Displacement error dynamic compensation method, device, equipment and medium

Abstract

The present application relates to motor control and sensor technology field, disclose a kind of displacement error dynamic compensation method, device, equipment and medium, comprising: obtaining the temperature data and original displacement data of multiple temperature measuring points in motion component inside and environment, call the nonlinear mapping relationship between temperature gradient and displacement deviation pre-constructed, temperature data and original displacement data are adaptively filtered based on the nonlinear mapping relationship, separate out the trend component characterized temperature drift, obtain the displacement data of removing trend component, generate dynamic compensation instruction based on trend component and nonlinear mapping relationship, adjust the displacement data of removing trend component and output compensation displacement data.The present application constructs the nonlinear mapping model of temperature gradient and displacement deviation, combines multiple-point temperature sensing and adaptive filtering extraction drift trend, dynamically generates compensation instruction, realizes the accurate real-time correction of displacement error, improves the responsiveness and accuracy of compensation.

Description

Technical Field

[0001] This invention relates to the field of motor control and sensor technology, and in particular to a method, device, equipment and storage medium for dynamic compensation of displacement error. Background Technology

[0002] In high-precision motor control systems, encoders are key components for displacement detection, and their accuracy directly affects the overall positioning performance of the system. However, traditional encoders generally lack the ability to monitor the internal temperature of the motor in real time, often relying on external sensors to measure ambient or housing temperatures. These indirect monitoring methods not only fail to reflect the true temperature distribution inside the encoder or at critical locations of moving parts, but also suffer from response delays, making them unsuitable for dynamic control systems with high requirements for thermal response sensitivity.

[0003] Furthermore, most existing displacement compensation methods are based on static temperature compensation models, which only correct errors at a constant temperature and fail to fully consider the nonlinear offset caused by temperature changes over time. Due to uneven distribution of internal heat sources during motor operation, the thermal expansion of various structural components differs, leading to localized thermal deformation. The lack of a dynamic compensation mechanism results in error accumulation, causing continuous interference with positioning accuracy.

[0004] More importantly, currently used temperature compensation strategies fail to establish a systematic correlation between displacement error and temperature changes, making it impossible to adjust the compensation strategy based on real-time temperature gradients. This lack of targeted compensation is particularly pronounced in high-speed or high-load applications, resulting in limited overall compensation accuracy and making it difficult to meet the actual needs of high-precision servo control systems for dynamic elimination of minute errors. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, and storage medium for dynamic compensation of displacement errors, aiming to solve the technical problem that existing technologies cannot achieve the correlation modeling and dynamic compensation of nonlinear temperature gradients and displacement deviations based on multi-point temperature sensing, resulting in low accuracy and poor real-time performance of displacement error compensation in complex thermal environments.

[0006] To achieve the above objectives, the present invention provides a dynamic compensation method for displacement error, comprising:

[0007] Temperature data are acquired at multiple temperature measurement points inside the moving part and in its environment, and the original displacement data of the moving part is also acquired.

[0008] The pre-built nonlinear mapping relationship between temperature gradient and displacement deviation is invoked, and the nonlinear mapping relationship is established by calibrating the displacement deviation curves at different temperatures;

[0009] based on the nonlinear mapping relationship, the temperature data and the original displacement data are adaptively filtered to separate a trend component representing temperature drift, and displacement data without the trend component is obtained;

[0010] based on the trend component and the nonlinear mapping relationship, dynamic compensation instructions are generated;

[0011] based on the dynamic compensation instructions, the displacement data without the trend component is adjusted to output compensated displacement data.

[0012] Further, to achieve the above object, the present application provides a displacement error dynamic compensation device, comprising:

[0013] a temperature and displacement acquisition module for acquiring temperature data at multiple temperature measuring points inside a moving component and its environment, and acquiring original displacement data of the moving component;

[0014] a nonlinear mapping construction module for calling a nonlinear mapping relationship between temperature gradient and displacement deviation pre-constructed, the nonlinear mapping relationship being established by calibrating displacement deviation curves at different temperatures;

[0015] an adaptive filtering module for adaptively filtering the temperature data and the original displacement data based on the nonlinear mapping relationship to separate a trend component representing temperature drift, and obtaining displacement data without the trend component;

[0016] a compensation instruction generation module for generating dynamic compensation instructions based on the trend component and the nonlinear mapping relationship;

[0017] a displacement compensation execution module for adjusting the displacement data without the trend component according to the dynamic compensation instructions to output compensated displacement data.

[0018] Further, to achieve the above object, the present application further provides a computer device, which comprises a memory, a processor, and a displacement error dynamic compensation program stored in the memory and executable on the processor, and the displacement error dynamic compensation program, when executed by the processor, implements the steps of the displacement error dynamic compensation method as described above.

[0019] Further, to achieve the above object, the present application further provides a computer readable storage medium, which stores a displacement error dynamic compensation program, and the displacement error dynamic compensation program, when executed by a processor, implements the steps of the displacement error dynamic compensation method as described above.

[0020] Beneficial effects: The present application relates to the field of motor control and sensor technology, and discloses a displacement error dynamic compensation method, device, equipment and medium, which comprises the following steps: acquiring temperature data of multiple temperature measuring points in a moving component and the environment thereof, acquiring original displacement data of the moving component, calling a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation, adaptively filtering the temperature data and the original displacement data based on the nonlinear mapping relationship to separate a trend component representing temperature drift, and obtaining displacement data from which the trend component is removed, generating a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship, adjusting the displacement data from which the trend component is removed according to the dynamic compensation instruction, and outputting compensated displacement data. The present application establishes a nonlinear mapping relationship between temperature gradient and displacement deviation through multi-point temperature sensing, so that the influence of temperature change on displacement error can be dynamically perceived and modeled. In combination with an adaptive filtering method for extracting a temperature drift trend and generating a dynamic compensation instruction, real-time compensation of displacement error under different thermal load conditions is realized, and the compensation accuracy and response speed are improved. BRIEF DESCRIPTION OF DRAWINGS

[0021] The present application will be further described below in conjunction with the accompanying drawings and embodiments. In the drawings:

[0022] Figure 1 An application environment diagram of the displacement error dynamic compensation method in an embodiment of the present application;

[0023] Figure 2 A flow diagram of the displacement error dynamic compensation method in an embodiment of the present application;

[0024] Figure 3 A functional module diagram of the displacement error dynamic compensation device in a preferred embodiment of the present application;

[0025] Figure 4 A structure diagram of a computer device in an embodiment of the present application;

[0026] Figure 5 Another structure diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0027] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0028] The displacement error dynamic compensation method provided by the embodiments of the present application can be applied to, for example, Figure 1The application environment of the application is that a user terminal communicates with a server terminal through a network. The server terminal can obtain temperature data at multiple temperature measuring points inside a moving component and its environment, obtain original displacement data of the moving component, call a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation, perform adaptive filtering processing on the temperature data and the original displacement data based on the nonlinear mapping relationship to separate a trend component representing temperature drift, and obtain displacement data from which the trend component is removed. The server terminal generates a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship, adjusts the displacement data from which the trend component is removed according to the dynamic compensation instruction, and outputs compensated displacement data. The application can dynamically perceive and model the influence of temperature change on displacement error by multi-point temperature sensing and establishing a nonlinear mapping relationship between temperature gradient and displacement deviation, and can extract a temperature drift trend and generate a dynamic compensation instruction by combining an adaptive filtering method, so that real-time compensation of displacement error is realized under different thermal load conditions, and the compensation accuracy and response speed are improved. The user terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server terminal can be implemented by an independent server or a server cluster composed of multiple servers. The application will be described in detail below through specific embodiments.

[0029] Please refer to Figure 2 , Figure 2 The flowchart of an embodiment of the displacement error dynamic compensation method provided by the application is shown in the figure. It should be noted that although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown.

[0030] As shown in Figure 2 , the displacement error dynamic compensation method provided by the application includes the following steps:

[0031] S10, obtaining temperature data at multiple temperature measuring points inside a moving component and its environment, and obtaining original displacement data of the moving component;

[0032] In this embodiment, in the process of dynamically compensating displacement error, first, temperature data at multiple temperature measuring points inside the moving component and its environment is obtained. The temperature data sources include different structural regions of the moving component. In order to more accurately capture the change of heat source and its influence on the structure, multiple temperature sensors need to be arranged at the stator assembly, the rotor region, the encoder region, and the heat exchange boundary. These sensor types can include thermocouples, thermistors, or integrated digital temperature acquisition chips, and their deployment methods can be adjusted according to the installation space and thermal coupling characteristics, such as being arranged in close contact with the stator winding surface or using a wireless temperature acquisition module to realize dynamic data transmission in the rotor.

[0033] In addition to the thermal information collection within the structure, the temperature influence of the external environment also needs to be obtained, so an environmental temperature sensor needs to be arranged near the shell or cooling interface of the moving part. These temperature information can be transmitted to the processing unit in real time through real-time sampling, forming a temperature monitoring data set with a time stamp. The data collected by each sensor needs to be corrected by a unified time synchronization mechanism to avoid spatial thermal state misjudgment caused by collection delay or network jitter.

