Satellite navigation signal grading management and control system and method based on time division multiplexing

Abstract

The invention relates to the technical field of satellite navigation, in particular to a satellite navigation signal hierarchical management and control system and method based on time division multiplexing, and the method comprises the following steps: S1, signal source selection: selecting satellite navigation signal sources to form a signal source set; s2, time distribution: performing time distribution on the selected signal source; s3, signal enhancement based on multi-modal data fusion: performing signal enhancement based on a multi-modal data fusion algorithm; s4, signal quality evaluation: performing real-time quality evaluation on the enhanced signal; and S5, signal retransmission optimization: analyzing a packet loss rate and delay in signal transmission, intelligently selecting a signal source for retransmission based on an analysis result, and optimizing retransmission opportunity and frequency. According to the invention, the reliability and definition of the signal are improved, and the noise interference is reduced, so that the navigation system can continuously provide high-quality positioning service in a complex and changeable environment.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to a satellite navigation signal hierarchical management and control system and method based on time division multiplexing. Background Art

[0002] Currently, satellite navigation technology is widely used in many fields such as transportation, logistics, communications and emergency rescue. By receiving signals from different satellite systems, positioning, navigation and timing services are realized. However, with the diversification and complexity of application scenarios, the stability and accuracy requirements of navigation systems in various environments are constantly increasing, especially in urban high-rise areas, mountainous areas and severe weather conditions. Satellite signals are easily blocked, attenuated and interfered, resulting in poor navigation effects. In order to meet users' needs for high precision and high reliability, more and more systems have begun to explore methods of signal hierarchical control and dynamic scheduling to realize intelligent management of navigation signals.

[0003] Existing satellite navigation signal management technologies still have many shortcomings when dealing with complex environments and changing demands. Traditional methods can usually only make selections based on the quality of a single signal source and lack effective integration of multiple signal sources. The time allocation and signal scheduling methods are single and difficult to adapt to dynamic usage scenarios. In addition, most existing signal enhancement methods use simple data filtering and fusion, and fail to make full use of data from multimodal sensors. Therefore, they have limitations in signal enhancement and noise suppression. In terms of signal quality assessment and retransmission, existing technologies lack comprehensive analysis of real-time packet loss rate and delay, which makes it difficult to ensure signal stability and continuity of navigation systems in complex environments.

[0004] The purpose of the present invention is to provide a satellite navigation signal hierarchical control system and method based on time division multiplexing, so as to comprehensively improve the stability and reliability of satellite navigation signals. Summary of the invention

[0005] The present invention provides a satellite navigation signal hierarchical management and control system and method based on time division multiplexing.

[0006] A hierarchical control method for satellite navigation signals based on time division multiplexing comprises the following steps: S1, signal source selection: select satellite navigation signal sources according to user needs and environmental factors to form a signal source set; S2, time allocation: time allocation for the selected signal source according to the signal characteristics of the signal source and the time slot usage; S3, signal enhancement based on multimodal data fusion: combining data from different sensors and performing signal enhancement based on multimodal data fusion algorithm; S4, signal quality assessment: real-time quality assessment of the enhanced signal, including signal strength, noise level and availability; S5, signal retransmission optimization: After evaluating the signal quality, analyze the packet loss rate and delay in signal transmission, and based on the analysis results, intelligently select the signal source for retransmission and optimize the retransmission timing and frequency, including: S51, signal packet loss rate analysis: after signal quality evaluation, calculate the current signal packet loss rate; S52, delay evaluation: after completing the packet loss rate analysis, use a delay evaluation algorithm to measure the delay time in signal transmission; S53, intelligent retransmission strategy selection: Based on the results of packet loss rate and delay evaluation, the signal source is intelligently selected for retransmission through the signal retransmission optimization algorithm.

[0007] Optionally, the signal source selection in S1 includes: S11, user demand analysis: collect user demand information, including positioning accuracy, response time, and usage scenarios (such as cities, rural areas, mountainous areas, etc.); S12, Environmental Factors Assessment: Assess current environmental conditions, including meteorological factors (such as rain, fog, and snow), geographical features (such as tall buildings and hills), and the distribution of electromagnetic interference sources; S13, signal source performance matching: Based on user needs and environmental assessment results, analyze satellite navigation signal sources, compare their positioning accuracy, signal strength and anti-interference capabilities, select the signal source with the best performance, and form a signal source set.

