CN119789065B - Multi-mode data communication method and system using Beidou satellite system - Google Patents

Abstract

The invention discloses a multi-mode data communication method and a system applying a Beidou satellite system, which particularly relate to the technical field of data transmission, wherein in the communication process, the influence of time delay deviation and environmental factors of different orbit satellites on signal attenuation can be analyzed, the transmission time delay degree of satellite signals is dynamically estimated, based on an estimation result, the satellite signals are divided into high-time delay signals and low-time delay signals by the system, a multi-mode data fusion and signal optimization strategy is adopted aiming at the high-time delay signals, command response errors are effectively reduced, the signal time delay characteristics and command response error data are combined, the communication strategy is dynamically adjusted, a data transmission path is optimized, the execution efficiency of rescue tasks and the real-time performance of information interaction are improved.

Description

A multi-modal data communication method and system using Beidou satellite system

Technical Field

The present invention relates to the technical field of data transmission, and in particular to a multimodal data communication method and system using a Beidou satellite system.

Background Art

With the rapid development of global satellite communication technology, satellite communication has become an important supporting technology in key areas such as emergency rescue, disaster response, and remote command. Beidou Satellite System (BDS), as a global satellite navigation and communication system independently developed by my country, not only has high-precision positioning functions, but also provides short message communication capabilities, and can still maintain smooth communication in extreme environments. However, traditional satellite communication methods mainly rely on a single mode of data transmission, which is easily affected by factors such as orbital characteristics, signal attenuation, and communication delays, resulting in a decrease in the efficiency of emergency command information transmission. In order to solve these problems, multimodal data communication methods have emerged. By integrating multiple data transmission modes (such as short messages, data links, ground networks, etc.), communication stability is improved to ensure efficient and low-latency information transmission in complex environments.

The prior art has the following deficiencies:

In emergency rescue scenarios, the existing satellite communication technology is susceptible to factors such as orbital delay and signal attenuation in a highly dynamic environment, resulting in uncertainty in the communication between the command center and the rescue unit. The traditional single-mode data transmission method lacks flexibility and cannot be effectively optimized for different signal delay conditions, which may lead to command response errors. In addition, the existing communication strategy cannot be adjusted dynamically, and it is difficult to optimize the data transmission path according to the real-time transmission status, which affects the timeliness and reliability of information interaction. Therefore, there is an urgent need for a multimodal data communication method based on the Beidou satellite system that can make full use of the various communication modes of Beidou satellites to improve the efficiency and accuracy of emergency command data transmission, thereby improving the overall rescue response capability.

Summary of the invention

The object of the present invention is to provide a multimodal data communication method and system using the BeiDou satellite system to address the deficiencies in the background technology.

In order to achieve the above object, the present invention provides the following technical solution: a multimodal data communication method using the Beidou satellite system, comprising the following steps:

S1: Establish communication connection between the command center and multiple rescue units through the multi-modal data communication link of the Beidou satellite system;

S2: After receiving the emergency command from the command center, the emergency command is transmitted to the rescue unit using the communication link of the Beidou satellite system combined with multimodal data transmission technology;

S3: During the communication process, based on the orbital characteristics of the BeiDou satellite system, analyze the delay deviation of satellites in different orbits in signal transmission and the impact of environmental factors on signal attenuation, and evaluate the delay degree of satellite signal transmission;

S4: Based on the evaluation results, satellite signals are divided into high-latency satellite signals and low-latency satellite signals, and multi-modal data fusion and signal optimization strategies are used for high-latency satellite signals to reduce command response errors;

S5: Dynamically adjust the communication strategy and optimize the data transmission path based on the signal transmission delay characteristics of the Beidou satellite system and the on-site command response error data.

Preferably, in S1, the communication link uses at least one type of satellite among low earth orbit satellites, medium earth orbit satellites or geostationary orbit satellites.

Preferably, in S2, a signal delay deviation index is generated after analyzing the delay deviation conditions existing in the signal transmission process of satellites in different orbits. The method for obtaining the signal delay deviation index is:

Set an interval and a range of environmental factors Δtenvironment, and perform N random samplings according to a pre-set probability distribution to simulate multiple signal transmission delays. Each simulation calculates the delay based on different satellite orbit heights and environmental factors.

For each simulation, values are drawn from the distribution of each parameter and then substituted into the signal delay model to calculate: ; where i represents the i-th simulation, h(i) and is a value drawn from the corresponding distribution, h is the distance between the satellite and the ground, and c is the speed of light;

A set of signal delay values are obtained through multiple simulations The delay deviation is defined as the deviation between the simulation result and the theoretical value. The theoretical delay is set to , then the delay deviation for: ; Calculate the signal delay deviation index, the expression is: ; In the formula, is the signal delay deviation index, and N is the total number of signal delay values.

