Star Flash connection intelligent switching decision system and method based on device status prediction

Abstract

The present invention discloses a Star Flash connection intelligent switching decision system and method based on device status prediction, which specifically relates to the field of Star Flash connection technology; in response to the problem of Star Flash network chain failure caused by the coupling of multi-manipulator coordinated motion and electromagnetic disturbance in industrial collaborative robot systems, a prediction model integrating the characteristics of spatial posture coordinated change and transient electromagnetic disturbance amplitude gradient characteristics is constructed, thereby realizing high-sensitivity identification and risk warning of the Star Flash connection network coupling state; dynamically adjusting the communication channel and communication parameters through the fuzzy logic decision mechanism, effectively improving the link stability and interference robustness; at the same time, introducing a model adaptive optimization mechanism driven by switching effect feedback, which significantly improves the prediction accuracy and system self-evolution capability, thereby ensuring the continuity, security and high reliability of multi-node collaboration in industrial communication scenarios.

Description

Technical Field

[0001] The present invention relates to the field of star flash connection technology, and in particular to a star flash connection intelligent switching decision system and method based on device state prediction. Background Art

[0002] SparkLink intelligent switching decision means that in devices using SparkLink communication technology, the system can intelligently judge and automatically select the optimal connection method or node based on the current network conditions, device status and application requirements, thereby achieving efficient, stable and low-latency communication.

[0003] The existing technology has the following shortcomings:

[0004] In industrial collaborative robotic systems, when multiple robotic arms simultaneously adjust their positions and the surrounding electromagnetic environment fluctuates dramatically, the previously stable Star Flash connection network can experience cascading failures due to minor coupling imbalances, causing multiple nodes to disconnect simultaneously. Because these issues lack obvious precursory characteristics and exhibit strong nonlinear coupling between states, existing prediction mechanisms are unable to provide early warning, severely impacting industrial production continuity and equipment safety. Summary of the Invention

[0005] The purpose of the present invention is to provide a star flash connection intelligent switching decision system and method based on device status prediction to solve the shortcomings in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent switching decision-making of a StarFlash connection based on device status prediction, comprising:

[0007] Obtain operating status parameters of multiple devices with Star Flash communication capabilities, including device displacement, connection signal strength, and network latency;

[0008] Performing real-time analysis on the operating status parameters to predict the connection quality change trend of the communication link, and extracting the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics from the operating status parameters;

[0009] The spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics are integrated and input into a pre-built device state prediction model to determine the coupling state of the star flash connection network and obtain the coupling imbalance risk result;

[0010] If the risk result is greater than the preset risk threshold, an early warning signal is immediately generated, and the Star Flash connection strategy is adjusted according to the predicted level of coupling imbalance, including switching communication channels or adjusting communication parameter configurations;

[0011] According to the switching effect feedback data, the weight parameters in the device state prediction model are updated to optimize the accuracy of subsequent predictions and decisions.

[0012] Preferably, the device displacement is collected by an inertial measurement unit, gyroscope, accelerometer or external visual positioning system integrated in each device, and the spatial position and posture changes of the device per unit time are recorded in the form of a three-dimensional vector.

[0013] Preferably, the specific calculation method of the spatial posture coordinated change value is: for each device i, at time points t and Get its displacement or attitude change vector : ;in, , represents the position coordinate of device i at time t, and T is the vector transpose; calculate the average displacement vector of all devices in the current time window, and for each device i, calculate the square of the Euclidean distance difference between it and the average change vector, and take the weighted average of the collaborative difference values ​​of all devices to obtain the spatial posture collaborative change value JSVD at the current time point.

[0014] Preferably, the transient electromagnetic disturbance amplitude gradient is calculated by collecting the electromagnetic disturbance signal received by the device per unit time, dividing the signal into multiple overlapping time windows, performing Fourier transform on each window, and calculating the frequency domain amplitude energy of the STFT spectrum under each window: ; Where: E(s) represents the total energy of the spectrum at time point s, fmin and fmax are the frequency ranges of analysis; is the instantaneous amplitude at frequency f. The first-order difference of the time domain energy change is performed through the sliding window to calculate the transient electromagnetic disturbance amplitude gradient. The expression is: ; Where: Δs is the sliding step length, is the transient electromagnetic disturbance amplitude gradient.