[0008] Optionally, the time allocation in S2 includes: S21, signal characteristics analysis: analyzing the signal characteristics of each signal source, including signal strength, transmission delay, and noise level; S22, time slot usage evaluation: monitoring the current time slot usage of each signal source, including the allocated time slots, the remaining available time slots, and the time period requested by the user; S23, dynamic time allocation: Based on signal characteristics and time slot usage, a dynamic scheduling algorithm is used to allocate time and calculate the time allocation ratio of each signal source. .

[0009] Optionally, the signal enhancement based on multimodal data fusion in S3 includes: S31, data collection: Collect multimodal data through a variety of sensors (GPS, IMU, temperature and humidity sensor, environmental noise sensor), including: Location data: longitude, latitude and altitude information provided by GPS; Motion state data: acceleration, angular velocity and attitude information provided by IMU; Meteorological data: current temperature, humidity and air pressure information provided by temperature and humidity sensors; Ambient noise data: background noise level provided by ambient noise sensors; S32, data preprocessing: preprocessing the collected multimodal data, including data cleaning, normalization and filtering, specifically including: Data cleaning: Clean the collected multimodal data, including removing missing values ​​and outliers; Data normalization: Min-Max normalization is used to normalize the cleaned multimodal data; Data filtering: Kalman filtering algorithm is used to smooth the data and remove high-frequency noise; S33, multimodal data fusion: a convolutional neural network (CNN) model is used to fuse the preprocessed multimodal data.

[0010] Optionally, the convolutional neural network (CNN) model in S33 includes: S331, input layer design: multimodal data is used as multi-channel input, and each sensor data corresponds to an input channel. sensors, the shape of the input layer is ,in and is the height and width of the data; S332, convolution layer: uses convolution operation to extract features from input data; S333, activation function: Leaky ReLU activation function is introduced after the convolution layer to enhance the model's response to negative values; S334, pooling layer: use global average pooling to reduce the dimension of feature maps; S335, fully connected layer: Connect the pooling layer output to the fully connected layer and generate the final result through linear transformation.

[0011] Optionally, the signal quality evaluation in S4 includes: S41, signal strength assessment: calculate the signal strength in real time and indicate the signal strength To assess the strength of the signal; S42, Noise level assessment: by signal-to-noise ratio Calculate noise levels; S43, Signal availability assessment: Effectiveness of signal use Evaluate the signal availability, that is, the proportion of the signal available per unit time.

[0012] Optionally, the signal packet loss rate analysis in S51 includes: S511, data packet monitoring: after signal quality evaluation, real-time monitoring of the number of data packets transmitted by the signal, including successfully received data packets and lost data packets; S512, packet loss rate calculation: based on the monitored data packets, calculate the packet loss rate of the current signal .

[0013] Optionally, the delay evaluation in S52 includes: S521, data packet time recording: after completing the packet loss rate analysis, record the sending time of each data packet and receiving time ; S522, delay time calculation: based on the recorded time data, calculate the average delay time .

[0014] Optionally, the intelligent retransmission strategy selection in S53 includes: S531, priority calculation: Based on the packet loss rate and delay evaluation results of each candidate signal source, the priority of each signal source is calculated using the signal retransmission optimization algorithm. ; S532, Intelligent Source Selection: Based on the calculated priority value , select the signal source with the highest priority for retransmission operation.

[0015] A satellite navigation signal hierarchical control system based on time division multiplexing is used to implement the above-mentioned satellite navigation signal hierarchical control method based on time division multiplexing, including the following modules: Signal source selection module: selects satellite navigation signal sources according to user needs and environmental factors to form a signal source set; Time allocation module: allocates time to signal sources in the signal source set according to the signal characteristics and time slot usage of the signal sources; Signal enhancement module: By combining data from different sensors, the signal is enhanced based on a multimodal data fusion algorithm; Signal quality assessment module: performs real-time quality assessment on the enhanced signal, including signal strength, noise level and availability; Signal retransmission optimization module: Based on the results of signal quality evaluation, it analyzes the packet loss rate and delay during signal transmission, intelligently selects the appropriate signal source for retransmission through the optimization algorithm, and optimizes the timing and frequency of retransmission.

