Node time slot distribution method, device and system for burst service and storage medium

Abstract

The invention discloses a node time slot allocation method, device and system for burst service and a storage medium, and relates to the technical field of communication. The method comprises the following steps: dividing a frame structure; acquiring actual service traffic data, constructing a parallel LSTM neural network to predict the actual service traffic data to obtain a third service traffic prediction value, calculating a fixed time slot required by a node based on the third service traffic prediction value, and adjusting a frame structure based on the required fixed time slot. According to the node time slot allocation method provided by the embodiment of the invention, the communication quality of key services is ensured, the utilization efficiency of network resources is improved, the requirements of emergent services are more efficiently met, the communication delay or loss caused by insufficient resources is reduced, time slot conflicts and communication chaos are avoided, the throughput of the whole network is improved, and the user experience is improved. The performance reduction caused by resource competition is avoided, the fairness of resource allocation is realized, and the overall performance of the network is improved; and the stability and robustness of the system are enhanced.

Description

Technical Field

[0001] The present application belongs to the field of communication technology, and in particular, relates to a method, device, system and storage medium for allocating node time slots for burst services. Background Art

[0002] In tactical communication networks, with the continuous growth of demand for efficient and reliable communications, the limited nature of tactical channel resources has become increasingly prominent. How to maximize system throughput and effectively reduce communication latency while ensuring fairness in communication resource allocation has become a technical challenge that needs to be urgently addressed in the field of tactical communications. As one of the key technologies for solving the problem of wireless channel resource allocation, the channel access time slot allocation algorithm plays a vital role. Through an effective time slot allocation strategy, it can not only ensure the fairness of resource allocation, but also optimize network performance and achieve efficient communication. However, during the operation of tactical data links on the battlefield, due to unpredictable problems such as rapid changes in the environment, temporary adjustments to mission instructions, or sudden changes in enemy and friendly situations, the task execution nodes often face a large number of burst services. If the burst services are not handled in a timely manner, the communication efficiency will be reduced.

[0003] In the prior art, dynamic time slot allocation, static time slot allocation and LSTM-based service prediction technology are usually used for processing, wherein:

[0004] Although dynamic time slot allocation is highly flexible and adaptable, when a node does not send data, its allocated time slot may be idle, which leads to low utilization of idle time slots. In order to achieve dynamic time slot allocation, complex algorithms need to be designed to monitor network status, analyze transmission requirements, and dynamically adjust time slot allocation. These algorithms may have high computational complexity and implementation difficulty, increasing the complexity and cost of the system.

[0005] The disadvantages of static time slot allocation mainly come from the fact that its fixed resource allocation method cannot adapt to the dynamic changes of network environment and transmission requirements. Static time slot allocation cannot release these unused time slot resources to other users in need in time, resulting in low resource utilization. Static time slot allocation is based on the configuration and planning of the network initialization phase. Once the allocation is completed, it remains unchanged during the entire communication process. This leads to its lack of flexibility in the face of changes in network environment and user needs, and it is unable to adjust the allocation of time slot resources according to real-time conditions.

[0006] The business prediction technology based on LSTM has high computational complexity. The complexity of LSTM makes its internal operation mechanism less intuitive and it is difficult to explain the decision-making process of the network. Summary of the invention

[0007] The purpose of this application is to provide a node time slot allocation method, device, system and storage medium for burst services, based on a parallel LSTM neural network prediction algorithm, to achieve time slot reconfiguration according to the business volume prediction results, thereby alleviating the problems of untimely processing of burst services and low system time slot utilization.

[0008] In order to achieve the above objectives, the solution of this application is:

[0009] In a first aspect, an embodiment of the present application provides a method for allocating node time slots for burst services, including: dividing the node time slots in a frame structure into control time slots and data time slots, and dividing the data time slots into fixed time slots and shared time slots;

[0010] Acquire actual business traffic data, build a parallel LSTM neural network to predict the actual business traffic data, and obtain a third business volume prediction value; the parallel LSTM neural network includes a first LSTM neural network and a second LSTM neural network, and the first LSTM neural network and the second LSTM neural network run in parallel;

[0011] The required fixed time slot of the node is calculated based on the third traffic volume prediction value, and the formula is as follows:

[0012]

[0013] in, Indicates the fixed time slot required by the node, A indicates Class A service, Indicates the fixed time slot required for node A class service, Indicates the total time slots required for Class A services, Indicates the number of continuous frames of Class A services. represents the third traffic volume prediction value, which is obtained from the prediction algorithm result, α represents the node traffic volume burst parameter, α is calculated based on the third traffic volume prediction value The value is determined by the size of SC, which represents the service capacity of a single time slot, delay, which represents the delay requirement, and flame. length Indicates the frame length, the single time slot transmission service capacity SC, the delay requirement delay and the frame length flame length is the default value;

