Aircraft environment monitoring method and system integrated with semiconductor sensor

Abstract

The invention discloses an aircraft environment monitoring method and system integrated with a semiconductor sensor. The method comprises the steps that S10, a semiconductor sensor array deployed on the surface of an aircraft is used for collecting temperature, humidity, air pressure, gas concentration and surface wind speed data in real time; s20, performing format standardization and error correction on the collected original environment data through a multi-channel data fusion engine, and constructing a unified input vector; s30, calculating a multi-dimensional fluctuation amplitude value of the input vector in the flight mission time window, and judging whether to trigger a high-frequency monitoring mode or not based on the value; s40, performing convolution fitting on the fluctuation amplitude value and a historical trend curve, and calculating a real-time environment disturbance index for updating a steady-state control parameter of the aircraft; s50, dynamically adjusting a control surface response strategy of the aircraft according to the disturbance index and synchronously generating a safety prompt level; and S60, uploading whole-process monitoring and response data to a ground monitoring center for intelligent optimization of model retraining and subsequent task planning.

Description

Technical Field

[0001] The present invention relates to the field of aircraft environmental monitoring and intelligent control, and particularly to an aircraft environmental monitoring method and system integrated with semiconductor sensors. Background Art

[0002] During the execution of complex flight missions by aircraft, external environmental factors (such as temperature, humidity, air pressure, wind speed, and gas concentration) will significantly affect the attitude control, flight trajectory stability, and mission execution effect of the aircraft. Traditional aircraft mainly rely on navigation systems and limited meteorological data for flight scheduling, but often lack the fine-grained perception ability of the aircraft surface environment and are difficult to respond to local disturbances or sudden environmental changes in a timely manner.

[0003] Although some existing flight control systems already have sensor sampling and attitude adjustment functions, they generally have the following problems:

[0004] Insufficient coverage of sensing data, failure to achieve structured multi-point perception of key areas of the aircraft (such as wings, tails, fuselages, etc.); lack of disturbance quantification mechanism, making it difficult for the system to quantitatively evaluate environmental fluctuations with high precision through mathematical models; fixed rudder surface response strategy, making it difficult for flight control parameters to be dynamically and adaptively adjusted according to the real-time environment, and the control strategy update lags behind; data cannot be effectively transmitted back for optimization, lacking a complete data closed-loop system, and unable to support model retraining and subsequent mission intelligent planning.

[0005] Therefore, there is an urgent need for an integrated monitoring method and system with multi-source environmental perception, disturbance modeling, adaptive control, and intelligent transmission capabilities to solve the deficiencies of the existing technology in terms of accuracy, flexibility, and closed-loop capabilities. Summary of the Invention

[0006] The present invention provides an aircraft environmental monitoring method integrated with semiconductor sensors, which includes:

[0007] S10. Using a semiconductor sensor array deployed on the aircraft surface to collect real-time temperature, humidity, air pressure, gas concentration, and surface wind speed data;

[0008] S20. Performing format standardization and error correction on the collected original environmental data through a multi-channel data fusion engine to construct a unified input vector;

[0009] S30. Calculating the multi-dimensional fluctuation amplitude value of the input vector within the flight mission time window, and determining whether to trigger the high-frequency monitoring mode based on this value;

[0010] S40. Performing convolution fitting on the fluctuation amplitude value and the historical trend curve to calculate the real-time environmental disturbance index for updating the steady-state control parameters of the aircraft.

[0011] S50. Dynamically adjust the rudder surface response strategy of the aircraft according to the disturbance index and synchronously generate the safety prompt level;

[0012] S60. Upload the whole-process monitoring and response data to the ground monitoring center for intelligent optimization of model retraining and subsequent task planning.

[0013] An aircraft environmental monitoring method integrating semiconductor sensors as described above, wherein using the semiconductor sensor array deployed on the aircraft surface to collect temperature, humidity, air pressure, gas concentration and surface wind speed data in real time includes:

[0014] S11. The acquisition unit arranges the sensor array on the aircraft surface in a structural partition manner to respectively detect the environmental information on the surfaces of the wings, tail wings and fuselage;

[0015] S12. The sensor array starts the self-calibration mechanism and periodically verifies the accuracy and response speed of the sampling channels through a preset reference signal;

[0016] S13. After the sensor data is locally buffered, it is synchronously transmitted to the airborne processing unit in the form of a relay signal.

[0017] An aircraft environmental monitoring method integrating semiconductor sensors as described above, wherein formatting standardization and error correction are performed on the collected original environmental data through a multi-channel data fusion engine to construct a unified input vector, including:

[0018] S21. The parsing engine performs type identification on various original sensing data and uniformly converts them into a standard vector format;

[0019] S22. The error correction module combines the historical model and the statistical characteristics of the current data to discriminate abnormal points and perform replacement operations;

[0020] S23. The fusion engine aligns the data from different sources according to the time stamp and generates a unified input vector for subsequent analysis.