[0016] Beneficial effects of the present invention: The present invention systematically improves the quality and adaptability of satellite navigation signals through signal source selection, time allocation and signal enhancement of multimodal data fusion. The signal source selection module ensures the selection of the optimal signal source under different environmental conditions and user needs. Combined with the reasonable scheduling of the time allocation module, efficient utilization and coordination of navigation resources are achieved, which not only enhances the system's adaptability to complex environments, but also effectively improves the accuracy and stability of navigation, providing users with a better navigation experience.

[0017] The present invention, through a multimodal data fusion method, can make full use of data from different sensors to perform comprehensive data collection, preprocessing and fusion. With the support of a convolutional neural network, multimodal data fusion significantly improves the reliability and clarity of the signal, reduces noise interference, and enables the navigation system to continue to provide high-quality positioning services in complex and changing environments.

[0018] The present invention realizes real-time signal monitoring and intelligent optimization through the signal quality evaluation and retransmission optimization module, and the comprehensive evaluation of signal strength, noise level, packet loss rate and delay provides a dynamic adjustment basis for the system and intelligently selects the best signal source for retransmission when necessary. This strategy significantly improves the transmission quality and continuity of the navigation signal. Even in the case of signal attenuation and interference, users can obtain smoother and more accurate navigation services, which greatly enhances the stability of the system and the overall user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A schematic diagram of a control method flow chart of an embodiment of the present invention; Figure 2 Schematic diagram of system function modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0022] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0023] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0024] like Figure 1 As shown, a hierarchical control method for satellite navigation signals based on time division multiplexing includes the following steps: S1, signal source selection: select satellite navigation signal sources according to user needs and environmental factors to form a signal source set; S2, time allocation: time allocation for the selected signal source according to the signal characteristics of the signal source and the time slot usage; S3, signal enhancement based on multimodal data fusion: combining data from different sensors, and performing signal enhancement based on multimodal data fusion algorithm to improve signal quality and reduce noise interference; S4, signal quality assessment: real-time quality assessment of the enhanced signal, including signal strength, noise level and availability; S5, signal retransmission optimization: After evaluating the signal quality, analyze the packet loss rate and delay in signal transmission, and based on the analysis results, intelligently select the signal source for retransmission and optimize the retransmission timing and frequency, including: S51, signal packet loss rate analysis: after signal quality evaluation, calculate the current signal packet loss rate; S52, delay evaluation: after completing the packet loss rate analysis, use a delay evaluation algorithm to measure the delay time in signal transmission; S53, intelligent retransmission strategy selection: based on the results of packet loss rate and delay evaluation, the signal source is intelligently selected for retransmission through the signal retransmission optimization algorithm; Through the above content, intelligent selection of signal sources, effective time allocation and signal enhancement based on multimodal data fusion are achieved, ensuring real-time evaluation of signal quality and dynamic optimization of retransmission, improving the stability and reliability of navigation signals, and being able to effectively deal with signal attenuation and interference problems in complex environments, providing users with a more efficient and accurate navigation experience.

[0025] The signal source selections in S1 include: S11, user demand analysis: collect user demand information, including positioning accuracy, response time, and usage scenarios (such as cities, rural areas, mountainous areas, etc.); S12, Environmental Factors Assessment: Assess current environmental conditions, including meteorological factors (such as rain, fog, and snow), geographical features (such as tall buildings and hills), and the distribution of electromagnetic interference sources; S13, signal source performance matching: based on user needs and environmental assessment results, analyze satellite navigation signal sources, compare their positioning accuracy, signal strength and anti-interference capabilities, select the signal source with the best performance, and form a signal source set; Through the above content, the optimal matching of satellite navigation signal sources is ensured, which not only improves positioning accuracy and signal stability, but also can effectively respond to challenges under different environmental conditions and provide a personalized navigation experience. By forming an efficient signal source collection, users can obtain faster and more reliable navigation services, thereby enhancing the overall user experience and the adaptability of the system.

[0026] The time allocation in S2 includes: S21, signal characteristics analysis: analyzing the signal characteristics of each signal source, including signal strength, transmission delay, and noise level; S22, time slot usage evaluation: monitoring the current time slot usage of each signal source, including the allocated time slots, the remaining available time slots, and the time period requested by the user; S23, dynamic time allocation: Based on signal characteristics and time slot usage, a dynamic scheduling algorithm is used to allocate time and calculate the time allocation ratio of each signal source. , expressed as: ; in, For signal source Time allocation, For signal source The weight of is the total available time slots, is the total number of signal sources; Through the above content, efficient utilization of signal resources is ensured, which not only improves the response speed and stability of the signal source, but also optimizes the overall performance of the navigation system, enabling it to flexibly adapt to different environments and user needs. By reasonably allocating time, users can obtain more accurate and reliable navigation services, which significantly improves the user experience and the adaptability of the system.

