Plate-type viscoelastic damper integrated with semiconductor active temperature control system

Abstract

This invention relates to a plate viscoelastic damper with an integrated semiconductor active temperature control system, comprising: a plate viscoelastic damper module, a semiconductor temperature control system hardware module, a hardware control circuit module, and a temperature control algorithm module. The semiconductor temperature control system hardware module includes a TEC1-12706 semiconductor cooling chip, an NTC thermistor, an STM32F407VGT6 main control chip, and a water-cooling module. Eight TEC1-12706 chips are symmetrically arranged on both sides of the plate viscoelastic damper and connected in parallel. The hardware control circuit design module transforms the hardware scheme into an implementable electrical connection blueprint, ensuring coordinated control of the cooling chip and accurate feedback from the sensor. The temperature control algorithm module inputs data to the main control chip to realize the device's temperature control function. After integrating the temperature control system, the various performance indicators of the plate viscoelastic damper remain stable within the target temperature range, effectively suppressing stiffness decay and energy efficiency fluctuations caused by temperature rise.

Description

Technical Field

[0001] This invention relates to the field of seismic resistance and vibration reduction technology in civil engineering, and particularly to the field of plate viscoelastic dampers, specifically a plate viscoelastic damper with an integrated semiconductor active temperature control system. Background Technology

[0002] Plate viscoelastic dampers rely on the viscoelastic dissipation mechanism of polymer materials to achieve vibration control, but their engineering applications face two challenges: First, the intrinsic temperature sensitivity of the material is significant: at low temperatures, the glass transition causes a surge in brittleness and a sharp drop in damping efficiency, while at high temperatures, excessive activation of molecular chain segments leads to modulus decay and energy dissipation capacity degradation. This strong correlation between temperature and performance severely restricts its reliability in extreme climatic environments. Second, the heat accumulation effect under long-term service conditions exacerbates performance degradation: in typical application scenarios such as wind vibration control, the damper needs to withstand long-cycle dynamic loads, and the heat generated by continuous energy consumption is difficult to dissipate effectively, causing the internal temperature to gradually increase with the duration of operation, further inducing material softening.

[0003] Current technologies focus on material modification to broaden temperature range adaptability, but generally neglect the fatigue softening problem under high-frequency cyclic loading. Wind-induced vibration control is precisely the most widely used application area for plate viscoelastic dampers. Its operating conditions dictate that the dampers must undergo cyclic loading, where insufficient internal heat dissipation efficiency creates a vicious cycle. Existing passive heat dissipation designs and static material optimization cannot solve the thermal coupling failure problem under dynamic loading. This technological gap leads to multimodal performance degradation in dampers in practical engineering, including insufficient low-temperature efficiency, high-temperature stiffness degradation, and long-term service fatigue failure. Innovative research is needed from the perspective of synergistic optimization of thermal conductivity modification and fatigue resistance.

[0004] Semiconductor cooling technology, based on the Peltier effect, achieves cooling by using electric current to drive the transfer of heat between electrons and holes in a semiconductor material. It boasts advantages such as no moving parts, compact structure, and fast response. The core of a semiconductor cooling device is the thermoelectric module, typically composed of multiple P-type and N-type semiconductor thermocouple pairs. When current flows through the thermocouple pair, heat is transferred from the cold junction to the hot junction, thus achieving a cooling effect. The performance of the thermoelectric module mainly depends on the Seebeck coefficient, electrical conductivity, and thermal conductivity of the material; these parameters collectively determine the quality factor of the thermoelectric material. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a plate viscoelastic damper with an integrated semiconductor active temperature control system. This damper achieves precise dynamic control of the damper's operating temperature, maintaining it stable at the optimal operating temperature and preventing abnormal performance degradation of the viscoelastic material due to overheating or overcooling, thus ensuring reliable application of the damper in long-term vibration control.

