Thz energy output adaptive regulation method and system based on multi-sensor feedback

Abstract

The application provides a terahertz energy output adaptive regulation method and system based on multi-sensor feedback, and belongs to the technical field of control systems. In view of the defects of single parameter regulation, fixed strategy and insufficient precision in the prior art, the application collects multi-dimensional parameters of terahertz output characteristics and transmission environment through a multi-sensor module, and outputs feedback parameters after filtering, calibration and fusion by a signal processing module; an adaptive regulation module dynamically matches a regulation strategy according to parameter deviation and generates an instruction to drive a terahertz source module to adjust output. The system forms a closed-loop iterative regulation link, realizes adaptive and accurate regulation of terahertz energy output, improves regulation comprehensiveness, adaptability and output stability, and is suitable for terahertz devices in multiple fields such as communication and security check.

Description

Technical Field

[0001] This invention relates to the field of control system technology, specifically to a terahertz energy output adaptive control method and system based on multi-sensor feedback. Background Technology

[0002] Terahertz technology has broad application prospects in fields such as communication, security inspection, and medical testing. The stability and accuracy of its energy output directly determine the application effect. Existing terahertz energy control technologies mostly adopt a single-parameter feedback mode, adjusting only a single indicator such as terahertz energy amplitude, ignoring the influence of output characteristic parameters such as frequency and phase, as well as transmission environment parameters such as temperature and humidity, on energy output, resulting in insufficient comprehensiveness of control. Furthermore, traditional control strategies are mostly fixed modes, unable to dynamically adapt to the degree of output deviation, easily leading to control lag and insufficient accuracy in complex operating conditions. In addition, the sensor data acquired by existing systems has not undergone system filtering, calibration, and fusion processing; noise and system errors in the raw data can easily lead to misjudgments in control, further reducing the stability of energy output. To address the above-mentioned shortcomings of existing technologies, there is an urgent need for a terahertz energy output control scheme that can achieve multi-dimensional parameter feedback, adaptive control strategy matching, and accurate data processing.

[0003] To address this, a terahertz energy output adaptive control method and system based on multi-sensor feedback is proposed. Summary of the Invention

[0004] The present invention aims to solve the problems mentioned in the background art by providing a method and system for adaptive control of terahertz energy output based on multi-sensor feedback.

[0005] The specific technical solution is as follows: A terahertz energy output adaptive control system based on multi-sensor feedback includes a multi-sensor module, a signal processing module, an adaptive control module, and a terahertz source module, wherein: The multi-sensor module is connected to the signal processing module and is used to collect multi-dimensional physical parameters related to terahertz energy output and transmit them to the signal processing module. The signal processing module is connected to the adaptive control module and is used to filter, calibrate and fuse the parameters collected by the multi-sensor module to output standardized feedback parameters. The adaptive control module is connected to the terahertz source module and is used to generate control commands and send them to the terahertz source module based on the feedback parameters output by the signal processing module and the preset energy output target through an adaptive control strategy. The terahertz source module is used to respond to the control commands of the adaptive control module and adjust its own working state to achieve adaptive output of terahertz energy.

[0006] The aforementioned terahertz energy output adaptive control system based on multi-sensor feedback includes a multi-sensor module comprising an energy sensor, a frequency sensor, a phase sensor, and an environmental parameter sensor. The energy sensor is used to acquire the terahertz energy amplitude parameters output by the terahertz source module; the frequency sensor is used to acquire the terahertz wave frequency parameters output by the terahertz source module; the phase sensor is used to acquire the terahertz wave phase parameters output by the terahertz source module; and the environmental parameter sensor is used to acquire temperature, humidity, and air pressure parameters along the terahertz transmission path.

[0007] The aforementioned terahertz energy output adaptive control system based on multi-sensor feedback includes a signal processing module comprising a filtering unit, a calibration unit, and a data fusion unit. The filtering unit performs noise suppression processing on the raw parameters acquired by the multi-sensor module. The calibration unit performs system error correction on the filtered parameters based on a preset standard parameter library. The data fusion unit uses a multi-source data fusion algorithm to fuse information from the calibrated multi-dimensional parameters and output comprehensive feedback parameters that characterize the terahertz energy output state.

[0008] The aforementioned terahertz energy output adaptive control system based on multi-sensor feedback includes an adaptive control module comprising a target setting unit, a strategy selection unit, and an instruction generation unit. The target setting unit stores or receives externally input terahertz energy output target parameters. The strategy selection unit adaptively selects a corresponding control strategy based on the degree of deviation between the feedback parameters and the target parameters. These control strategies include proportional-integral-derivative (PID) control strategies, model prediction control strategies, and neural network adaptive control strategies. The instruction generation unit generates corresponding voltage or current control instructions based on the selected control strategy and the deviation data.

[0009] The aforementioned terahertz energy output adaptive control system based on multi-sensor feedback includes a terahertz source module comprising a terahertz generator, a drive circuit, and a matching circuit. The drive circuit is controlled and connected to the adaptive control module, and is used to output a corresponding drive signal in response to control commands. The terahertz generator is electrically connected to the drive circuit, and is used to generate terahertz waves under the action of the drive signal. The matching circuit is connected to the output terminal of the terahertz generator, and is used to optimize the output impedance matching of the terahertz waves and improve energy transmission efficiency.