[0015] Preferably, the spatial attitude coordinated change feature and the transient electromagnetic disturbance amplitude gradient feature are fused and input into a pre-built device state prediction model to determine the coupling state of the star flash connection network, specifically including:

[0016] The acquired space attitude coordinated change value and transient electromagnetic disturbance amplitude gradient are used as the input of fuzzy logic, and the star flash connection network coupling state risk value is used as the output of fuzzy logic.

[0017] Set the corresponding membership function for each input and output language variable; formulate several fuzzy inference rules and calculate their membership values ​​under each language variable;

[0018] Using fuzzy reasoning method, the input variables are matched with fuzzy rules to obtain the activation degree of each rule on the output variable. The reasoning result of each rule will generate a fuzzy output subset, and the outputs of multiple rules are fused;

[0019] The synthesized fuzzy output results are defuzzified to convert them into clear numerical results, and finally the coupling state risk value is output.

[0020] Preferably, the output value is compared with a preset risk threshold. If the coupling state risk value exceeds the predetermined threshold, it is judged as high risk. The system will immediately issue an early warning and start the connection switching decision process, including switching the communication channel or adjusting the communication parameter configuration.

[0021] Preferably, set the original equipment status prediction model , introduce the MAML structure, and fine-tune the model parameters according to the switching feedback data after each communication cycle, specifically:

[0022] Collecting switching feedback data sets ;in: is the state parameter before switching, Evaluate labels for actual switching effects;

[0023] Under the current model parameters θ, use Execute training to obtain temporarily updated model parameters: ; where α is the inner loop learning rate, is the loss function of the feedback data, is the gradient vector;

[0024] Model Based on the performance on the main task, the original model parameters are updated, and the updated parameters θ replace the original model weights to generate an enhanced state prediction model for star flash connection switching decision.

[0025] The present invention also provides a Star Flash connection intelligent switching decision system based on device status prediction, including a data acquisition module, a feature extraction module, a risk prediction module, a Star Flash connection strategy adjustment module and an accuracy optimization module;

[0026] Data acquisition module: obtains the operating status parameters of multiple devices with Star Flash communication capabilities, including device displacement, connection signal strength and network delay;

[0027] Feature extraction module: performs real-time analysis on the operating status parameters to predict the connection quality change trend of the communication link, and extracts the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics from the operating status parameters;

[0028] Risk prediction module: This module integrates the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics into a pre-built device state prediction model to determine the coupling state of the star flash connection network and obtain the coupling imbalance risk results.

[0029] Star Flash connection strategy adjustment module: If the risk result is greater than the preset risk threshold, it will immediately generate an early warning signal and adjust the Star Flash connection strategy according to the predicted level of coupling imbalance, including switching communication channels or adjusting communication parameter configurations;

[0030] Accuracy optimization module: based on the switching effect feedback data, updates the weight parameters in the device state prediction model to optimize the accuracy of subsequent predictions and decisions.

[0031] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0032] 1. This invention addresses the problem of simultaneous displacement of multiple robotic arms and strong electromagnetic disturbances in industrial collaborative robotic systems, which can easily trigger cascading failures of the Starflash network. By introducing the Joint Spatial Situation Variation (JSVD) feature and the Transient Electromagnetic Disturbance Amplitude Gradient (TEMG) feature, this technology achieves highly sensitive perception of device coordination and environmental interference. Fuzzy logic inference is used to assess the risk level of the Starflash network coupling state. In high-risk situations, intelligent communication channel switching or parameter adjustment strategies are triggered, significantly improving the stability and interference resistance of the communication link.