Preferably, the signal attenuation anomaly index is generated after analyzing the satellite signal attenuation degree under the interference of environmental factors. The method for obtaining the signal attenuation anomaly index is:

Prepare input data related to signal attenuation: Signal attenuation data , that is, the signal attenuation value measured at each time point; environmental data: temperature T, humidity H, precipitation P, for each observation point, construct a feature vector: ; Before applying Isolation Forest, use the training data set to build the IsolationForest model. Use multiple decision trees to isolate data points. Each tree constructs a path by randomly selecting a feature and dividing the value of the selected feature. Isolation Forest calculates the degree of isolation of each data point, that is, the length of the path where the data point is isolated in the tree. After training the Isolation Forest model, for each data point, the model generates an anomaly score, which indicates the degree of abnormality of the data point. The anomaly score The calculation formula is: ;in: is the i-th data point The average path length isolated in all trees, c(n) is a normalization constant, the formula is: ; n is the total number of data points in the training dataset, and the signal attenuation anomaly index is the weighted average of all signal attenuation anomaly scores.

Preferably, the delay degree value of satellite signal transmission is calculated by a machine learning model according to the acquired signal delay deviation index and signal attenuation anomaly index;

The signal delay deviation index and the signal attenuation anomaly index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model predicts the delay degree value label of satellite signal transmission for each group of comprehensive feature vectors as the prediction target, and minimizes the sum of prediction errors of the delay degree value labels of all satellite signal transmissions as the training target. The machine learning model is trained until the sum of prediction errors converges and the model training is stopped. The delay degree value of satellite signal transmission is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

Preferably, in S4, based on the evaluation result, the satellite signals are divided into high-latency satellite signals and low-latency satellite signals, specifically:

The acquired delay degree value of satellite signal transmission is compared with a preset reference threshold of the delay degree value. If the delay degree value of satellite signal transmission is greater than or equal to the preset reference threshold of the delay degree value, it indicates that the delay degree of satellite signal transmission is high, and the satellite signal is classified as a high-latency satellite signal; if the delay degree value of satellite signal transmission is less than the preset reference threshold of the delay degree value, it indicates that the delay degree of satellite signal transmission is low, and the satellite signal is classified as a low-latency satellite signal.

Preferably, in S5, the on-site command response error is calculated based on the instruction issuance time, reception time, and response time, specifically:

The delay of satellite signal transmission refers to the time difference between the command issued by the command center and the time when the on-site rescue unit receives the command, and the expression is: command time difference = command reception time − command issuance time; the command response error refers to the difference between the response time of the on-site rescue unit and the time when the response should theoretically start, response time difference = command response time − (command issuance time + Δt reaction time); where Δt reaction time is the standard response time set according to the task type and command system; the on-site command response error is obtained by taking the weighted average sum of the command time difference and the response time difference.

Preferably, the acquired satellite signal transmission delay value and the on-site command response error are used as input items of fuzzy logic, and are divided into different fuzzy sets respectively;

The communication strategy is taken as the output item of fuzzy logic and divided into different fuzzy sets;

Formulate fuzzy rules to describe the impact of satellite signal transmission delay and on-site command response error definition on communication strategy;

Perform fuzzy reasoning based on fuzzy rules and dynamically adjust communication strategies.

The present invention also provides a multi-modal data communication system using the Beidou satellite system, including a satellite communication link module, an instruction transmission module, a delay evaluation module, a signal optimization strategy module and a communication strategy dynamic adjustment module;

Satellite communication link module: Establish communication connection between the command center and multiple rescue units through the multi-modal data communication link of the Beidou satellite system;

Command transmission module: After receiving the emergency command from the command center, it uses the communication link of the Beidou satellite system and combines multimodal data transmission technology to transmit the emergency command to the rescue unit;

Delay evaluation module: During the communication process, based on the orbital characteristics of the Beidou satellite system, the delay deviation of satellites in different orbits in signal transmission and the impact of environmental factors on signal attenuation are analyzed to evaluate the delay degree of satellite signal transmission;

Signal optimization strategy module: Based on the evaluation results, satellite signals are divided into high-latency satellite signals and low-latency satellite signals, and multi-modal data fusion and signal optimization strategies are used for high-latency satellite signals to reduce command response errors;

Communication strategy dynamic adjustment module: dynamically adjusts communication strategies and optimizes data transmission paths based on the signal transmission delay characteristics of the Beidou satellite system and on-site command response error data.

In the above technical solution, the technical effects and advantages provided by the present invention are:

1. The present invention discloses a multimodal data communication method and system using the Beidou satellite system. By constructing a multimodal data communication link, combined with intelligent signal analysis, machine learning prediction and dynamic communication optimization strategy, the stability, real-time and adaptability of emergency command communication are effectively improved. The method makes full use of the short message communication, data link communication and other modes of the Beidou satellite system, and integrates ground network resources to ensure that efficient information transmission can be maintained in various complex environments (such as disaster areas, maritime rescue, remote unmanned areas, etc.). Through the calculation and analysis of the signal delay deviation index and the signal attenuation anomaly index, the present invention can accurately evaluate the quality of satellite signals, and use the machine learning model to predict the delay, so that the communication strategy can be intelligently adjusted according to the real-time channel status, thereby reducing the command response error and improving the accuracy of task execution.

