Wind turbine gearbox gear strength optimization method considering multi-load condition load distribution

Abstract

This invention belongs to the field of data processing, specifically relating to a method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution. It aims to address the problem that existing technologies only perform strength optimization and verification for a single condition, affecting the optimization effect. The method includes: acquiring gearbox operating data under multiple preset conditions; performing load spectrum decoupling processing on the operating data to determine a dynamic load distribution matrix synchronized with the tooth surface meshing cycle, and performing stress field transformation on the dynamic load distribution matrix to obtain the tooth root bending stress field and the tooth surface contact stress field; inputting the tooth root bending stress field and the tooth surface contact stress field into a pre-constructed dual-constraint optimization model to obtain the tooth surface strengthening parameter tensor output by the model; and inputting the tooth surface strengthening parameter tensor into a preset process mapping system. Based on the method proposed in this invention, multi-condition operating data of the gearbox are acquired simultaneously for optimization calculation, overcoming the limitations of traditional single-load analysis.

Description

Technical Field

[0001] This invention belongs to the field of data processing, and specifically relates to a method for optimizing the tooth surface strength of wind turbine gearboxes that considers the load distribution under multiple operating conditions. Background Technology

[0002] As a core component of the transmission system, the gearbox of a wind turbine is subjected to alternating loads and complex operating conditions for extended periods. Pitting, spalling, and fatigue fracture are its main failure modes. To ensure the normal operation of the wind turbine gearbox, traditional tooth surface strength optimization methods mainly rely on a combination of static load assumptions and empirical design. The optimization process is based on a simplified load model under rated operating conditions, calculating the tooth root bending stress and tooth surface contact stress using theoretical formulas.

[0003] However, existing technologies typically only perform strength optimization and verification for a single operating condition (such as rated power generation). In actual operation, wind turbines need to cope with sudden changes in dynamic loads such as grid frequency regulation and emergency braking. The spatiotemporal distribution of such dynamic loads (such as local overload on the tooth surface) is difficult to quantify, leading to design redundancy or insufficient strength, which in turn affects the technical performance. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, namely, the limitation to strength optimization verification only under a single operating condition, which affects the optimization effect, this invention, in its first aspect, proposes a method for optimizing the tooth surface strength of wind turbine gearboxes considering load distribution under multiple operating conditions. The method includes:

[0005] Acquire operating data of the gearbox under various preset operating conditions;

[0006] The load spectrum decoupling process is performed on the working condition data to determine the dynamic load distribution matrix that is synchronized with the tooth surface meshing cycle. The stress field transformation is performed on the dynamic load distribution matrix to obtain the tooth root bending stress field and the tooth surface contact stress field.

[0007] The tooth root bending stress field and the tooth surface contact stress field are input into a pre-constructed dual-constraint optimization model to obtain the tooth surface strengthening parameter tensor output by the model. The dual-constraint optimization model includes: a stress feature extraction module, used to determine the geometric coordinate set of the high stress danger area based on the tooth root bending stress field; a Hertz contact analysis module, used to calculate the local contact pressure gradient based on the tooth surface contact stress field; and a parameter generation module, used to determine the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient.

[0008] The tooth surface strengthening parameter tensor is input into a preset process mapping system, and the process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor.

[0009] In some preferred embodiments, the load spectrum decoupling process includes:

[0010] The rotational speed pulse signal of the gearbox input shaft is collected as a time reference signal;

[0011] Based on the time reference signal, determine the analysis time window that is synchronized with the tooth surface meshing cycle;

[0012] Within each analysis time window, a dynamic envelope extraction operation is performed on the operating condition data to construct a two-dimensional matrix characterizing the dynamic load intensity of different meshing phases as the dynamic load distribution matrix.

[0013] In some preferred embodiments, the stress field transformation of the dynamic load distribution matrix includes:

[0014] Obtain the 3D geometric model of the gear;

[0015] Based on the three-dimensional geometric model of the gear, a finite element mesh model containing the gear morphology is constructed;

[0016] Based on the dynamic load distribution matrix, determine the dynamic load components and map the dynamic load components to the corresponding nodes of the finite element mesh model;

[0017] Based on the mapped nodes, a spatial distribution dataset of the tooth root bending stress field and the tooth surface contact stress field is generated synchronously using a preset stress field iterative algorithm.

[0018] In some preferred embodiments, determining the geometric coordinate set of the high-stress-critical region based on the tooth root bending stress field includes:

[0019] Gradient distribution characteristics of the bending stress field at the tooth root;

[0020] Identify continuous stress concentration regions that exceed a preset material strength threshold;

[0021] Extract the geometric center coordinates of each stress concentration region to obtain the geometric coordinate set.