[0014] The frame structure is adjusted based on the required fixed time slots, the required fixed time slots are evenly distributed in the frame structure, and the local existing fixed time slots are obtained. The local existing fixed time slots are pre-allocated time slots, and the pre-allocated time slots are preset values ​​evenly distributed in the frame structure. The required fixed time slots are compared with the local existing fixed time slots. If the required fixed time slots are larger than the local existing fixed time slots, the number of time slots is increased, the increased time slots are marked as pre-occupied state, and pre-occupied broadcast is performed. After other nodes receive the pre-occupied broadcast, they mark the increased time slots as fixed time slots; if the required fixed time slots are smaller than the local existing fixed time slots, the number of time slots is reduced, the reduced time slots are marked as idle state, and idle broadcast is performed. After other nodes receive the idle broadcast, they mark the reduced time slots as shared time slots.

[0015] According to the above method of the embodiment of the present application, the following additional technical features may also be provided:

[0016] Furthermore, the required fixed time slot Limited by the maximum value Max, after calculating the required fixed time slot of the node based on the third traffic volume prediction value, the required fixed time slot The constraints are as follows:

[0017] Furthermore, in the parallel LSTM neural network, the first LSTM neural network is a main predictor, which is used to process the actual business flow data x(t) to obtain a first business volume prediction value, and the second LSTM neural network is used as an auxiliary predictor, which simultaneously receives the actual business flow data x(t) and the simulated emergency event sequence y(t) as input to obtain a second business volume prediction value. The second LSTM neural network is used to compare the first business volume prediction value with the second business volume prediction value to obtain a comparison result, and identify the occurrence time of the emergency event according to the comparison result;

[0018] During the training phase, the first LSTM neural network and the second LSTM neural network use the same actual business traffic data x(t) and simulated emergency event sequence y(t) as input. The first LSTM neural network and the second LSTM neural network have the same weights and share internal states with each other, including cell states and hidden states.

[0019] Furthermore, the first LSTM neural network includes a state register and a result register. At time t-1, the first LSTM neural network stores the cell state c1(t-1) and the hidden state h1(t-1) in the state register, takes the actual business flow data x(t-1) at time t-1 as input, and outputs the first business volume prediction value at time t. And store it in the result register.

[0020] Furthermore, the second LSTM neural network includes a buffer, and the second LSTM neural network copies the cell state c1(t-1) and hidden state h1(t-1) stored in the state register of the first LSTM neural network as its internal state, and combines the actual business flow data x(t-1) at time t-1 and the simulated emergency event sequence y(t-1) at time t-1 as input to form an input vector x(t-1)=[x(t-1)y(t-1)]. When the input vector x(t-1) is formed, the second LSTM neural network completes forward propagation, and the input vector x(t-1) is used to predict the second business volume prediction value at time t. The prediction results are stored in the buffer, and the internal states c2(t) and h2(t) of the second LSTM neural network when the forward propagation is completed and the input simulated emergency event sequence y(t-1) are retained and saved in the buffer.

[0021] Furthermore, at time t, the first LSTM neural network and the second LSTM neural network independently make predictions. The first LSTM neural network predicts the first traffic volume forecast value at time t+1 based on the actual traffic flow data x(t). The second LSTM neural network predicts the second traffic volume forecast value at time t+1 based on the actual traffic flow data x(t) and the simulated emergency event sequence y(t). The first business volume forecast value and the second traffic volume prediction value Compare and judge whether there is a sudden change in the actual business traffic. The formula is as follows:

[0022] and

[0023] If the above formula is satisfied, it is determined that a mutation has occurred. If the above formula is not satisfied, it is determined that no mutation has occurred. 1 and TH 2 is the preset threshold.

[0024] Furthermore, it is determined whether there is a sudden change in the actual service traffic, including:

[0025] If it is determined that a mutation occurs, the internal states c2(t) and h2(t) stored in the buffer of the second LSTM neural network are updated to the state register of the first LSTM neural network, and the simulated emergency event sequence y(t) is used as one of the inputs of the first LSTM neural network. The third traffic volume forecast value at time t+1 is generated in the first LSTM neural network in combination with x(t), y(t) and the internal states c2(t) and h2(t). The third traffic volume forecast value is used to correct the first traffic volume forecast value. And output to the node. After the update is completed, the new internal states c1(t) and h1(t) of the first LSTM neural network are stored in the state register;

[0026] If it is determined that no mutation occurs, the step at time t-1 is repeated.

