Communication link dynamic channel allocation method based on swan gap system

Abstract

The invention belongs to the technical field of computer communication, and particularly relates to a communication link dynamic channel allocation method based on a swan gap system. The method comprises the implementation steps that a cooperative communication monitoring module is established at a gap communication node, multi-dimensional link state parameters such as channel idle degree are collected in a distributed mode, and the collection period is dynamically adjusted along with state changes; constructing an evaluation model by using an improved LSTM algorithm, fusing the space coordinate frequency domain features and the time sequence parameters, and generating a channel state evaluation matrix; dynamically allocating channels in combination with channel stability, task priorities and data types; and a rapid re-evaluation mechanism is set, and when the channel score deviates from a threshold value, re-allocation is performed, so that the communication stability is guaranteed. Through dynamic monitoring, intelligent evaluation and self-adaptive distribution, the channel resource utilization rate and communication stability are improved, the method is suitable for a distributed internet of things scene of the swan mongolian system, and the problems of channel competition and real-time scheduling under high-density deployment are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer communication, and particularly relates to a method for dynamically allocating communication channels in a communication link based on the HarmonyOS. Background Art

[0002] With the continuous promotion of the concept of Internet of Everything, the HarmonyOS has been widely applied to various scenarios such as home automation, industrial Internet of Things, and vehicle Internet of Things by virtue of its distributed architecture and soft bus capabilities. In the HarmonyOS ecosystem, frequent data communication and status coordination between distributed terminals and edge devices have become the norm. Especially in high-density deployment environments, higher requirements are put forward for the quality and stability of communication links. However, existing communication systems generally adopt static channel allocation strategies, that is, presetting the binding method of communication channels and devices, and it is difficult to dynamically adjust channel resources according to device state changes, link interference fluctuations, or access requirements. Especially in the face of task scheduling scenarios with high-concurrency access, multi-source link competition, and high real-time requirements, the static allocation scheme not only leads to low utilization rate of channel resources, but also may cause problems such as link congestion, communication delay, and even connection interruption. At the same time, the traditional channel selection mechanism lacks the ability to real-time perceive and intelligently evaluate the channel state, and cannot support the dynamic scheduling requirements of complex heterogeneous devices under the HarmonyOS. Summary of the Invention

[0003] In view of the technical problems existing in the above background art, the present invention proposes a method for dynamically allocating communication channels in a communication link based on the HarmonyOS.

[0004] To achieve the above object, the technical solution adopted by the present invention includes the following steps:

[0005] S1. Establish a cooperative communication monitoring module among multiple communication nodes running the HarmonyOS, which is used to collect the link state parameters of the communication channels between the local node and the surrounding nodes in real time. The link state parameters include: channel idle degree, signal strength, bit error rate, and interference index;

[0006] S2. Construct a channel state evaluation model based on the collected multi-source link state parameters. The model is used to generate a state evaluation matrix of all current channels. The specific implementation process includes the following steps: Assign a unique channel number to each channel to be evaluated, and establish a corresponding data input channel, and input the link state parameters of the channel into the channel state evaluation model; The evaluation model outputs the corresponding score values of each channel respectively, and arranges all the score values in the order of channel numbers to form a one-dimensional score vector; In each scoring cycle, perform the step of forming a one-dimensional score vector for all channels, and vertically splice all the score vectors in the time dimension to form a two-dimensional channel state evaluation matrix;

[0007] S3. Calculate the channel stability through the status evaluation matrix, and complete the channel allocation by combining the task priority and data type;

[0008] S4. Continuously monitor the real-time status parameters of the allocated channels during the communication process. If the difference between its scoring value and the mean value of the corresponding scoring period at the time of allocation exceeds the set threshold, trigger the fast re-evaluation mechanism, re-evaluate the channels and perform channel allocation to ensure communication stability.

[0009] Preferably, the cooperative communication monitoring module adopts a distributed deployment architecture, realizes cross-node data synchronization through the soft bus technology of the HarmonyOS system, and the acquisition period of each node is dynamically matched with the channel state change frequency. The calculation method is: , where is the acquisition period of the current node, respectively represent the predicted minimum and maximum periods, is the variance of the channel state parameters in the previous period.