[0022] In some preferred embodiments, calculating the local contact pressure gradient based on the tooth surface contact stress field includes:

[0023] Determine the maximum and minimum values ​​of tooth surface contact pressure within a single meshing cycle;

[0024] Calculate the absolute difference between the maximum contact pressure and the minimum contact pressure;

[0025] Divide the absolute difference by the actual length of the tooth surface contact line to obtain the quantization factor of the pressure distribution gradient.

[0026] In some preferred embodiments, determining the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient includes:

[0027] The material strength enhancement factor is determined based on the local contact pressure gradient.

[0028] Based on the geometric coordinate set, an interpolation algorithm is used to generate a profile modification distribution map of the tooth surface.

[0029] The modified shape distribution map and the material strength enhancement coefficient are integrated into the tooth surface strengthening parameter tensor.

[0030] In some preferred embodiments, the process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor, including:

[0031] Based on the tooth surface strengthening parameter tensor, the tooth surface modification amount distribution data is obtained analytically.

[0032] Based on the tooth surface strengthening parameter tensor and the root strength enhancement coefficient threshold, select the corresponding strengthening process type;

[0033] Based on the tooth surface modification amount distribution data and the strengthening process type, a set of machining control parameters is determined;

[0034] Based on the set of processing control parameters, determine the set of process control instructions.

[0035] In some preferred embodiments, the method further includes:

[0036] The vibration response signal of the gearbox is collected in real time during the operation of the wind turbine.

[0037] Based on the vibration response signal, compare the degree of deviation between the actual vibration spectrum characteristics and the design expectation value;

[0038] When the deviation exceeds the tolerance threshold, the iterative update process of the tooth surface strengthening parameters is triggered.

[0039] In some preferred embodiments, the iterative update process of the tooth surface strengthening parameters includes:

[0040] Recalculate the local contact pressure gradient;

[0041] Update the profile modification distribution map in the tooth surface strengthening parameter tensor;

[0042] Based on the updated local contact pressure gradient and the shaping amount distribution map, the process control instruction set is updated, and the CNC equipment is driven to perform compensation machining.

[0043] The beneficial effects of this invention are:

[0044] Based on the method proposed in this invention, the limitations of traditional single load analysis are overcome by simultaneously acquiring multi-condition operating data of the gearbox. When the gearbox faces complex operating conditions such as power grid frequency regulation and sudden wind speed changes, the dynamic load characteristics within the meshing cycle are accurately captured based on the load spectrum decoupling processing technology, achieving seamless connection from data acquisition to physical field calculation. This enables maintenance personnel to accurately locate high-stress danger areas that are difficult to detect using traditional methods.

[0045] Based on the method proposed in this invention, a dual-constraint optimization model is used to achieve precise coordinated optimization of tooth surface strength and geometry through a collaborative mechanism of stress feature extraction, Hertzian contact analysis and parameter generation. The final output reinforcement parameter tensor can directly drive the intelligent manufacturing system, breaking through the empirical limitations of traditional gear design methods. Attached Figure Description

[0046] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0047] Figure 1 This is a flowchart illustrating a method for optimizing the tooth surface strength of a wind turbine gearbox that considers the load distribution under multiple operating conditions, as proposed in an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of the structure of an electronic device proposed in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of a computer system proposed in an embodiment of the present invention. Detailed Implementation

[0050] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0051] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0052] Please refer to Figure 1 The first embodiment of this application provides a method for optimizing the tooth surface strength of a wind turbine gearbox considering multi-condition load distribution, including:

[0053] Step S10: Obtain the operating condition data of the gearbox under various preset operating conditions;

[0054] The types of preset operating conditions include, but are not limited to:

[0055] Rated power generation condition, which is the operating state in which the wind turbine continuously outputs its rated power;

[0056] Power grid frequency regulation is the power regulation process in response to power grid frequency fluctuations.

[0057] Emergency braking condition, i.e. the instantaneous torque impact state when overspeed protection is triggered;

[0058] The wind speed change condition is a transient process in which the wind speed changes by ≥5m / s within 10 seconds. Apart from this, any stable and continuous condition can be used as the preset operation of the gearbox.

[0059] Specifically, the operating condition data refers to the torque timing sequence and speed pulse signal corresponding to the gearbox under various operating conditions.

[0060] It is easy to understand that the torque time series can be obtained by installing a strain gauge torque sensor on the gearbox input shaft and continuously recording the sensing data at a certain sampling frequency. It is used to characterize the dynamic load distribution of the transmission chain and capture the torque fluctuation characteristics during power grid frequency regulation.