[0010] This invention also provides a terahertz energy output adaptive control method based on multi-sensor feedback, applied to the aforementioned terahertz energy output adaptive control system based on multi-sensor feedback. The method includes the following steps: Step 1: Parameter acquisition. Multi-dimensional physical parameters related to terahertz energy output are acquired in real time through a multi-sensor module. Step two, signal processing: the collected multi-dimensional physical parameters are filtered, calibrated and fused sequentially to obtain standardized feedback parameters; Step 3, control decision-making: Calculate the deviation between the feedback parameters and the preset energy output target parameters, adaptively select the control strategy based on the deviation results, and generate control instructions. Step four: Energy regulation. The terahertz source module responds to the regulation command and adjusts its own working state to adjust the terahertz energy output, thereby achieving adaptive regulation of the terahertz energy output.

[0011] The aforementioned terahertz energy output adaptive control method based on multi-sensor feedback, wherein the fusion processing in step two specifically involves: employing a weighted fusion algorithm to perform weighted calculations on the calibrated multi-dimensional parameters according to the measurement accuracy weights of each sensor, thereby obtaining comprehensive feedback parameters; wherein the measurement accuracy weights are determined through error analysis of the historical measurement data of each sensor.

[0012] The aforementioned adaptive control method for terahertz energy output based on multi-sensor feedback, wherein the deviation calculation in step three specifically involves: calculating the absolute and relative deviations between the terahertz energy amplitude and the target energy amplitude in the feedback parameters, and simultaneously calculating the deviations between the frequency parameters and the target frequency parameters; when the relative deviation is less than a preset threshold, a proportional-integral-derivative control strategy is selected; when the relative deviation is greater than or equal to the preset threshold but less than twice the preset threshold, a model prediction control strategy is selected; and when the relative deviation is greater than or equal to twice the preset threshold, a neural network adaptive control strategy is selected.

[0013] The aforementioned adaptive control method for terahertz energy output based on multi-sensor feedback further includes step five, feedback iteration. After the terahertz source module completes energy regulation, the multi-sensor module collects the relevant parameters of terahertz energy output again, and steps two to four are repeated until the deviation between the feedback parameters and the target parameters is less than the preset stability threshold, thereby achieving stable control of terahertz energy output.

[0014] In the aforementioned adaptive control method for terahertz energy output based on multi-sensor feedback, the parameter acquisition in step one adopts a synchronous acquisition method, and the acquisition frequency of the multi-sensor module is dynamically adjusted according to the output frequency of the terahertz source module, with the acquisition frequency not less than ten times the output frequency of the terahertz source module.

[0015] The present invention has the following beneficial effects: 1. Enhance the comprehensiveness and precision of control: By using a multi-sensor module to achieve full-dimensional acquisition of terahertz output characteristics and transmission environment parameters, combined with the step-by-step optimization processing of the signal processing module, the influence of single parameter acquisition and raw data noise and error on control is avoided, making the feedback parameters more consistent with the actual output state, providing reliable data support for precise control, and significantly improving the accuracy of control. 2. Enhanced control adaptability and efficiency: The adaptive control module dynamically matches the control strategy based on the degree of deviation, breaking through the adaptation limitations of traditional fixed strategies. It can select the optimal control method under different deviation scenarios, realize stable control in small deviation scenarios and rapid response in large deviation scenarios, and improve the system's adaptability and control efficiency to complex working conditions. 3. Ensure energy output stability: Through a closed-loop iterative control process, the system can continuously correct energy output deviations, avoiding the problem of insufficient precision in a single control; at the same time, the matching circuit optimizes impedance matching, reduces energy transmission loss, and ensures that the terahertz energy output is stable within the target range for a long time, thus improving the reliability of terahertz technology in various application scenarios. 4. Enhanced system usability and operability: The functions of each module in the system are clearly defined and the connection logic is clear. During use, only the initial deployment and parameter settings need to be completed, and the subsequent control process can be executed automatically without continuous manual intervention. At the same time, the dynamically adjusted acquisition frequency adapts to the different output frequency requirements of the terahertz source, further enhancing the usability of the system. Attached Figure Description

[0016] Figure 1 A schematic diagram of the electrical connection relationship of each module in the terahertz energy output adaptive control system based on multi-sensor feedback provided in an embodiment of the present invention; Figure 2 A flowchart of a terahertz energy output adaptive control method based on multi-sensor feedback provided in an embodiment of the present invention; Figure 3 To improve the comprehensiveness and accuracy of the control curve; Figure 4 A curve illustrating adaptability to complex operating conditions; Figure 5 This is a graph showing the stability of energy output. Figure 6 The graph shows the efficiency curve of the feed-through iteration. Detailed Implementation

[0017] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0019] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0020] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] Reference Figure 1-6 The specific implementation method provides the following Examples 1 and 2, wherein... Figure 1 The electrical connections of the various modules in this system are shown. Figure 2 The flowchart of this terahertz energy output adaptive control method based on multi-sensor feedback is shown; Figure 3 The X-axis represents time / number of iterations, and the Y-axis represents the control accuracy (deviation). This shows that the curve of this system drops rapidly and tends to stabilize, while the control group system drops more slowly and fluctuates significantly, demonstrating the precise control advantage of the multi-sensor feedback system. Figure 4 The X-axis represents changes in environmental parameters (temperature / humidity fluctuations) | The Y-axis represents the system output stability, showing that the system maintains stable output (smooth curve) under complex operating conditions. The comparison group system fluctuates significantly, verifying the system's adaptability to dynamic environments. Figure 5 The X-axis represents time and the Y-axis represents output energy. This shows that the output of this system closely follows the target energy line (fluctuation ±0.5%), while the comparison group system fluctuates significantly around the target line (±15%), intuitively demonstrating the difference in the stability of energy output. Figure 6 The X-axis represents the number of iterations, and the Y-axis represents the time it takes for the deviation to drop to the threshold, demonstrating that the system converges quickly through finite iterations, reflecting the efficiency of the multi-sensor feedback mechanism. Example