[0034] The displacement data acquisition depends on the displacement detection encoder on the moving part. The encoder can be an incremental photoelectric encoder, an absolute encoder, or a magnetic encoder, which needs to be installed near the observed displacement axis to ensure accurate perception of the thermal deformation sensitive area. The original position signal generated by the encoder is usually a pulse sequence or digital angle data, which needs to be converted to physical displacement values through decoding logic.

[0035] After the temperature data and displacement data are collected, spatial point binding and data alignment operations need to be performed, that is, the spatial coordinates of each temperature sampling point are mapped to the corresponding mechanical structure area, and the data of each channel is aligned in the time dimension to ensure the synchronization of temperature change and displacement change, thereby providing accurate input basis for subsequent temperature drift trend extraction and displacement error modeling.

[0036] In specific implementation, three symmetrical positions in the stator assembly can be selected to paste thermocouple sensors, and analog signals can be transmitted to the main control board in real time through an analog-to-digital conversion module; wireless temperature sensors can be used in the rotor to send data through Bluetooth or radio frequency channels; NTC sensors can be embedded in the encoder housing and read synchronously through an I2C interface; and DS18B20 type digital temperature sensors can be used for environmental temperature sensors and fixed on the air cooling channel or the outside of the machine shell.

[0037] The displacement detection encoder uses a magnetic absolute encoder with a resolution of not less than 0.001 mm, and the position signal is uploaded to the processing platform through the RS485 protocol. The sampling frequency of all sensors and encoders is uniformly set to 10 Hz, and a synchronous clock marker is set to align the data. If deployed in high-speed rotating occasions, a jitter prevention mechanism or a filter buffer needs to be added to process transient jump data.

[0038] In the data preprocessing stage, a multi-channel data fusion mechanism is used to perform spatial interpolation calculation on multiple temperature points to form a three-dimensional heat distribution matrix, and the encoder displacement data is smoothed to remove abnormal mutation sample points, ensuring that the basic data for nonlinear mapping modeling is accurate and stable.

[0039] Example description: In a class of high-precision servo motor applications, in order to compensate for the axial displacement error caused by the stator winding temperature rise due to long-time operation, three K-type thermocouple sensors are respectively arranged at the winding slot openings, a wireless infrared temperature module is arranged at the center of the rotor, a digital thermometer is arranged in the environmental control box, and an absolute encoder is installed at the output shaft end. When the equipment starts to run, the system periodically collects the data of all sensors and encoders, synchronously to the host computer through the data acquisition card, and combines the subsequent mapping model and filtering algorithm processing procedures to update the displacement compensation value in real time, effectively compresses the displacement deviation caused by thermal drift, and improves the stability and precision of the system operation.

[0040] In this embodiment, by simultaneously acquiring temperature information of multiple key structural parts and actual displacement data, and establishing a joint data set in space and time, the influence of local heat source induced micro deformation on displacement error can be effectively captured, and the lag and deviation caused by traditional static correction using only environmental temperature or single point temperature can be avoided. This method can improve the resolution and accuracy of temperature field monitoring, thereby providing a more detailed input basis for high-precision displacement compensation.

[0041] S20, a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation is called, the nonlinear mapping relationship is established by calibrating displacement deviation curves at different temperatures;

[0042] In this embodiment, in order to realize dynamic displacement compensation based on temperature state, an established nonlinear mapping relationship is needed, which is used to describe the correspondence between temperature gradient and displacement deviation. This nonlinear mapping relationship is not a direct assumption function form, but is generated through an experimental calibration process, the core of which is to establish displacement deviation curves at multiple temperature states, and to extract the influence of temperature field change on structural displacement from them.

[0043] The construction of the nonlinear mapping relationship depends on the spatio-temporal coupling characteristics between the temperature gradient and the displacement offset. The temperature gradient refers to the temperature difference between different temperature measurement points, which produces local thermal stress in the structure material, resulting in micro or macro displacement deviation. This deviation does not have a simple linear response relationship, so it needs to be fitted through experimental or machine learning modeling methods to establish a nonlinear function mapping between multivariate input and target displacement deviation. Generally, this process covers the following characteristic inputs: spatial position coordinates of temperature measurement points, corresponding temperature values, temperature change rate per unit time, and initial displacement reference value. The target output is the cumulative displacement offset under a specific thermal state.

[0044] The expression of the nonlinear mapping relationship can adopt a neural network model, a support vector regression model, a radial basis function network, or a multi-dimensional fitting surface based on interpolation. The collection process of the calibration data involves multiple rounds of control experiments. Under the condition of setting constant speed and load, the environmental temperature is gradually increased, and the displacement deviation value is collected synchronously. Finally, a set of function parameters or a trained model for subsequent compensation calculation is obtained through a fitting tool.

[0045] When calling the nonlinear mapping relationship, the current collected temperature data needs to be input into the corresponding model, and the displacement error estimate corresponding to the current time is output by the model. This estimate serves as the numerical basis for the displacement deviation trend caused by temperature, providing key data support for subsequent filtering and compensation.

[0046] In specific implementation, a certain type of motor can be calibrated using historical experimental data. The environmental temperature is set to gradually increase from 25°C to 85°C, and the difference between the internal temperature measurement point data and the actual displacement data at different time points is recorded. Through these experimental points, a function mapping relationship between the temperature gradient vector and the corresponding displacement error is established. In the modeling stage, a multilayer perceptron neural network is selected, the input layer includes the temperature values of each temperature measurement point, the initial state of displacement, the current environmental temperature and the change rate, and the output is the displacement deviation at the corresponding time point.

[0047] After training, the model is deployed to the operation module. In the subsequent running stage, the real-time temperature data vector collected is directly input, and the corresponding displacement deviation prediction value is output. This process realizes low-delay compensation calculation through high-frequency parallel calculation, ensuring the unity of real-time and accuracy.

[0048] In high-stability scenarios, a fitting function can also be established through mathematical methods such as spline interpolation or Chebyshev polynomial fitting, and stored in the system parameter table in the form of a lookup table. In the calling process, the displacement error estimate under the current temperature state is quickly obtained through multi-dimensional indexing, simplifying the calculation burden.

[0049] Example: Taking an automated precision platform as an example, in the early stage of construction, a standard test platform is used to sample the axial displacement error under different temperature distributions for a long period of time, obtaining 100 sets of temperature-displacement paired data. Based on these data, a neural network is built through the TensorFlow framework in Python, and finally a model with an input dimension of 6, a hidden layer of 3 layers, and an output of a single value deviation prediction is trained. In the subsequent system operation, the temperature values of the current six temperature measurement points are collected in real time, input into the model, and the current estimated displacement deviation is obtained once per second, which is used to assist displacement filtering processing and dynamic compensation instruction generation, significantly improving the platform positioning stability and repeatability.

[0050] The embodiment can effectively model the complex influence of temperature gradient on displacement offset by constructing and calling the nonlinear mapping relationship established based on the calibration experiment, and break through the limitation that the traditional linear correction model cannot adapt to nonlinear errors. The mechanism has good generalization ability and real-time responsiveness, can accurately identify and predict the displacement error change trend under different temperature conditions, thereby providing high-precision input for subsequent compensation strategies.

[0051] S30, based on the nonlinear mapping relationship, performing adaptive filtering processing on the temperature data and the original displacement data, separating out a trend component representing temperature drift, and obtaining displacement data without the trend component;

[0052] In the embodiment, to further reduce the displacement measurement error caused by changes in the thermal environment, adaptive filtering processing is performed on the original displacement data and temperature data based on the establishment of the nonlinear mapping relationship. The core of this processing procedure is to separate the temperature drift trend superimposed in the displacement change signal, and extract the displacement response without temperature influence, to obtain more real structural displacement data.

[0053] Adaptive filtering processing means dynamically adjusting filtering parameters according to changes in external environment and measured system state, to realize extraction and suppression of non-stationary interference components in the signal. The filter used here does not use fixed weights, but uses the output of the nonlinear mapping relationship as the adjustment factor of the filter, so that the filter can perceive the current thermal field change, thereby specifically enhancing the ability to capture the drift component. This process is not simply a high-pass or low-pass process, but through the introduction of a trend modeling mechanism, the slowly varying term in the displacement signal caused by temperature gradient changes is accurately extracted.

[0054] The trend component is defined here as a displacement offset trajectory that changes slowly over time and has a high correlation with the current temperature gradient characteristics. After extracting it, the original displacement data is subtracted from the trend component to obtain the structural response value without temperature influence. In this process, the trend component is based on the temperature response offset estimated by the output of the nonlinear mapping relationship, and then the corresponding component in the actual data is identified and separated through the filter.

[0055] The key point of this process is the dynamic updating strategy of the filter parameters, which includes the current temperature change rate, temperature gradient amplitude, nonlinear model residual, and displacement data change speed. By setting a sliding window or exponential weighted moving average mechanism, the adaptive filter has strong robustness while maintaining response speed.

[0056] In practical applications, the system first inputs the multi-point temperature data collected in real time into the nonlinear mapping model, and outputs a set of current predicted displacement offset trend values caused by temperature. Then, the predicted trend is taken as the expected drift signal, and compared with the original displacement data for adaptive filtering calculation.

