A Closed-Loop Calibration System and Method for a Doppler Very High Frequency Omnidirectional Range Transmitting Channel

Abstract

The present invention discloses a closed-loop calibration system and method for a Doppler very high frequency omnidirectional range (VOR) transmitter channel, which relates to the technical field of calibration optimization. The system includes: retrieving the transmission data of the beacon device under different environmental conditions; constructing a beacon prediction model according to a deep learning model, training the transmission data combined with the environmental state by inputting them into the beacon prediction model, predicting the change trend of the beacon transmission performance, and marking the current environmental state; generating an environmental state transition matrix according to the environmental state marking result and the performance change trend prediction result, and dynamically updating the environmental state transition probability; calculating the influence rate of environmental changes on the beacon transmission performance according to the historical transmission data, the environmental state transition probability, and the phase noise level corresponding to each historical transmission data; dynamically adjusting the calibration parameters of the beacon transmission channel according to the calculated influence rate. The present invention solves the problem that the beacon transmission performance is affected by environmental changes through a deep learning model and a dynamic calibration mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of calibration optimization, and particularly to a closed-loop calibration system and method for a Doppler very high frequency omnidirectional range (VOR) transmitter channel. Background Art

[0002] Doppler very high frequency omnidirectional range technology occupies an important position in the fields of aviation navigation and radio communication. Its core function is to provide accurate navigation information by transmitting high-precision radio signals. In traditional very high frequency omnidirectional range systems, the stability and accuracy of the transmitted signal are crucial for the overall performance of the navigation system. With the rapid development of aviation and communication technologies, users have put forward higher requirements for the accuracy, stability, and adaptability of beacon systems. Especially under changing environmental conditions, such as the influence of external factors like temperature, humidity, and electromagnetic interference, the transmission performance of beacon systems often fluctuates, resulting in a decline in signal quality and further affecting navigation accuracy. To address the impact of environmental changes on beacon performance, existing calibration methods mainly rely on periodic manual calibration or automatic adjustment based on simple feedback loops. However, existing calibration methods usually have difficulty coping with complex environmental changes and cannot accurately calibrate the beacon transmitter channel in real-time and dynamically.

[0003] Currently, many beacon transmission systems adopt a static calibration mechanism, that is, calibrating the transmitter channel within a fixed time rather than making real-time adjustments according to environmental changes. Although this static calibration can ensure a certain signal stability under certain specific environmental conditions, under the influence of complex and changeable external environments (high temperature, high humidity, and strong electromagnetic interference), the amplitude and phase of the signal are prone to unpredictable deviations. In addition, in existing technologies, the prediction of changes in the performance of transmitted beacons mostly uses simple linear models and fails to fully consider the complex influence of environmental conditions on beacon performance. With the progress of machine learning and data processing technologies, it has become possible to use deep learning models to construct a prediction model for beacon transmission performance. However, in existing technologies, deep learning methods have not been effectively combined with beacon transmission performance calibration, resulting in lag and inaccuracy in the calibration system when dealing with environmental changes, thereby restricting the application scope of beacon systems. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a closed-loop calibration system and method for a Doppler very high frequency omnidirectional range transmitter channel to solve the problem that in the prior art, deep learning methods have not been effectively combined with beacon transmission performance calibration, resulting in lag and inaccuracy in the calibration system when dealing with environmental changes, thereby restricting the application scope of beacon systems.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a closed-loop calibration method for a Doppler very high frequency omnidirectional range (VOR) transmitter channel, which includes:

[0008] Retrieve the transmission data of the beacon device under different environmental conditions;

[0009] Construct a beacon prediction model based on a deep learning model, input the transmission data combined with the environmental state into the beacon prediction model for training, predict the changing trend of the beacon transmission performance, and mark the current environmental state;

[0010] Generate an environmental state transition matrix according to the environmental state marking result and the performance change trend prediction result, and dynamically update the environmental state transition probability;

[0011] Calculate the influence rate of environmental changes on the beacon transmission performance according to the historical transmission data, the environmental state transition probability, and the phase noise level corresponding to each historical transmission data;

[0012] Dynamically adjust the calibration parameters of the beacon transmitter channel according to the calculated influence rate.

