Self-adaptive temperature compensation intelligent resistance system and control method

Abstract

The invention relates to the technical field of intelligent resistor control, and discloses a self-adaptive temperature compensation intelligent resistor system and a control method, and the system comprises a sensor module, a temperature compensation module, a self-adaptive control module, an electromagnetic shielding structure and a power management module. By applying an advanced temperature compensation algorithm, a reasonable electromagnetic shielding design, an intelligent self-adaptive control strategy and a perfect self-diagnosis function, the stability problem caused by the fact that a traditional resistance system is affected by environmental interference and element aging is effectively solved. According to the system, the resistance drift can be controlled to be + / -0.05% within the temperature range of-40 DEG C to 125 DEG C; in the aspect of electromagnetic interference suppression, the signal-to-noise ratio can be increased by 30dB; and the element aging compensation rate exceeds 90%.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent resistance control, and particularly relates to an intelligent resistance system with adaptive temperature compensation and a control method therefor. Background Art

[0002] There are many problems in the operation of existing intelligent resistance systems, seriously affecting their stability and performance.

[0003] 1. Temperature drift problem: The temperature coefficient of traditional resistance materials is usually between ±0.01% / °C - ±0.1% / °C. When the ambient temperature changes by more than 10°C, the resistance value drift can reach 0.1% - 1%. For example, in a certain precision resistance system within the temperature range of 25°C - 55°C, the resistance value deviation exceeds 0.5%, which greatly reduces the accuracy of the equipment relying on this resistance system. In electronic measuring instruments with extremely high precision requirements, such resistance value deviation will cause large errors in measurement results.

[0004] 2. Influence of electromagnetic interference: In an industrial environment, the electromagnetic interference intensity can reach 100 V / m - 1000 V / m. When a certain test system operates near a frequency converter, due to the strong electromagnetic interference generated by the frequency converter, the measurement error suddenly rises from the original 0.3% to 2.5%, resulting in a signal acquisition error exceeding 2%, seriously interfering with the accurate measurement and control of the resistance value by the system.

[0005] 3. Component aging effect: Long-term use of electronic components will cause parameter drift. The annual drift rate of metal film resistors is about 0.01% - 0.1%, and the zero drift of sensors can reach 0.5% - 1% per year. Taking a resistance system used on a certain production line for 3 years as an example, the measurement error has increased by 3 times, greatly affecting the long-term stability and reliability of the system.

[0006] 4. Lack of dynamic compensation: Traditional systems rely on fixed compensation parameters and cannot adapt to the real-time changing environment. For example, in a certain temperature control system during a step temperature change, the response time is as long as 15 seconds, and the overshoot exceeds 10%, unable to adjust the resistance value in a timely and effective manner and unable to meet the requirements of complex and changeable working scenarios.

[0007] 5. Defects of the existing technology: The temperature compensation algorithm is simple and cannot handle non-linear temperature characteristics; the electromagnetic shielding structure design is unreasonable, and the suppression effect on high-frequency interference is poor; there is a lack of an online compensation mechanism for component aging; the adaptive control strategy is single and it is difficult to cope with multi-variable coupling interference. Summary of the Invention

[0008] The present invention aims to provide an intelligent resistance system with adaptive temperature compensation and a control method therefor. Through multi-dimensional interference suppression and dynamic compensation strategies, it effectively solves the stability problems of traditional systems caused by environmental interference and component aging, and improves the accuracy and reliability of the system.

[0009] To achieve the above object, the present invention provides the following technical solutions: An intelligent resistor system with adaptive temperature compensation, comprising: A sensor module for real-time acquisition of ambient temperature and operating current; A temperature compensation module, including a non-linear compensation algorithm and a look-up interpolation unit; An adaptive control module, adopting a fuzzy PID controller and a neural network online learning mechanism; An electromagnetic shielding structure, composed of a double shielding layer and an optimized grounding system; A power management module, including dual power redundancy and a voltage fluctuation compensation circuit.

[0010] Preferably, the non-linear compensation algorithm of the temperature compensation module is based on cubic spline interpolation, and the compensation formula is .

[0011] Preferably, the temperature compensation module includes a pre-stored temperature-compensation coefficient look-up table, which contains the α, β, and γ parameters corresponding to temperature nodes.

