An RTU encryption transmission device for high-altitude monitoring scenarios

Abstract

The present invention discloses an RTU encryption transmission device for high-altitude monitoring scenarios, which relates to the technical field of RTU encryption transmission. It includes a signal initialization transmission module, a signal strength detection and data acquisition module, a signal data preprocessing module, a signal attenuation feature extraction and analysis module, a signal attenuation prediction module, a signal attenuation classification module, a risk attenuation stage transmission rate adjustment module, and a non-risk attenuation stage transmission rate adjustment module. Through the initially set data transmission rate, real-time signal strength monitoring, data preprocessing, and signal attenuation feature extraction, and combined with the long short-term memory network model for intelligent prediction, the present invention realizes the dynamic distinction between the "risk attenuation" and "non-risk attenuation" stages. According to the prediction results, the transmission rate is reduced in the "risk attenuation" stage to reduce the risk of data loss; the rate is increased in the "non-risk attenuation" stage to accelerate the transmission of backlogged data and ensure the timeliness of real-time data.

Description

Technical Field

[0001] The present invention relates to the technical field of RTU encrypted transmission, and particularly relates to an RTU encrypted transmission device for high-altitude monitoring scenarios. Background Art

[0002] The RTU encrypted transmission device for high-altitude monitoring scenarios refers to a telemetry terminal unit (RTU, Remote Terminal Unit) encrypted communication device designed for specific environmental requirements in high-altitude areas. RTUs are usually used for remote monitoring and data collection, capable of collecting environmental data (such as temperature, humidity, atmospheric pressure, etc.) at different monitoring points and transmitting it to the central control system. In high-altitude regions, due to factors such as low atmospheric pressure, large temperature fluctuations, and strong electromagnetic interference, traditional communication devices are easily affected or unable to operate stably. Therefore, RTU devices need to be specially designed to work stably under extreme conditions such as low temperature, low air pressure, and high radiation. At the same time, encrypted transmission technology is used to ensure the information security during data transmission, preventing monitoring data from being illegally stolen or tampered with. Such devices usually integrate advanced encryption algorithms to ensure the secure transmission of data from the collection end to the receiving end, ensuring the integrity and confidentiality of the data, which is particularly crucial in sensitive environmental monitoring.

[0003] The existing technologies have the following deficiencies:

[0004] In high-altitude monitoring scenarios, RTU encrypted transmission devices face challenges in wireless signal stability. In high-altitude areas, the air is thin, the air pressure is low, and meteorological conditions change frequently, all of which have an adverse impact on the wireless communication system. Specifically, the lower air density leads to increased energy loss during the propagation of radio waves, and the signal strength is significantly lower than that under sea-level conditions, thereby increasing the risk of signal attenuation, which may result in signal loss or instability. When the attenuation degree of the wireless signal is large, the existing transmission technologies may not be able to effectively cope with it, especially in the case of a large amount of data, and the phenomenon of data loss is particularly serious. This not only means that real-time monitoring data cannot be transmitted in a timely manner, but also may lead to a delay or complete failure of the monitoring system's response to environmental changes. For example, if key monitoring data (such as temperature, air pressure, etc.) fails to be transmitted to the central control system in a timely manner, it may lead to the failure of early warnings for emergencies (such as meteorological disasters, landslides, fires, etc.), seriously affecting the timeliness and accuracy of emergency responses.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide an RTU encryption transmission device for high-altitude monitoring scenarios. Through the initially set data transmission rate, real-time signal strength monitoring, data preprocessing, and signal attenuation feature extraction, combined with a long short-term memory network model for intelligent prediction, the dynamic distinction between the risk attenuation stage and the risk-free attenuation stage is achieved. According to the prediction results, the transmission rate is reduced during the risk attenuation stage to reduce the risk of data loss; during the risk-free attenuation stage, the rate is increased to accelerate the transmission of backlogged data, ensuring the timeliness of real-time data. This intelligent transmission strategy guarantees data integrity, improves the system response speed, enhances the stability, accuracy, and emergency response ability of the monitoring system in high-altitude environments, and improves the efficiency of environmental monitoring and emergency response to solve the problems in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: An RTU encryption transmission device for high-altitude monitoring scenarios, including a signal initialization transmission module, a signal strength detection and data acquisition module, a signal data preprocessing module, a signal attenuation feature extraction and analysis module, a signal attenuation prediction module, a signal attenuation classification module, a transmission rate adjustment module for the risk attenuation stage, and a transmission rate adjustment module for the risk-free attenuation stage;

[0008] The signal initialization transmission module starts the wireless signal transmission at the initially set data transmission rate to ensure that the data transmission rate under normal signal conditions meets the requirements;

[0009] The signal strength detection and data acquisition module continuously detects and records the real-time changes in the wireless signal strength through the wireless module during data transmission, and obtains the signal strength of the wireless signal in real time to provide the original data for subsequent signal attenuation analysis;

[0010] The signal data preprocessing module preprocesses the acquired data after obtaining the original signal strength data to improve the quality of the data and ensure the accuracy of subsequent analysis;

[0011] The signal attenuation feature extraction and analysis module extracts the features reflecting the signal strength attenuation from the preprocessed signal strength data, and further analyzes the extracted attenuation features under the monitoring window to quantify the severity and change trend of signal attenuation;

[0012] The signal attenuation prediction module, after analyzing the attenuation features, inputs the analyzed features into a pre-trained long short-term memory network model to make an intelligent prediction of the signal strength attenuation;

[0013] The signal attenuation classification module divides the signal attenuation situation into two categories: risk attenuation and risk-free attenuation based on the prediction results of the long short-term memory network model;

[0014] During the risk attenuation stage, the transmission rate adjustment module dynamically reduces the actual data transmission rate according to the prediction results of the long short-term memory network model to reduce the risks of signal loss and transmission failure.