[0033] 2. This paper further introduces a meta-learning model based on MAML, using communication handoff feedback data as the basis for model training to achieve adaptive updating and optimization of the device state prediction model. Compared to traditional fixed rule systems, this method possesses continuous learning capabilities and environmental adaptability, enabling efficient and reliable connection decision-making in dynamic, multi-disturbance industrial environments, thereby effectively ensuring the continuity, security, and system robustness of multi-device collaborative communication during industrial production. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0035] Example 1: The method for intelligent switching decision-making for a Star Flash connection based on device status prediction described in this embodiment includes:

[0036] Obtain operating status parameters of multiple devices with Star Flash communication capabilities, including device displacement, connection signal strength, and network latency;

[0037] Performing real-time analysis on the operating status parameters to predict the connection quality change trend of the communication link, and extracting the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics from the operating status parameters;

[0038] The spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics are integrated and input into a pre-built device state prediction model to determine the coupling state of the star flash connection network and obtain the coupling imbalance risk result;

[0039] If the risk result is greater than the preset risk threshold, an early warning signal is immediately generated, and the Star Flash connection strategy is adjusted according to the predicted level of coupling imbalance, including switching communication channels or adjusting communication parameter configurations;

[0040] According to the switching effect feedback data, the weight parameters in the device state prediction model are updated to optimize the accuracy of subsequent predictions and decisions.

[0041] Comprehensive operating status collection is performed on all devices in the system that have SparkLink communication capabilities, in order to provide basic data support for subsequent status prediction and intelligent switching decisions. The status parameters obtained include but are not limited to the following:

[0042] Device displacement: Acquired in real time by a high-precision inertial measurement unit (IMU), gyroscope, accelerometer integrated within the device, or an external vision / laser positioning system (such as VSLAM, lidar). This indicates the device's relative position change and movement speed in three-dimensional space, including translational displacement (x, y, z axes) and attitude angle changes (pitch, roll, yaw). This can be recorded in a time series format and used to determine whether the device is in a high-speed dynamic state or undergoing coordinated displacement changes. It is a fundamental indicator for determining potential changes in network topology.

[0043] Connection signal strength: Through the interface provided by the Star Flash communication protocol stack, the received signal strength (RSSI), link quality index (LQI) or signal-to-noise ratio (SNR) of each communication link can be obtained in real time. The acquisition frequency can be adjusted according to the communication density, generally in the range of 5ms to 50ms. A dynamic communication topology map can be further constructed to reflect the stability and spatial attenuation model of the network connection. In the presence of obstruction, interference or long distance, a decrease in signal strength can indicate the risk of connection quality degradation in advance.

[0044] Network latency: By periodically sending probe packets or communication heartbeat packets between devices, point-to-point latency or round-trip time (RTT) is measured. Frequent latency fluctuations or a rising average value may indicate impending link instability, serving as an early signal for intelligent switching decisions. For industrial collaboration scenarios, time synchronization drift can also be combined to improve the accuracy of system-level communication predictions.

[0045] The continuously collected operating status parameters (device displacement, signal strength, network delay, etc.) are divided into sliding windows in chronological order; a multi-dimensional state vector sequence is formed in each window to describe the evolution process of the current system's operating status.

[0046] Build a multivariate time series analysis model for link quality prediction, such as one based on an LSTM neural network, a Bayesian time series model, or a Kalman filter. Output the link's connection quality trends for several future moments, such as signal attenuation rate, delay variation trend, and connection loss probability. The prediction time step Δt can be set. For example, if the link quality drops above a threshold within the next 0.5 or 1 second, an early warning mechanism is triggered.

[0047] To identify non-explicit interference that may cause chain connection failure in complex collaborative scenarios, the spatial posture coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics are extracted:

[0048] Spatial Posture Cooperative Variation (JSVD): This feature represents the spatial posture change of each device per unit time as a set of three-dimensional motion vectors. The spatial posture cooperative variation value is calculated by calculating the cosine of the angle between the posture change vectors or the variance of the vector differences between any two devices in the group.

[0049] For example, the specific calculation method of the spatial posture coordinated change value is:

[0050] For each device i, at time t and Get its displacement or attitude change vector : ;in, , represents the position coordinates of device i at time t, T is the vector transpose; calculate the average displacement vector of all devices in the current time window , for each device i, calculate the square of the Euclidean distance difference between it and the average change vector , the expression is: ; Take the weighted average of the collaborative difference values ​​of all devices to obtain the spatial posture collaborative change value JSVD at the current time point.