[0012] Preferably, the inner layer of the electromagnetic shielding structure is copper foil, the outer layer is permalloy, and the distance between the two layers is 5 mm - 10 mm.

[0013] Preferably, the dual power switching threshold of the power management module is 180 VAC - 220 VAC, and the backup power supply uses a supercapacitor or a lithium battery.

[0014] Preferably, the fuzzy PID controller of the adaptive control module includes 49 fuzzy control rules, and the input variables are temperature deviation and deviation change rate.

[0015] Preferably, the system further includes a self-diagnosis module for real-time monitoring of the aging state of components and updating of compensation parameters.

[0016] Preferably, the self-diagnosis module judges the aging degree of components by comparing the deviation between historical data and current data, and triggers the online learning mechanism.

[0017] Preferably, the sensor module adopts a platinum resistance temperature sensor with an accuracy of ±0.1℃D and a Hall current sensor with a linearity of 0.05%.

[0018] Preferably, the operating temperature range of the system is -40℃ - 125℃, and the electromagnetic shielding effectiveness reaches more than 100 dB in the frequency band of 10 MHz - 1 GHz.

[0019] A control method for an intelligent resistor system with adaptive temperature compensation, comprising the following steps: 1) Collect the ambient temperature and operating current in real time; 2) Perform compensation through a hierarchical control architecture: a. The feedforward compensation layer uses a cubic spline interpolation formula combined with a look-up table method to obtain compensation coefficients α, β, and γ; b. The feedback control layer uses a fuzzy PID controller to dynamically adjust the compensation voltage (range ±5V, 12-bit DAC accuracy); c. The online optimization layer updates the compensation parameters every 5 minutes through a BP neural network (3-5-1 structure); 3) Implement a multi-modal cooperative control strategy: a. Switch to the high-frequency filtering mode when the detected electromagnetic interference intensity > 50V / m; b. Start the power compensation circuit and adjust the sampling frequency when the voltage fluctuation > 10%; c. Trigger neural network retraining when the component aging deviation > 0.2%; 4) Execute the real-time control process: a. Dynamically adjust the data acquisition period (50ms - 200ms); b. Use an adaptive notch filter for interference compensation; c. Output the compensation voltage through a DAC (0 - 5V, 1μV resolution); 5) Implement a fault tolerance mechanism: a. Detect sensor disconnection / saturation; b. Support manual / automatic / safe mode switching; c. Automatically store abnormal data (50 groups of cache).

[0020] Preferably, in the hierarchical control architecture, the feedforward compensation layer pre-compensates the non-linear temperature characteristics, the feedback control layer introduces differential lead processing, and the online optimization layer uses the Levenberg-Marquardt algorithm to update the weights; The multi-modal cooperative control strategy includes a component life model based on the Arrhenius equation and a redundant component switching mechanism; The real-time control process includes Kalman filter noise reduction and a dynamic step size adjustment algorithm; The fault tolerance mechanism has the function of manually setting compensation coefficients through RS485 instructions.

[0021] Preferably, the system self-calibration process includes power-on automatic calibration (zero calibration at 25°C ± 2°C and full-scale calibration at rated current) and periodic reference resistance comparison (once a week, full-system calibration is triggered when the deviation > 0.05%); The fuzzy PID controller contains 49 fuzzy control rules, the input variables are temperature deviation and deviation change rate, and the output compensation voltage is processed by differential lead; The online learning of the BP neural network is trained based on 1000 sets of historical compensation data, and the target error is <0.01%.

[0022] Preferably, during the data acquisition stage, temperature (with a 100 ms cycle), current (synchronous sampling), and power supply status (updated every 50 ms) are synchronously acquired; The compensation calculation stage includes temperature compensation, interference compensation (adaptive notch filtering), and power supply compensation (PI regulation based on voltage feedforward); During the execution and output stage, the compensation level (green / yellow / red) is displayed through status indicator lights, and a compensation voltage output with a resolution of μV level is achieved.

[0023] Preferably, there is a dynamic step size adjustment mechanism: when the temperature change rate > 2 °C / min, the sampling period is shortened to 50 ms, and in the stable state, it is extended to 200 ms; The adaptive filtering algorithm automatically adjusts the filtering coefficient according to the environmental noise intensity; The dual power supply switching threshold is 180 VAC - 220 VAC. The backup power supply uses a super capacitor or a lithium battery, and the maintenance time is ≥ 10 minutes.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows: Improved temperature compensation accuracy: In the range of -40 °C - 125 °C, the resistance drift is controlled within ±0.05%, which is 5 times higher than the traditional method, greatly enhancing the stability and accuracy of the system in different temperature environments.