[0015] During the risk-free attenuation stage, the transmission rate adjustment module accelerates the transmission of backlogged data by adjusting the actual data transmission rate to achieve fast response.

[0016] Preferably, the initially set data transmission rate is determined based on environmental conditions and the estimated data volume. The specific steps are as follows:

[0017] First, evaluate the communication environment in high-altitude areas.

[0018] Then, calculate the initial data transmission rate range according to the expected monitoring data volume.

[0019] Finally, select an optimal data transmission rate as the initial data transmission rate to balance communication efficiency and signal stability.

[0020] Preferably, after the signal strength data is preprocessed, features reflecting signal strength attenuation are extracted. Among them, the extracted features include the energy lost by the wireless signal during propagation and the change in link loss during the wireless signal transmission process. The energy lost by the wireless signal during propagation and the change in link loss during the wireless signal transmission process are further analyzed under the monitoring window to generate a signal propagation loss evaluation value and a wireless link loss evaluation value respectively, and the severity and change trend of signal attenuation are quantified through the signal propagation loss evaluation value and the wireless link loss evaluation value.

[0021] Preferably, the specific steps for further analyzing the energy lost by the wireless signal during propagation under the monitoring window to generate a signal propagation loss evaluation value are as follows:

[0022] Set the monitoring window as where and represent the start time and end time of the monitoring window respectively;

[0023] Within the monitoring window, the actual received signal strength and transmit power are recorded in real time. At the same time, the theoretical propagation strength is calculated using the extended model of the free space path loss formula. The calculation expression is: where: is the theoretical propagation strength, representing the expected strength when the signal propagates along the free space path, represents the signal transmit power at time point t, is the real-time distance of signal propagation, is the path loss factor, used to adjust the sensitivity of signal loss in different propagation environments, is the exponential factor of the propagation distance;

[0024] By calculating the difference between the actual received signal strength and the theoretical propagation strength, the propagation loss characteristic is obtained. The calculation expression of the propagation loss characteristic is: , where is the propagation loss characteristic, which is used to describe the deviation degree between the actual received signal strength and the theoretical propagation strength, is the actual received signal strength;

[0025] In the monitoring window , the change trend of the propagation loss characteristic is quantified as the signal propagation loss evaluation value. The quantification expression is: , where, is the signal propagation loss evaluation value, which is used to quantify the severity of attenuation, , represents the instantaneous change rate of the propagation loss characteristic, indicating the trend of the loss increasing or decreasing with time, is the weight function, which is used to dynamically adjust the time importance of the loss characteristic, is the weighted accumulation of the propagation loss change rate, is the normalization factor, which ensures that the calculation of the signal propagation loss evaluation value is independent of the duration of the monitoring window.

[0026] Preferably, the specific steps for further analyzing the change of the link loss in the wireless signal transmission process under the monitoring window to generate the wireless link loss evaluation value are as follows:

[0027] Under the monitoring window, calculate the link loss in the wireless signal transmission process. The calculation expression is: , where: represents the link loss value at time point t, represents the signal reception power at time point t, represents the environmental correction factor;

[0028] Input the link loss value into the exponential decay model to generate the wireless link loss evaluation value, and further analyze the change trend of the link loss. The generation expression of the wireless link loss evaluation value is: , where: represents the exponential decay factor related to time t, which is used to represent the attenuation intensity, represents the wireless link loss evaluation value, which reflects the cumulative impact of the link loss.

[0029] Preferably, after analyzing the attenuation characteristics, the evaluated signal propagation loss value and the wireless link loss evaluation value after analysis are input into a pre-trained long short-term memory network model. Based on the long short-term memory network model, a signal attenuation evaluation index is generated, and the attenuation situation of the signal strength is intelligently predicted through the signal attenuation evaluation index.

[0030] Preferably, when the attenuation situation of the signal strength is intelligently predicted through a pre-trained long short-term memory network model, the signal attenuation evaluation index generated is compared and analyzed with a pre-set signal attenuation evaluation index reference threshold to classify the signal attenuation situation. The specific classification steps are as follows:

[0031] If the signal attenuation evaluation index is greater than the signal attenuation evaluation index reference threshold, the signal attenuation situation in this monitoring window is classified as risk attenuation;

[0032] If the signal attenuation evaluation index is less than or equal to the signal attenuation evaluation index reference threshold, the signal attenuation situation in this monitoring window is classified as risk-free attenuation.

[0033] Preferably, in the risk attenuation stage, the actual data transmission rate is dynamically reduced according to the prediction result of the long short-term memory network model. The specific steps are as follows:

[0034] When it is detected that the signal attenuation evaluation index is greater than the signal attenuation evaluation index reference threshold, enter the risk attenuation stage and dynamically reduce the actual data transmission rate. The specific expression for the reduction of the actual data transmission rate is: , where is the initially set data transmission rate, is the signal attenuation evaluation index in the current monitoring window, is the pre-set signal attenuation evaluation index reference threshold, is the adjustment coefficient, which is used to control the sensitivity of the reduction of the transmission rate.