[0051] A small JSVD indicates similar motion directions and amplitudes between devices, a high degree of coordination, and a stable network topology. A large JSVD indicates a significant deviation from the group's motion trend, potentially leading to occlusion, interference, and link interruptions. In industrial robotic arms and collaborative robot scenarios, a sudden increase in JSVD often indicates a sudden change in the robotic arm group's state, which can easily trigger the need for rapid Starlink link reconfiguration.

[0052] Transient electromagnetic disturbance amplitude gradient characteristics (TEMG): The electromagnetic field strength values ​​EM(t) at multiple time points are collected through the electromagnetic sensor module on the device. The first-order difference of the electromagnetic disturbance signal at consecutive moments is performed to obtain the disturbance change gradient. Alternatively, in a multi-device environment, the gradient distribution is calculated by collecting EMi(t) at different spatial points to reflect the disturbance fluctuation range and directionality.

[0053] For example, the transient electromagnetic disturbance amplitude gradient is calculated as follows: the electromagnetic disturbance signal received by the acquisition device per unit time is expressed as: EM(s), s∈[0,T]; where EM(s) represents the electromagnetic field intensity at time s (which can be the electric field amplitude, voltage, or power spectrum density, etc.). The signal is divided into multiple overlapping time windows, and a Fourier transform is performed on each window: ;in: is a window function (such as a Hamming window or a Gaussian window) with a length of Lw; f is a frequency variable, and STFT(s,f) is the spectrum at time s in the time-frequency domain. is the kernel function of Fourier transform, EM( ) indicates the time The electromagnetic field intensity at . Calculate the frequency domain amplitude energy for the STFT spectrum under each window: Where: E(s) represents the total energy (or energy density) of the spectrum at time point s, fmin and fmax are the frequency range of analysis (for example, 10kHz~500kHz); is the instantaneous amplitude at frequency f. The first-order difference of the time domain energy change is performed through the sliding window to calculate the transient electromagnetic disturbance amplitude gradient, which is expressed as: ; Where: Δs is the sliding step size (such as 5ms, 10ms), TEMG is the transient electromagnetic disturbance amplitude gradient. A sudden increase in the TEMG value indicates the presence of a short-term strong interference source in the environment (such as high-power equipment startup, electric welding, and EMI coupling), which can easily cause transient instability of the starlight link.

[0054] The spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics are integrated and input into a pre-built device state prediction model to determine the coupling state of the star flash connection network and obtain the coupling imbalance risk results, including:

[0055] The acquired space attitude coordinated change value and transient electromagnetic disturbance amplitude gradient are used as the input of fuzzy logic, and the star flash connection network coupling state risk value is used as the output of fuzzy logic.

[0056] Assign a corresponding membership function to each input and output linguistic variable. Common membership functions include triangular, trapezoidal, or Gaussian functions. For example, for JSVD, the following membership function is assigned: "Low covariance" indicates high consistency in system motion; "Medium covariance" indicates slight covariance deviation; and "High covariance" indicates significant motion inconsistency. Fuzzy levels and membership curves are assigned in the same manner for TEMG and output items.

[0057] Based on your understanding of system behavior, develop several fuzzy inference rules (IF-THEN rules). For example, if JSVD is "high" and TEMG is "high," the coupling risk is "high"; if JSVD is "medium" and TEMG is "high," the coupling risk is "medium-high"; if JSVD is "low" and TEMG is "low," the coupling risk is "low"; if JSVD is "medium" and TEMG is "low," the coupling risk is "medium," and so on. The rule base should cover all possible combinations of input variables to ensure that the system fully responds to various operating conditions.

[0058] After the actual system collects specific values ​​of JSVD and TEMG, its membership values ​​for each linguistic variable are calculated. Next, fuzzy inference methods (such as the Mamdani model) are used to match the input variables with fuzzy rules, determining the degree of activation of each rule on the output variable. The inference results of each rule generate a fuzzy output subset, and the outputs of multiple rules are fused using methods such as "maximum membership synthesis" or "weighted averaging."

