Doppler very high frequency omnidirectional beacon transmission channel closed-loop calibration system and method

Abstract

The invention discloses a Doppler very high frequency omnidirectional beacon transmission channel closed-loop calibration system and method, and relates to the technical field of calibration optimization, and the method comprises the steps: calling the transmission data of beacon equipment under different environment conditions; constructing a beacon prediction model according to the deep learning model, inputting the emission data in combination with the environment state into the beacon prediction model for training, predicting the change trend of the beacon emission performance, and marking the current environment state; generating an environment state transition matrix according to an environment state marking result in combination with a performance change trend prediction result, and dynamically updating an environment state transition probability; according to the historical emission data, the environmental state transition probability and the phase noise level corresponding to each piece of historical emission data, calculating the influence rate of the environmental change on the beacon emission performance; dynamically adjusting calibration parameters of the beacon transmitting channel according to the calculated influence rate; through a deep learning model and a dynamic calibration mechanism, the problem that the beacon emission performance is influenced by environmental changes is solved.

Description

Technical Field

[0001] The invention relates to the technical field of calibration optimization, in particular to a Doppler very high frequency omnidirectional beacon transmission channel closed-loop calibration system and method. Background Art

[0002] Doppler VOR technology plays an important role 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 VOR systems, the stability and accuracy of transmitted signals are crucial to the overall performance of navigation systems. 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 such as temperature, humidity, and electromagnetic interference, the transmission performance of beacon systems often fluctuates, resulting in a decrease in signal quality, which in turn affects navigation accuracy. In order to cope with 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 are usually difficult to cope with complex environmental changes and cannot accurately calibrate beacon transmission channels in real time and dynamically. At present, many beacon transmission systems adopt a static calibration mechanism, that is, the transmission channel is calibrated within a fixed time, rather than real-time adjustment 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 environment (high temperature, high humidity and strong electromagnetic interference), the amplitude and phase of the signal are prone to unpredictable deviations. In addition, the existing technology mostly uses a simple linear model to predict the performance changes of the transmitting beacon, and fails to fully consider the complex impact of environmental conditions on the beacon performance. With the advancement of machine learning and data processing technology, it has become possible to use deep learning models to construct a prediction model for beacon transmission performance. However, the existing technology has not yet effectively combined deep learning methods with beacon transmission performance calibration, resulting in lag and inaccuracy in the calibration system when responding to environmental changes, which in turn limits the scope of application of the beacon system. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a Doppler very high frequency omnidirectional beacon transmission channel closed-loop calibration system and method to solve the problem that the deep learning method has not been effectively combined with the beacon transmission performance calibration in the prior art, resulting in lag and inaccuracy in the calibration system when responding to environmental changes, thereby limiting the application scope of the beacon system.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a Doppler very high frequency omnidirectional beacon transmission channel closed-loop calibration method, which includes: 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; According to the calculated impact rate, the calibration parameters of the beacon transmission channel are dynamically adjusted.

[0006] As a preferred solution of the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method of the present invention, wherein: the transmission data of the beacon device under different environmental conditions is retrieved, and the specific steps are as follows: Different environmental conditions refer to indoor environment, outdoor environment and special environment; Choose to collect data in two different time periods: day and night; 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; The preprocessed emission data and environmental status are stored in a SQL database.

[0007] As a preferred solution of the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method of the present invention, wherein: the beacon prediction model is constructed according to the deep learning model, and the transmission data is combined with the environmental state to input the beacon prediction model for training, and the specific steps are as follows: 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.

[0008] As a preferred solution of the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method of the present invention, wherein: according to the environmental state marking result, combined with the performance change trend prediction result, the environmental state transfer matrix is ​​generated, 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.

[0009] As a preferred solution of the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method of the present invention, wherein: the influence rate of environmental changes on beacon transmission performance is calculated based on historical transmission data, environmental state transition probability and the phase noise level corresponding to each historical transmission data, and the specific steps are: According to the historical data and 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 expression of environmental impact rate 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.

[0010] As a preferred solution of the closed-loop calibration method of the Doppler VHF omnidirectional beacon transmission channel of the present invention, wherein: the calibration parameters of the beacon transmission channel are dynamically adjusted according to the calculated influence rate, and the specific steps are as follows: 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; 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.