[0006] To achieve the above objectives, the plate viscoelastic damper of the integrated semiconductor active temperature control system of the present invention is as follows:

[0007] The plate viscoelastic damper of this integrated semiconductor active temperature control system is characterized by comprising: a plate viscoelastic damper module, a semiconductor temperature control system hardware module, a hardware control circuit module, and a temperature control algorithm module; among which,

[0008] The plate-type viscoelastic damper module includes an outer horizontal constraint steel plate, a middle horizontal constraint steel plate, and an inner horizontal constraint steel plate. A viscoelastic energy-dissipating core material is fixed between each horizontal steel plate. Each horizontal constraint steel plate and the viscoelastic energy-dissipating core material are mechanically fastened to the outside of the core material area by multiple sets of high-strength bolts that penetrate the structure. Circular mounting holes are provided at the four corners of the two outermost constraint plates for connecting to the docking base plate of the building structure.

[0009] The semiconductor temperature control system hardware module is symmetrically arranged on both sides of the plate viscoelastic damper module, and is connected in parallel with the plate viscoelastic damper module to achieve superposition of cooling capacity and uniform distribution of temperature difference control.

[0010] The hardware control circuit module includes a temperature acquisition circuit and a drive circuit. The main control chip performs temperature control processing through the hardware control circuit.

[0011] The temperature control algorithm module controls the main control chip to perform temperature control through an NTC thermistor calibration algorithm and a fuzzy PID control algorithm for the temperature control system.

[0012] Preferably, the semiconductor temperature control system hardware module includes a semiconductor cooling chip array, a temperature sensor, a main control chip, and a water cooling system, wherein,

[0013] The semiconductor cooling chip array consists of several semiconductor cooling chips, which are symmetrically arranged on both sides of the plate viscoelastic damper module.

[0014] The temperature sensor is an NTC thermistor, which is placed next to the semiconductor cooling chip array to monitor the actual cooling or heating temperature.

[0015] The main control chip, in conjunction with the semiconductor cooling chip, generates a PWM signal, which is then acquired and processed by an ADC to achieve closed-loop temperature control.

[0016] The water cooling system includes a water cooling head, a water pump, coolant, water pipes, and connectors. The water cooling head transfers heat from the hot end of the semiconductor cooler (TEC) to the coolant. The water pump drives the coolant to circulate, and the coolant transfers heat. The components are connected by water pipes and connectors to achieve a seal.

[0017] Preferably, the temperature acquisition circuit employs a constant voltage circuit, a subtractor circuit, or a Wheatstone circuit.

[0018] Preferably, the driving circuit adopts an H-bridge topology, which realizes the cooling or heating function of the thermoelectric cooler by switching the current direction, and controls the cooling or heating power by controlling the current magnitude; the control logic of the driving circuit is as follows:

[0019] Cooling mode: GPIO1 = high level, GPIO2 = low level;

[0020] Heating mode: GPIO1 = low level, GPIO2 = high level.

[0021] More preferably, the NTC thermistor calibration algorithm employs an adaptive Steinhart-Hart coefficient calibration algorithm, which updates the model parameters in real time using a recursive least squares method, and calculates the absolute temperature using the updated coefficients. Specifically:

[0022] The resistance-temperature relationship of a thermistor is defined by the following nonlinear equation: ,in, Absolute temperature Let y = 1 / T be the measurement vector, and A, B, and C be the Steinhart-Hart coefficients. Construct the state vector. and measurement vector The equation is transformed into a linear form: ;

[0023] Assume that the temperature measured at the k-th sampling time is The corresponding resistance value is The model output error is then defined as: This yields the coefficient estimates from the previous iteration;

[0024] Let the sampling data point of the kth time be ( First, calculate the gain matrix. , For the first The covariance matrix of the next sample; updating the parameter estimates. Update the covariance matrix Finally, the coefficients are updated in real time. Calculate absolute temperature .

[0025] Preferably, the fuzzy PID control algorithm of the temperature control system takes the temperature difference and error change rate as input, and after fuzzification, maps them to a 7-level fuzzy set {NB, NM, NS, ZO, PS, PM, PB}. The input variables adopt triangular membership functions, and the output variable correction amount adopts a Gaussian function. A 7×7 fuzzy rule base is constructed according to the combination of different errors and change rates, where NB is a negative large error, NM is a negative medium error, NS is a negative small error, ZO is a zero error, PS is a positive small error, PM is a positive medium error, and PB is a positive large error. Finally, the centroid method is used to calculate the precise value of the correction amount.