[0022] The terahertz energy output adaptive control system based on multi-sensor feedback provided in this embodiment, such as Figures 1-5 As shown, the system includes a multi-sensor module, a signal processing module, an adaptive control module, and a terahertz source module. The multi-sensor module is signal-connected to the signal processing module and is used to collect multi-dimensional physical parameters related to terahertz energy output and transmit them to the signal processing module. The signal processing module is signal-connected to the adaptive control module and is used to filter, calibrate, and fuse the parameters collected by the multi-sensor module, outputting standardized feedback parameters. The adaptive control module is control-connected to the terahertz source module and is used to generate control commands based on the feedback parameters output by the signal processing module and a preset energy output target, using an adaptive control strategy, and send them to the terahertz source module. The terahertz source module responds to the control commands from the adaptive control module and adjusts its own operating state to achieve adaptive terahertz energy output.

[0023] This solution constructs a collaborative architecture consisting of a multi-sensor module, a signal processing module, an adaptive control module, and a terahertz source module, forming a closed-loop link of multi-dimensional parameter acquisition, processing, and control. This overcomes the limitations of single parameter feedback, enabling more comprehensive and precise control of terahertz energy output and ensuring that the output state can stably adapt to the preset target requirements.

[0024] Specifically, in this embodiment, the multi-sensor module includes an energy sensor, a frequency sensor, a phase sensor, and an environmental parameter sensor. The energy sensor, frequency sensor, and phase sensor are all located at the output end of the terahertz source module and are all connected to the signal processing module. The energy sensor is used to collect the terahertz energy amplitude parameters output by the terahertz source module and transmit them to the signal processing module. The frequency sensor is used to collect the terahertz wave frequency parameters output by the terahertz source module and transmit them to the signal processing module. The phase sensor is used to collect the terahertz wave phase parameters output by the terahertz source module and transmit them to the signal processing module. The environmental parameter sensor is located on the terahertz transmission path and is connected to the signal processing module. It is used to collect the temperature, humidity, and air pressure parameters on the terahertz transmission path and transmit them to the signal processing module.

[0025] This scheme places energy, frequency, and phase sensors at the output end of the terahertz source, and environmental parameter sensors at the transmission path. All sensors are connected to the signal processing module to achieve full-dimensional acquisition of terahertz output characteristics and transmission environmental parameters, supplementing a complete data foundation, reducing the impact of environmental interference and parameter missingness on regulation, and improving the targeting and reliability of regulation.

[0026] Specifically, in this embodiment, the signal processing module includes a filtering unit, a calibration unit, and a data fusion unit. The filtering unit is signal-connected to the multi-sensor module and is used to perform noise suppression processing on the raw parameters collected by the multi-sensor module. The filtering unit is also signal-connected to the calibration unit and is used to transmit the filtered parameters to the calibration unit. The calibration unit is signal-connected to the data fusion unit and is used to perform system error correction on the filtered parameters based on a preset standard parameter library and transmit the calibrated parameters to the data fusion unit. The data fusion unit is signal-connected to the adaptive control module and is used to perform information fusion on the calibrated multi-dimensional parameters using a multi-source data fusion algorithm, output a comprehensive feedback parameter that can characterize the terahertz energy output state, and transmit it to the adaptive control module.

[0027] This scheme uses a step-by-step signal connection and processing through filtering, calibration, and data fusion units to first suppress noise in the original parameters, then correct system errors, and finally integrate multi-dimensional parameters to improve the accuracy and representativeness of feedback parameters. This avoids misjudgments in regulation caused by single parameter deviations or noise, and provides reliable data support for adaptive regulation.

[0028] Specifically, in this embodiment, the adaptive control module includes a target setting unit, a strategy selection unit, and an instruction generation unit. The target setting unit is signal-connected to the strategy selection unit and is used to store or receive externally input terahertz energy output target parameters and transmit them to the strategy selection unit. The strategy selection unit is also signal-connected to the output of the signal processing module and is used to receive feedback parameters output by the signal processing module. Based on the degree of deviation between the feedback parameters and the target parameters, the corresponding control strategy is adaptively selected. The control strategies include proportional-integral-derivative control strategies, model prediction control strategies, and neural network adaptive control strategies. The strategy selection unit is signal-connected to the instruction generation unit, and the instruction generation unit is also control-connected to the terahertz source module. It is used to generate corresponding voltage or current control instructions based on the selected control strategy and deviation data, and transmit the control instructions to the terahertz source module.

[0029] This solution overcomes the limitations of a single control strategy by linking the signals of the target setting, strategy selection, and command generation unit with the adaptive matching control strategy based on the degree of deviation. It enables the optimal control method to be quickly selected under different deviation scenarios, improving control efficiency and adaptability, and ensuring that the generated control commands accurately match the adjustment needs of the terahertz source.

[0030] Specifically, in this embodiment, the terahertz source module includes a terahertz generator, a driving circuit, and a matching circuit. The driving circuit is controlled and connected to the adaptive control module, and is used to receive the control command output by the adaptive control module and respond to the control command by outputting a corresponding driving signal. The terahertz generator is electrically connected to the driving circuit and is used to generate a terahertz wave under the action of the driving signal. The input terminal of the matching circuit is connected to the output terminal of the terahertz generator, and the output terminal of the matching circuit is the terahertz wave output terminal of the terahertz source module, which is used to optimize the output impedance matching of the terahertz wave and improve the energy transmission efficiency.