[0057] The variable parameter Kalman filter structure can be used to introduce the nonlinear model output into the state transition equation, and combine the measurement noise of the original displacement data for state estimation update. When the nonlinear mapping output changes dramatically, the weight of the drift component in the filter is dynamically increased; when the temperature changes slowly, the original signal is mainly used to return to the response trajectory.

[0058] Another implementation is a combined filtering method based on empirical mode decomposition (EMD) and nonlinear function adjustment. First, each order modal component is decomposed by EMD, and then it is judged by the nonlinear model which order modal is related to the temperature drift, and the component screening and reconstruction are performed to retain the true response component.

[0059] The final result output by the system is the displacement data without the trend component, which can be used for subsequent dynamic compensation.

[0060] Example: Taking a certain servo positioning system as an example, the original encoder displacement data shows a periodic slow drift in long-term operation, and analysis shows that it is consistent with the body temperature rise trend. In the experiment, the six temperature measurement points are input into the three-layer neural network model to obtain the current drift trend estimation value, and a variable step weighting filter is used to adjust the estimation value in real time. The filter is initially set to a 50 ms response window, which is shortened to 20 ms when the thermal deformation rises rapidly, and then automatically adjusted according to the temperature change rate. After filtering, the detrended displacement data has a standard deviation of about 60% lower than the original data, providing a reliable input for subsequent compensation instruction generation.

[0061] This embodiment can realize real-time identification and separation of temperature-driven displacement error components by combining nonlinear mapping relationship with adaptive filtering strategy, so that the original displacement signal after removing the data affected by heat is more accurate and stable. Compared with the fixed parameter filter, the dynamically adjusted filtering method can respond more sensitively to temperature field changes, effectively improve the accuracy of pre-processing compensation, and reduce cumulative error.

[0062] S40, generating a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship;

[0063] In this embodiment, after the extraction of the trend component is completed, a compensation control signal, i.e., a dynamic compensation instruction, for the trend component needs to be constructed to further improve the immediacy and accuracy of the compensation response. The generation of the instruction relies on the functional linkage between the trend component and the established nonlinear mapping relationship, and the control parameters required for compensation are back calculated through the mapping model, thereby adapting to the dynamic displacement correction requirements under different thermal states.

[0064] The dynamic compensation instruction is a control parameter sequence for correcting the current displacement response, and usually contains information such as displacement direction, compensation amplitude, and action duration. Its essence is to generate a corresponding reverse correction path based on the nonlinear relationship between the thermal-induced trend and the original displacement, so that the system produces an adjustment effect to offset the thermal drift in the actual response.

[0065] The trend component is regarded as an estimated value of the displacement offset caused by the current thermal field, and the nonlinear mapping relationship provides a displacement change function model under temperature driving. By performing reverse mapping on the trend component, the compensation value range that the system should output at present is obtained, and it is converted into a continuous compensation instruction stream according to the time sequence characteristics.

[0066] The generation of the compensation instruction not only considers the amplitude characteristics of the trend component itself, but also introduces the trend change rate as a response adjustment factor to ensure that the compensation instruction is updated in time when the thermal drift accelerates or reverses. In addition, the compensation instruction is usually encapsulated in a structured data form, with fields such as time stamp, execution priority, and reset flag to support real-time parsing and scheduling execution of subsequent modules.

[0067] The system can functionally express the nonlinear mapping relationship based on polynomial fitting or neural network structure, receive the trend component as input, and calculate the reverse correction value required for current compensation. For example, if the nonlinear mapping model uses a radial basis function network (RBF), the trend component input will activate the corresponding hidden unit, output the compensation coefficient, and construct a vectorized compensation instruction through the coefficient.

[0068] In implementation, the dynamic compensation instruction can be represented in difference control format: compensation displacement = -F(trend component), where F is a regression function based on the nonlinear mapping relationship. This function structure supports continuous input update and sliding prediction, i.e., when the trend component changes over time, the compensation instruction is also dynamically adjusted.

[0069] Another implementation is to construct a pre-trained lookup table, which corresponds the trend component under different amplitude and rate combinations to a specific compensation instruction template, obtains the optimal matching compensation parameter set through fast lookup table method, and injects it into the downstream execution module in real time.

[0070] Optionally, to improve the robustness and fault tolerance of the instructions, a dynamic instruction smoothing mechanism can also be introduced to perform linear or nonlinear interpolation on the compensation parameters of adjacent time steps, so that the compensation control process is more continuous and stable, and the mechanical system resonance caused by compensation fluctuation is prevented.

[0071] Example: Taking a high-precision numerical positioning platform as an example, under the action of a heat source, the structural thermal deformation of the platform leads to the gradual accumulation of displacement deviation, forming a clear trend drift. The current temperature distribution is collected by the deployed multi-point temperature measurement unit, and the trend component is extracted in combination with the filtering module. The built-in neural network nonlinear mapping model of the system accepts the trend component as input and outputs the corresponding negative compensation value, generating a dynamic compensation instruction containing compensation amplitude and direction. The instruction is continuously sent to the displacement execution controller at a 10 ms update period, realizing rapid tracking and correction of thermal drift.

[0072] This embodiment generates a dynamic compensation instruction by combining the trend component and the nonlinear mapping relationship, achieving the adaptability and accuracy of compensation control. This mechanism can calculate and output a compensation control sequence that matches the trend of the displacement change caused by the heat field in real time, avoiding the cumulative distortion caused by compensation delay or static error estimation, and effectively improving the stability and controllability of high-precision displacement systems in complex thermal environments.

[0073] S50, according to the dynamic compensation instruction, adjusting the displacement data after removing the trend component, outputting compensation displacement data.

[0074] In this embodiment, after the generation of the dynamic compensation instruction, the system needs to apply the compensation control signal to the displacement data after removing the trend component, thereby outputting the compensation displacement data after thermal drift correction. The key in this process is how to accurately and without delay integrate the dynamic compensation instruction into the displacement result after filtering out the influence of temperature drift in the form of numerical superposition, state control or response interpolation, etc.

[0075] The displacement data after removing the trend component is a numerical sequence that has removed the disturbance components strongly related to temperature changes through filtering, and retains the true displacement response characteristics of the measured object under actual physical driving. Taking this data as the basis for compensation processing can correct the displacement without introducing redundant disturbances.

[0076] As a control input, the dynamic compensation instruction usually contains compensation displacement amplitude, direction, application time window, etc. The system can perform point-to-point mapping of the instruction with the displacement data after removing the trend component, or perform sliding weighted fusion on the time axis, and finally obtain the compensation displacement data after correcting the influence of the deviation.

[0077] The compensation mode can adopt a weighted superposition mechanism. At each time sampling point, the dynamic compensation value is directly superimposed on the displacement value after removing the trend component to construct a new displacement output sequence. Such superposition operation needs to be performed under synchronous timing control to ensure accurate alignment of the compensation instruction and the displacement data on the time axis.

[0078] To improve the smoothness and stability of the output results, the system can also set a buffer module to input a plurality of continuous compensation instructions and corresponding displacement data into a linear interpolator or a Bezier curve fitter to construct a smooth and continuous compensation displacement trajectory, preventing mechanical shock or data anomalies caused by displacement mutation.

[0079] The system can perform one-to-one compensation operation on the dynamic compensation instruction and the displacement data after removing the trend component according to the time step during processing. In specific implementation, the following compensation model can be defined:

[0080] Compensation displacement = displacement after removing trend component + compensation instruction output value

[0081] The model supports interrupt mode calling in a standard controller, that is, at each sampling point, the displacement value after removing the trend component is read first, then the compensation instruction value at the current time is read, and the compensation displacement data buffer is updated after summation.

[0082] When the compensation instruction has multiple dimensions (such as direction vector), a vector superposition method can be introduced to combine the displacement vector and the compensation vector according to the component direction to construct the compensation response data of a three-dimensional space or a multi-axis system. For non-continuous sampling or compensation packet loss, a difference prediction model can be constructed to estimate the current frame compensation parameter using the compensation values of the previous and next frames to enhance the integrity of the data stream.

[0083] At the output layer, a data formatting and timestamp binding mechanism can be set to structure and package the compensation displacement data, including fields such as compensated value, original displacement value, compensation amplitude, and action window, to facilitate direct analysis and verification by downstream devices, which is suitable for various scenarios such as precision motion control and thermal stability monitoring.

[0084] Example: In a type of high-precision linear displacement measurement system, the displacement of the target object is collected in real time by a magnetic grid sensor. Due to the inconsistent thermal expansion of the sensor body and structural parts, the collected displacement has temperature-related deviation. The system obtains the displacement data after removing the trend component through a pre-processing module and generates real-time updated dynamic compensation instructions according to the trend component.

[0085] The control module performs a sum operation on the compensation instruction and current displacement data to output real-time compensation displacement data. The output data is sent to an upper controller through a serial port to realize high-precision feedback control in a thermal drift environment. Experimental results show that the processing step reduces the overall displacement error to about 20% of the original error, and still maintains high stability and repeatability in a working interval where the temperature changes by more than 15 DEG C.

[0086] The embodiment can realize reverse correction of thermal drift disturbance by applying a dynamic compensation instruction to displacement data from which a trend component is removed, and significantly improve the precision and availability of displacement data in a variable temperature environment. The processing mechanism supports continuity and adaptive adjustment, can dynamically offset the error caused by thermal changes, avoids system performance degradation caused by incorrect compensation or lag response, and provides accurate and stable data basis for subsequent feedback control and state judgment.

