophysical properties and phase change kinetic parameters;

[0016] The thermal radiation-convection coupling boundary condition is set based on the variation law of heat flow on the drill surface;

[0017] Based on the above steps, a three-dimensional temperature field simulation model of the drill is constructed.

[0018] Preferably, the method of obtaining the gradient temperature distribution characteristics based on the distributed thermocouple array and dividing the drill bit axially into a head strengthening section, a transition temperature equalization section and a tail slow cooling section specifically includes:

[0019] A temperature measuring point is arranged along the axial direction of the drill bit, and 4 sensors are arranged in the circumferential direction of each section to monitor the radial temperature uniformity;

[0020] Calculating the temperature change rate based on the measured temperature data, and constructing a nonlinear gradient temperature change function based on the temperature change rate;

[0021] Based on the nonlinear gradient temperature change function, the drill bit is divided into the head strengthening section, the transition temperature equalization section and the tail slow cooling section.

[0022] Preferably, the method of calculating the temperature-stress evolution curve of each segment through transient thermal-structural coupling analysis based on historical heat treatment data, training the model, and constructing an ideal temperature change model of each segment during the heat treatment of the drill specifically includes:

[0023] Based on historical heat treatment data, transient thermal-structural coupling modeling is performed, boundary conditions are obtained, and solvers are set up;

[0024] Based on the transient thermal-structural coupling model, the typical working conditions of each segment are simulated, and the temperature-stress evolution curve function of each segment is generated;

[0025] The acquired temperature-stress evolution curve function data is input into the neural network model for training, and the ideal temperature model is reversely solved through Pareto front analysis to construct the ideal temperature change model of each section in the heat treatment process of Zhiluo drill.

[0026] Preferably, the heat treatment of each segment of the drill bit based on the ideal temperature change model and the real-time acquisition of the temperature data of each segment by a distributed thermocouple array monitoring system specifically include:

[0027] Based on the acquired ideal temperature change model, the ideal temperature curve is discretized into a time-temperature matrix, written into the heat treatment equipment through the OPC UA protocol, and a three-dimensional coordinate mapping is established to bind the theoretical model coordinates with the actual thermocouple physical position;

[0028] Based on the distributed thermocouple array, the temperature data of each segment is collected and acquired in real time.

[0029] Preferably, the step of calculating the error between the stress data of each segment of the drill bit under the temperature data of each segment and the temperature-stress evolution curve of each segment and feeding it back to the control system specifically includes:

[0030] The clock synchronization between the thermocouple array and the strain sensor is achieved through the IEEE 1588 PTP protocol;

[0031] Based on the acquired temperature data and stress data, cubic spline interpolation is performed to uniformly process the timestamps;

[0032] Establish a coordinate mapping table between the temperature sensor and the strain gauge to ensure that each temperature measurement point corresponds to three strain gauges;

[0033] Obtain stress data in each current segment through the stress solution model;

[0034] Based on the acquired stress data of each segment, nonlinear alignment is performed with the temperature-stress evolution curve of each segment, and the regularized cumulative distance matrix is ​​calculated to extract the minimum path error value;

[0035] Based on the obtained error value data, it is fed back to the possible perception system.

[0036] Preferably, the induction heating parameters are dynamically adjusted based on the error size to the real-time temperature field data, and the closed-loop temperature control of each segment specifically includes:

[0037] Based on the obtained error size data of each segment, error classification assessment is performed to determine whether adjustments should be made;

[0038] Based on the segments that need to be adjusted, the required power of each segment is calculated according to the error distribution, and the frequency is dynamically adjusted through the skin depth formula;

[0039] Multi-stage collaborative closed-loop control is performed through the design of decoupling control architecture.