2. The present invention realizes real-time monitoring and dynamic optimization of communication link quality through calculation of delay and signal attenuation abnormality index, thereby ensuring the stability of information transmission; secondly, by adopting fuzzy logic reasoning and multimodal data fusion strategy, the communication system can flexibly adjust the transmission mode according to actual conditions, effectively reducing the impact of high-delay satellite signals on rescue command; finally, this method can significantly improve emergency response efficiency, reduce decision-making errors caused by communication delays, thereby enhancing the collaborative combat capability of rescue missions, and providing strong technical support for various emergency communications and remote command.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

FIG1 is a flow chart of the method of the present invention.

FIG. 2 is a system module diagram of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1, referring to FIG. 1 , a multimodal data communication method using a Beidou satellite system described in this embodiment includes the following steps:

S1: Establish communication connection between the command center and multiple rescue units through the multi-modal data communication link of the Beidou satellite system;

S2: After receiving the emergency command from the command center, the emergency command is transmitted to the rescue unit using the communication link of the Beidou satellite system combined with multimodal data transmission technology;

S3: During the communication process, based on the orbital characteristics of the BeiDou satellite system, analyze the delay deviation of satellites in different orbits in signal transmission and the impact of environmental factors on signal attenuation, and evaluate the delay degree of satellite signal transmission;

S4: Based on the evaluation results, satellite signals are divided into high-latency satellite signals and low-latency satellite signals, and multi-modal data fusion and signal optimization strategies are used for high-latency satellite signals to reduce command response errors;

S5: Dynamically adjust the communication strategy and optimize the data transmission path based on the signal transmission delay characteristics of the Beidou satellite system and the on-site command response error data.

In scenarios such as emergency rescue, disaster response, and remote command, the command center needs to maintain efficient and stable communications with multiple rescue units to ensure rapid response and accurate execution of rescue operations. Traditional single communication modes (such as cellular networks or single satellite links) are susceptible to signal attenuation, delay fluctuations, and link interruptions in complex environments (such as earthquakes, floods, mountainous areas, and oceans), which affects the real-time and reliability of command and dispatch.

BeiDou Satellite System (BDS), as a global satellite navigation system independently developed by my country, not only provides high-precision positioning and timing functions, but also has short message communication and two-way data link communication capabilities. Among them:

It supports users to send and receive short messages via Beidou satellites in areas without ground network coverage. It is suitable for low-bandwidth emergency information transmission, such as command sending, location information sharing, etc.

Supports high-capacity data transmission and can be used for transmission of real-time voice, video streams and other critical data.

In order to ensure stable communication between the command center and multiple rescue units, the multimodal data communication link based on the Beidou satellite system is mainly connected in the following ways:

Beidou satellite link as the core communication method: The command center establishes direct communication with multiple rescue units through Beidou satellites, which can ensure the stability of information transmission in the event of ground network failure.

Integrate ground networks (such as public networks, private networks or ad hoc networks): When ground communication conditions are available, combine public networks (5G/4G), ad hoc networks (Wi-Fi, Mesh networks) and other communication methods to enhance the flexibility and reliability of data transmission.

Short message + data link fusion: When broadband links are limited, short messages are used to transmit critical information (such as location and status updates), while high-throughput data links are enabled when broadband is available to transmit complex data (such as images and videos).

Intelligent data diversion: Based on the urgency of the transmission content and the bandwidth requirements, data is intelligently allocated to different links. For example, low-latency high-priority data (such as command and dispatch information) can be transmitted through Beidou short messages or low-latency channels, while large amounts of information (such as high-definition video) can be transmitted through ground networks or satellite broadband.

The dynamic link management algorithm is used to intelligently select the optimal transmission path based on satellite orbital position, signal quality, environmental factors (such as weather, electromagnetic interference), etc. In the case of high latency or link interruption, it automatically switches to a low-latency link or alternative communication mode to ensure that information is not interrupted.

In emergency rescue missions, the command center needs to transmit instructions to multiple rescue units in real time to ensure the efficient execution of rescue operations. However, relying solely on traditional communication methods (such as cellular networks or radio) may not meet the needs of fast and reliable instruction transmission when the ground network may be damaged or coverage is insufficient. Therefore, using the communication link of the Beidou Satellite System (BDS) and combining it with multimodal data transmission technology can efficiently transmit emergency instructions in different environments and improve the rescue response speed.

In emergency rescue scenarios, the command center usually needs to issue various types of instructions to rescue units, such as task arrangement, operation area adjustment, emergency evacuation orders, etc. The transmission of these instructions can be carried out in the following multi-modal ways:

Applicable scenarios: When the ground network is unavailable or the bandwidth is limited, short message communication can ensure the delivery of basic instructions. Transmission content: including mission adjustment information, GPS coordinates, personnel status reports, etc. Transmission characteristics: supports up to 1KB data transmission, which can ensure the delivery of emergency information in extreme environments.