[0087] The application relates to the fields of motor control and sensor technology, and discloses a displacement error dynamic compensation method, device, equipment and medium, which comprises the following steps: acquiring temperature data of multiple temperature measuring points in a moving component and an environment where the moving component is located, acquiring original displacement data of the moving component, calling a pre-constructed nonlinear mapping relationship between a temperature gradient and a displacement deviation, adaptively filtering the temperature data and the original displacement data based on the nonlinear mapping relationship to separate a trend component representing temperature drift, and obtaining displacement data from which the trend component is removed, generating a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship, adjusting the displacement data from which the trend component is removed according to the dynamic compensation instruction, and outputting compensation displacement data. The application can dynamically perceive and model the influence of temperature change on displacement error by perceiving multiple temperature points and establishing a nonlinear mapping relationship between a temperature gradient and a displacement deviation, and can further extract a temperature drift trend and generate a dynamic compensation instruction in combination with an adaptive filtering method, so that real-time compensation of displacement error can be realized under different thermal load conditions, and the precision and response speed of compensation are improved.

[0088] In one embodiment, the above step S10 comprises:

[0089] S101, deploying multiple stator temperature sensors in a stator assembly of the moving component;

[0090] S102, deploying multiple rotor temperature sensors in a rotor of the moving component;

[0091] S103, deploying multiple encoder temperature sensors in an encoder of the moving component;

[0092] S104, deploying an environment temperature sensor at a heat exchange boundary of the moving component;

[0093] S105, installing a displacement detection encoder on a rotor of the moving component;

[0094] S106, collecting stator temperature data through the multi-channel stator temperature sensor;

[0095] S107, collecting rotor temperature data through the multi-channel rotor temperature sensor;

[0096] S108, collecting encoder temperature data through the multi-channel encoder temperature sensor;

[0097] S109, collecting ambient temperature data through the ambient temperature sensor;

[0098] S110, integrating the stator temperature data, rotor temperature data, encoder temperature data, and ambient temperature data to form a temperature monitoring data set;

[0099] S111, collecting rotor position signals through the displacement detection encoder and generating raw displacement data based on the rotor position signals.

[0100] In this embodiment, in order to effectively compensate for displacement errors under the influence of thermal drift, it is necessary to first construct a complete temperature sensing and displacement observation channel. By setting multiple temperature measurement points inside the moving parts and their environment, and accurately collecting various temperature information and rotor displacement information, a foundation is provided for subsequent data fusion and compensation processing.

[0101] Deploying multiple stator temperature sensors inside the stator assembly can obtain local thermal distribution data at the stator winding, electromagnetic core, etc. These locations are often the starting source of temperature change due to the combined effects of power heating and external heat conduction, and have a decisive influence on thermal field distribution. The multi-point arrangement helps to capture spatial non-uniform temperature rise conditions and provide high-resolution temperature profiles for thermal gradient modeling.

[0102] Deploying multiple rotor temperature sensors inside the rotor can be used to detect the temperature rise distribution caused by eddy currents, motor load, and centrifugal effects of the rotating part. The rotor is in a rotating state, causing its heat conduction path to change dynamically, so the temperature data collected reflects the true thermal response characteristics during operation. In order to ensure real-time and accuracy, temperature information can be obtained through wireless temperature acquisition modules or non-contact infrared temperature measurement technology.

[0103] The encoder is a precision displacement detection unit, and the internal temperature change directly affects the signal stability and sensitivity. Deploying multiple encoder temperature sensors inside the encoder can accurately obtain the temperature rise around key sensitive components, especially the local temperature near the photoelectric encoder sheet, sensing elements, and signal amplification circuit, which helps to analyze the temperature offset causes of the encoder output signal.

[0104] The ambient temperature sensor is arranged at the heat exchange boundary to obtain the heat conduction boundary condition between the moving component and the external system, and to evaluate the disturbance of the working condition of the system on the overall temperature field. The change of the ambient temperature often affects the cooling efficiency and thermal stability of the system, and therefore the measurement data is indispensable in dynamic compensation modeling.

[0105] A displacement detection encoder is installed on the rotor to obtain a rotor angle position signal. The encoder can adopt an incremental or absolute structure to ensure that the displacement change at different angular velocities can be perceived with high precision. Based on the collected angular displacement signal, position integration processing can be performed in combination with time information to obtain original displacement data as a reference benchmark for subsequent filtering and compensation processing.

[0106] The system synchronously collects temperature values of each temperature measurement point through the above-mentioned multiple temperature channels, and integrates the temperature data of the stator, the rotor, the encoder and the environment to form a temperature monitoring data set corresponding to time sequence and position association. The data set reflects the evolution characteristics of the thermal field in two dimensions of space and time, which is helpful for subsequent construction of a multivariate thermal drift model and execution of dynamic error compensation.

[0107] The original displacement data acquisition process is performed synchronously with temperature monitoring to ensure that each set of displacement data has a matching temperature observation background, thereby realizing the alignment of basic data for thermal-displacement coupling modeling. This synchronous acquisition mechanism improves the accuracy and response speed of dynamic compensation, and is particularly suitable for high dynamic and severe thermal disturbance application scenarios.

[0108] The embodiment can comprehensively perceive the thermal gradient evolution during system operation by arranging multiple types of temperature sensors inside the moving component and in the environment where the moving component is located, and synchronously collecting data related to displacement detection. In addition, the embodiment can construct a temperature monitoring channel with high spatial resolution and strong real-time response. The original displacement data and the temperature monitoring data set are synchronously acquired to provide stable input for thermal drift analysis and nonlinear error modeling. Through this acquisition mechanism, the adaptability and response efficiency of the subsequent compensation model can be significantly improved, and the displacement measurement error caused by local overheating, thermal conduction lag or external environmental disturbance can be reduced.

[0109] In one embodiment, the above step S20 comprises:

[0110] S201, operating the moving component at a plurality of predetermined temperature points;

[0111] S202, recording steady-state displacement deviation data when maintaining a constant temperature state at each predetermined temperature point;

[0112] S203, generating a displacement deviation curve cluster based on the recorded steady-state displacement deviation data;

[0113] S204, extracting a feature parameter based on the displacement deviation curve cluster;​​​​​​​​​

[0114] S205, constructing a nonlinear mapping model using the extracted characteristic parameters;

[0115] S206, storing the nonlinear mapping model into a relational database;

[0116] S207, calling the nonlinear mapping model in the relational database in response to a displacement compensation instruction.

[0117] In the embodiment, in order to realize dynamic compensation of displacement error based on temperature change, a nonlinear mapping model between temperature gradient and displacement deviation needs to be established, which reflects the steady-state error response of the moving part under different thermal field distribution states. The establishment of the mapping model is based on systematic calibration, which needs to sample the running state of the moving part in multiple temperature control environments and extract the functional law of temperature affecting displacement precision.

[0118] Running the moving part at multiple predetermined temperature points is the starting stage of model construction. The selection of the predetermined temperature points needs to cover the typical temperature values in the working range of the moving part, including normal temperature, steady-state temperature after heating, and extreme high temperature working point. By controlling the environmental cabin or built-in heat source to heat and maintain the system stable, each key temperature measurement point reaches the expected temperature level and keeps the temperature rise trend in the stable interval.

[0119] When maintaining a constant temperature state at each predetermined temperature point, record the steady-state displacement deviation data, which reflects the static displacement error caused by the combined action of temperature change on material thermal expansion, motor magnetic property change, and structure stress response. The purpose of steady-state collection is to filter out the instantaneous changes caused by dynamic disturbance and extract the representative offset value under specific thermal equilibrium conditions. These data are usually obtained by comparing high-precision laser interferometer, grating ruler or encoder with standard working conditions.

[0120] Based on the recorded steady-state displacement deviation data, a displacement deviation curve cluster is generated, i.e. the displacement offset at different temperature points is plotted as a function relationship diagram, thereby constructing a thermal-displacement response surface expression. Since the thermal response of different parts is significantly nonlinear, the obtained curve cluster reflects the trend characteristics that the curvature is not constant and the slope changes nonlinearly with the increase of temperature.

[0121] Based on the above curve cluster, characteristic parameters are extracted, including slope change rate, inflection point position, local maximum offset value, inflection point spacing, fluctuation amplitude and other high-order description variables. These parameters are used to quantify the strength, sensitive interval and nonlinearity of temperature affecting displacement offset, providing structured input for subsequent modeling.

[0122] The extracted characteristic parameters are used to construct a nonlinear mapping model, which can be constructed using various data fitting methods, including radial basis function network, support vector regression, polynomial fitting, or deep neural network. The input of the model is the temperature value of each temperature monitoring point in the temperature monitoring data set, and the output is the predicted displacement offset value under the current temperature distribution. A regularization mechanism is introduced in the modeling process to prevent overfitting, and cross-validation is used to ensure generalization ability.

[0123] The nonlinear mapping model is stored in a relational database to ensure that the model can be called for different runtime tasks. The database contains information such as model structure, parameter set, version number, and applicable range, supporting fast indexing and scheduling by time tag or thermal condition tag.