[0027] In a second aspect, an embodiment of the present application provides a node time slot allocation device for a burst service, including:

[0028] A time slot division module is configured to divide the node time slots in the frame structure into control time slots and data time slots, and divide the data time slots into fixed time slots and shared time slots;

[0029] The business prediction module is configured to obtain actual business flow data, construct a parallel LSTM neural network to predict the actual business flow data, and obtain a third business volume prediction value; the parallel LSTM neural network includes a first LSTM neural network and a second LSTM neural network, and the first LSTM neural network and the second LSTM neural network run in parallel;

[0030] The time slot calculation module is configured to calculate the fixed time slot required by the node based on the third traffic volume prediction value, and the formula is as follows:

[0031]

[0032] in, Indicates the fixed time slot required by the node, A indicates Class A service, Indicates the fixed time slot required for node A class service, Indicates the total time slots required for Class A services, Indicates the number of continuous frames of Class A services. represents the third traffic volume prediction value, which is obtained from the prediction algorithm result, α represents the node traffic volume burst parameter, α is calculated based on the third traffic volume prediction value The value is determined by the size of SC, which represents the service capacity of a single time slot, delay, which represents the delay requirement, and flame. length Indicates the frame length, the single time slot transmission service capacity SC, the delay requirement delay and the frame length flame length is the default value;

[0033] The time slot allocation module is configured to adjust the frame structure based on the required fixed time slots, evenly distribute the required fixed time slots in the frame structure, obtain the existing local fixed time slots, the existing local fixed time slots are pre-allocated time slots, and the pre-allocated time slots are preset values ​​evenly distributed in the frame structure, compare the required fixed time slots with the existing local fixed time slots, if the required fixed time slots are larger than the existing local fixed time slots, increase the number of time slots, mark the increased time slots as pre-occupied state, and perform pre-occupied broadcast, after other nodes receive the pre-occupied broadcast, mark the increased time slots as fixed time slots; if the required fixed time slots are smaller than the existing local fixed time slots, reduce the number of time slots, mark the reduced time slots as idle state, and perform idle broadcast, after other nodes receive the idle broadcast, mark the reduced time slots as shared time slots.

[0034] In a third aspect, an embodiment of the present application provides a node time slot allocation system for burst services, the system comprising a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement a node time slot allocation method for burst services as provided in the first aspect of the embodiment of the present application.

[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement a node time slot allocation method for burst services as in the first aspect of the embodiment of the present application.

[0036] Compared with the prior art, the node time slot allocation method for burst services provided by the embodiment of the present application has the following beneficial technical effects:

[0037] The embodiment of the present application ensures the communication quality of key services and improves the utilization efficiency of network resources by dividing fixed time slots and shared time slots, and adjusts the number of fixed time slots in combination with the forecast results of the business volume, which can more efficiently respond to the needs of sudden business, ensure that communication resources can be allocated to the required nodes in a timely manner in sudden situations, and reduce communication delays or losses caused by insufficient resources;

[0038] The embodiment of the present application uses a broadcast mechanism to ensure that all nodes can obtain the latest time slot allocation information in a timely manner, avoid time slot conflicts and communication confusion, and mark the excess time slots as shared time slots for use by other nodes when there are fewer burst services, thereby improving the throughput of the entire network. When burst services increase, the communication quality of key services is ensured by adding fixed time slots, avoiding performance degradation caused by resource competition.

[0039] The embodiment of the present application adjusts the number and allocation method of fixed time slots and shared time slots to ensure that all nodes can obtain sufficient communication resources when needed, thereby achieving fairness in resource allocation and improving the overall performance of the network;

[0040] The embodiment of the present application utilizes a parallel LSTM neural network to perform traffic volume forecasting and emergency event identification, thereby improving the system's ability to respond to emergencies; by adjusting the time slot allocation strategy in advance, communication delays or losses caused by insufficient resources or conflicts are reduced, thereby enhancing the stability and robustness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram showing a flow chart of a method for allocating node time slots for burst services according to an embodiment of the present application;

[0042] Figure 2 A structural block diagram of a node time slot allocation device for burst services according to an embodiment of the present application is shown;

[0043] Figure 3 A structural block diagram of a computer device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some structures related to the present application are shown in the accompanying drawings, rather than all structures. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0045] The terms "including" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0046] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] like Figure 1 As shown, the embodiment of the present application provides a method for allocating node time slots for burst services, comprising the following steps:

[0048] Step 101: divide the node time slots in the frame structure into control time slots and data time slots, and divide the data time slots into fixed time slots and shared time slots.

[0049] In the embodiment of the present application, the node time slots in the frame structure are divided into control time slots and data time slots, and the data time slots are further subdivided into fixed time slots and shared time slots.