When the command center needs to send audio, video, pictures, map information and other data in real time. Mission planning documents, geographic information, disaster area images, rescue personnel status data, etc. Support larger data transmission to ensure that the rescue unit obtains detailed command information. When the ground network (such as 4G/5G, Wi-Fi, ad hoc network) is available, the ground network is used first to improve transmission efficiency. If the ground network has signal attenuation or interruption, the system will automatically switch to the Beidou communication link to ensure uninterrupted communication. Low-latency mission information is transmitted preferentially through Beidou short messages or ground networks; large data volume information (such as video, voice) is transmitted through data links or 5G/4G; important instructions adopt a redundant transmission strategy (that is, they are sent through multiple links at the same time to ensure information delivery).

In order to ensure the efficient transmission of emergency commands, the following optimization technologies are combined to improve the quality and timeliness of communication: Commands are prioritized according to the degree of urgency and data type: Level 1 (highest priority): emergency evacuation commands, life safety related information (preferentially transmitted via Beidou short message + ground network). Level 2 (high priority): mission adjustment information, GPS location, voice message (transmitted via Beidou data link + 4G/5G). Level 3 (normal priority): environmental information, non-emergency video (transmission method selected based on network conditions).

Dynamic adjustment based on network quality: If the signal attenuation or latency of a link is too high, it will automatically switch to a low-latency link. Bandwidth load balancing: When multiple links are available, data distribution is optimized to avoid network congestion. During emergency communications, the confidentiality of instructions is ensured by using Beidou encrypted short messages + end-to-end encrypted data transmission to prevent information leakage or tampering.

S3: The delay of satellite communication is mainly affected by the satellite orbit type, the distance between the satellite and the ground station, and the signal propagation path. Depending on the different orbit types of the satellite, the delay of signal transmission will vary significantly.

Low Earth Orbit (LEO) satellite orbit altitude: LEO satellites are usually located between 500 km and 2000 km above the ground and are relatively close to the ground. Due to the low altitude of the satellite, the signal transmission delay is shorter. The signal round-trip time is usually between 30 milliseconds and 200 milliseconds, which is a great advantage for emergency command systems with high real-time requirements. Since LEO satellites usually move at high speeds in orbit, the distance between them and ground stations is constantly changing, which may cause certain fluctuations in latency. Nevertheless, the lower latency of LEO satellites makes them more advantageous in scenarios that require a quick response.

Medium Earth Orbit (MEO) Orbital Altitude: MEO satellites are located in an orbital altitude range of approximately 8,000 km to 20,000 km and are commonly used in global navigation and communication systems (such as GPS, Galileo). Compared with LEO satellites, MEO satellites have longer transmission delays. The signal round-trip time is approximately 150 milliseconds to 500 milliseconds, depending on the specific orbital position. The signal transmission of MEO satellites is more stable, but still not as low as that of LEO satellites. MEO satellites generally have higher orbital stability and smaller orbital deviations, so their fluctuations in signal transmission delay are smaller than those of LEO satellites.

Geostationary Earth Orbit (GEO) Orbital Altitude: GEO satellites are located at an orbital altitude of approximately 35,786 km and are high-orbit satellites. Since GEO satellites are far from the ground, the round-trip signal latency is high, generally between 500 milliseconds and 700 milliseconds. This may have an impact on some applications that require real-time interaction (such as voice or video calls). Since GEO satellites are relatively stationary, their positions are relatively fixed and the latency changes little. However, high latency may lead to misjudgment of command or untimely response in some high-risk environments.

The signal delay deviation index is generated by analyzing the delay deviation of satellites in different orbits during signal transmission. The method for obtaining the signal delay deviation index is as follows:

Set an interval (for example, LEO satellite altitude is 500 km to 2000 km, GEO satellite altitude is 35786 km), and set its distribution to follow uniform distribution or normal distribution.

Environmental factors Δtenvironment: For example, atmospheric attenuation and weather changes. The range of environmental factors is set through historical data or prediction models, and is usually set to obey a normal distribution. According to the set probability distribution, N random samplings are performed to simulate multiple signal transmission delays. Each simulation calculates the delay based on different satellite orbit altitudes and environmental factors.

For each simulation, values are drawn from the distribution of each parameter and then substituted into the signal delay model to calculate: ; where i represents the i-th simulation, h(i) and is a value drawn from the corresponding distribution, h is the distance between the satellite and the ground, and c is the speed of light.

A set of signal delay values are obtained through multiple simulations The delay deviation is defined as the deviation between the simulation result and the theoretical value. For example, if the theoretical delay is , then the delay deviation for: ; Calculate the signal delay deviation index, the expression is: ; In the formula, is the signal delay deviation index, N is the total number of signal delay values, and this deviation index represents the average level of signal delay deviation in multiple simulations. If the deviation index is high, it means that the uncertainty of signal delay is large, which may affect the real-time performance and stability of the system.

