Intelligent simulation analysis method and system for heavy-duty gear machining

Abstract

The invention relates to the technical field of intelligent simulation analysis, in particular to an intelligent simulation analysis method and system for heavy-duty gear machining. The method comprises the following steps that gear heat treatment machining data are collected; performing gear heat treatment analogue simulation based on the gear heat treatment processing data to generate gear heat treatment analogue simulation data; performing gear heat treatment strain reaction quantitative distribution analysis based on the gear heat treatment analogue simulation data to generate gear heat treatment strain reaction quantitative distribution data; analyzing heat treatment strain reaction performance-demand index fitting data through preset gear heat treatment strain demand indexes and gear heat treatment strain reaction quantitative distribution data; and performing strain reaction demand processing parameter analysis according to the heat treatment strain reaction performance-demand index fitting data to generate strain reaction demand processing parameters. Simulation analysis is conducted on heat treatment machining of the heavy-duty gear, and intelligent heat treatment machining with higher efficiency and precision is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent simulation analysis, and particularly to an intelligent simulation analysis method and system for heavy-duty gear machining. Background Art

[0002] In the field of modern construction machinery, the gear system, as a key transmission component, is widely used in various heavy-duty and high-load mechanical equipment. Due to its extremely high load-bearing capacity requirements, the machining quality of heavy-duty gears directly affects the performance and service life of the equipment. There are many problems with traditional heavy-duty gear machining methods. Especially during the heat treatment process, the heat treatment quality and strain behavior control of gears are often affected by changes in the machining environment and process parameters, resulting in unstable gear performance, and even defects such as heat treatment deformation and residual stress, which in turn affect the load-bearing capacity and working life of the gears. However, the existing heavy-duty gear machining methods that rely on empirical formulas and fixed process parameters for heat treatment control can no longer meet the requirements of modern industry for machining accuracy and efficiency. It is difficult to monitor and adjust the temperature field and strain field in real time, resulting in uncontrollable deformation during the machining process, thus affecting the machining quality of the final gears. In addition, the technology often lacks intelligent feedback adjustment means for the dynamic changes during the gear heat treatment process, leading to problems such as high energy consumption, low efficiency, and insufficient accuracy during the machining process. Summary of the Invention

[0003] Based on this, the present invention provides an intelligent simulation analysis method and system for heavy-duty gear machining to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent simulation analysis method for heavy-duty gear machining includes the following steps: Step S1: Use a distributed sensor network to perform real-time monitoring and processing of heat treatment processing signals for heavy-duty gear heat treatment operations to generate gear heat treatment processing data; perform gear heat treatment simulation processing based on the gear heat treatment processing data to generate gear heat treatment simulation data; perform timing, temperature, and strain analysis of gear heat treatment processing based on the gear heat treatment simulation data to generate gear heat treatment timing-temperature-strain data; Step S2: Perform gear heat treatment strain reaction quantitative distribution analysis based on the gear heat treatment timing-temperature-strain data to generate gear heat treatment strain reaction quantitative distribution data; Step S3: Perform demand index fitting processing of heat treatment strain reactions through a preset gear heat treatment strain demand index and the gear heat treatment strain reaction quantitative distribution data to generate heat treatment strain reaction performance-demand index fitting data; perform strain reaction demand processing parameter analysis based on the heat treatment strain reaction performance-demand index fitting data to generate strain reaction demand processing parameters; Step S4: Perform a demand processing simulation analysis of the strain response based on the strain response demand processing parameters to generate strain response demand processing simulation data; perform a simulation response processing residual heat analysis based on the strain response demand processing simulation data to generate simulation response processing residual heat data; Step S5: Perform a safety detection and adjustment process on the strain response demand processing parameters through the simulation response processing residual heat data to obtain strain response demand safety processing parameters; perform an intelligent optimization and iteration process on the processing parameters based on the strain response demand safety processing parameters to generate strain response demand optimized processing parameters; perform an intelligent processing parameter feedback operation for the heat treatment of heavy-duty gears through the strain response demand optimized processing parameters.

[0005] Furthermore, step S1 includes the following steps: Step S11: Use a distributed sensor network to perform real-time monitoring and processing of heat treatment processing signals for the heat treatment operation of heavy-duty gears to generate gear heat treatment processing data; Step S12: Perform a gear heat treatment simulation process based on the gear heat treatment processing data to generate gear heat treatment simulation data; Step S13: Perform an analysis of the gear heat treatment temperature field distribution based on the gear heat treatment simulation data to generate gear heat treatment temperature field distribution data; Step S14: Perform an analysis of the gear heat treatment strain behavior based on the gear heat treatment simulation data to generate gear heat treatment strain behavior data; Step S15: Perform a timing, temperature, and strain analysis of the gear heat treatment process based on the gear heat treatment temperature field distribution data and the gear heat treatment strain behavior data to generate gear heat treatment timing-temperature-strain data.

[0006] Furthermore, step S2 includes the following steps: Step S21: Analyze the gear heat treatment strain influence factors based on the gear heat treatment timing-temperature-strain data to generate gear heat treatment strain influence factors; Step S22: Use the gear heat treatment strain response quantification evaluation algorithm to perform a strain response quantification evaluation process on the gear heat treatment strain influence factors under the influence of timing and temperature of the gear heat treatment to generate gear heat treatment strain response quantification data; Step S23: Perform a distribution mapping process on the gear heat treatment strain response quantification data to generate gear heat treatment strain response quantification distribution data.

[0007] Furthermore, the gear heat treatment strain influence factors in step S21 include the gear heat treatment strain elastic modulus factor, the gear heat treatment strain expansion factor, and the gear heat treatment strain tensile strength factor.

[0008] Furthermore, the gear heat treatment strain reaction quantification evaluation algorithm in step S22 is as follows: ; In the formula, is expressed as, represents the continuous reaction time of the heat treatment, represents the reaction temperature data of the gear heat treatment, represents the end reaction time of the heat treatment, represents the start reaction time of the heat treatment, represents the quantified information on the relationship between the gear heat treatment strain elastic modulus factor and temperature, represents the temperature change rate of the gear heat treatment, represents the quantified information on the relationship between the gear heat treatment strain expansion factor and temperature, represents the quantified information on the relationship between the gear heat treatment strain tensile strength factor and temperature, represents the actual reaction temperature data of the gear heat treatment, represents the temperature-dependent heat treatment reaction effect data varying with temperature, represents the temperature change difference between the start and the end.

[0009] Furthermore, step S3 includes the following steps: Step S31: Conduct spatial clustering analysis of the heat treatment strain reaction based on the gear heat treatment strain reaction quantification distribution data to generate heat treatment strain reaction spatial clustering data; Step S32: Conduct grouping processing of the heat treatment strain reaction performance based on the heat treatment strain reaction feature spatial clustering data to generate heat treatment strain reaction performance group data; Step S33: Conduct fitting processing of the demand indicators for each strain reaction performance group based on the preset gear heat treatment strain demand indicators and the heat treatment strain reaction performance group data to generate heat treatment strain reaction performance-demand indicator fitting data; Step S34: Conduct analysis of the processing parameters required for the strain reaction demand based on the heat treatment strain reaction performance-demand indicator fitting data to generate the processing parameters required for the strain reaction demand.

[0010] Furthermore, step S4 includes the following steps: Step S41: Conduct simulation analysis of the processing required for the strain reaction demand based on the processing parameters required for the strain reaction demand to generate simulation data for the processing required for the strain reaction demand; Step S42: Perform temperature transfer distribution analysis of gear heat treatment based on the gear heat treatment temperature field distribution data to generate gear heat treatment temperature transfer distribution data; Step S43: Perform simulated response heat gradient distribution analysis based on the gear heat treatment temperature transfer distribution data and the strain reaction demand machining simulation data to generate simulated response heat gradient distribution data; Step S44: Perform simulated response machining residual heat analysis through the simulated response heat gradient distribution data to generate simulated response machining residual heat data.

[0011] Further, Step S5 includes the following steps: Step S51: Perform safety detection and adjustment processing of the strain reaction demand machining parameters through the simulated response machining residual heat data, including: when the simulated response machining residual heat data is greater than the preset machining residual heat abnormal deformation threshold, execute Step S511; or, when the simulated response machining residual heat data is not greater than the preset machining residual heat abnormal deformation threshold, execute Step S512; Step S511: Mark the strain reaction demand machining parameters corresponding to the simulated response machining residual heat data as residual heat abnormal strain reaction demand machining parameters, and perform strain reaction demand machining parameter correction adjustment for the residual heat abnormal deformation of the residual heat abnormal strain reaction demand machining parameters to obtain the residual heat abnormal corrected strain reaction demand machining parameters, and feedback the residual heat abnormal corrected strain reaction demand machining parameters to Step S34 for abnormal correction adjustment operation of the strain reaction demand machining parameters; Step S512: Mark the strain reaction demand machining parameters corresponding to the simulated response machining residual heat data as strain reaction demand safe machining parameters; Step S52: Perform intelligent optimization iteration processing of the machining parameters based on the strain reaction demand safe machining parameters to generate strain reaction demand optimized machining parameters; Step S53: Perform intelligent machining parameter feedback operation of heavy-duty gear heat treatment through the strain reaction demand optimized machining parameters.

[0012] Further, Step S52 includes the following steps: Step S521: Perform data integration modeling processing based on the strain reaction demand safe machining parameters and the corresponding simulated response machining residual heat data to generate a strain reaction demand safe machining parameter-residual heat data model; Step S522: Perform simulated analysis of the hardened layer machining of the residual heat based on the gear heat treatment strain reaction quantization distribution data and the strain reaction demand safe machining parameter-residual heat data model to generate simulated data of the hardened layer machining of the residual heat; Step S523: Analyze the hardening layer processing benefit characteristics of the residual heat hardening layer processing simulation data to generate residual heat hardening layer processing benefit characteristic data; Step S524: Use the residual heat hardening layer processing benefit characteristic data as the processing parameter optimization coefficient, and perform intelligent optimization iteration processing on the strain response demand safe processing parameters through the processing parameter optimization coefficient to generate strain response demand optimized processing parameters.