[0061] For the speed pulse signal, it can be obtained by acquiring the input shaft speed through a magnetic encoder, which is used to provide a reference for the meshing phase of the tooth surface and solve the synchronization problem under variable speed conditions.

[0062] Step S20: Perform load spectrum decoupling processing on the working condition data to determine the dynamic load distribution matrix that is synchronized with the tooth surface meshing cycle, and perform stress field transformation on the dynamic load distribution matrix to obtain the tooth root bending stress field and the tooth surface contact stress field.

[0063] In this step, a dynamic load distribution matrix is ​​generated through load spectrum decoupling processing. The magnetic encoder pulse signal of the gearbox input shaft mentioned above is used as the time reference signal for load spectrum decoupling processing, providing a precise phase reference for the meshing cycle. Specifically, this includes:

[0064] The rotational speed pulse signal of the gearbox input shaft is collected as a time reference signal; based on the time reference signal, an analysis time window synchronized with the gear meshing cycle is determined; within each analysis time window, a dynamic envelope extraction operation is performed on the working condition data to construct a two-dimensional matrix characterizing the dynamic load intensity of different meshing phases as the dynamic load distribution matrix.

[0065] The rotational speed pulse signal is the magnetic encoder pulse signal mentioned above. Based on this, the torque envelope feature is extracted, the alternating load component is separated, and then a two-dimensional dynamic load distribution matrix is ​​generated with the meshing phase as the row index and the load component as the column element.

[0066] Specifically, the stress field transformation of the dynamic load distribution matrix includes:

[0067] Obtain a three-dimensional geometric model of the gear; construct a finite element mesh model containing the gear morphology based on the three-dimensional geometric model of the gear; determine the dynamic load components based on the dynamic load distribution matrix, and map the dynamic load components to the corresponding nodes of the finite element mesh model; based on the mapped nodes, synchronously generate a spatial distribution dataset of the tooth root bending stress field and the tooth surface contact stress field through a preset stress field iteration algorithm.

[0068] Specifically, an accurate 3D model can be built using NX or SolidWorks based on gear design parameters (module, number of teeth, pressure angle). On this basis, the tooth profile error between the actual gear and the model can be compared using a coordinate measuring machine.

[0069] In this embodiment, different mesh types are selected for different gear morphologies. Specifically, a hexahedral dominant mesh can be used for the tooth surface contact area, a pyramidal transition unit can be used for the tooth root transition area, and a tetrahedral simplified mesh can be used for the spoke structure. The specific parameters of the mesh, such as the unit size, aspect ratio, and Jacobian, can be set according to the specific morphological characteristics. This embodiment does not limit these parameters.

[0070] As a feasible implementation method, the dynamic load mapping mechanism includes:

[0071] The meshing cycle is discretized into several phase points. Based on the phase markers in the dynamic load distribution matrix, the load components are mapped to the tooth surface mesh nodes of the corresponding phases.

[0072] As a feasible implementation method, a spatial distribution dataset of the tooth root bending stress field and the tooth surface contact stress field is generated simultaneously using a preset stress field iterative algorithm, including:

[0073] A stress integration path is set at the tooth root fillet, the normal stress component is calculated through the material constitutive relation, the pressure distribution along the major axis of the contact ellipse is calculated based on the modified Hertzian contact theory (considering the plastic deformation of the material), the convergence criterion of the stress change rate is set, and the iteration process ends when the stress change rate bracelet is stacked.

[0074] Step S30: Input the tooth root bending stress field and the tooth surface contact stress field into the pre-constructed dual-constraint optimization model to obtain the tooth surface strengthening parameter tensor output by the model. The dual-constraint optimization model includes: a stress feature extraction module, used to determine the geometric coordinate set of the high stress danger area based on the tooth root bending stress field; a Hertz contact analysis module, used to calculate the local contact pressure gradient based on the tooth surface contact stress field; and a parameter generation module, used to determine the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient.

[0075] The tooth surface strengthening parameter tensor includes spatial coordinates, modification amount, and material strength coefficient. The spatial coordinates are used to indicate the tooth surface strengthening position, the modification amount is used to indicate the amount of geometric correction of the tooth profile, and the material strength coefficient is used to indicate the degree of material strengthening.

[0076] The dual-constraint optimization model used in this embodiment is a dedicated computational framework designed to solve the problem of synergistic optimization of tooth root bending stress and tooth surface contact stress. It uses a stress feature extraction module to locate the dangerous area of ​​the tooth root bending stress field; a Hertz contact analysis module to quantify the non-uniformity of tooth surface contact pressure distribution; and a parameter generation module to integrate input parameters to generate a tooth surface strengthening parameter tensor.