[0021] An aircraft environmental monitoring method integrating semiconductor sensors as described above, wherein calculating the multi-dimensional fluctuation amplitude value of the input vector within the flight mission time window and judging whether to trigger the high-frequency monitoring mode based on this value includes:

[0022] S31. Extract the time window parameters of the current flight mission and select the corresponding environmental data sequence paragraph;

[0023] S32. Analyze the overall change range of the data sequence and calculate the average deviation degree of each index;

[0024] S33. The monitoring system judges whether to start the high-frequency sampling mechanism to enhance the accuracy according to the deviation degree.

[0025] An aircraft environmental monitoring method integrating a semiconductor sensor, wherein convolving and fitting the fluctuation amplitude value with the historical trend curve to calculate a real-time environmental disturbance index for updating the steady-state control parameters of the aircraft includes:

[0026] S41. The disturbance analysis module calls the historical flight data curve to perform a morphological comparison on the current fluctuation data;

[0027] S42. Identify the change inflection point in the trend fitting and generate a set of disturbance trend parameters;

[0028] S43. Input the disturbance trend result into the flight control stability module to update the flight regulation parameters.

[0029] An aircraft environmental monitoring method integrating a semiconductor sensor, wherein dynamically adjusting the rudder surface response strategy of the aircraft according to the disturbance index and synchronously generating a safety prompt level includes:

[0030] S51. The system selects the corresponding rudder surface response strategy template according to the disturbance index level;

[0031] S52. The command control module generates a rudder deflection adjustment signal in real time according to the selected template;

[0032] S53. The cockpit interface synchronously updates the current safety level prompt icon according to the strategy output result.

[0033] An aircraft environmental monitoring method integrating a semiconductor sensor, wherein uploading the full-process monitoring and response data to the ground monitoring center for intelligent optimization of model retraining and subsequent task planning includes:

[0034] S61. The communication module packs and compresses the whole-process monitoring data and performs encryption processing;

[0035] S62. The encrypted data is transmitted to the monitoring center at regular intervals through the wireless channel between the aircraft and the ground;

[0036] S63. The data received by the ground processing center is automatically stored in the task database and used to update the environmental pre-assessment model.

[0037] The present invention also provides an aircraft environmental monitoring system integrating a semiconductor sensor, wherein it includes:

[0038] A data acquisition unit for acquiring information such as the surface temperature, humidity, air pressure, gas concentration, and wind speed of the aircraft and performing multi-frequency calibration;

[0039] A data processing unit for performing unified structure conversion on the environmental information and performing error correction operations;

[0040] A perturbation calculation unit, configured to evaluate the amplitude of environmental fluctuations during the flight time period and calculate a perturbation index;

[0041] A control decision-making unit, configured to automatically adjust the response strategy of the aircraft rudder surface according to the perturbation index;

[0042] A data synchronization unit, configured to upload all monitoring and response data to the ground control center and perform intelligent archiving processing.

[0043] The beneficial effects achieved by the present invention are as follows: The present invention can not only solve the problems of low environmental perception accuracy of the aircraft, fixed control strategy, and insufficient data utilization rate, but also construct a dynamic monitoring and regulation closed loop during the flight process, providing full-link support for the efficient operation of the intelligent flight control system, and having good engineering application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0045] Figure 1 It is a flowchart of a method for monitoring the environment of an aircraft integrated with a semiconductor sensor provided in Embodiment 1 of the present application;

[0046] Figure 2 It is a schematic diagram of a system for monitoring the environment of an aircraft integrated with a semiconductor sensor provided in Embodiment 2 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1

[0049] As Figure 1 shown, Embodiment 1 of the present application provides a method for monitoring the environment of an aircraft integrated with a semiconductor sensor, including the following steps:

[0050] S10. Use the semiconductor sensor array deployed on the aircraft surface to collect real-time data of temperature, humidity, air pressure, gas concentration, and surface wind speed;

[0051] The main purpose of this step is to obtain the key environmental parameters of the space where the aircraft is located during the flight mission in real time through a distributed sensing network. The sensor array consists of multiple types of micro-sensors based on semiconductor processes, which have the characteristics of fast response, high integration, and low power consumption, and can adapt to complex environments at different altitudes and flight states. The array is initialized before the aircraft mission starts, autonomously enters the sampling working state, and provides a stable data source for the data processing link. It specifically includes the following sub-steps:

[0052] S11. The acquisition unit arranges the sensor array on the aircraft surface in a structural partition manner to respectively detect the environmental information on the surfaces of the wings, tail wings, and fuselage;

[0053] To adapt to the aerodynamic force distribution and structural differences of the aircraft during flight, the acquisition unit divides the entire aircraft surface into multiple functional areas and arranges them differentially according to the sensitivity of different areas to the external environment. The wing area, as the main position for generating lift, is equipped with wind speed and pressure sensors; sensors for monitoring temperature and humidity changes are installed in the middle section of the fuselage to obtain the body heat exchange information; the tail wing area is configured with gas concentration sensors to obtain the characteristics of gas composition changes in the wake area. The sensor density and distribution pattern in each area are dynamically adjusted according to the aerodynamic simulation results, so as to achieve refined sensing oriented to structural characteristics.