[0031] In this scheme, the drive circuit is connected to the adaptive control module, the terahertz generator is electrically connected to the drive circuit, and the matching circuit is connected to the generator output, forming a complete command response-energy generation-output optimization link. This enables the terahertz source energy to accurately respond to the control command, while reducing energy transmission loss and improving the effectiveness and utilization of energy output. Example

[0032] This embodiment provides a terahertz energy output adaptive control method based on multi-sensor feedback, applied to the terahertz energy output adaptive control system based on multi-sensor feedback in Embodiment 1. The method includes the following steps: Step 1, parameter acquisition: real-time acquisition of multi-dimensional physical parameters related to terahertz energy output through a multi-sensor module; Step 2, signal processing: sequential filtering, calibration, and fusion processing of the acquired multi-dimensional physical parameters to obtain standardized feedback parameters; Step 3, control decision: deviation calculation based on the feedback parameters and preset energy output target parameters, adaptive selection of control strategy based on the deviation result, and generation of control command; Step 4, energy adjustment: the terahertz source module responds to the control command, adjusts its own working state to adjust the terahertz energy output, and realizes adaptive control of terahertz energy output.

[0033] This scheme transforms the system architecture into an orderly process of parameter acquisition, signal processing, control decision-making, and energy regulation. It clarifies the execution logic and connection relationship of each link, making adaptive control operable, ensuring the orderly coordination of the functions of each module of the system, realizing the standardization of terahertz energy output control, and ensuring the stable implementation of the adaptive control target.

[0034] Specifically, in this embodiment, the fusion process in step two is as follows: a weighted fusion algorithm is used to calculate the calibrated multi-dimensional parameters by weighting them according to the measurement accuracy weights of each sensor, so as to obtain the comprehensive feedback parameters; wherein, the measurement accuracy weights are determined by error analysis of the historical measurement data of each sensor.

[0035] This scheme employs a weighted fusion algorithm, which integrates the calibrated parameters by combining the measurement accuracy weights of each sensor. It fully considers the differences in measurement reliability among different sensors, making the fused comprehensive feedback parameters more closely match the actual output state of the terahertz, improving the reliability of the feedback data, and further ensuring the rationality of the control decision.

[0036] Specifically, in this embodiment, the deviation calculation in step three is as follows: calculate the absolute and relative deviations between the terahertz energy amplitude and the target energy amplitude in the feedback parameters, and simultaneously calculate the deviation between the frequency parameters and the target frequency parameters; when the relative deviation is less than a preset threshold, select the proportional-integral-derivative control strategy; when the relative deviation is greater than or equal to the preset threshold but less than twice the preset threshold, select the model prediction control strategy; when the relative deviation is greater than or equal to twice the preset threshold, select the neural network adaptive control strategy.

[0037] This scheme avoids blind strategy selection by clarifying the calculation dimensions of terahertz energy amplitude and frequency deviations, as well as the corresponding rules for different deviation ranges and control strategies. It ensures stable control in small deviation scenarios and efficient control in large deviation scenarios, thereby improving the system's adaptability to different output deviations.

[0038] Specifically, in this embodiment, step five, feedback iteration, is also included. After the terahertz source module completes energy regulation, the multi-sensor module collects the relevant parameters of terahertz energy output again, and steps two to four are repeated until the deviation between the feedback parameters and the target parameters is less than the preset stable threshold, thereby achieving stable regulation of terahertz energy output.

[0039] This scheme adds iterative acquisition and repeated control steps after energy regulation to form a continuously optimized closed-loop control, gradually correcting the residual deviation after a single control, avoiding the problem of insufficient control accuracy, further improving the long-term stability of terahertz energy output, and ensuring that the output deviation is always controlled within a stable range.

[0040] Specifically, in this embodiment, the parameter acquisition in step one adopts a synchronous acquisition method. The acquisition frequency of the multi-sensor module is dynamically adjusted according to the output frequency of the terahertz source module, and the acquisition frequency is not less than ten times the output frequency of the terahertz source module.

[0041] This scheme adopts a synchronous acquisition method and dynamically adjusts the acquisition frequency to ensure the timing consistency of multi-sensor parameter acquisition, avoid control lag caused by acquisition delay or asynchrony, and adapt to the acquisition requirements of different output frequencies of terahertz sources, ensuring the timeliness and integrity of the acquired data, and providing a guarantee for the timeliness of subsequent processing and control.

[0042] Specifically, in this embodiment, the deviation calculation further employs a multi-dimensional weighted fusion control error evaluation equation for comprehensive control error evaluation, which is expressed as: ; in: To comprehensively control the error; These are the measured and target energy amplitudes, respectively. These are the measured and target frequencies, respectively. The measured and target phases are shown separately (unit: radians). This represents the measured value of the i-th environmental parameter. This represents the standard value of the i-th environmental parameter. α, β, γ, δ are the weighting coefficients of energy, frequency, phase, and environmental parameters, respectively, and satisfy α+β+γ+δ=1; These are the weighting coefficients within each environmental parameter, reflecting their degree of influence on energy output; All error terms have been normalized to ensure It is a dimensionless evaluation index.