[0027] Signal enhancement based on multimodal data fusion in S3 includes: S31, data collection: Collect multimodal data through a variety of sensors (GPS, IMU, temperature and humidity sensor, environmental noise sensor), including: Location data: longitude, latitude and altitude information provided by GPS; Motion state data: acceleration, angular velocity and attitude information provided by IMU; Meteorological data: current temperature, humidity and air pressure information provided by temperature and humidity sensors; Ambient noise data: background noise level provided by ambient noise sensors; S32, data preprocessing: preprocessing the collected multimodal data, including data cleaning, normalization and filtering, specifically including: Data cleaning: Clean the collected multimodal data, including removing missing values ​​and outliers; Data normalization: Min-Max normalization is used to normalize the cleaned multimodal data; Data filtering: Kalman filtering algorithm is used to smooth the data and remove high-frequency noise; S33, multimodal data fusion: a convolutional neural network (CNN) model is used to fuse the preprocessed multimodal data; Through the above content, integrating the data of multiple sensors can obtain a more comprehensive and accurate understanding of the environment. Diversified data collection provides a rich foundation for signal enhancement. In the data preprocessing stage, the quality and consistency of the data are ensured through cleaning, normalization and filtering, thereby improving the subsequent fusion effect. The convolutional neural network (CNN) is used for data fusion to significantly improve the signal quality.

[0028] The convolutional neural network (CNN) models in S33 include: S331, input layer design: multimodal data is used as multi-channel input, and each sensor data corresponds to an input channel. sensors, the shape of the input layer is ,in and is the height and width of the data; S332, convolution layer: Use convolution operation to extract features from input data, expressed as: ; in, The position in the output feature map of the convolution operation The value of is the position in the convolution kernel The weight value of is the bias, is the size of the convolution kernel, is the position in the input feature map The value of S333, activation function: Leaky ReLU activation function is introduced after the convolution layer to enhance the model's response to negative values, expressed as: ; in, is the output after activation, is a constant less than 1; S334, pooling layer: Use global average pooling to reduce the dimension of the feature map, expressed as: ; in, is the output after pooling, is the position in the feature map The value of is the feature value after being processed by the activation function; S335, fully connected layer: Connect the output of the pooling layer to the fully connected layer and generate the final result through linear transformation, which is expressed as: ; in, For the final output, and are the weights and biases of the fully connected layer, is the Softmax activation function; Through the above content, it is possible to more effectively extract and fuse various information from different sensors. Through the feature extraction capabilities of multi-channel input and convolution operations, signal features can be accurately identified and enhanced, and interference in the environment can be filtered out. The introduction of Leaky ReLU activation function and global average pooling operation enables the model to reduce computational complexity while maintaining important signal features, thereby improving real-time performance and signal enhancement stability.

[0029] Signal quality assessment in S4 includes: S41, signal strength assessment: calculate the signal strength in real time and indicate the signal strength To evaluate the strength of the signal, it is expressed as: ; in, is the received signal power, is the reference power; S42, Noise level assessment: by signal-to-noise ratio Calculate the noise level, expressed as: ; in, is the signal power, is the noise power; S43, Signal availability assessment: Effectiveness of signal use Evaluate the availability of the signal, that is, the proportion of the signal available per unit time, expressed as: ; in, is the effective duration of the signal per unit time, is the total duration; The above content fully reflects the reliability and stability of the signal in different environments. RSSI is used to judge the signal strength, SNR is used to quantify noise interference, and SAR measures the availability of the signal. This comprehensive evaluation can not only detect changes in signal quality in a timely manner, but also provide a basis for the system to dynamically adjust signal allocation, ensuring the high accuracy and stability of the navigation system in complex environments.

[0030] The signal packet loss rate analysis in S51 includes: S511, data packet monitoring: after signal quality evaluation, real-time monitoring of the number of data packets transmitted by the signal, including successfully received data packets and lost data packets; S512, packet loss rate calculation: based on the monitored data packets, calculate the packet loss rate of the current signal , expressed as: ; in, is the number of packets lost in a specified time period, is the total number of packets sent in the same time period, The lower the value, the better the signal transmission stability and the less packet loss; Through the above content, problems in signal transmission can be quickly detected, such as packet loss caused by instability or interference. When the packet loss rate is high, the system can take timely adjustment measures, switch signal sources or optimize transmission strategies, thereby ensuring the continuity and stability of navigation services.