[0026] More preferably, the fuzzy PID control algorithm of the temperature control system is specifically designed as follows:

[0027] ;

[0028] in, This is a proportionality coefficient used to respond proportionally to the current error; These are the integral coefficients used to eliminate the steady-state error of the system; These are the differential coefficients, used to predict the future trend of the error and reduce overshoot; This indicates the control signal output by the system at the current moment, used to regulate the temperature. The error at the current moment, i.e., the set temperature. Compared with actual temperature The difference; Indicates error over time The change, the integral from the initial time up to the current moment The cumulative historical error value of t is ;

[0029] The data from the internal temperature sensor of the plate viscoelastic damper were processed using a weighted median value, and the mean was calculated after removing outliers. ;

[0030] in, The actual temperature of the internal temperature sensor. For the first Weighting factors for each data point; For the first One temperature measurement value; For all temperature data , , ... , the median;

[0031] A two-dimensional fuzzy PID control strategy is adopted to adjust the PID parameters in real time.

[0032] ;

[0033] in, =2.79, =0.0106, =14.3 is the initial PID parameter; proportionality coefficient The change in; Integral coefficient The change in; Differential coefficients The change in;

[0034] The controller uses temperature difference The sum of the error change rate is used as input, and after fuzzification, it is mapped to a 7-level fuzzy set {NB, NM, NS, ZO, PS, PM, PB}. The input variables use triangular membership functions.

[0035] ;

[0036] in, To represent temperature difference In fuzzy sets Membership function in;

[0037] The output variable correction uses a Gaussian function:

[0038] ;

[0039] in, Indicates the rate of change of error The membership function in the fuzzy set NS is used to smoothly adjust the parameters.

[0040] This paper describes the application of a plate viscoelastic damper with an integrated semiconductor active temperature control system as described above in building structure vibration reduction.

[0041] The plate viscoelastic damper of this integrated semiconductor active temperature control system combines semiconductor refrigeration technology with intelligent control algorithms. It employs an adaptive Steinhart-Hart coefficient calibration algorithm, using recursive least squares to correct sensor nonlinearity errors in real time, thus solving the temperature drift problem of traditional fixed-coefficient models. Simultaneously, a fuzzy PID composite control strategy is designed, utilizing fuzzy logic to dynamically adjust PID parameters without requiring a precise mathematical model. Its hardware design offers advantages in modularity and miniaturization, and the control algorithm is universally applicable to nonlinear and time-varying systems. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the control principle of the hardware module of the semiconductor temperature control system of the present invention.

[0043] Figure 2 This is a schematic diagram illustrating the workflow of the semiconductor temperature control system hardware module of the present invention.

[0044] Figure 3 This is a flowchart of the fuzzy PID control algorithm for the semiconductor temperature control system of the present invention.

[0045] Figure 4 This is a comparison chart of the temperature response curves of three control algorithms.

[0046] Figure 5 This is a schematic diagram of the dimensions of the plate viscoelastic damper specimen of the present invention.

[0047] Figure 6 The hysteresis curves of the plate viscoelastic damper specimen of the present invention under different ambient temperature conditions with a given strain amplitude of 5 mm and a given frequency of 1 Hz, without the integrated temperature control system.

[0048] Figure 7 The hysteresis curves of the plate viscoelastic damper specimen of the present invention under different ambient temperature conditions with a given strain amplitude of 5 mm and a given frequency of 1 Hz are obtained. Detailed Implementation

[0049] To more clearly describe the technical content of the present invention, the following description is provided in conjunction with specific embodiments.

[0050] Before describing the embodiments of the present invention in detail, it should be noted that, in the following, the terms “comprising,” “including,” or any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0051] The integrated semiconductor active temperature control system's plate viscoelastic damper includes: a plate viscoelastic damper module, a semiconductor temperature control system hardware module, a hardware control circuit module, and a temperature control algorithm module; wherein...

[0052] The plate-type viscoelastic damper module includes an outer horizontal constraint steel plate, a middle horizontal constraint steel plate, and an inner horizontal constraint steel plate. A viscoelastic energy-dissipating core material is fixed between each horizontal steel plate. Each horizontal constraint steel plate and the viscoelastic energy-dissipating core material are mechanically fastened to the outside of the core material area by multiple sets of high-strength bolts that penetrate the structure. Circular mounting holes are provided at the four corners of the two outermost constraint plates for connecting to the docking base plate of the building structure.