[0059] The synthesized fuzzy output is defuzzified to convert it into a clear numerical result. Common methods include the centroid method or the maximum membership method. The final output, the coupling state risk value, is a continuous value (e.g., a decimal between 0 and 1). This value can be used to determine whether the system has entered a high-risk state and decide whether to trigger a connection switch or early warning strategy.

[0060] The output value is compared with the preset risk threshold. If the coupling status risk value exceeds the predetermined threshold (such as ≥0.7), it is judged as high risk. The system will immediately issue an early warning and initiate the connection switching decision process, including switching the communication channel or adjusting the communication parameter configuration.

[0061] When the current communication channel is determined to be interfered or unstable, the system will perform the following operations:

[0062] Communication frequency band reselection: Scans available StarFlash communication frequency bands (such as those operating in the 2.4GHz or 5.8GHz bands); performs interference assessment on each candidate channel (signal-to-noise ratio, packet loss rate, occupancy rate, etc.); selects the channel with the least interference and the best link quality based on priority or policy weight for switching; during the switching process, ensures that the master and slave devices hop synchronously to avoid connection interruption.

[0063] Fast spectrum sensing and dynamic channel assessment: Uses an embedded spectrum analysis module to perform short-term spectrum scanning; evaluates the current channel's channel idle rate, bandwidth availability, and adjacent signal interference level in real time; and quickly selects a switching target based on a channel quality scoring model.

[0064] Channel blacklist management mechanism: If a channel experiences multiple communication failures in a short period of time, it will be temporarily added to the blacklist to prevent it from being selected again; the blacklisted channel can be automatically restored after a set cooling time.

[0065] If you do not need to switch frequency bands or channels but want to optimize the interference immunity of the current link, you can adjust the communication parameters in the following ways:

[0066] Modulation and Coding Scheme (MCS) adjustment: Automatically selects a more robust modulation scheme (such as downgrading from 64-QAM to 16-QAM or QPSK) based on link quality; reduces the bit error rate and improves transmission stability, but may sacrifice throughput; and automatically reverts to a higher-rate configuration after interference is alleviated.

[0067] Dynamically adjust transmit power: If there is instantaneous attenuation or obstruction in the channel, the transmit power (TXPower) can be temporarily increased. To avoid adjacent channel interference caused by excessive power, a power cap must be set. In the absence of interference, the power can be gradually reduced to save energy.

[0068] Enable adaptive retransmission mechanism (ARQ / HARQ): For environments with high packet loss rates, enable the automatic retransmission request mechanism; set parameters such as the maximum number of retransmissions, waiting interval, and ACK confirmation mechanism; combined with the fast retransmission channel in Star Flash, improve stability in high-interference environments.

[0069] Increase link redundancy or multi-path backup: Establish a primary-backup connection mechanism so that critical equipment can quickly switch to the backup channel when the primary channel fails; support multi-link concurrent transmission (such as link aggregation or load balancing) to reduce the risk of single point failure.

[0070] Optimize MAC layer scheduling strategies: prioritize allocating time slices or transmission windows to critical devices; temporarily limit the data rate or transmission cycle of low-priority devices when network resources are tight; and enable time slot rescheduling mechanisms to avoid periods of concentrated interference.

[0071] In the Star Flash connection switching system based on device state prediction, the initial state prediction model is often built based on fixed rules or training samples, making it difficult to cope with complex environmental changes during operation. To this end, it is necessary to introduce a model adaptive optimization mechanism that uses feedback from the switching effect to continuously adjust the model weights, thereby improving its prediction accuracy and generalization ability in real-world scenarios.

[0072] After each connection handover, the system collects feedback data to evaluate the handover's effectiveness. This data includes: the change in signal strength (ΔRSSI) before and after the handover; the change in network latency (ΔRTT) before and after the handover; the success of the handover (success rate / failure flag); the duration of network stability after the handover; and performance metrics such as bit error rate and packet loss rate after the handover. This feedback data can be used to measure the accuracy and reliability of the current prediction model's decisions.