[0050] Fixed time slots are used to ensure stable transmission of services that have strict requirements on time, bandwidth or delay and require stable transmission guarantees. By allocating fixed time slots to these services, their priority transmission in the network can be ensured, thereby improving the overall network performance and user experience. Shared time slots are used for flexible allocation to adapt to burst services.

[0051] Specifically, in the fixed time slot allocation algorithm, the frame structure consists of multiple time slots, which are pre-fixed and allocated to each communication node of the data link to ensure orderly and conflict-free service transmission. Based on the above frame structure, the embodiment of the present application divides the time slot into a control time slot and a data time slot considering the time requirements of node information processing. Among them, the control time slot realizes the transmission of control information, and the data time slot realizes the transmission of service data. The purpose of this design is to ensure the basic transmission capacity of the network node and meet the quality of service (QoS) requirements of various services. Furthermore, the data time slot is divided into a fixed time slot and a shared time slot to meet the transmission requirements and service quality requirements of different services.

[0052] Considering that battlefield data link nodes often encounter burst services, each communication node needs its own control time slot resources, which need to be configured according to the plan for the node to use. Since each node has a unique identifier, when determining the control resource, a mapping method can be used to map the node identifier to the control resource number. One common method is direct modulo mapping. For example, if the node identifier is m, the corresponding control time slot resource number is m%K1 (where K1 is the total number of control resources). Such a mapping method can ensure that each node can obtain unique and effective control resources to support its communication and control needs in the network.

[0053] Considering the needs and characteristics of different services, data time slot resources are divided into fixed time slots and shared time slots. Fixed time slots are configured in a static allocation mode to meet the needs of low latency and urgent services; while shared time slots are configured in a dynamic time slot allocation mode, allowing nodes to apply for time slots based on immediate needs, thereby improving the overall efficiency of network resources. When planning fixed time slots, each communication node is fixedly allocated these time slots, and there is no need to apply for and release time slots. The number of fixed time slots depends on the service transmission requirements of the node. In order to ensure the basic transmission capacity of the node, the fixed time slots are usually evenly distributed within the frame period to reduce the average delay of service transmission and avoid service accumulation. Shared time slots occupy the remaining part outside the fixed resources and are suitable for high-bandwidth services with high delay tolerance. These resources are managed through dynamic allocation, allowing time slot sharing and scheduling between nodes, reducing control overhead.

[0054] The fixed time slot allocation algorithm follows specific principles to ensure efficient and fair time slot resource utilization. First, each node is allocated the same type of pre-allocated time slots, which are evenly distributed within a frame period, ensuring uniform access opportunities for each node in the network. Secondly, in order to improve resource utilization, pre-allocated time slots do not support multiplexing mechanisms. This strategy can indeed avoid conflicts and data transmission delays, thereby ensuring the stability and reliability of transmission, while meeting the specific needs of different nodes in different business scenarios. Therefore, whether to pre-allocate and determine the number of pre-allocated time slots are entirely determined by business needs. This approach emphasizes business-driven resource allocation strategies, aiming to optimize network performance while meeting the needs of various business scenarios.

[0055] Step 102, obtaining the node traffic volume, constructing a parallel LSTM neural network to predict the node traffic volume, and obtaining a third traffic volume prediction value; the parallel LSTM neural network includes a first LSTM neural network and a second LSTM neural network, and the first LSTM neural network and the second LSTM neural network run in parallel.

[0056] The parallel LSTM neural network constructed in the embodiment of the present application includes two LSTM neural networks, wherein the first LSTM neural network is a main predictor, which is used to process actual business flow data x(t) to obtain a first business volume prediction value, and the second LSTM neural network is an auxiliary predictor, which simultaneously receives the actual business flow data x(t) and the simulated emergency event sequence y(t) as input to obtain a second business volume prediction value. Through the LSTM neural networks running in parallel, not only can future business volume be predicted, but also potential emergencies can be detected and responded to in real time to ensure business continuity and stability.

[0057] Both networks receive actual business traffic data x(t), but the second LSTM neural network also receives simulated emergency event sequence y(t) in addition. Before the data is input into the parallel LSTM neural network, a series of data preprocessing steps are usually performed, including but not limited to removing noise, missing values ​​and inconsistent data points; converting the data to a uniform scale so that the network can better learn and predict; dividing the time series data into continuous windows, each window containing business traffic data within a certain time period as the input of the network.

[0058] LSTM (Long Short-Term Memory) neural networks are particularly good at processing time series data with long-term dependencies, which makes them ideal for predicting traffic. By running two LSTM networks in parallel, the system is able to generate two forecasts at the same time and use the difference between them to detect emergencies. The second LSTM neural network is used to compare the first traffic forecast with the second traffic forecast to obtain a comparison result, identify the time of occurrence of the emergency based on the comparison result, set a reasonable threshold based on business needs and historical data to determine whether the difference between the forecasts is sufficient to indicate the occurrence of an emergency, and immediately switch to emergency mode once an emergency is detected, using the specific state of the second LSTM neural network (auxiliary predictor) and simulated emergency sequences to generate more accurate forecasts, and take appropriate measures to respond to the emergency.

