Signal processing method and system of aviation intelligent flow sensor

Abstract

The invention discloses a signal processing method and system of an aviation intelligent flow sensor. The method comprises the following steps: collecting real-time signal data and generating a flow characteristic data set; determining an initial measurement parameter based on a preset flow signal model; the real-time deviation between the initial parameter and the actual operation state is obtained, the initial parameter is subjected to self-adaptive optimization through a normalized weighted deviation correction formula, and an optimized measurement parameter is obtained; and performing dynamic intelligent processing on the sensor based on the optimized parameters, evaluating a measurement error variance and a precision fluctuation rate through working condition segmentation and a fuzzy control algorithm, and generating measurement stability information. According to the method, through non-dimensionalized deviation correction and dynamic working condition segmentation processing, the flow metering precision and the signal stability in the aviation complex environment are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of aviation sensor technology and signal processing, specifically relating to a signal processing method and system for an aviation intelligent flow sensor based on an adaptive optimization algorithm. Background Technology

[0002] With the rapid development of aviation technology, aircraft engines and fuel systems have placed higher demands on the accuracy and stability of flow measurement. As a core component for monitoring aircraft fuel consumption, hydraulic system status, and environmental control systems, the quality of the signal processing capabilities of intelligent aviation flow sensors directly affects flight safety and mission efficiency.

[0003] In real-world aviation operating environments, flow sensors face extremely complex operating challenges:

[0004] Complex environmental interference: factors such as high altitude and low pressure, severe vibration, wide temperature variation and fluid pulsation can easily cause nonlinear drift and high-frequency noise in the sensor output signal, making it difficult for traditional fixed parameter filtering algorithms to maintain high-precision measurement.

[0005] Multi-parameter coupling effects: Flow measurement is affected by multiple factors such as velocity distribution, pressure changes, and fluid density fluctuations. Existing processing methods often focus on the calibration of a single physical quantity and lack a collaborative optimization mechanism for multi-dimensional deviations, resulting in lag in response and increased error when the flow regime changes drastically.

[0006] Insufficient stability assessment: Most traditional systems only provide instantaneous flow rates, lacking in-depth assessment of the stability of the measurement process. When operating across different conditions (such as takeoff, cruise, and landing), it is difficult to identify the fluctuation relationship between signal power loss and conversion accuracy in real time, making it impossible to provide reliable measurement quality predictions for the flight control system.

[0007] Therefore, how to develop an intelligent signal processing system that can adapt to different aviation operating conditions, coordinate the correction of deviations of multiple physical quantities, and generate measurement stability information in real time has become a key technical problem that urgently needs to be solved in the field of aviation sensing technology. Summary of the Invention

[0008] The purpose of this invention is to overcome the technical shortcomings of existing aviation flow sensors, such as low signal processing accuracy under complex operating conditions, weak multi-parameter coupling correction capability, and lack of in-depth stability assessment, and to provide a signal processing method and system for an intelligent aviation flow sensor. This invention aims to solve the problem of coordinated optimization among parameters of different dimensions, such as flow velocity and density, by establishing a preset flow signal model and a real-time deviation feedback mechanism, and introducing a normalized weighted deviation correction formula, thereby achieving accurate compensation for nonlinear deviations across the entire measurement range. Simultaneously, this invention aims to improve the sensor's adaptive capability to transient changes in complex aviation environments by dynamically segmenting the operating conditions and combining them with fuzzy control algorithms. Furthermore, it generates quantitative stability indicators based on signal power loss variance and conversion accuracy fluctuation rate, ultimately achieving the invention's objective of significantly improving the measurement accuracy, environmental reliability, and signal quality prediction capability of the intelligent aviation flow sensor.

[0009] To achieve the above objectives, the present invention provides a signal processing method and system for an aviation intelligent flow sensor, the method comprising:

[0010] Collect real-time signal data from intelligent airflow sensors to generate corresponding airflow characteristic datasets;

[0011] Based on a preset flow signal model, the flow characteristic dataset is analyzed to determine the initial measurement parameters of the flow process;

[0012] The system obtains real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status, and adaptively optimizes the initial measurement parameters based on the deviation information to obtain optimized measurement parameters.

[0013] Based on the optimized measurement parameters, the aviation intelligent flow sensor is dynamically and intelligently processed to generate measurement stability information of the flow process.

[0014] Optionally, the step of collecting real-time signal data from the intelligent airflow sensor and generating a corresponding airflow feature dataset includes:

[0015] The flow rate, pressure parameters, and fluid density parameters of the airflow process are collected using airborne sensors.

[0016] The real-time signal data is preprocessed in the time and frequency domain and features are extracted to suppress noise interference and extract flow accuracy-related features.

[0017] Based on the extracted features, a flow feature dataset is constructed that includes flow accuracy, signal noise level, and the operating status of air traffic sensors.

[0018] Optionally, the step of analyzing the flow characteristic dataset based on a preset flow signal model to determine the initial measurement parameters of the flow process includes:

[0019] The flow characteristic dataset is subjected to signal calibration analysis using a preset flow signal model to identify the measurement response characteristics and signal noise distribution of the airflow process;

[0020] Based on the measurement response characteristics and signal-noise distribution, a multi-objective optimization algorithm is used to generate initial measurement parameters.

[0021] If the initial measurement parameters meet the preset flow performance constraints, they are used as the initial measurement parameters; otherwise, an alarm message indicating that the measurement parameter initialization failed is output.

[0022] Optionally, the step of obtaining real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status, and adaptively optimizing the initial measurement parameters based on the deviation information to obtain optimized measurement parameters includes:

[0023] Based on the distribution of air traffic flow conditions, a first monitoring point group and a second monitoring point group are set up with the initial measurement parameters as a reference. The first monitoring point group corresponds to the flow velocity distribution, and the second monitoring point group corresponds to the flow rate distribution.

[0024] Calculate the measurement deviation between the initial measurement parameters and the first and second monitoring point groups, and generate real-time deviation information;

[0025] Based on the real-time deviation information, the initial measurement parameters are adaptively optimized using the following formula. ;in, To optimize measurement parameters, These are the initial measurement parameters. Let be the flow velocity deviation at the i-th monitoring point. Let i be the flow deviation at the i-th monitoring point. and The weighting coefficients for flow velocity and flow rate are respectively, and satisfy the following conditions: , As a regulating factor, and These are the reference flow rate and reference flow rate, respectively, and N is the total number of monitoring points.