[0025] Enhanced electromagnetic interference suppression: In the frequency band of 10 MHz - 1 GHz, the signal-to-noise ratio (SNR) is increased by 30 dB, and the bit error rate is reduced to 10⁻ 6 The following effectively reduces the impact of electromagnetic interference on the system and improves the accuracy of data transmission and processing.

[0026] Component aging compensation: Through the online learning algorithm, it can compensate for an annual drift rate of components exceeding 90%, extend the system life by more than 2 years, and reduce the equipment maintenance cost and replacement frequency.

[0027] Optimized dynamic response: The step response time is shortened to 3 seconds, and the overshoot is controlled within 5%. It can quickly adapt to the rapidly changing industrial environment, improving the real-time performance and reliability of the system. Detailed implementation manners

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] The present invention provides the following technical solutions: Sensor module: Adopting a platinum resistance temperature sensor (accuracy ±0.1°C) and a Hall current sensor (linearity 0.05%), it can achieve synchronous acquisition of multiple physical quantities such as ambient temperature and working current, providing an accurate data basis for subsequent compensation and control.

[0030] Temperature compensation module: Nonlinear temperature compensation algorithm: Based on cubic spline interpolation, the compensation formula is... Through this formula, the resistance value is accurately compensated to cope with the nonlinear temperature characteristics.

[0031] Look-up table method combined with linear interpolation: Establish a temperature-compensation coefficient look-up table (as shown in Table 1). According to the current temperature, obtain the corresponding α, β, γ parameters from the table. When the temperature is between two nodes in the table, linear interpolation is used to calculate the compensation coefficient to achieve more accurate compensation.

[0032] Table 1 Temperature-compensation coefficient look-up table

[0033] Adaptive control module: Fuzzy PID controller: The input variables are temperature deviation (e) and deviation change rate (ec), and the output is compensation voltage (u). By formulating 49 fuzzy control rules, intelligent adjustment of the compensation voltage is achieved.

[0034] The following is the complete 49 - rule fuzzy control rule table, constructed based on 7 fuzzy subsets (NB / NM / NS / ZO / PS / PM / PB): Table 2 Fuzzy control rule table

[0035] Complete rule description: If e is NB and ec is NB, then u is PB If e is NB and ec is NM, then u is PB If e is NB and ec is NS, then u is PM If e is NB and ec is ZO, then u is PM If e is NB and ec is PS, then u is PS If e is NB and ec is PM, then u is ZO If e is NB and ec is PB, then u is ZO If e is NM and ec is NB, then u is PB If e is NM and ec is NM, then u is PM If e is NM and ec is NS, then u is PM If e is NM and ec is ZO, then u is PS If e is NM and ec is PS, then u is PS If e is NM and ec is PM, then u is ZO If e is NM and ec is PB, then u is NS If e is NS and ec is NB, then u is PM If e is NS and ec is NM, then u is PM If e is NS and ec is NS, then u is PS If e is NS and ec is ZO, then u is PS If e is NS and ec is PS, then u is ZO If e is NS and ec is PM, then u is NS If e is NS and ec is PB, then u is NS If e is ZO and ec is NB, then u is PM If e is ZO and ec is NM, then u is PS If e is ZO and ec is NS, then u is PS If e is ZO and ec is ZO, then u is ZO If e is ZO and ec is PS, then u is NS If e is ZO and ec is PM, then u is NS If e is ZO and ec is PB, then u is NM If e is PS and ec is NB, then u is PS If e is PS and ec is NM, then u is PS If e is PS and ec is NS, then u is ZO If e is PS and ec is ZO, then u is NS If e is PS and ec is PS, then u is NS If e is PS and ec is PM, then u is NM If e is PS and ec is PB, then u is NM If e is PM and ec is NB, then u is PS If e is PM and ec is NM, then u is ZO If e is PM and ec is NS, then u is NS If e is PM and ec is ZO, then u is NS If e is PM and ec is PS, then u is NM If e is PM and ec is PM, then u is NM If e is PM and ec is PB, then u is NB If e is PB and ec is NB, then u is ZO If e is PB and ec is NM, then u is ZO If e is PB and ec is NS, then u is NS If e is PB and ec is ZO, then u is NM If e is PB and ec is PS, then u is NM If e is PB and ec is PM, then u is NB If e is PB and ec is PB, then u is NB Rule design description: Response priority: When the deviation (e) is large, eliminate the deviation first (e.g., the PB / PB combination outputs PB). Stability control: When the rate of change of deviation (ec) is large, appropriately reduce the output (e.g., the PB / PB combination outputs NB). Transition smoothness: The output change gradient between adjacent rules does not exceed 2 levels. Symmetry: The outputs of rules symmetric about the ZO / ZO point have opposite signs. Anti-overshoot mechanism: When the signs of ec and e are opposite, the output level is reduced by 1 - 2 levels. This rule table has been optimized for membership functions using the MATLAB Fuzzy Logic Toolbox. Triangular membership functions are adopted, with the quantization factor ke = 0.1, kec = 0.05, and the output scale factor ku = 5. In Simulink, the step response time is verified to be shortened to 2.8 seconds, the overshoot is 4.2%, and the steady-state error is 0.03%.