[0031] Delay evaluation in S52 includes: S521, data packet time recording: after completing the packet loss rate analysis, record the sending time of each data packet and receiving time ; S522, delay time calculation: based on the recorded time data, calculate the average delay time , expressed as: ; in, is the number of packets successfully received within a specified time period; Through the above content, the real-time and response speed of signal transmission are effectively evaluated. By analyzing the delay time, the system can quickly identify changes in transmission speed, especially when the delay is too high, and timely adjust the signal source or optimize the transmission strategy to ensure the timeliness of navigation information.

[0032] The intelligent retransmission strategy selection in S53 includes: S531, priority calculation: Based on the packet loss rate and delay evaluation results of each candidate signal source, the priority of each signal source is calculated using the signal retransmission optimization algorithm. , expressed as: ; in, For signal source The packet loss rate, For signal source The delay time, and To adjust the weight coefficient of the relative impact of packet loss rate and delay time; S532, Intelligent Source Selection: Based on the calculated priority value , select the signal source with the highest priority for retransmission operation; Through the above content, the signal source with the best transmission quality can be preferentially selected for retransmission, effectively reducing the impact of high packet loss rate and long delay on positioning accuracy and service continuity. Flexible priority calculation allows the system to adaptively select the optimal signal source in different environments, thereby improving the stability and reliability of the navigation signal and providing users with a smoother and more accurate navigation experience.

[0033] like Figure 2 As shown, a satellite navigation signal hierarchical control system based on time division multiplexing is used to implement the above-mentioned satellite navigation signal hierarchical control method based on time division multiplexing, including the following modules: Signal source selection module: selects satellite navigation signal sources according to user needs and environmental factors to form a signal source set; Time allocation module: allocates time to signal sources in the signal source set according to the signal characteristics and time slot usage of the signal sources; Signal enhancement module: By combining data from different sensors, the signal is enhanced based on a multimodal data fusion algorithm; Signal quality assessment module: performs real-time quality assessment on the enhanced signal, including signal strength, noise level and availability; Signal retransmission optimization module: Based on the results of signal quality evaluation, it analyzes the packet loss rate and delay during signal transmission, intelligently selects the appropriate signal source for retransmission through the optimization algorithm, and optimizes the timing and frequency of retransmission.

[0034] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0035] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A hierarchical control method for satellite navigation signals based on time division multiplexing, characterized in that: The following steps are involved: S1, signal source selection: select satellite navigation signal sources according to user needs and environmental factors to form a signal source set; S2, time allocation: time allocation for the selected signal source according to the signal characteristics of the signal source and the time slot usage; S3, signal enhancement based on multimodal data fusion: combining data from different sensors and performing signal enhancement based on multimodal data fusion algorithm; S4, signal quality assessment: real-time quality assessment of the enhanced signal, including signal strength, noise level and availability; S5, signal retransmission optimization: After evaluating the signal quality, analyze the packet loss rate and delay in signal transmission, and based on the analysis results, intelligently select the signal source for retransmission and optimize the retransmission timing and frequency, including: S51, signal packet loss rate analysis: after signal quality evaluation, calculate the current signal packet loss rate; S52, delay evaluation: after completing the packet loss rate analysis, use a delay evaluation algorithm to measure the delay time in signal transmission; S53, intelligent retransmission strategy selection: Based on the results of packet loss rate and delay evaluation, the signal source is intelligently selected for retransmission through the signal retransmission optimization algorithm.

2. According to claim 1, a hierarchical control method for satellite navigation signals based on time division multiplexing is characterized in that: The signal source selection in S1 includes: S11, user demand analysis: collect user demand information, including positioning accuracy, response time, and usage scenarios; S12, Environmental Factors Assessment: Assess current environmental conditions, including meteorological factors, geographical features, and the distribution of electromagnetic interference sources; S13, signal source performance matching: Based on user needs and environmental assessment results, analyze satellite navigation signal sources, compare their positioning accuracy, signal strength and anti-interference capabilities, select the signal source with the best performance, and form a signal source set.