[0026] Iterative optimization continues until the measurement deviation meets the preset convergence threshold, thus obtaining the optimized measurement parameters.

[0027] Optionally, the step of adaptively optimizing the initial measurement parameters based on the real-time deviation information further includes:

[0028] Based on the flow velocity deviation of the first monitoring point group, the flow velocity-related component of the initial measurement parameter is dynamically adjusted until the flow velocity deviation is balanced.

[0029] Based on the flow deviation of the second monitoring point group, the flow-related component of the measurement parameters is further adjusted until the flow deviation meets the preset equilibrium condition, and the optimized measurement parameters are determined.

[0030] Optionally, the step of dynamically and intelligently processing the airborne intelligent flow sensor based on the optimized measurement parameters to generate measurement stability information of the flow process includes:

[0031] Based on the changes in flow accuracy and measurement error rate in the distribution of air traffic flow conditions, the operating conditions of the flow process are divided into multiple dynamic segments.

[0032] The system collects measurement fluctuation data of each dynamic segment in real time, and dynamically adjusts the processing parameters of the aviation intelligent flow sensor based on the differences in measurement fluctuation data between segments using a fuzzy control algorithm.

[0033] Based on the adjusted processing parameters, a measurement stability index is calculated based on the measurement error variance and the fluctuation rate of flow conversion accuracy, and measurement stability information is generated.

[0034] Optionally, the measurement fluctuation data includes the measurement error and flow conversion accuracy of the airborne intelligent flow sensor, and the step of generating measurement stability information for the flow process includes:

[0035] The measurement error and flow conversion accuracy of each dynamic segment are statistically analyzed, and the variance of the measurement error and the fluctuation rate of the flow conversion accuracy between each segment are calculated.

[0036] If both the variance and volatility are within the preset stability range, then the measurement stability information indicates that the flow process measurement is stable.

[0037] If the variance or volatility exceeds the preset stability range, the measurement stability information indicates that the flow process measurement is unstable, and a dynamic adjustment command is triggered.

[0038] The present invention also provides a signal processing system for an aviation intelligent flow sensor, the system comprising:

[0039] The data acquisition module is used to collect real-time signal data from the intelligent airflow sensor and generate the corresponding airflow characteristic dataset.

[0040] The signal analysis module is used to analyze the flow characteristic dataset based on a preset flow signal model to determine the initial measurement parameters of the flow process.

[0041] The signal optimization module is used to acquire real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status, and to adaptively optimize the initial measurement parameters based on the deviation information to obtain optimized measurement parameters.

[0042] The intelligent processing module is used to perform dynamic intelligent processing on the aviation intelligent flow sensor based on the optimized measurement parameters, and generate measurement stability information of the flow process.

[0043] Optionally, the signal optimization module includes:

[0044] The deviation analysis unit is used to set up a first monitoring point group and a second monitoring point group based on the air traffic flow condition distribution, corresponding to the velocity distribution and the flow rate distribution respectively, and to calculate the measurement deviation between the initial measurement parameters and each monitoring point group.

[0045] The optimization calculation unit is used to adaptively optimize the initial measurement parameters based on the nonlinear optimization model and the normalized weight deviation formula to obtain optimized measurement parameters.

[0046] Optionally, the intelligent processing module includes:

[0047] The segmentation unit is used to divide the operating conditions of the flow process into multiple dynamic segments based on the changes in flow accuracy and the rate of change in measurement error in the distribution of air flow conditions.

[0048] The stability assessment unit is used to collect measurement fluctuation data of each dynamic segment, dynamically adjust the processing parameters of the aviation intelligent flow sensor based on the fuzzy control algorithm, and generate measurement stability information based on the measurement error variance and the fluctuation rate of flow conversion accuracy.

[0049] This invention provides a signal processing method and system for an intelligent airflow sensor, which offers significant advantages over existing technologies. First, it achieves multi-dimensional collaborative improvement in measurement accuracy. By introducing a normalized weighted deviation correction formula, it effectively solves the problem of inconsistent dimensions and difficulty in direct fusion of multiple physical quantities such as flow velocity distribution and flow density. Utilizing reference values ​​for dimensionless processing and adaptive adjustment of weighting coefficients, it can accurately correct nonlinear deviations under different airflow conditions. Second, it enhances environmental adaptability under dynamic conditions. Employing a dynamic segmentation mechanism based on flow accuracy gradient and signal loss rate of change, combined with a fuzzy control algorithm to dynamically adjust processing parameters, the sensor can adapt in real time to transient changes under different pressure and flow velocity environments during takeoff, cruise, and landing. This overcomes the shortcomings of traditional fixed-parameter models in terms of response hysteresis. Furthermore, it provides deep-level signal stability assurance by generating measurement stability information through calculation of signal power loss variance and flow conversion accuracy fluctuation rate, providing a reliable signal quality monitoring method for aviation flight control systems. This enables real-time anomaly identification and triggering dynamic adjustments. Finally, it constructs a systematic closed-loop optimization mechanism, forming a complete closed loop from real-time data acquisition and initial parameter establishment to adaptive feedback optimization. This not only effectively suppresses high-frequency noise and electromagnetic interference in aviation but also reduces power consumption loss through multi-objective optimization algorithms, extending the sensor's lifespan in harsh environments. Attached Figure Description

[0050] Figure 1 Flowchart of the signal processing method for the intelligent airflow sensor provided by the present invention;

[0051] Figure 2 A flowchart of a method for adaptively optimizing initial measurement parameters based on real-time deviation information provided by the present invention;

[0052] Figure 3 This is a schematic diagram of the signal processing system structure of the aviation intelligent flow sensor provided by the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of this application, unless otherwise stated, "multiple" means two or more.

[0055] To more clearly illustrate the technical solution of the present invention, the present invention will be described in detail below with reference to specific embodiments, but it should not be construed as a limitation on the scope of protection of the present invention.

[0056] This embodiment effectively solves the problem of decreased accuracy in air traffic flow measurement under extreme conditions such as wide temperature variations and high vibration by introducing an adaptive optimization mechanism and multi-dimensional deviation correction logic. Figure 1 As shown, by dynamically aligning the initial model prediction with the real-time operating status, the system can quickly eliminate systematic errors caused by fluctuations in fluid physical properties. This ensures high measurement accuracy while outputting a stability evaluation reflecting the health of the signal, greatly enhancing the reliability of the sensor throughout the entire flight mission cycle.