[0053] The semiconductor temperature control system hardware module is symmetrically arranged on both sides of the plate viscoelastic damper module, and is connected in parallel with the plate viscoelastic damper module to achieve superposition of cooling capacity and uniform distribution of temperature difference control.

[0054] The hardware control circuit module includes a temperature acquisition circuit and a drive circuit. The main control chip performs temperature control processing through the hardware control circuit.

[0055] The temperature control algorithm module controls the main control chip to perform temperature control through an NTC thermistor calibration algorithm and a fuzzy PID control algorithm for the temperature control system.

[0056] like Figure 1 and 2 As shown, in practical applications, the semiconductor temperature control system hardware module includes a TEC1-12706 semiconductor cooling chip, an NTC thermistor, an STM32F407VGT6 main control chip, and a water cooling system. Eight TEC1-12706 chips are arranged in a symmetrical layout, with four chips installed on each side of the damper. These are connected in parallel to achieve superposition of cooling capacity and uniform temperature distribution. A symmetrical heat flow field is formed on the damper surface, preventing performance degradation caused by localized overheating.

[0057] The installation position of the NTC thermistor directly affects the dynamic response and accuracy of the temperature control system. When monitoring the cold end or heating, it should be placed near the semiconductor cooling chip to monitor the actual cooling or heating temperature. The deployment of all sensors must follow the principle of thermal symmetry to avoid measurement deviations caused by local temperature gradients.

[0058] The STM32F407VGT6 main control chip generates a PWM signal, which is then acquired and processed by an ADC to achieve closed-loop temperature control.

[0059] The water cooling system includes a water cooling head, a water pump, coolant, water pipes, and connectors;

[0060] The water block transfers heat from the hot end of the TEC to the coolant. The water pump drives the coolant to circulate, and the coolant transfers heat. It must have high thermal conductivity and corrosion resistance. Water pipes and connectors connect various components to ensure airtightness.

[0061] After the above hardware is integrated, a copper sheet with good thermal conductivity is attached to one side of the semiconductor cooling chip with thermally conductive adhesive. The copper sheet is then attached to both sides of the plate viscoelastic damper to generate heat or cool through physical contact.

[0062] In a preferred embodiment of the present invention, the hardware control circuit module transforms the hardware solution into an implementable electrical connection blueprint, including temperature acquisition circuit design and drive circuit design.

[0063] The temperature acquisition circuit design includes a constant voltage circuit, a subtractor circuit, and a Wheatstone circuit.

[0064] The Wheatstone circuit, as the core sensing and conversion unit, converts the physical quantity of temperature into a weak voltage signal. The constant voltage circuit provides a stable reference voltage to ensure the Wheatstone circuit operates under constant conditions, avoiding measurement deviations caused by power supply voltage variations. The subtractor circuit further processes the differential signal output by the Wheatstone circuit, effectively suppressing common-mode interference.

[0065] The voltage signal output by the temperature acquisition circuit after collaborative processing has high stability, strong anti-interference and suitable amplitude. It can be directly connected to devices such as microcontrollers or data acquisition cards to complete the complete conditioning process from temperature physical quantity to precise electrical signal, providing a foundation for subsequent temperature data analysis and control.

[0066] The core of the drive circuit design adopts an H-bridge topology, which realizes the cooling or heating function of the semiconductor thermoelectric chip by switching the current direction, and controls the cooling or heating power by controlling the current magnitude.

[0067] In a specific embodiment of the present invention, the drive circuit is designed based on TEC1-12706 (12V / 6A). In the H-bridge drive circuit design, the BTS7960B drive chip is used, whose voltage and current both meet the usage requirements. Detailed parameter information for this chip is shown in Table 1 below:

[0068] ;

[0069] The control logic of the drive circuit is as follows: when the GPIO control signal is "positive," current flows to the TEC through the upper left and lower right switches of the H-bridge, absorbing heat at the cold end; when the signal is "reverse," current flows in the opposite direction through the upper right and lower left switches, releasing heat at the cold end; when the signal is "stop," all switches are turned off, and no current flows through the TEC. As shown in Table 2 below, the control logic of this drive circuit achieves bidirectional mode switching through two complementary GPIO signals, and uses a PWM signal to adjust the duty cycle and control the current magnitude, ensuring that the TEC accurately switches between hot and cold functions according to temperature control requirements.