[0013] As a preferred solution of the closed-loop calibration method for the Doppler very high frequency omnidirectional range transmitter channel of the present invention, wherein: the specific steps of retrieving the transmission data of the beacon device under different environmental conditions are as follows:

[0014] Different environmental conditions refer to indoor environment, outdoor environment, and special environment;

[0015] Select two different time periods, day and night, for data collection;

[0016] Use a thermometer to record the environmental temperature during each transmission test, use a hygrometer to record the environmental humidity during each transmission test, and use an electromagnetic interference detector to record the electromagnetic interference intensity during each transmission test;

[0017] At the same time, record the signal frequency, signal phase, signal amplitude, and transmission time of each transmission;

[0018] Preprocess the collected data;

[0019] Store the preprocessed transmission data and environmental state in an SQL database.

[0020] As a preferred solution of the closed-loop calibration method for the Doppler very high frequency omnidirectional range transmitter channel of the present invention, wherein: the specific steps of constructing a beacon prediction model based on a deep learning model and inputting the transmission data combined with the environmental state into the beacon prediction model for training are as follows:

[0021] Use the Long Short-Term Memory network (LSTM) as the basis of the beacon prediction model;

[0022] Based on the LSTM, construct an architecture that includes an input layer, a hidden layer, and an output layer;

[0023] By inputting the transmitted data and the environmental state into the beacon prediction model, obtain the predicted values of two performance indicators, namely the signal amplitude and the signal phase. The expression is:

[0024] ;

[0025] ;

[0026] where and represent the signal amplitude and the signal phase predicted by the beacon prediction model at time , represents the signal amplitude, represents the signal phase, and represent the initial signal amplitude and the initial signal phase, represents the natural attenuation coefficient of the signal amplitude, represents the cosine function, represents the amplitude of the sine wave in the signal amplitude, represents the amplitude of the cosine wave in the signal phase, represents the angular frequency of the sine function and the cosine function, represents time;

[0027] According to the output of the signal prediction model and the change of the environmental state, mark the current environmental state;

[0028] According to different environmental data, the environmental state is divided into a normal state, a high-temperature state, a high-humidity state, and a strong electric field interference state;

[0029] Set thresholds T1, T2, and T3 for the environmental data. When the predicted value of the beacon prediction model exceeds the corresponding threshold, the beacon prediction model automatically determines the current environmental state according to the environmental data.

[0030] As a preferred solution of the Doppler VHF omnidirectional range (VOR) transmitter channel closed-loop calibration method described in the present invention, wherein: according to the environmental state marking result, combined with the performance change trend prediction result, generate an environmental state transition matrix, and dynamically update the environmental state transition probability. The specific steps are as follows:

[0031] According to the result of the environmental state marking, combined with the performance change trend predicted by the model, generate an environmental state transition matrix to define the transition probability between different environmental states. The environmental state transition matrix The expression is:

[0032] ;

[0033] Wherein, , , and respectively represent the probability values of the normal state, high-temperature state, high-humidity state, and strong electric field interference state, represents the probability from the normal state to the normal state, represents the probability from the strong electric field interference state to the strong electric field interference state;

[0034] According to the obtained environmental state transition probability, combined with the historical data and the prediction results of the signal prediction model, the environmental state transition probability is dynamically updated through the Bayesian update formula.

[0035] As a preferred solution of the closed-loop calibration method for the Doppler very high frequency omnidirectional range (VOR) transmitter channel of the present invention, wherein: calculating the influence rate of environmental changes on the transmitter performance of the beacon according to the historical transmission data, environmental state transition probability, and the phase noise level corresponding to each historical transmission data, the specific steps are as follows,

[0036] According to the historical data and the environmental state transition matrix , combined with the predicted signal amplitude and signal phase changes, calculate the influence rate of environmental changes on the transmitter performance of the beacon, and the expression of the environmental influence rate is:

[0037] ;

[0038] Wherein, is the environmental influence rate, representing the comprehensive influence of environmental changes on the transmission performance, represents the time period, is the function of environmental temperature changing with time, is the coefficient of the influence of temperature on the transmitter performance of the beacon, is the function of environmental humidity changing with time, is the coefficient of the influence of humidity on the transmitter performance of the beacon, is the function of electromagnetic interference intensity changing with time, is the coefficient of the influence of electromagnetic interference on the transmitter performance of the beacon, represents the definite integral.