[0057] Specifically, real-time signal data from intelligent airflow sensors are collected to generate corresponding airflow characteristic datasets;

[0058] In actual operation, sensors installed in aviation fuel or hydraulic lines continuously capture the electromagnetic or ultrasonic pulses generated as fluid passes through, converting them into raw voltage signals reflecting changes in physical quantities. These signals are then sent to a signal conditioning unit for precise analog-to-digital conversion. To eliminate background noise in the aviation environment, the system digitally filters the discrete signals and simultaneously correlates them with pressure, temperature, and density compensation parameters. By performing time alignment and normalization on this multi-dimensional information, the system constructs a dataset that comprehensively characterizes the fluid dynamics, laying the foundation for subsequent intelligent analysis.

[0059] Furthermore, based on a preset flow signal model, the flow characteristic dataset is analyzed to determine the initial measurement parameters of the flow process;

[0060] Specifically, the system's pre-built physical model integrates prior knowledge of the sensor under standard flow fields. By inputting the real-time generated flow characteristic dataset into this model, the theoretical response value under the current operating conditions can be quickly mapped. This process involves a deep analysis of signal transmission efficiency. By analyzing the energy attenuation distribution of the signal in complex pipeline structures, the evolution trend of the current flow state is identified. Using a multi-objective search strategy, the system calculates a set of initial values ​​that can represent the current measurement benchmark, while satisfying preset flow performance constraints. These values ​​serve as the logical starting point for subsequent fine-tuning calibration, defining the initial scale for flow conversion.

[0061] Furthermore, real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status is obtained, and the initial measurement parameters are adaptively optimized based on the deviation information to obtain optimized measurement parameters;

[0062] To achieve a higher level of accuracy, the system compares the initial model predictions with the actual physical feedback distributed across various monitoring points. This comparison reveals subtle discrepancies between the model and the actual flow field, generating real-time deviation information including velocity gradient and flow density deviations. In handling these deviations, the system employs a weighted correction strategy, treating the differences in velocity and flow rate across different dimensions as dimensionless using reference values. This allows for coordinated correction of various deviations within a unified mathematical framework. Through the dynamic intervention of adjustment factors, the system continuously fine-tunes the initial parameters until the fluctuations caused by these deviations converge to an extremely small threshold range. The optimized measurement parameters thus locked in approximate the actual airflow conditions to a very high degree.

[0063] Furthermore, based on the optimized measurement parameters, the aviation intelligent flow sensor is dynamically and intelligently processed to generate measurement stability information of the flow process.

[0064] After acquiring optimized parameters, the sensor's processing logic enters a dynamic adjustment mode. Based on current changes in flow accuracy and error evolution trends, the flight conditions are automatically divided into different evaluation intervals. Within each segment, the system statistically analyzes the fluctuation characteristics of the measured values ​​in real time and uses fuzzy logic to fine-tune the processing parameters to cope with instantaneous environmental shocks. By analyzing the variance of the measurement error and the smoothness of the flow conversion efficiency, the system ultimately calculates a stability index reflecting signal quality. This information not only serves as a confidence reference for the flow values ​​but also proactively triggers the system's underlying self-adjustment mechanism when stability degradation is detected, ensuring the entire flow monitoring process operates with extremely high consistency.

[0065] This embodiment significantly improves the measurement accuracy and operational reliability of sensors under complex aviation conditions by constructing a complete closed loop from data acquisition and model initialization to adaptive optimization and stability assessment. The method utilizes a pre-set flow signal model to provide a scientific initial logical starting point for the measurement process. Combined with an adaptive optimization mechanism based on real-time deviation information, it can dynamically eliminate signal offsets caused by environmental interference, ensuring a high degree of consistency between measurement parameters and the actual physical flow field. Furthermore, by applying optimized parameters to dynamic intelligent processing and generating stability information, it overcomes the limitation of traditional sensors that can only output a single value, achieving in-depth quantitative monitoring of measurement quality. This provides high-confidence decision support for the flight control system and comprehensively enhances the robustness and safety of the aviation flow monitoring system in transient and variable environments.

[0066] In some embodiments, by performing in-depth time-frequency domain feature mining and noise suppression on the raw sensing data, the high purity and strong correlation of the flow feature dataset at the source are ensured. This multi-parameter, multi-feature construction method enables the system to accurately extract key information reflecting the essence of fluid physics from the complex aerospace electromagnetic environment and mechanical vibration, providing solid data support for subsequent signal calibration and parameter optimization.

[0067] Specifically, flow velocity signals, pressure parameters, and fluid density parameters of the airflow process are collected using airborne sensors;

[0068] Understandably, in the complex environment of aviation piping, a distributed array of sensors can perceive the fluid's operating status in multiple dimensions. Flow velocity sensors capture the pulses or frequency shifts generated by fluid flow in real time, converting them into raw signals that reflect the instantaneous flow rate. Simultaneously, pressure sensing units continuously monitor changes in static and dynamic pressure within the piping, while density measurement components acquire the fluid's density characteristics through changes in sound velocity or vibration frequency. These parameters, as the most basic physical inputs, are synchronously transmitted to the signal processing terminal, ensuring that the measurement system can perceive fluid property fluctuations under different flight altitudes and dynamic loads in real time.

[0069] Furthermore, the real-time signal data undergoes time-frequency domain preprocessing and feature extraction to suppress noise interference and extract flow accuracy-related features;

[0070] Understandably, due to the severe electromagnetic interference and structural vibrations in the aviation environment, the acquired raw signals are often mixed with a large amount of noise. The system first digitally samples the signal and smooths signal fluctuations in the time domain using moving average or adaptive filtering techniques to eliminate random spikes. Subsequently, spectral analysis is used to convert the signal to the frequency domain, accurately identifying and filtering out noise components that overlap with mechanical vibration frequencies. Based on this, the system uses feature extraction algorithms to extract key indicators such as energy distribution, zero-crossing rate, and crest coefficient from the purified signal. These indicators are directly related to the conversion accuracy of the flow rate and can significantly enhance the system's ability to distinguish between the effective signal and the environmental background.

[0071] Specifically, based on the extracted features, a flow feature dataset is constructed that includes flow accuracy, signal noise level, and the operating status of air traffic sensors.