[0054] S12. The sensor array activates the self-calibration mechanism and periodically verifies the accuracy and response speed of the sampling channels through a preset reference signal;

[0055] To ensure that the sensors can still output high-precision and highly consistent data during continuous operation, the system configures a self-calibration mechanism for the array nodes. This mechanism periodically activates a preset reference signal source, injects a standard signal into the sampling path, and then verifies the response time, linear output range, and deviation degree of the current sensor. Once the system detects that the response curve deviates from the set error range, the node will call the built-in correction table generated based on the calibration samples and automatically fine-tune the sampling output. This process is carried out without affecting normal data transmission, ensuring that the acquisition quality is always stable within the system tolerance range.

[0056] S13. After being locally buffered, the sensor data is synchronously transmitted to the on-board processing unit in the form of a relay signal.

[0057] Due to the possible sudden fluctuations in the communication environment during flight, in order to ensure data integrity and processing efficiency, each sensor node is equipped with a lightweight cache module. The collected data first enters the local buffer for timestamp sorting and is packed into a standard frame format according to the data priority. This standard frame format is compatible with the in-aircraft internal transmission protocol specification. When the communication channel is stable or the main control processing unit issues a request signal, the node synchronously uploads the cached data through the relay method to ensure that the information has good anti-interference ability and transmission consistency at the link level.

[0058] S20. Standardize the format and correct the errors of the collected original environmental data through a multi-channel data fusion engine to construct a unified input vector.

[0059] After the multi-source environmental data collection of the aircraft surface sensor array is completed, the system needs to perform unified format conversion and error correction on the data output by various sensors to construct a standard input vector for subsequent modeling calls. Since there are differences in the sampling structure, data bit width, and response frequency among different types of sensors, the raw data will directly affect the analysis accuracy and model stability if not processed. Therefore, in this step, the data structure and quality are uniformly regulated through the collaborative processing of the parsing engine, error correction module, and fusion engine. It specifically includes the following sub-steps:

[0060] S21. The parsing engine identifies the types of various raw sensing data and uniformly converts them into a standard vector format.

[0061] The parsing engine first performs a type identification operation on the incoming raw data. The system determines the type of sensor and sampling source to which it belongs based on the identifier field and numerical feature information carried in the data packet. After the identification is completed, the system reorganizes the data structure according to the preset standard template and uniformly maps various data into a normalized multi-dimensional input vector. This vector arranges dimensions such as temperature, humidity, air pressure, gas concentration, and wind speed in a fixed order, and at the same time ensures that all data has consistent physical units and numerical accuracies, facilitating efficient processing and unified calling of the model.

[0062] S22. The error correction module discriminates abnormal points in combination with the historical model and the statistical characteristics of the current data and performs replacement operations.

[0063] After format standardization is completed, the system performs rapid statistical analysis on the current batch of data through an error correction module. The module extracts the extreme value intervals, mean deviations, and fluctuation trend characteristics of each dimension parameter, and compares them with the reference data in the historical stable model. If a data point deviates from its confidence interval or has a residual exceeding the threshold in the trend model, it is determined as a potential outlier. The system will first call the redundant channel data of the same type of sensor for replacement; if there is no redundant source, the sliding window interpolation method is used to estimate the approximate value. This mechanism improves data integrity while maintaining the response efficiency and numerical robustness of the overall processing process.

[0064] S23. The fusion engine aligns the data from different sources according to the time stamp and generates a unified input vector for subsequent analysis.

[0065] The multi-channel data after error elimination and replacement repair will be uniformly time-aligned and structure-stitched by the fusion engine. The system uses the time stamp as the reference window, sorts and pairs the data entries from different sensors, and completes the synchronous fusion operation according to the set time tolerance. After processing, the valid data of each channel will be stitched into a standard input vector of equal length, where each dimension represents a stable and standardized environmental parameter value. This input vector will be used as the basic input for the perturbation modeling and trend recognition module to ensure that the subsequent analysis process is based on a dataset with consistent time, clear structure, and reliable quality.