[0010] Preferably, the construction of the channel state evaluation model based on the collected multi-source link state parameters in step S2 includes:

[0011] First, normalize the link state parameters collected for each channel;

[0012] Construct a channel feature vector according to the normalized state parameters , where represents the channel idle degree parameter of the i-th channel, represents the signal strength parameter of the i-th channel, represents the bit error rate parameter of the i-th channel, represents the interference index parameter of the i-th channel;

[0013] Finally, establish a channel state evaluation model using the improved LSTM algorithm model and perform training.

[0014] Preferably, the specific implementation of the improved LSTM algorithm model is:

[0015] First, add a spatial feature embedding module in front of the LSTM input layer. Obtain the geographical location coordinates of each communication node through the HarmonyOS soft bus, map the coordinates to a frequency domain feature vector through Fourier transform, and splice it with the time series link state parameters into a multi-dimensional input tensor;

[0016] Improve the LSTM forget gate structure, and adaptively adjust the weight of the forget gate according to the current channel interference index. The calculation method is: , where represents the activation function, represents the forget gate weight, The hidden state at the previous moment, is a multi-dimensional input tensor, representing the bias of the forget gate, representing the adjustment factor, representing the channel interference index at the current moment;

[0017] Insert a channel attention module before the LSTM output layer to obtain weighted features;

[0018] Finally, map the weighted features through a fully connected layer to a single dimension and output a normalized channel score value.

[0019] Preferably, the specific steps of inserting a channel attention module before the LSTM output layer to obtain weighted features are as follows:

[0020] First, perform a dimensional transformation on the feature tensor output by the last hidden layer of the LSTM, and generate channel descriptors through a global average pooling layer and a global max pooling layer respectively;

[0021] Concatenate the above two channel descriptors and input them into a two-layer fully connected network. The first layer uses the ReLU activation function for dimensionality reduction, and the second layer uses the Sigmoid activation function to generate channel attention weights;

[0022] Finally, multiply the generated attention weights element-wise with the output features to obtain weighted features.

[0023] Preferably, each scoring cycle in step S2 consists of 10 acquisition cycles.

[0024] Preferably, the process of calculating the channel stability through a state evaluation matrix and completing channel allocation in combination with task priority and data type includes the following steps:

[0025] First, extract the scoring sequence of each channel from the state evaluation matrix, and calculate the channel stability according to the fluctuation range of its scoring value;

[0026] Analyze the priority and data type of the task to be scheduled, and determine the sensitive requirements for channel performance and stability according to the communication demand characteristics corresponding to the task, and generate a task scheduling constraint set;

[0027] Match and analyze the scoring values and channel stability of each channel in the current period with the task scheduling constraint set to obtain a set of schedulable candidate channels;

[0028] In the set of candidate channels, determine the optimal communication channel for allocation according to the sorting weight set by the scoring value and task priority level.

[0029] Compared with the prior art, the advantages and positive effects of the present invention are:

[0030] 1. Through the distributed module, multi-dimensional channel state parameters are collected in real time, and the improved LSTM model is combined with spatiotemporal features to achieve accurate evaluation of channel status, breaking through the limitations of traditional static allocation that lacks real-time perception and intelligent analysis.

[0031] 2. Dynamically match channel resources based on channel stability, task priority, and data type to improve channel utilization and ensure communication quality for high-priority tasks, solving the problems of static resource allocation waste and inefficient scheduling.

[0032] 3. Real-time monitoring of the status of allocated channels, triggering rapid re-evaluation and switching, proactively responding to channel fluctuations, significantly enhancing communication stability, and making up for the defect that traditional solutions cannot dynamically adapt to environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 This is a structural flow chart of a dynamic channel allocation method for communication links based on the Hongmeng system. DETAILED DESCRIPTION

[0035] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments of the following disclosure.

[0037] Embodiment: In the Internet of Everything environment supported by the Hongmeng system (HarmonyOS), there are a large number of distributed terminals and device communication needs. Traditional static channel allocation cannot adapt to the complex environment of high concurrency, link fluctuations, and multi-source access. In order to cope with the problems of complex channel states, frequent device access, and drastic link fluctuations in the distributed communication scenario of the Hongmeng system, the present invention provides a communication link dynamic channel allocation method based on the Hongmeng system. The specific implementation steps are as follows: Figure 1 shown.