[0059] During the training phase, the first LSTM neural network and the second LSTM neural network use the same actual business traffic data x(t) and simulated emergency event sequence y(t) as input. The first LSTM neural network and the second LSTM neural network have the same weights and share internal states with each other. The internal states include cell states and hidden states, which helps them jointly learn the inherent laws and patterns of the data.

[0060] Specifically, the first LSTM neural network includes a state register and a result register. The first LSTM neural network uses the state register to continuously update its internal state to ensure the continuity and consistency of data during the continuous prediction process. At time t-1, the first LSTM neural network stores the cell state c1(t-1) and the hidden state h1(t-1) in the state register, takes the actual business flow data x(t-1) at time t-1 as input, and outputs the first business volume prediction value at time t. The node traffic volume prediction value at time t is output and stored in the result register.

[0061] The second LSTM neural network includes a buffer, and the second LSTM neural network uses the buffer to store its internal state, input data, and prediction results so that it can respond quickly when an emergency is detected. The second LSTM neural network copies the cell state c1(t-1) and hidden state h1(t-1) stored in the state register of the first LSTM neural network as its internal state, and combines the actual business traffic data x(t-1) at time t-1 and the simulated emergency event sequence y(t-1) at time t-1 as input to form an input vector x(t-1)=[x(t-1)y(t-1)]. When the input vector x(t-1) is formed, the second LSTM neural network completes forward propagation, and the input vector x(t-1) is used to predict the second business volume prediction value at time t. The prediction results are stored in the buffer, and the internal states c2(t) and h2(t) of the second LSTM neural network when the forward propagation is completed and the input simulated emergency event sequence y(t-1) are retained and saved in the buffer.

[0062] At time t, the first LSTM neural network and the second LSTM neural network make predictions independently. The first LSTM neural network predicts the first traffic volume forecast value at time t+1 based on the actual traffic flow data x(t). The second LSTM neural network predicts the second traffic volume forecast value at time t+1 based on the actual traffic flow data x(t) and the simulated emergency event sequence y(t). The first business volume forecast value and the second traffic volume prediction value Compare and judge whether there is a sudden change in the actual business traffic. The formula is as follows:

[0063] and

[0064] If the above formula is satisfied, it is determined that a mutation has occurred. If the above formula is not satisfied, it is determined that no mutation has occurred. 1 and TH 2 is the preset threshold.

[0065] Determine whether there is a sudden change in the actual business traffic, including:

[0066] If a sudden change is determined, the internal states c2(t) and h2(t) of the second LSTM neural network stored in the buffer are updated to the state register of the first LSTM neural network, and the simulated emergency sequence y(t) is used as one of the inputs of the first LSTM neural network. After detecting an emergency, the system will correct the prediction value of the first LSTM neural network based on the prediction value of the second LSTM neural network and its internal state to generate a more accurate third business volume prediction value. The corrected prediction value will be output to the node or related system to guide business decisions and resource allocation. At the same time, the system will also collect feedback data on actual business volume for subsequent training and optimization.

[0067] The embodiment of the present application combines x(t), y(t) and the internal states c2(t) and h2(t) to generate a third traffic volume forecast value at time t+1 in the first LSTM neural network. The third traffic volume forecast value is used to correct the first traffic volume forecast value. And output to the node. After the update is completed, the new internal states c1(t) and h1(t) of the first LSTM neural network are stored in the state register;

[0068] If it is determined that no mutation occurs, the step at time t-1 is repeated.

[0069] Such a design can effectively combine the predictive capabilities of two LSTM neural networks, while utilizing simulated emergency event sequences to enhance the ability to detect and respond to emergencies.

[0070] Step 103: Calculate the fixed time slot required by the node based on the third traffic volume prediction value.

[0071] In the embodiment of the present application, after the node obtains the third traffic volume prediction value, the dynamic resource allocation algorithm is used to reconfigure the time slot resources in the network. The specific process is as follows:

[0072] For Class A services, when determining the number of fixed time slots required by a node, the single service volume is the third service volume prediction value Taking into account that burst services often occur in data links, α is directly set as the service burst parameter here. In order to adapt to the service requirements of different priorities and burst services, for services with large fixed time slot requirements, namely Class A services, the service burst parameter α can be set to 3, or even α>3, because it provides an average of the traffic prediction level, and this type of service usually has a higher priority. For services with near real-time requirements, namely Class B services, the service burst parameter α can be set to 1, so that the near real-time requirements of data transmission can be guaranteed under certain conditions. For services with low fixed time slot requirements, namely Class C services, due to the uncertainty of such services and the lack of need to guarantee real-time transmission, the service burst parameter α is set to <1 to prevent the waste of fixed time slots and delay the real-time requirements of high-level services. Therefore, for the next Tw The number of fixed time slots is determined by Calculated, where T w Represents a time window.