[0011] According to the real-time environmental impact rate The calibration parameter adjustment formula is 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 ; When adjusting the calibration parameters After that, continue to monitor the beacon transmission performance and fine-tune the calibration parameters based on subsequent transmission data.

[0012] In a second aspect, the present invention provides a Doppler VHF omnidirectional beacon transmission channel closed-loop calibration system, including a data acquisition module, a prediction model module, a state update module, an influence rate module and a 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.

[0013] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the Doppler very high frequency omnidirectional beacon transmission channel closed-loop calibration method as described in the first aspect of the present invention is implemented.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the Doppler very high frequency omnidirectional beacon transmission channel closed-loop calibration method as described in the first aspect of the present invention is implemented.

[0015] The beneficial effects of the present invention are as follows: 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. The core of this method is to obtain the transmission data of the beacon device under different environmental conditions, combined with the factors of environmental temperature, humidity, and electromagnetic interference, to monitor and calibrate the beacon transmission performance in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a flow chart of the closed-loop calibration method for the Doppler VHF omnidirectional beacon transmission channel in Example 1.

[0018] Figure 2 This is a flow chart of the Doppler VHF omnidirectional beacon transmission channel closed-loop calibration system in Example 1. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a Doppler VHF omnidirectional beacon transmission channel closed-loop calibration method, comprising the following steps: S1 retrieves the transmission data of the beacon device under different environmental conditions; Different environmental conditions refer to indoor environment, outdoor environment and special environment; This step ensures the extensiveness and diversity of data by collecting data indoors, outdoors and in special environments. Different from the prior art, the present invention can more accurately evaluate the impact of environmental conditions on beacon transmission performance by fully capturing transmission data in different environments. For example, in high temperature or strong electromagnetic interference environments, the signal strength may drop significantly. By collecting these data, the system can establish a corresponding calibration model to ensure the stability of beacon transmission in extreme environments. Choose to collect data in two different time periods: day and night; This step further enriches the data dimension by collecting data in different time periods. Since temperature, humidity and electromagnetic interference may differ greatly during the day and at night, the present invention can capture the potential impact of time factors on beacon transmission performance through this detailed data collection strategy. This multi-dimensional data collection method is far superior to the traditional method of testing only in a single time period, ensuring the adaptability of the system in different time scenarios. 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, including data cleaning and standardization; The pre-processed emission data and environmental status are stored in the SQL database. By storing the pre-processed data in the SQL database, the structured management and efficient access of the data are ensured; Special environments include high temperature, high humidity and strong electromagnetic interference environments; This step is specifically for data collection in special environments such as high temperature, high humidity and strong electromagnetic interference, which has important technical significance. Beacon devices often show different characteristics in these extreme environments. Therefore, collecting transmission data in these environments helps to establish a more robust calibration model. Compared with the existing technology that only tests in conventional environments, the present invention can better ensure the comprehensiveness and adaptability of beacon transmission by expanding data collection to special environments, especially the reliability in harsh environments. The transmission data includes signal frequency, signal phase, signal amplitude and transmission time; Environmental conditions include ambient temperature, ambient humidity and electromagnetic interference intensity.

