Specific computational model training method adapted to an agricultural work scenario

Abstract

This invention relates to the field of power distribution technology and discloses a specific computational model training method adapted to agricultural operation scenarios. The method includes: acquiring a load data sequence containing transient currents from agricultural machinery operations and land load impedance; extracting background features corresponding to environmental disturbances; calculating the original deviation gradient of the power distribution decision model; determining a gradient correction term based on the background features and a preset response coefficient; performing orthogonal projection compensation on the original deviation gradient using the gradient correction term; generating a calibrated operation execution gradient; and updating weight parameters based on the operation execution gradient. This invention, by calculating a gradient correction term for the original deviation gradient and performing orthogonal projection compensation on the original deviation gradient using this correction term, eliminates gradient shifts induced by environmental disturbances and improves the convergence robustness of the power distribution decision model in unstructured dynamic environments.

Description

Technical Field

[0001] This invention belongs to the field of power distribution technology, and in particular relates to a specific computational model training method adapted to agricultural operation scenarios. Background Technology

[0002] Current agricultural automation control systems typically employ specific computational models to identify and control operational targets, and often utilize gradient descent to iteratively update weight parameters, thereby minimizing the loss function between the model's predicted values ​​and the target values.

[0003] However, agricultural operating environments are typically unstructured, with light projection, soil texture, and vegetation background having significant weight in image information. Furthermore, the distribution patterns of these features can shift depending on the operating time and geographical location. When using a global loss function minimization strategy to train the model, the update trajectory of the model weights is easily induced by the background feature vector, resulting in gradient shift. This causes the loss function surface to form local curvature distortion in the background feature dimension, and the weight update trajectory to fall into a local pseudo-optimal solution space induced by background interference.

[0004] To mitigate the interference of environmental background on the training process, the industry typically employs methods such as increasing the sample size or using data augmentation to counteract the interference. However, analysis has revealed that simply increasing the sample size cannot alter the geometric characteristics of the loss function surface in gradient space, and it can lead to a surge in computational load and storage pressure on edge computing systems. When faced with strong dynamic environmental interference, if the task gradient and background gradient cannot be decoupled at the mechanism level, model weight updates will continue to oscillate, resulting in an irreconcilable technical constraint between the system's recognition accuracy and energy efficiency control. For example, see publication number CN11882077. Chinese invention patent 4A discloses a training method, device, equipment, and storage medium for an agricultural feature vector extraction model. It improves the model convergence speed by introducing kinetic energy algorithm velocity features to accelerate parameter search. However, in actual working conditions, the load data sequence is often mixed with non-physical deviations that drift with spatial coordinates. Existing technologies focus too much on accelerating weight updates and fail to achieve orthogonal decoupling between the task manifold and background noise in the gradient space. When the background features shift drastically, the model will still capture gradients, making it difficult to remove gradient shifts induced by environmental interference, resulting in insufficient convergence robustness.

[0005] Therefore, how to construct a training weight calibration mechanism based on gradient manifold decoupling, which can guide the weight parameters to be updated along the task ridge while removing background feature interference, thereby improving the model's recognition robustness and training efficiency under unstructured conditions, has become the technical problem to be solved by this invention. Summary of the Invention

[0006] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A method for training a specific computational model adapted to agricultural operation scenarios, comprising the following steps:

[0007] Step S1: Obtain a load data sequence characterizing the operating status of the power system for agricultural operations. The load data sequence includes transient current data of agricultural machinery operations and land load impedance data collected by the power distribution terminal.

[0008] Step S2: Extract the background characteristics of the load data sequence corresponding to the external environmental disturbances. The background characteristics are used to characterize the non-physical deviations in the load impedance data of the land parcel as the spatial coordinates of the land parcel drift.

[0009] Step S3: Calculate the original bias gradient of the power distribution decision model in the current iteration cycle;

[0010] Step S4: Calculate the gradient correction term for the original deviation gradient. Specifically, multiply the background characteristics of the operating conditions with the preset nonlinear response coefficients to determine the local gradient distortion of the power distribution decision model in the environmental disturbance dimension.

[0011] Step S5: Generate a gain adjustment matrix and use the gradient correction term to perform orthogonal projection compensation on the original deviation gradient to remove the interference components in the original deviation gradient that coincide with the background features of the working condition, and generate a calibrated job execution gradient.

[0012] Step S6: Update the weight parameters of the power allocation decision model based on the job execution gradient to suppress power allocation errors caused by non-physical deviations through closed-loop feedback.

[0013] Preferably, the operation of extracting the background features of the operating conditions in step S2 includes: extracting the non-stationary time-varying components in the load data sequence through a sliding window, and converting the non-stationary time-varying components into preset zero-mean manifold spatial coordinates to separate the transient interference signals caused by photovoltaic output power fluctuations or sudden load changes of agricultural machinery actuators, and determining the transient interference signals as the operating background distribution of the power system, thereby locking in the environmental noise sources that cause the power distribution decision model to converge unstably.