[0038] First, to achieve channel state acquisition and synchronization across communication nodes, a collaborative communication monitoring module is established among multiple communication nodes running the HarmonyOS system. This module is used to collect in real-time the link state parameters of the communication channels between the local node and its surrounding nodes. The link state parameters include: channel idle degree, signal strength, bit error rate, and interference index. The calculation method of the interference index is obtained by measuring the proportion of the energy in the non-expected frequency band in the received signal and combining it with the noise benchmark. The collaborative communication monitoring module adopts a distributed deployment architecture and realizes cross-node data synchronization through the soft bus technology of the HarmonyOS system. Moreover, the acquisition period of each node is dynamically matched with the change frequency of the channel state. The calculation method is as follows: , where is the acquisition period of the current node, respectively represent the predicted minimum and maximum periods, is the variance of the channel state parameters in the previous period. Through the distributed deployment architecture and the HarmonyOS soft bus technology, the collaborative monitoring data synchronization among multiple communication nodes is realized, and the acquisition period of each node is dynamically adjusted based on the change frequency of the channel state, thereby optimizing the system resource utilization while ensuring the real-time monitoring, and avoiding the problems of resource waste or state lag caused by fixed-period acquisition. Specifically, the acquisition period is dynamically calculated through the parameter variance of the previous period, enabling the node to automatically shorten the period for high-frequency sampling when the channel state fluctuates violently, and extend the period to reduce power consumption when the state is stable, improving the monitoring efficiency and adaptability in the distributed communication environment of the HarmonyOS system.

[0039] Then, to convert the multi-source heterogeneous link state parameters into a unified evaluation index, a channel state evaluation model is constructed based on the collected multi-source link state parameters. The construction method steps of the channel state evaluation model are as follows: First, the link state parameters collected for each channel are normalized, and a channel feature vector is constructed according to the normalized state parameters , where represents the channel idle degree parameter of the i-th channel, represents the signal strength parameter of the i-th channel, represents the bit error rate parameter of the i-th channel, The interference index parameter of the \(i\)-th channel. Finally, an improved LSTM algorithm model is used to establish a channel state evaluation model and train it. Specifically, first, in the actual communication environment, there are problems such as inconsistent physical magnitudes and large differences in value ranges for the link state parameters collected by each channel. Directly inputting the original data will cause the model learning to deviate and affect the overall prediction accuracy. Therefore, in this step, the link state parameters of all channels are first normalized to ensure that each feature is comparable in the numerical dimension. Specifically, the system calculates the historical maximum and minimum values of each parameter respectively, and uses the Min-Max normalization method to map it to the interval [0,1]. After normalization, the four core link state parameters are concatenated in a predefined order to construct a channel feature vector in a unified format, which is used to characterize the overall operating state of each channel in the current time slice. For the \(i\)-th channel, its feature vector can be defined as , where represents the channel idle degree parameter of the \(i\)-th channel, represents the signal strength parameter of the \(i\)-th channel, represents the bit error rate parameter of the \(i\)-th channel, represents the interference index parameter of the \(i\)-th channel. This vector, as the input feature of the model, has a clear semantic structure and physical meaning, and can provide a complete channel state representation ability in the subsequent LSTM model. Then, the LSTM algorithm model is improved to establish a channel state evaluation model. The specific improvement is to first add a spatial feature embedding module before the LSTM input layer. The geographical location coordinates of each communication node are obtained through the HarmonyOS soft bus, and the coordinates are mapped to a frequency domain feature vector through Fourier transform and concatenated with the link state parameters of the time series into a multi-dimensional input tensor. Specifically, the system first uses the soft bus technology of the HarmonyOS system to obtain the location information of each communication node in real time, including longitude and latitude or relative coordinates, and standardizes it into a two-dimensional vector in a unified coordinate system. Considering the problems of periodicity and difficulty in expressing similarity in the original coordinate system for spatial position information, Fourier transform is used to perform frequency domain mapping on the coordinate vector to extract its spatial spectrum features. The frequency domain vector can more effectively express the spatial coupling relationship and distribution law between nodes, and is suitable for feature fusion with time series models. Finally, the obtained frequency domain spatial feature vector is concatenated with the link state parameters collected by each channel at consecutive moments in dimensions to construct a unified multi-dimensional input tensor. This tensor contains both time dynamic characteristics and spatial structure information, providing richer context semantic input for the LSTM model. Then, the LSTM forget gate structure is improved, and the weight of the forget gate is adaptively adjusted according to the current channel interference index. The calculation method is: , where represents the activation function, represents the weight of the forget gate, the hidden state of the previous moment, is a multi-dimensional input tensor, represents the bias of the forget gate, represents the adjustment factor, represents the channel interference index at the current moment. Then, a channel attention module is inserted before the output layer of the LSTM to obtain the weighted features. Specifically, first, the dimensionality of the feature tensor output by the last hidden layer of the LSTM is transformed to adapt to the input format of the channel attention module. Subsequently, global average pooling and global max pooling operations are respectively applied to the feature tensor to extract the average response and the maximum response of each channel over the overall time dimension, generating two channel descriptors that respectively characterize the overall contribution and local saliency of the channel. After concatenating the two descriptors, they are input into a weight generator composed of two fully-connected networks. The first layer uses the ReLU activation function for dimensionality compression to extract high-order semantic features, and the second layer uses the Sigmoid activation function to output the normalized channel attention weights. Finally, using the broadcasting mechanism, the weights are multiplied element-wise with the original feature tensor to obtain the weighted feature output. Through this mechanism, the model can automatically focus on the feature channels that are more critical for channel state determination, effectively suppressing the interference of redundant information, thereby improving the accuracy and generalization ability of channel state scoring. Finally, the weighted features are mapped to a single dimension through a fully-connected layer to output the normalized channel score value. Specifically, first, the weighted feature tensor is flattened into a one-dimensional vector as the input of the fully-connected layer. Through a linear transformation operation, a single scalar output is obtained. To ensure that the output values have a unified scale range for subsequent normalization processing and horizontal comparison, the output values are mapped to the [0,1] interval through the Sigmoid activation function to obtain the final channel score value. This score value provides the core quantitative basis for subsequent channel stability evaluation, task priority matching, and dynamic channel allocation.