[0043] The derivation of the equation: This equation is constructed based on the practical needs of multi-sensor feedback control, comprehensively considering four major categories of factors affecting the stability of terahertz output: energy, frequency, phase, and environmental parameters. The derivation process is as follows: 1. Normalization: To eliminate the influence of different physical dimensions on error assessment, all errors are expressed as relative errors or normalized errors. 2. Weighted fusion: Different weighting coefficients α, β, γ, δ are assigned according to the degree of influence of each parameter on the output stability. The weights can be dynamically adjusted based on historical data or expert experience. 3. Environmental parameter integration: Environmental parameters (such as temperature, humidity, and air pressure) are introduced through a weighted summation method to reflect their comprehensive impact on terahertz propagation; 4. Dimensionless output: Final output It is a dimensionless indicator, which is easy to compare with preset thresholds and used for adaptive strategy selection.

[0044] Example: Assume the system is currently collecting the following parameters: =102mW, =100mW; =2.01THz, =2.00THz; =0.52rad, =0.50 rad; Environmental parameters: temperature =26℃, =25℃; Humidity =55%, =50%; Weighting coefficients are set as follows: α=0.5, β=0.2, γ=0.2, δ=0.1, representing the weights within the environment. =0.6, =0.4.

[0045] Substitute into the equation to calculate: ; The system according to The value is compared with the preset threshold, and the corresponding control strategy is selected.

[0046] Parameter description: α, β, γ, δ: can be dynamically optimized through learning algorithms or experimental calibration, reflecting the importance of each parameter under the current working conditions; The internal weights of environmental parameters can be determined through correlation analysis or transfer function models.

[0047] All sensor data must be filtered and calibrated by the signal processing module to ensure the reliability of the input data.

[0048] Technical effects: 1. Improve the comprehensiveness and scientific nature of error assessment: Integrate multi-dimensional parameters to avoid the one-sidedness of assessment based on a single indicator; 2. Enhance the accuracy of adaptive regulation: Through quantitative evaluation, provide more refined decision-making basis for strategy selection; 3. Adaptability to complex operating conditions: The dynamic weighting of environmental parameters enhances the system's robustness in controlling changes under varying environments; 4. Facilitates system integration and debugging: The equation structure is clear and the parameters are adjustable, which facilitates the implementation and optimization of the actual system.

[0049] Working principle and process: In the "regulation decision-making" step: 1. Obtain standardized feedback parameters from the signal processing module; 2. Call the target parameters; 3. Substitute into the multi-dimensional weighted fusion control error evaluation equation to calculate ; 4. According to Compare with the preset threshold range and select the appropriate control strategy (such as PID, MPC, neural network). 5. Output control commands to the terahertz source module; 6. Continuously update the weight coefficients during the iteration process to achieve dynamic optimization.

[0050] In summary, the terahertz energy output adaptive control method and system based on multi-sensor feedback provided in this embodiment has the following advantages: 1. Enhance the comprehensiveness and precision of control: By using a multi-sensor module to achieve full-dimensional acquisition of terahertz output characteristics and transmission environment parameters, combined with the step-by-step optimization processing of the signal processing module, the influence of single parameter acquisition and raw data noise and error on control is avoided, making the feedback parameters more consistent with the actual output state, providing reliable data support for precise control, and significantly improving the accuracy of control. 2. Enhanced control adaptability and efficiency: The adaptive control module dynamically matches the control strategy based on the degree of deviation, breaking through the adaptation limitations of traditional fixed strategies. It can select the optimal control method under different deviation scenarios, realize stable control in small deviation scenarios and rapid response in large deviation scenarios, and improve the system's adaptability and control efficiency to complex working conditions. 3. Ensure energy output stability: Through a closed-loop iterative control process, the system can continuously correct energy output deviations, avoiding the problem of insufficient precision in a single control; at the same time, the matching circuit optimizes impedance matching, reduces energy transmission loss, and ensures that the terahertz energy output is stable within the target range for a long time, thus improving the reliability of terahertz technology in various application scenarios. 4. Enhanced system usability and operability: The functions of each module in the system are clearly defined and the connection logic is clear. During use, only the initial deployment and parameter settings need to be completed, and the subsequent control process can be executed automatically without continuous manual intervention. At the same time, the dynamically adjusted acquisition frequency adapts to the different output frequency requirements of the terahertz source, further enhancing the usability of the system.

[0051] Working principle: This system achieves adaptive control of terahertz energy output based on the logic of multi-dimensional acquisition, precise processing, adaptive regulation, and closed-loop optimization. The modules work together to form a complete closed-loop link: 1. Parameter acquisition stage: The multi-sensor module serves as the data input end. Energy, frequency, and phase sensors are deployed at the output end of the terahertz source to acquire output characteristic parameters such as terahertz energy amplitude, frequency, and phase in real time. Environmental parameter sensors are deployed along the transmission path to acquire environmental parameters such as temperature, humidity, and air pressure. All sensors synchronously transmit the raw parameters acquired to the signal processing module. 2. Signal Processing Stage: The signal processing module performs step-by-step optimization on the raw parameters. The filtering unit first suppresses noise and removes interference signals from the raw parameters. The calibration unit corrects the systematic error of the filtered parameters based on the standard parameter library to improve data accuracy. The data fusion unit uses a weighted fusion algorithm to integrate information from multi-dimensional parameters by combining the measurement accuracy weights of each sensor. It outputs comprehensive feedback parameters that can fully characterize the terahertz energy output state and transmits them to the adaptive control module. 3. Control Decision Stage: The target setting unit in the adaptive control module provides preset energy output target parameters. After receiving the feedback parameters and target parameters, the strategy selection unit calculates the degree of deviation between the two and adaptively matches the control strategy according to the magnitude of the deviation. The instruction generation unit generates the corresponding voltage or current control instruction based on the selected strategy and deviation data and transmits it to the terahertz source module. 4. Energy Output Stage: After receiving the control command, the drive circuit of the terahertz source module outputs the corresponding drive signal to drive the terahertz generator to produce terahertz waves. The matching circuit optimizes the impedance matching of the terahertz wave output, reduces energy transmission loss, and achieves precise energy output. At the same time, the multi-sensor module continuously collects the adjusted output parameters, repeating the above process to form a closed-loop iteration, ensuring that the energy output is stable and adapts to the target requirements.