[0070] ;

[0071] like Figure 3 As shown, in a preferred embodiment of the present invention, the temperature control algorithm module includes an NTC thermistor calibration algorithm and a fuzzy PID control algorithm for the temperature control system.

[0072] The NTC thermistor calibration algorithm adopts the adaptive Steinhart-Hart coefficient calibration algorithm, which updates the model parameters in real time through recursive least squares (RLS).

[0073] The resistance-temperature relationship of a thermistor is defined by the following nonlinear equation: , T Let y = 1 / T, and construct the state vector. (Note: The original text contains some inconsistencies and unclear punctuation, which have been omitted from the translation.) and measurement vector The equation is transformed into a linear form: ;

[0074] The parameter estimation problem is transformed into a linear regression problem through linearization, and dynamic parameter calibration is achieved using recursive least squares (RLS). Assume that at the k-th sampling, the measured temperature... and corresponding resistance values The model output error is then defined as: , which is the coefficient estimate from the previous iteration;

[0075] The goal of the RLS algorithm is to minimize the weighted sum of squared historical errors: In the formula, Let be the forgetting factor, used to adjust the weights of historical data. Let the k-th sampled data point be ( First, initialize the algorithm, including initial values ​​for the parameter vector. =0, initial value of the covariance matrix , =106;

[0076] First, calculate the gain matrix. Update parameter estimation Update the covariance matrix Finally, the coefficients are updated in real time. Calculate absolute temperature .

[0077] The fuzzy PID control algorithm of the temperature control system consists of three parts: proportional (P), integral (I), and derivative (D), and its formula is as follows: , This is a scaling factor used to respond proportionally to the current error. These are integral coefficients used to eliminate the steady-state error of the system. These are the differential coefficients, used to predict the future trend of the error and reduce overshoot;

[0078] The data from the internal temperature sensor of the damper were processed using a weighted median value, and the mean was calculated after removing outliers.

[0079] ;

[0080] A two-dimensional fuzzy PID control strategy is adopted, which adjusts the PID parameters in real time through fuzzy logic to achieve adaptive control under complex operating conditions. Its basic structure is as follows: ;

[0081] =2.79, =0.0106, =14.3 is the initial PID parameter; proportionality coefficient The change in; Integral coefficient The change in; Differential coefficients The change in;

[0082] The controller uses temperature difference The sum of the error change rate is used as input, and after fuzzification, it is mapped to a 7-level fuzzy set {NB, NM, NS, ZO, PS, PM, PB}. The input variables use triangular membership functions.

[0083] ,

[0084] The vertex coordinates are (-60,0), (-50,1), and (-40,0), and the output variable correction uses a Gaussian function:

[0085] ;

[0086] This is used to smoothly adjust parameters.

[0087] As shown in Table 3 below, the proportionality coefficients are constructed. The 7×7 fuzzy rule base covers parameter adjustment strategies under different combinations of errors and rates of change, ensuring adaptability under all operating conditions.

[0088] ;

[0089] ;

[0090] ;

[0091] Tables 4 and 5 above represent the integral coefficients in the fuzzy rule base. and differential coefficients The correction rules also cover parameter adjustment strategies under different combinations of errors and rates of change.

[0092] The application process of this technical solution is further illustrated below with reference to a specific embodiment:

[0093] Example 1

[0094] This embodiment verifies the control performance of the fuzzy PID control algorithm in practical applications, and compares it with the Bang-Bang control algorithm and the traditional PID control algorithm. A semiconductor temperature control system is installed on both sides of the plate viscoelastic damper, and traditional PID parameters are set. =2.79, =0.0106, =14.3, the initial parameters of the fuzzy PID are the same, the ambient temperature is 28℃, and the target temperature is set to 12℃.

[0095] Figure 4 The temperature response curves of the fuzzy PID control algorithm, the Bang-Bang control algorithm, and the traditional PID control algorithm are shown. The fuzzy PID control uses fuzzy logic to adaptively adjust the PID parameters. The curve starts from 22℃ and decreases monotonically according to an exponential law. There is no obvious overshoot or oscillation throughout the process. It stabilizes at the target temperature of 12℃ with the smoothest transition and the shortest adjustment time.