[0077] Step S40: Input the tooth surface strengthening parameter tensor into a preset process mapping system. The process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor.

[0078] In this embodiment, a parameter-process direct mapping is achieved based on a preset process mapping system. For example, the input tooth surface strengthening parameter tensor (containing 217 strengthening points) is parsed by the process mapping system to automatically generate an instruction set, which includes: grinding path points: 1528, laser quenching area: 8 blocks, shot peening strengthening area: 3 blocks, total processing time: 3.2 hours, etc. The specific process mapping logic can be set by those skilled in the art, and this embodiment does not make any improvements in this regard.

[0079] In some embodiments, determining the geometric coordinate set of the high-stress-critical region based on the tooth root bending stress field includes:

[0080] The gradient distribution characteristics of the bending stress field at the tooth root are scanned; continuous stress concentration regions exceeding a preset material strength threshold are identified; and the geometric center coordinates of each stress concentration region are extracted to obtain the geometric coordinate set.

[0081] In some embodiments, calculating the local contact pressure gradient based on the tooth surface contact stress field includes:

[0082] Determine the maximum and minimum values ​​of tooth surface contact pressure within a single meshing cycle; calculate the absolute difference between the maximum and minimum contact pressures; divide the absolute difference by the actual length of the tooth surface contact line to obtain the quantization factor of the pressure distribution gradient.

[0083] In some embodiments, determining the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient includes:

[0084] Based on the local contact pressure gradient, the material strength enhancement coefficient is determined; based on the geometric coordinate set, an interpolation algorithm is used to generate a profile modification distribution map of the tooth surface; the profile modification distribution map and the material strength enhancement coefficient are integrated into the tooth surface strengthening parameter tensor.

[0085] In some embodiments, the process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor, including:

[0086] Based on the tooth surface strengthening parameter tensor, the tooth surface modification amount distribution data is obtained by analysis; based on the tooth surface strengthening parameter tensor and the root strength enhancement coefficient threshold, the corresponding strengthening process type is selected; based on the tooth surface modification amount distribution data and the strengthening process type, the set of machining control parameters is determined; based on the set of machining control parameters, the set of process control instructions is determined.

[0087] In some embodiments, the method further includes:

[0088] The vibration response signal of the gearbox is collected in real time during the operation of the wind turbine; based on the vibration response signal, the deviation between the actual vibration spectrum characteristics and the design expectation value is compared; when the deviation exceeds the preset tolerance threshold, the iterative update process of the tooth surface strengthening parameters is triggered.

[0089] The preset tolerance threshold is used to indicate the maximum acceptable tolerance range of the system.

[0090] In some embodiments, the iterative update process of the tooth surface strengthening parameters includes:

[0091] Recalculate the local contact pressure gradient; update the profile distribution map in the tooth surface strengthening parameter tensor; update the process control instruction set based on the updated local contact pressure gradient and the profile distribution map, and drive the CNC equipment to perform compensation machining.

[0092] The second embodiment of this application provides a wind turbine gearbox tooth surface strength optimization system considering multi-condition load distribution, including:

[0093] The data acquisition module is configured to acquire the gearbox's operating condition data under various preset operating conditions.

[0094] The load decoupling module is configured to perform load spectrum decoupling processing on the working condition data, determine the dynamic load distribution matrix synchronized with the tooth surface meshing cycle, and perform stress field transformation on the dynamic load distribution matrix to obtain the tooth root bending stress field and the tooth surface contact stress field.

[0095] The data output module is configured to input the tooth root bending stress field and the tooth surface contact stress field into a pre-constructed dual-constraint optimization model to obtain the tooth surface strengthening parameter tensor output by the model. The dual-constraint optimization model includes: a stress feature extraction module, used to determine the geometric coordinate set of the high stress danger area based on the tooth root bending stress field; a Hertz contact analysis module, used to calculate the local contact pressure gradient based on the tooth surface contact stress field; and a parameter generation module, used to determine the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient.

[0096] The process mapping module is configured to input the tooth surface strengthening parameter tensor to a preset process mapping system, and the process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor.

[0097] Reference Figure 2 The third embodiment of this application also proposes an electronic device, including:

[0098] At least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor to implement the method as described in the first embodiment.

[0099] The fourth embodiment of this application also proposes a computer-readable storage medium storing computer instructions for execution by the computer to implement the method described in the first embodiment.