[0035] Preferably, when the signal attenuation evaluation index is less than or equal to the signal attenuation evaluation index reference threshold, enter the risk-free attenuation stage and dynamically increase the actual data transmission rate. The specific expression for the increase of the actual data transmission rate is: , where is the rate increase coefficient, which controls the rate increase ratio based on the backlogged data volume, is the currently backlogged data volume, is the maximum backlogged data volume allowed by the system, is the rate increase sensitivity coefficient, which adjusts the rate adjustment amplitude based on the signal attenuation evaluation index.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention:

[0037] Ensure efficient transmission under normal signal conditions through the initially set data transmission rate. Combine real-time signal strength monitoring with data preprocessing to accurately extract and quantify signal attenuation characteristics. Use a pre-trained long short-term memory network model for intelligent prediction, enabling the system to dynamically distinguish between risk attenuation and non-risk attenuation stages and flexibly adjust the transmission rate according to the prediction results. During the risk attenuation stage, dynamically reducing the transmission rate effectively reduces the risk of data loss and transmission failure; during the non-risk attenuation stage, increasing the transmission rate speeds up the transmission of backlogged data, ensuring the timeliness of real-time monitoring data and the rapidity of system response. This intelligent and dynamically adjusted transmission strategy not only guarantees the integrity and accuracy of key monitoring data but also significantly improves the timeliness of emergency response and the operating efficiency of the system, ensuring that the monitoring system can operate stably and reliably in the complex and changeable high-altitude environment, timely warn and respond to emergencies, and greatly enhance the capabilities of environmental monitoring and emergency management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0039] Figure 1 It is a module schematic diagram of an RTU encryption transmission device for high-altitude monitoring scenarios according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0041] The present invention provides an RTU encryption transmission device for high-altitude monitoring scenarios as shown in Figure 1 which includes a signal initialization transmission module, a signal strength detection and data acquisition module, a signal data preprocessing module, a signal attenuation feature extraction and analysis module, a signal attenuation prediction module, a signal attenuation classification module, a transmission rate adjustment module for the risk attenuation stage, and a transmission rate adjustment module for the non-risk attenuation stage;

[0042] The signal initialization transmission module starts wireless signal transmission at the initially set data transmission rate to ensure that the data transmission rate under normal signal conditions meets the requirements;

[0043] The initially set data transmission rate is usually set based on environmental conditions and the estimated data volume. Its main function is to ensure that under normal signal conditions, the system can start data transmission at a moderate rate to guarantee the stability and efficiency of the transmission. The initially set transmission rate is the baseline rate of the system, and subsequent adjustments are optimized based on this rate. If the initial rate is set too low, it may lead to untimely responses; if set too high, it may cause packet loss and data transmission failures due to high signal attenuation. Therefore, the selection of the initial rate is crucial as it provides the basis for subsequent data transmission adjustments.

[0044] The initially set data transmission rate is mainly determined based on environmental conditions (such as altitude, meteorological factors, geographical location, etc.) and the estimated data volume. First, it is necessary to evaluate the communication environment in high-altitude areas, including factors such as the frequency of meteorological changes and the stability of wireless communication links. Then, based on the expected monitoring data volume (including the frequency of data, the size of data packets, etc.), a suitable range of initial data transmission rates is calculated. The system selects an optimal data transmission rate as the initially set data transmission rate according to these evaluation results to ensure that in most cases, the communication efficiency and signal stability can be balanced, avoiding packet loss caused by too high a rate or response delays caused by too low a rate. The setting of this rate provides a basic reference for subsequent adjustments of data transmission.

[0045] The signal strength detection and data acquisition module continuously detects and records the real-time changes in the wireless signal strength through the wireless module during data transmission, and obtains the signal strength of the wireless signal in real time, providing the original data for subsequent signal attenuation analysis;

[0046] Signal strength is an important indicator for evaluating the quality of wireless communication. It reflects factors such as signal attenuation degree and noise interference during the transmission process. In high-altitude scenarios, due to factors such as low air pressure and thin air, the signal strength may fluctuate frequently. Therefore, it is necessary to monitor the signal changes in real time. The acquisition of real-time signal strength provides data support for the entire intelligent prediction process to ensure the accuracy of subsequent processing.

[0047] The signal data preprocessing module preprocesses the acquired data after obtaining the original signal strength data to improve the quality of the data and ensure the accuracy of subsequent analysis;

[0048] The preprocessing step cleans, filters, denoises, etc. these data. The goal of this process is to eliminate unnecessary noise, smooth the signal, improve the quality of the data, and ensure the accuracy of subsequent analysis. The signal strength data may be interfered by external environments, equipment errors, meteorological factors, etc., resulting in fluctuations or outliers in the data. Through preprocessing, these interference factors are removed to ensure that the data is more stable and reliable. The preprocessed signal strength data can accurately reflect the true situation of the wireless signal, thus providing high-quality basic data for subsequent feature extraction and attenuation analysis.