[0013] This specification provides an intelligent simulation analysis system for heavy-duty gear processing, which is used to execute the intelligent simulation analysis method for heavy-duty gear processing as described above. The intelligent simulation analysis system for heavy-duty gear processing includes: A gear heat treatment simulation analysis module, which is used to use a distributed sensor network to perform real-time monitoring and processing of heat treatment processing signals for heavy-duty gear heat treatment processing operations to generate gear heat treatment processing data; perform gear heat treatment simulation processing based on the gear heat treatment processing data to generate gear heat treatment simulation data; perform timing, temperature, and strain analysis of gear heat treatment processing based on the gear heat treatment simulation data to generate gear heat treatment timing-temperature-strain data; A gear heat treatment strain response analysis module, which is used to perform gear heat treatment strain response quantitative distribution analysis based on the gear heat treatment timing-temperature-strain data to generate gear heat treatment strain response quantitative distribution data; A strain response demand processing parameter analysis module, which is used to perform fitting processing of the demand index of heat treatment strain response through a preset gear heat treatment strain demand index and gear heat treatment strain response quantitative distribution data to generate heat treatment strain response performance-demand index fitting data; perform strain response demand processing parameter analysis based on the heat treatment strain response performance-demand index fitting data to generate strain response demand processing parameters; A simulation response processing residual heat analysis module, which is used to perform demand processing simulation analysis of strain response based on the strain response demand processing parameters to generate strain response demand processing simulation data; perform simulation response processing residual heat analysis based on the strain response demand processing simulation data to generate simulation response processing residual heat data; A processing parameter intelligent optimization iteration module, which is used to perform safety detection and adjustment processing of the strain response demand processing parameters through the simulation response processing residual heat data to obtain strain response demand safe processing parameters; perform intelligent optimization iteration processing on the processing parameters based on the strain response demand safe processing parameters to generate strain response demand optimized processing parameters; execute intelligent processing parameter feedback operations for heavy-duty gear heat treatment through the strain response demand optimized processing parameters.

[0014] The beneficial effects of this application are as follows. By introducing a distributed sensor network to monitor the heat treatment process of heavy-duty gears in real time, the present invention can accurately obtain various key parameters during the processing, such as temperature, strain and other data, and conduct more refined simulation analysis on the gear heat treatment process. Through heat treatment simulation based on these processing data, the temperature field and strain field distributions during the processing can be accurately reproduced, revealing potential strain and temperature changes during the heat treatment process and predicting processing defects in advance. In addition, by further combining time series, temperature and strain analysis, the correlations between temperature, strain and time series at each moment during the processing are comprehensively analyzed, providing comprehensive theoretical support for subsequent processing optimization, ensuring the effective control of the gear heat treatment quality, and significantly improving the processing accuracy and stability. Through strain response quantitative distribution analysis based on gear heat treatment time series-temperature-strain data, the strain response characteristics during the heat treatment process can be further refined and its distribution can be quantified. The core of this process is to identify the key factors affecting gear heat treatment quality, such as strain elastic modulus, strain expansion factor, strain tensile strength factor, etc. through the analysis of gear heat treatment strain influence factors, so as to comprehensively evaluate various strain responses during the gear heat treatment process. Through the quantitative evaluation of these influence factors, the strain behavior of gear materials at different temperatures and times can be accurately captured and its distribution mapping can be carried out. This method can not only accurately describe the strain response characteristics during the gear heat treatment process, but also provide a quantitative basis for subsequent processing optimization, optimize processing parameters, reduce the negative impact of strain response on gear performance, and thus improve the quality and reliability of gear processing. By fitting the demand index to the gear heat treatment strain response quantitative distribution data, the actual performance of the strain response during the heat treatment process can be accurately matched with the preset strain demand index, ensuring that the heat treatment process meets the specific performance requirements of the gear material. Through spatial clustering analysis and strain response performance grouping, the behavior characteristics of gear heat treatment strain under different working conditions are refined, helping to identify the key factors with greater influence on gear performance and further quantifying their influence on gear performance. By fitting these influencing factors with the demand index, processing parameters are formulated to ensure that the strain behavior of the gear during the processing is consistent with the performance requirements. Through the analysis of strain response demand processing parameters, highly accurate control of the processing process can be achieved, the heat treatment process can be optimized, the overall quality and stability of the gear can be improved, and its service life can be extended. Based on the strain response demand processing parameters, demand processing simulation analysis can accurately predict the change trend of strain response under different processing conditions, providing data support for the adjustment in the actual processing process. Through temperature transfer distribution analysis, the influence of temperature changes on material properties during gear heat treatment can be deeply understood, the temperature control strategy can be further optimized, and the negative impact of temperature fluctuations on processing quality can be reduced.The simulated response heat gradient distribution analysis can reveal the heat transfer and distribution rules during the processing, thereby helping to predict the residual heat generated during processing and its impact on the gear structure. The simulated response processing residual heat analysis provides a scientific basis for the adjustment of processing parameters, ensuring that the residual heat generation is minimized while ensuring the quality of the gear, avoiding thermal strain and deformation problems during processing, improving the accuracy and stability of gear processing, and further optimizing the processing efficiency and quality. The safe detection and adjustment of the simulated response processing residual heat data can effectively avoid the material deformation problem caused by abnormal residual heat during the heat treatment process. By performing dynamic detection according to the preset threshold, when the residual heat data exceeds the threshold, the correction process is immediately started, the abnormal processing parameters are marked and adjusted, thereby preventing the gear deformation or damage caused by uneven heat distribution, ensuring that the temperature control during the processing is within a reasonable range, and thus ensuring the accuracy and quality of the gear. Through the safe adjustment and intelligent optimization of the processing parameters required by the strain response, accurate processing process optimization can be achieved. Intelligent optimization iteration based on safe processing parameters not only improves the processing efficiency, but also achieves the best heat treatment effect, reduces energy consumption, and reduces process costs by continuously optimizing the processing parameters. The optimized processing parameters are fed back to the processing system to form a closed-loop control system, ensuring that each round of heat treatment process can be adjusted and improved based on real-time data. Through this efficient feedback mechanism, the heat treatment process of the gear has been continuously improved, thereby improving the overall performance and service life of the gear and reducing potential risks in the production process.

[0015] Therefore, the intelligent simulation analysis method of heavy-duty gear processing of the present invention simulates and analyzes the heat treatment of heavy-duty gears, obtains processing data in real time, and conducts a comprehensive analysis of timing, temperature and strain, so as to accurately capture the slight changes in the gear processing process, accurately analyze the strain response of each distribution of heavy-duty gears to temperature changes, and ensure that the deformation in the processing process can be pre-controlled, meet the requirements of modern industry for processing accuracy and efficiency, and achieve the processing quality requirements of gears. In addition, based on the intelligent feedback adjustment mechanism, it can respond to the dynamic changes in the heat treatment process of the gear in real time, automatically optimize the processing parameters, and through continuous optimization of the processing parameters, the processing parameters maintain the cracking, deformation and other problems of the gear heat treatment processing, and ensure that the processing parameters can achieve the goal of the surface hardening layer of the gear, which not only improves production efficiency and reduces energy consumption, but also can achieve more precise control under different working conditions, thereby ensuring the efficiency and accuracy of the processing process, so as to provide an efficient, accurate and energy-saving heavy-duty gear processing method, and meet the needs of modern industry for high-precision and high-efficiency processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1Schematic diagram of the step process of an intelligent simulation analysis method for heavy-duty gear machining according to the present invention; Figure 2 is Figure 1 Schematic diagram of the detailed implementation steps of step S3 in; Figure 3 is Figure 1 Schematic diagram of the detailed implementation steps of step S4 in; The realization of the object of the present invention, functional features and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0017] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work fall within the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0019] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0020] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an intelligent simulation analysis method for heavy-duty gear machining, including the following steps: Step S1: Use a distributed sensor network to monitor and process the heat treatment processing signals of heavy-duty gear heat treatment processing operations in real time to generate gear heat treatment processing data; perform gear heat treatment simulation processing based on the gear heat treatment processing data to generate gear heat treatment simulation data; perform timing, temperature and strain analysis of gear heat treatment processing based on the gear heat treatment simulation data to generate gear heat treatment timing-temperature-strain data;In the embodiments of the present invention, during the heat treatment process of heavy-duty gears, a group of distributed sensor networks are first deployed. The types of sensors include temperature sensors (such as thermocouple sensors), strain sensors (such as fiber optic sensors or resistance strain gauges), and acceleration sensors (such as piezoelectric acceleration sensors). These sensors are evenly arranged on the gear surface and key areas (such as tooth surfaces, tooth roots, and key contact surfaces). The sensors monitor and collect the signals generated during the processing in real time, including temperature changes (for example, temperature range: 100°C to 1000°C), strain changes (strain range: -0.01% to 0.05%), and vibration and other parameters, in order to obtain the gear heat treatment processing data. Based on the gear heat treatment processing data, data simulation and simulation are carried out using heat treatment simulation software (such as ANSYS or ABAQUS). The software is used to create a geometric model of the gear, define its material properties and geometric dimensions, and input external environmental parameters. A multi-physical field coupling model is established, and numerical calculations are carried out by combining the heat conduction equation, fluid mechanics equation, and mechanical strain model. During the simulation process, the thermal loads, external fluid convection, and radiation cooling effects that the gear undergoes in different heating, holding, and cooling stages are considered. The gear heat treatment simulation data generated by the simulation results, including the temperature field, strain field, and the thermal stress conditions that occur during the heating and cooling processes, provide a basis for further analysis. Based on the gear heat treatment simulation data, temperature field distribution analysis is carried out. The finite element method (FEM) is used to establish a three-dimensional temperature field model in the simulation software, and the temperature distribution at different time nodes during the heating, holding, and cooling processes of the gear is analyzed. During the simulation process, the heat source distribution (such as the uniform temperature distribution inside the heating furnace), the thermal conductivity of the gear (such as the thermal conductivity of steel is 45 W / m·K), the specific heat capacity (such as the specific heat capacity of steel is 500 J / kg·K), and the thermal convection and radiation effects during the cooling process are considered. Through numerical calculations, the temperature field distribution data of each region at different time nodes are generated. Based on the temperature field distribution data, gear heat treatment strain behavior analysis is carried out. According to the temperature field distribution, the thermal stress formula and elastic deformation theory are applied, combined with the temperature changes generated during the heat treatment process of the gear, to carry out strain analysis. The finite element method is used to analyze the influence of thermal stress on the gear during the heating and cooling processes, predict the maximum strain values of different parts of the gear (such as the maximum strain on the tooth surface is 0.02%), and further calculate its strain distribution. The generated strain behavior data can provide the internal and external stress distributions of the gear during the heat treatment process, and help evaluate the deformation problems that occur during the processing. According to the gear heat treatment temperature field distribution data and the gear heat treatment strain behavior data, the timing, temperature, and strain analysis of the gear heat treatment processing are carried out, and the principal component analysis (PCA) method is used to analyze the temperature and strain data at different time nodes to extract key characteristic patterns.For example, the PCA model calculates the covariance matrix of temperature and strain data and extracts the first two principal components. The first principal component represents the influence of temperature change on strain, and the second principal component represents the coupling relationship between temperature and strain. Further, regression analysis (such as linear regression or polynomial regression) is used to analyze the relationship between temperature and strain data, and the specific functional relationship between temperature and strain is obtained. Time series-temperature-strain data is generated to provide a basis for optimizing the processing technology and parameter adjustment. Through this data, the influence of temperature change on strain during gear heat treatment can be accurately predicted and used for subsequent process optimization.