[0040] Furthermore, a segmented heat treatment process optimization system for Zhiluo drill based on temperature field simulation is proposed, including:

[0041] Temperature field simulation modeling module: The temperature field simulation modeling module is mainly used to build a three-dimensional nonlinear temperature field model based on the drill geometry, material and phase change parameters;

[0042] Temperature gradient partition module: The temperature gradient partition module is mainly used to divide the head strengthening section, transition temperature equalization section and tail slow cooling section through the distributed thermocouple array monitoring data;

[0043] Transient coupling analysis module: The transient coupling analysis module is mainly used to train the thermal-structural coupling model using historical data to generate the ideal temperature-stress evolution curve of each segment;

[0044] Real-time data acquisition module: The real-time data acquisition module is mainly used to realize the synchronous acquisition and mapping of temperature data through the distributed thermocouple array and the OPCUA protocol;

[0045] Error calculation feedback module: The error calculation feedback module is mainly used to calculate the temperature-stress error using the stress solution model and the nonlinear alignment algorithm and feed it back to the control system;

[0046] Dynamic closed-loop control module: The dynamic closed-loop control module is mainly used to dynamically adjust the induction heating parameters based on the error classification and skin depth formula to achieve multi-stage coordinated temperature control;

[0047] Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0048] Compared with the prior art, the advantages of the present invention are:

[0049] By establishing a three-dimensional temperature field simulation model that integrates the nonlinear characteristics of materials and phase change dynamics, combined with the axial gradient partition temperature control strategy and real-time feedback mechanism, the accuracy and stability of the Zhiluo drill heat treatment process have been significantly improved. Based on the axial segmented monitoring of the distributed thermocouple array, it is possible to implement differentiated temperature control for the drill head strengthening section (which requires rapid quenching to obtain high-hardness martensite), the transition temperature equalization section (which requires balanced phase change stress), and the tail slow cooling section (which requires retaining residual austenite to improve toughness), effectively solving the hardness-toughness mismatch problem caused by traditional integral heat treatment. Through the transient thermal-structural coupling model trained with historical data, the temperature-stress evolution path of each segment can be accurately predicted. Combined with the real-time collected stress error feedback, the induction heating frequency, power, and cooling medium flow rate are dynamically adjusted to greatly reduce the temperature field fluctuation range, thereby suppressing quenching cracks and optimizing carbide dispersion. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the method proposed by the present invention;

[0051] Figure 2 A schematic diagram of constructing a temperature field simulation model proposed by the present invention;

[0052] Figure 3 This is a schematic diagram of the segmented support drill proposed by the present invention;

[0053] Figure 4 A schematic diagram of the ideal temperature change model proposed by the present invention;

[0054] Figure 5 A schematic diagram of the heat treatment proposed by the present invention;

[0055] Figure 6 This is a schematic diagram of error calculation and feedback proposed by the present invention;

[0056] Figure 7 Schematic diagram of temperature closed-loop control proposed by the present invention

[0057] Figure 8 This is a schematic diagram of the electronic device in this solution;

[0058] Fig. 9 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0059] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0060] The segmented heat treatment process optimization system of Zhiluo drill based on temperature field simulation includes:

[0061] Temperature field simulation modeling module: The temperature field simulation modeling module is mainly used to build a three-dimensional nonlinear temperature field model based on the drill geometry, material and phase change parameters;

[0062] Temperature gradient partition module: The temperature gradient partition module is mainly used to divide the head strengthening section, transition temperature equalization section and tail slow cooling section through the distributed thermocouple array monitoring data;

[0063] Transient coupling analysis module: The transient coupling analysis module is mainly used to train the thermal-structural coupling model using historical data to generate the ideal temperature-stress evolution curve of each segment;

[0064] Real-time data acquisition module: The real-time data acquisition module is mainly used to realize the synchronous acquisition and mapping of temperature data through the distributed thermocouple array and the OPCUA protocol;

[0065] Error calculation feedback module: The error calculation feedback module is mainly used to calculate the temperature-stress error using the stress solution model and the nonlinear alignment algorithm and feed it back to the control system;

[0066] Dynamic closed-loop control module: The dynamic closed-loop control module is mainly used to dynamically adjust the induction heating parameters based on the error classification and skin depth formula to achieve multi-stage coordinated temperature control;

[0067] Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0068] See also Figure 1 As shown, the optimization method of the segmented heat treatment process of the drill bit based on the temperature field simulation includes:

[0069] Step 1: Based on the geometric parameters, material parameters and phase change kinetic parameters of the drill, a three-dimensional temperature field simulation model of the drill including the nonlinear properties of the material is established;