[0066] S30. Calculate the multi-dimensional fluctuation amplitude value of this input vector within the flight mission time window, and judge whether to trigger the high-frequency monitoring mode based on this value;

[0067] When the aircraft enters a critical mission segment (such as climbing, orbit changing, turning, or cruise switching), the system needs to judge whether there are significant disturbances in its external environment, especially paying attention to the occurrence of non-steady-state fluctuations within a short period of time. For this reason, the system performs volatility modeling on each parameter based on the multi-dimensional environmental input vector within the current flight time window, and quantitatively generates a perturbation amplitude vector. This vector reflects the perturbation intensity of different dimension parameters in the current flight stage and serves as the trigger basis for whether to enable the high-frequency monitoring mechanism. The specific steps are as follows:

[0068] S31. Extract the time window parameters of the current flight mission and select the corresponding environmental data sequence paragraph;

[0069] The flight control system first obtains the time window parameters of the current flight stage from the mission control unit, including the start and end times [t1, t T , the type of flight mission segment and its corresponding flight environment number. Subsequently, the system retrieves the environmental parameter data sequence within this window from the cache database. The parameters include temperature, humidity, atmospheric pressure, wind speed, and gas concentration, etc. Each type of parameter corresponds to a time series, denoted as Among them, d represents the parameter dimension, and T represents the number of sampling points within the window.

[0070] The system calculates the sample mean of each dimension based on the above data and the sample standard deviation σ d , and introduces a time position factor to reflect the relative position of each sampling point within the window. In addition, the system uses a sliding window method to estimate the local information entropy value at each moment to characterize the uncertainty level of the parameter at time t.

[0071] S32. Analyze the overall variation range of the data sequence and calculate the average deviation degree of each index;

[0072] After extracting the multi-dimensional data within the time window, the system first calculates the maximum value, minimum value and range of each parameter dimension, and identifies the mutation points, local jump amplitudes and durations to roughly judge its fluctuation trend state. To obtain a representative disturbance intensity index, the system constructs a weighted deviation function for each parameter dimension, and the specific definition is as follows: Among them, Δ d is the disturbance amplitude value (i.e., the average deviation degree) of the d-th parameter; T represents the total number of sampling points in the time window; ω d is the disturbance weight; α d is the amplification coefficient; κ d is the time position enhancement factor; γ d is the entropy weight index coefficient, which is used to adjust the response sensitivity of the system to uncertainty changes; is the standardized value of the d-th parameter at time t; σ d is the mean and standard deviation of this parameter within the window; ∈ is a constant, which is used to prevent division by zero errors when the standard deviation is 0; is the time position factor; is the sliding information entropy of this point, which measures the local data uncertainty. Finally, the system combines the disturbance values of all dimensions into a disturbance vector: Δ = [Δ1, Δ2, …, Δ D , and this vector will be used as the input basis for the high-frequency sampling judgment mechanism.

[0073] S33. The monitoring system determines whether to start the high-frequency sampling mechanism to enhance the accuracy according to the deviation degree.

[0074] Based on the preset disturbance level determination model, the system compares the disturbance amplitude value Δ d of each dimension with the corresponding risk interval. This model can be set based on historical flight mission data or flight control expert experience, and is divided into a "normal interval", a "medium risk interval" and a "high risk interval" according to the disturbance intensity, which is used to drive the dynamic switching of the sensor sampling rhythm.

[0075] When the perturbation value Δ in any dimension d exceeds its high-risk threshold, or when multiple dimensions are simultaneously at the medium-risk level or above, the system will determine that the current state is in the "unsteady perturbation zone". At this time, the monitoring module automatically enters the high-frequency sampling mode, dynamically increasing the sampling frequency of each sensor to 2-4 times the normal frequency, and synchronously compressing the cache refresh cycle to enhance the response ability to sudden perturbations.

[0076] The high-frequency sampling mode will continue to execute until all Δ values of the perturbed dimensions d all fall back to their corresponding safe fluctuation intervals, and the system will automatically switch back to the normal monitoring state to achieve an adaptive balance between accuracy and resource utilization.

[0077] S40. Convolve and fit the fluctuation amplitude value with the historical trend curve to calculate the real-time environmental perturbation index for updating the steady-state control parameters of the aircraft;

[0078] After calculating the perturbation amplitude vector, the system needs to further determine whether there is a reference historical evolution pattern for the current perturbation form, and generate a more structured perturbation index accordingly. To achieve this goal, the system uses the convolution fitting method to align the current perturbation sequence with the predefined typical trend templates in the historical flight missions for morphological alignment and response analysis, so as to extract the trend similarity features and generate a quantitative perturbation score. This perturbation score will be used as the dynamic input of the flight control strategy module to drive the automatic update of the steady-state control parameters of the current aircraft. Specifically, it includes the following sub-steps:

[0079] S41. The perturbation analysis module calls the historical flight data curve to compare the morphology of the current fluctuation data;

[0080] The system's built-in historical data management module stores a flight mission template library with multiple classification labels. Each type of template contains several representative perturbation trend curves. The perturbation analysis module calls the template set corresponding to the current flight mission stage, extracts K historical trend curves, which are respectively denoted as R1(t), R2(t), …, R K (t), and the domain length of which is consistent with the current flight time window. The perturbation amplitude sequence obtained in the current task is denoted as Δ(t), which will be used as the target input and perform time series convolution operations with each historical trend template to obtain the response similarity of each template, used to measure its fitting degree in the current environment.