[0021] Step S2: Based on the time series-temperature-strain data of gear heat treatment, perform a quantitative distribution analysis of the strain response during gear heat treatment to generate quantitative distribution data of the strain response during gear heat treatment; In the embodiments of the present invention, based on the time-sequence - temperature - strain data of gear heat treatment, the analysis of the strain influence factors during gear heat treatment is carried out. Statistical analysis methods (such as correlation analysis and regression analysis) are used to conduct a detailed study on the relationship between temperature, strain, and gear material. During the analysis process, first, the temperature, strain data, and material properties (such as elastic modulus, expansion coefficient, strain tensile strength, etc.) at each time node during gear heat treatment are extracted. By calculating the temperature change rate, temperature gradient during heating and cooling processes, etc., the key factors affecting strain are identified. By calculating the correlation between these factors, the gear heat treatment strain influence factor data is generated. The gear heat treatment strain response quantification evaluation algorithm is used to conduct the strain response quantification evaluation processing of the gear heat treatment strain influence factors under the influence of time sequence and temperature of gear heat treatment. A self-developed gear heat treatment strain response quantification evaluation algorithm is adopted. By inputting the temperature field, strain field, and their influence factors (such as expansion coefficient, temperature gradient, elastic modulus, strain tensile strength, etc.), the strain response of the gear under different processing conditions is calculated. The algorithm will calculate the strain distribution based on these inputs and generate the strain response quantification data, which reflects the degree of strain that occurs in the gear during the actual processing and its impact on the heat treatment quality. Through this algorithm, the influence of different temperature fields and the involved time on the strain effect of gear heat treatment can be evaluated more accurately. According to the gear heat treatment strain response quantification data, the distribution mapping processing of the strain response quantification is carried out. Through spatial analysis and data visualization techniques, the distribution map of the strain response during gear heat treatment is generated. The gear is divided into multiple small regions (for example, each gear tooth surface is divided into 5 regions, and each region is 20mm×20mm). For each region, the strain data at each time point in the heat treatment simulation result is used for mapping, and color identification is carried out according to the magnitude of the strain value. The generated strain response quantification distribution map visually shows the strain degree of each part of the gear during heat treatment. For example, the strain response of the tooth surface region during heat treatment is 0.015%, and the tooth root region is 0.02%. This distribution map provides a visual basis and can quantify the distribution information of the strain response, providing effective support for optimizing the heat treatment process and avoiding excessive local deformation.

[0022] Step S3: Through the preset gear heat treatment strain requirement index and the gear heat treatment strain response quantification distribution data, the fitting processing of the heat treatment strain response requirement index is carried out to generate the heat treatment strain response performance - requirement index fitting data; according to the heat treatment strain response performance - requirement index fitting data, the analysis of the processing parameters required for the strain response is carried out to generate the processing parameters required for the strain response; In the embodiments of the present invention, spatial clustering analysis of the heat treatment strain response is performed based on the quantified distribution data of the gear heat treatment strain response. The spatial clustering analysis algorithm is used to classify the strain response data of different regions of the gear. The K-means clustering algorithm or the DBSCAN algorithm is used for data classification. In this process, first, the strain data of the gear is divided into several regions. For the strain data of each region, clustering processing is performed according to its strain magnitude and pattern. The initial center points of the K-means clustering algorithm can be randomly selected as 5 points. The algorithm calculates the distance from each data point to these center points and iteratively updates until the clustering result converges. The clustering result divides the strain data into multiple subsets, and each subset represents a similar strain response pattern. The generated spatial clustering data of the heat treatment strain response provides a basis for subsequent strain performance grouping. Based on the spatial clustering data of the heat treatment strain response, heat treatment strain response performance grouping processing is performed. For each strain characteristic data after spatial clustering, its performance under different temperatures and process parameters is analyzed. By comparing the strain changes under different temperatures and cooling rates, the strain characteristic pattern of this region can be determined and compared with the strain responses of other regions. Through clustering analysis, similar strain response characteristics are classified into the same group. Heat treatment strain response performance group data is generated, and the strain characteristics within each group are relatively consistent, facilitating subsequent targeted demand index fitting processing. According to the strain performance group data and combined with the preset gear heat treatment strain demand index, strain response performance-demand index fitting processing is performed. The data of each strain performance group is compared with these preset strain demand indexes. If the strain response in the tooth root region exceeds the maximum allowable strain, the process parameters need to be adjusted (such as reducing the heating temperature or changing the cooling rate). Through mathematical fitting methods (such as the least squares method or genetic algorithm), the strain response characteristics of each group are optimized to ensure that they meet the preset demand indexes, and heat treatment strain response performance-demand index fitting data is generated. According to the heat treatment strain response performance-demand index fitting data, the processing parameters for strain response requirements are analyzed. By analyzing the processing parameters of each strain performance group under different strain requirements, the processing parameters of each group are determined. These parameters will be used for the next processing simulation and optimization. Through these processing parameters, it is ensured that the gear achieves the minimum deformation and stress distribution during the heat treatment process, meets the quality requirements, and improves production efficiency.

[0023] Step S4: Perform demand processing simulation analysis of the strain response based on the processing parameters for strain response requirements to generate strain response demand processing simulation data; perform simulation response processing residual heat analysis based on the strain response demand processing simulation data to generate simulation response processing residual heat data; In the embodiments of the present invention, a demand processing simulation analysis of strain response is carried out based on the processing parameters of strain response requirements. Using CAE (Computer Aided Engineering) simulation software, such as Simulia or COMSOL, a demand processing simulation analysis of strain response is carried out for the gear heat treatment process. In the software, a three-dimensional geometric model of the gear is established, and the temperature range, cooling rate and other relevant process parameters are set according to the processing parameters. During the simulation process, through the finite element analysis (FEA) method, various index changes during the strain response process are simulated, such as thermal deformation, thermal stress and strain distribution. The simulation results will predict the strain behavior of the gear under specific process conditions and give the deformation and thermal stress distribution. This simulation result provides a basis for the subsequent adjustment of processing parameters to ensure that the strain during the processing is controlled within the allowable range. According to the simulation data and the gear heat treatment temperature field distribution data, a temperature transfer distribution analysis is carried out. By establishing a finite element model of the temperature field, the geometric model of the gear is coupled with the temperature field to simulate the heat transfer process inside the gear. Assuming that the initial temperature of the gear is room temperature, during the heating process, the heat provided by the external heat source is transferred from the gear surface to the inside, and the temperature gradually rises. In the simulation, considering the thermal conductivity, specific heat capacity of the gear material and boundary conditions, such as the temperature of the heating furnace is 900 °C, analyze how heat is transferred in each area of the gear. The generated temperature transfer distribution data can show the temperature distribution of each point inside the gear. This data reveals the heat transfer law of different areas of the gear, which can help optimize the temperature control during cooling and heating and improve the consistency and uniformity of heat treatment. Based on the temperature transfer distribution data, the change of the heat gradient is further analyzed. By combining the thermal stress and strain data during the gear processing, the simulated response heat gradient distribution data is calculated and generated. The heat gradient refers to the temperature difference between different areas of the gear, which directly affects the thermal stress distribution of the gear. Using finite element analysis, by simulating the change of the heat gradient, the generated simulated response heat gradient distribution data reveals the stress and strain response of the gear under different cooling rates, provides guidance for the control of heat distribution during the processing, and helps to ensure the uniform distribution of thermal stress and strain during the processing. According to the simulated response heat gradient distribution data, the problem of residual heat during the processing is analyzed. During the heat treatment process, due to the temperature gradient difference in different areas, residual heat will be generated inside the gear, resulting in uneven thermal stress and deformation problems. For example, assuming that during the gear heating process, due to the too large temperature gradient, the root area of the gear fails to reach the target temperature completely, while the temperature of the tooth surface area is too high, resulting in large thermal deformation of the tooth surface. By calculating the residual heat distribution in different areas inside the gear, the simulated response processing residual heat data is obtained. This data shows the difference in residual heat between the tooth surface and the tooth root. The residual heat in the tooth surface area is 15 J / g, while that in the tooth root area is 5 J / g.Further adjust the temperature control during the heating and cooling processes based on these data, avoid excessive temperature differences, reduce the influence of residual heat, and ultimately optimize the machining quality of the gears.