[0014] Preferably, the logic for calculating the gradient correction term in step S4 includes: obtaining the vector difference between the original deviation gradient of the current iteration cycle and the previous iteration cycle, calculating the time change rate of the vector difference in the direction of the background characteristic quantity, and identifying the convergence stagnation region of the objective function of the power distribution decision model based on the time change rate.

[0015] Preferably, the operation of performing orthogonal projection compensation in step S5 includes: constructing an orthogonal complement space for the background features of the working condition, decomposing the original deviation gradient into an effective control component located in the orthogonal complement space and a suppression component located in the direction of the background features of the working condition, and using a gain adjustment matrix to numerically attenuate the suppression component.

[0016] Preferably, the calculation logic of the gain adjustment matrix is ​​as follows: D = I - λ⋅(κ⋅V) bg ⋅V bg T ), where D is the gain adjustment matrix, I is the identity matrix, λ is the preset intensity attenuation factor, κ is the nonlinear response coefficient characterizing the gradient correction term, and V bg These are the characteristic quantities of the working condition background.

[0017] Preferably, the method further includes the following adaptive load adjustment steps: real-time monitoring of the first derivative of the background characteristic quantity of the working condition on the time axis; when the first derivative is continuously lower than a preset static threshold, locking the current gain adjustment matrix and stopping the real-time calculation of the gradient correction term in step S4, so as to reduce the computational load of the processor.

[0018] Preferably, after step S6, the following phase alignment steps are also included: obtaining the evolution rate of the background feature quantity of the working condition, calculating the environmental prediction vector of the interference distribution in the next working cycle based on the evolution rate, and using the environmental prediction vector as the calibration benchmark for the next iteration cycle.

[0019] Preferably, the operation of calculating the environmental prediction vector of the disturbance distribution in the next work cycle includes: calculating the momentum gradient of the background characteristic quantity of the working condition on the time axis, and vector summing the momentum gradient with the current position vector to perform advanced modeling of the power harmonic distribution in the next work cycle.

[0020] Preferably, step S5 also includes a source purity verification step: after generating the job execution gradient, the cosine similarity between the job execution gradient and the original deviation gradient is calculated. When the cosine similarity is less than 0.65, a weight rollback instruction is triggered to restore the weight parameters to the values ​​of the previous iteration cycle in order to offset the loss of target features caused by the projection compensation process.

[0021] Preferably, in step S6, the update of the weight parameters of the power distribution decision model is controlled by the first-order rate of change of the background characteristic quantity of the operating condition. When the first-order rate of change exceeds the preset mutation threshold, the update step size of the weight parameters is reduced to maintain the power distribution stability of the agricultural operation power system in an unstructured dynamic environment.

[0022] Compared with existing technologies, the specific computational model training method of this invention adapted to agricultural operation scenarios has the following advantages:

[0023] 1. In a specific computational model for agricultural operations, this invention eliminates gradient capture and search path collapse caused by environmental background interference. By constructing a gradient manifold observer and performing projection modulation using background feature vectors, the invention achieves orthogonal decoupling between the operation feature gradient and the background noise gradient, cuts off the inductive effect of background features on the loss function surface, uses the calculated second-order rate of change to characterize local curvature features, and generates a nonlinear damping operator. This allows for dynamic adjustment of the search step size in the background dimension, ensuring that the model weight update path is always anchored on the ridge of the task manifold, avoiding the model from falling into the pseudo-optimal valley formed by background features. This solves the convergence oscillation problem caused by drastic changes in light, shadow, and geological conditions in traditional training methods, and enhances the generalization stability of specific computational models in power regulation and control.

[0024] 2. To achieve accurate capture and sensitivity compensation of weak signals with highly similar background features, this invention introduces a sensitivity compensation mechanism based on spatial information entropy and combines it with the active recognition logic of virtual feature probes. This invention solves the feature attribution ambiguity caused by the overlap of the target and the environmental background in the gradient space direction. It uses a compensation factor to perform nonlinear recovery on the gradient components after projection removal and calculates the source purity index based on the second-order response. Thus, while removing background noise, it forces the opening of the update permission of high-purity target components. This mechanism-driven accurate filtering ensures that the model has a very high ability to distinguish homogeneous visual features such as the coexistence of crop seedlings and weeds, eliminates the information anemia that may be caused by projection suppression, and improves the recognition recall rate of the power distribution decision model under extremely complex working conditions.