[0072] By logically associating and encapsulating the extracted multidimensional features, the system generates a high-dimensional feature vector. This process not only integrates core data reflecting flow accuracy but also quantifies the current noise level by evaluating the signal-to-noise ratio in real time. Simultaneously, the system incorporates the electrical performance and physical aging status of the sensors by analyzing signal attenuation and response hysteresis coefficients. The resulting flow feature dataset is a composite data package integrating flow field physical properties, signal environment quality, and equipment health, providing a comprehensive and structured input reference for subsequent model-based signal analysis and intelligent parameter adjustment.

[0073] In some embodiments, by introducing a preset flow signal model to perform in-depth computation on the feature data, a complete logical transformation from signal transmission efficiency to parameter optimization generation is achieved. This model-driven parameter determination method can automatically identify response patterns in complex airflow fields, eliminate measurement blind spots caused by sensor installation location or abrupt changes in flow regime, and ensure that the system has a parameter benchmark that highly matches the current physical conditions before entering the adaptive optimization stage, thereby significantly improving the efficiency of subsequent signal correction.

[0074] Specifically, a preset flow signal model is used to perform signal calibration analysis on the flow characteristic dataset to identify the measurement response characteristics and signal noise distribution of the air traffic process;

[0075] After acquiring the flow characteristic dataset, the processing terminal loads it into a pre-defined flow signal model. This model uses pre-stored fluid dynamics mapping relationships to perform comprehensive signal balance calculations on the input data. The system focuses on analyzing the energy distribution of the signal on the sensor's sensitive elements, identifying the dynamic response curves between flow velocity pulsations, pressure fluctuations, and sensor electrical outputs, thereby characterizing the precise measurement response characteristics under the current operating conditions. During this process, the model automatically separates the signal components generated by flow field disturbances from the background noise. By analyzing the energy density of the noise within the frequency range, it accurately identifies the distribution characteristics of the signal noise, providing a "signal-to-noise ratio" dimension for decision-making reference in the fine-tuning of parameters.

[0076] Based on the measurement response characteristics and signal-noise distribution, a multi-objective optimization algorithm is used to generate initial measurement parameters.

[0077] Furthermore, based on the identified response characteristics and noise environment, the system initiates multi-objective optimization calculations. This process does not seek a single optimal solution, but rather seeks the best balance between measurement accuracy, dynamic response time, and signal anti-interference capability. The algorithm automatically iterates and searches, calculating calibration coefficients that enable the sensor to maintain measurement response sensitivity while greatly suppressing the influence of noise, tailored to the current complex flow characteristics. These coefficients are weighted and fused into a set of globally representative technical indicators, serving as the initial measurement parameters for the flow conversion logic, thus enabling the measurement system to achieve a high degree of coupling with the actual operating conditions from the startup phase.

[0078] If the initial measurement parameters meet the preset flow performance constraints, they are used as the initial measurement parameters; otherwise, an alarm message indicating that the measurement parameter initialization failed is output.

[0079] Understandably, to ensure system safety and reliability, the generated initial measurement parameters must undergo rigorous compliance verification. The system incorporates these parameters into a pre-defined air traffic flow performance constraint framework to verify whether they fall within a reasonable range of the sensor's physical measurement range and whether they meet the measurement accuracy thresholds required by civil or military aviation standards. If the parameters pass this verification process, the system officially recognizes them as the initial parameters for the current mission cycle and sends them to the computational core. If parameters are detected to exceed safety boundaries or the model calculation fails to converge, the system immediately terminates the current process and outputs an alarm message indicating parameter initialization failure to the cockpit or ground monitoring system, effectively preventing erroneous traffic flow data output due to model failure.

[0080] Through this process, the present invention ensures that every step of flow measurement is based on scientific model analysis and rigorous logical verification. You can continue to generate embodiments of other claims as needed, or let me optimize the overall content flow for you.

[0081] In some embodiments, by introducing normalized deviation correction logic and a multi-monitoring point weighted algorithm, the initial measurement parameters are made to approximate the optimal values ​​of the actual operating conditions. By converting the absolute deviations of flow velocity and flow rate into dimensionless relative deviations, this invention effectively eliminates the interference of differences in the dimensions of different physical quantities on the optimization process, ensuring that the measurement parameters can achieve highly stable iterative convergence under extreme aerospace conditions such as high-speed flow fields and variable density environments, thereby obtaining optimization results that reflect the true flow state.

[0082] Specifically, based on the distribution of air traffic flow conditions, a first monitoring point group and a second monitoring point group are set up with the initial measurement parameters as a reference. The first monitoring point group corresponds to the flow velocity distribution, and the second monitoring point group corresponds to the flow rate distribution.

[0083] Understandably, after the system enters the optimization phase, the processing core will retrieve the corresponding operating condition distribution map based on the current flight phase (such as takeoff climb, high-altitude cruise, or landing deceleration). Using the initially determined measurement parameters as a reference, the system delineates two sets of monitoring points in the key flow characteristic areas within the sensor pipeline: the first set of monitoring points is concentrated in the central flow region and near the wall region where the velocity gradient changes drastically, used to capture minute disturbances in the velocity distribution in real time; the second set of monitoring points is distributed in the core cross-section reflecting the overall fluid properties, focusing on monitoring the correlation between fluid density and mass flow rate. The spatial layout and number N of these monitoring points are pre-set according to the pipe diameter and sensor accuracy requirements, ensuring that the deviation extraction can cover the geometric and physical characteristics of the flow field.

[0084] Further, the measurement deviation between the initial measurement parameters and the first and second monitoring point groups is calculated to generate real-time deviation information;

[0085] Specifically, the system acquires measured physical data from each monitoring point via a high-speed acquisition bus and compares it point-by-point with the theoretical expected values ​​under the initial measurement parameters. For the first group of monitoring points, the system calculates the flow velocity deviation at each point through subtraction. For the second monitoring point group, the corresponding flow deviation is calculated simultaneously. These deviation data reflect the degree of inaccuracy of the initial model in the current actual airflow field. To eliminate the problem of incompatibility caused by the different dimensions of various physical quantities, the system synchronously retrieves a preset reference flow velocity from non-volatile memory. and reference flow (This reference value is determined based on the sensor's factory calibration value) to prepare the necessary benchmark operator for subsequent normalization processing.