[0024] Step S5: Perform safety detection and adjustment processing on the strain response required machining parameters through the simulated response machining residual heat data to obtain the strain response required safe machining parameters; perform intelligent optimization iteration processing on the machining parameters based on the strain response required safe machining parameters to generate the strain response required optimized machining parameters; execute the intelligent machining parameter feedback operation of the heavy-duty gear heat treatment through the strain response required optimized machining parameters.

[0025] In the embodiments of the present invention, safety detection and adjustment are performed on the processing parameters required for the strain response during the gear heat treatment according to the simulated response of the residual heat of processing. A preset abnormal deformation threshold for the residual heat of processing is set. For example, the threshold is set to a residual heat of 30 J / g. When the simulated response of the residual heat of processing is greater than this threshold, it indicates that there is a relatively significant residual heat during the heat treatment process, resulting in abnormal deformation or unqualified processing. Abnormal correction is performed on the processing parameters required for the strain response. Conversely, when the simulated response of the residual heat of processing is less than or equal to the preset threshold, the processing parameters required for the strain response are marked as safe processing parameters and no adjustment is made. When the simulated response of the residual heat of processing is greater than the preset abnormal deformation threshold for the residual heat of processing, correction adjustment for the abnormal deformation of the residual heat is performed on the relevant processing parameters required for the strain response. The processing parameters required for the strain response corresponding to the simulated response of the residual heat of processing are marked as the processing parameters required for the abnormal strain response of the residual heat. These parameters include heating temperature, cooling rate, time period, etc. In this case, the purpose of correcting these parameters is to reduce the generation of residual heat and avoid thermal stress and non-uniform deformation. The adjusted processing parameters required for the strain response will be returned to step S3 for further abnormal correction adjustment operations until the output simulated response of the residual heat of processing is not greater than the preset abnormal deformation threshold for the residual heat of processing, thereby ensuring that the strain control and heat distribution during the processing reach an ideal state. When the simulated response of the residual heat of processing is less than or equal to the preset abnormal deformation threshold for the residual heat of processing, the processing parameters required for the strain response are marked as the safe processing parameters required for the strain response, which means that these processing parameters can be used in actual production without causing abnormal thermal stress or deformation problems. In this case, no further adjustment is required and production can be directly carried out through these parameters. Based on the safe processing parameters required for the strain response, enter the intelligent optimization and iteration processing stage of the processing parameters. In this stage, machine learning algorithms (such as support vector machine SVM or reinforcement learning) are applied to further optimize the safe processing parameters required for the strain response. Assume that the safe processing parameters are: heating temperature 880°C, cooling rate 1.5°C / min. After optimization processing, the optimized processing parameters required for the strain response generated are: heating temperature 875°C, cooling rate 1.4°C / min. During this optimization process, the algorithm will comprehensively consider multiple factors, such as processing accuracy, time efficiency, energy efficiency, etc., to generate a set of optimized processing parameters. For example, the optimized heating temperature and cooling rate will be able to minimize thermal deformation and thermal stress, while improving production efficiency and reducing energy consumption. Perform intelligent processing parameter feedback operations for the heat treatment of heavy-duty gears according to the optimized processing parameters required for the strain response. In this stage, the optimized processing parameters will be fed back to the heat treatment equipment on the production line, and the control parameters of the equipment, such as temperature setting, cooling rate, heating duration, etc., will be adjusted in real time.For example, in the actual operation of heat treatment for heavy-duty gears, the heating temperature of the equipment will be automatically adjusted to 875 °C, and the cooling rate will be adjusted to 1.4 °C / min to ensure that the strain response during the processing meets the preset safety and quality standards.

[0026] Furthermore, step S1 includes the following steps: Step S11: Use a distributed sensor network to monitor and process the heat treatment processing signals of the heavy-duty gear heat treatment operation in real time, and generate gear heat treatment processing data; Step S12: Based on the gear heat treatment processing data, perform gear heat treatment simulation processing to generate gear heat treatment simulation data; Step S13: Analyze the temperature field distribution of gear heat treatment according to the gear heat treatment simulation data, and generate gear heat treatment temperature field distribution data; Step S14: Analyze the strain behavior of gear heat treatment according to the gear heat treatment simulation data, and generate gear heat treatment strain behavior data; Step S15: Analyze the timing, temperature, and strain of gear heat treatment processing according to the gear heat treatment temperature field distribution data and the gear heat treatment strain behavior data, and generate gear heat treatment timing-temperature-strain data.

[0027] In the embodiments of the present invention, during the heat treatment process of heavy-duty gears, a group of distributed sensor networks are first deployed. The types of sensors include temperature sensors (such as thermocouple sensors), strain sensors (such as fiber optic sensors or resistance strain gauges), and acceleration sensors (such as piezoelectric acceleration sensors). These sensors are evenly arranged on the gear surface and key areas (such as tooth surfaces, tooth roots, and key contact surfaces). For example, 6 temperature sensors, 3 strain sensors, and 2 acceleration sensors are arranged in the tooth surface and tooth root areas of the gear. The sensors monitor and collect the signals generated during the processing in real time, including temperature changes (for example, temperature range: 100°C to 1000°C), strain changes (strain range: -0.01% to 0.05%), and vibration and other parameters. All data is transmitted to the central processing unit (CPU) through a wireless network. The central processing unit filters, denoises, and cleans the received raw data, and uses a band-pass filter (such as 2 Hz to 500 Hz) to remove high-frequency noise and low-frequency fluctuations. After processing, gear heat treatment processing data is generated and stored in a database for subsequent analysis use to ensure the data is accurate and complete. Based on the gear heat treatment processing data, data simulation and simulation are carried out using heat treatment simulation software (such as ANSYS or ABAQUS). The software is used to create a geometric model of the gear, define its material properties (for example, the thermal conductivity of alloy steel material is 45 W / m·K, and the specific heat capacity is 500 J / kg·K), and geometric dimensions (such as gear diameter 200 mm, thickness 20 mm), and input external environment parameters (such as environmental temperature is 25°C, and the cooling medium is water). A multi-physical field coupling model is established, and numerical calculations are carried out by combining the heat conduction equation, fluid mechanics equation, and mechanical strain model. During the simulation process, the thermal loads, external fluid convection, and radiation cooling effects borne by the gear during different heating, holding, and cooling stages are considered. The gear heat treatment simulation data generated by the simulation results, including the temperature field, strain field, and the thermal stress conditions occurring during the heating and cooling processes, provides a basis for further analysis. Based on the gear heat treatment simulation data, temperature field distribution analysis is carried out. The finite element method (FEM) is used to establish a three-dimensional temperature field model in the simulation software, and the temperature distribution at different time nodes during the heating, holding, and cooling processes of the gear is analyzed. For example, during the heating process, assume the heating temperature is 950°C, the holding time is 30 minutes, and the cooling time is 15 minutes. During the simulation process, the heat source distribution (such as the internal temperature of the heating furnace is evenly distributed), the thermal conductivity of the gear (such as the thermal conductivity of steel is 45 W / m·K), the specific heat capacity (such as the specific heat capacity of steel is 500 J / kg·K), and the thermal convection and radiation effects during the cooling process are considered.Through numerical calculations, temperature field distribution data of each region (such as the gear tooth root, tooth surface, etc.) at different time nodes are generated (for example, the tooth root temperature is 850°C and the tooth surface temperature is 900°C), providing data support for subsequent strain analysis and optimization of processing parameters. Based on the temperature field distribution data, the strain behavior analysis of gear heat treatment is carried out. According to the temperature field distribution, using the thermal stress formula and elastic deformation theory, combined with the temperature changes generated during the gear heat treatment process, strain analysis is performed. For example, assuming that when the gear is heated to 950°C, the maximum thermal stress is 300 MPa, according to the material elastic modulus of the strain (such as the elastic modulus of steel is 210 GPa), the strain changes on the gear surface and inside are calculated through the strain formula. The finite element method is used to analyze the influence of thermal stress on the gear during the heating and cooling processes, predict the maximum strain values of different parts of the gear (such as the maximum strain on the tooth surface is 0.02%), and further calculate its strain distribution. The generated strain behavior data can provide the internal and external stress distributions of the gear during the heat treatment process, helping to evaluate the deformation problems that occur during the processing. According to the gear heat treatment temperature field distribution data and the gear heat treatment strain behavior data, the timing, temperature, and strain analysis of gear heat treatment processing are carried out. The principal component analysis (PCA) method is used to analyze the temperature and strain data at different time nodes to extract key characteristic patterns. For example, the PCA model calculates the covariance matrix of the temperature and strain data and extracts the first two principal components. The first principal component represents the influence of temperature changes on strain, and the second principal component represents the coupling relationship between temperature and strain. During the analysis process, the set principal component threshold is the proportion of explained variance of 95%, and the first two principal components are selected for calculation. Further, regression analysis (such as linear regression or polynomial regression) is used to analyze the relationship between temperature and strain data, and the specific functional relationship between temperature and strain is obtained. Time series-temperature-strain data are generated (such as at the time node of 50 seconds, when the temperature is 600°C, the strain is 0.01%), providing a basis for optimizing the processing technology and parameter adjustment. Through this data, the influence of temperature changes on strain during the gear heat treatment process can be accurately predicted and used for subsequent process optimization.