[0070] Step 2: Based on the distributed thermocouple array, the gradient temperature distribution characteristics are obtained, and the drill bit is divided into a head strengthening section, a transition temperature equalization section, and a tail slow cooling section along the axial direction;

[0071] Step 3: Based on the historical heat treatment data, the temperature-stress evolution curve of each segment is calculated through transient thermal-structural coupling analysis, the model is trained, and the ideal temperature change model of each segment during the heat treatment process of the drill bit is constructed;

[0072] Step 4: Based on the ideal temperature change model, each section of the drill bit is heat treated, and the temperature data of each section is collected in real time through the distributed thermocouple array monitoring system;

[0073] Step 5: Based on the obtained stress data of each segment of the drill under the temperature data of each segment, the error between the stress data and the temperature-stress evolution curve of each segment is calculated and fed back to the control system;

[0074] Step 6: Dynamically adjust the induction heating parameters based on the real-time temperature field data based on the error size, and perform closed-loop control on the temperature of each segment.

[0075] See also Figure 2 As shown in the figure, based on the geometric parameters, material parameters and phase change kinetic parameters of the drill, a three-dimensional temperature field simulation model of the drill including the nonlinear properties of the material is established, which specifically includes:

[0076] Based on the three-dimensional structure of the drill, a parametric model is generated using the Carl coordinate system through CAD software;

[0077] Obtain material thermophysical properties and phase change kinetic parameters;

[0078] The thermal radiation-convection coupling boundary condition is set based on the variation law of heat flow on the drill surface;

[0079] Based on the above steps, a three-dimensional temperature field simulation model of the drill is constructed.

[0080] Specifically, the Cartesian coordinate system is used to define the geometric parameters of the drill: drill diameter D, total length L, helix angle , blade length l, number of spiral grooves N;

[0081] The phase change volume fraction f is described by the Johnson-Mehl-Avrami equation, as follows:

[0082]

[0083] When the thermal radiation-convection coupling boundary condition is set, the radiation heat flux density is obtained based on the Stefan-Boltzmann law. The formula is:

[0084]

[0085] in, is the emissivity, is the ambient temperature.

[0086] See also Figure 3 As shown in the figure, based on the distributed thermocouple array, the gradient temperature distribution characteristics are obtained, and the drill bit is divided into the head strengthening section, the transition temperature section and the tail slow cooling section along the axial direction, which specifically include:

[0087] A temperature measuring point is arranged along the axial direction of the drill bit, and 4 sensors are arranged in the circumferential direction of each section to monitor the radial temperature uniformity;

[0088] Calculating the temperature change rate based on the measured temperature data, and constructing a nonlinear gradient temperature change function based on the temperature change rate;

[0089] Based on the nonlinear gradient temperature change function, the drill bit is divided into the head strengthening section, the transition temperature equalization section and the tail slow cooling section.

[0090] Specifically, m temperature measurement sections are arranged at equal intervals along the axis of the drill bit (with a spacing of ), 4 sensors are evenly arranged in the circumferential direction of each section (with an angle interval of 90°);

[0091] Collecting time series temperature data , where i=1,2,…,m is the section number, j=1,2,3,4 is the sensor number in the circumferential direction, is a discrete time point ( is the sampling interval);

[0092] For each sensor data, calculate the instantaneous temperature change rate: , calculate the average axial temperature change rate: ;

[0093] By analyzing The critical point of the sudden change of the temperature change rate is determined by the gradient, and the segmentation is carried out based on the critical point. The head strengthening section corresponds to the drill bit cutting area, with high heat flux density and large temperature gradient; the transition temperature section is dominated by heat conduction and the temperature distribution tends to be uniform; the tail slow cooling section is far away from the heat source and mainly uses natural convection for heat dissipation;

[0094] Calculate the temperature standard deviation of each section and perform segmented verification. The formula is:

[0095]

[0096] See also Figure 4 As shown in the figure, based on the historical heat treatment data, the temperature-stress evolution curve of each segment is calculated through transient thermal-structural coupling analysis, the model is trained, and the ideal temperature change model of each segment during the heat treatment of the drill is constructed. Specifically, it includes:

[0097] Based on historical heat treatment data, transient thermal-structural coupling modeling is performed, boundary conditions are obtained, and solvers are set up;

[0098] Based on the transient thermal-structural coupling model, the typical working conditions of each segment are simulated, and the temperature-stress evolution curve function of each segment is generated;

[0099] The acquired temperature-stress evolution curve function data is input into the neural network model for training, and the ideal temperature model is reversely solved through Pareto front analysis to construct the ideal temperature change model of each section in the heat treatment process of Zhiluo drill.