[0096] Example 2

[0097] This embodiment presents a performance test of a plate viscoelastic damper with an integrated semiconductor active temperature control system. The dimensions of the plate viscoelastic damper specimen are as follows: Figure 5 As shown.

[0098] Through various loading tests, the force and displacement hysteresis curves of the damper under different seasons, excitation frequencies, and displacement amplitudes were obtained. After data processing, key performance parameters such as equivalent stiffness, equivalent damping ratio, single-turn energy consumption, and loss factor were calculated to verify the temperature control accuracy of the semiconductor temperature control device at the target temperature and its effect on improving the performance stability of the damper.

[0099] Figure 6 Hysteresis curves of a plate viscoelastic damper specimen under different ambient temperature conditions with a given strain amplitude of 5 mm and a given frequency of 1 Hz, without an integrated temperature control system.

[0100] Figure 7 The hysteresis curves of the plate viscoelastic damper specimen under different ambient temperature conditions with a given strain amplitude of 5 mm and a given frequency of 1 Hz are obtained.

[0101] The experimental data clearly show that, under the same parameter conditions, the integrated semiconductor active temperature control system of this invention significantly improves the temperature control accuracy and the stability of the damper performance.

[0102] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0103] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution device.

[0104] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0105] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0106] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0107] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0108] The plate viscoelastic damper using the integrated semiconductor active temperature control system of this invention can maintain stable performance indicators of the plate viscoelastic damper within the target temperature range after the integration of the temperature control system. This effectively suppresses stiffness decay and energy dissipation efficiency fluctuations caused by temperature rise, highlighting the key role of the temperature control system in improving the environmental adaptability and long-term performance reliability of the damper.

[0109] In this specification, the invention has been described with reference to specific embodiments thereof. However, it will be apparent that various modifications and variations can be made without departing from the spirit and scope of the invention. Therefore, the specification and drawings should be considered illustrative rather than restrictive.

Claims

1. A plate viscoelastic damper integrating a semiconductor active temperature control system, characterized in that, include: The system comprises a plate-type viscoelastic damper module, a semiconductor temperature control system hardware module, a hardware control circuit module, and a temperature control algorithm module; among which... The plate-type viscoelastic damper module includes an outer horizontal constraint steel plate, a middle horizontal constraint steel plate, and an inner horizontal constraint steel plate. A viscoelastic energy-dissipating core material is fixed between each horizontal steel plate. Each horizontal constraint steel plate and the viscoelastic energy-dissipating core material are mechanically fastened to the outside of the core material area by multiple sets of high-strength bolts that penetrate the structure. Circular mounting holes are provided at the four corners of the two outermost constraint plates for connecting to the docking base plate of the building structure. The semiconductor temperature control system hardware module is symmetrically arranged on both sides of the plate viscoelastic damper module, and is connected in parallel with the plate viscoelastic damper module to achieve superposition of cooling capacity and uniform distribution of temperature difference control. The hardware control circuit module includes a temperature acquisition circuit and a drive circuit. The main control chip performs temperature control processing through the hardware control circuit. The temperature control algorithm module controls the main control chip to perform temperature control through the NTC thermistor calibration algorithm and the fuzzy PID control algorithm of the temperature control system. The NTC thermistor calibration algorithm described above adopts an adaptive Steinhart-Hart coefficient calibration algorithm, which updates the model parameters in real time using the recursive least squares method, and calculates the absolute temperature using the updated coefficients. The fuzzy PID control algorithm for the temperature control system uses temperature difference and error change rate as inputs. After fuzzification, it is mapped to a 7-level fuzzy set {NB, NM, NS, ZO, PS, PM, PB}. The input variables adopt triangular membership functions, and the output variable correction amount adopts a Gaussian function. A 7×7 fuzzy rule base is constructed according to the combination of different errors and change rates. NB represents negative large error, NM represents negative medium error, NS represents negative small error, ZO represents zero error, PS represents positive small error, PM represents positive medium error, and PB represents positive large error. Finally, the centroid method is used to calculate the precise value of the correction amount.