[0073] Traditional model parameter update methods, such as backpropagation or reinforcement learning, are commonly used for static or semi-dynamic systems. However, in starlight systems, where state transitions are frequent and perturbations are complex, a small-sample learning model that can quickly adapt to new environmental changes is required. MAML meta-learning can be used to rapidly adjust model parameters to achieve optimal performance on a small number of new samples (i.e., feedback data).

[0074] Setting up the original equipment status prediction model , which is used to predict the risk level of the communication link; by introducing the MAML structure, the model parameters are fine-tuned according to the switching feedback data after each communication cycle, specifically:

[0075] Collecting switching feedback data sets ;in: Status parameters before switching (such as JSVD, TEMG, signal strength, delay, etc.), Evaluate labels for actual handover effects (e.g., success / failure, network quality improvement rate);

[0076] Under the current model parameters θ, use Execute training to obtain temporarily updated model parameters: ; where α is the inner loop learning rate, is the loss function of the feedback data (such as prediction error, classification loss, etc.), It is a gradient vector, which represents the set of partial derivatives of a loss function (or objective function) with respect to the model parameters θ.

[0077] Model Based on the performance on the main task (such as future state prediction), update the original model parameters: ; Where: β is the learning rate of the outer loop, This step measures the generalization ability of the prediction model in real-world environments. The goal of this optimization step is to enable the model to quickly adapt to environmental changes in any future feedback samples. The updated parameters θ replace the original model weights to generate an enhanced state prediction model, which is used for the next Star Flash connection handover decision.

[0078] Example 2: The Star Flash connection intelligent switching decision system based on device status prediction described in this embodiment includes a data acquisition module, a feature extraction module, a risk prediction module, a Star Flash connection strategy adjustment module, and an accuracy optimization module;

[0079] Data acquisition module: obtains the operating status parameters of multiple devices with Star Flash communication capabilities, including device displacement, connection signal strength and network delay;

[0080] Feature extraction module: performs real-time analysis on the operating status parameters to predict the connection quality change trend of the communication link, and extracts the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics from the operating status parameters;

[0081] Risk prediction module: This module integrates the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics into a pre-built device state prediction model to determine the coupling state of the star flash connection network and obtain the coupling imbalance risk results.

[0082] Star Flash connection strategy adjustment module: If the risk result is greater than the preset risk threshold, it will immediately generate an early warning signal and adjust the Star Flash connection strategy according to the predicted level of coupling imbalance, including switching communication channels or adjusting communication parameter configurations;

[0083] Accuracy optimization module: based on the switching effect feedback data, updates the weight parameters in the device state prediction model to optimize the accuracy of subsequent predictions and decisions.

[0084] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0085] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0086] Those skilled 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 beyond the scope of this application.