[0036] Online learning mechanism of neural network: The BP neural network is adopted with a structure of 3-5-1 and trained using 1000 sets of historical compensation data. The network weights are updated every 5 minutes to adapt to the parameter changes caused by component aging and further optimize the control effect.

[0037] Electromagnetic shielding structure: Double-layer shielding design: The inner layer is copper foil (thickness 0.05 mm, shielding effectiveness SE = 85 dB), and the outer layer is permalloy (thickness 0.1 mm, SE = 120 dB). The distance between the two layers is 5 mm - 10 mm. This design can effectively block external electromagnetic interference and improve the anti-interference ability of the system.

[0038] Optimization of grounding system: The star-shaped grounding structure is adopted with a grounding resistance less than 0.1 Ω. By optimizing the grounding, the interference in the grounding loop is reduced to ensure the stable operation of the system.

[0039] Power management module: Dual-power redundancy design: The main power supply is a switching power supply (efficiency 92%), and the backup power supply is a supercapacitor (capacity 50 F, maintenance time ≥ 10 minutes) or a lithium battery. When the main power supply voltage is lower than 180 VAC - 220 VAC, it automatically switches to the backup power supply to ensure uninterrupted operation of the system.

[0040] Voltage fluctuation compensation: Based on the Buck-Boost circuit, the input voltage range is 90 - 264 VAC, and the output ripple ≤ 5 mV. The voltage is stabilized through this circuit to ensure stable power supply for the system and reduce the impact of voltage fluctuations on the resistance system.

[0041] Control method of an intelligent resistance system with adaptive temperature compensation, including the following steps: 1) Real-time collect the ambient temperature and working current; 2) Perform compensation through a hierarchical control architecture: a. The feedforward compensation layer uses the cubic spline interpolation formula combined with the look-up table method to obtain the compensation coefficients α, β, γ; b. The feedback control layer uses a fuzzy PID controller to dynamically adjust the compensation voltage (range ±5 V, 12-bit DAC accuracy); c. The online optimization layer updates the compensation parameters every 5 minutes through a BP neural network (3-5-1 structure); 3) Implement a multi-modal collaborative control strategy: a. Switch to the high-frequency filtering mode when the detected electromagnetic interference intensity > 50 V / m; b. Start the power compensation circuit and adjust the sampling frequency when the voltage fluctuation > 10%; c. Trigger neural network retraining when the component aging deviation > 0.2%; 4) Execute the real-time control process: a. Dynamically adjust the data acquisition period (50ms - 200ms); b. Use an adaptive notch filter for interference compensation; c. Output the compensation voltage through DAC (0 - 5V, 1μV resolution); 5) Implement a fault tolerance mechanism: a. Sensor disconnection / saturation detection; b. Support manual / automatic / safe mode switching; c. Automatically store abnormal data (50 - group cache).

[0042] In the hierarchical control architecture, the feedforward compensation layer pre - compensates the nonlinear temperature characteristics, the feedback control layer introduces differential preview processing, and the online optimization layer uses the Levenberg - Marquardt algorithm to update the weights; The multi - modal cooperative control strategy includes a component life model based on the Arrhenius equation and a redundant component switching mechanism; The real - time control process includes Kalman filter noise reduction and a dynamic step - size adjustment algorithm; The fault tolerance mechanism has the function of manually setting the compensation coefficient by RS485 instructions.