[0124] In the compensation execution phase, the system automatically calls the corresponding nonlinear mapping model in the relational database in response to the displacement compensation instruction, and performs offset prediction and compensation control based on the current real-time temperature input data. This design based on the calling mechanism improves the flexibility and configurability of the model, allowing different tasks or devices to load different versions of the model.

[0125] The present embodiment can accurately capture the nonlinear influence of temperature gradient on displacement error by systematically calibrating the steady-state displacement deviation of the moving part at multiple predetermined temperature points and constructing a nonlinear mapping model based on the characteristic parameters of the curve cluster. The model is stored in a database and responds quickly to compensation tasks through a calling mechanism, and still has good real-time performance and precision adaptation ability in application scenarios with severe temperature rise and frequent dynamic load, thereby significantly improving the robustness and precision control ability of the overall displacement compensation system.

[0126] In one embodiment, the above step S30 comprises:

[0127] S301, inputting the temperature data into a reference signal channel to generate a reference reference signal;

[0128] S302, inputting the original displacement data into a main signal channel to generate a mixed displacement signal;

[0129] S303, initializing adaptive filter coefficients according to the nonlinear mapping relationship;

[0130] S304, processing the mixed displacement signal using the adaptive filter coefficients to generate an initial filtered output signal;

[0131] S305, calculating an error signal based on the reference reference signal and the initial filtered output signal;

[0132] S306, iteratively updating the adaptive filter coefficients according to the error signal to generate an optimized adaptive filter;

[0133] S307, processing the mixed displacement signal using the optimized adaptive filter to generate a temperature drift trend component;

[0134] S308, subtracting the temperature drift trend component from the mixed displacement signal to obtain displacement data with trend component removed.

[0135] In this embodiment, in order to extract and eliminate the interference of temperature drift on displacement measurement from systematic bias caused by temperature influence, a filtering mechanism with dynamic self-learning ability is needed, which can adaptively adjust the filtering strategy according to different thermal field inputs. The processing flow is based on the structural constraint of the nonlinear mapping relationship to ensure that the filtering result can accurately reflect the drift trend under the temperature driving.

[0136] Firstly, input the temperature data into the reference signal channel to generate a reference signal. The temperature data includes time series information collected by the stator, rotor, encoder and environment monitoring points. After preprocessing such as unit normalization, time alignment and missing interpolation, the temperature data is converted into an estimated sequence of theoretical displacement response through a nonlinear mapping model, which is used as a reference signal. The reference signal reflects the response trend of displacement that may be caused by temperature change, and is used as a guide signal for filter adjustment direction.

[0137] Then, input the original displacement data into the main signal channel to generate a mixed displacement signal. The original displacement data comes from the displacement change recorded by the encoder at the current time, which superimposes the slow-changing bias component caused by thermal drift on the real motion state, and therefore contains the interference information that needs to be stripped. The mixed displacement signal is synchronized with the reference signal in time series, and is used as the input for filter training.

[0138] The adaptive filter coefficients are initialized according to the nonlinear mapping relationship. The initial coefficients are not arbitrarily set, but are set based on the weight prior information of the influence of each heat source point on the total displacement according to the temperature-displacement mapping model. This initial value selection based on the nonlinear model helps to improve the convergence speed and directionality correctness of the initial filter output.

[0139] The adaptive filter coefficients are used to process the mixed displacement signal to generate an initial filter output signal. The initial output reflects the first estimation of the thermal drift trend in the displacement under the current filter configuration. The output provides a reference for subsequent error calculation.

[0140] The error signal is calculated by comparing the initial filter output signal with the reference signal. The error measures the deviation between the current filter output and the theoretical temperature response. The error signal is used as the basis for feedback adjustment to control the update direction and amplitude of the adaptive filter coefficients.

[0141] Subsequently, the adaptive filter coefficients are iteratively updated according to the error signal to generate an optimized adaptive filter. This process can employ LMS (Least Mean Square Error) or RLS (Recursive Least Square) algorithms to adjust the channel weights in real-time so that the output minimizes the prediction error. Through multiple iterations, the filter continuously enhances its ability to perceive temperature-driven drift.

[0142] Finally, the mixed displacement signal is processed using the optimized adaptive filter to extract a trend component that characterizes the system's drift trend under temperature driving. This trend component is a low-frequency, slowly-varying drift quantity that, in most industrial scenarios, corresponds to the thermal inertia of the mechanical structure.

[0143] By removing this trend component from the mixed displacement signal, high-fidelity displacement data after drift removal is obtained. This data more accurately reflects the actual dynamic response state of the mechanical system after excluding thermal field interference, providing a reliable basis for subsequent position control or precision closed-loop control.

[0144] For example, the root mean square evaluation formula of temperature drift error is:

[0145]

[0146] This formula is used to quantitatively evaluate the accuracy of the adaptive filter in extracting the thermal drift trend component. By calculating the root mean square error (Root Mean Square Error, RMSE) between the reference signal and the filter output, it measures whether the current filter configuration can effectively capture and extract the temperature-driven systematic shift trend. The smaller the error, the closer the filter output is to the theoretical temperature response signal, indicating more accurate temperature drift modeling.

[0147] where e(n) = d(n) - y(n); N is the total number of sampling points, representing the number of samples used for evaluation, usually the number of data points within a time window; e(n) is the error signal at the nth time, representing the difference between the reference signal d(n) and the filter output y(n), i.e. the current modeling error; d(n) is the theoretical drift response (reference signal) at the nth time, generated by non-linear mapping of temperature data, representing the theoretical influence of temperature on displacement trend; y(n) is the filter output signal at the nth time, estimated by the adaptive filter based on the original displacement signal to obtain the temperature drift trend component; is the sum of error squares, measuring the overall deviation between the output and the reference at all time points.

[0148] The embodiment can separate the systematic displacement drift trend caused by temperature accurately, maintain the continuous optimization ability of the filter parameters in the dynamic thermal change environment, and obtain purified displacement data with high precision, low delay and strong anti-drift capability, thereby significantly improving the dynamic adaptability and environmental robustness of the displacement measurement system.

[0149] In one embodiment, the above step S40 comprises:

[0150] S401, inputting the trend component into a compensation decision module;

[0151] S402, calling the current parameters of the nonlinear mapping relationship in the compensation decision module;

[0152] S403, calculating the axial displacement compensation and the radial displacement compensation respectively using the current parameters and the trend component;

[0153] S404, fusing the axial displacement compensation and the radial displacement compensation in the compensation decision module to generate a multi-dimensional compensation vector;

[0154] S405, converting the multi-dimensional compensation vector into a dynamic compensation instruction through the compensation decision module.

[0155] In the embodiment, in order to realize the compensation of the multi-dimensional displacement drift caused by temperature, the trend component needs to be combined with the pre-constructed nonlinear mapping model to quantize and dynamically correct the actual error, and then output the compensation instruction with physical meaning. The processing process not only considers the absolute value change of displacement, but also considers the directional difference in space, so as to guarantee the pertinence and precision of compensation.

[0156] Firstly, the trend component is input into the compensation decision module. The trend component is a low-frequency drift term separated from the original displacement data under the driving of temperature, which reflects the thermal response state of the current system. The compensation decision module receives the data as an execution center and triggers the compensation process. The module is generally deployed in the controller firmware or works cooperatively with the temperature monitoring system as an embedded signal processing unit.

[0157] The current parameters of the nonlinear mapping relationship are called in the compensation decision module. The parameters here are not static constants, but a set of thermal coupling model weights retrieved on demand from the database and loaded in real time through historical calibration results. The parameters include how the temperature gradient affects the corresponding relationship between the axial and radial displacements under different thermal field distributions, which may exist in the form of polynomial coefficients, neural network weights or lookup table functions.

[0158] Subsequently, the axial displacement compensation and the radial displacement compensation are calculated using the current parameters and the trend component, respectively. Here, the trend component needs to be decomposed into responses in different structural directions, which are mapped to the axial and radial compensation channels, respectively. The axial compensation is mainly aimed at the position drift caused by the longitudinal thermal expansion of the motor, while the radial compensation involves transverse influences such as bearing displacement and armature deflection. In the mapping process, response prediction is completed through interpolation fitting, nonlinear function or neural network inference-based reasoning to ensure the dynamic accuracy of the compensation results.

[0159] The axial displacement compensation and the radial displacement compensation are fused in the compensation decision module to generate a multi-dimensional compensation vector. The compensation vector is usually a vector quantity in two-dimensional or three-dimensional space, with directionality and amplitude, representing the geometric displacement deviation that the system should actively offset under the current thermal state. The fusion strategy can adopt weighted synthesis, vector superposition or polar coordinate conversion, etc., to make the compensation more consistent with the system structural mechanics constraints and sensor configuration characteristics.

[0160] Finally, the multi-dimensional compensation vector is converted into a dynamic compensation instruction by the compensation decision module. The instruction can be used to update the position estimation value in the control system, or directly drive the hardware to perform offset correction, such as adjusting the servo system zero point, modifying the feedback loop reference value or reconfiguring the reference point of the displacement calculation module. The instruction format is determined according to the system bus protocol and the control system structure, which can be a PWM parameter correction instruction, an incremental encoder compensation factor or a digital displacement calibration command.