[0028] Further, step S2 includes the following steps: Step S21: Analyze the gear heat treatment strain influence factors based on the gear heat treatment time series-temperature-strain data to generate gear heat treatment strain influence factors; Step S22: Use the gear heat treatment strain response quantification evaluation algorithm to perform strain response quantification evaluation processing on the gear heat treatment strain influence factors under the influence of the timing and temperature of gear heat treatment to generate gear heat treatment strain response quantification data; Step S23: Perform distribution mapping processing on the gear heat treatment strain response quantification data to generate gear heat treatment strain response quantification distribution data.

[0029] In the embodiment of the present invention, based on the gear heat treatment time sequence-temperature-strain data, the strain influence factors during the gear heat treatment process are analyzed. Statistical analysis methods (such as correlation analysis and regression analysis) are used to conduct a detailed study on the relationship between temperature, strain, and gear material. During the analysis process, first, the temperature, strain data, and material properties (such as elastic modulus, expansion coefficient, strain tensile strength, etc.) at each time node during the gear heat treatment process are extracted. By calculating the temperature change rate, temperature gradient during heating and cooling processes, etc., the key factors affecting strain are identified. For example, the expansion coefficient of the gear (such as the expansion coefficient of steel is 12×10 -6( / °C), elastic modulus (for example, the elastic modulus of steel is 210 GPa), temperature change rate (for example, the heating rate from room temperature to 900 °C is 5 °C / min), and temperature gradient (for example, the temperature difference between the tooth root and the tooth surface during heating is 100 °C) are important factors affecting strain. By calculating the correlations between these factors, data on the influencing factors of gear heat treatment strain are generated. For example, the influence of the temperature change rate on strain is 0.4, the influence of the expansion coefficient on strain is 0.3, the influence of the elastic modulus is 0.2, and the influence of the temperature gradient is 0.1. Using the gear heat treatment strain response quantification evaluation algorithm, the influencing factors of gear heat treatment strain are subjected to quantification evaluation processing of the strain response under the influence of time sequence and temperature during gear heat treatment. Using a self-developed gear heat treatment strain response quantification evaluation algorithm, by inputting the temperature field, strain field, and their influencing factors (such as expansion coefficient, temperature gradient, elastic modulus, strain tensile strength, etc.), the strain response of the gear under different processing conditions is calculated. For example, during the process of heating the gear to 900 °C, the strain response model of the gear will consider the temperature change rate (such as the heating rate of 5 °C / min), material properties (such as the elastic modulus of steel is 210 GPa), and strain field data. The algorithm will calculate the strain distribution based on these inputs and generate strain response quantification data. For example, the strain response in the tooth root area is 0.02%, and the strain response in the tooth surface area is 0.015%. These quantification data represent the strain degree of each area of the gear during heat treatment and output a comprehensive evaluation value (such as the gear strain index is 0.18), which reflects the strain degree that occurs in the gear during the actual processing and its impact on the heat treatment quality. Through this algorithm, the influence of different temperature fields and time involved on the strain effect of gear heat treatment can be evaluated more precisely. Perform distribution mapping processing of strain response quantification according to the gear heat treatment strain response quantification data. Through spatial analysis and data visualization techniques, a distribution map of the strain response during gear heat treatment is generated. First, the gear is divided into multiple small areas (such as each tooth surface of the gear is divided into 5 areas, each area is 20 mm × 20 mm). For each area, the strain data at each time point in the heat treatment simulation results are used for mapping, and color identification is performed according to the magnitude of the strain value. For example, 0.02% strain is mapped to red, 0.01% strain is mapped to yellow, and 0.005% strain is mapped to green. The generated strain response quantification distribution map intuitively shows the strain degree of each part of the gear during heat treatment. For example, the strain response in the tooth surface area during heat treatment is 0.015%, and the tooth root area is 0.02%. This distribution map provides a visual basis and can quantify the distribution information of the strain response, providing effective support for optimizing the heat treatment process and avoiding excessive local deformation.

[0030] Further, the gear heat treatment strain influence factors described in step S21 include the gear heat treatment strain elastic modulus factor, the gear heat treatment strain expansion factor, and the gear heat treatment strain tensile strength factor.

[0031] Further, the gear heat treatment strain response quantification evaluation algorithm described in step S22 is as follows: ; In the formula, is expressed as, represents the continuous reaction time of the heat treatment, represents the reaction temperature data of the gear heat treatment, represents the end reaction time of the heat treatment, represents the start reaction time of the heat treatment, represents the quantification information of the relationship between the gear heat treatment strain elastic modulus factor and temperature, represents the temperature change rate of the gear heat treatment, represents the quantification information of the relationship between the gear heat treatment strain expansion factor and temperature, represents the quantification information of the relationship between the gear heat treatment strain tensile strength factor and temperature, represents the actual reaction temperature data of the gear heat treatment, represents the temperature-dependent heat treatment reaction effect data varying with temperature, represents the temperature change difference between the start and the end.

[0032] The gear heat treatment strain response quantification evaluation algorithm in the present invention. This gear heat treatment strain response quantification evaluation algorithm precisely simulates the strain response of the gear during the heat treatment process by establishing a comprehensive evaluation model based on temperature, strain, and material properties. Each parameter in the formula represents different physical characteristics and key factors in the heat treatment process. Specifically, in the algorithm, Represents the quantified value of the strain response of the gear at a given time and reaction temperature. This value reflects the degree of deformation experienced by the gear during the heat treatment process, taking into account the cumulative effect from the start to the end of the heat treatment. Among them, the strain elastic modulus factor at different temperatures describes the ability of the gear material to respond to external loads at different temperatures. Through its relationship with temperature, it can provide quantitative information on the change of material properties of the gear during the heat treatment process for the algorithm. The temperature change rate is used to describe the speed of temperature change during the heat treatment process, which has an important impact on the deformation and stress of the gear. The expansion factor of the gear at different temperatures depicts the expansion effect of temperature change on the size and structure of the gear. The tensile strength factor of the gear material at different temperatures is a key factor in measuring the stress-bearing capacity of the material during the heat treatment process. Another key term in this formula is the heat treatment reaction effect data of the gear that changes with temperature at different temperatures. It can capture the change laws of heat treatment reactions (such as hardening, tempering, etc.) under different temperature conditions and quantify them as temperature-dependent effects. The range of the inner integral is from the initial temperature at the start of the heat treatment to the current temperature, reflecting the long-term cumulative effect of temperature change on the heat treatment effect. Through this algorithm, the strain response of the gear during the entire heat treatment process and its impact on quality can be accurately calculated. Especially in the heat treatment process, how to predict strain according to the changes of factors such as temperature and time, so as to optimize the heat treatment process and prevent gear deformation or insufficient strength caused by temperature non-uniformity or thermal gradient problems. This algorithm based on multi-physics field coupling can provide detailed analysis of each stage during the heat treatment process, provide a scientific basis for accurately controlling the process parameters of gear heat treatment and optimizing the process design, and ultimately improve the performance and quality of the gear. By establishing a quantified model of the strain response during the heat treatment process, the strain change trend of the gear during the heat treatment process can be accurately evaluated, and the complex relationships between factors such as temperature, strain, and tensile strength can be precisely quantified. The core advantage of this algorithm lies in its highly physical modeling foundation. Through the dynamic coupling of multiple key parameters such as the temperature change rate, strain elastic modulus, and expansion factor, it provides a multi-dimensional and adjustable strain evaluation framework. The integration of the algorithm enables real-time and accurate calculation of the strain problems that occur during the gear heat treatment process, timely discovery and correction of thermal deformation caused by temperature non-uniformity or temperature gradient, and avoidance of quality problems during the processing.

[0033] Further, step S3 includes the following steps: Step S31: Conduct spatial clustering analysis of the heat treatment strain response based on the quantified distribution data of the gear heat treatment strain response to generate spatial clustering data of the heat treatment strain response; In the embodiments of the present invention, spatial clustering analysis of heat treatment strain response is performed based on the quantified distribution data of gear heat treatment strain response, and a spatial clustering analysis algorithm is used to classify the strain response data of different regions of the gear. Specifically, the K-means clustering algorithm or the DBSCAN algorithm is used for data classification. In this process, first, the strain data of the gear is divided into several regions. For example, the tooth surface, tooth root, and other key regions of the gear (such as the size of each region is 20mm×20mm). For the strain data of each region (for example, the tooth surface strain response is 0.015%, and the tooth root strain response is 0.02%), clustering processing is performed according to its strain magnitude and pattern. The initial center points of the K-means clustering algorithm can randomly select 5 points (for example, representing strain responses of 0.01%, 0.02%, 0.03%, 0.05%, and 0.07% respectively). The algorithm calculates the distance from each data point to these center points and performs iterative updates until the clustering result converges. Finally, the clustering result divides the strain data into multiple subsets, and each subset represents a similar strain response pattern. For example, the tooth surface region is grouped into one group, the tooth root region is another group, and other regions are classified according to the characteristics of the strain response. The generated spatial clustering data of heat treatment strain response provides a basis for subsequent strain performance grouping.