[0052] How to use: 1. System Deployment: Install each module according to the preset positions, fix the energy, frequency, and phase sensors at the output end of the terahertz source module, and deploy the environmental parameter sensors at key nodes in the terahertz transmission path; complete the signal and control connections between the multi-sensor module and the signal processing module, the signal processing module and the adaptive control module, and the adaptive control module and the terahertz source module to ensure smooth link operation; 2. Parameter setting: Input or call the preset terahertz energy output target parameters, including core indicators such as energy amplitude and frequency, through the target setting unit of the adaptive control module; at the same time, confirm the standard parameter library, filtering and fusion algorithm parameters, and key parameters such as the deviation threshold of the adaptive control strategy of the signal processing module. 3. Start-up and operation: Start the terahertz source module, and the system will automatically enter the working state. The multi-sensor module will synchronously collect multi-dimensional physical parameters according to the dynamically adjusted acquisition frequency and transmit them to the signal processing module. 4. Adaptive control: The signal processing module filters, calibrates, and fuses the acquired parameters to output comprehensive feedback parameters; the adaptive control module calculates the deviation between the feedback parameters and the target parameters, matches the corresponding control strategy, and generates control commands to drive the terahertz source module to adjust its working state. 5. Closed-loop maintenance: The system continuously collects the adjusted output parameters through multi-sensor modules, repeats the signal processing, control decision, and energy regulation process until the deviation between the feedback parameter and the target parameter is less than the stable threshold, thus achieving stable control. In subsequent operation, the system automatically responds to parameter fluctuations and continuously executes the closed-loop control process without manual intervention.

[0053] In addition, this application provides the following examples: Based on the control system of Embodiment 1 and the control method of Embodiment 2, the specific selection and connection of each module are as follows: 1. Multi-sensor module: An InGaAs terahertz energy sensor (compatible with 0.1-10THz frequency band), a terahertz frequency counter (measurement accuracy down to one-thousandth of the frequency band), and a terahertz phase detector are selected as output characteristic acquisition components. All three are connected to the signal input terminal of the signal processing module via an SMA interface and are physically fixed on the flange of the terahertz source output terminal. An integrated temperature, humidity, and air pressure sensor (measurement range covering conventional indoor and outdoor environments) is selected as the environmental parameter acquisition component. It is connected to the signal processing module via an I2C interface and deployed at the intermediate node position of the terahertz transmission path. 2. Signal Processing Module: The core processing unit is an FPGA chip (model XC7K325T), which integrates filtering, calibration, and data fusion functions. The filtering unit uses a Kalman filter algorithm to suppress noise, the calibration unit has a built-in library of preset standard parameters (covering error correction coefficients for each sensor under different operating conditions), and the data fusion unit uses a weighted fusion algorithm. The signal processing module is connected to the adaptive control module via an SPI interface to transmit comprehensive feedback parameters. 3. Adaptive Control Module: An ARM processor (model STM32H743) is used to build the target setting unit, strategy selection unit, and instruction generation unit. The target setting unit supports inputting and storing terahertz energy amplitude and frequency target parameters via host computer software. The strategy selection unit presets deviation thresholds (small deviation threshold and twice the small deviation threshold) and has built-in proportional-integral-derivative control algorithms, model prediction control algorithms, and neural network adaptive control algorithms. The instruction generation unit outputs 0-5V adjustable voltage control instructions through a DAC interface, which are connected to the drive circuit of the terahertz source module for control. 4. Terahertz Source Module: The core component is a quantum cascaded terahertz generator (output frequency band 1-5THz). The drive circuit uses an adjustable drive circuit composed of high-speed operational amplifiers, and the matching circuit uses a microstrip line impedance matching network (adapted to 50Ω characteristic impedance). The input of the drive circuit is connected to the DAC output of the adaptive control module, and the output of the drive circuit is electrically connected to the terahertz generator. The input of the matching circuit is soldered to the output of the terahertz generator, and the output of the matching circuit serves as the terahertz wave output port. 5. Control Process Adaptation: Parameter acquisition adopts a synchronous acquisition method. The acquisition timing of each sensor is synchronously controlled by the timer of the FPGA chip. The acquisition frequency is dynamically adjusted according to the output frequency of the terahertz generator to ensure that the acquisition frequency is not less than ten times the output frequency. The measurement accuracy weight of each sensor in the fusion processing is determined based on historical measurement error analysis after multiple calibration experiments of each sensor in the early stage. In the feedback iteration process, the stability threshold is set as the allowable deviation range of the target parameter to ensure that the energy output is stable after the iteration ends.