[0039] As a preferred solution of the closed-loop calibration method for the Doppler very high frequency omnidirectional range (VOR) transmitter channel of the present invention, wherein: dynamically adjusting the calibration parameters of the beacon transmitter channel according to the calculated influence rate, the specific steps are as follows,

[0040] When the beacon device leaves the factory or is first deployed, the initial calibration value is preset to be , which is the reference calibration value of the beacon transmission channel under ideal environmental conditions;

[0041] During the operation of the device, the current environmental temperature, environmental humidity, and electromagnetic interference intensity are monitored in real time to generate corresponding environmental status data 、 and ;

[0042] The real-time environmental impact rate is calculated through the environmental impact rate expression according to the real-time environmental data ;

[0043] According to the signal amplitude and signal phase emitted by the beacon monitored in real time, and calculate their rates of change over time. The formula is:

[0044] ;

[0045] Among them, represents the differential symbol, represents a small change in time.

[0046] According to the real-time environmental impact rate The calibration parameter adjustment formula is expressed as:

[0047] ;

[0048] Among them, represents the dynamic calibration parameter of the transmission channel, represents the initial calibration value, represents the calibration coefficient;

[0049] According to the calculated dynamic calibration parameter ;

[0050] After adjusting the calibration parameter , continue to monitor the beacon transmission performance, and fine-tune the calibration parameter according to the subsequent transmission data.

[0051] In a second aspect, the present invention provides a closed-loop calibration system for a Doppler very high frequency omnidirectional range transmission channel, including a data acquisition module, a prediction model module, a state update module, an impact rate module, and a calibration adjustment module,

[0052] The data acquisition module is used to collect beacon transmission data and environmental status data and store them in a database;

[0053] The prediction model module is used to predict the changing trend of the beacon emission performance based on the deep learning model LSTM. This module analyzes the environmental data and emission data to generate predicted values of the signal amplitude and phase.

[0054] The state update module is used to generate an environmental state transition matrix based on the environmental state marking result and dynamically update the state transition probability.

[0055] The influence rate module is used to calculate the influence rate of environmental changes on the beacon emission performance according to the historical emission data, environmental state transition probability, and phase noise level.

[0056] The calibration adjustment module is used to dynamically adjust the calibration parameters of the beacon emission channel according to the calculated environmental influence rate, so that the beacon emission performance remains stable under different environmental conditions.

[0057] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the closed-loop calibration method for the Doppler very high frequency omnidirectional beacon emission channel as described in the first aspect of the present invention is implemented.

[0058] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by the processor, any step of the closed-loop calibration method for the Doppler very high frequency omnidirectional beacon emission channel as described in the first aspect of the present invention is implemented.

[0059] The beneficial effects of the present invention are as follows: The present invention solves the problem that the beacon emission performance is affected by environmental changes through a deep learning model and a dynamic calibration mechanism. The core of this method is to call the emission data of the beacon device under different environmental conditions, and combine factors such as environmental temperature, humidity, and electromagnetic interference to monitor and calibrate the beacon emission performance in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0061] Figure 1 It is a flowchart of the closed-loop calibration method for the Doppler very high frequency omnidirectional beacon emission channel in Embodiment 1.

[0062] Figure 2 It is a flowchart of the closed-loop calibration system for the Doppler very high frequency omnidirectional beacon emission channel in Embodiment 1. Detailed implementation manners

[0063] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0065] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0066] Embodiment 1, referring to Figure 1 and Figure 2 , which is the first embodiment of the present invention. This embodiment provides a closed-loop calibration method for the Doppler very high frequency omnidirectional range (VOR) transmitter channel, including the following steps:

[0067] S1 Retrieve the transmission data of the beacon device under different environmental conditions;

[0068] The different environmental conditions refer to indoor environment, outdoor environment, and special environment;

[0069] This step ensures the extensiveness and diversity of the data by collecting data in indoor, outdoor, and special environments. Different from the prior art, the present invention can more accurately evaluate the impact of environmental conditions on the beacon transmission performance by comprehensively capturing the transmission data under different environments. For example, in a high-temperature or strong electromagnetic interference environment, the signal strength may decrease significantly. Through the collection of these data, the system can establish a corresponding calibration model to ensure the stability of the beacon transmission in extreme environments;

[0070] Select two different time periods, day and night, for data collection;

[0071] This step further enriches the dimension of the data by collecting data at different time periods. Since there may be significant differences in temperature, humidity, and electromagnetic interference between day and night, the present invention can capture the potential impact of time factors on the beacon transmission performance through this detailed data collection strategy. This multi-dimensional data collection method is far superior to the traditional method of only testing in a single time period, ensuring the adaptability of the system in different time scenarios;

[0072] Use a thermometer to record the ambient temperature during each emission test, a hygrometer to record the ambient humidity during each emission test, and an electromagnetic interference detector to record the electromagnetic interference intensity during each emission test;