[0034] Step S32: Perform heat treatment strain response performance grouping processing based on the heat treatment strain response feature spatial clustering data to generate heat treatment strain response performance group data; In the embodiments of the present invention, based on the heat treatment strain response spatial clustering data, heat treatment strain response performance grouping processing is performed. For each strain feature data after spatial clustering (for example, the strain value of a certain type of aggregated data is between 0.015% and 0.03%, representing the tooth surface region), its performance under different temperatures and process parameters is analyzed. Taking the tooth surface region as an example, assuming that its temperature range during the heating process is 600°C to 900°C and the cooling rate is 2°C / min, analyze the strain response performance of this region under different process parameters. In this process, by comparing the strain changes under different temperatures and cooling rates, the strain feature pattern of this region can be determined and compared with the strain responses of other regions. Through clustering analysis, similar strain response characteristics are divided into the same group (for example, the tooth surface region with a temperature change range of 600°C to 900°C is grouped into one group). Heat treatment strain response performance group data is generated, and the strain characteristics within each group are relatively consistent, facilitating subsequent targeted demand index fitting processing.

[0035] Step S33: Perform demand index fitting processing for each strain response performance group based on the preset gear heat treatment strain demand index and the heat treatment strain response performance group data to generate heat treatment strain response performance-demand index fitting data; In the embodiments of the present invention, according to the strain performance group data and in combination with the preset strain requirement indexes for gear heat treatment, a fitting process of strain response performance - requirement indexes is carried out. Assume that the preset strain requirement indexes include the maximum allowable strain (for example, the maximum allowable strain in the tooth surface area is 0.02%) and the strain distribution uniformity in the tooth root area (for example, the maximum difference in tooth root strain distribution is 0.005%). The data of each strain performance group is compared with these preset strain requirement indexes. For example, for the tooth surface area, its strain response is 0.015%, which meets the preset maximum allowable strain (0.02%). If the strain response in the tooth root area is 0.025%, exceeding the allowable maximum strain, the process parameters need to be adjusted (such as reducing the heating temperature or changing the cooling rate). The strain response characteristics of each group are optimized through mathematical fitting methods (such as the least - squares method or genetic algorithm) to ensure that they meet the preset requirement indexes, and the fitting data of heat treatment strain response performance - requirement indexes is generated.

[0036] Step S34: Analyze the processing parameters required for strain response according to the fitting data of heat treatment strain response performance - requirement indexes, and generate the processing parameters required for strain response; In the embodiments of the present invention, according to the fitting data of heat treatment strain response performance - requirement indexes, the processing parameters required for strain response are analyzed. By analyzing the processing parameters of each strain performance group under different strain requirements, the processing parameters of each group are determined. Assume that the optimal processing parameters for the tooth surface area are a heating temperature of 850 °C and a cooling rate of 2 °C / min, and the processing parameters for the tooth root area are a heating temperature of 900 °C and a cooling rate of 1 °C / min to obtain the processing parameters required for strain response. These parameters will be used for the next processing simulation and optimization. Through these processing parameters, it is ensured that the gear achieves the minimum deformation and stress distribution during the heat treatment process, meets the quality requirements, and improves the production efficiency.

[0037] Further, step S4 includes the following steps: Step S41: Based on the processing parameters required for strain response, carry out a simulation analysis of the processing required for strain response, and generate the simulation data of the processing required for strain response; In the embodiments of the present invention, a demand processing simulation analysis of strain response is carried out based on the processing parameters of strain response requirements. Using CAE (Computer Aided Engineering) simulation software, such as Simulia or COMSOL, a demand processing simulation analysis of strain response is carried out for the gear heat treatment process. First, a three-dimensional geometric model of the gear is established in the software, and the temperature range, cooling rate and other relevant process parameters are set according to the processing parameters. For example, assume the processing parameters are: heating temperature 850 °C, cooling rate 2 °C / min. During the simulation process, through the finite element analysis (FEA) method, various index changes during the strain response process are simulated, such as thermal deformation, thermal stress and strain distribution. The simulation results will predict the strain behavior of the gear under specific process conditions and give the deformation and thermal stress distribution. For example, the simulation shows that during the heating process of the gear, the temperature in the tooth surface area rises relatively fast, resulting in an increase in strain, while the strain in the tooth root area is smaller. This simulation result provides a basis for the subsequent adjustment of processing parameters to ensure that the strain during the processing is controlled within the allowable range.

[0038] Step S42: Perform a temperature transfer distribution analysis of gear heat treatment based on the gear heat treatment temperature field distribution data to generate gear heat treatment temperature transfer distribution data; In the embodiments of the present invention, a temperature transfer distribution analysis is carried out according to the simulation data and the gear heat treatment temperature field distribution data. First, by establishing a finite element model of the temperature field, the geometric model of the gear is coupled with the temperature field to simulate the heat transfer process inside the gear. Assume the initial temperature of the gear is room temperature (20 °C). During the heating process, the heat provided by the external heat source is transferred from the gear surface to the inside, and the temperature gradually rises. In the simulation, considering the thermal conductivity, specific heat capacity of the gear material and boundary conditions, such as the temperature of the heating furnace is 900 °C, analyze how the heat is transferred in each area of the gear. The generated temperature transfer distribution data can show the temperature distribution of each point inside the gear. For example, the temperature in the tooth surface area is 850 °C, and the temperature in the tooth root area is 780 °C. This data reveals the heat transfer law of different areas of the gear, which can help optimize the temperature control during cooling and heating and improve the consistency and uniformity of heat treatment.

[0039] Step S43: Perform a simulated response heat gradient distribution analysis according to the gear heat treatment temperature transfer distribution data and the strain response demand processing simulation data to generate simulated response heat gradient distribution data; In the embodiments of the present invention, based on the temperature transfer distribution data, the change of the heat gradient is further analyzed. By combining the thermal stress and strain data in the gear machining process, the simulated response heat gradient distribution data is calculated and generated. The heat gradient refers to the temperature difference between different regions of the gear, which directly affects the thermal stress distribution of the gear. Assume that during the cooling process, the temperature of the gear surface rapidly drops from 850 °C to 300 °C, while the internal temperature drops more slowly, resulting in an obvious temperature gradient. Using finite element analysis, by simulating the change of the heat gradient, the generated simulated response heat gradient distribution data reveals the stress and strain responses of the gear at different cooling rates. For example, when the cooling rate is 1 °C / min, the temperature difference between the tooth surface and the tooth root of the gear is 50 °C; while when the cooling rate is 3 °C / min, the temperature difference between the tooth surface and the tooth root increases to 80 °C. This data can provide guidance for the control of the heat distribution in the machining process, helping to ensure the uniform distribution of thermal stress and strain during the machining process.

[0040] Step S44: Perform a simulated response analysis of the machining residual heat through the simulated response heat gradient distribution data to generate simulated response machining residual heat data.

[0041] In the embodiments of the present invention, based on the simulated response heat gradient distribution data, the problem of residual heat in the machining process is analyzed. During the heat treatment process, due to the temperature gradient difference in different regions, residual heat will be generated inside the gear, resulting in uneven thermal stress and deformation problems. For example, assume that during the gear heating process, due to the excessive temperature gradient, the tooth root region fails to reach the target temperature completely, while the temperature of the tooth surface region is too high, resulting in large thermal deformation of the tooth surface. By calculating the residual heat distribution in different regions inside the gear, the simulated response machining residual heat data is obtained. This data shows the difference in residual heat between the tooth surface and the tooth root. The residual heat in the tooth surface region is 15 J / g, while that in the tooth root region is 5 J / g. Analyzing this data is used to identify the deformation problems caused by uneven heat, such as the higher residual heat in the tooth surface region causing the gear to bend or warp. According to these data, further adjust the temperature control during the heating and cooling processes, avoid excessive temperature differences, reduce the influence of residual heat, and finally optimize the machining quality of the gear.

[0042] Further, step S5 includes the following steps: Step S51: Perform a safety detection and adjustment process on the strain response required machining parameters through the simulated response machining residual heat data, including: when the simulated response machining residual heat data is greater than the preset machining residual heat abnormal deformation threshold, execute step S511; or, when the simulated response machining residual heat data is not greater than the preset machining residual heat abnormal deformation threshold, execute step S512; Step S511: Mark the strain reaction demand processing parameters corresponding to the simulated response processed residual heat data as residual heat abnormal strain reaction demand processing parameters, and perform correction adjustment of the strain reaction demand processing parameters for residual heat abnormal deformation to obtain the strain reaction demand processing parameters with residual heat abnormal correction. Feed back the strain reaction demand processing parameters with residual heat abnormal correction to step S34 for abnormal correction adjustment operation of the strain reaction demand processing parameters; Step S512: Mark the strain reaction demand processing parameters corresponding to the simulated response processed residual heat data as strain reaction demand safety processing parameters; Step S52: Perform intelligent optimization iteration processing on the processing parameters based on the strain reaction demand safety processing parameters to generate strain reaction demand optimized processing parameters; Step S53: Execute the intelligent processing parameter feedback operation of heavy-duty gear heat treatment through the strain reaction demand optimized processing parameters.