[0054] How this example works: This example follows the logic of multi-dimensional data collection, precise processing, adaptive adjustment, and closed-loop optimization. The specific workflow is as follows: 1. Parameter acquisition stage: After the terahertz generator is started, the energy sensor, frequency counter and phase detector synchronously acquire the output terahertz energy amplitude, frequency and phase parameters. The integrated temperature, humidity and air pressure sensor synchronously acquires the environmental parameters of the transmission path. All raw parameters are transmitted to the FPGA chip in real time through the corresponding interface. 2. Signal Processing Stage: The filtering unit within the FPGA chip uses the Kalman filter algorithm to suppress noise in the original parameters, eliminating environmental interference and random noise from the sensor itself; the calibration unit calls the built-in standard parameter library to correct system errors in the filtered parameters, compensating for sensor temperature drift errors and installation errors; the data fusion unit integrates information from multi-dimensional calibration parameters according to preset measurement accuracy weights for each sensor, using a weighted fusion algorithm to generate comprehensive feedback parameters that fully characterize the terahertz energy output state, and then transmits them to the ARM processor via the SPI interface; 3. Control Decision Stage: The ARM processor's target setting unit calls the preset energy output target parameters. The strategy selection unit calculates the deviation between the comprehensive feedback parameters and the target parameters (including the relative deviation of energy amplitude and frequency deviation), and matches the control strategy according to the magnitude of the deviation—when the relative deviation is less than the small deviation threshold, the proportional-integral-derivative control strategy is selected; when the relative deviation is greater than or equal to the small deviation threshold but less than twice the small deviation threshold, the model prediction control strategy is selected; when the relative deviation is greater than or equal to twice the small deviation threshold, the neural network adaptive control strategy is selected. The instruction generation unit outputs the corresponding voltage control instruction to the drive circuit through the DAC interface according to the selected strategy and deviation data. 4. Energy Regulation and Closed-Loop Iteration Stage: After receiving the voltage regulation command, the drive circuit adjusts the amplitude of the output drive signal, thereby regulating the operating current of the terahertz generator to achieve terahertz energy output adjustment; the matching circuit optimizes the output impedance through a microstrip line impedance matching network to reduce energy loss during terahertz wave transmission; at the same time, the multi-sensor module continuously collects the adjusted output parameters and environmental parameters, repeating the above signal processing, regulation decision, and energy regulation process until the deviation between the comprehensive feedback parameters and the target parameters is less than the stability threshold, thus achieving stable terahertz energy output.

[0055] Experimental data: The experiment was conducted under two operating conditions: Condition 1 was a normal indoor environment (temperature 25℃, humidity 50%, air pressure standard atmospheric pressure), with the target energy amplitude being the preset normal value and the target frequency being 2THz; Condition 2 was a complex environmental condition (temperature fluctuation range 15-35℃, humidity fluctuation range 30%-70%, air pressure slightly lower than standard atmospheric pressure), with the target energy amplitude and frequency being the same as Condition 1; at the same time, a traditional single-parameter control system was set up as a control group (only the energy amplitude parameter was collected, and a fixed proportional integral derivative control strategy was adopted).

[0056] Experimental results show that: Under operating condition one, the sample system can quickly bring the terahertz energy output to the target state and maintain a stable output state without significant fluctuations; the control group system takes longer to reach the target state and exhibits slight fluctuations. Under operating condition two, the sample system can maintain stable terahertz energy output through environmental parameter acquisition and adaptive strategy matching; the control group system, due to not considering the influence of environmental parameters, shows significant output deviation and cannot stably maintain the target output state. During the feedback iteration process, the sample system can reduce the deviation to below the stability threshold within a finite number of iterations, and the output state remains stable after the iteration is terminated.

[0057] The technical effect of this example: 1. Improve the comprehensiveness and accuracy of regulation: By selecting multiple types of sensors to collaboratively collect output characteristic parameters and environmental parameters, and combining Kalman filtering and weighted fusion algorithms to process the data, the limitations of single parameter acquisition and noise interference in the raw data are avoided, making the feedback parameters more consistent with the actual output state, providing reliable data support for precise regulation, and thus improving the comprehensiveness and accuracy of regulation. 2. Enhanced adaptability to complex working conditions: By pre-setting multiple control strategies and deviation matching rules, the system can adaptively select the optimal control method according to the degree of output deviation. At the same time, it combines environmental parameter acquisition to compensate for the impact of environmental interference, breaking through the adaptability limitations of traditional fixed strategies under complex working conditions, and ensuring stable control under both normal and complex environmental working conditions. 3. Ensure energy output stability: Through a closed-loop iterative control process, the system can continuously correct energy output deviations, avoiding the problem of insufficient precision in a single control; at the same time, the impedance matching design of the matching circuit reduces energy transmission loss, so that the control command can be effectively converted into stable energy output, ensuring that the terahertz energy remains stable within the target range for a long time. 4. Enhance system usability: The selection of each module takes into account both performance and versatility, and the connection interface is standardized to facilitate installation and deployment; after the system starts up, no continuous manual intervention is required, and it can automatically complete the entire process of acquisition, processing, regulation and iteration, and adapt to terahertz sources with different output frequencies, further enhancing the system's usability and scalability.