[0073] The minimum number of time slots required for each service frame is calculated based on the frame structure, transmission rate and service delay requirements, that is, the node As well as the single time slot transmission service capacity SC, delay requirement delay, frame length flame length , calculate the number of frames flame required num And the total number of time slots required for this service sneed And the number of slots required per frame need , and the local fixed time slot resource number slot all Comparison is made to calculate the number of time slots that need to be applied for or released this time. The formula for calculating the fixed time slots required by the node based on the third traffic volume prediction value in the embodiment of the present application is as follows:

[0074]

[0075] in, Indicates the fixed time slot required by the node, A indicates Class A service, Indicates the fixed time slot required for node A class service, Indicates the total time slots required for Class A services, Indicates the number of continuous frames of Class A services. represents the third traffic volume prediction value, which is obtained from the prediction algorithm result, α represents the node traffic volume burst parameter, α is calculated based on the third traffic volume prediction value The value is determined by the size of SC, which represents the service capacity of a single time slot, delay, which represents the delay requirement, and flame. length Indicates the frame length, the single time slot transmission service capacity SC, the delay requirement delay and the frame length flame length is the default value.

[0076] To meet the basic transmission needs of the node, at least one fixed time slot is reserved. At the same time, to prevent excessive occupancy from affecting the flexibility of the TDMA protocol, the number of fixed time slots should be limited by the maximum value Max, so Max≥ The formula is as follows:

[0077] In satisfying Under the constraints of , plan the location of each node's required fixed time slot and follow the following principles:

[0078] a. Ensure that the fixed time slots preset for each node are not reused.

[0079] b. The fixed time slots preset for each node should be evenly distributed within a frame as much as possible.

[0080] Principle a ensures that each node avoids sending service conflicts when using its fixed time slot for service transmission; Principle b balances resource usage and minimizes the maximum waiting delay of service transmission by evenly distributing the fixed time slots of each node within the frame.

[0081] Planning the fixed time slot position of the node in advance does not mean that the node is occupied. The following principles still need to be followed:

[0082] Each node has at least one fixed time slot. Unoccupied fixed time slots are regarded as shared time slots. All nodes can apply for them and prioritize them. Nodes follow the principle of uniform distribution when occupying their own fixed time slots.

[0083] The above principles guarantee the transmission performance of the node, and when a node applies to occupy a preset fixed time slot, it does not need to wait for the response of other nodes, and the application is declared to be occupied.

[0084] During the operation of the tactical data link, nodes performing different tasks are often faced with emergent services due to unpredictable factors such as rapid changes in the battlefield environment, temporary adjustments to mission instructions, or sudden changes in the enemy and friendly situation. These emergent services not only require nodes to have the ability to respond quickly and process efficiently, but also pose a severe test to the resource allocation of the entire tactical data link system, especially in the core resource of time slots. Considering that time slots are essentially allocated for each node to send services, different nodes and different services have different degrees of importance, so the priority weights of nodes and services need to be considered when handling conflicts.

[0085] This application divides the node burst traffic volume, node type and service type into three priority levels, with the highest priorities being z0, p0 and g0, and the lowest priorities being z2, p2 and g2. The node burst traffic volume priority, node priority and service priority are weighed in a ratio of 4:3:3, that is, the weight of the node burst traffic volume priority is 40%, and the weights of the node priority and service priority are both 30%. Therefore, the priority calculation formula is as follows:

[0086] S s =S t ×0.4+S n ×0.3+S d ×0.3

[0087] Among them, S s Indicates mixed priority, S t Indicates the priority of burst traffic, S n Indicates the priority of the communication node, S d Indicates the priority of the service.

[0088] After weighted calculation using the priority calculation formula, we get the mixed priority table, as shown in the following table:

[0089] Table 1

[0090]

[0091]

[0092] In Table 1, the weighted priorities are further divided into three levels: P0, P1, and P2, where P0 is the highest priority and P2 is the lowest priority. When resolving time slot conflicts, if the mixed priorities are different, the occupancy of the time slot is determined according to the size of the mixed priorities; if the mixed priorities are the same, first determine the weighted priority value, if the weighted priority values ​​are different, determine the time slot occupancy according to the weighted priority value; if the weighted priority values ​​are the same, further determine according to the node burst traffic priority, node priority, and service priority, where the node burst traffic priority is the largest and the node priority is the smallest, and determine the occupancy of the time slot accordingly, resolve the time slot conflict, and distinguish priorities.