[0049] The signal attenuation feature extraction and analysis module, after the signal strength data is preprocessed, extracts the features reflecting the signal strength attenuation from it, and further analyzes the extracted attenuation features under the monitoring window to quantify the severity and change trend of the signal attenuation;

[0050] After the signal strength data is preprocessed, the features reflecting the signal strength attenuation are extracted from it. Among them, the extracted features include the energy lost by the wireless signal during propagation and the change of the link loss during the wireless signal transmission process. The energy lost by the wireless signal during propagation and the change of the link loss during the wireless signal transmission process are further analyzed under the monitoring window, and the signal propagation loss evaluation value and the wireless link loss evaluation value are generated respectively. The severity and change trend of the signal attenuation are quantified through the signal propagation loss evaluation value and the wireless link loss evaluation value.

[0051] The greater the energy lost by the wireless signal during propagation, usually the more severe the signal attenuation. The propagation loss of the wireless signal mainly includes factors such as free space path loss, environmental scattering, refraction, absorption, etc. Especially in high-altitude areas, the air is thin and the air pressure is low, and the energy loss suffered by the signal during propagation is more significant. When the signal propagates, for every certain distance passed, the signal energy will attenuate with the square increase of the distance, and as the air density decreases, the energy loss during signal propagation will be more aggravated. This attenuation will not only cause the signal strength to weaken, but may also cause the signal quality to decline, resulting in problems such as error codes and packet loss, further affecting the stability and accuracy of data transmission. When the signal attenuation reaches a certain level, even if a higher transmission power is used, the signal may still not be able to reach the receiving end or cannot meet the communication quality requirements. Therefore, the increase in propagation loss is usually a direct manifestation of severe signal attenuation.

[0052] The specific steps for further analyzing the energy lost by the wireless signal during propagation under the monitoring window to generate the signal propagation loss evaluation value are as follows:

[0053] The energy lost during signal propagation is quantified by the deviation between the received signal strength and the theoretical propagation model. Set the monitoring window as , where and respectively represent the start time and end time of the monitoring window. Within the monitoring window, the actual received signal strength and transmission power are recorded in real time. Meanwhile, the theoretical propagation strength is calculated using the extended model of the free space path loss formula, and the calculation expression is: , where: is the theoretical propagation strength, representing the expected strength when the signal propagates along the free space path, represents the signal transmission power at time point t, is the real-time distance of signal propagation, which can be calculated from the location information of the device, is the path loss factor, used to adjust the sensitivity of signal loss to different propagation environments, is the exponential factor of the propagation distance, usually taking a value of 2 (free space) or higher (complex environment);

[0054] By calculating the difference between the actual received signal strength and the theoretical propagation strength, the propagation loss characteristics are obtained. The calculation expression of the propagation loss characteristics is: , where is the propagation loss characteristic, used to describe the deviation degree between the actual received signal strength and the theoretical propagation strength, is the actual received signal strength;

[0055] This step extracts multi-dimensional factors (such as distance, environmental changes, etc.) that affect signal propagation loss and describes the instantaneous characteristics of propagation loss through the propagation loss characteristic to provide a basis for subsequent quantification.

[0056] Within the monitoring window , the change trend of the propagation loss characteristic is quantified into the signal propagation loss evaluation value. The quantification expression is: , where is the signal propagation loss evaluation value, used to quantify the severity of attenuation, , represents the instantaneous change rate of the propagation loss characteristic, indicating the trend of increasing or decreasing loss over time, is the weight function, used to dynamically adjust the time importance of the loss characteristic, is the weighted accumulation of the propagation loss change rate, is the normalization factor, ensuring that the calculation of the signal propagation loss evaluation value is independent of the duration of the monitoring window;

[0057] This step converts the propagation loss characteristic into an intuitive signal propagation loss evaluation value, quantifying the severity and change trend of signal attenuation. The non-linear characteristic of the signal propagation loss evaluation value makes it more sensitive to severe attenuation, and at the same time, the time weighting strengthens the impact of recent attenuation on the index, thereby improving the timeliness and accuracy of the calculation result.

[0058] It can be seen from the signal propagation loss evaluation value that the larger the performance value of the signal propagation loss evaluation value generated by further analyzing the energy lost by the wireless signal during the propagation process under the monitoring window, the more serious the energy loss of the wireless signal during the propagation process. The signal propagation loss evaluation value is calculated based on the propagation loss characteristics of the signal and its change trend, and its value reflects the degree of energy attenuation of the signal from transmission to reception. Specifically, the signal propagation loss evaluation value amplifies the severity of signal attenuation by weighted accumulation of signal attenuation characteristics and exponential processing of change trends. A larger signal propagation loss evaluation value indicates that the signal loss increases rapidly or significantly during the monitoring window, indicating that the attenuation of the wireless signal is more serious, which may lead to data loss or reduced transmission quality. A smaller signal propagation loss evaluation value means that the signal attenuation is lighter, the loss is smaller, and the signal quality during transmission is better. Therefore, the attenuation degree of the signal can be directly quantified by the size of the signal propagation loss evaluation value, which helps to dynamically adjust the data transmission strategy and optimize the communication quality in complex environments such as high altitudes.