[0161] For example, the instantaneous axial (or radial) error compensation quantity is:

[0162] Δ(n) = α·e(n)

[0163] Δ(n) represents the instantaneous compensation component that needs to be added in the multi-dimensional compensation vector at the current time n, with the same unit as displacement (μm or arc-sec). α represents the dynamic gain coefficient, which is derived from the "current parameter" retrieval result of the compensation decision module for the nonlinear mapping relationship, and is self-adjusted in real time with the motor thermal state, usually in the range of 0.0-2.0. The greater the gain, the more sensitive the compensation to the latest error. e(n) = d(n) - y(n) is the real-time error signal, d(n) is the mixed displacement signal (main channel), and y(n) is the output of the optimized adaptive filter (reference channel).

[0164] When calculating the axial displacement compensation and the radial displacement compensation using the current parameters and the trend component, Δ(n) can be independently obtained in each direction, constituting the first part of the "axial displacement compensation" or "radial displacement compensation", which is used to depict the direct correction amount of the instantaneous error, and is written into the compensation quantity before entering the fusion link.

[0165] Cumulative axial (or radial) error compensation quantity:

[0166]

[0167] represents the cumulative compensation amount for the past m sampling period errors, used to correct the residual error caused by slow drift or long period temperature rise; β represents the integral coefficient, also from the "current parameters", which size reflects the model's tolerance and compensation strength for long-term error; represents the error summation in the sliding window of length m, where the selection of m corresponds to the thermal inertia constant of the temperature field and the sampling period.

[0168] Cumulative axial (or radial) error compensation and immediate axial (or radial) error compensation together constitute the second part of the "axial displacement compensation" or "radial displacement compensation". The compensation decision module fuses Δ(n) and Superimposed to form the total compensation value in each direction, and then combined with the value of another direction to form a multi-dimensional compensation vector.

[0169] The embodiment can dynamically convert the multi-directional displacement drift caused by heat into a spatial compensation vector in the form of a compensation instruction recognizable by the control system by introducing a compensation decision module and based on joint reasoning of the trend component and nonlinear mapping parameters, significantly improving the accuracy, real-time performance and multi-dimensional adaptability of displacement compensation, solving the problems of decoupling difficulty and large response delay in axial-radial compensation of traditional methods, and being applicable to high-precision, multi-heat source distributed motion component operating environments.

[0170] In one embodiment, the above step S405 includes:

[0171] S4051, obtaining historical temperature change data;

[0172] S4052, based on the historical temperature change data, applying a sliding window mechanism to predict the temperature change trend;

[0173] S4053, generating a pre-compensation amount according to the predicted temperature change trend;

[0174] S4054, superimposing the pre-compensation amount into the multi-dimensional compensation vector to generate an updated multi-dimensional compensation vector;

[0175] S4055, converting the updated multi-dimensional compensation vector into a dynamic compensation instruction through the compensation decision module.

[0176] In the embodiment, in order to further enhance the forward response capability of the compensation process to temperature dynamic changes, before generating the dynamic compensation instruction, the historical temperature change data is introduced and a short-term prediction model is constructed to evaluate the trend of the upcoming temperature fluctuation, thereby realizing the feedforward correction and dynamic adjustment of the compensation vector.

[0177] Firstly, the compensation decision module acquires historical temperature change data. The historical data is usually derived from continuous sampling records of various temperature sensors during previous operation cycles, including stator, rotor, encoder and environment, etc. Such data can be cached in a ring queue structure in real time, or stored in a low-latency local memory to support high-frequency time series analysis and calling.

[0178] After obtaining the historical temperature data, a sliding window mechanism is applied to predict the temperature change trend. The sliding window mechanism divides the historical data into multiple overlapping or non-overlapping time periods, and performs gradient analysis and fluctuation amplitude modeling on the temperature change in each time period to extract the temperature rise or fall rate information. The prediction mechanism can use simple linear regression model, exponential smoothing, or time series analysis methods such as ARIMA model for short-term trend extrapolation to evaluate the temperature evolution path in the future period.

[0179] Based on the predicted temperature change trend, a pre-compensation amount is generated. The pre-compensation amount represents the displacement deviation front response caused by the imminent temperature change, and the calculation method is similar to the trend component compensation, but the input is the estimated future temperature gradient. In the generation process, the predicted temperature slope or difference value is input into a nonlinear mapping model to obtain the corresponding displacement influence estimate value, and the axial and radial directions are distinguished to output respective pre-compensation sub-amounts.

[0180] Then, the above pre-compensation amount is added to the current multi-dimensional compensation vector to generate an updated multi-dimensional compensation vector. This process is completed by vector addition or weighted fusion, combining trend-driven compensation and prediction-driven compensation to ensure that the compensation instruction not only responds to the current temperature, but also anticipates the imminent change, improving the feedforward adaptability of the system. The weighting factor can be dynamically adjusted according to the prediction confidence, temperature change rate or compensation impact level.

[0181] Finally, the compensation decision module converts the updated multi-dimensional compensation vector into a dynamic compensation instruction. The instruction form remains consistent with the previously generated basic compensation instruction, with a structured format and system compatibility, used to correct the current displacement data or as a controller feedback correction amount, and to achieve higher robustness of multi-source thermal drift elimination. The output method can use encoding parameter reset, displacement offset instruction sending or controller interrupt triggering strategy to ensure the executability and transmission timeliness of the compensation instruction.

[0182] The embodiment predicts the influence of the temperature fluctuation that may occur in the future on displacement by introducing a trend prediction mechanism of historical temperature data, and superimposes the influence into the current compensation vector, so that pre-compensation is completed before the actual change of temperature, thereby effectively reducing the cumulative error caused by compensation lag. While considering the current thermal response compensation, the feedforward correction capability for future disturbance is enhanced, and dynamic and robust control of a high-precision displacement measurement system under a complex thermal field is realized.

[0183] In one embodiment, the above step S50 comprises:

[0184] S501, inputting the dynamic compensation instruction into an execution controller;

[0185] S502, generating a current control signal and a displacement correction parameter by parsing the dynamic compensation instruction through the execution controller;

[0186] S503, adjusting the motor driving current based on the current control signal to generate an actual displacement change;

[0187] S504, quantifying the actual displacement change as a displacement compensation increment;

[0188] S505, correcting the original displacement data using the displacement correction parameter to generate corrected original displacement data;

[0189] S506, performing three-dimensional fusion on the corrected original displacement data, the displacement data without the trend component, and the displacement compensation increment to generate a three-dimensional fusion result;

[0190] S507, generating compensation displacement data based on the three-dimensional fusion result and outputting the compensation displacement data.

[0191] In the embodiment, when the dynamic compensation instruction is generated, the instruction is first input into an execution controller for parsing. The execution controller can be a special processing module integrated in an encoder control unit, a servo driver, or a main control module, and its main task is to parse the input structured compensation instruction, extract the multi-dimensional compensation vector parameters contained therein, and convert them into electrical and logical control signals. The content contained in the compensation instruction generally includes displacement compensation values in axial and radial directions, control response delay tolerance, pre-compensation time window, etc.

[0192] In the analysis process, the execution controller maps the compensation vector elements in the dynamic compensation instruction into two types of control quantities: one is the current control signal of the drive end, which is used to control the motor drive system to produce fine displacement adjustment; the other is the displacement correction parameter used in the encoder or the controller, which is used to directly correct the measurement deviation generated in the sensing path. These two types of control quantities act on the execution layer and the data layer in the system respectively, ensuring that the compensation process has a double-channel path of closed-loop adjustment and original signal correction.

[0193] The current control signal is issued through the interface with the motor drive module and is used to adjust the actual driving current of the motor, producing continuous and tiny rotational corrections inside the motor to achieve fine-grained displacement changes in space. The actual displacement changes brought about by this are collected and quantified in real time by the position feedback system to form displacement compensation increments. This increment represents the mechanical motion response directly caused by the current compensation instruction and plays a fundamental role in further building the fusion model.

[0194] At the same time, the displacement correction parameter is used to digitally correct the original displacement data. The original displacement data has not been filtered and compensated, and there are temperature drift residuals and system nonlinear response errors. The correction parameter generates corrected original displacement data by performing arithmetic transformation or gain adjustment on the original displacement data. The correction method can be weighted translation, proportional amplification, zero offset adjustment, etc., depending on the sensor model and error calibration method in the system.

[0195] After forming the compensation increment and the corrected data, the corrected original displacement data, the displacement data with the trend component removed, and the displacement compensation increment are fused in three dimensions. This fusion process is completed using a spatial feature weighting method, which extracts and jointly expresses the feature information in each data source by constructing a fusion space containing three directions or three sources. The fusion strategy can be implemented based on priority weighting, principal component analysis, or Bayesian fusion algorithm, with the focus on suppressing redundant components in each path data and retaining the most representative stable compensation information.

[0196] Finally, the compensation displacement data is generated based on the three-dimensional fusion result. This data is the consistency result after fusing the current dynamic compensation, trend removal, and sensor correction, and has the comprehensive characteristics of thermal drift elimination, nonlinear response suppression, and control correction unification. The compensation displacement data, as the final output result, can be used for feedback control, precision positioning, error recording, or high-precision path planning, ensuring that the motion control system still has high consistency and high precision in response under high temperature gradient and multi-source disturbance conditions.