[0023] S2 builds a beacon prediction model based on the deep learning model, inputs the emission data into the beacon prediction model for training in combination with the environmental status, predicts the trend of beacon emission performance changes, and marks the current environmental status; Using the long short-term memory network LSTM as the basis of the beacon prediction model, LSTM can effectively process time series data, especially suitable for analyzing the changing trend of the beacon's transmission performance in different time periods; Build an architecture including input layer, hidden layer and output layer based on long short-term memory network LSTM; The input layer is used to input preprocessed environmental data and emission data; The hidden layer contains several LSTM units, which are used to capture long-term dependencies in time series; The output layer is used to output the predicted beacon transmission performance (predicted values ​​of signal amplitude and signal phase); The innovation of this step is to use LSTM network to analyze the time series data of beacon transmission performance. LSTM network can effectively process long-term dependencies in time series, showing the ability to remember historical data, and is suitable for processing the changing trend of beacon transmission performance over time. Traditional neural network algorithms often cannot capture long-term dependencies when processing time series data, while LSTM can effectively capture the long-term dependency problem in beacon transmission data through its unique gating mechanism, thus solving the common time span problem in beacon performance prediction; 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; This formula not only takes into account 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 combining time series analysis, it can more accurately fit the actual changes in beacon transmission performance, especially suitable for scenarios with large environmental fluctuations; In order to ensure that the model can accurately predict the impact of environmental changes on beacon transmission performance, the loss function is constructed by comprehensively considering the signal amplitude and phase noise of the transmission performance. The loss function expression is: ; in, represents the loss function value, and Represents the actual signal amplitude and actual signal phase at each time point, Represents an index variable, represents the total number of samples used to train the beacon prediction model, represents the coefficient of the regularization term; 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; According to the output of the signal prediction model and the change of the environmental state, the current environmental state is marked. 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. 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 environmental data. When the prediction value of the beacon prediction model exceeds the corresponding threshold, the beacon prediction model automatically determines the current environmental state based on the environmental data. When the predicted temperature value exceeds the set threshold value T1, it indicates a high temperature state; When the predicted humidity value exceeds the set threshold value T2, it indicates a high humidity state; When the predicted electromagnetic interference value exceeds the set threshold value T3, it indicates a strong electromagnetic interference state; When the predicted environmental data values ​​are all within the normal range, they are marked as normal; The invention marks the current environmental status by predicting the beacon transmission performance and combining it with environmental data. The innovation lies in that the mark is not only used to evaluate the beacon performance, but also can be used by the system to automatically adjust the calibration parameters of the transmission channel. Through real-time marking of the environment, the system can make timely adjustments under different environmental conditions (such as high temperature, high humidity and strong electromagnetic interference) to ensure the stability of the beacon transmission 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 and response speed of the system.

[0024] S3 generates an environmental state transfer matrix based on the environmental state marking results and combines the performance change trend prediction results to dynamically update the environmental state transfer probability; 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, and so on; This step generates an environmental state transfer matrix by combining the environmental state marking results and the beacon performance change trend , the uniqueness of this matrix is ​​that it can quantify the transition probability between different states of the environment, for example, represents the probability that the environment remains in the "normal state" from the "normal state", and It represents the probability of transferring from the "normal state" to the "high temperature state". By quantifying this probability, the dynamic changes of the environmental state can be effectively captured. This matrix processing is not only applicable to the beacon transmission environment of the present invention, but can also be extended to other dynamic environmental monitoring fields. Compared with the prior art, the present invention simplifies and quantifies the complex environmental state transfer problem through the introduction of the transfer matrix, which effectively improves the response speed and judgment accuracy of the system.

[0025] 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, and the expression is: ; in, represents the updated environment state transition probability, Indicates environmental conditions, Indicates from Status to The probability of the state, , and is an index variable with a value range of 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, Indicates that at a given signal amplitude and signal phase In the case of The probability of 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 makes the inference of environmental state no longer rely on simple historical data, but can respond to the current signal performance in real time. Secondly, 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 of high temperature or high humidity, this step can update the transfer matrix in time to ensure that the system can quickly adjust the beacon transmission parameters to avoid beacon performance degradation.

[0026] S4 calculates the impact rate of environmental changes on beacon transmission performance based on historical transmission data, environmental state transition probability and phase noise level corresponding to each historical transmission data; According to the historical data and 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 expression of environmental impact rate 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 equation; Through the environmental impact rate expression, the system can comprehensively evaluate the impact of temperature, humidity and electromagnetic interference on the beacon transmission performance. The innovation of this step is that the effects of multiple environmental factors are unified in an integral form to provide a quantitative evaluation index. Compared with the traditional static threshold judgment, the present invention can more accurately reflect the long-term impact of the environment on the transmission performance through integral calculation, which is particularly suitable for the working conditions of the beacon for long-term operation. Environmental impact rate The value range of is positive. The larger the value is, the greater the impact of environmental changes on the transmission performance is, and vice versa; Ambient temperature as a function of time , the function of environmental humidity changing with time And the electromagnetic interference intensity changes as a function of time The calculation formula is: ; ; ; in, represents the average temperature, represents the amplitude of temperature fluctuation, represents the sine function, represents the angular frequency of time, Indicates the signal phase; represents the average humidity, represents the amplitude of humidity fluctuations, 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; Through the sine wave function and To simulate the changes in temperature and humidity, it effectively captures the periodic change characteristics of the environment. Especially for outdoor scenes or scenes that are greatly affected by the environment, the temperature and humidity have obvious periodic changes. The sine function can fit these changes very accurately. Compared with the simple linear model, this description method can better reflect the actual impact of environmental changes on beacon performance, thereby improving the prediction accuracy of transmission performance. Randomness and time correlation of electromagnetic interference intensity through autoregressive model The model is beneficial in that it can capture the hysteresis effect and random fluctuation characteristics of electromagnetic interference, and is particularly 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 transmission performance, allowing the beacon system to make adjustments in advance to avoid a significant performance degradation. The environmental impact rate is a comprehensive assessment of environmental changes, indicating the degree of disturbance of the environment on the beacon transmission performance; 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; 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.