Satellite signals are subject to interference from many factors during transmission, such as the atmosphere, climate change, geographical environment, and ground obstacles. These factors can cause signal attenuation and transmission quality degradation, which in turn affects the change in latency. The impact of environmental factors can be divided into the following categories:

Atmosphere affects the troposphere and mesosphere: Satellite signals pass through the Earth's atmosphere during transmission, and water vapor, pressure changes, and weather systems (such as cyclones and storms) in the troposphere and mesosphere can cause signal attenuation. Signal attenuation is more significant when humidity is high or the temperature changes dramatically. Usually, this attenuation is mainly manifested in higher-frequency signals (such as the Ka band), resulting in a decrease in the signal transmission rate, which may cause fluctuations in latency.

Ionosphere influence: The change of electron density in the ionosphere will also affect the propagation of satellite signals, especially the signals of GEO satellites will be propagated through the ionosphere. The change of electron density in the ionosphere may cause refraction or reflection of the signal, thereby increasing the transmission delay. This effect is more obvious in the high frequency band.

Meteorological factors Precipitation and clouds: Heavy precipitation (such as rainstorms, thunderstorms) and thick clouds can cause satellite signal attenuation. In these cases, satellite signals may attenuate or even temporarily lose connection, resulting in a sharp increase in latency. Satellite communication systems usually need to have anti-rain attenuation technology to mitigate the impact of these factors.

Snowstorms and hail: These extreme weather conditions will aggravate signal attenuation, especially at Ka-band frequencies, where signal attenuation is most significant, which may lead to a decrease in signal quality and unstable latency.

Geographical factors and obstacles Terrain and buildings: In complex terrain environments such as cities or mountainous areas, satellite signals may be blocked or reflected by obstacles such as ground buildings and mountains, resulting in signal attenuation or increased latency. In extreme environments, the latency of command transmission may fluctuate significantly, affecting the real-time nature of communications.

The placement of ground stations and terminal equipment: The antenna direction and installation location of ground stations and terminals may also affect the quality of satellite signals and thus latency. If the antenna of the ground station is not properly pointed at the satellite, the signal may be lost, resulting in increased latency.

After analyzing the satellite signal attenuation degree under the interference of environmental factors, the signal attenuation anomaly index is generated. The method for obtaining the signal attenuation anomaly index is as follows:

Prepare input data related to signal attenuation. Signal attenuation data usually consists of multiple environmental factors and time series data, such as meteorological conditions (precipitation, temperature, wind speed, etc.) and actual measured signal strength attenuation values.

Signal attenuation data : Signal attenuation value measured at each time point.

Environmental data: factors that affect signal attenuation, such as temperature (T), humidity (H), precipitation (P), etc. For each observation point (i.e., timestamp), construct a feature vector: ; Before applying Isolation Forest, it is usually necessary to standardize or normalize the data, especially when the dimensions of the data are inconsistent. Through standardization, the mean of each feature is adjusted to 0 and the standard deviation is 1, thus preventing some features from having too much influence on the model results.

Use the training data set to build an Isolation Forest model. This model "isolates" data points through multiple decision trees. Each tree builds a path by randomly selecting a feature and partitioning the values of the selected feature, with the goal of isolating data points as quickly as possible. Isolation Forest calculates the "degree of isolation" of each data point, that is, the length of the path where the data point is isolated in the tree.

Model training process: Initialization: Set the number of trees (usually 100 or more) and the depth limit of the tree. Build trees: Each tree randomly extracts samples from the data set, gradually "isolating" each data point. The more trees, the more robust the model. Path length calculation: Each data point will be randomly divided until it is isolated. The shorter the path length, the easier it is for the data point to be isolated and may be an outlier.

After training the Isolation Forest model, for each data point, the model generates an anomaly score, which indicates the degree of anomaly of the data point. The calculation formula is: ;in: is the i-th data point The average path length isolated in all trees, c(n) is a normalization constant, usually calculated based on the sample size n, and the formula is: ; n is the total number of data points in the training data set. When the value of is close to 1, it means that the data point is normal; When the value is close to 0, it means that the data point is abnormal. The signal attenuation anomaly index is the weighted average of all signal attenuation anomaly scores.

According to the acquired signal delay deviation index and signal attenuation anomaly index, the delay value of satellite signal transmission is calculated through a machine learning model;

For example, the signal delay deviation index and the signal attenuation anomaly index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the delay degree value label of satellite signal transmission as the prediction target, and takes minimizing the sum of prediction errors of the delay degree value labels of all satellite signal transmissions as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The delay degree value of satellite signal transmission is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

The method for obtaining the delay value of satellite signal transmission is to obtain the corresponding function expression from the comprehensive feature vector training data of the trained machine learning model: ; In the formula, is the output function of the model, is the signal delay deviation index, is the signal attenuation anomaly index, It is the delay value of satellite signal transmission.