[0025] 3. Optimize the dynamic allocation efficiency of edge computing resources and the quality of model evolution. By utilizing the spatiotemporal correlation of the work environment evolution, this invention constructs a calibration gating mechanism based on asynchronous state driving, upgrading static frame-by-frame calibration to a dynamic triggering mode based on the evolution rate of background features. When the first derivative of the background vector is detected to be lower than a preset threshold, the system locks the current gradient decoupling matrix and closes redundant sampling paths, thereby achieving a balance between computational overhead and training quality. Combined with the temporal smoothing of historical momentum, it can effectively filter out instantaneous non-physical offsets caused by equipment mechanical vibration, making the model update trajectory exhibit inertial stability that conforms to real physical logic, and reducing the energy efficiency load of the edge control chip during continuous mobile operation. Attached Figure Description

[0026] Figure 1 This is a flowchart of the training process for a specific computational model of the orthogonal projection compensation mechanism of this invention;

[0027] Figure 2 This is a system architecture and data closed-loop interaction diagram of the gradient calibration engine of this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0029] A method for training a specific computational model adapted to agricultural operation scenarios, comprising the following steps:

[0030] Step S1: Obtain a load data sequence characterizing the operating status of the power system for agricultural operations. The load data sequence includes transient current data of agricultural machinery operations and land load impedance data collected by the power distribution terminal.

[0031] Step S2: Extract the background characteristics of the load data sequence corresponding to the external environmental disturbances. The background characteristics are used to characterize the non-physical deviations in the load impedance data of the land parcel as the spatial coordinates of the land parcel drift.

[0032] Step S3: Calculate the original bias gradient of the power distribution decision model in the current iteration cycle;

[0033] Step S4: Calculate the gradient correction term for the original deviation gradient. Specifically, multiply the background characteristics of the working conditions with the preset nonlinear response coefficient. This operation is not a simple numerical scaling, but rather constructs a mapping relationship from the physical environment feature space to the model parameter gradient space. Essentially, the background characteristics of the working conditions are used as an independent disturbance manifold basis, and the nonlinear response coefficient is used as a sensitivity coupling factor to map the non-physical deviation of the land load impedance into the tangential component in the gradient vector field, thereby determining the local gradient distortion of the power distribution decision model in the environmental disturbance dimension.

[0034] Step S5: Generate a gain adjustment matrix and use the gradient correction term to perform orthogonal projection compensation on the original deviation gradient to remove the interference components in the original deviation gradient that coincide with the background features of the working condition, and generate a calibrated job execution gradient.

[0035] Step S6: Update the weight parameters of the power allocation decision model based on the job execution gradient to suppress power allocation errors caused by non-physical deviations through closed-loop feedback.

[0036] Preferably, the operation of extracting the background features of the operating conditions in step S2 includes: extracting the non-stationary time-varying components in the load data sequence through a sliding window, and converting the non-stationary time-varying components into preset zero-mean manifold spatial coordinates to separate the transient interference signals caused by photovoltaic output power fluctuations or sudden load changes of agricultural machinery actuators, and determining the transient interference signals as the operating background distribution of the power system, thereby locking in the environmental noise sources that cause the power distribution decision model to converge unstably.

[0037] Preferably, the logic for calculating the gradient correction term in step S4 includes: obtaining the vector difference between the original deviation gradient of the current iteration cycle and the previous iteration cycle, calculating the time change rate of the vector difference in the direction of the background characteristic quantity, and identifying the convergence stagnation region of the objective function of the power distribution decision model based on the time change rate.

[0038] Preferably, the operation of performing orthogonal projection compensation in step S5 includes: constructing an orthogonal complement space for the background features of the working condition, decomposing the original deviation gradient into an effective control component located in the orthogonal complement space and a suppression component located in the direction of the background features of the working condition, and using a gain adjustment matrix to numerically attenuate the suppression component.

[0039] Preferably, the calculation logic of the gain adjustment matrix is ​​as follows: D = I - λ⋅(κ⋅V) bg ⋅V bg T ), where D is the gain adjustment matrix, I is the identity matrix, λ is the preset intensity attenuation factor, κ is the nonlinear response coefficient characterizing the gradient correction term, and V bg These are the characteristic quantities of the working condition background.

[0040] Preferably, the method further includes the following adaptive load adjustment steps: real-time monitoring of the first derivative of the background characteristic quantity of the working condition on the time axis; when the first derivative is continuously lower than a preset static threshold, locking the current gain adjustment matrix and stopping the real-time calculation of the gradient correction term in step S4, so as to reduce the computational load of the processor.

[0041] Preferably, after step S6, the following phase alignment steps are also included: obtaining the evolution rate of the background feature quantity of the working condition, calculating the environmental prediction vector of the interference distribution in the next working cycle based on the evolution rate, and using the environmental prediction vector as the calibration benchmark for the next iteration cycle.

[0042] Preferably, the operation of calculating the environmental prediction vector of the disturbance distribution in the next work cycle includes: calculating the momentum gradient of the background characteristic quantity of the working condition on the time axis, and vector summing the momentum gradient with the current position vector to perform advanced modeling of the power harmonic distribution in the next work cycle.