[0086] Furthermore, based on the real-time deviation information, the initial measurement parameters are adaptively optimized using the following formula:

[0087]

[0088] Understandably, this formula is the core mathematical logic for achieving adaptive parameter correction in this embodiment. The specific calculation steps are as follows: First, the system performs dimensionless processing, converting the flow velocity deviation at each monitoring point... Divide by reference flow rate and the flow deviation Divide by reference flow This transforms the relative deviation into a purely numerical form; secondly, the system introduces weighting factors for fusion, utilizing weighting coefficients. and (satisfy The two relative deviations are weighted, with the weighting determined by the control strategy for the current flight phase. Then, spatial averaging is performed, summing the weighted relative deviations of N monitoring points and dividing by N to smooth out single-point measurement noise. Finally, the resulting average relative deviation is multiplied by an adjustment factor. (Used to control the sensitivity of the correction step size), and this increment is compensated to the initial measurement parameters. This allows for the acquisition of optimized measurement parameters. .

[0089] Furthermore, the optimization is iterated until the measurement deviation meets the preset convergence threshold, thus obtaining the optimized measurement parameters.

[0090] The optimization process is carried out in a cyclical iterative manner. In each iteration, a new formula is derived using the above formula. Afterwards, the system will re-input the data into the flow field analysis model and calculate the residuals between the updated parameters and the measured values ​​of the monitoring point group. The algorithm will automatically determine whether the residuals have fallen below a preset convergence threshold. If the threshold is not met, the system will revert to the current... As the next iteration The above calculation process is repeated. When the convergence condition is met, the system considers that the current parameters have largely eliminated the difference between the model and the actual working conditions, and the locked final value is the optimized measurement parameter, which serves as a high-precision benchmark for the subsequent intelligent processing module.

[0091] In some embodiments, such as Figure 2 As shown, by decoupling and adjusting the velocity and flow components step by step, a refined reconstruction of the measurement parameters is achieved. This phased optimization logic effectively eliminates coupling interference between different physical properties, ensuring that the velocity gradient and density distribution reach an ideal equilibrium state during airborne operations with intense flow conditions, thereby significantly improving the static accuracy and dynamic response consistency of the measurement results.

[0092] Specifically, based on the flow velocity deviation of the first monitoring point group, the flow velocity-related component of the initial measurement parameter is dynamically adjusted until the flow velocity deviation is balanced;

[0093] After acquiring real-time deviation information, the system first initiates a dedicated calibration of the velocity component. The processing unit focuses on extracting the velocity gradient data acquired from the first set of monitoring points and analyzing the degree of deviation of each point within the flow channel relative to the initial measurement parameters. Using an adaptive control algorithm, the system performs targeted fine-tuning of the weighting operators or correction coefficients in the initial measurement parameters that characterize the velocity distribution of the flow field. This adjustment is a dynamic iterative process; the system monitors the velocity residuals at each monitoring point in real time after adjustment, aiming to flatten and unify the velocity distribution error across the flow channel cross-section. Through this local balancing strategy, the system can effectively compensate for velocity distribution distortions caused by pipeline vibration or changes in fluid viscosity, laying a precise velocity benchmark for the subsequent determination of overall parameters.

[0094] Furthermore, based on the flow deviation of the second monitoring point group, the flow-related component of the measurement parameters is further adjusted until the flow deviation meets the preset equilibrium condition, and the optimized measurement parameters are determined.

[0095] Specifically, after the velocity component reaches equilibrium, the system enters a secondary optimization stage for flow characteristics. At this point, the processing unit, combining the flow deviation feedback from the second monitoring point group, focuses on deeply correcting the components of the measurement parameters related to mass flow conversion and fluid density compensation. Since the velocity component has already been initially aligned, this step can focus more on the impact of fluid compressibility, density non-uniformity, and other physical properties on the measurement results. The system continuously iterates the flow-related components in small steps according to the constraints of mass and momentum conservation until the overall flow deviation at each sampling point falls within the preset equilibrium range. Through this decoupled adjustment method—from local to global, velocity first, then flow rate—the optimized measurement parameters ultimately locked by the system possess extremely high robustness, ensuring that the sensor maintains stable measurement output even under complex aerospace operating conditions.

[0096] In some embodiments, by introducing a dynamic segmentation mechanism and a fuzzy control algorithm, the flow measurement process is transformed from "static parameter calculation" to "dynamic intelligent sensing." By finely dividing the operating environment and combining it with fuzzy logic to process the real-time evolution of parameters, this invention can effectively cope with instantaneous measurement fluctuations caused by sudden pressure changes and flow regime transitions during flight, significantly improving the smoothness and stability of the signal under complex and variable operating conditions, and providing a high-confidence decision-making basis for the back-end flight control system.

[0097] Specifically, based on the changes in flow accuracy and the rate of change in measurement error in the distribution of air traffic flow conditions, the operating conditions of the flow process are divided into multiple dynamic segments.

[0098] In actual flight, the sensor's operating environment is not static. The system monitors the fluctuation characteristics of flow accuracy and the trend of measurement error over time (i.e., the error rate of change) in real time, and combines this with a preset operating condition map to analyze the entire measurement task into several dynamic segments with distinct characteristics. For example, during the high-thrust phase of engine takeoff, due to the extremely high flow velocity and large pressure gradient, the system identifies this as a high-dynamic segment; while during the stable cruise phase at high altitude, the error rate of change tends to level off, and the system defines this as a steady-state segment. This segmentation strategy allows subsequent processing logic to adopt more targeted parameter control schemes based on the physical characteristics of the flow field at different stages.

[0099] Furthermore, the measurement fluctuation data of each dynamic segment is collected in real time, and the processing parameters of the aviation intelligent flow sensor are dynamically adjusted based on the differences in measurement fluctuation data between segments using a fuzzy control algorithm.

[0100] Specifically, the system continuously captures the fluctuation patterns of the measured signal within each dynamic segment. These measured fluctuation data reflect the intensity of environmental disturbances affecting the signal under the current operating conditions. The system compares the fluctuation differences between adjacent segments or different operating points and uses these differences as inputs to the fuzzy controller. The fuzzy control algorithm calculates the correction increment of the processing parameters through its internal membership function and inference rules (such as "if the fluctuation rate is large and the error grows rapidly, then the filtering depth is significantly increased"). This adjustment does not rely on a precise mathematical analytical model, but rather utilizes fuzzy logic simulation expert experience to smoothly switch the sensor's sampling frequency, filtering time constant, or compensation coefficient, effectively avoiding signal jumps that may occur when parameters are adjusted across segments.

[0101] Furthermore, based on the adjusted processing parameters, a measurement stability index is calculated based on the measurement error variance and the fluctuation rate of flow conversion accuracy, thereby generating measurement stability information.