[0081] S42. Identify the change inflection points in the trend fitting and generate a set of perturbation trend parameters;

[0082] After completing the trend fitting, the system further identifies the key change features in the current disturbance sequence through trend gradient analysis and sliding window detection methods, including turning points, local oscillation sections, and trend reversal behaviors within short periods. Each disturbance structure is extracted as a set of trend parameters, such as the extreme value of the change rate, trend period, jitter duration, etc. All trend parameters form a disturbance feature set P = {p1, p2, …, p M}, where p i is the i-th trend feature parameter, and M is the number of identified structure dimensions. To quantify the overall intensity and trend complexity of the current disturbance within the time window, the system defines the following disturbance index function: where S env is the environmental disturbance score under the current time window; Δ(t) is the value of the current disturbance sequence at time point t; R k (t) is the value of the k-th historical template sequence at time point t; λ k represents the risk enhancement factor of template k; ρ t is the time factor; μ is the amplification coefficient of the local information entropy difference; is the sliding information entropy between the current disturbance sequence and the template sequence at time point t; η is the non-linear response control coefficient; T is the total number of sampling points within the time window; K is the number of historical trend templates. This function structurally fits and quantifies the score of the current disturbance and historical behavior through a triple mechanism - trend convolution, time weighting, and local information entropy alignment. The finally calculated S env value will be used as the core basis for disturbance level judgment and flight control strategy switching.

[0083] S43. Input the disturbance trend result into the flight control stability module to update the flight regulation parameters.

[0084] The system inputs the calculated disturbance score S env and the trend feature set P into the steady-state control module of the flight control system. This module presets a multi-dimensional strategy mapping matrix. The system automatically matches the corresponding rudder control strategy templates according to the grade interval where the current disturbance score is located and the trend feature combination, including but not limited to: adjusting the rudder deflection sensitivity curve, correcting the attitude control gain, scaling the attitude response period, reallocating the control weights, etc.

[0085] The selected strategy will be injected into the flight control command generation logic in real time to form a control parameter update vector and directly act on the execution layer. This adjustment process is automatically completed during the execution of the flight mission without manual intervention, ensuring that the aircraft can still maintain attitude stability, heading accuracy, and control response continuity in the scenario of enhanced disturbance.

[0086] S50. Dynamically adjust the rudder response strategy of the aircraft according to the disturbance index and synchronously generate the safety prompt level;

[0087] After completing the calculation of the environmental disturbance index S env the system enters the flight control response stage. The core objective is to automatically adjust the rudder control strategy when the environmental changes are significant, ensuring the attitude continuity and response stability of the aircraft in unsteady airflow. Meanwhile, the system also needs to synchronize the current disturbance level and control status to the cockpit display module in real time, generating corresponding safety prompt levels to enhance the visualization ability of flight risks. Specifically, it includes the following sub-steps:

[0088] S51. The system selects the corresponding rudder response strategy template according to the disturbance index level;

[0089] After the system receives the disturbance index, the flight control scheduling module compares it with the internally preset disturbance level criteria, which are divided into four levels: "stable", "mild disturbance", "medium disturbance", and "high-risk disturbance". Each level corresponds to a set of trained rudder response strategy templates, and the templates contain parameter combinations such as response sensitivity functions, adjustment frequencies, buffer times, and response smoothing coefficients. The system automatically selects the most suitable template according to the current level as the configuration basis for generating control instructions, ensuring the dynamic linkage between the flight control system and environmental risks.

[0090] S52. The command control module generates the rudder deflection adjustment signal in real time according to the selected template;

[0091] After receiving the strategy template, the command control module combines the current state of the aircraft and the feedback from the attitude sensors, and loads the parameter group defined in the template to generate the rudder control instruction. Its decision-making basis includes: disturbance direction, flight speed change rate, and the historical execution status of each rudder. The system conducts fusion analysis on the above information through internal control logic, and judges the target deflection angle and adjustment speed of the executive surfaces such as the elevator, aileron, and rudder in real time.

[0092] The generated control instructions are sent to the servo execution module at a high frequency, and the instruction refresh frequency can be automatically adjusted according to the disturbance level, ensuring that the flight attitude can still maintain stable response and continuous correction in a high-disturbance environment.

[0093] S53. The cockpit interface synchronously updates the current safety level prompt icon according to the strategy output result.

[0094] While adjusting the flight control strategy, the system synchronously sends the disturbance level and strategy information to the cockpit interface display module. This module automatically renders the corresponding safety status icons according to the disturbance level: green indicates "flight stable", yellow indicates "mild disturbance", orange indicates "suggested avoidance", and red indicates "high-risk warning". The icons will be updated simultaneously in the main navigation interface, the head-mounted display system, and the auxiliary monitoring area. All status change events and corresponding strategy numbers will be automatically recorded in the flight log for subsequent retrospective analysis and model training iteration.