[0197] Example: In a type of high-precision motor drive system, to eliminate the displacement measurement errors caused by thermal drift and ensure the precision positioning performance in high dynamic load environment, the following specific implementation process is adopted.

[0198] Firstly, sensors are deployed at multiple key locations of the motor under test. Multi-channel stator temperature sensors are arranged inside the stator assembly to sense the heat accumulation of the coils; rotor temperature sensors are arranged inside the rotor to monitor the temperature rise of the rotating body; a dedicated temperature sensor is set inside the encoder to collect the temperature variation of the reading mechanism; environmental temperature sensors are set on the motor housing or the air-cooled area to form the thermal boundary conditions; in addition, a high-precision displacement detection encoder is installed at the end of the rotor shaft to collect angular displacement signals. After the system is powered on and enters a stable running state, all temperature sensors start continuous sampling and upload real-time temperature data of the stator, rotor, encoder and environment to the data aggregation module, while the displacement detection encoder synchronously collects the original position signal and generates the original displacement data.

[0199] Then, the temperature data set and the original displacement data formed above are input into the dynamic compensation module. The module calls the nonlinear mapping relationship constructed in the previous calibration stage, which is based on running the motor at multiple constant temperature points and recording the displacement deviation curve at steady state, and then extracting feature parameters based on the curve set and establishing a nonlinear mapping model through a regression algorithm. In operation, the module inputs the temperature data into the reference signal channel and generates a reference signal, while the original displacement data are input into the main signal channel to generate a mixed signal. The adaptive filter initializes to load the nonlinear mapping model parameters, and performs filtering processing on the mixed signal. The filter updates its weight values based on the LMS or RLS algorithm, and continuously optimizes the filter coefficients through the error signal, finally separates the temperature drift trend component from the mixed signal. The trend component is deducted from the original displacement signal to obtain the de-drift displacement data.

[0200] Next, the trend component is input into the compensation decision module as input, and the current temperature mapping model parameters are loaded to calculate the displacement compensation amount in the axial and radial directions of the current system, and are fused into a multi-dimensional compensation vector. On this basis, a sliding window temperature prediction mechanism is introduced to predict the future temperature change trend using historical temperature change data, and to generate a pre-compensation amount. The predicted amount and the current compensation vector are superimposed to form an updated compensation vector, which is further converted into a structured dynamic compensation instruction.

[0201] Subsequently, the execution controller parses the compensation instruction and generates two control quantities: one is a current control signal for motor driving, and the control quantity is issued to the driver in real time to guide the motor to produce a small amplitude displacement adjustment; the other is a displacement correction parameter for correcting the original displacement data, which acts on the encoder feedback data in the signal processing path. The displacement change generated by the motor is quantized as a compensation increment, which is input into a three-dimensional fusion unit together with the corrected original displacement data and the data without the trend component. The fusion unit integrates the three-way data based on multi-channel weight calculation or principal component analysis method, generates the final compensation displacement data with correction, compensation and drift suppression capability, and outputs to the control system or display terminal for precision control and monitoring.

[0202] The embodiment constructs a double-path control link of electric control driving and measurement correction by parsing the dynamic compensation instruction into a current control signal and a displacement correction parameter, and improves the accuracy and stability of the compensation data by introducing a data fusion mechanism of three sources. The mechanism can effectively eliminate the cumulative effect of displacement error under thermal dynamic change conditions, realize the coordinated response of the compensation strategy from the perception layer to the control layer, and ensure that the system maintains high-precision displacement measurement and stable execution control in a complex thermal field.

[0203] In an embodiment, a displacement error dynamic compensation device is provided, which corresponds to the displacement error dynamic compensation method in the above embodiment. Referring to Figure 3 , Figure 3 The functional module schematic diagram of a preferred embodiment of the displacement error dynamic compensation device of the present application is shown in FIG. 1. The temperature and displacement acquisition module 10, the nonlinear mapping construction module 20, the adaptive filtering module 30, the compensation instruction generation module 40 and the displacement compensation execution module 50. The detailed description of each functional module is as follows:

[0204] The temperature and displacement acquisition module 10 is used to acquire temperature data at multiple temperature measurement points inside the moving part and the environment in which the moving part is located, and to acquire original displacement data of the moving part;

[0205] The nonlinear mapping construction module 20 is used to call a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation, and the nonlinear mapping relationship is established by calibrating the displacement deviation curve at different temperatures;

[0206] The adaptive filtering module 30 is used to perform adaptive filtering processing on the temperature data and the original displacement data based on the nonlinear mapping relationship, separate out the trend component representing temperature drift, and obtain displacement data without the trend component;

[0207] The compensation instruction generation module 40 is used to generate a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship;

[0208] The displacement compensation execution module 50 is configured to adjust the displacement data after the trend component is removed according to the dynamic compensation instruction, and output compensated displacement data.

[0209] In an embodiment, the temperature and displacement acquisition module 10 is specifically configured to:

[0210] deploying multiple stator temperature sensors inside the stator assembly of the moving component;

[0211] deploying multiple rotor temperature sensors inside the rotor of the moving component;

[0212] deploying multiple encoder temperature sensors inside the encoder of the moving component;

[0213] deploying an environmental temperature sensor at the heat exchange boundary of the moving component;

[0214] installing a displacement detection encoder on the rotor of the moving component;

[0215] acquiring stator temperature data through the multiple stator temperature sensors;

[0216] acquiring rotor temperature data through the multiple rotor temperature sensors;

[0217] acquiring encoder temperature data through the multiple encoder temperature sensors;

[0218] acquiring environmental temperature data through the environmental temperature sensor;

[0219] integrating the stator temperature data, the rotor temperature data, the encoder temperature data and the environmental temperature data to form a temperature monitoring data set;

[0220] acquiring rotor position signals through the displacement detection encoder, and generating original displacement data based on the rotor position signals.

[0221] In an embodiment, the nonlinear mapping construction module 20 is specifically configured to:

[0222] running the moving component at multiple predetermined temperature points;

[0223] recording steady-state displacement deviation data when maintaining a constant temperature state at each predetermined temperature point;

[0224] generating a displacement deviation curve cluster based on the recorded steady-state displacement deviation data;

[0225] extracting feature parameters based on the displacement deviation curve cluster;

[0226] constructing a nonlinear mapping model using the extracted feature parameters;

[0227] storing the non-linear mapping model into a relational database;

[0228] calling the non-linear mapping model in the relational database in response to a displacement compensation instruction.

[0229] In an embodiment, the adaptive filtering module 30 is specifically configured to:

[0230] inputting the temperature data into a reference signal channel to generate a reference reference signal;

[0231] inputting the original displacement data into a main signal channel to generate a mixed displacement signal;

[0232] initializing adaptive filter coefficients according to the non-linear mapping relationship;

[0233] processing the mixed displacement signal using the adaptive filter coefficients to generate an initial filtering output signal;

[0234] calculating an error signal based on the reference reference signal and the initial filtering output signal;

[0235] iteratively updating the adaptive filter coefficients according to the error signal to generate an optimized adaptive filter;

[0236] processing the mixed displacement signal using the optimized adaptive filter to generate a temperature drift trend component;

[0237] subtracting the temperature drift trend component from the mixed displacement signal to obtain displacement data with a trend component removed.

[0238] In an embodiment, the compensation instruction generation module 40 is specifically configured to:

[0239] inputting the trend component into a compensation decision module;

[0240] calling current parameters of the non-linear mapping relationship in the compensation decision module;

[0241] calculating an axial displacement compensation amount and a radial displacement compensation amount using the current parameters and the trend component, respectively;

[0242] fusing the axial displacement compensation amount and the radial displacement compensation amount in the compensation decision module to generate a multi-dimensional compensation vector;

[0243] converting the multi-dimensional compensation vector into a dynamic compensation instruction through the compensation decision module.

[0244] In an embodiment, the compensation instruction generation module 40 is specifically configured to:

[0245] obtaining historical temperature change data;

[0246] predicting a temperature change trend based on the historical temperature change data by using a sliding window mechanism;

[0247] generating a pre-compensation amount according to the predicted temperature change trend;

[0248] superimposing the pre-compensation amount into the multi-dimensional compensation vector to generate an updated multi-dimensional compensation vector;

[0249] converting the updated multi-dimensional compensation vector into dynamic compensation instructions by the compensation decision module.

[0250] In an embodiment, the displacement compensation execution module 50 is specifically configured to:

[0251] inputting the dynamic compensation instructions into an execution controller;

[0252] generating a current control signal and a displacement correction parameter by parsing the dynamic compensation instructions through the execution controller;

[0253] adjusting a motor driving current based on the current control signal to generate an actual displacement change amount;

[0254] quantifying the actual displacement change amount into a displacement compensation increment;

[0255] correcting the original displacement data using the displacement correction parameter to generate corrected original displacement data;

[0256] performing three-dimensional fusion on the corrected original displacement data, the displacement data after removing the trend component and the displacement compensation increment to generate a three-dimensional fusion result;

[0257] generating compensation displacement data based on the three-dimensional fusion result and outputting the compensation displacement data.