[0043] The system self - calibration process includes power - on automatic calibration (zero - point calibration at 25℃ ± 2℃ and full - scale calibration at rated current) and periodic reference resistor comparison (once a week, full - system calibration is triggered when the deviation > 0.05%); The fuzzy PID controller contains 49 fuzzy control rules. The input variables are temperature deviation and deviation change rate, and the output compensation voltage is processed by differential preview; The online learning of the BP neural network is trained based on 1000 groups of historical compensation data, and the target error < 0.01%.

[0044] In the data acquisition stage, temperature (100ms cycle), current (synchronous sampling), and power supply status (updated every 50ms) are synchronously collected; The compensation calculation stage includes temperature compensation, interference compensation (adaptive notch filtering), and power supply compensation (PI regulation based on voltage feedforward); In the execution output stage, the compensation level (green / yellow / red) is displayed through status indicator lights, and the compensation voltage output with μV - level resolution is achieved.

[0045] Dynamic step - size adjustment mechanism: When the temperature change rate > 2℃ / min, the sampling period is shortened to 50ms, and in the stable state, it is extended to 200ms; The adaptive filtering algorithm automatically adjusts the filtering coefficient according to the environmental noise intensity; The dual - power switching threshold is 180VAC - 220VAC. The backup power supply uses supercapacitors or lithium batteries, and the maintenance time is ≥10 minutes.

[0046] Example 1: Implementation of temperature compensation algorithm Data acquisition: The temperature sensor collects the ambient temperature every 100ms, and its accuracy can reach ±0.1°C, ensuring the accuracy and reliability of the collected temperature data.

[0047] Look - up table interpolation: According to the currently collected temperature T, obtain the α, β, γ parameters from the pre - stored temperature - compensation coefficient table. If the temperature is between two nodes in the table, linear interpolation is used to calculate the compensation coefficient. For example, when the temperature is 30°C, which is between the two nodes of 25°C and 40°C, the corresponding compensation coefficient is calculated by linear interpolation.

[0048] Real - time compensation: According to the formula Calculate the compensation resistance value, and then adjust the DAC output voltage to achieve dynamic compensation of the resistance value. In the constant - temperature oven test, when the temperature rises from 25°C to 85°C, the system output resistance is stable within ±0.03% of the set value, effectively verifying the accuracy and effectiveness of the temperature compensation algorithm.

[0049] Example 2: Design of electromagnetic shielding structure Shielding layer parameters: The inner layer uses copper foil with a thickness of 0.05mm, and the outer layer uses permalloy with a thickness of 0.1mm. The distance between the two layers is set to 5mm. Such parameter design can give full play to the shielding characteristics of the two materials and effectively block electromagnetic interference in different frequency bands.

[0050] Grounding design: Separate the analog signal ground and the digital signal ground, and connect them in a single - point grounding manner. This grounding design can reduce the mutual interference between different signal grounds and improve the anti - interference ability of the system.

[0051] Shielding effectiveness test: In the frequency band of 100MHz - 1GHz, the shielding effectiveness of this electromagnetic shielding structure reaches 105dB, which is 40dB higher than that of the single - layer shielding, indicating that this double - layer shielding structure has a significant effect in suppressing electromagnetic interference.

[0052] Example 3: Implementation of adaptive control module Fuzzy rule design: Carefully formulate 49 fuzzy control rules, which are based on the logical relationship between the temperature deviation (e), the deviation change rate (ec), and the compensation voltage (u). For example, when the temperature deviation is negative large (NB) and the deviation change rate is also negative large (NB), the output compensation voltage is positive large (PB), so as to achieve intelligent adjustment of the compensation voltage.

[0053] Neural network training: The BP neural network is trained using 1000 sets of historical compensation data, with the target error set to be less than 0.01%. Through continuous training, the neural network can better learn and predict the compensation requirements of the system.

[0054] Online learning: The network weights are updated every 5 minutes to adapt to changes in system parameters caused by factors such as component aging. During actual operation, as components age, the system can adjust the control strategy in a timely manner through online learning to maintain good control effects.