[0059] Large changes in link loss during wireless signal transmission usually mean more severe signal attenuation. Link loss refers to the energy loss caused by various factors (such as distance, obstacles, atmospheric conditions, terrain undulations, etc.) during signal transmission. When link loss changes dramatically, it means that the signal has encountered strong attenuation or interference during propagation. For example, in high-altitude areas, the loss of wireless signal transmission will increase due to thin air, low air pressure, and frequent changes in meteorological conditions. The increase in link loss may be caused by factors such as atmospheric refraction, signal path loss, and frequency interference. As the change in link loss increases, the signal strength will be significantly reduced, resulting in unstable signal quality, and even packet loss, transmission delay, or communication interruption. Therefore, large changes in link loss usually indicate that the signal attenuation is more serious, which may affect the normal operation of the communication system.

[0060] The specific steps for further analyzing the changes in link loss during wireless signal transmission in the monitoring window to generate a wireless link loss assessment value are as follows:

[0061] In the monitoring window, the link loss during wireless signal transmission is calculated. Link loss is the signal strength loss caused by various factors (such as distance, obstacles, atmospheric changes, etc.) during signal propagation. In order to quantify the impact of link loss, the link loss is calculated using the following formula: ,in: represents the link loss value at time point t, ( represents the signal transmission power at time point t) Indicates the signal reception power at time point t, Indicates an additional environmental correction factor caused by factors such as meteorological conditions and path loss;

[0062] By calculating the link loss at each time point, the attenuation of the signal can be preliminarily quantified. The higher the link loss value, the more severe the signal attenuation. At this time, environmental factors (such as meteorological changes and terrain obstacles) during signal transmission also need to be corrected. Therefore, an environmental correction factor is added to enhance the capture of environmental impacts.

[0063] Input the link loss value into the exponential decay model to generate a wireless link loss evaluation value, and further analyze the change trend of the link loss. The generation expression of the wireless link loss evaluation value is: , where: (where and represent the start time and end time of the monitoring window respectively) Indicates the exponential decay factor related to time t, which is usually related to environmental changes (such as air pressure, temperature, humidity, meteorological changes, etc.) and is used to represent the attenuation intensity, Indicates the wireless link loss evaluation value, which reflects the cumulative impact of the link loss;

[0064] By introducing the change of the link loss value into the exponential decay model, a wireless link loss evaluation value is generated. The change trend of the wireless link loss evaluation value can reflect the cumulative effect and intensity of signal attenuation. When the wireless link loss evaluation value increases significantly, it indicates severe attenuation and the stability of the system is greatly affected. The wireless link loss evaluation value not only examines the instantaneous loss but also evaluates its continuous impact in the time dimension, and can effectively quantify the severity and trend change of signal attenuation. Therefore, the generated wireless link loss evaluation value is an important indicator for dynamically monitoring signal attenuation and its potential risks.

[0065] From the wireless link loss evaluation value, it can be seen that the larger the performance value of the wireless link loss evaluation value generated by further analyzing the change of the link loss during the wireless signal transmission process under the monitoring window, generally means that the attenuation of the wireless signal is more serious. This is because the wireless link loss evaluation value is generated based on the cumulative effect and change trend of the link loss during the signal propagation process. As the link loss increases, the exponential value grows exponentially. When the link loss is large, the interference and attenuation suffered by the signal during transmission are also more serious, resulting in a significant decrease in the received signal strength and unstable signal quality. Therefore, a higher wireless link loss evaluation value reflects a higher signal attenuation risk. When the wireless link loss evaluation value is small, it means that the signal attenuation is relatively light, the signal transmission quality is good, and the system stability is high. Therefore, the performance value of the wireless link loss evaluation value can effectively quantify the severity of signal attenuation and help monitor and evaluate the health status of signal transmission.

[0066] The signal attenuation prediction module, after analyzing the attenuation characteristics, inputs the analyzed characteristics into a pre-trained long short-term memory network model to intelligently predict the attenuation of the signal strength;

[0067] After analyzing the attenuation characteristics, the analyzed signal propagation loss evaluation value and wireless link loss evaluation value are input into a pre-trained long short-term memory network model, and a signal attenuation evaluation index is generated based on the long short-term memory network model. The attenuation of the signal strength is intelligently predicted through the signal attenuation evaluation index.

[0068] The pre-trained long short-term memory network model (LSTM, Long Short-Term Memory) refers to that in practical applications, through a large amount of historical data and known signal propagation characteristics, it is trained using deep learning algorithms so that it can capture and predict the temporal change trend of signal attenuation. LSTM is a special type of recurrent neural network (RNN), specifically designed to process and predict temporal data. Different from traditional neural networks, LSTM can effectively remember the data characteristics within a long time range, and through its unique gating mechanism (such as input gate, forget gate, output gate), it can avoid the common gradient disappearance problem in traditional RNNs, thus maintaining high learning ability in long time series.

[0069] In a given scenario, the inputs to the LSTM model include the signal propagation loss evaluation value and the wireless link loss evaluation value. These two parameters reflect the severity and trend of signal attenuation. This data itself has temporal characteristics and may fluctuate due to factors such as environmental changes, meteorological conditions, and physical obstacles. Therefore, it is difficult for traditional machine learning models to accurately capture these temporal dynamic relationships. The LSTM network can predict the signal attenuation trend in the future for a period of time by learning the temporal patterns of historical link losses and signal attenuations, and calculate the signal attenuation evaluation index. In this way, the LSTM can not only predict the change of signal strength at the current moment, but also respond in advance on the time axis, identify the upcoming attenuation trend, and thus provide an important basis for subsequent decisions.