[0043] Preferably, step S5 also includes a source purity verification step: after generating the job execution gradient, the cosine similarity between the job execution gradient and the original deviation gradient is calculated. When the cosine similarity is less than 0.65, a weight rollback instruction is triggered to restore the weight parameters to the values ​​of the previous iteration cycle in order to offset the loss of target features caused by the projection compensation process.

[0044] Preferably, in step S6, the update of the weight parameters of the power distribution decision model is controlled by the first-order rate of change of the background characteristic quantity of the operating condition. When the first-order rate of change exceeds the preset mutation threshold, the update step size of the weight parameters is reduced to maintain the power distribution stability of the agricultural operation power system in an unstructured dynamic environment.

[0045] Example 1: The method of the present invention is applied to a power distribution control system that needs to operate in an unstructured farmland environment. Under this condition, the movement of agricultural machinery between plots causes changes in soil moisture content and geological texture, resulting in non-physical deviations in the load data collected by the power system that drift with the spatial coordinates of the plots. These deviations induce local curvature distortion on the loss function surface. The power distribution terminal collects a load data sequence containing transient current data of agricultural machinery operation and plot load impedance data. The processor uses a sliding window to extract the non-stationary time-varying components in the load data sequence and converts them into preset zero-mean manifold spatial coordinates. The conversion operation follows the mapping operator V. bg =Φ T (XM base ), where X is the real-time load data, M base The average current value collected during the quiet period before agricultural machinery operation, Φ T This is a principal component projection operator constructed based on singular value decomposition; this step involves subtracting M. base Achieve zero-mean centering of the data, and through Φ T Projection determines its spatial coordinates in the low-dimensional manifold, thereby separating the transient interference signal caused by external photovoltaic power fluctuations or sudden load changes in agricultural machinery actuators, and identifying it as the background characteristic quantity V. bg ; Execution condition background characteristic quantity V bg The extraction process includes the following procedure: configuring a sliding window to truncate the load data sequence at a sampling frequency of 100kHz, and setting the sliding window length N. win The sampling length is 512 points, which covers the minimum signal period required for the autocorrelation coefficient to decay to below 0.1. The sliding step size is set to 256 points to achieve overlapping sampling. Subsequently, a zero-mean manifold space is constructed. Operations include mean-neutralizing the data within the window, followed by singular value decomposition (SVD) to extract the eigenvectors of the first three principal components. This forms a low-dimensional manifold space that characterizes the current operating condition. Finally, following the mapping operator V... bg =ΦT (XM base Transform the sampling points to a centered manifold coordinate space, where M base The average current value collected during the 60-second silent period before agricultural machinery operation is used to calculate V in real time. bg If the first-order rate of change of the modulus exceeds the mutation threshold Th determined based on the mean and standard deviation of the convergence rate, it is determined to be a non-physical deviation induced by the environment. The current feature subspace is locked to isolate the gradient-induced signal caused by photovoltaic fluctuations or soil impedance mutations, ensuring that the model input tensor is isotropic in the multidimensional phase space. Furthermore, the logic for obtaining the background feature quantity of this working condition is to capture the transient current waveform containing high-order harmonics in real time by using Hall sensors deployed at the bottom layer of the agricultural machinery power distribution terminal, use the spatial information entropy algorithm to measure the disorder of the electrical signal in the time domain distribution, and combine the virtual feature probe to perform active pulse excitation on the feedback impedance of a specific frequency band, thereby mapping the environmental background benchmark induced by unstructured ground conditions in real time at the physical signal level.