[0043] In the embodiments of the present invention, the safety detection and adjustment of the processing parameters required for the strain response during the gear heat treatment process are carried out according to the simulated response of the residual heat data of the machining. Specifically, during implementation, first, a preset abnormal deformation threshold for the residual heat of machining is set. For example, the threshold is set to a residual heat of 30 J / g. When the simulated response of the residual heat data of the machining is greater than this threshold, it indicates that there is a relatively significant residual heat during the heat treatment process, resulting in abnormal deformation or unqualified machining. At this time, the system executes step S511 to perform abnormal correction on the processing parameters required for the strain response. Conversely, when the simulated response of the residual heat data of the machining is less than or equal to the preset threshold, step S512 is executed to mark the processing parameters required for the strain response as safe processing parameters without adjustment. When the simulated response of the residual heat data of the machining is greater than the preset abnormal deformation threshold for the residual heat of machining, correction and adjustment of the abnormal deformation of the residual heat are performed on the relevant processing parameters required for the strain response. The processing parameters required for the strain response corresponding to the simulated response of the residual heat data of the machining are marked as the processing parameters required for the abnormal strain response of the residual heat. These parameters include heating temperature, cooling rate, time period, etc. In this case, the purpose of correcting these parameters is to reduce the generation of residual heat and avoid thermal stress and non-uniform deformation. For example, assume the original processing parameters are: heating temperature 920 °C, cooling rate 2 °C / min. After calculation and adjustment, the adjusted processing parameters are: heating temperature 880 °C, cooling rate 1.5 °C / min. The optimized cooling rate and temperature will help reduce the residual heat generated during the machining process. The adjusted processing parameters required for the strain response will be returned to step S34 for further abnormal correction and adjustment operations, and the corresponding steps after step S34 will be re-executed until the output simulated response of the residual heat data of the machining is not greater than the preset abnormal deformation threshold for the residual heat of machining, thereby ensuring that the strain control and heat distribution during the machining process reach an ideal state. When the simulated response of the residual heat data of the machining is less than or equal to the preset abnormal deformation threshold for the residual heat of machining, the processing parameters required for the strain response are marked as the safe processing parameters required for the strain response, which means that these processing parameters can be used in actual production without causing abnormal thermal stress or deformation problems. In this case, no further adjustment is required, and production can be directly carried out using these parameters. For example, if the simulated response of the residual heat data of the machining is 25 J / g, lower than the threshold of 30 J / g, it indicates that the heat treatment process of the gear is within a safe range, and production can continue according to the original parameters without additional adjustment. Based on the safe processing parameters required for the strain response, enter the intelligent optimization and iterative processing stage of the processing parameters. In this stage, machine learning algorithms (such as support vector machine SVM or reinforcement learning) are applied to further optimize the safe processing parameters required for the strain response.Suppose the safe processing parameters are: heating temperature 880°C, cooling rate 1.5°C / min. After optimization, the optimized processing parameters for the generated strain reaction requirements are: heating temperature 875°C, cooling rate 1.4°C / min. During this optimization process, the algorithm comprehensively considers multiple factors, such as processing accuracy, time efficiency, energy efficiency, etc., to generate a set of optimized processing parameters. For example, the optimized heating temperature and cooling rate will be able to minimize thermal deformation and thermal stress to the greatest extent, while improving production efficiency and reducing energy consumption. Perform intelligent processing parameter feedback operations for the heat treatment of heavy-duty gears according to the optimized processing parameters for the strain reaction requirements. At this stage, the optimized processing parameters will be fed back to the heat treatment equipment on the production line, and the control parameters of the equipment, such as temperature settings, cooling rates, heating durations, etc., will be adjusted in real time. For example, in the actual operation of the heat treatment of heavy-duty gears, the heating temperature of the equipment will be automatically adjusted to 875°C, and the cooling rate will be adjusted to 1.4°C / min to ensure that the strain reaction during the processing meets the preset safety and quality standards.

[0044] Further, step S52 includes the following steps: Step S521: Perform data integration modeling processing based on the safe processing parameters for the strain reaction requirements and the corresponding quasi-response processing residual heat data to generate a safe processing parameter-residual heat data model for the strain reaction requirements; Step S522: Perform simulation analysis of the hardened layer processing of the residual heat based on the strain reaction quantification distribution data of the gear heat treatment and the safe processing parameter-residual heat data model for the strain reaction requirements to generate simulation data for the hardened layer processing of the residual heat; Step S523: Analyze the benefit characteristics of the hardened layer processing of the residual heat for the simulation data of the hardened layer processing of the residual heat to generate benefit characteristic data for the hardened layer processing of the residual heat; Step S524: Use the benefit characteristic data of the hardened layer processing of the residual heat as the optimization coefficient for the processing parameters, and perform intelligent optimization iteration processing of the safe processing parameters for the strain reaction requirements through the optimization coefficient for the processing parameters to generate optimized processing parameters for the strain reaction requirements.

[0045] In the embodiments of the present invention, the data integration modeling technology is adopted to integrate the strain response demand safety machining parameters and their corresponding residual heat data. First, collect machining parameters under different heat treatment conditions, such as heating temperature, cooling rate, heating time, etc., and combine the residual heat data generated during the machining of gears to establish a complete data set. Then, use the support vector machine (SVM) or decision tree algorithm for modeling. In the SVM modeling process, select the Gaussian kernel function (RBF), adjust the penalty parameter C to be between 1.0 and 10.0, set the maximum number of iterations to 1000 times, and select the optimal support vectors through cross-validation. Through this modeling process, the relationship between the distribution of residual heat and machining parameters during the gear heat treatment process under different machining conditions can be predicted, thereby providing data support for the subsequent optimization of the heat treatment process. According to the strain response demand safety machining parameter-residual heat data model, perform simulation analysis on the machining of the residual heat hardened layer. Use simulation software (such as ANSYS or COMSOL), combined with the physical characteristics of the hardened layer effect, to simulate the quality changes of the hardened layer under different machining parameters (such as heating time, temperature, and cooling rate). In the simulation of the hardened layer effect, set the heating temperature to 850 °C and the cooling rate to 1.5 °C / min, and simulate the depth (0.8 mm to 1.5 mm), hardness (HRC58 to HRC62), and uniformity (uniform hardened layer distribution of 0.8 to 1.2 mm) of the hardened layer. The simulation results will provide key data for the uniformity and quality of the gear hardened layer, and reveal the influence of heat treatment on the gear hardened layer under different machining conditions, providing a basis for the subsequent optimization plan. Based on the simulation data of the machining of the residual heat hardened layer, perform analysis on the benefit characteristics of the hardened layer machining. Through feature analysis algorithms, such as clustering analysis or principal component analysis (PCA), extract the key factors affecting the quality of the hardened layer. When using PCA, perform data dimensionality reduction processing on the characteristics such as the hardness, thickness, and uniformity of the hardened layer. Set the number of principal components to 2, maintain 95% of the variance, and after standardizing all input data (mean is 0, variance is 1), transform the quality characteristic data of the hardened layer into a new feature space and extract the principal components that can best represent the performance of the hardened layer. Through feature analysis, identify the key factors affecting the quality of the hardened layer, providing guidance for the subsequent optimization of the machining plan. Using the benefit characteristic data of the hardened layer machining as the coefficient for optimizing the machining parameters, perform intelligent optimization iteration processing on the machining parameters. Adopt machine learning algorithms such as neural networks or gradient boosting decision trees (GBDT) to optimize the machining parameters, and design the constraint function of the model based on the simulated response machining residual heat data corresponding to the strain response demand safety machining parameters not being greater than the preset machining residual heat abnormal deformation threshold. The neural network adopts a 3-layer structure, with 64 neurons in each layer, selects the ReLU activation function, the learning rate is 0.001, and the number of training times is 100 times; the batch size is set to 32.The Gradient Boosting Decision Tree (GBDT) uses a maximum tree depth of 5, a learning rate of 0.1, and 100 weak learners. Using these algorithms, the hardening layer processing benefit feature data is converted into optimization coefficients, and then the safe processing parameters for strain response requirements are optimized. Through the optimized processing parameters (such as the temperature adjusted to 900 °C and the cooling rate of 1.0 °C / min), the processing effect is ensured to be optimal, and it is ensured that no deformation or breakage of the gear occurs during the heat treatment process.

[0046] This specification provides an intelligent simulation analysis system for heavy-duty gear processing, which is used to execute the intelligent simulation analysis method for heavy-duty gear processing as described above. The intelligent simulation analysis system for heavy-duty gear processing includes: A gear heat treatment simulation and analysis module, which is used to use a distributed sensor network to monitor and process the heat treatment processing signals of heavy-duty gear heat treatment operations in real time, and generate gear heat treatment processing data; perform gear heat treatment simulation processing based on the gear heat treatment processing data to generate gear heat treatment simulation data; perform time series, temperature, and strain analysis of gear heat treatment processing based on the gear heat treatment simulation data to generate gear heat treatment time series-temperature-strain data; A gear heat treatment strain response analysis module, which is used to perform quantitative distribution analysis of gear heat treatment strain responses based on gear heat treatment time series-temperature-strain data to generate gear heat treatment strain response quantitative distribution data; A processing parameter analysis module for strain response requirements, which is used to perform fitting processing of the demand indicators of heat treatment strain responses through preset gear heat treatment strain demand indicators and gear heat treatment strain response quantitative distribution data to generate heat treatment strain response performance-demand indicator fitting data; perform strain response demand processing parameter analysis based on the heat treatment strain response performance-demand indicator fitting data to generate strain response demand processing parameters; A simulation response processing residual heat analysis module, which is used to perform demand processing simulation analysis of strain responses based on strain response demand processing parameters to generate strain response demand processing simulation data; perform simulation response processing residual heat analysis based on the strain response demand processing simulation data to generate simulation response processing residual heat data; A processing parameter intelligent optimization and iteration module, which is used to perform safety detection and adjustment processing of strain response demand processing parameters through simulation response processing residual heat data to obtain strain response demand safe processing parameters; perform processing parameter intelligent optimization and iteration processing based on the strain response demand safe processing parameters to generate strain response demand optimized processing parameters; execute the intelligent processing parameter feedback operation of heavy-duty gear heat treatment through the strain response demand optimized processing parameters.