[0093] Step 104, adjusting the frame structure based on the required fixed time slot.

[0094] The frame structure is adjusted based on the required fixed time slots, the required fixed time slots are evenly distributed in the frame structure, and the local existing fixed time slots are obtained. The local existing fixed time slots are pre-allocated time slots, and the pre-allocated time slots are preset values ​​evenly distributed in the frame structure. The required fixed time slots are compared with the local existing fixed time slots. If the required fixed time slots are larger than the local existing fixed time slots, the number of time slots is increased, the increased time slots are marked as pre-occupied state, and pre-occupied broadcast is performed. After other nodes receive the pre-occupied broadcast, they mark the increased time slots as fixed time slots; if the required fixed time slots are smaller than the local existing fixed time slots, the number of time slots is reduced, the reduced time slots are marked as idle state, and idle broadcast is performed. After other nodes receive the idle broadcast, they mark the reduced time slots as shared time slots.

[0095] Since steps 101 to 103 implement the adjustment of node fixed time slot resources according to the traffic volume prediction results, it is similar to the dynamic application of shared data time slot resources, but there are still differences. The dynamic adjustment of fixed time slot resources introduces two states, "pre-occupied" and "occupied", which are used to declare and confirm the time slot type conversion to ensure the clarity of the adjustment process. In the fixed time slot resource adjustment, the node completes the reconfiguration and starts using the time slot only after receiving the confirmation response from other nodes.

[0096] Specifically, when a node needs to increase the number of fixed time slots, it should select the corresponding time slot, mark its time slot status as "pre-occupied" and broadcast this status. After receiving the control message, other nodes mark the time slot type as a fixed time slot. If other nodes previously occupied the time slot to transmit services, they will unconditionally give up the time slot and re-apply for the time slot for transmission. When a node needs to reduce the number of fixed time slots, it selects the corresponding time slot, marks its time slot status as "idle" and broadcasts it. After receiving it, other nodes can regard the time slot as a shared resource that can be applied.

[0097] A node needs to increase the number of fixed data time slot resources in the current window. It selects one from the preset time slots, marks it as "pre-occupied" and broadcasts it. After receiving this information, other nodes also mark the time slot as "pre-occupied" and respond in the control time slot. After receiving the response from all nodes, the original node updates the time slot status to "occupied" and broadcasts it again to complete the reconfiguration.

[0098] The control message structure means that the communication node will integrate its own time slot status table in the control time slot phase, generate and send control messages based on the update of its own time slot resources. These control messages contain the node's demand for time slot resources and occupancy. In this way, the node can promptly inform other nodes of its time slot usage for effective time slot coordination and management. In the listening phase, the node will receive and parse the control messages sent by other nodes, and then update its own time slot status table accordingly based on this information.

[0099] In order to achieve effective management and coordination of time slot resources, the communication node sends a control message in the control time slot phase so that other communication nodes can update the local time slot table according to the status of the node and make time slot reservations and allocations. Such control messages are intended to ensure the correct transmission of information and the rational reuse of time slots. Therefore, in the control message, the status of the time slot can be integrated and described. The control message structure of the node usually includes the source node ID number, that is, the identifier of the node to which the time slot table belongs. The content of the time slot table in the control message may be different from that of the local time slot table. Among them, the broadcast flag item has a special meaning in the control message. When the time slot status is "self-sent", the broadcast flag indicates that the service applying for the time slot is a broadcast service, which helps other nodes understand the usage and characteristics of the time slot. There are 3 types of time slot status here:

[0100] Self-transmit: When the state of the node in the time slot maintenance table is "self-transmit", the state in the control message is still self-transmit, and the node address is the address of the destination node. After receiving the message, other nodes change their time slot state to "self-receive" or "cannot send or receive" or remain unchanged according to the destination node address.

[0101] Self-receiving: When the state of the node's time slot maintenance table is "self-receiving", the state in the control message is also "self-receiving", and the node address is the address of the sending node. After receiving the message, other communication nodes change their own time slot state to "cannot send or receive" or maintain the state according to the node address information.

[0102] Idle: When the status in the node's time slot maintenance table is "idle", the status in the control message is also "idle".

[0103] In the control information exchange of each frame, the communication node receives the control message from the neighbor and maintains and updates its own local time slot table. When applying for a time slot, it can select an available time slot according to its own local time slot table. This reservation mechanism reduces time slot conflicts to a certain extent, improves time slot utilization, and has the ability to adapt to network scenarios with different loads.