[0058] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A terahertz energy output adaptive control system based on multi-sensor feedback, characterized in that, It includes a multi-sensor module, a signal processing module, an adaptive control module, and a terahertz source module, among which: The multi-sensor module is connected to the signal processing module and is used to collect multi-dimensional physical parameters related to terahertz energy output and transmit them to the signal processing module. The signal processing module is connected to the adaptive control module and is used to filter, calibrate and fuse the parameters collected by the multi-sensor module to output standardized feedback parameters. The adaptive control module is connected to the terahertz source module and is used to generate control commands and send them to the terahertz source module based on the feedback parameters output by the signal processing module and the preset energy output target through an adaptive control strategy. The terahertz source module is used to respond to the control instructions of the adaptive control module and adjust its own working state to achieve adaptive output of terahertz energy. The multi-sensor module includes an energy sensor, a frequency sensor, a phase sensor, and an environmental parameter sensor; the energy sensor is used to collect the terahertz energy amplitude parameter output by the terahertz source module; the frequency sensor is used to collect the terahertz wave frequency parameter output by the terahertz source module; the phase sensor is used to collect the terahertz wave phase parameter output by the terahertz source module; and the environmental parameter sensor is used to collect temperature, humidity, and air pressure parameters along the terahertz transmission path. The adaptive control module includes a target setting unit, a strategy selection unit, and an instruction generation unit. The target setting unit is used to store or receive externally input terahertz energy output target parameters. The strategy selection unit is used to adaptively select a corresponding control strategy based on the degree of deviation between the feedback parameters and the target parameters. The control strategies include proportional-integral-derivative control strategies, model prediction control strategies, and neural network adaptive control strategies. The instruction generation unit is used to generate corresponding voltage or current control instructions based on the selected control strategy and deviation data. The data fusion unit uses a multi-dimensional weighted fusion control error evaluation equation to perform comprehensive control error evaluation. This equation is expressed as: ； in: To comprehensively control the error; These are the measured and target energy amplitudes, respectively. These are the measured and target frequencies, respectively. These are the measured and target phases, respectively. This represents the measured value of the i-th environmental parameter. Let be the standard value of the i-th environmental parameter; α, β, γ, δ are the weighting coefficients of energy, frequency, phase, and environmental parameter, respectively, and satisfy α+β+γ+δ=1; These are the weighting coefficients within each environmental parameter; all error terms have been normalized to ensure... It is a dimensionless evaluation index.

2. The terahertz energy output adaptive control system based on multi-sensor feedback according to claim 1, characterized in that, The signal processing module includes a filtering unit, a calibration unit, and a data fusion unit. The filtering unit is used to suppress noise in the raw parameters collected by the multi-sensor module. The calibration unit is used to correct system errors in the filtered parameters based on a preset standard parameter library. The data fusion unit is used to fuse information from the calibrated multi-dimensional parameters using a multi-source data fusion algorithm, and output a comprehensive feedback parameter that can characterize the terahertz energy output state.

3. The terahertz energy output adaptive control system based on multi-sensor feedback according to claim 1, characterized in that, The terahertz source module includes a terahertz generator, a drive circuit, and a matching circuit; the drive circuit is controlled and connected to the adaptive control module, and is used to output a corresponding drive signal in response to the control command; the terahertz generator is electrically connected to the drive circuit, and is used to generate terahertz waves under the action of the drive signal. The matching circuit is connected to the output of the terahertz generator to optimize the output impedance matching of the terahertz wave and improve energy transmission efficiency.

4. A terahertz energy output adaptive control method based on multi-sensor feedback, characterized in that, The method, applied to the terahertz energy output adaptive control system based on multi-sensor feedback as described in any one of claims 1 to 3, comprises the following steps: Step 1: Parameter acquisition. Multi-dimensional physical parameters related to terahertz energy output are acquired in real time through a multi-sensor module. Step two, signal processing: the collected multi-dimensional physical parameters are filtered, calibrated and fused sequentially to obtain standardized feedback parameters; Step 3, control decision-making: Calculate the deviation between the feedback parameters and the preset energy output target parameters, adaptively select the control strategy based on the deviation results, and generate control instructions. Step four: Energy regulation. The terahertz source module responds to the regulation command and adjusts its own working state to adjust the terahertz energy output, thereby achieving adaptive regulation of the terahertz energy output.

5. The terahertz energy output adaptive control method based on multi-sensor feedback according to claim 4, characterized in that, The fusion process described in step two is as follows: a weighted fusion algorithm is used to calculate the calibrated multi-dimensional parameters by weighting them according to the measurement accuracy weights of each sensor, so as to obtain the comprehensive feedback parameters; wherein, the measurement accuracy weights are determined by error analysis of the historical measurement data of each sensor.

6. The terahertz energy output adaptive control method based on multi-sensor feedback according to claim 4, characterized in that, The deviation calculation in step three specifically involves: calculating the absolute and relative deviations between the terahertz energy amplitude and the target energy amplitude in the feedback parameters, and simultaneously calculating the deviation between the frequency parameters and the target frequency parameters; when the relative deviation is less than a preset threshold, a proportional-integral-derivative control strategy is selected. When the relative deviation is greater than or equal to the preset threshold and less than twice the preset threshold, the model prediction control strategy is selected. When the relative deviation is greater than or equal to twice the preset threshold, the neural network adaptive control strategy is selected.

7. The terahertz energy output adaptive control method based on multi-sensor feedback according to claim 4, characterized in that, It also includes step five, feedback iteration. After the terahertz source module completes energy regulation, the multi-sensor module collects the relevant parameters of terahertz energy output again, and steps two to four are repeated until the deviation between the feedback parameters and the target parameters is less than the preset stability threshold, so as to achieve stable control of terahertz energy output.

8. The terahertz energy output adaptive control method based on multi-sensor feedback according to claim 4, characterized in that, The parameter acquisition described in step one adopts a synchronous acquisition method. The acquisition frequency of the multi-sensor module is dynamically adjusted according to the output frequency of the terahertz source module, and the acquisition frequency is not less than ten times the output frequency of the terahertz source module.

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