[0047] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0048] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent simulation analysis method for heavy-loaded gear processing, characterized in that: The following steps are involved: Step S1: using a distributed sensor network to perform real-time monitoring and processing of heat treatment processing signals for heavy-duty gear heat treatment processing operations, and generating gear heat treatment processing data; performing gear heat treatment simulation processing based on the gear heat treatment processing data, and generating gear heat treatment simulation data; performing timing, temperature and strain analysis of gear heat treatment processing based on the gear heat treatment simulation data, and generating gear heat treatment timing-temperature-strain data; Step S2: performing a quantitative distribution analysis of the gear heat treatment strain response based on the gear heat treatment time series-temperature-strain data to generate quantitative distribution data of the gear heat treatment strain response; Step S3: performing heat treatment strain response demand index fitting processing through preset gear heat treatment strain demand index and gear heat treatment strain response quantitative distribution data to generate heat treatment strain response performance-demand index fitting data; performing strain response demand processing parameter analysis based on the heat treatment strain response performance-demand index fitting data to generate strain response demand processing parameters; Step S4: performing strain response demand processing simulation analysis based on the strain response demand processing parameters to generate strain response demand processing simulation data; performing simulated response processing residual heat analysis based on the strain response demand processing simulation data to generate simulated response processing residual heat data; Step S5: By simulating the response to the residual heat data of the processing, the strain response requirement processing parameters are subjected to safety detection and adjustment processing of the strain response requirement processing parameters to obtain the strain response requirement safety processing parameters; based on the strain response requirement safety processing parameters, the processing parameters are intelligently optimized and iteratively processed to generate the strain response requirement optimized processing parameters; and the intelligent processing parameter feedback operation of the heat treatment of heavy-loaded gears is performed through the strain response requirement optimized processing parameters.

2. The intelligent simulation analysis method for heavy-duty gear processing according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using a distributed sensor network to monitor and process heat treatment processing signals of heavy-duty gear heat treatment processing operations in real time, and generating gear heat treatment processing data; Step S12: performing gear heat treatment simulation processing based on the gear heat treatment processing data to generate gear heat treatment simulation data; Step S13: performing gear heat treatment temperature field distribution analysis according to the gear heat treatment simulation data to generate gear heat treatment temperature field distribution data; Step S14: performing gear heat treatment strain behavior analysis according to the gear heat treatment simulation data to generate gear heat treatment strain behavior data; Step S15: performing timing, temperature and strain analysis of the gear heat treatment process according to the gear heat treatment temperature field distribution data and the gear heat treatment strain behavior data, and generating the gear heat treatment timing-temperature-strain data.

3. The intelligent simulation analysis method for heavy-duty gear processing according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: analyzing the gear heat treatment strain influence factor according to the gear heat treatment time series-temperature-strain data to generate the gear heat treatment strain influence factor; Step S22: using a gear heat treatment strain response quantitative evaluation algorithm to perform a strain response quantitative evaluation process on the gear heat treatment strain influencing factor under the influence of the timing and temperature of the gear heat treatment, and generating gear heat treatment strain response quantitative data; Step S23: performing distribution mapping processing of the gear heat treatment strain response quantification according to the gear heat treatment strain response quantification data to generate the gear heat treatment strain response quantification distribution data.

4. The intelligent simulation analysis method for heavy-duty gear machining according to claim 3 is characterized in that: The gear heat treatment strain influencing factors in step S21 include a gear heat treatment strain elastic modulus factor, a gear heat treatment strain expansion factor, and a gear heat treatment strain tensile strength factor.

5. The intelligent simulation analysis method for heavy-duty gear processing according to claim 4 is characterized in that: The gear heat treatment strain response quantitative evaluation algorithm in step S22 is as follows: ； In the formula, It is expressed as, It is expressed as the continuous reaction time of heat treatment, Expressed as reaction temperature data for gear heat treatment, It is expressed as the end reaction time of heat treatment, It is expressed as the starting reaction time of heat treatment, It is expressed as the quantitative information of the relationship between the gear heat treatment strain elastic modulus factor and temperature. Expressed as the temperature change rate of gear heat treatment, It is expressed as the quantitative information of the relationship between the gear heat treatment strain expansion factor and temperature. It is expressed as the quantitative information of the relationship between the gear heat treatment strain tensile strength factor and temperature. It is the actual reaction temperature data of gear heat treatment. The temperature-dependent heat treatment reaction effect data expressed as a function of temperature, It is expressed as the difference in temperature between the start and end.

6. The intelligent simulation analysis method for heavy-loaded gear processing according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing a spatial clustering analysis of the heat treatment strain response according to the quantitative distribution data of the gear heat treatment strain response to generate the heat treatment strain response spatial clustering data; Step S32: performing heat treatment strain reaction performance grouping processing according to the heat treatment strain reaction characteristic space clustering data to generate heat treatment strain reaction performance group data; Step S33: performing a demand index fitting process for each strain reaction performance group based on the preset gear heat treatment strain demand index and heat treatment strain reaction performance group data to generate heat treatment strain reaction performance-demand index fitting data; Step S34: performing strain response requirement processing parameter analysis based on the heat treatment strain response performance-requirement index fitting data to generate strain response requirement processing parameters.

7. The intelligent simulation analysis method for heavy-duty gear machining according to claim 2 is characterized in that: Step S4 includes the following steps: Step S41: performing strain response demand processing simulation analysis based on strain response demand processing parameters to generate strain response demand processing simulation data; Step S42: performing a gear heat treatment temperature transfer distribution analysis based on the gear heat treatment temperature field distribution data to generate gear heat treatment temperature transfer distribution data; Step S43: performing simulated response heat gradient distribution analysis according to the gear heat treatment temperature transfer distribution data and the strain response requirement processing simulation data to generate simulated response heat gradient distribution data; Step S44: Perform simulated response processing residual heat analysis by simulating the response heat gradient distribution data to generate simulated response processing residual heat data.

8. The intelligent simulation analysis method for heavy-loaded gear processing according to claim 6 is characterized in that: Step S5 includes the following steps: Step S51: performing safety detection and adjustment processing of the strain response demand processing parameters by simulating the response processing residual heat data, including: when the simulated response processing residual heat data is greater than the preset processing residual heat abnormal deformation threshold, executing step S511; or, when the simulated response processing residual heat data is not greater than the preset processing residual heat abnormal deformation threshold, executing step S512; Step S511: marking the strain response requirement processing parameter corresponding to the simulated response processing residual heat data as the residual heat abnormal strain response requirement processing parameter, and performing residual heat abnormal deformation strain response requirement processing parameter correction adjustment on the residual heat abnormal strain response requirement processing parameter to obtain residual heat abnormal corrected strain response requirement processing parameter, and feeding back the residual heat abnormal corrected strain response requirement processing parameter to step S34 to perform abnormal correction adjustment operation on the strain response requirement processing parameter; Step S512: marking the strain response requirement processing parameter corresponding to the simulated response processing residual heat data as the strain response requirement safety processing parameter; Step S52: performing intelligent optimization iterative processing of processing parameters based on the strain response requirement safety processing parameters to generate strain response requirement optimized processing parameters; Step S53: Optimizing processing parameters by strain response requirements to perform intelligent processing parameter feedback for heat treatment of heavy-duty gears.

9. The intelligent simulation analysis method for heavy-loaded gear processing according to claim 8 is characterized in that: Step S52 includes the following steps: Step S521: Perform data integration modeling processing according to the strain response requirement safety processing parameters and the corresponding pseudo-response processing residual heat data to generate a strain response requirement safety processing parameter-residual heat data model; Step S522: Perform residual heat hardening layer processing simulation analysis based on the gear heat treatment strain response quantitative distribution data and the strain response requirement safety processing parameter-residual heat data model to generate residual heat hardening layer processing simulation data; Step S523: performing a hardening layer processing benefit characteristic analysis on the residual heat hardening layer processing simulation data to generate residual heat hardening layer processing benefit characteristic data; Step S524: The residual heat hardening layer processing benefit characteristic data is used as a processing parameter optimization coefficient, and the strain response requirement safety processing parameters are iteratively optimized through the processing parameter optimization coefficient to generate strain response requirement optimized processing parameters.

10. An intelligent simulation analysis system for heavy-duty gear processing, characterized in that: The intelligent simulation analysis method for heavy-loaded gear processing according to claim 1 is used to execute the intelligent simulation analysis system for heavy-loaded gear processing, comprising: The gear heat treatment simulation analysis module is used to use a distributed sensor network to perform real-time monitoring and processing of heat treatment processing signals for heavy-duty gear heat treatment processing operations, and generate gear heat treatment processing data; perform gear heat treatment simulation processing based on the gear heat treatment processing data, and generate gear heat treatment simulation data; perform timing, temperature and strain analysis of gear heat treatment processing based on the gear heat treatment simulation data, and generate gear heat treatment timing-temperature-strain data; Gear heat treatment strain response analysis module, used to perform quantitative distribution analysis of gear heat treatment strain response based on gear heat treatment time series-temperature-strain data, and generate quantitative distribution data of gear heat treatment strain response; The strain response demand processing parameter analysis module is used to perform a demand index fitting process for heat treatment strain response through preset gear heat treatment strain demand index and gear heat treatment strain response quantitative distribution data, and generate heat treatment strain response performance-demand index fitting data; perform strain response demand processing parameter analysis based on the heat treatment strain response performance-demand index fitting data, and generate strain response demand processing parameters; The simulation response processing residual heat analysis module is used to perform strain response demand processing simulation analysis based on the strain response demand processing parameters to generate strain response demand processing simulation data; perform simulation response processing residual heat analysis based on the strain response demand processing simulation data to generate simulation response processing residual heat data; The intelligent optimization and iteration module of processing parameters is used to perform safety detection and adjustment processing on the strain response requirement processing parameters by simulating the response to the residual heat data of processing, so as to obtain the strain response requirement safety processing parameters; perform intelligent optimization and iteration processing on the processing parameters based on the strain response requirement safety processing parameters to generate the strain response requirement optimized processing parameters; and perform intelligent processing parameter feedback operation of heat treatment of heavy-loaded gears through strain response requirement optimized processing parameters.

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