[0100] Specifically, in the transient thermal-structural coupling modeling process, the transient heat conduction equation is:

[0101]

[0102] in, is the density, is the specific heat capacity, is the thermal conductivity, It is an internal heat source;

[0103] The structural equilibrium equation considering thermal strain is:

[0104]

[0105] in, is the stress tensor, is the strain tensor, is the elasticity matrix, is the coefficient of thermal expansion;

[0106] When configuring the solver, select implicit time integration (such as the Newmark-β method), time step adaptive control, and direct coupling (such as COMSOL) or sequential coupling (thermal analysis first, then structural analysis) as the coupling solution strategy;

[0107] The drill bit is divided into multiple sections (such as heating section, insulation section, cooling section), typical process parameters are applied to each section, and the temperature T(t) and stress of each section are extracted. (t) time-varying data, fitted as an explicit function;

[0108] Minimizing residual stresses through Pareto optimization and maximizing hardness optimization multi-objective problems;

[0109] The optimal process parameter combination is predicted by neural network, and the temperature curve is inferred. The formula is:

[0110]

[0111] in, For the reverse mapping of the neural network, the ideal temperature model is input into the coupled simulation to verify whether the stress and hardness meet the standards.

[0112] See also Figure 5 As shown in the figure, based on the ideal temperature change model, each section of the drill is heat treated, and the temperature data of each section is collected in real time through the distributed thermocouple array monitoring system, including:

[0113] Based on the acquired ideal temperature change model, the ideal temperature curve is discretized into a time-temperature matrix, written into the heat treatment equipment through the OPC UA protocol, and a three-dimensional coordinate mapping is established to bind the theoretical model coordinates with the actual thermocouple physical position;

[0114] Based on the distributed thermocouple array, the temperature data of each segment is collected and acquired in real time.

[0115] Specifically, the continuous ideal temperature model By time step Discretized into a time-temperature matrix , the format is as follows:

[0116]

[0117] in, , Determined by the equipment control cycle;

[0118] Based on the segmented heat treatment equipment, an independent matrix is ​​generated for each segment;

[0119] In the process of three-dimensional coordinate mapping and thermocouple position binding, the mapping relationship between theoretical coordinates and thermocouple positions is established:

[0120]

[0121] in, is the coordinate transformation function;

[0122] Based on PID control, the heating power is adjusted according to the deviation between the measured temperature and the set value. , the formula is:

[0123]

[0124] If the temperature of a certain section continues to deviate, update , and rewrite the OPC UA node.

[0125] See also Figure 6 As shown, based on the obtained stress data of each segment of the drill under the temperature data of each segment, the error between the stress data and the temperature-stress evolution curve of each segment is calculated and fed back to the control system, which specifically includes:

[0126] The clock synchronization between the thermocouple array and the strain sensor is achieved through the IEEE 1588 PTP protocol;

[0127] Based on the acquired temperature data and stress data, cubic spline interpolation is performed to uniformly process the timestamps;

[0128] Establish a coordinate mapping table between the temperature sensor and the strain gauge to ensure that each temperature measurement point corresponds to three strain gauges;

[0129] Obtain stress data in each current segment through the stress solution model;

[0130] Based on the acquired stress data of each segment, nonlinear alignment is performed with the temperature-stress evolution curve of each segment, and the regularized cumulative distance matrix is ​​calculated to extract the minimum path error value;

[0131] Based on the obtained error value data, it is fed back to the possible perception system.