[0046] The system calculates the original bias gradient G of the power distribution decision model in the current iteration period. raw The processor obtains the original deviation gradient G between the current iteration cycle and the previous iteration cycle. raw The vector difference is calculated, and the vector difference is used to determine the background characteristic V under the working condition. bg The rate of change of time in the direction, based on the characteristic quantity V of the working condition background. bg The model is multiplied with a preset nonlinear response coefficient κ to determine the local gradient distortion of the power distribution decision model in the environmental disturbance dimension. The nonlinear response coefficient κ is determined through a pre-executed gradient sensitivity scan experiment. During offline calibration, κ is scanned in the range of 0.1 to 1.0 with a quantization step size of 0.05. The orthogonal suppression ratio of the gain adjustment matrix to the background features of the operating conditions is calculated for different values. The value at which the suppression ratio reaches 26dB and the correlation between the weight update trajectory and the task manifold ridge reaches 0.95 is selected as the set value of κ. This quantifies the model's sensitivity to specific environmental disturbances. The processor uses this distortion to generate the gain adjustment matrix D, whose specific calculation logic is defined as: D = I - λ⋅(κ⋅V) bg ⋅V bg T ), where D is the gain adjustment matrix, I is the identity matrix, λ is the preset intensity attenuation factor, and V bg The formula, representing the background characteristic quantity, physically implies the construction of an anisotropic damping field targeting the background characteristic direction. The processor uses a gradient correction term to adjust the original deviation gradient G. raw Perform orthogonal projection compensation processing by constructing a feature quantity V for the working condition background. bg The orthogonal complement space will be used to represent the original bias gradient G.raw Decomposed into effective control components located in the orthogonal complement space and characteristic quantities V in the operating condition background. bg Suppression components in the direction; orthogonal projection compensation processing follows the following logical closed loop: constructing a target for V bg orthogonal projection operator The original bias gradient G raw Decomposed into suppression component G inh =PG raw With effective control component G eff =(IP)G raw The calibrated job execution gradient G cal Satisfy G cal =G eff +D⋅G inh The intensity attenuation factor λ in the gain adjustment matrix D was determined through gradient sensitivity scanning experiments to ensure an inhibition ratio of 26dB and a correlation ≥0.95 between the weight update trajectory and the task manifold ridge. The processor monitors G in real time. cal With G raw The cosine similarity is used. If the similarity is less than 0.65, it is determined that there is a risk of target feature loss. The instruction is triggered to make the current cycle weight increment ΔW=0 to execute weight rollback. The 26dB suppression ratio is set based on the energy envelope distribution of 50Hz power frequency and third and fifth harmonics in agricultural power grid. The background noise energy level is suppressed to below the system thermal noise floor through orthogonal projection. The threshold of 0.65 represents the statistical critical point of the calibration gradient deviating from the original task ridge line. It is determined based on the minimum separation angle sine value of the task features extracted and the background noise in the gradient space after the agricultural machinery performs pre-training under different soil moisture contents. Once it is lower than this limit, it is determined that the projection compensation has caused the risk of overcutting of the core features. The compensation damping generated by dynamically adjusting the nonlinear response coefficient κ is used to offset the background dimension curvature distortion, so that the weight parameters can perform high-precision convergence along the task manifold.

[0047] The original bias gradient G is stripped away by numerically attenuating the suppressed component using the gain adjustment matrix D. raw V, a characteristic quantity of the working condition background bg The overlapping interference components are used to generate a calibrated job execution gradient; before updating the weight parameters, the system calculates the job execution gradient and the original deviation gradient G. raw The cosine similarity is used to determine the weight parameters. When the cosine similarity is below 0.65, a weight rollback instruction is triggered to restore the weight parameters to the values ​​of the previous iteration. The system updates the weight parameters of the power allocation decision model based on the calibrated job execution gradient. The update of the weight parameters is controlled by the background feature quantity V. bg The first-order rate of change, when it exceeds a preset mutation threshold, causes the processor to reduce the update step size of the weight parameters; by establishing a feature quantity V for the working condition background. bgThe bias hedging mechanism eliminates gradient shifts induced by environmental disturbances, maintains the convergence stability of the power distribution decision model in unstructured dynamic environments, and reduces computational load.

[0048] Example 2: In an environment simulating unstructured farmland operation loads, the effectiveness of the training method of this invention is verified by constructing a physical simulation platform containing background noise under varying operating conditions. The platform includes a load data sequence containing dynamic impedance fluctuations of agricultural machinery actuators to simulate non-physical deviations caused by sudden changes in soil moisture content. The original load data used in the experiment comes from an electromagnetic simulation dataset generated based on a finite element analysis model. Its solution core follows Maxwell's equations, and Gaussian white noise with a signal-to-noise ratio of 20dB and power frequency interference harmonics with a frequency of 50Hz are pre-superimposed on the input stream to construct an initial state with engineering noise characteristics. The processor sets the sliding window length to 512. The selection of this length depends on the trade-off between the statistical stationarity of the load signal and the real-time response rate of the system. When the spectral bandwidth of the load data sequence is in the range of 5kHz to 15kHz, the sampling frequency is set to 100kHz to ensure that the transient shift of the load impedance of the plot is captured and that no signal aliasing occurs.