[0102] Understandably, after the processing parameters are dynamically optimized, the system performs a closed-loop evaluation of the output signal quality. By statistically optimizing the measurement sequence, the system calculates the dispersion (variance) of the measurement error in the time domain and the frequency of fluctuations in the flow conversion accuracy per unit time. These two indicators together constitute a multi-dimensional measurement stability evaluation system. The system maps the calculation results to quantified stability levels, ultimately generating measurement stability information. This information not only provides real-time feedback on the reliability of the current sensor output values ​​but also serves as an important reference for subsequent operating condition identification and system self-diagnosis, ensuring that the flow monitoring system always operates within the optimal linear response range.

[0103] In some embodiments, by performing refined statistical analysis on the signal waveform characteristics within each dynamic segment, the measurement stability is transformed from "qualitative perception" to "quantitative assessment." By establishing a dual judgment criterion based on variance and volatility, the system can capture minute performance degradation or transient disturbances during air traffic flow measurement in real time, and trigger underlying adaptive adjustments using a closed-loop command mechanism, thereby ensuring high confidence and continuity of traffic flow data throughout the entire flight cycle.

[0104] Specifically, the measurement error and flow conversion accuracy of each dynamic segment are statistically analyzed, and the variance of the measurement error and the fluctuation rate of the flow conversion accuracy between each segment are calculated.

[0105] Understandably, during each preset dynamic segment operation, the system's internal statistical unit scans the processed data stream in real time. For each monitoring interval, the system calculates the dispersion between the measured flow rate and the ideal reference value, thus determining the measurement error distribution within that segment. Simultaneously, the system analyzes the conversion efficiency of the sensor's electrical signal to the physical flow rate value, calculating the flow conversion accuracy for that stage. To evaluate the stability during the switching process between different operating conditions, the system further calculates the variance of the measurement error within each segment. This is used to characterize the discrete stability of the signal; and to calculate the fluctuation rate of the flow conversion accuracy, that is, the jump amplitude of the accuracy value per unit time, to characterize the dynamic response quality of the signal.

[0106] Furthermore, if both the variance and volatility are within a preset stability range, then the measurement stability information indicates that the flow process measurement is stable.

[0107] Specifically, the system sends the calculated variance and volatility to the logic judgment module in real time and compares them with stability thresholds pre-stored in aerospace-grade memory. When the detection results show that the error variance is extremely small and the accuracy volatility is at an extremely low level, it indicates that the current fluid flow is stable and the sensor's processing parameters are highly matched with the current aviation environment. In this state, the system marks the generated measurement stability information as "measurement stable". This status information is transmitted to the flight management computer along with the flow data, assigning the current flow value the highest confidence weight to ensure that the flight control system performs fuel consumption calculations or load allocation based on the most reliable data.

[0108] Furthermore, if the variance or volatility exceeds a preset stability range, the measurement stability information indicates that the flow process measurement is unstable, and a dynamic adjustment command is triggered.

[0109] Once an increase in variance or an abnormal jump in volatility is detected, the system will keenly detect this trend of signal quality deterioration, even if the current average error is still within the allowable range. The system will immediately update the measurement stability information to "measurement unstable" and generate a corresponding anomaly characteristic code. At this time, the closed-loop control unit inside the system will synchronously trigger a dynamic adjustment command, forcibly executing a new round of parameter adaptive optimization, or adjusting the intervention intensity of the fuzzy controller. Through this early warning-based dynamic feedback, the present invention can effectively suppress measurement risks caused by external electromagnetic pulse interference, cavitation in aviation pipelines, or sudden changes in fluid viscosity, ensuring that the system always maintains a controlled and robust operating state.

[0110] This embodiment provides a signal processing system 100 for an aviation intelligent flow sensor, such as... Figure 3 As shown, by constructing an integrated hardware and software functional architecture, the entire processing logic of the aviation intelligent flow sensor, from physical perception to intelligent decision-making, is realized. This system 100 utilizes a modular design to deeply couple data acquisition, deviation optimization, and stability assessment, ensuring efficient collaboration among functional units under complex flight conditions. This significantly improves the response speed and measurement accuracy of the flow monitoring system 100, providing a robust and accurate signal processing foundation for aircraft.

[0111] Specifically, the data acquisition module 101 is used to acquire real-time signal data from the intelligent airflow sensor and generate a corresponding airflow characteristic dataset.

[0112] Understandably, at the physical front end of system 100, data acquisition module 101 is connected to sensing elements deployed in the pipeline via a high-speed signal conditioning circuit. This module utilizes a high-sampling-rate analog-to-digital converter to capture raw physical signals such as fluid flow velocity, system 100 pressure, and fluid temperature. The acquired data undergoes preliminary digital denoising processing within module 101 and is encapsulated according to a unified time reference. Furthermore, module 101 performs feature alignment on different physical quantities to generate a structured flow feature dataset containing instantaneous amplitude, frequency distribution, and environmental factors, providing a standardized, high signal-to-noise ratio input data source for backend processing.

[0113] Furthermore, the signal analysis module 102 is used to analyze the flow characteristic dataset based on a preset flow signal model to determine the initial measurement parameters of the flow process;

[0114] Specifically, the signal analysis module 102, as the core of the logic preprocessing of system 100, internally contains a flow physics model for specific aviation conditions. Upon receiving the flow characteristic dataset, module 102 invokes the preset model to perform flow field simulation and calculates the theoretical measurement characteristics under the current conditions. Through in-depth analysis of signal response sensitivity and energy attenuation characteristics, module 102 can identify the basic dynamic parameters in the fluid flow process. Based on this, using a multi-objective search mechanism, module 102, while satisfying aviation performance constraints, determines a set of initial measurement parameters that can represent the current measurement benchmark, serving as the benchmark input for subsequent adaptive optimization.

[0115] Furthermore, the signal optimization module 103 is used to acquire real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status, and to adaptively optimize the initial measurement parameters based on the deviation information to obtain optimized measurement parameters;

[0116] Understandably, the signal optimization module 103 is the core unit for improving the system's accuracy, responsible for aligning the theoretical model with the actual flow field in a closed loop. This module 103 uses deviation analysis logic to compare the initial parameters with the measured values ​​fed back from distributed monitoring points, calculating a multi-dimensional deviation vector reflecting uneven velocity distribution and flow density fluctuations. Subsequently, module 103 calls its built-in normalized weighted optimization algorithm to perform step-by-step corrections on the initial measurement parameters. By dynamically adjusting the correction weights and step sizes, module 103 can guide the parameters to converge rapidly towards the true physical state, ultimately outputting highly robust optimized measurement parameters, effectively eliminating system accuracy errors caused by sudden environmental changes.