S4: Compare the acquired satellite signal transmission delay value with a preset reference threshold of the delay value. If the satellite signal transmission delay value is greater than or equal to the preset reference threshold of the delay value, it indicates that the satellite signal transmission delay is high, and the satellite signal is classified as a high-latency satellite signal. If the satellite signal transmission delay value is less than the preset reference threshold of the delay value, it indicates that the satellite signal transmission delay is low, and the satellite signal is classified as a low-latency satellite signal.

For high-latency satellite signals, the following signal transmission optimization strategies can be used to reduce the impact of latency:

Increase the redundancy of satellite links: For example, use multiple satellite links for communication backup to reduce the delay caused by a single satellite signal problem.

Adjust satellite orbit or attitude: Dynamically adjust the satellite's orbit or attitude based on real-time signal evaluation to optimize the signal transmission path.

Optimize data encoding and compression algorithms: During signal transmission, use efficient encoding and compression algorithms to reduce the amount of data during signal transmission, thereby reducing latency.

Use low-latency satellites: In the case of high-latency signal transmission, low-latency low-Earth orbit satellites are preferred for signal transmission.

Dynamically select ground stations: By evaluating the reception conditions of different ground stations in real time, the ground station with the shortest response time is dynamically selected for data transmission.

For low-latency satellite signals, the existing communication configuration can continue to be maintained, optimizing system stability and reducing maintenance costs.

S5: The method for obtaining the on-site command response error data is as follows: When the command center issues a command to the site, it will record the time when each command is issued. This time marks the start time of the command. It can be recorded through the timestamp in the command system. Every time a command is issued, the system will automatically record the exact time when the command is issued.

When the rescue unit on the scene receives the emergency command from the command center, it will also record the specific time when the command was received. This time record can accurately reflect the delay of satellite signal transmission.

The time from when the rescue unit receives the command to when it actually executes the task and responds. The response time refers to the time when the rescue unit starts to act.

Record the time it takes to complete a rescue mission in order to measure the accuracy and efficiency of the response time. It can be combined with the command response time for analysis.

The on-site command response error is calculated based on the command issuance time, reception time, and response time. The specific steps are as follows:

The delay of satellite signal transmission refers to the time difference between the command issued by the command center and the time received by the on-site rescue unit. The expression is: command time difference = command reception time − command issuance time;

The command response error refers to the difference between the response time of the on-site rescue unit and the time when the response should theoretically begin. The theoretical response time can be assumed to be the time when the command is issued plus a predetermined reaction time (set according to factors such as the urgency of the task and the familiarity of the personnel). Response time difference = command response time − (command issuance time + Δt reaction time); where Δt reaction time is the standard response time set according to the task type and command system.

The on-site command response error is obtained by taking the weighted average of the command time difference and the response time difference.

The acquired satellite signal transmission delay value and on-site command response error are used as the input of fuzzy logic, and the communication strategy is used as the output of fuzzy logic. The communication strategy is dynamically adjusted through fuzzy logic to improve the efficiency of information transmission between the command center and the rescue unit.

The delay value of satellite signal transmission (such as the result predicted by the machine learning model) is usually a quantitative value, which we need to convert into a fuzzy value. Common fuzzy sets can include the following categories: Low Delay, Medium Delay, High Delay;

The on-site command response error is also a quantitative value, indicating the response deviation caused by signal delay. We fuzzify it into the following fuzzy sets: low error, medium error, high error;

Through fuzzification, the actual value is mapped to the above fuzzy set. Fuzzification usually uses triangular or trapezoidal membership functions. Taking the "delay value" as an example, assuming that the value ranges from 0 to 1000 milliseconds, the specific membership function can be defined as follows:

Low latency: The membership value of high latency is low (0-300 milliseconds), and the membership of low latency will decrease. Medium latency: Between 300 and 700 milliseconds, the membership gradually increases. High latency: The membership value of high latency is high (700-1000 milliseconds), and the membership of high latency gradually decreases. Through these membership functions, the delay value of satellite signals can be fuzzified into a fuzzy set of "low latency", "medium latency", or "high latency".

According to the delay degree and the response error, a fuzzy rule base is defined. For example, the rules in the rule base may include: if the delay degree is high delay and the error is high error, the communication strategy is to optimize the delay. If the delay degree is low delay and the error is low error, the communication strategy is to maintain the current configuration. If the delay degree is medium delay and the error is medium error, the communication strategy is to perform partial optimization. These rules reflect the choice of communication strategies in different situations.

The output of the communication strategy is the goal we need to optimize. The output fuzzy set can be defined according to different strategy goals, for example:

Optimize Delay: Reduce communication delay, for example by switching to low-latency satellites or changing satellite orbits.

Keep Current Configuration: Keep the current communication configuration with low error and short latency.

Partial Optimization: Make appropriate adjustments to signal delay and response error.

The output of the communication strategy needs to be represented by fuzzy sets, which usually also involves the definition of membership functions. For example, the degree of latency optimization of the output can be divided into "strong optimization", "moderate optimization" and "slight optimization".