[0132] Specifically, when the cubic spline interpolation unifies the timestamp, the temperature data and stress data are sorted by time, and the spline function is constructed. The formula is:

[0133]

[0134] The coefficient , , , Solve through boundary conditions;

[0135] Similarly, construct the stress spline function;

[0136] Compute interpolated values ​​at uniform timestamps:

[0137]

[0138] Insufficient number of strain gauges around thermocouples or excessive distance between them will lead to mapping failure. Therefore, the temperature-strain gauge coordinate mapping problem is solved by increasing the density of strain gauges or using inverse distance weighted (IDW) interpolation.

[0139] At the same time, the O(nm) complexity of long-sequence DTW leads to poor real-time performance and high computational complexity. We can sacrifice accuracy for speed by using constrained DTW (such as Sakoe-Chiba Band to limit the path search range) or downsampling or segmented DTW.

[0140] See also Figure 7 As shown, the induction heating parameters are dynamically adjusted based on the real-time temperature field data based on the error size, and the temperature closed-loop control of each segment specifically includes:

[0141] Based on the obtained error size data of each segment, error classification assessment is performed to determine whether adjustments should be made;

[0142] Based on the segments that need to be adjusted, the required power of each segment is calculated according to the error distribution, and the frequency is dynamically adjusted through the skin depth formula;

[0143] Multi-stage collaborative closed-loop control is performed through the design of decoupling control architecture.

[0144] Specifically, the error threshold is defined, the errors of each segment are normalized and weighted, and the adjustment priority is calculated. The skin depth formula is:

[0145]

[0146] in, is the resistivity, is the magnetic permeability, is the current frequency, by adjusting Control the heating depth so that the temperature gradient matches the error distribution;

[0147] The power compensation formula for frequency modulation does not take into account the nonlinearity of eddy current loss. The actual energy distribution can be inferred by introducing the eddy current loss coefficient or using an infrared thermal imager. Caused by temperature changes The calculated deviation, resulting in a mismatch between skin depth and material properties, is resolved by updating material parameters online and dynamically adjusting based on impedance analyzer feedback.

[0148] Furthermore, the method according to the embodiment of the present application can also be performed by Figure 8 The electronic device architecture shown in FIG. Figure 8 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store the method and system for optimizing the segmented heat treatment process of the drill based on the temperature field simulation provided in the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 8 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 8 One or more components of an electronic device are shown.

[0149] Fig. 9 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Fig. 9 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, the method and system for optimizing the segmented heat treatment process of the drill based on the temperature field simulation according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0150] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0151] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation, characterized in that: include: Based on the geometric parameters, material parameters and phase change kinetic parameters of the drill, a three-dimensional temperature field simulation model of the drill including the nonlinear properties of the material is established; Based on the distributed thermocouple array, the gradient temperature distribution characteristics are obtained, and the drill bit is divided into the head strengthening section, the transition temperature section and the tail slow cooling section along the axial direction; Based on historical heat treatment data, the temperature-stress evolution curve of each segment is calculated through transient thermal-structural coupling analysis, the model is trained, and the ideal temperature change model of each segment during the heat treatment process of the drill is constructed; Based on the ideal temperature change model, each section of the drill bit is heat treated, and the temperature data of each section is collected in real time through the distributed thermocouple array monitoring system. Based on the obtained stress data of each segment of the drill under the temperature data of each segment, the error between the stress data and the temperature-stress evolution curve of each segment is calculated and fed back to the control system; The induction heating parameters are dynamically adjusted based on the real-time temperature field data based on the error size, and the temperature of each segment is closed-loop controlled.

2. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The establishment of a three-dimensional temperature field simulation model of a drill including nonlinear properties of the material based on the drill geometric parameters, material parameters and phase change kinetic parameters specifically includes: Based on the three-dimensional structure of the drill, a parametric model is generated using the Carl coordinate system through CAD software; Obtain material thermophysical properties and phase change kinetic parameters; The thermal radiation-convection coupling boundary condition is set based on the variation law of heat flow on the drill surface; Based on the above steps, a three-dimensional temperature field simulation model of the drill is constructed.

3. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of obtaining the gradient temperature distribution characteristics based on the distributed thermocouple array and dividing the drill bit axially into a head strengthening section, a transition temperature equalization section and a tail slow cooling section specifically includes: A temperature measuring point is arranged along the axial direction of the drill bit, and 4 sensors are arranged in the circumferential direction of each section to monitor the radial temperature uniformity; Calculating the temperature change rate based on the measured temperature data, and constructing a nonlinear gradient temperature change function based on the temperature change rate; Based on the nonlinear gradient temperature change function, the drill bit is divided into the head strengthening section, the transition temperature equalization section and the tail slow cooling section.

4. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of calculating the temperature-stress evolution curve of each segment through transient thermal-structural coupling analysis based on historical heat treatment data, training the model, and constructing the ideal temperature change model of each segment during the heat treatment of the drill specifically includes: Based on historical heat treatment data, transient thermal-structural coupling modeling is performed, boundary conditions are obtained, and solvers are set up; Based on the transient thermal-structural coupling model, the typical working conditions of each segment are simulated, and the temperature-stress evolution curve function of each segment is generated; The acquired temperature-stress evolution curve function data is input into the neural network model for training, and the ideal temperature model is reversely solved through Pareto front analysis to construct the ideal temperature change model of each section in the heat treatment process of Zhiluo drill.

5. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: Based on the ideal temperature change model, the heat treatment process is performed on each segment of the drill bit, and the temperature data of each segment is collected in real time through the distributed thermocouple array monitoring system, which specifically includes: Based on the acquired ideal temperature change model, the ideal temperature curve is discretized into a time-temperature matrix, written into the heat treatment equipment through the OPC UA protocol, and a three-dimensional coordinate mapping is established to bind the theoretical model coordinates with the actual thermocouple physical position; Based on the distributed thermocouple array, the temperature data of each segment is collected and acquired in real time.

6. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of calculating the error between the stress data of each segment of the drill bit under the temperature data of each segment and the temperature-stress evolution curve of each segment and feeding back the error to the control system specifically includes: The clock synchronization between the thermocouple array and the strain sensor is achieved through the IEEE 1588 PTP protocol; Based on the acquired temperature data and stress data, cubic spline interpolation is performed to uniformly process the timestamps; Establish a coordinate mapping table between the temperature sensor and the strain gauge to ensure that each temperature measurement point corresponds to three strain gauges; Obtain stress data in each current segment through the stress solution model; Based on the acquired stress data of each segment, nonlinear alignment is performed with the temperature-stress evolution curve of each segment, and the regularized cumulative distance matrix is ​​calculated to extract the minimum path error value; Based on the obtained error value data, it is fed back to the possible perception system.

7. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of dynamically adjusting the induction heating parameters based on the real-time temperature field data based on the error size and controlling the temperature closed loop of each segment specifically includes: Based on the obtained error size data of each segment, error classification assessment is performed to determine whether adjustments should be made; Based on the segments that need to be adjusted, the required power of each segment is calculated according to the error distribution, and the frequency is dynamically adjusted through the skin depth formula; Multi-stage collaborative closed-loop control is performed through the design of decoupling control architecture.

8. A method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation is combined to realize a system for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation as claimed in any one of claims 1 to 7, characterized in that: include: Temperature field simulation modeling module: The temperature field simulation modeling module is mainly used to build a three-dimensional nonlinear temperature field model based on the drill geometry, material and phase change parameters; Temperature gradient partition module: The temperature gradient partition module is mainly used to divide the head strengthening section, transition temperature equalization section and tail slow cooling section through the distributed thermocouple array monitoring data; Transient coupling analysis module: The transient coupling analysis module is mainly used to train the thermal-structural coupling model using historical data to generate the ideal temperature-stress evolution curve of each segment; Real-time data acquisition module: The real-time data acquisition module is mainly used to realize the synchronous acquisition and mapping of temperature data through the distributed thermocouple array and the OPC UA protocol; Error calculation feedback module: The error calculation feedback module is mainly used to calculate the temperature-stress error using the stress solution model and the nonlinear alignment algorithm and feed it back to the control system; Dynamic closed-loop control module: The dynamic closed-loop control module is mainly used to dynamically adjust the induction heating parameters based on the error classification and skin depth formula to achieve multi-stage coordinated temperature control; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 method for optimizing the segmented heat treatment process of the drill bit based on temperature field simulation as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation described in any one of claims 1 to 7 is implemented.

Images