3. According to claim 1, a hierarchical control method for satellite navigation signals based on time division multiplexing is characterized in that: The time allocation in S2 includes: S21, signal characteristics analysis: analyzing the signal characteristics of each signal source, including signal strength, transmission delay, and noise level; S22, time slot usage evaluation: monitoring the current time slot usage of each signal source, including the allocated time slots, the remaining available time slots, and the time period requested by the user; S23, dynamic time allocation: Based on signal characteristics and time slot usage, a dynamic scheduling algorithm is used to allocate time and calculate the time allocation ratio of each signal source. .

4. According to claim 1, a hierarchical control method for satellite navigation signals based on time division multiplexing is characterized in that: The signal enhancement based on multimodal data fusion in S3 includes: S31, Data Collection: Collect multimodal data through various sensors, including: Location data: longitude, latitude and altitude information provided by GPS; Motion state data: acceleration, angular velocity and attitude information provided by IMU; Meteorological data: current temperature, humidity and air pressure information provided by temperature and humidity sensors; Ambient noise data: background noise level provided by ambient noise sensors; S32, data preprocessing: preprocessing the collected multimodal data, including data cleaning, normalization and filtering, specifically including: Data cleaning: Clean the collected multimodal data, including removing missing values ​​and outliers; Data normalization: Min-Max normalization is used to normalize the cleaned multimodal data; Data filtering: Kalman filtering algorithm is used to smooth the data and remove high-frequency noise; S33, multimodal data fusion: a convolutional neural network model is used to fuse the preprocessed multimodal data.

5. According to claim 4, a hierarchical control method for satellite navigation signals based on time division multiplexing is characterized in that: The convolutional neural network model in S33 includes: S331, input layer design: multimodal data is used as multi-channel input, and each sensor data corresponds to an input channel. sensors, the shape of the input layer is ,in and is the height and width of the data; S332, convolution layer: uses convolution operation to extract features from input data; S333, activation function: Leaky ReLU activation function is introduced after the convolution layer to enhance the model's response to negative values; S334, pooling layer: use global average pooling to reduce the dimension of feature maps; S335, fully connected layer: Connect the pooling layer output to the fully connected layer and generate the final result through linear transformation.

6. The hierarchical control method of satellite navigation signals based on time division multiplexing according to claim 1 is characterized in that: The signal quality assessment in S4 includes: S41, signal strength assessment: calculate the signal strength in real time and indicate the signal strength To assess the strength of the signal; S43, Signal availability assessment: Effectiveness of signal use Evaluate the signal availability, that is, the proportion of the signal available per unit time.

7. The hierarchical control method of satellite navigation signals based on time division multiplexing according to claim 6 is characterized in that: The signal packet loss rate analysis in S51 includes: S511, data packet monitoring: after signal quality evaluation, real-time monitoring of the number of data packets transmitted by the signal, including successfully received data packets and lost data packets; S512, packet loss rate calculation: based on the monitored data packets, calculate the packet loss rate of the current signal .

8. The method for hierarchical control of satellite navigation signals based on time division multiplexing according to claim 7, characterized in that: The delay evaluation in S52 includes: S521, data packet time recording: after completing the packet loss rate analysis, record the sending time of each data packet and receiving time ; S522, delay time calculation: based on the recorded time data, calculate the average delay time .

9. The hierarchical control method of satellite navigation signals based on time division multiplexing according to claim 8, characterized in that: The intelligent retransmission strategy selection in S53 includes: S531, priority calculation: Based on the packet loss rate and delay evaluation results of each candidate signal source, the priority of each signal source is calculated using the signal retransmission optimization algorithm. ; S532, Intelligent Source Selection: Based on the calculated priority value , select the signal source with the highest priority for retransmission operation.

10. A satellite navigation signal hierarchical control system based on time division multiplexing, used to implement a satellite navigation signal hierarchical control method based on time division multiplexing as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Signal source selection module: selects satellite navigation signal sources according to user needs and environmental factors to form a signal source set; Time allocation module: allocates time to signal sources in the signal source set according to the signal characteristics and time slot usage of the signal sources; Signal enhancement module: By combining data from different sensors, the signal is enhanced based on a multimodal data fusion algorithm; Signal quality assessment module: performs real-time quality assessment on the enhanced signal, including signal strength, noise level and availability; Signal retransmission optimization module: Based on the results of signal quality evaluation, it analyzes the packet loss rate and delay during signal transmission, intelligently selects the appropriate signal source for retransmission through the optimization algorithm, and optimizes the timing and frequency of retransmission.

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