Fuzzy reasoning is the core of fuzzy logic. It derives the corresponding output (communication strategy) through the fuzzy values of input items (delay degree and response error) and the rules in the fuzzy rule base.

Reasoning is performed based on the fuzzy values of the input items and the fuzzy rules in the rule base. For example, if the input is "high latency" and "high error", then according to the fuzzy rule base, the output may be "optimal latency". The reasoning process usually takes the following steps:

Fuzzy input: Map the actual value of satellite signal delay and command response error into fuzzy sets.

Apply fuzzy rules: According to the fuzzy rule base, check the combination of input variables and obtain a fuzzy reasoning result.

Aggregate rule results: Aggregate the output results of all rules into a fuzzy set to represent the overall communication strategy.

Defuzzification: Convert the fuzzy output set into an accurate communication strategy value. For example, use the Centroid Method to convert the fuzzy output result into a specific numerical communication strategy.

The defuzzification process transforms the fuzzy set obtained by fuzzy reasoning into a specific communication strategy. Through defuzzification, the most suitable communication strategy for the current situation is obtained.

According to the fuzzification results of each satellite signal transmission delay and command response error, the communication strategy is adjusted in real time through the fuzzy reasoning model. Specifically:

High latency and high error: When both latency and response error are high, the inference results may suggest “optimizing latency”, such as switching to a low-latency satellite orbit or adopting a signal enhancement strategy.

Low latency and low error: If both latency and error are low, the system is likely to maintain the current communication configuration.

Moderate delay and error: Some optimization strategies may be adopted at this time, such as adjusting the communication link or enabling backup signal channels.

Through continuous monitoring and feedback adjustment, the system can continuously optimize the command response process, reduce command response errors caused by time delays and errors, and improve emergency command efficiency.

Embodiment 2, referring to FIG. 2 , a multimodal data communication system using the Beidou satellite system described in this embodiment includes a satellite communication link module, an instruction transmission module, a delay evaluation module, a signal optimization strategy module, and a communication strategy dynamic adjustment module;

Satellite communication link module: Establish communication connection between the command center and multiple rescue units through the multi-modal data communication link of the Beidou satellite system;

Command transmission module: After receiving the emergency command from the command center, it uses the communication link of the Beidou satellite system and combines multimodal data transmission technology to transmit the emergency command to the rescue unit;

Delay evaluation module: During the communication process, based on the orbital characteristics of the Beidou satellite system, the delay deviation of satellites in different orbits in signal transmission and the impact of environmental factors on signal attenuation are analyzed to evaluate the delay degree of satellite signal transmission;

Signal optimization strategy module: Based on the evaluation results, satellite signals are divided into high-latency satellite signals and low-latency satellite signals, and multi-modal data fusion and signal optimization strategies are used for high-latency satellite signals to reduce command response errors;

Communication strategy dynamic adjustment module: dynamically adjusts communication strategies and optimizes data transmission paths based on the signal transmission delay characteristics of the Beidou satellite system and on-site command response error data.

The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

It should be understood that the term "and/or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character "/" in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and/or" relationship. Please refer to the context for specific understanding.

Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims (6)