2. The plate viscoelastic damper of the integrated semiconductor active temperature control system according to claim 1, characterized in that, The semiconductor temperature control system hardware module includes a semiconductor cooling chip array, a temperature sensor, a main control chip, and a water cooling system. The semiconductor cooling chip array consists of several semiconductor cooling chips, which are symmetrically arranged on both sides of the plate viscoelastic damper module. The temperature sensor is an NTC thermistor, which is placed next to the semiconductor cooling chip array to monitor the actual cooling or heating temperature. The main control chip, in conjunction with the semiconductor cooling chip, generates a PWM signal, which is then acquired and processed by an ADC to achieve closed-loop temperature control. The water cooling system includes a water cooling head, a water pump, coolant, water pipes, and connectors. The water cooling head transfers heat from the hot end of the semiconductor cooler (TEC) to the coolant. The water pump drives the coolant to circulate, and the coolant transfers heat. The components are connected by water pipes and connectors to achieve a seal.

3. The plate viscoelastic damper of the integrated semiconductor active temperature control system according to claim 1, characterized in that, The temperature acquisition circuit uses a constant voltage circuit, a subtractor circuit, or a Wheatstone circuit.

4. The plate viscoelastic damper of the integrated semiconductor active temperature control system according to claim 2, characterized in that, The driving circuit adopts an H-bridge topology, which realizes the cooling or heating function of the thermoelectric cooler by switching the current direction, and controls the cooling or heating power by controlling the current magnitude; the control logic of the driving circuit is as follows: Cooling mode: GPIO1 = high level, GPIO2 = low level; Heating mode: GPIO1 = low level, GPIO2 = high level.

5. The plate viscoelastic damper of the integrated semiconductor active temperature control system according to claim 1, characterized in that, The NTC thermistor calibration algorithm specifically includes the following processing steps: The resistance-temperature relationship of a thermistor is defined by the following nonlinear equation: ,in, Absolute temperature Let y = 1 / T be the measurement vector, and A, B, and C be the Steinhart-Hart coefficients. Construct the state vector. and measurement vector The equation is transformed into a linear form: ; Assume that the temperature measured at the k-th sampling time is The corresponding resistance value is The model output error is then defined as: We obtain the coefficient estimates from the previous iteration; let the sampled data points of the kth iteration be... First, calculate the gain matrix. , For the first The covariance matrix of the next sample; updating the parameter estimates. Update the covariance matrix Finally, the coefficients are updated in real time. Calculate absolute temperature .

6. The plate viscoelastic damper of the integrated semiconductor active temperature control system according to claim 1, characterized in that, The specific design of the fuzzy PID control algorithm for the temperature control system is as follows: ； in, This is a proportionality coefficient used to respond proportionally to the current error; These are the integral coefficients used to eliminate the steady-state error of the system; These are the differential coefficients, used to predict the future trend of the error and reduce overshoot; This indicates the control signal output by the system at the current moment, used to regulate the temperature. The error at the current moment, i.e., the set temperature. Compared with actual temperature The difference; Indicates error over time The change, the integral from the initial time up to the current moment The cumulative historical error value is ; The data from the internal temperature sensor of the plate viscoelastic damper were processed using a weighted median value, and the mean was calculated after removing outliers. ; in, The actual temperature of the internal temperature sensor. For the first Weighting factors for each data point; For the first One temperature measurement value; For all temperature data the median; A two-dimensional fuzzy PID control strategy is adopted to adjust the PID parameters in real time. ； in, =2.79, =0.0106, =14.3 is the initial PID parameter; proportionality coefficient The change in; Integral coefficient The change in; Differential coefficients The change in; The controller uses temperature difference The sum of the error change rate is used as input, and after fuzzification, it is mapped to a 7-level fuzzy set {NB, NM, NS, ZO, PS, PM, PB}. The input variables use triangular membership functions. ； in, To represent temperature difference e Membership function in fuzzy set NB; The output variable correction uses a Gaussian function: ； in, Indicates the rate of change of error ec The membership function in the fuzzy set NS is used to smoothly adjust the parameters.

7. The application of a plate viscoelastic damper utilizing the integrated semiconductor active temperature control system according to any one of claims 1 to 6 in vibration reduction of building structures.

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