[0095] S60. Upload the full-process monitoring and response data to the ground monitoring center for intelligent optimization of model retraining and subsequent task planning.

[0096] During the flight, the system continuously records the whole-process data such as environmental parameters, disturbance analysis results, flight control adjustment behaviors, and rudder surface execution responses. To achieve continuous optimization of the model and intelligent task control, the system synchronously uploads this data to the ground monitoring center through the communication link as input samples for environmental modeling and strategy training. The uploaded data can not only supplement the flight log but also support the risk prediction and strategy planning of future tasks. It specifically includes the following sub-steps:

[0097] S61. The communication module packs and compresses the whole-process monitoring data and performs encryption processing;

[0098] The communication module automatically sorts and segment-packs the collected key data, including information such as sensor raw values, disturbance scores, rudder surface execution records, strategy numbers, and safety level prompts. The system adopts a structured packing method and performs differential compression for low-variation fields to reduce redundancy. After compression, the data packet is encrypted by a symmetric encryption algorithm (such as AES-256) and additional verification information based on the timestamp and task ID is attached to ensure the security and integrity of the data transmission process.

[0099] S62. The encrypted data is periodically transmitted to the monitoring center through the wireless channel between the aircraft and the ground;

[0100] The communication scheduling module periodically opens the air-ground wireless channel according to the set time window strategy and pushes the encrypted data packet to the ground monitoring center. The system adopts a time slot rotation mechanism and a segmented sending strategy to give priority to transmitting high-priority data. Each data segment contains an encryption label and a sequence number identifier to support reconstruction and verification at the receiving end. At the same time, the communication link supports status feedback and breakpoint resumption functions, and automatically caches and reissues in case of unstable or interrupted channels to ensure the complete delivery of key data.

[0101] S63. The data received by the ground processing center is automatically stored in the task database and used to update the environmental pre-assessment model.

[0102] After receiving the encrypted data, the ground monitoring center first completes decryption and integrity verification through the security processing module. The decrypted data is reconstructed into a structured record according to the task number and timestamp order and classified and stored in the task database. The system automatically incorporates the new data samples into the training process of the environmental pre-assessment model, updates the model parameters through a continuous learning mechanism, and improves its prediction capabilities for disturbance level judgment, risk trend identification, and strategy response effects, thereby enhancing the environmental adaptability and control intelligence level of subsequent tasks.

[0103] Example Two

[0104] As Figure 2 shown, Example Two of the present application provides an aircraft environment monitoring system integrated with semiconductor sensors, including:

[0105] A data acquisition unit 21, configured to obtain information such as the temperature, humidity, air pressure, gas concentration, and wind speed on the surface of the aircraft and perform multi-frequency calibration;

[0106] The data acquisition unit 21 is used to obtain key physical parameters in the environment where the aircraft surface is located, including information such as temperature, humidity, atmospheric pressure, gas concentration, and surface wind speed. This unit arranges a semiconductor sensor array on the aircraft structure surface according to functional areas, and respectively collects multi-point environment data of parts such as the wing, tail wing, and fuselage to ensure that the perception covers all key structural areas of the whole aircraft.

[0107] To adapt to the environmental changes in different flight stages, this unit has the capabilities of structural partition acquisition and multi-channel sampling. Each sensing node has built-in self-calibration logic, periodically detects the sampling response deviation by injecting a reference signal, automatically corrects the drift error, and ensures the sampling accuracy. The collected data is sorted and packed in the local buffer area according to the time stamp, and then transmitted to the data processing unit in a relay manner to achieve low latency and high consistency of data acquisition and transmission.

[0108] A data processing unit 22, configured to perform unified structure conversion on the environmental information and execute error correction operations;

[0109] The data processing unit 22 is used to perform unified structure conversion and error correction processing on the collected multi-source environmental data to construct a standard input vector for modeling. The system first identifies the data type through the built-in parsing engine, uniformly converts it into a standard physical unit and precision format, and generates a multi-dimensional input vector composed of temperature, humidity, air pressure, gas concentration, and wind speed according to the preset dimension.

[0110] To improve the data stability, the data processing unit is configured with an error correction module, which identifies abnormal points by combining the historical model and the current sampling statistical characteristics, and preferentially calls the redundant channel data for replacement; in case of missing data, a sliding interpolation method is used for approximate compensation. Finally, the system aligns the multi-channel data according to the time stamp to generate a unified vector input, providing a structured, complete, and consistent analysis basis for the perturbation calculation unit.