[0040] Next, the constructed model is used to generate the state evaluation matrix of all current channels. The process of generating the state evaluation matrix of all current channels by the channel state evaluation model includes the following steps: First, a unique channel number is assigned to each channel to be evaluated, and a corresponding data input channel is established, and the link state parameters of the channel are input into the channel state evaluation model. The evaluation model respectively outputs the score values corresponding to each channel, and all the score values are arranged in the order of channel numbers to form a one-dimensional score vector. Finally, in each scoring cycle, the step of forming a one-dimensional score vector is executed for all channels, and all the score vectors are vertically concatenated in the time dimension to form a two-dimensional channel state evaluation matrix. Each scoring cycle consists of 10 acquisition cycles, and each time the acquisition cycle is updated, the scoring cycle is updated, that is, the 1st - 10th is a scoring cycle, and the 2nd - 11th is a new scoring cycle.

[0041] Then, the channel stability is calculated through the state evaluation matrix, and the channel allocation is completed by combining the task priority and data type. First, extract the scoring sequence of each channel from the state evaluation matrix, and calculate the channel stability according to the fluctuation range of its scoring value. Secondly, analyze the priority and data type of the tasks to be scheduled, and determine the sensitive requirements for channel performance and stability according to the communication requirement characteristics corresponding to the tasks, so as to generate a task scheduling constraint set. In addition, match and analyze the scoring values and channel stability of each channel in the current period with the task scheduling constraint set to obtain a set of schedulable candidate channels. Finally, in the set of candidate channels, determine the optimal communication channel for allocation according to the sorting weights set by the scoring value and task priority level. Specifically, after the channel state evaluation matrix is constructed, the system first extracts the scoring values of each channel in multiple consecutive acquisition periods to form a scoring time series. To evaluate the stability of the channel, perform volatility analysis on this scoring sequence and calculate its standard deviation as the stability index. A smaller standard deviation indicates that the channel scoring changes little between periods, indicating that the channel performs stably; on the contrary, a large volatility indicates that the channel quality may change drastically and the communication reliability is low. After calculating the channel stability, analyze the current communication tasks to be scheduled, extract their priority levels and data type attributes, and establish a task scheduling constraint set based on this. The task priority can be divided into multiple levels (such as high, medium, low) according to the system configuration, representing the degree of competition for communication resources and the real-time requirements of the tasks; the data type refers to the data transmission characteristics of the tasks, such as video streams, status instructions, heartbeat packets, etc., and their sensitivities to channel bandwidth, delay, and bit error rate are different. The system maps different types of tasks to their required communication characteristics by referring to the task attribute table. For example, high-priority video tasks have high requirements for channel bandwidth and low bit error rate, while low-priority log tasks are more tolerant of delays. Finally, convert the task attributes into a formalized set of scheduling constraint parameters, including the minimum channel scoring threshold, the maximum allowable scoring fluctuation, the lower limit of the channel stability coefficient, etc. This scheduling constraint set serves as a rule template for screening available channels to ensure the fit between tasks and channels in terms of performance and risk, and prevent problems such as data packet loss, delay, or scheduling failure caused by channel mismatch. Based on the generated channel scoring values and stability, as well as the task scheduling constraint set, perform a one-by-one matching analysis on all channels in the current period. Specifically, first filter out channels with scoring values lower than the task minimum threshold or stability lower than the set standard; then, according to the requirements of the task for parameters such as bit error rate and signal strength, further extract the set of channels that meet