[0070] The data during the training process usually contains information in multiple dimensions, such as link loss, transmission rate, environmental conditions, signal strength, etc. These input features can reflect different aspects of signal attenuation. By training on historical signal attenuation data, the LSTM network can automatically extract the temporal patterns hidden in these complex data and form an adaptive prediction mechanism. The model continuously adjusts and optimizes the network weights, enabling it to accurately predict the future signal attenuation situation and generate a signal attenuation evaluation index. This evaluation index is based on historical data and the learning ability of the network, and reflects the health status and future trend of the current network signal in real time, providing decision support for the stable operation of wireless communication systems in complex environments such as high altitudes.

[0071] Through the pre-trained LSTM model, the system can identify immediate signal attenuation problems. This intelligent prediction mechanism can effectively improve the reliability of the communication system, especially in extreme environments such as high altitudes, timely respond to challenges brought by environmental changes, prevent signal loss or transmission delay, and thus ensure the accuracy of real-time monitoring data and the timeliness of system response.

[0072] The above long short-term memory network model is not specifically limited here, and any long short-term memory network model that can achieve comprehensive analysis of the signal propagation loss evaluation value and the wireless link loss evaluation value to generate a signal attenuation evaluation index is acceptable. To implement the technical solution of the present invention, the present invention provides a specific implementation; the calculation formula for generating the signal attenuation evaluation index is: , where, , are the preset proportionality coefficients of the signal propagation loss evaluation value and the wireless link loss evaluation value respectively, and , are both greater than 0.

[0073] From the signal attenuation evaluation index, the larger the performance value of the signal propagation loss evaluation value generated by further analyzing the energy lost by the wireless signal during propagation under the monitoring window, and the larger the performance value of the wireless link loss evaluation value generated by further analyzing the change in the link loss during the wireless signal transmission process under the monitoring window. That is, when the intelligent prediction of the attenuation of the signal strength is performed through a pre-trained long short-term memory network model, the larger the performance value of the signal attenuation evaluation index generated, it indicates that the wireless signal attenuates more severely. On the contrary, it indicates that the wireless signal attenuation is not severe.

[0074] The signal attenuation classification module divides the signal attenuation situation into two categories: risk attenuation and risk-free attenuation based on the prediction results of the long short-term memory network model;

[0075] Compare and analyze the signal attenuation evaluation index generated when the intelligent prediction of the attenuation of the signal strength is performed through a pre-trained long short-term memory network model with the pre-set signal attenuation evaluation index reference threshold to divide the signal attenuation situation. The specific division steps are as follows:

[0076] If the signal attenuation evaluation index is greater than the signal attenuation evaluation index reference threshold, the signal attenuation situation under this monitoring window is divided into risk attenuation;

[0077] Risk attenuation means that the degree of signal attenuation exceeds the range that the system can tolerate, indicating that the signal quality has dropped to a level that may cause communication failure or data loss.

[0078] If the signal attenuation evaluation index is less than or equal to the signal attenuation evaluation index reference threshold, the signal attenuation situation under this monitoring window is divided into risk-free attenuation.

[0079] Risk-free attenuation means that the degree of signal attenuation is within an acceptable range and will not have a significant impact on communication quality.

[0080] The transmission rate adjustment module in the risk attenuation stage dynamically reduces the actual data transmission rate according to the prediction results of the long short-term memory network model during the risk attenuation stage to reduce the risk of signal loss and transmission failure;

[0081] During the risk attenuation stage, dynamically reduce the actual data transmission rate according to the prediction results of the long short-term memory network model. The specific steps are as follows:

[0082] When it is detected that the signal attenuation evaluation index is greater than the signal attenuation evaluation index reference threshold, enter the risk attenuation stage. At this time, in order to reduce the risk of signal loss and transmission failure, it is necessary to dynamically reduce the actual data transmission rate. The specific expression for the reduction of the actual data transmission rate is: , where is the initially set data transmission rate, is the signal attenuation evaluation index under the current monitoring window, is the pre-set reference threshold of the signal attenuation evaluation index, is an adjustment coefficient used to control the sensitivity of the transmission rate reduction, usually determined through experiments;

[0083] The key role of this step is to ensure the stability of the transmission process by adapting to the changes in signal quality. When the wireless signal attenuation is severe, reducing the data transmission rate can reduce signal conflicts, avoid network congestion, and improve the reliability of data transmission.

[0084] The transmission rate adjustment module in the risk-free attenuation stage. In the risk-free attenuation stage, that is, when the signal attenuation is small, it speeds up the transmission of backlogged data by adjusting the actual data transmission rate to achieve fast response;

[0085] When the signal attenuation evaluation index is less than or equal to the reference threshold of the signal attenuation evaluation index, it enters the risk-free attenuation stage. At this time, in order to speed up the transmission of backlogged data and achieve fast response, it is necessary to dynamically increase the actual data transmission rate. The specific expression for the increase in the actual data transmission rate is: , where is the rate increase coefficient, which controls the rate increase ratio based on the amount of backlogged data, is the current amount of backlogged data, is the maximum amount of backlogged data allowed by the system, is the rate increase sensitivity coefficient, which adjusts the rate adjustment amplitude based on the signal attenuation evaluation index;

[0086] This formula combines the amount of backlogged data and the current signal attenuation situation, and dynamically increases the data transmission rate through linear and non-linear combination methods. When the amount of backlogged data is large and the signal attenuation is low, the transmission rate is significantly increased to quickly clear the backlogged data and ensure that the system can respond to environmental changes and emergencies in a timely manner. At the same time, by adjusting the rate increase sensitivity coefficient , the system can flexibly adjust the rate increase amplitude according to the real-time signal quality, avoiding new transmission problems caused by excessive rate increase.