[0111] A perturbation calculation unit 23, configured to evaluate the environmental fluctuation amplitude during the flight time period and calculate the perturbation index;

[0112] The perturbation calculation unit 23 is used to model the changing trend of environmental data within a specific time window of the flight mission, and calculate the perturbation amplitude value to construct a perturbation vector. First, the system extracts the multi-dimensional environmental parameter sequence within the time period according to the mission parameters, and combines the sample mean, standard deviation, information entropy and time weight of each dimension to construct a non-linear perturbation amplitude function to quantify the deviation intensity of each dimension within the current window.

[0113] The system performs statistical fusion of the perturbation values of all dimensions to generate a perturbation vector for subsequent response judgment. If the perturbation value of a certain dimension exceeds the set threshold, or multiple dimensions are in the medium-risk or above state at the same time, the system automatically triggers a high-frequency monitoring mechanism to dynamically increase the sampling frequency and refresh period, and enhance the real-time perception ability of sudden perturbations.

[0114] The control decision-making unit 24 is used to automatically adjust the rudder surface response strategy of the aircraft according to the perturbation index;

[0115] The control decision-making unit 24 is used to generate the current perturbation score and dynamically match the flight control strategy based on the fitting result between the perturbation vector and the historical trend template. This unit calls the perturbation curve corresponding to the current mission segment in the historical template library, calculates the trend similarity through the structural convolution and information entropy difference alignment method, extracts the set of trend characteristic parameters, and constructs the perturbation score value accordingly.

[0116] The system automatically matches the rudder surface response strategy template according to the perturbation score level and the set of trend characteristics, and adjusts key parameters such as response sensitivity, control period and attitude adjustment weight. At the same time, the system dynamically generates a rudder surface command in combination with the current flight state, controls the elevators, ailerons and rudders to perform corresponding deflection operations, and maintains the stability of the flight attitude. This process is completed automatically in real time to construct a closed-loop response chain from perturbation recognition to flight control command output.

[0117] In addition, this unit synchronously sends the strategy level and response status to the cockpit interaction module to update the flight safety prompt icon, and assist the pilot to perceive the environmental perturbation level in real time. The icon is linked to the main navigation interface and the flight log system to record the perturbation level, strategy number and control time node, and improve the evaluation and reproduction ability of subsequent tasks.

[0118] The data synchronization unit 25 is used to upload all monitoring and response data to the ground control center and perform intelligent archiving processing.

[0119] The data synchronization unit 25 is used to structurally package and encrypt the monitoring and control full-process data during the flight and transmit it to the ground monitoring center for model retraining and task strategy optimization. The system listens to the output content of the acquisition, processing, control and interaction modules in real time, classifies and organizes it according to the time stamp and task label, and generates a data packet through the differential compression strategy.

[0120] The encryption module performs encryption based on a symmetric encryption algorithm (such as AES-256), appending the task ID and verification information. The compressed data is periodically sent to the ground via a wireless channel, using a time-slot push and receipt verification mechanism to ensure transmission integrity. Under unstable channel conditions, the system automatically enables a breakpoint retransmission mechanism.

[0121] After receiving the data, the ground monitoring center completes decryption and structure reconstruction, automatically stores it in the task database, and injects it as an input sample into the training pipeline of the environmental pre-assessment model. Based on this, the system continuously optimizes the disturbance level division and strategy matching accuracy, realizing the closed-loop self-evolution of the flight control strategy and the intelligence of task execution.

[0122] Corresponding to the above embodiments, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0123] The memory is used to store one or more program instructions;

[0124] The processor is used to run one or more program instructions to execute a method for monitoring the environment of an aircraft integrated with semiconductor sensors.

[0125] Corresponding to the above embodiments, an embodiment of the present invention provides a computer-readable storage medium. The computer storage medium contains one or more program instructions, and the one or more program instructions are used to be executed by a processor to execute a method for monitoring the environment of an aircraft integrated with semiconductor sensors.

[0126] The disclosed embodiment of the present invention provides a computer-readable storage medium. Computer program instructions are stored in the computer-readable storage medium. When the computer program instructions run on a computer, the computer is caused to execute the above-mentioned method for monitoring the environment of an aircraft integrated with semiconductor sensors.

[0127] In the embodiment of the present invention, the processor may be an integrated circuit chip with signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP for short), an application-specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0128] The various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. The processor reads the information in the storage medium and combines its hardware to complete the steps of the above method.

[0129] The storage medium can be a memory, for example, it can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0130] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0131] The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).

[0132] The storage media described in the embodiments of the present invention are intended to include, but not be limited to, these and any other suitable types of memories.

[0133] Those skilled in the art should be able to realize that in one or more of the above examples, the functions described in the present invention can be implemented by a combination of hardware and software. When applying software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. The computer-readable medium includes computer storage media and communication media, where the communication media includes any medium that facilitates the transmission of a computer program from one place to another. The storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0134] The specific embodiments described above have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the protection scope of the present invention.