1. A multi-modal data communication method using the Beidou satellite system, characterized in that it comprises the following steps: S1: Establish communication connection between the command center and multiple rescue units through the multi-modal data communication link of the Beidou satellite system; S2: After receiving the emergency command from the command center, the emergency command is transmitted to the rescue unit using the communication link of the Beidou satellite system combined with multimodal data transmission technology; S3: During the communication process, based on the orbital characteristics of the BeiDou satellite system, analyze the delay deviation of satellites in different orbits in signal transmission and the impact of environmental factors on signal attenuation, and evaluate the delay degree of satellite signal transmission, specifically: The signal delay deviation index is generated by analyzing the delay deviation of satellites in different orbits during signal transmission. The method for obtaining the signal delay deviation index is as follows: Set an interval and a range of environmental factors Δtenvironmentt, and perform N random samplings according to a pre-set probability distribution to simulate multiple signal transmission delays. Each simulation calculates the delay based on different satellite orbit heights and environmental factors. For each simulation, values are drawn from the distribution of each parameter and then substituted into the signal delay model to calculate: Where i represents the i-th simulation, and is a value drawn from the corresponding distribution, h is the distance between the satellite and the ground, and c is the speed of light; A set of signal delay values are obtained through multiple simulations The delay deviation is defined as the deviation between the simulation result and the theoretical value. The theoretical delay is set to , then the delay deviation for: ; Calculate the signal delay deviation index, the expression is: ; In the formula, is the signal delay deviation index, N is the total number of signal delay values; After analyzing the satellite signal attenuation degree under the interference of environmental factors, the signal attenuation anomaly index is generated. The method for obtaining the signal attenuation anomaly index is as follows: Prepare input data related to signal attenuation: Signal attenuation data , that is, the signal attenuation value measured at each time point; environmental data: temperature T, humidity H, precipitation P, for each observation point, construct a feature vector: ; Before applying Isolation Forest, use the training data set to build an Isolation Forest model. Multiple decision trees are used to isolate data points. Each tree constructs a path by randomly selecting a feature and dividing the value of the selected feature. Isolation Forest calculates the degree of isolation of each data point, that is, the length of the path where the data point is isolated in the tree. After training the Isolation Forest model, for each data point, the model generates an anomaly score, which indicates the degree of abnormality of the data point. The anomaly score The calculation formula is: ;in: is the i-th data point The average path length isolated in all trees, c(n) is a normalization constant, the formula is: ; n is the total number of data points in the training dataset, and the signal attenuation anomaly index is the weighted average of all signal attenuation anomaly scores; According to the acquired signal delay deviation index and signal attenuation anomaly index, the delay value of satellite signal transmission is calculated through a machine learning model; The signal delay deviation index and the signal attenuation anomaly index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model predicts the delay degree value label of the satellite signal transmission with each group of comprehensive feature vectors as the prediction target, and minimizes the sum of the prediction errors of the delay degree value labels of all satellite signal transmissions as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the model training is stopped. The delay degree value of the satellite signal transmission is determined according to the model output result, wherein the machine learning model is a polynomial regression model; S4: Based on the evaluation results, satellite signals are divided into high-latency satellite signals and low-latency satellite signals, and multi-modal data fusion and signal optimization strategies are used for high-latency satellite signals to reduce command response errors; S5: Dynamically adjust the communication strategy and optimize the data transmission path based on the delay value of the satellite signal transmission of the Beidou satellite system and the on-site command response error data. 2. A multimodal data communication method using the BeiDou satellite system according to claim 1, characterized in that: in S1, the communication link uses at least one type of satellite among low earth orbit satellites, medium earth orbit satellites or geostationary orbit satellites. 3. According to a multimodal data communication method using the Beidou satellite system according to claim 1, it is characterized in that: in S4, based on the evaluation result, the satellite signal is divided into a high-latency satellite signal and a low-latency satellite signal, specifically: The acquired delay degree value of satellite signal transmission is compared with a preset reference threshold of the delay degree value. If the delay degree value of satellite signal transmission is greater than or equal to the preset reference threshold of the delay degree value, it indicates that the delay degree of satellite signal transmission is high, and the satellite signal is classified as a high-latency satellite signal; if the delay degree value of satellite signal transmission is less than the preset reference threshold of the delay degree value, it indicates that the delay degree of satellite signal transmission is low, and the satellite signal is classified as a low-latency satellite signal. 4. A multimodal data communication method using the Beidou satellite system according to claim 1, characterized in that: in S5, the on-site command response error is calculated based on the instruction sending time, receiving time, and response time, specifically: The delay of satellite signal transmission refers to the time difference between the command issued by the command center and the time when the on-site rescue unit receives the command, and the expression is: command time difference = command reception time − command issuance time; the command response error refers to the difference between the response time of the on-site rescue unit and the time when the response should theoretically start, response time difference = command response time − (command issuance time + Δt reaction time); where Δt reaction time is the standard response time set according to the task type and command system; the on-site command response error is obtained by taking the weighted average sum of the command time difference and the response time difference. 5. A multimodal data communication method using the BeiDou satellite system according to claim 4, characterized in that: the acquired satellite signal transmission delay value and the on-site command response error are used as input items of fuzzy logic, and are divided into different fuzzy sets respectively; The communication strategy is taken as the output item of fuzzy logic and divided into different fuzzy sets; Formulate fuzzy rules to describe the impact of satellite signal transmission delay and on-site command response error definition on communication strategy; Perform fuzzy reasoning based on fuzzy rules and dynamically adjust communication strategies. 6. A multimodal data communication system using the BeiDou satellite system, used to implement a multimodal data communication method using the BeiDou satellite system as described in any one of claims 1 to 5, characterized in that it includes a satellite communication link module, an instruction transmission module, a delay evaluation module, a signal optimization strategy module, and a communication strategy dynamic adjustment module; Satellite communication link module: Establish communication connection between the command center and multiple rescue units through the multi-modal data communication link of the Beidou satellite system; Command transmission module: After receiving the emergency command from the command center, it uses the communication link of the Beidou satellite system and combines multimodal data transmission technology to transmit the emergency command to the rescue unit; Delay evaluation module: During the communication process, based on the orbital characteristics of the Beidou satellite system, the delay deviation of satellites in different orbits in signal transmission and the impact of environmental factors on signal attenuation are analyzed to evaluate the delay degree of satellite signal transmission; Signal optimization strategy module: Based on the evaluation results, satellite signals are divided into high-latency satellite signals and low-latency satellite signals, and multi-modal data fusion and signal optimization strategies are used for high-latency satellite signals to reduce command response errors; Communication strategy dynamic adjustment module: dynamically adjusts communication strategies and optimizes data transmission paths based on the delay value of satellite signal transmission of the Beidou satellite system and the on-site command response error data.