[0117] The intelligent processing module 104 is used to perform dynamic intelligent processing on the aviation intelligent flow sensor based on the optimized measurement parameters to generate measurement stability information of the flow process.

[0118] The intelligent processing module 104, located at the top layer of the system 100 architecture, is responsible for performing the final dynamic control and quality assessment. This module 104 utilizes optimized measurement parameters to reconstruct the sensor's output characteristics in real time and triggers fuzzy control logic based on changes in operating conditions to dynamically adjust the filtering intensity and compensation coefficients. Simultaneously with data processing, module 104 evaluates the smoothness of the current measurement process through statistical analysis of key indicators such as signal dispersion and conversion accuracy. Finally, module 104 generates quantified measurement stability information, which serves as a reliability label for the sensor output, ensuring that the air traffic management system 100 can monitor the quality status of the flow monitoring data in real time.

[0119] In some embodiments, through the collaborative work of the internal units of the signal optimization module, precise control from deviation quantization to parameter reconstruction is achieved. By decomposing the complex differences in the airflow field into physical deviations in two core dimensions—flow velocity and flow rate—and using a nonlinear optimization model for normalization solution, this system 100 can ensure that even when the sensor faces non-ideal operating conditions such as cavitation and pressure pulsation, it can still quickly lock the optimal parameter solution, thereby guaranteeing dynamic high accuracy of flow measurement at the system 100 level.

[0120] Specifically, the deviation analysis unit is used to set up a first monitoring point group and a second monitoring point group based on the air traffic flow condition distribution, corresponding to the velocity distribution and the flow rate distribution respectively, and to calculate the measurement deviation between the initial measurement parameters and each monitoring point group;

[0121] After the signal optimization module receives the initial measurement parameters, the deviation analysis unit first initiates the spatial mapping logic. Based on the operating condition information fed back by the current flight control system 100 (such as fuel pump speed and pipeline static pressure), this unit constructs the corresponding flow field distribution topology within the algorithm. Subsequently, the unit activates the first monitoring point group at key locations in the topology to obtain the velocity gradient deviation, and activates the second monitoring point group at the core flow cross-section to obtain the flow density deviation. By comparing the real-time data sequence fed back from the monitoring points with the theoretical sequence generated by the initial parameters, the unit calculates the instantaneous offset of velocity and flow rate within each sampling period. This multi-point, multi-dimensional deviation extraction method provides highly targeted error feedback characteristics for subsequent optimization calculations.

[0122] The optimization calculation unit is used to adaptively optimize the initial measurement parameters based on the nonlinear optimization model and the normalized weight deviation formula to obtain optimized measurement parameters.

[0123] Specifically, the optimization computing unit is the core of the signal optimization module's computing power, and its operation is based on a nonlinear optimization mathematical model. This unit receives flow velocity deviations. and flow deviation After that, instead of performing an arithmetic summation directly, it calls the reference base in memory. and Perform normalization. This is done by executing the formula:

[0124]

[0125] Furthermore, the unit maps deviations of different dimensions to a dimensionless weight space. During this process, the unit dynamically adjusts the regulation factor based on the severity of the real-time operating conditions. The value of is chosen to balance the convergence speed and stability of parameter adjustment. Through multiple rounds of nonlinear iteration, the unit continuously corrects the error components in the initial measurement parameters until the magnitude of the comprehensive deviation vector is minimized. The final optimized measurement parameters have a deep fitting capability to the current physical flow field, thereby significantly reducing the system measurement error of the sensor.

[0126] In some embodiments, through deep collaboration among the functional units within the intelligent processing module, a complete logical closed loop for air traffic monitoring, from "segmented perception" to "stability closed-loop assessment," is achieved. By refining the flight envelope through the segmentation unit and employing fuzzy logic decision-making by the stability assessment unit, the system 100 can effectively suppress signal jitter caused by aero-engine vibration, fluid pulsation, or sudden changes in the electromagnetic environment, ensuring that the output flow stability information accurately reflects the sensor's measurement confidence level under any complex operating conditions.

[0127] Specifically, the segmentation unit is used to divide the operating conditions of the flow process into multiple dynamic segments based on the changes in flow accuracy and the rate of change in measurement error in the distribution of air flow operating conditions.

[0128] Furthermore, the segmentation unit, serving as the logical starting point for intelligent processing, monitors the dynamic characteristics of the sensor output stream in real time. This unit continuously tracks the real-time gradient of flow accuracy and the rate of change of measurement error over time, identifying non-stationary characteristics of the signal waveform. When the system 100 detects that the rate of change of error exceeds a preset threshold (e.g., during aircraft takeoff or afterburner), the segmentation unit automatically marks the current period as a specific dynamic response segment; while during stable cruise, it is segmented as a steady-state maintenance segment. This segmentation method based on error evolution trends allows the system 100 to break away from the fixed "one-size-fits-all" processing mode, reserving differentiated parameter configuration space for each specific operating condition.

[0129] Understandably, the stability assessment unit is used to collect measurement fluctuation data of each dynamic segment, dynamically adjust the processing parameters of the aviation intelligent flow sensor based on the fuzzy control algorithm, and generate measurement stability information based on the measurement error variance and the fluctuation rate of flow conversion accuracy.

[0130] Specifically, the stability assessment unit is the intelligent decision-making center of the entire system. This unit performs high-frequency sampling of the original fluctuation characteristics within each dynamic segment to acquire real-time data reflecting the degree of signal dispersion. Subsequently, the unit uses these fluctuation characteristics as inputs to the built-in fuzzy controller. The fuzzy control algorithm, based on preset "fuzzy sets" and "inference rules," assesses whether the current signal is severely disturbed and outputs optimal processing parameter correction values, such as adaptively adjusting the filter bandwidth or gain coefficient. Based on this, the unit performs in-depth statistical calculations, analyzing the variance of the measurement error. In addition to the volatility of traffic conversion accuracy, a quantitative stability rating is generated. The final stability information serves as a core appendix to the output data, directly guiding subsequent flight management computer decisions regarding the acceptance of traffic data.