[0100] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system suitable for implementing the methods, systems, and apparatus embodiments of this application. Figure 3 The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0101] like Figure 3 As shown, the computer system includes a central processing unit (CPU), which performs various appropriate actions and processes based on programs stored in read-only memory (ROM) or loaded from memory into random access memory (RAM). The RAM also stores various programs and data required for system operation. The CPU, ROM, and RAM are interconnected via a bus. I / O interfaces are also connected to the bus.

[0102] The following components are connected to the I / O interface: input sections including keyboards, mice, etc.; output sections including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage sections including hard drives; and communication sections including network interface cards such as LAN (Local Area Network) cards and modems. The communication sections perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as required.

[0103] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.

[0104] More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0105] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0107] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0108] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0109] The technical solution of the present invention has now been described in conjunction with the preferred embodiments shown in the accompanying drawings.

[0110] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for optimizing the tooth surface strength of a wind turbine gearbox considering multi-condition load distribution, characterized in that, The method includes: Acquire operating data of the gearbox under various preset operating conditions; The load spectrum decoupling process is performed on the working condition data to determine the dynamic load distribution matrix that is synchronized with the tooth surface meshing cycle. The stress field transformation is performed on the dynamic load distribution matrix to obtain the tooth root bending stress field and the tooth surface contact stress field. The tooth root bending stress field and the tooth surface contact stress field are input into a pre-constructed dual-constraint optimization model to obtain the tooth surface strengthening parameter tensor output by the model. The dual-constraint optimization model includes: a stress feature extraction module, used to determine the geometric coordinate set of the high stress danger area based on the tooth root bending stress field; a Hertz contact analysis module, used to calculate the local contact pressure gradient based on the tooth surface contact stress field; and a parameter generation module, used to determine the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient. The tooth surface strengthening parameter tensor is input into a preset process mapping system, and the process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor. The load spectrum decoupling process includes: The rotational speed pulse signal of the gearbox input shaft is collected as a time reference signal; Based on the time reference signal, determine the analysis time window that is synchronized with the tooth surface meshing cycle; Within each analysis time window, a dynamic envelope extraction operation is performed on the operating condition data to construct a two-dimensional matrix characterizing the dynamic load intensity of different meshing phases as the dynamic load distribution matrix. The determination of the geometric coordinate set of the high-stress-critical region based on the tooth root bending stress field includes: Gradient distribution characteristics of the bending stress field at the tooth root; Identify continuous stress concentration regions that exceed a preset material strength threshold; Extract the geometric center coordinates of each stress concentration region to obtain the geometric coordinate set; The calculation of the local contact pressure gradient based on the tooth surface contact stress field includes: Determine the maximum and minimum values ​​of tooth surface contact pressure within a single meshing cycle; Calculate the absolute difference between the maximum contact pressure and the minimum contact pressure; Divide the absolute difference by the actual length of the tooth surface contact line to obtain the quantization factor of the pressure distribution gradient; The step of determining the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient includes: The material strength enhancement factor is determined based on the local contact pressure gradient. Based on the geometric coordinate set, an interpolation algorithm is used to generate a profile modification distribution map of the tooth surface. The modified shape distribution map and the material strength enhancement coefficient are integrated into the tooth surface strengthening parameter tensor.

2. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The stress field transformation of the dynamic load distribution matrix includes: Obtain the 3D geometric model of the gear; Based on the three-dimensional geometric model of the gear, a finite element mesh model containing the gear morphology is constructed; Based on the dynamic load distribution matrix, determine the dynamic load components and map the dynamic load components to the corresponding nodes of the finite element mesh model; Based on the mapped nodes, a spatial distribution dataset of the tooth root bending stress field and the tooth surface contact stress field is generated synchronously using a preset stress field iterative algorithm.

3. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor, including: Based on the tooth surface strengthening parameter tensor, the tooth surface modification amount distribution data is obtained analytically. Based on the tooth surface strengthening parameter tensor and the root strength enhancement coefficient threshold, select the corresponding strengthening process type; Based on the tooth surface modification amount distribution data and the strengthening process type, a set of machining control parameters is determined; Based on the set of processing control parameters, determine the set of process control instructions.

4. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The method further includes: The vibration response signal of the gearbox is collected in real time during the operation of the wind turbine. Based on the vibration response signal, compare the degree of deviation between the actual vibration spectrum characteristics and the design expectation value; When the deviation exceeds the tolerance threshold, the iterative update process of the tooth surface strengthening parameters is triggered.

5. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 4, characterized in that, The iterative update process for the tooth surface strengthening parameters includes: Recalculate the local contact pressure gradient; Update the profile modification distribution map in the tooth surface strengthening parameter tensor; Based on the updated local contact pressure gradient and the shaping amount distribution map, the process control instruction set is updated, and the CNC equipment is driven to perform compensation machining.

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