[0073] Meanwhile, record the signal frequency, signal phase, signal amplitude, and emission time of each emission;

[0074] Preprocess the collected data, including data cleaning and standardization;

[0075] Store the preprocessed emission data and environmental status in an SQL database. By storing the preprocessed data in the SQL database, the structured management and efficient access of the data are ensured;

[0076] Special environments include high-temperature, high-humidity, and strong electromagnetic interference environments;

[0077] This step specifically conducts data collection for special environments such as high temperature, high humidity, and strong electromagnetic interference, which has important technical significance. Beacon devices often exhibit different characteristics in these extreme environments. Therefore, collecting emission data in these environments helps to establish a more robust calibration model. Compared with the existing technology that only conducts tests in conventional environments, the present invention can better ensure the comprehensiveness and adaptability of beacon emission, especially the reliability in harsh environments through the data collection extended to special environments;

[0078] Emission data includes signal frequency, signal phase, signal amplitude, and emission time;

[0079] Environmental status includes ambient temperature, ambient humidity, and electromagnetic interference intensity.

[0080] S2 Construct a beacon prediction model according to the deep learning model. Input the emission data combined with the environmental status into the beacon prediction model for training, predict the changing trend of the beacon emission performance, and mark the current environmental status;

[0081] Use the long short-term memory network LSTM as the basis of the beacon prediction model. LSTM can effectively process time series data and is especially suitable for analyzing the changing trend of the emission performance of beacons at different time periods;

[0082] Construct an architecture including an input layer, a hidden layer, and an output layer based on the long short-term memory network LSTM;

[0083] The input layer is used to input the preprocessed environmental data and emission data;

[0084] The hidden layer contains several LSTM units, and the LSTM units are used to capture the long-term dependencies in the time series;

[0085] The output layer is used to output the predicted beacon emission performance (predicted values of signal amplitude and signal phase);

[0086] The innovation of this step lies in using an LSTM network to analyze the time series data of beacon emission performance. The LSTM network can effectively handle the long-term dependencies in the time series, showing the ability to remember historical data, and is suitable for dealing with the changing trend of beacon emission performance over time. When traditional neural network algorithms process time series data, they often fail to capture the dependencies over a long time. However, through its unique gating mechanism, LSTM can effectively capture the long-term dependency problems in beacon emission data, thus solving the common time span problem in beacon performance prediction;

[0087] By inputting the emission data and environmental state into the beacon prediction model, the predicted values of two performance indicators, signal amplitude and signal phase, are obtained. The expression is:

[0088] ;

[0089] ;

[0090] Among them, and represent the signal amplitude and signal phase predicted by the beacon prediction model at time , represents the signal amplitude, represents the signal phase, and represent the initial signal amplitude and initial signal phase, represents the natural attenuation coefficient of the signal amplitude, represents the cosine function, represents the amplitude of the sine wave in the signal amplitude, represents the amplitude of the cosine wave in the signal phase, represents the angular frequency of the sine function and cosine function, represents time;

[0091] This formula not only considers the natural attenuation of the signal but also introduces sine and cosine functions to describe the fluctuation characteristics of the signal. This combination is relatively rare in the existing technology, especially in the scenario of combined time series analysis, and can more accurately fit the actual changes in beacon emission performance, especially suitable for scenarios with large environmental fluctuations;

[0092] To ensure that the model can accurately predict the impact of environmental changes on beacon emission performance, a loss function is constructed by comprehensively considering the signal amplitude and phase noise of the emission performance. The expression of the loss function is:

[0093] ;

[0094] Among them, represents the loss function value, and represent the actual signal amplitude and actual signal phase at each time point, represents the index variable, represents the total number of samples used to train the beacon prediction model, represents the coefficient of the regularization term;

[0095] Set the number of iterations, and stop the iteration when the number of iterations is reached or the loss function value meets the user's requirements;

[0096] According to the output of the signal prediction model and the change of the environmental state, mark the current environmental state. The marking of the environmental state can help the system identify the current working environment and adjust the calibration parameters of the transmission channel accordingly;

[0097] According to different environmental data, the environmental state is divided into normal state, high-temperature state, high-humidity state, and strong electric field interference state;

[0098] Set thresholds T1, T2, and T3 for the environmental data. When the prediction value of the beacon prediction model exceeds the corresponding threshold, the beacon prediction model automatically judges the current environmental state according to the environmental data;

[0099] When the predicted temperature value exceeds the set threshold T1, it indicates a high-temperature state;

[0100] When the predicted humidity value exceeds the set threshold T2, it indicates a high-humidity state;