[0258] In an embodiment, a computer device is provided, which can be a server. An internal structure diagram of the computer device can be as shown in Figure 4 The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is configured to communicate with an external user terminal through a network connection. The computer program is executed by the processor to implement the functions or steps of a displacement error dynamic compensation method on the server side.

[0259] In one embodiment, a computer device is provided, which may be a user terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a dynamic displacement error compensation method on the user side.

[0260] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0261] Temperature data are acquired at multiple temperature measurement points inside the moving part and in its environment, and the original displacement data of the moving part is also acquired.

[0262] The pre-built nonlinear mapping relationship between temperature gradient and displacement deviation is invoked, and the nonlinear mapping relationship is established by calibrating the displacement deviation curves at different temperatures;

[0263] Based on the nonlinear mapping relationship, the temperature data and the original displacement data are subjected to adaptive filtering to separate the trend component characterizing the temperature drift and obtain the displacement data after removing the trend component.

[0264] Based on the trend component and the nonlinear mapping relationship, a dynamic compensation instruction is generated;

[0265] According to the dynamic compensation instruction, the displacement data after removing the trend component is adjusted, and the compensated displacement data is output.

[0266] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0267] Temperature data are acquired at multiple temperature measurement points inside the moving part and in its environment, and the original displacement data of the moving part is also acquired.

[0268] The pre-built nonlinear mapping relationship between temperature gradient and displacement deviation is invoked, and the nonlinear mapping relationship is established by calibrating the displacement deviation curves at different temperatures;

[0269] Based on the nonlinear mapping relationship, the temperature data and the original displacement data are adaptively filtered to separate a trend component representing temperature drift, and displacement data without the trend component is obtained.

[0270] Based on the trend component and the nonlinear mapping relationship, dynamic compensation instructions are generated.

[0271] According to the dynamic compensation instructions, the displacement data without the trend component is adjusted to output compensated displacement data.

[0272] It should be noted that the functions or steps described above with respect to the computer-readable storage medium or the computer device can correspond to the related descriptions of the server side and the user side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0273] Those skilled in the art can understand that all or part of the processes in the foregoing embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the foregoing embodiments. In the embodiments provided in the present application, any reference to memory, storage, database or other medium can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0274] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified. In actual applications, the above functions can be completed by different functional units or modules according to needs, i.e. the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0275] It should be explained that if the software tools or components of other companies appear in the embodiments of the present application, they are only used for example introduction and do not represent actual use. The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of dynamic displacement error compensation, characterized in that, The method comprises the following steps: obtaining temperature data at multiple temperature measuring points inside the moving component and the environment in which the moving component is located, and obtaining original displacement data of the moving component; calling a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation, the nonlinear mapping relationship being established by calibrating displacement deviation curves at different temperatures; based on the nonlinear mapping relationship, performing adaptive filtering processing on the temperature data and the original displacement data to separate out a trend component representing temperature drift, and obtaining displacement data from which the trend component is removed; based on the trend component and the nonlinear mapping relationship, generating dynamic compensation instructions, including: inputting the trend component into a compensation decision module; calling current parameters of the nonlinear mapping relationship in the compensation decision module; using the current parameters and the trend component to respectively calculate axial displacement compensation and radial displacement compensation; fusing the axial displacement compensation and the radial displacement compensation in the compensation decision module to generate a multi-dimensional compensation vector; and converting the multi-dimensional compensation vector into dynamic compensation instructions through the compensation decision module; adjusting the displacement data from which the trend component is removed according to the dynamic compensation instructions to output compensation displacement data, including: inputting the dynamic compensation instructions into an execution controller; analyzing the dynamic compensation instructions through the execution controller to generate current control signals and displacement correction parameters; adjusting motor driving current based on the current control signals to generate actual displacement variation; quantifying the actual displacement variation into displacement compensation increments; using the displacement correction parameters to correct the original displacement data to generate corrected original displacement data; performing three-dimensional fusion on the corrected original displacement data, the displacement data from which the trend component is removed, and the displacement compensation increments to generate a three-dimensional fusion result; and generating compensation displacement data based on the three-dimensional fusion result and outputting the compensation displacement data.

2. The displacement error dynamic compensation method of claim 1, wherein, Obtaining temperature data at multiple temperature measuring points inside the moving component and the environment in which the moving component is located, and obtaining original displacement data of the moving component, comprises: deploying multiple stator temperature sensors inside a stator assembly of the moving component; deploying multiple rotor temperature sensors inside a rotor of the moving component; deploying multiple encoder temperature sensors inside an encoder of the moving component; deploying environmental temperature sensors at a heat exchange boundary of the moving component; installing a displacement detection encoder on the rotor of the moving component; collecting stator temperature data through the multiple stator temperature sensors; collecting rotor temperature data through the multiple rotor temperature sensors; collecting encoder temperature data through the multiple encoder temperature sensors; collecting environmental temperature data through the environmental temperature sensors; integrating the stator temperature data, the rotor temperature data, the encoder temperature data, and the environmental temperature data to form a temperature monitoring data set; collecting rotor position signals through the displacement detection encoder, and generating original displacement data based on the rotor position signals.

3. The method of dynamic displacement error compensation of claim 1, wherein, calling a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation, the nonlinear mapping relationship being established by calibrating displacement deviation curves at different temperatures, comprising: running the moving component at a plurality of predetermined temperature points; recording steady-state displacement deviation data while maintaining a constant temperature at each predetermined temperature point; generating a displacement deviation curve cluster based on the recorded steady-state displacement deviation data; extracting feature parameters based on the displacement deviation curve cluster; constructing a nonlinear mapping model using the extracted feature parameters; storing the nonlinear mapping model to a relational database; calling the nonlinear mapping model in the relational database in response to a displacement compensation instruction.

4. The method of dynamic displacement error compensation of claim 1, wherein, based on the nonlinear mapping relationship, adaptively filtering the temperature data and the original displacement data to separate out a trend component representing temperature drift, and obtaining displacement data with the trend component removed, comprising: inputting the temperature data into a reference signal channel to generate a benchmark reference signal; inputting the original displacement data into a main signal channel to generate a mixed displacement signal; initializing adaptive filter coefficients according to the nonlinear mapping relationship; processing the mixed displacement signal using the adaptive filter coefficients to generate an initial filtered output signal; calculating an error signal based on the benchmark reference signal and the initial filtered output signal; iteratively updating the adaptive filter coefficients according to the error signal to generate an optimized adaptive filter; processing the mixed displacement signal using the optimized adaptive filter to generate a temperature drift trend component; subtracting the temperature drift trend component from the mixed displacement signal to obtain displacement data with the trend component removed.

5. The method of dynamic displacement error compensation of claim 1, wherein, the compensation decision module converts the multi-dimensional compensation vector into a dynamic compensation instruction, comprising: obtaining historical temperature change data; based on the historical temperature change data, applying a sliding window mechanism to predict temperature change trends; generating a pre-compensation amount according to the predicted temperature change trends; adding the pre-compensation amount to the multi-dimensional compensation vector to generate an updated multi-dimensional compensation vector; the compensation decision module converts the updated multi-dimensional compensation vector into a dynamic compensation instruction.

6. A displacement error dynamic compensation device, characterized by, the displacement error dynamic compensation device comprises: a temperature and displacement collection module for obtaining temperature data at a plurality of temperature measurement points inside a moving component and its environment, and obtaining original displacement data of the moving component; a nonlinear mapping construction module for calling a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation, the nonlinear mapping relationship being established by calibrating displacement deviation curves at different temperatures; an adaptive filtering module for adaptively filtering the temperature data and the original displacement data based on the nonlinear mapping relationship to separate out a trend component representing temperature drift, and obtaining displacement data with the trend component removed; The compensation instruction generation module is configured to generate a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship, including: inputting the trend component into a compensation decision module; calling a current parameter of the nonlinear mapping relationship in the compensation decision module; calculating an axial displacement compensation amount and a radial displacement compensation amount using the current parameter and the trend component, respectively; fusing the axial displacement compensation amount and the radial displacement compensation amount in the compensation decision module to generate a multi-dimensional compensation vector; and converting the multi-dimensional compensation vector into a dynamic compensation instruction by the compensation decision module. The displacement compensation execution module is configured to adjust the displacement data from which the trend component is removed according to the dynamic compensation instruction to output compensated displacement data, including: inputting the dynamic compensation instruction into an execution controller; analyzing the dynamic compensation instruction by the execution controller to generate a current control signal and a displacement correction parameter; adjusting a motor driving current based on the current control signal to generate an actual displacement change amount; quantifying the actual displacement change amount into a displacement compensation increment; correcting the original displacement data using the displacement correction parameter to generate corrected original displacement data; performing three-dimensional fusion on the corrected original displacement data, the displacement data from which the trend component is removed, and the displacement compensation increment to generate a three-dimensional fusion result; and generating compensated displacement data based on the three-dimensional fusion result and outputting the compensated displacement data.

7. A computer device, comprising: The computer device includes a memory, a processor, and a displacement error dynamic compensation program stored on the memory and executable on the processor, and the displacement error dynamic compensation program, when executed by the processor, implements the steps of the displacement error dynamic compensation method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium has a displacement error dynamic compensation program stored thereon, and the displacement error dynamic compensation program, when executed by the processor, implements the steps of the displacement error dynamic compensation method according to any one of claims 1-5.

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