Claims

1. An aircraft environmental monitoring method integrating a semiconductor sensor, characterized in that, It includes the following steps: S10. Use the semiconductor sensor array deployed on the surface of the aircraft to collect temperature, humidity, air pressure, gas concentration and surface wind speed data in real time; S20. Through the multi-channel data fusion engine, perform format standardization and error correction on the collected original environmental data to construct a unified input vector; S30. Calculate the multi-dimensional fluctuation amplitude value of the input vector within the flight mission time window, and judge whether to trigger the high-frequency monitoring mode based on this value; S40. Convolve and fit the fluctuation amplitude value with the historical trend curve to calculate the real-time environmental disturbance index for updating the steady-state control parameters of the aircraft; S50. Dynamically adjust the rudder surface response strategy of the aircraft according to the disturbance index and synchronously generate the safety prompt level; S60. Upload the whole-process monitoring and response data to the ground monitoring center for intelligent optimization of model retraining and subsequent mission planning.

2. The aircraft environmental monitoring method with an integrated semiconductor sensor according to claim 1, characterized in that, Using the semiconductor sensor array deployed on the surface of the aircraft to collect temperature, humidity, air pressure, gas concentration and surface wind speed data in real time includes the following sub-steps: S11. The acquisition unit arranges the sensor array on the surface of the aircraft in a structural partition manner to detect the environmental information on the surfaces of the wing, tail and fuselage respectively; S12. The sensor array starts the self-calibration mechanism and periodically verifies the accuracy and response speed of the sampling channels through the preset reference signal; S13. After the sensor data is locally buffered, it is synchronously transmitted to the airborne processing unit in the form of a relay signal.

3. The aircraft environmental monitoring method with an integrated semiconductor sensor as claimed in claim 1, wherein Performing format standardization and error correction on the collected original environmental data through the multi-channel data fusion engine to construct a unified input vector includes the following sub-steps: S21. The parsing engine performs type recognition on various original sensing data and uniformly converts them into the standard vector format; S22. The error correction module combines the historical model and the current data statistical characteristics to identify and replace the abnormal points; S23. The fusion engine aligns the data from different sources according to the time stamp and generates a unified input vector for subsequent analysis.

4. The aircraft environmental monitoring method with an integrated semiconductor sensor as claimed in claim 1, wherein Calculating the multi-dimensional fluctuation amplitude value of the input vector within the flight mission time window and judging whether to trigger the high-frequency monitoring mode based on this value includes the following sub-steps: S31. Extract the time window parameters of the current flight mission and select the corresponding environmental data sequence segment; S32. Analyze the overall change range of the data sequence and calculate the average deviation degree of each index; S33. The monitoring system judges whether to start the high-frequency sampling mechanism to enhance the accuracy according to the deviation degree.

5. A method for monitoring the flight vehicle environment integrating a semiconductor sensor, as claimed in claim 1, characterized in that Convolving and fitting the fluctuation amplitude value with the historical trend curve to calculate the real-time environmental disturbance index for updating the steady-state control parameters of the aircraft includes the following sub-steps: S41. The disturbance analysis module calls the historical flight data curve to compare the current fluctuation data in terms of morphology; S42. Identify the change inflection points in the trend fitting and generate a set of disturbance trend parameters; S43. Input the disturbance trend result into the flight control stability module to update the flight control parameters.

6. The aircraft environment monitoring method with an integrated semiconductor sensor as claimed in claim 1, wherein Dynamically adjusting the rudder surface response strategy of the aircraft according to the disturbance index and synchronously generating the safety prompt level includes the following sub-steps: S51. The system selects the corresponding rudder surface response strategy template according to the disturbance index level; S52. The instruction control module generates a rudder deflection adjustment signal in real time according to the selected template; S53. The cockpit interface synchronously updates the current safety level prompt icon according to the policy output result.

7. The aircraft environmental monitoring method with an integrated semiconductor sensor as claimed in claim 1, characterized in that, Uploading the whole-process monitoring and response data to the ground monitoring center for intelligent optimization of model retraining and subsequent task planning includes the following sub-steps: S61. The communication module packs and compresses the whole-process monitoring data and performs encryption processing; S62. The encrypted data is periodically transmitted to the monitoring center through the wireless channel between the aircraft and the ground; S63. The data received by the ground processing center is automatically stored in the task database and used to update the environmental pre-assessment model.

8. An aircraft environmental monitoring system integrated with a semiconductor sensor, characterized in that, Including: A data acquisition unit, which is used to obtain information such as the surface temperature, humidity, air pressure, gas concentration and wind speed of the aircraft and perform multi-frequency calibration; A data processing unit, which is used to perform unified structural conversion on the environmental information and execute error correction operations; A disturbance calculation unit, which is used to evaluate the environmental fluctuation amplitude during the flight period and calculate the disturbance index; A control decision unit, which is used to automatically adjust the aircraft rudder surface response strategy according to the disturbance index; A data synchronization unit, which is used to upload all monitoring and response data to the ground control center and perform intelligent archiving processing.

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