[0101] When the predicted electromagnetic interference value exceeds the set threshold T3, it indicates a strong electromagnetic interference state;

[0102] When the predicted environmental data values are all within the normal range, it is marked as the normal state;

[0103] The invention marks the current environmental state by predicting the beacon emission performance and combining the environmental data. The innovation lies in that the marking is not only used for the evaluation of the beacon performance, but also can be used for the system to automatically adjust the calibration parameters of the transmission channel. Through the real-time marking of the environment, the system can make timely adjustments under different environmental states (such as high temperature, high humidity, and strong electromagnetic interference) to ensure the stability of the beacon emission performance. Compared with the simple environmental monitoring and manual adjustment in the prior art, the automatic marking and adjustment method of the present invention greatly improves the intelligence level and response speed of the system.

[0104] S3 generates an environmental state transition matrix according to the environmental state marking result and the performance change trend prediction result, and dynamically updates the environmental state transition probability;

[0105] Generate an environmental state transition matrix based on the results of environmental state marking and in combination with the performance change trend predicted by the model. To define the transition probability between different states of the environment, the environmental state transition matrix The expression is:

[0106] ;

[0107] Among them, , , and respectively represent the probability values of the normal state, high-temperature state, high-humidity state, and strong electric field interference state. represents the probability from the normal state to the normal state, represents the probability from the strong electric field interference state to the strong electric field interference state, and so on;

[0108] This step generates an environmental state transition matrix by combining the environmental state marking results and the beacon performance change trend. , and the uniqueness of this matrix lies in that it can quantify the transition probability between different states of the environment. For example, in the matrix represents the probability that the environment remains in the "normal state" from the "normal state", while represents the probability of transitioning from the "normal state" to the "high-temperature state". Through this quantification of probabilities, the dynamic changes of the environmental state can be effectively captured. This matrix processing is not only applicable to the beacon emission environment of the present invention but can also be extended to other dynamic environmental monitoring fields. Compared with the prior art, the introduction of the transition matrix in the present invention simplifies and quantifies the complex environmental state transition problem, effectively improving the response speed and judgment accuracy of the system.

[0109] Based on the obtained environmental state transition probability, combined with historical data and the prediction results of the signal prediction model, dynamically update the environmental state transition probability through the Bayesian update formula. The expression is:

[0110] ;

[0111] Among them, represents the updated environmental state transition probability, represents the th environmental state, represents the probability from state to state, , and are index variables, and their value ranges are 1, 2, 3, and 4, corresponding to the normal state, high-temperature state, high-humidity state, and strong electric field interference state respectively. represents the posterior probability, indicating that given the signal amplitude and signal phase , the probability of being in state ;

[0112] The step of dynamically updating the environmental state transition probability through the Bayesian update formula has significant technical advantages. First, it takes into account the changes in signal amplitude and phase, which enables the inference of the environmental state to no longer rely on simple historical data but to be able to respond in real time to the current signal performance. Second, the updated transition probability reflects the latest change trend of the environmental state. For example, if the system detects abnormal fluctuations in signal amplitude and phase, indicating that there may be an abnormal environment such as high temperature or high humidity, this step can update the transition matrix in a timely manner to ensure that the system can quickly adjust the beacon transmission parameters and avoid the degradation of beacon performance.

[0113] S4 calculates the influence rate of environmental changes on the beacon transmission performance according to historical transmission data, environmental state transition probability, and the phase noise level corresponding to each historical transmission data;

[0114] According to historical data and the environmental state transition matrix , combined with the predicted changes in signal amplitude and signal phase, calculate the influence rate of environmental changes on the beacon transmission performance. The expression of the environmental influence rate is:

[0115] ;

[0116] where is the environmental influence rate, representing the comprehensive influence of environmental changes on the transmission performance, represents the time period, is the function of environmental temperature changing with time, is the coefficient of the influence of temperature on the beacon transmission performance, is the function of environmental humidity changing with time, is the coefficient of the influence of humidity on the beacon transmission performance, is the function of electromagnetic interference intensity changing with time, is the coefficient of the influence of electromagnetic interference on the beacon transmission performance, represents the definite integral;

[0117] Through the environmental influence rate expression, the system can comprehensively evaluate the influence of temperature, humidity, and electromagnetic interference on the beacon transmission performance. The innovation of this step lies in unifying the effects of multiple environmental factors in an integral form, providing a quantitative evaluation index. Compared with the traditional static threshold judgment, the present invention can more accurately reflect the long-term influence of the environment on the transmission performance through integral calculation, especially suitable for the working conditions of long-term operation of the beacon;