specific dimensional indicators on the premise of meeting the scoring and stability. The matching process supports the configuration of hard and soft conditions, where the key constraint items (such as the minimum score) are hard conditions that must be met; while the non-key constraint items (such as channel idle degree) are set with weights and only participate in the subsequent decision-making as the sorting basis. Finally, output the set of channel numbers that meet all hard constraints and preferentially meet soft constraint conditions as the set of schedulable candidate channels.Finally, after obtaining the set of schedulable candidate channels, the candidate channels are sorted and optimized by combining the priority level of the task and the channel score value to determine the final communication channel allocation result. The specific method is as follows: First, calculate the comprehensive scoring index for each candidate channel, which is obtained by the weighted sum of the current channel score value, stability, and task priority. All candidate channels are sorted in descending order according to this comprehensive scoring index, and the channel with the highest score is taken as the target communication channel to complete the binding and allocation.

[0042] Finally, during the communication process, continuously monitor the real-time state parameters of the allocated channels. If the difference between the channel score value and the mean value of the corresponding scoring period at the time of allocation exceeds the set threshold, trigger the fast re-evaluation mechanism, re-evaluate the channels, and perform channel allocation to ensure communication stability. Specifically, during the execution of the communication task, continuously monitor the state parameters of the allocated channels in real time and regularly calculate their current score values. If it is found that the score value of a certain channel deviates from the historical mean value of the scoring period to which it belongs at the time of allocation by more than the set threshold, it indicates that the channel may be interfered with or its performance has degraded. At this time, immediately trigger the fast re-evaluation mechanism, re-call the channel state evaluation model, perform instant scoring and stability calculation on the currently available channels, and combine the current task scheduling constraint set to screen and allocate new optimal channels. Through this mechanism, proactive adjustment can be made before the communication quality deteriorates, avoiding communication interruption or data loss caused by channel degradation, and significantly improving the dynamic adaptability and communication stability of the system.

[0043] The above is only a preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic channel allocation method for communication links based on the HarmonyOS, characterized in that Including the following steps: S1. Establish a collaborative communication monitoring module among multiple communication nodes running HarmonyOS, which is used to collect the link state parameters of the communication channels between the local node and the surrounding nodes in real time. The link state parameters include: channel idle degree, signal strength, bit error rate, and interference index; S2. Construct a channel state evaluation model based on the collected multi-source link state parameters. The model is used to generate a state evaluation matrix of all current channels. The specific implementation process includes the following steps: Assign a unique channel number to each channel to be evaluated, and establish a corresponding data input channel, and input the link state parameters of the channel into the channel state evaluation model; The evaluation model outputs the score values corresponding to each channel respectively, and arranges all the score values in the order of the channel numbers to form a one-dimensional score vector; In each scoring cycle, perform the step of forming a one-dimensional score vector for all channels, and vertically splice all the score vectors in the time dimension to form a two-dimensional channel state evaluation matrix; S3. Calculate the channel stability through the state evaluation matrix, and complete the channel allocation in combination with the task priority and data type; S4. Continuously monitor the real-time state parameters of the allocated channels during the communication process. If the difference between its score value and the mean value of the corresponding scoring cycle at the time of allocation exceeds the set threshold, trigger a fast re-evaluation mechanism, re-evaluate the channels and perform channel allocation to ensure communication stability.