[0087] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for intelligent switching decision-making for Star Flash connections based on device status prediction, characterized by: include: Obtain operating status parameters of multiple devices with Star Flash communication capabilities, including device displacement, connection signal strength, and network latency; Performing real-time analysis on the operating status parameters to predict the connection quality change trend of the communication link and extracting the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics from the operating status parameters; The specific calculation method of the spatial posture collaborative change value is: for each device i, at time points t and Get its displacement or attitude change vector : ;in, , represents the position coordinates of device i at time t, and T is the vector transpose; calculate the average displacement vector of all devices in the current time window, and for each device i, calculate the square of the Euclidean distance difference between it and the average change vector, and take the weighted average of the collaborative difference values ​​of all devices to obtain the spatial posture collaborative change value JSVD at the current time point; The calculation method of the transient electromagnetic disturbance amplitude gradient is as follows: the electromagnetic disturbance signal received by the device per unit time is collected, the signal is divided into multiple overlapping time windows, a Fourier transform is performed on each window, and the frequency domain amplitude energy is calculated for the STFT spectrum under each window: ; Where: E(s) represents the total energy of the spectrum at time point s, fmin and fmax are the frequency ranges of analysis; is the instantaneous amplitude at frequency f. The first-order difference of the time domain energy change is performed through the sliding window to calculate the transient electromagnetic disturbance amplitude gradient. The expression is: ; Where: Δs is the sliding step length, is the transient electromagnetic disturbance amplitude gradient; The spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics are integrated and input into a pre-built device state prediction model to determine the coupling state of the star flash connection network and obtain the coupling imbalance risk result, specifically including: using the obtained spatial attitude coordinated change value and transient electromagnetic disturbance amplitude gradient as input items of fuzzy logic, and using the star flash connection network coupling state risk value as output item of fuzzy logic; Set the corresponding membership function for each input and output language variable; formulate several fuzzy inference rules and calculate their membership values ​​under each language variable; Using fuzzy reasoning method, the input variables are matched with fuzzy rules to obtain the activation degree of each rule on the output variable. The reasoning result of each rule will generate a fuzzy output subset, and the outputs of multiple rules are fused; Defuzzify the synthesized fuzzy output results, convert them into clear numerical results, and finally output the coupling state risk value; If the risk result is greater than the preset risk threshold, an early warning signal is immediately generated, and the Star Flash connection strategy is adjusted according to the predicted level of coupling imbalance, including switching communication channels or adjusting communication parameter configurations; Based on the switching effect feedback data, the weight parameters in the device state prediction model are updated to optimize the accuracy of subsequent predictions and decisions; Setting up the original equipment status prediction model , introduce the MAML structure, and fine-tune the model parameters according to the switching feedback data after each communication cycle, specifically: Collecting switching feedback data sets ;in: is the state parameter before switching, Evaluate labels for actual switching effects; Under the current model parameters θ, use Execute training to obtain temporarily updated model parameters: ; where α is the inner loop learning rate, is the loss function of the feedback data, is the gradient vector; Model Based on the performance on the main task, update the original model parameters: ; Where: β is the learning rate of the outer loop, It is used to measure the generalization ability of the prediction model in the real environment; the updated parameters θ replace the original model weights to generate an enhanced state prediction model for the next star flash connection switching decision.

2. The method for intelligent switching decision-making of a StarFlash connection based on device status prediction according to claim 1 is characterized in that: The device displacement is collected by an inertial measurement unit, gyroscope, accelerometer or external visual positioning system integrated in each device, and the spatial position and posture changes of the device per unit time are recorded in the form of a three-dimensional vector.

3. The method for intelligent switching decision-making of a StarFlash connection based on device status prediction according to claim 1 is characterized in that: The output value is compared with the preset risk threshold. If the coupling status risk value exceeds the predetermined threshold, it is judged as high risk. The system will immediately issue an early warning and initiate the connection switching decision process, including switching the communication channel or adjusting the communication parameter configuration.

4. A Star Flash connection intelligent switching decision system based on device state prediction, used to implement the Star Flash connection intelligent switching decision method based on device state prediction according to any one of claims 1 to 3, characterized in that: It includes data acquisition module, feature extraction module, risk prediction module, star flash connection strategy adjustment module and accuracy optimization module; Data acquisition module: obtains the operating status parameters of multiple devices with Star Flash communication capabilities, including device displacement, connection signal strength and network delay; Feature extraction module: performs real-time analysis on the operating status parameters to predict the connection quality change trend of the communication link, and extracts the spatial attitude coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics from the operating status parameters; Risk prediction module: The spatial posture coordinated change characteristics and transient electromagnetic disturbance amplitude gradient characteristics are integrated into a pre-built equipment status prediction model to determine the coupling status of the star flash connection network and obtain the coupling imbalance risk results. Star Flash connection strategy adjustment module: If the risk result is greater than the preset risk threshold, it will immediately generate an early warning signal and adjust the Star Flash connection strategy according to the predicted level of coupling imbalance, including switching communication channels or adjusting communication parameter configurations; Accuracy optimization module: based on the switching effect feedback data, updates the weight parameters in the device state prediction model to optimize the accuracy of subsequent predictions and decisions.

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