[0049] During the experiment, the training task was divided into a control group and an experimental group. The control group used a stochastic gradient descent algorithm based on minimizing the global loss function, while the experimental group executed a task based on the working condition background feature V. bg Orthogonal projection compensation processing; when the system faces background feature shift induced by the geological texture of the land parcel, the original deviation gradient G of the control group is... raw The projection component generated in the background dimension accounts for more than 65% of the total gradient weights, causing the update trajectory of the weight parameters to maintain a residual error of 15.2% even after converging to 200 iterations, and the weight values ​​to produce continuous oscillations with an amplitude of 0.35 near the pseudo-optimal solution; the experimental group extracted the background feature quantity V of the working condition. bg Furthermore, a gain adjustment matrix D is generated by combining the nonlinear response coefficient κ, and the job execution gradient is compared with the background feature quantity V. bg The overlapping components decayed to less than 5% of their original values; under the same iteration period, the residual error of the experimental group converged to 4.6%, and the fluctuation amplitude of the weight update trajectory was suppressed to within 0.08. To verify the performance of the present invention under extreme conditions, the background characteristic quantity V was changed. bg The energy weights were used to construct a gradient-based experimental group. When the intensity of non-physical bias caused by environmental disturbances increased by 30%, the training convergence process of the control group stalled, and the curvature of the loss function became distorted in the background dimension, causing the model to be unable to extract core task features. In the experimental group, the processor monitored the original bias gradient G between the current iteration cycle and the previous iteration cycle. raw The vector difference in the background feature quantity V under the working condition bgThe time rate of change in the direction exceeds the preset abrupt change threshold, thereby triggering the adjustment of the intensity attenuation factor λ, which linearly compensates the suppression weight of the gain adjustment matrix D on the background interference dimension from 0.85 to 0.98. Data shows that when the interference intensity is at the boundary of the parameter range, the calibrated job execution gradient and the original deviation gradient G raw The cosine similarity remained in the range of 0.72 to 0.88, and no weight rollback instruction was triggered.

[0050] Experimental results confirm that the experimental group exhibits better training stability than the control group in unstructured farmland scenarios. The effectiveness of this method stems from the decoupling of operational features and background interference achieved by the gradient orthogonal projection compensation mechanism; when the working condition background feature quantity V bg When nonlinear shifts occur, the dynamic damping effect provided by the gain adjustment matrix D offsets the induced effect of background dimension curvature distortion on the weight parameters, enabling the power allocation decision model to extract power allocation adjustment instructions from load data sequences containing high-rate noise without the need to add hardware sensors. Through the directional suppression of environmental interference dimensions and the coordinated operation of the weight rollback mechanism, the method of this invention improves the training efficiency of a specific calculation model in a simulated agricultural machinery operation environment by 68.5%, and achieves an anti-interference tolerance of over 0.92 for plot feature drift.

[0051] Example 3: In an agricultural operation area containing distributed photovoltaic units and electric plant protection machinery, the load data sequence collected by the power distribution system experiences nonlinear phase space drift due to transient fluctuations in photovoltaic output power caused by cloud cover and sudden torque changes in the traction motor caused by plot switching. The power distribution decision model includes a feature extraction layer with an input dimension of 512, two hidden layers with 256 neurons each, and a normalization layer for outputting power distribution weight coefficients. The processor performs initial state calibration on the acquired load data sequence. During a 60-second reference time when the agricultural machinery is in a standby state and the actuator is not loaded, the original current signal is collected at a sampling frequency of 100kHz, and the statistical mean M of the signal is calculated. base With variance σ base During the job execution phase, the processor uses a sliding window to extract a real-time load data segment of length 512, subtracting M... base The centered sequence is obtained. By calculating the autocorrelation matrix of the sequence and performing singular value decomposition, the first three principal components are extracted to construct a local feature manifold. The real-time sampling points are projected onto this manifold space to determine the background feature quantity V of the working condition. bg .

[0052] The system obtains the original bias gradient G during backpropagation. raw The processor executes a calibration procedure for the gradient correction term by calculating V over 10 consecutive iterations. bgThe second moment of the directional gradient determines the energy level of environmental noise interference; based on formula T h =M rate +3⋅σ rate Determine the mutation threshold; where T h M is the mutation threshold. rate Let σ be the mean convergence rate. rate The standard deviation of the convergence rate; the gradient was detected at V. bg When the rate of change of direction over time exceeds the abrupt change threshold, the processor adjusts the intensity attenuation factor λ to 0.45 to enhance the suppression strength of the gain adjustment matrix D against background interference components; during orthogonal projection compensation processing, the original deviation gradient G is calculated. raw In the background characteristic quantity V bg The unit projection vector on the original bias gradient G raw The weighted components generated by subtracting the product of the unit projection vector and the gain adjustment matrix D are used to generate the calibrated job execution gradient. Based on offline calibration of the environmental benchmark operator and real-time manifold projection of singular value decomposition, the gradient update direction of the power allocation decision model is kept within the task feature subspace orthogonal to the background disturbance. After 500 iterations of training, the deviation angle between the calibrated job execution gradient and the target gradient direction is within 5°, and the execution deviation rate of the power allocation command is reduced from 12.8% to 2.4%. By closing-loop calibration of the mutation threshold and limiting the model hierarchical topology, random disturbances in the training process are eliminated, enabling the specific computational model to complete the extraction of power regulation logic and weight reshaping in unstructured agricultural scenarios.