2. The dynamic channel allocation method for a communication link based on the HarmonyOS according to claim 1, wherein The collaborative communication monitoring module adopts a distributed deployment architecture, realizes cross-node data synchronization through the soft bus technology of the HarmonyOS, and the acquisition period of each node is dynamically matched with the channel state change frequency. The calculation method is as follows: , where is the acquisition period of the current node, respectively represent the predicted minimum and maximum periods, is the variance of the channel state parameters in the previous period.

3. A dynamic channel allocation method for a communication link based on the HarmonyOS system according to claim 1, wherein The construction of the channel state evaluation model based on the collected multi-source link state parameters in step S2 includes: First, normalize the link state parameters collected for each channel; Construct a channel feature vector based on the normalized state parameters , where represents the channel idle degree parameter of the i-th channel, represents the signal strength parameter of the i-th channel, represents the bit error rate parameter of the i-th channel, represents the interference index parameter of the i-th channel; Finally, use an improved LSTM algorithm model to establish a channel state evaluation model and perform training.

4. A dynamic channel allocation method for a communication link based on the HarmonyOS system according to claim 3, characterized in that, The specific implementation of the improved LSTM algorithm model is: First, add a spatial feature embedding module before the LSTM input layer. Obtain the geographical location coordinates of each communication node through the HarmonyOS soft bus, and map the coordinates to a frequency domain feature vector through Fourier transform, and splice it with the link state parameters of the time series to form a multi-dimensional input tensor; Improve the LSTM forget gate structure, adaptively adjust the weight of the forget gate according to the current channel interference index, and the calculation method is as follows: , where represents the activation function, represents the weight of the forget gate, is the hidden state at the previous moment, is a multi-dimensional input tensor, represents the bias of the forget gate, represents the adjustment factor, represents the channel interference index at the current moment; Insert a channel attention module before the LSTM output layer to obtain the weighted features; Finally, map the weighted features to a single dimension through a fully connected layer, and output the normalized channel score value.

5. A dynamic channel allocation method for a communication link based on the HarmonyOS system according to claim 4, characterized in that The specific steps of inserting a channel attention module before the LSTM output layer to obtain the weighted features are: First, perform a dimension transformation on the feature tensor output by the last hidden layer of the LSTM, and generate channel descriptors through a global average pooling layer and a global max pooling layer respectively; Splice the above two channel descriptors and input them into a two-layer fully connected network. The first layer uses a ReLU activation function for dimensionality reduction, and the second layer uses a Sigmoid activation function to generate channel attention weights; Finally, multiply the generated attention weights element by element with the output features to obtain the weighted features.

6. A dynamic channel allocation method for a communication link based on the HarmonyOS system according to claim 1, characterized in that Each scoring cycle in step S2 consists of 10 acquisition cycles.

7. A dynamic channel allocation method for a communication link based on the HarmonyOS system according to claim 1, characterized in that The process of calculating the channel stability through the state evaluation matrix and completing the channel allocation in combination with the task priority and data type in step S3 includes the following steps: First, extract the scoring sequence of each channel from the state evaluation matrix, and calculate the channel stability according to the fluctuation range of its scoring value; Analyze the priority and data type of the task to be scheduled, and determine the sensitivity requirements for channel performance and stability based on the communication requirement characteristics corresponding to the task, so as to generate a task scheduling constraint set; Match and analyze the scoring values and channel stabilities of each channel in the current cycle with the task scheduling constraint set to obtain a set of schedulable candidate channels; In the set of candidate channels, determine the optimal communication channel for allocation according to the sorting weight set by the scoring value and the task priority level.

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