[0055] Example 4: Power management module design Dual power supply switching: When the main power supply voltage is lower than 180VAC, the system automatically switches to the supercapacitor for power supply. Taking an industrial device as an example, when the main power supply has a short-term fault, the supercapacitor can take over the power supply in a timely manner to ensure the continuous and stable operation of the resistance system of the device, and avoid data loss and device damage caused by power outages.

[0056] Voltage compensation circuit: An LM2596 chip is used to form a Buck - Boost circuit to stabilize the input voltage at 12V ± 0.1V. This circuit can automatically adjust the output voltage according to the change of the input voltage to ensure the stability of the system power supply.

[0057] Ripple suppression: A 100μF electrolytic capacitor and a 0.1μF ceramic capacitor are connected in parallel at the output end to reduce the ripple voltage to 3mV. In this way, the ripple of the power supply output is effectively suppressed, the interference to the resistance system is reduced, and the stability of the system is improved.

[0058] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent resistance system with adaptive temperature compensation, characterized in that: include: Sensor module, used to collect ambient temperature and working current in real time; Temperature compensation module, including nonlinear compensation algorithm and table lookup interpolation unit; Adaptive control module, using fuzzy PID controller and neural network online learning mechanism; Electromagnetic shielding structure, consisting of a double shielding layer and an optimized grounding system; The power management module includes dual power supply redundancy and voltage fluctuation compensation circuit.

2. The intelligent resistor system with adaptive temperature compensation according to claim 1, characterized in that: The nonlinear compensation algorithm of the temperature compensation module is based on cubic spline interpolation, and the compensation formula is: .

3. The intelligent resistor system with adaptive temperature compensation according to claim 1, characterized in that: The inner layer of the electromagnetic shielding structure is copper foil, the outer layer is Permalloy, and the distance between the two layers is 5mm-10mm.

4. The intelligent resistor system with adaptive temperature compensation according to claim 1, characterized in that: The dual power switching threshold of the power management module is 180VAC-220VAC, and the backup power supply adopts a super capacitor or a lithium battery.

5. The adaptive temperature-compensated intelligent resistor system according to claim 1, characterized in that: The fuzzy PID controller of the adaptive control module includes 49 fuzzy control rules, and the input variables are temperature deviation and deviation change rate.

6. The intelligent resistor system with adaptive temperature compensation according to claim 1, characterized in that: The system also includes a self-diagnosis module for real-time monitoring of component aging status and updating compensation parameters.

7. The intelligent resistor system with adaptive temperature compensation according to claim 1, characterized in that: The self-diagnosis module determines the aging degree of the component by comparing the deviation between the historical data and the current data, and triggers the online learning mechanism.

8. The intelligent resistor system with adaptive temperature compensation according to claim 1, characterized in that: The sensor module adopts a platinum resistance temperature sensor with an accuracy of ±0.1°C and a Hall current sensor with a linearity of 0.05%.

9. The intelligent resistor system with adaptive temperature compensation according to claim 1, characterized in that: The operating temperature range of the system is -40°C-125°C, and the electromagnetic shielding effectiveness reaches more than 100dB in the 10MHz-1GHz frequency band.

10. A control method for an intelligent resistor system with adaptive temperature compensation according to any one of claims 1 to 9, characterized in that: The following steps are involved: 1) Real-time collection of ambient temperature and working current; 2) Compensation through hierarchical control architecture: a. The feedforward compensation layer uses the cubic spline interpolation formula combined with the table lookup method to obtain the compensation coefficients α, β, and γ; b. The feedback control layer uses a fuzzy PID controller to dynamically adjust the compensation voltage, with a range of ±5V and 12-bit DAC accuracy; c. The online optimization layer updates the compensation parameters every 5 minutes through the BP neural network with a 3-5-1 structure; 3) Implement multi-modal collaborative control strategy: a. Switch to high-frequency filtering mode when electromagnetic interference intensity >50V / m is detected; b. When the voltage fluctuation is >10%, start the power compensation circuit and adjust the sampling frequency; c. When the component aging deviation is >0.2%, the neural network retraining is triggered; 4) Execute real-time control process: a. Dynamic adjustment of data collection cycle; b. Adopt adaptive notch filter to compensate interference; c. Output compensation voltage through DAC; 5) Implement fault tolerance mechanism: a. Sensor disconnection / saturation detection; b. Support manual / automatic / safe mode switching; c. Abnormal data is automatically stored in 50 groups of cache.

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