[0087] This step ensures that the system can quickly transmit data when the signal conditions are good, reduce the delay caused by signal instability, and improve the real-time response ability. In a high-altitude environment, real-time performance is crucial, especially when emergencies (such as meteorological disasters, landslides, fires, etc.) require emergency response. Through this real-time data transmission and dynamic adjustment mechanism, the system can maintain efficient data flow, ensure that key monitoring data can be timely fed back to the central control system, and improve the efficiency and accuracy of emergency response.

[0088] Through the implementation of the above solution, the RTU encryption transmission device significantly improves the stability of wireless communication and the reliability of data transmission in high-altitude monitoring scenarios. This solution ensures efficient transmission under normal signal conditions through the initially set data transmission rate, combines real-time signal strength monitoring with data preprocessing, and accurately extracts and quantifies signal attenuation characteristics. An intelligent prediction is made using a pre-trained long short-term memory network (LSTM) model, enabling the system to dynamically distinguish between risk attenuation and non-risk attenuation stages and flexibly adjust the transmission rate according to the prediction results. During the risk attenuation stage, dynamically reducing the transmission rate effectively reduces the risk of data loss and transmission failure; during the non-risk attenuation stage, increasing the transmission rate speeds up the transmission of backlogged data, ensuring the timeliness of real-time monitoring data and the rapidity of system response. Overall, this intelligent and dynamically adjusted transmission strategy not only guarantees the integrity and accuracy of key monitoring data but also greatly improves the timeliness of emergency response and the operating efficiency of the system, ensuring that the monitoring system can operate stably and reliably in the complex and changeable high-altitude environment, promptly warning and responding to emergencies, and greatly enhancing the capabilities of environmental monitoring and emergency management.

[0089] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0090] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0091] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. An RTU encrypted transmission device for high altitude monitoring scenarios, characterized in that: It includes a signal initialization transmission module, a signal strength detection and data acquisition module, a signal data preprocessing module, a signal attenuation feature extraction and analysis module, a signal attenuation prediction module, a signal attenuation classification module, a risk attenuation stage transmission rate adjustment module, and a risk-free attenuation stage transmission rate adjustment module; The signal initialization transmission module starts the wireless signal transmission at the initially set data transmission rate to ensure that the data transmission rate meets the requirements under normal signal conditions; Signal strength detection and data acquisition module: During the data transmission process, the wireless module continuously detects and records the real-time changes of wireless signal strength, obtains the signal strength of the wireless signal in real time, and provides raw data for subsequent signal attenuation analysis; The signal data preprocessing module preprocesses the acquired data after obtaining the original signal strength data to improve the data quality and ensure the accuracy of subsequent analysis; The signal attenuation feature extraction and analysis module extracts features reflecting signal strength attenuation from the signal strength data after preprocessing, and further analyzes the extracted attenuation features under the monitoring window to quantify the severity and change trend of signal attenuation; The signal attenuation prediction module, after analyzing the attenuation characteristics, inputs the analyzed characteristics into the pre-trained long short-term memory network model to make an intelligent prediction of the attenuation of signal strength; The signal attenuation classification module divides the signal attenuation into two categories: risk attenuation and risk-free attenuation based on the prediction results of the long short-term memory network model; The transmission rate adjustment module in the risk attenuation stage dynamically reduces the actual data transmission rate according to the prediction results of the long short-term memory network model in the risk attenuation stage to reduce the risk of signal loss and transmission failure; The transmission rate adjustment module in the risk-free attenuation phase speeds up the transmission of backlog data by adjusting the actual data transmission rate in the risk-free attenuation phase to achieve rapid response; After the signal strength data is preprocessed, features reflecting signal strength attenuation are extracted from the data, wherein the extracted features include energy lost during the propagation of the wireless signal and changes in link loss during the transmission of the wireless signal. The energy lost during the propagation of the wireless signal and changes in link loss during the transmission of the wireless signal are further analyzed under the monitoring window to generate a signal propagation loss evaluation value and a wireless link loss evaluation value, respectively, and the severity and change trend of signal attenuation are quantified by the signal propagation loss evaluation value and the wireless link loss evaluation value; The specific steps for further analyzing the energy lost by wireless signals during propagation in the monitoring window to generate the signal propagation loss evaluation value are as follows: In the monitoring window, the actual received signal strength and transmission power are recorded in real time. At the same time, the free space path loss formula is used to expand the model and calculate the theoretical propagation strength. By calculating the difference between the actual received signal strength and the theoretical propagation strength, the propagation loss characteristics are obtained; Within the monitoring window, the changing trend of the propagation loss characteristics is quantified as a signal propagation loss evaluation value; The specific steps for further analyzing the changes in link loss during wireless signal transmission in the monitoring window to generate a wireless link loss assessment value are as follows: Under the monitoring window, the link loss during the wireless signal transmission process is calculated; The link loss value is input into the exponential decay model to generate the wireless link loss assessment value, and the change trend of the link loss is further analyzed.