[0027] S5 dynamically adjusts the calibration parameters of the beacon transmission channel according to the calculated impact rate; 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; 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 ; 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; 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, which represents the difference between the signal amplitude and signal phase over time To derive the change of Indicates small changes in time; By calculating the rate of change of signal amplitude and phase, the system can capture the dynamic changes of beacon transmission performance. By introducing differential operation, it avoids relying on a single instantaneous signal value. By analyzing the speed of signal change to judge the fluctuation of transmission performance, it solves the error problem caused by the fluctuation of beacon signal in a short time. This is one of the core innovations of the present invention. Through the participation of signal change rate, the calibration formula not only takes into account the influence of the environment, but also can make more accurate calibration adjustments according to the rate of signal change. According to the real-time environmental impact rate The calibration parameter adjustment formula is expressed as: ; in, Indicates the dynamic calibration parameters of the transmission channel. This is the final calibration parameter after adjusting the environmental impact rate and signal change. change, represents the initial calibration value, represents the calibration factor; According to the calculated dynamic calibration parameters , automatically adjust the relevant settings of the beacon transmission channel so that the beacon's transmission performance remains stable under the current environmental conditions; When adjusting the calibration parameters After that, continue to monitor the beacon transmission performance and fine-tune the calibration parameters based on subsequent transmission data to ensure that the calibration process is adaptive and continuously optimized. The proposed formula solves the problem of how to effectively convert environmental impact rate and signal change into specific calibration parameters. It not only provides a scientific and reasonable calibration basis for beacon equipment, but also shows how to use mathematical models to guide the technical path of practical engineering applications. In this way, even in extreme or non-standard environments, beacons can achieve optimal transmission performance through self-adjustment, which is of great significance to improving the overall performance of wireless communication systems.

[0028] This embodiment also provides a Doppler VHF omnidirectional beacon transmission channel closed-loop calibration system, including: a data acquisition module, a prediction model module, a state update module, an influence rate module and a calibration adjustment module. A data acquisition module, 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 changing trend of beacon transmission performance based on the deep learning model LSTM. This module generates predicted values ​​of signal amplitude and phase by analyzing environmental data and transmission data; The state update module is used to generate the environment state transfer matrix according to the environment state marking result and dynamically update the state transfer probability; An 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.

[0029] This embodiment also provides a computer device, which is applicable to the case of a Doppler very high frequency omni-directional beacon transmission channel closed-loop calibration method, 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 Doppler very high frequency omni-directional beacon transmission channel closed-loop calibration method proposed in the above embodiment.

[0030] The computer device may be a terminal, and the 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 the computer device is used to provide computing and control capabilities. The memory of the 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 the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0031] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the closed-loop calibration method for the Doppler very high frequency omnidirectional beacon transmission channel proposed in the above embodiment is implemented; 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0032] In summary, 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. The core of this method is to obtain the transmission data of the beacon device under different environmental conditions, combined with the factors of environmental temperature, humidity, and electromagnetic interference, to monitor and calibrate the beacon transmission performance in real time.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in 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; According to the calculated impact rate, the calibration parameters of the beacon transmission channel are dynamically adjusted.

2. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 1, characterized in that: The specific steps of retrieving the transmission data of the beacon device under different environmental conditions are as follows: Different environmental conditions refer to indoor environment, outdoor environment and special environment; Choose to collect data in two different time periods: day and night; 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; The preprocessed emission data and environmental status are stored in a SQL database.

3. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 2, characterized in that: 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.

4. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 3, 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.

5. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 4, 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 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 expression of environmental impact rate 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.

6. The closed-loop calibration method for a Doppler VHF omnidirectional beacon transmission channel as claimed in claim 5, 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 ; When adjusting the calibration parameters After that, continue to monitor the beacon transmission performance and fine-tune the calibration parameters based on subsequent transmission data.

7. 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 6, 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.

8. 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 7 are implemented.

9. 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 7 are implemented.

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