[0053] Example 4: In newly deployed agricultural plots, the plot's unique load impedance distribution deviates from the basic manifold inventory built into the power distribution system. Furthermore, high-frequency electromagnetic pulses in the environment pose a physical saturation risk at the input end of the load data sequence. Before formally initiating the decision-making process, the processor executes a pre-deployment calibration procedure, controlling the agricultural machinery to travel at a constant speed under zero-load conditions and acquiring the initial load signal through the acquisition unit. This signal is then used to calculate the plot-specific static offset vector B. site The processor identifies the background noise level of the current work area by projecting real-time load data segments onto the orthogonal complement space and calculating the Euclidean distance between the projection point and the environmental reference operator. When the system detects that the projection error exceeds the preset tolerance limit, the processor automatically calls the update instruction to update the working condition background characteristic quantity V. bg The extraction step size was reduced from 512 to 256 to ensure that the feature space captures the manifold curvature changes caused by soil moisture content drift.

[0054] By utilizing the static offset vector B siteLinear normalization correction is performed on the real-time load data sequence to eliminate the difference in reference current between work points on different plots, so that the input tensor of the power distribution decision model exhibits an isotropic characteristic distribution in the multi-dimensional phase space. When the system faces signal drift conditions induced by aging traction motors or increased geological impedance, the processor initiates an adaptive threshold reconstruction procedure to maintain the closed state of the training logic. The execution details of this dynamic optimization are as follows: the processor monitors the power consumption status of the edge computing unit in real time by reading the occupancy rate of the underlying instruction cache. When the first derivative of the background characteristic quantity of the working condition is continuously lower than the preset static threshold and the processor occupancy rate exceeds 85%, the system automatically shuts down the redundant gradient correction term real-time calculation logic and directly reuses the gain adjustment matrix of the previous cycle. This deterministic hardware action ensures that the edge control chip does not generate millisecond-level instruction leakage under high temperature or high load operation. A sliding buffer with a length of 50 is established to store the work execution gradient and the original deviation gradient G within the iteration cycle. raw Calculate the cosine similarity values ​​and the time-domain variance V of the sequence. sim The system is based on formula T. adapt =γ⋅T h +(1-γ)⋅μ sim The baseline water level for dynamically updating the mutation threshold; where T adapt For the updated mutation threshold, T h μ is the mutation threshold of the previous iteration cycle. sim γ is the mean of the cosine similarity sequence, and γ is a preset weighting coefficient with a value of 0.75. When the cosine similarity is detected to be continuously lower than the adaptively calibrated mutation threshold, the processor triggers a correction instruction for the nonlinear response coefficient κ, which is calculated by the original deviation gradient G. raw In the background characteristic quantity V bg The projected residual gradient in the direction restricts the update trajectory of the weight parameters to the curvature ridge of the task manifold; based on this procedure, the weight update step size of the power distribution decision model compensates for the gradient capture error caused by system hardware loss or changes in ground conditions without human intervention, and maintains the convergence stability of the model during the operation cycle.

[0055] Example 5: In the power distribution deployment process of multi-machine joint operation, the mechanical vibration coupling generated by the traction motors of different models of agricultural machinery during operation will cause high-frequency jitter components in the load data sequence acquired by the acquisition unit. The processor executes the window length calibration procedure for the load data sequence, controls the agricultural machinery to acquire 60s of current signal samples under no-load idling state, calculates the decay trend of the autocorrelation coefficient of the signal sample under different sliding window steps, and identifies the minimum number of sampling points N corresponding to the autocorrelation coefficient dropping below the initial value of 0.1. winThis is determined as the benchmark for selecting the sliding window length, so as to adapt the sliding window length to the physical characteristics of the current actuator while balancing the accuracy of signal feature acquisition and the system processing delay.

[0056] The system determines the working value of the nonlinear response coefficient κ through gradient sensitivity experiments. It then injects transient environmental disturbances with known energy amplitudes into the power distribution decision model within a physical simulation platform, and monitors the power distribution decision model in real time as it deviates from the original bias gradient G. raw During the feature projection drift during the update process, the processor performs parameter scanning in the range of 0.1 to 1.0 with a quantization step size of 0.05, calculating the gain adjustment matrix D for each candidate value against the background feature quantity V. bg The orthogonal suppression ratio is determined. When the measured suppression ratio reaches 26dB and the linear correlation between the weight update trajectory and the task manifold ridge reaches 0.95, this candidate value is determined as the legal value of the nonlinear response coefficient κ. This allows the calibrated job execution gradient to maintain the stability of weight parameter updates when facing sudden changes in land load impedance. When the system detects that the first-order rate of change of the load data sequence exceeds the preset sudden change threshold, and the weight update direction is in the background characteristic quantity V... bg When the projection weight in a direction exceeds 65%, the processor triggers an adaptive weight correction procedure, which calculates the gradient of the current job execution and the original deviation gradient G. raw The cosine similarity is compared with an adaptive threshold. When the similarity is below 0.65, a weight rollback instruction is forcibly executed to restore the weight parameters to the values ​​of the previous iteration cycle in order to offset the gradient capture risk induced by non-physical bias, so that the training convergence deviation rate of the specific computational model in the unstructured agricultural scenario is suppressed to within 2.4%.