[0118] Environmental impact rate The value range is positive. When the value is larger, it indicates that the environmental change has a greater impact on the emission performance, and vice versa;

[0119] Function of environmental temperature changing with time , function of environmental humidity changing with time and function of electromagnetic interference intensity changing with time The calculation formula is:

[0120] ;

[0121] ;

[0122] ;

[0123] Among them, represents the average temperature, represents the amplitude of temperature fluctuation, represents the sine function, represents the angular frequency of time, represents the signal phase; represents the average humidity, represents the amplitude of humidity fluctuation, represents the coefficient of the autoregressive model, represents the lag order of the autoregressive model, represents the random component in the electromagnetic interference at the current moment;

[0124] The changes in temperature and humidity are simulated through the sine wave functions and , effectively capturing the periodic change characteristics in the environment. Especially for outdoor or scenarios with greater environmental influence, the temperature and humidity have obvious periodic changes, and the sine function can accurately fit these changes. Compared with a simple linear model, this description method can better reflect the actual impact of environmental changes on the beacon performance, thus improving the prediction accuracy of the emission performance;

[0125] The randomness and time correlation of the electromagnetic interference intensity are modeled through the autoregressive model . The beneficial effect of this model is that it can capture the lag effect and random fluctuation characteristics of the electromagnetic interference, especially suitable for complex electromagnetic environments. Compared with traditional instantaneous electromagnetic interference detection methods, the autoregressive model can better predict the future impact of interference on the emission performance, enabling the beacon system to make adjustments in advance to avoid a significant decline in performance;

[0126] The environmental impact rate is a comprehensive assessment of environmental changes, indicating the degree of disturbance of the environment on the beacon transmission performance;

[0127] According to the calculated environmental impact rate, the calibration parameters of the beacon transmission channel are dynamically adjusted to ensure the stable transmission performance of the beacon in different environments;

[0128] The calculated environmental impact rate is used to dynamically adjust the calibration parameters of the beacon transmission channel. This innovation solves the problem of how to convert complex environmental factors into operational calibration parameters. Traditional calibration methods are usually static and cannot adapt to real-time changes in the environment. Through dynamic calculation of the environmental impact rate, the system can adjust the parameters of the transmission channel in real time to ensure the stability of the transmission performance, especially in extreme or harsh environments. Dynamic adjustment can significantly improve the reliability and stability of the transmission performance.

[0129] S5 dynamically adjusts the calibration parameters of the beacon transmission channel according to the calculated impact rate;

[0130] When the beacon device leaves the factory or is first deployed, the preset initial calibration value is , the initial calibration value is the reference calibration value of the beacon transmission channel under ideal environmental conditions;

[0131] During equipment operation, the current ambient temperature, ambient humidity and electromagnetic interference intensity are monitored in real time to generate corresponding environmental status data , and ;

[0132] This step calculates the comprehensive environmental impact rate by real-time monitoring of the ambient temperature, humidity and electromagnetic interference. The beneficial effect of this calculation process is that it converts complex environmental factors into quantifiable impact parameters, so that the system can reasonably adjust the parameters of the beacon transmission channel based on the actual environmental conditions. Compared with the traditional static calibration method, the present invention ensures that the calibration parameters can be adjusted in real time with environmental changes by dynamically monitoring the environmental status, thus solving the problem that the beacon transmission performance is affected in extreme environments;

[0133] The real-time environmental impact rate is calculated based on the real-time environmental data through the environmental impact rate expression ;

[0134] According to the real-time monitoring beacon transmission signal amplitude and signal phase , and calculate its rate of change over time, the formula is:

[0135] ;

[0136] in, represents the differential symbol, indicating the derivative of the signal amplitude and signal phase with respect to time The change is differentiated, represents a small change in time;

[0137] By calculating the rate of change of the signal amplitude and phase, the system can capture the dynamic changes in the beacon transmission performance. Through the introduction of differential operations, the dependence on a single instantaneous signal value is avoided. By analyzing the speed of signal change to judge the fluctuation of the transmission performance, the error problem caused by the fluctuation of the beacon signal in a short time is solved, which is one of the core creative points of the present invention. Through the participation of the signal change rate, the calibration formula not only considers the influence of the environment, but also can perform more accurate calibration adjustment according to the rate of signal change;

[0138] According to the real-time environmental impact rate The calibration parameter adjustment formula is expressed as:

[0139] ;

[0140] Wherein, represents the dynamic calibration parameter of the transmission channel, which is the final calibration parameter after being adjusted by the environmental impact rate and signal change, and changes with time changes, represents the initial calibration value, represents the calibration coefficient;

[0141] According to the calculated dynamic calibration parameter , the relevant settings of the beacon transmission channel are automatically adjusted to keep the transmission performance of the beacon stable under the current environmental conditions;

[0142] After adjusting the calibration parameter , continue to monitor the beacon transmission performance, and fine-tune the calibration parameter according to the subsequent transmission data to ensure that the calibration process has self-adaptability and continuous optimization ability

[0143] The proposed formula solves the problem of how to effectively convert the environmental impact rate and signal change into specific calibration parameters. It not only provides a scientific and reasonable calibration basis for beacon devices, but also demonstrates the technical path of using mathematical models to guide practical engineering applications. In this way, even in extreme or non-standard environments, the beacon can achieve the best transmission performance through self-adjustment, which is of great significance for improving the overall efficiency of wireless communication systems.

[0144] This embodiment also provides a closed-loop calibration system for the Doppler very high frequency omnidirectional beacon transmission channel, including: a data acquisition module, a prediction model module, a state update module, an impact rate module, and a calibration adjustment module,

[0145] A data acquisition module, which is used to acquire beacon emission data and environmental status data and store them in a database;

[0146] A prediction model module, which is used to predict the change trend of beacon emission performance based on the deep learning model LSTM. This module generates predicted values of signal amplitude and phase by analyzing environmental data and emission data;

[0147] A status update module, which is used to generate an environmental status transition matrix according to the environmental status marking result and dynamically update the state transition probability;

[0148] An influence rate module, which is used to calculate the influence rate of environmental changes on beacon emission performance according to historical emission data, environmental state transition probability and phase noise level;

[0149] A calibration adjustment module, which is used to dynamically adjust the calibration parameters of the beacon emission channel according to the calculated environmental influence rate, so that the emission performance of the beacon remains stable under different environmental conditions.

[0150] This embodiment also provides a computer device, which is applicable to the case of the closed-loop calibration method for the Doppler very high frequency omnidirectional range emission channel, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the closed-loop calibration method for the Doppler very high frequency omnidirectional range emission channel proposed in the above embodiment.

[0151] This computer device can be a terminal. This computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (near field communication) or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of this computer device can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the computer device shell, or an external keyboard, a touchpad or a mouse, etc.

[0152] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the Doppler VHF omnidirectional range (VOR) transmitter channel closed-loop calibration method proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0153] In summary, the present invention resolves the problem that the beacon transmission performance is affected by environmental changes through a deep learning model and a dynamic calibration mechanism. The core of this method is to retrieve the transmission data of the beacon device under different environmental conditions and combine factors such as environmental temperature, humidity, and electromagnetic interference to monitor and calibrate the beacon transmission performance in real time.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A closed-loop calibration method for a Doppler very high frequency omnidirectional beacon transmission channel, characterized in that: include, Retrieve the transmission data of beacon equipment under different environmental conditions; Build a beacon prediction model based on the deep learning model, input the emission data and environmental status into the beacon prediction model for training, predict the trend of beacon emission performance changes, and mark the current environmental status; Based on the environmental state marking results and the performance change trend prediction results, the environmental state transfer matrix is ​​generated and the environmental state transfer probability is dynamically updated; The influence rate of environmental changes on beacon transmission performance is calculated based on historical transmission data, environmental state transition probability and phase noise level corresponding to each historical transmission data; Dynamically adjust the calibration parameters of the beacon transmission channel according to the calculated impact rate; The environmental conditions refer to indoor environment, outdoor environment and special environment; Use a thermometer to record the ambient temperature during each transmission test, use a hygrometer to record the ambient humidity during each transmission test, and use an electromagnetic interference detector to record the electromagnetic interference intensity during each transmission test; At the same time, the signal frequency, signal phase, signal amplitude and transmission time of each transmission are recorded; Preprocess the collected data; Store the preprocessed emission data and environmental status in a SQL database; The beacon prediction model is constructed according to the deep learning model, and the emission data is combined with the environmental state to input the beacon prediction model for training. The specific steps are: Use long short-term memory network LSTM as the basis of beacon prediction model; Build an architecture including input layer, hidden layer and output layer based on long short-term memory network LSTM; By inputting the transmission data and environmental conditions into the beacon prediction model, the predicted values ​​of the two performance indicators, signal amplitude and signal phase, are obtained. The expressions are: ； ； in, and Represents the beacon prediction model at time Predicted signal amplitude and signal phase, represents the signal amplitude, represents the signal phase, and represents the initial signal amplitude and initial signal phase, represents the natural attenuation coefficient of the signal amplitude, represents the cosine function, represents the sine wave amplitude in the signal amplitude, represents the cosine wave amplitude in the signal phase, represents the angular frequency of the sine and cosine functions, Indicates time; Mark the current environmental state according to the output of the signal prediction model and the change of environmental state; According to different environmental data, the environmental state is divided into normal state, high temperature state, high humidity state and strong electric field interference state; Thresholds T1, T2 and T3 are set for the environmental data. When the prediction value of the beacon prediction model exceeds the corresponding threshold, the beacon prediction model automatically determines the current environmental status based on the environmental data.

2. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 1, characterized in that: The environmental state transfer matrix is ​​generated based on the environmental state marking result and the performance change trend prediction result, and the environmental state transfer probability is dynamically updated. The specific steps are: Based on the results of environmental state labeling and the performance change trend predicted by the model, an environmental state transfer matrix is ​​generated. To define the transition probability between different states of the environment, the environment state transition matrix The expression is: ； in, , , and They represent the probability values ​​of normal state, high temperature state, high humidity state and strong electric field interference state respectively. represents the probability of going from the normal state to the normal state, represents the probability of going from a strong electric field interference state to a strong electric field interference state; According to the obtained environmental state transition probability combined with historical data and the prediction results of the signal prediction model, the environmental state transition probability is dynamically updated through the Bayesian update formula.

3. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 2, characterized in that: The method of calculating the impact rate of environmental changes on beacon transmission performance according to historical transmission data, environmental state transition probability and the phase noise level corresponding to each historical transmission data is specifically performed as follows: According to the historical data and the environmental state transfer matrix, combined with the predicted signal amplitude and signal phase changes, the impact rate of environmental changes on beacon transmission performance is calculated. The environmental impact rate expression is: ； in, is the environmental impact rate, which indicates the comprehensive impact of environmental changes on emission performance. Indicates the time period, is the function of ambient temperature changing with time, is the coefficient of temperature effect on beacon transmission performance, is the function of the change of ambient humidity over time, is the coefficient of humidity on beacon transmission performance, is the function of the electromagnetic interference intensity changing with time, is the coefficient of electromagnetic interference on beacon transmission performance, Represents differential.

4. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 3, characterized in that: The calibration parameters of the beacon transmission channel are dynamically adjusted according to the calculated influence rate. The specific steps are: When the beacon device leaves the factory or is first deployed, the preset initial calibration value is ; During equipment operation, the current ambient temperature, ambient humidity and electromagnetic interference intensity are monitored in real time to generate corresponding environmental status data , and ; The real-time environmental impact rate is calculated based on the real-time environmental data through the environmental impact rate expression ; According to the real-time monitoring beacon transmission signal amplitude and signal phase , and calculate its rate of change over time, the formula is: ； in, represents the differential symbol, Indicates small changes in time; According to the real-time environmental impact rate Construct the calibration parameter adjustment formula, expressed as: ； in, represents the dynamic calibration parameters of the transmit channel, represents the initial calibration value, represents the calibration factor; According to the calculated dynamic calibration parameters ; Adjusting the calibration parameters After that, continue to monitor the beacon transmission performance and fine-tune the calibration parameters based on subsequent transmission data.

5. A Doppler VHF omnidirectional beacon transmission channel closed-loop calibration system, based on the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method according to any one of claims 1 to 4, characterized in that: Including data collection module, prediction model module, status update module, impact rate module and calibration adjustment module, The data acquisition module is used to collect beacon emission data and environmental status data and store them in a database; The prediction model module is used to predict the change trend of the beacon transmission performance based on the deep learning model LSTM. The module generates the predicted values ​​of signal amplitude and phase by analyzing the environmental data and the transmission data; The state updating module is used to generate an environment state transfer matrix according to the environment state marking result, and dynamically update the state transfer probability; The impact rate module is used to calculate the impact rate of environmental changes on beacon transmission performance based on historical transmission data, environmental state transition probability and phase noise level; The calibration adjustment module is used to dynamically adjust the calibration parameters of the beacon transmission channel according to the calculated environmental impact rate, so that the beacon transmission performance remains stable under different environmental conditions.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method described in any one of claims 1 to 4 are implemented.

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