[0131] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A signal processing method for an aviation intelligent flow sensor, characterized in that, The method includes the following steps: Collect real-time signal data from intelligent airflow sensors to generate corresponding airflow characteristic datasets; Based on a preset flow signal model, the flow characteristic dataset is analyzed to determine the initial measurement parameters of the flow process; The system obtains real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status, and adaptively optimizes the initial measurement parameters based on the deviation information to obtain optimized measurement parameters. Based on the optimized measurement parameters, the aviation intelligent flow sensor is dynamically and intelligently processed to generate measurement stability information of the flow process.

2. The signal processing method for an aviation intelligent flow sensor as described in claim 1, characterized in that, The steps of collecting real-time signal data from the intelligent airflow sensor and generating the corresponding airflow feature dataset include: The flow rate, pressure parameters, and fluid density parameters of the airflow process are collected using airborne sensors. The real-time signal data is preprocessed in the time and frequency domain and features are extracted to suppress noise interference and extract flow accuracy-related features. Based on the extracted features, a flow feature dataset is constructed that includes flow accuracy, signal noise level, and the operating status of air traffic sensors.

3. The signal processing method for an aviation intelligent flow sensor as described in claim 1, characterized in that, The step of analyzing the flow characteristic dataset based on a preset flow signal model to determine the initial measurement parameters of the flow process includes: The flow characteristic dataset is subjected to signal calibration analysis using a preset flow signal model to identify the measurement response characteristics and signal noise distribution of the airflow process; Based on the measurement response characteristics and signal-noise distribution, a multi-objective optimization algorithm is used to generate initial measurement parameters. If the initial measurement parameters meet the preset flow performance constraints, they are used as the initial measurement parameters; otherwise, an alarm message indicating that the measurement parameter initialization failed is output.

4. The signal processing method for an aviation intelligent flow sensor as described in claim 1, characterized in that, The step of obtaining real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status, and adaptively optimizing the initial measurement parameters based on the deviation information to obtain optimized measurement parameters includes: Based on the air traffic flow condition distribution, a first monitoring point group and a second monitoring point group are set up with the initial measurement parameters as a reference. The first monitoring point group corresponds to the flow velocity distribution, and the second monitoring point group corresponds to the flow rate distribution. Calculate the measurement deviation between the initial measurement parameters and the first and second monitoring point groups, and generate real-time deviation information; Based on the real-time deviation information, the initial measurement parameters are adaptively optimized using the following formula: in, To optimize measurement parameters, These are the initial measurement parameters. Let be the flow velocity deviation at the i-th monitoring point. Let i be the flow deviation at the i-th monitoring point. and The weighting coefficients for flow velocity and flow rate are respectively, and satisfy the following conditions: , As a regulating factor, and These are the reference flow velocity and reference flow rate, respectively, and N is the total number of monitoring points; Iterative optimization continues until the measurement deviation meets the preset convergence threshold, thus obtaining the optimized measurement parameters.

5. The signal processing method for an aviation intelligent flow sensor as described in claim 4, characterized in that, The step of adaptively optimizing the initial measurement parameters based on the real-time deviation information further includes: Based on the flow velocity deviation of the first monitoring point group, the flow velocity-related component of the initial measurement parameter is dynamically adjusted until the flow velocity deviation is balanced. Based on the flow deviation of the second monitoring point group, the flow-related component of the measurement parameters is further adjusted until the flow deviation meets the preset equilibrium condition, and the optimized measurement parameters are determined.

6. A signal processing method for an aviation intelligent flow sensor as described in any one of claims 1 to 5, characterized in that, The step of dynamically and intelligently processing the airborne intelligent flow sensor based on the optimized measurement parameters to generate measurement stability information of the flow process includes: Based on the changes in flow accuracy and measurement error rate in the distribution of air traffic flow conditions, the operating conditions of the flow process are divided into multiple dynamic segments. The system collects measurement fluctuation data of each dynamic segment in real time, and dynamically adjusts the processing parameters of the aviation intelligent flow sensor based on the differences in measurement fluctuation data between segments using a fuzzy control algorithm. Based on the adjusted processing parameters, a measurement stability index is calculated based on the measurement error variance and the fluctuation rate of flow conversion accuracy, and measurement stability information is generated.

7. The signal processing method for an aviation intelligent flow sensor as described in claim 6, characterized in that, The measurement fluctuation data includes the measurement error and flow conversion accuracy of the airborne intelligent flow sensor, and the step of generating measurement stability information for the flow process includes: The measurement error and flow conversion accuracy of each dynamic segment are statistically analyzed, and the variance of the measurement error and the fluctuation rate of the flow conversion accuracy between each segment are calculated. If both the variance and volatility are within the preset stability range, then the measurement stability information indicates that the flow process measurement is stable. If the variance or volatility exceeds the preset stability range, the measurement stability information indicates that the flow process measurement is unstable, and a dynamic adjustment command is triggered.

8. A signal processing system for an aviation intelligent flow sensor, characterized in that, The system includes: The data acquisition module is used to collect real-time signal data from the intelligent airflow sensor and generate the corresponding airflow characteristic dataset. The signal analysis module is used to analyze the flow characteristic dataset based on a preset flow signal model to determine the initial measurement parameters of the flow process. The signal optimization module is used to acquire real-time deviation information between the initial measurement parameters and the actual air traffic flow operation status, and to adaptively optimize the initial measurement parameters based on the deviation information to obtain optimized measurement parameters. The intelligent processing module is used to perform dynamic intelligent processing on the aviation intelligent flow sensor based on the optimized measurement parameters, and generate measurement stability information of the flow process.

9. The signal processing system for an aviation intelligent flow sensor as described in claim 8, characterized in that, The signal optimization module includes: The deviation analysis unit is used to set up a first monitoring point group and a second monitoring point group based on the air traffic flow condition distribution, corresponding to the velocity distribution and the flow rate distribution respectively, and to calculate the measurement deviation between the initial measurement parameters and each monitoring point group. The optimization calculation unit is used to adaptively optimize the initial measurement parameters based on the nonlinear optimization model and the normalized weight deviation formula to obtain optimized measurement parameters.

10. The signal processing system for an aviation intelligent flow sensor as described in claim 8, characterized in that, The intelligent processing module includes: The segmentation unit is used to divide the operating conditions of the flow process into multiple dynamic segments based on the changes in flow accuracy and the rate of change in measurement error in the distribution of air flow conditions. The stability assessment unit is used to collect measurement fluctuation data of each dynamic segment, dynamically adjust the processing parameters of the aviation intelligent flow sensor based on the fuzzy control algorithm, and generate measurement stability information based on the measurement error variance and the fluctuation rate of flow conversion accuracy.

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