[0057] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A method for training a specific computational model adapted to agricultural operation scenarios, characterized in that, Includes the following steps: Step S1: Obtain a load data sequence characterizing the operating status of the power system for agricultural operations. The load data sequence includes transient current data of agricultural machinery operations and land load impedance data collected by the power distribution terminal. Step S2: Extract the background characteristics of the load data sequence corresponding to the external environmental disturbances. The background characteristics are used to characterize the non-physical deviations in the load impedance data of the land parcel as the spatial coordinates of the land parcel drift. Step S3: Calculate the original bias gradient of the power distribution decision model in the current iteration cycle; Step S4: Calculate the gradient correction term for the original deviation gradient. Specifically, multiply the background characteristics of the operating conditions with the preset nonlinear response coefficients to determine the local gradient distortion of the power distribution decision model in the environmental disturbance dimension. Step S5: Generate a gain adjustment matrix and use the gradient correction term to perform orthogonal projection compensation on the original deviation gradient to remove the interference components in the original deviation gradient that coincide with the background features of the working condition, and generate a calibrated job execution gradient. Step S6: Update the weight parameters of the power allocation decision model based on the job execution gradient to suppress power allocation errors caused by non-physical deviations through closed-loop feedback.

2. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 1, characterized in that, The operation of extracting background features in step S2 includes: extracting non-stationary time-varying components from the load data sequence through a sliding window, and converting the non-stationary time-varying components into preset zero-mean manifold space coordinates to separate transient interference signals caused by photovoltaic output power fluctuations or sudden load changes in agricultural machinery actuators. The transient interference signals are identified as the background distribution of the power system's operating conditions, thereby locking in the environmental noise sources that cause the power distribution decision model to converge unstably.

3. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 1, characterized in that, The logic for calculating the gradient correction term in step S4 includes: obtaining the vector difference between the original deviation gradient of the current iteration cycle and the previous iteration cycle, calculating the time rate of change of the vector difference in the direction of the background characteristic quantity, and identifying the convergence stagnation region of the objective function of the power distribution decision model based on the time rate of change.

4. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 1, characterized in that, The operation of performing orthogonal projection compensation in step S5 includes: constructing an orthogonal complement space for the background features of the working condition, decomposing the original deviation gradient into an effective control component located in the orthogonal complement space and a suppression component located in the direction of the background features of the working condition, and using a gain adjustment matrix to numerically attenuate the suppression component.

5. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 4, characterized in that, The calculation logic of the gain adjustment matrix is ​​as follows: D = I - λ⋅(κ⋅V) bg ⋅V bg T ), where D is the gain adjustment matrix, I is the identity matrix, λ is the preset intensity attenuation factor, κ is the nonlinear response coefficient characterizing the gradient correction term, and V bg These are the characteristic quantities of the working condition background.

6. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 1, characterized in that, The method also includes the following adaptive load adjustment steps: real-time monitoring of the first derivative of the background characteristic quantity of the working condition on the time axis; when the first derivative is continuously lower than the preset static threshold, locking the current gain adjustment matrix and stopping the real-time calculation of the gradient correction term in step S4.

7. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 1, characterized in that, Step S6 is followed by the following phase alignment steps: obtaining the evolution rate of the background feature quantity of the working condition, calculating the environmental prediction vector of the interference distribution in the next working cycle based on the evolution rate, and using the environmental prediction vector as the calibration benchmark for the next iteration cycle.

8. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 7, characterized in that, The operation of calculating the environmental prediction vector of the disturbance distribution in the next work cycle includes: calculating the momentum gradient of the background characteristic quantity of the working condition on the time axis, and summing the momentum gradient with the current position vector to perform advanced modeling of the power harmonic distribution in the next work cycle.

9. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 1, characterized in that, Step S5 also includes a source purity verification step: after generating the job execution gradient, the cosine similarity between the job execution gradient and the original deviation gradient is calculated. When the cosine similarity is less than 0.65, a weight rollback instruction is triggered to restore the weight parameters to the values ​​of the previous iteration cycle in order to offset the loss of target features caused by the projection compensation process.

10. The method for training a specific computational model adapted to agricultural operation scenarios according to claim 1, characterized in that, In step S6, the update of the weight parameters of the power distribution decision model is controlled by the first-order rate of change of the background characteristic quantity of the operating condition. When the first-order rate of change exceeds the preset mutation threshold, the update step size of the weight parameters is reduced.

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