2. According to claim 1, a RTU encrypted transmission device for high altitude monitoring scenarios is characterized in that: The initial data transmission rate is determined based on environmental conditions and the estimated amount of data. The specific steps are as follows: First, evaluate the communication environment at high altitudes; Then, the initial data transmission rate range is calculated based on the expected amount of monitoring data; Finally, an optimal data transmission rate is selected as the initial data transmission rate to balance communication efficiency and signal stability.

3. The RTU encrypted transmission device for high altitude monitoring scenarios according to claim 1 is characterized in that: The specific steps for further analyzing the energy lost by wireless signals during propagation in the monitoring window to generate the signal propagation loss evaluation value are as follows: Set the monitoring window to ,in and Respectively represent the start time and end time of the monitoring window; In the monitoring window, the actual received signal strength and transmission power are recorded in real time. At the same time, the free space path loss formula is used to expand the model to calculate the theoretical propagation strength. The calculation expression is: ,in: is the theoretical propagation intensity, which represents the expected intensity of the signal when propagating along the free space path. represents the signal transmission power at time point t, is the real-time distance of signal propagation, is the path loss factor, which is used to adjust the sensitivity of different propagation environments to signal loss. is the exponential factor of the propagation distance; By calculating the difference between the actual received signal strength and the theoretical propagation strength, the propagation loss characteristic is obtained. The calculation expression of the propagation loss characteristic is: ,in It is the propagation loss characteristic, which is used to characterize the deviation between the actual received signal strength and the theoretical propagation strength. is the actual received signal strength; In the monitoring window Within, the propagation loss characteristics The changing trend of is quantified into the signal propagation loss evaluation value, and the quantitative expression is: ,in, is the signal propagation loss assessment value, used to quantify the severity of attenuation. , represents the instantaneous rate of change of the propagation loss characteristics, indicating the trend of the loss increasing or decreasing over time, is a weight function used to dynamically adjust the time importance of loss features, is the weighted accumulation of the propagation loss change rate, is a normalization factor to ensure that the calculation of the signal propagation loss evaluation value is independent of the duration of the monitoring window.

4. The RTU encrypted transmission device for high altitude monitoring scenarios according to claim 3 is characterized in that: The specific steps for further analyzing the changes in link loss during wireless signal transmission in the monitoring window to generate a wireless link loss assessment value are as follows: In the monitoring window, the link loss during the wireless signal transmission process is calculated, and the calculation expression is: ,in: represents the link loss value at time point t, represents the signal receiving power at time point t, represents the environmental correction factor; The link loss value is input into the exponential decay model to generate the wireless link loss evaluation value. The change trend of the link loss is further analyzed. The generation expression of the wireless link loss evaluation value is: ,in: Represents the exponential decay factor related to time t, used to represent the decay intensity, Indicates the wireless link loss assessment value, reflecting the cumulative impact of link loss.

5. The RTU encrypted transmission device for high altitude monitoring scenarios according to claim 1 is characterized in that: After analyzing the attenuation characteristics, the analyzed signal propagation loss assessment value and wireless link loss assessment value are input into the pre-trained long short-term memory network model, and a signal attenuation assessment index is generated based on the long short-term memory network model. The signal attenuation assessment index is used to intelligently predict the attenuation of signal strength.

6. The RTU encrypted transmission device for high altitude monitoring scenarios according to claim 5, characterized in that: The signal attenuation evaluation index generated by the intelligent prediction of the signal strength attenuation by the pre-trained long short-term memory network model is compared and analyzed with the pre-set signal attenuation evaluation index reference threshold to divide the signal attenuation situation. The specific division steps are as follows: If the signal attenuation evaluation index is greater than the signal attenuation evaluation index reference threshold, the signal attenuation situation under the monitoring window is classified as risk attenuation; If the signal attenuation assessment index is less than or equal to the signal attenuation assessment index reference threshold, the signal attenuation situation in the monitoring window is classified as risk-free attenuation.

7. The RTU encrypted transmission device for high altitude monitoring scenarios according to claim 6 is characterized in that: In the risk attenuation stage, the actual data transmission rate is dynamically reduced according to the prediction results of the long short-term memory network model. The specific steps are as follows: When it is detected that the signal attenuation evaluation index is greater than the signal attenuation evaluation index reference threshold, the risk attenuation stage is entered, and the actual data transmission rate is dynamically reduced. The specific expression for the reduction of the actual data transmission rate is: ,in, The data transmission rate is set initially. is the signal attenuation evaluation index under the current monitoring window, is a preset signal attenuation evaluation index reference threshold, Adjustment factor that controls the sensitivity of the transmission rate reduction.

8. The RTU encrypted transmission device for high altitude monitoring scenarios according to claim 7, characterized in that: When the signal attenuation evaluation index is less than or equal to the signal attenuation evaluation index reference threshold, it enters the risk-free attenuation stage and dynamically increases the actual data transmission rate. The specific expression for the increase in the actual data transmission rate is: ,in, is the rate increase factor, which controls the rate increase ratio based on the amount of backlog data. is the current amount of backlog data, is the maximum amount of backlog data allowed by the system, For the rate boost sensitivity factor, adjust the rate adjustment amplitude based on the signal attenuation evaluation index.

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