[0104] The embodiment of the present application samples the service flow in units of each frame according to the service model of the data link, and takes the average service flow of F frames as a time step T w The Keras deep learning framework is used for simulation experiments, and the parameters in the neural network are shown in the following table:

[0105] Table 2

[0106] Parameter Description Value Data value length 15000 Training set length 80％ Test set length 20％ Number of hidden layers 1 Number of hidden layer neurons 32 Banch size 1

[0107] A comparative experiment is set up from the perspective of different service burst intensities, and the network performance indicators and time slot allocation behavior are statistically analyzed in the OPNET environment.

[0108] During the experimental prediction process, the business traffic of the next time step is predicted in units of 5 time steps. Each time step contains 100 frames of data, that is, for 500 consecutive frames of business traffic data, the model will predict the business volume of the next 100 frames. Its prediction results can basically fit the real data. It proves that the method provided in the embodiment of the present application has good predictability.

[0109] In order to verify the performance of the time slot resource reconfiguration algorithm based on the offline traffic prediction of the communication network, the Link16 data link is used, and the service model parameters are shown in the following table:

[0110] Table 3

[0111] Business Type Arrival distribution probability Video Services Poisson distribution Document business Periodic Distribution

[0112] The algorithm performance is still compared in terms of average service delay and time slot utilization.

[0113] Compared with fixed time slot allocation, the method proposed in the embodiment of the present application has better delay reduction performance. Whether it is a video service or a low-priority file service, it can well reduce service delay, ensure the timely delivery of high-priority services, and also ensure the delivery of low-priority services. The embodiment of the present application can greatly improve the time slot utilization rate, mainly because the added service prediction algorithm can predict the change of business volume in advance, thereby enabling the node to dynamically apply for time slot allocation, occupy the idle time slot, and improve the time slot allocation utilization rate.

[0114] By comparing the latency and time slot utilization of the two types of services, it can be concluded that compared with the fixed time slot allocation method, the node time slot allocation method for burst services proposed in the embodiment of the present application shows better performance advantages when processing multi-priority services.

[0115] like Figure 2 As shown, the embodiment of the present application provides a node time slot allocation device for burst services, including a time slot division module 201, a service prediction module 202, a time slot calculation module 203 and a time slot allocation module 204, wherein:

[0116] The time slot division module 201 is configured to divide the node time slots in the frame structure into control time slots and data time slots, and divide the data time slots into fixed time slots and shared time slots;

[0117] The business prediction module 202 is configured to obtain actual business flow data, construct a parallel LSTM neural network to predict the actual business flow data, and obtain a third business volume prediction value; the parallel LSTM neural network includes a first LSTM neural network and a second LSTM neural network, and the first LSTM neural network and the second LSTM neural network run in parallel;

[0118] The time slot calculation module 203 is configured to calculate the fixed time slot required by the node based on the third traffic volume prediction value, and the formula is as follows:

[0119]

[0120] in, Indicates the fixed time slot required by the node, A indicates Class A service, Indicates the fixed time slot required for node A class service, Indicates the total time slots required for Class A services, Indicates the number of continuous frames of Class A services. represents the third traffic volume prediction value, which is obtained from the prediction algorithm result, α represents the node traffic volume burst parameter, α is calculated based on the third traffic volume prediction value The value is determined by the size of SC, which represents the service capacity of a single time slot, delay, which represents the delay requirement, and flame. lengthIndicates the frame length, the single time slot transmission service capacity SC, the delay requirement delay and the frame length flame length is the default value;

[0121] The time slot allocation module 204 is configured to adjust the frame structure based on the required fixed time slots, evenly distribute the required fixed time slots in the frame structure, obtain the existing local fixed time slots, the existing local fixed time slots are pre-allocated time slots, and the pre-allocated time slots are preset values ​​evenly distributed in the frame structure, compare the required fixed time slots with the existing local fixed time slots, if the required fixed time slots are larger than the existing local fixed time slots, increase the number of time slots, mark the increased time slots as pre-occupied state, and perform a pre-occupied broadcast. After other nodes receive the pre-occupied broadcast, they mark the increased time slots as fixed time slots; if the required fixed time slots are smaller than the existing local fixed time slots, reduce the number of time slots, mark the reduced time slots as idle state, and perform an idle broadcast. After other nodes receive the idle broadcast, they mark the reduced time slots as shared time slots.

[0122] The node time slot allocation device for burst services in the embodiment of the present application can be a computer device, or a component in the computer device, such as an integrated circuit or a chip. The computer device can be a terminal, or other devices other than a terminal. Exemplarily, the computer device can be a mobile phone, a tablet computer, a laptop computer, a palmtop computer, a vehicle-mounted computer device, a mobile Internet device (Mobile Internet Device, MID), an ultra-mobile personal computer (Ultra-Mobile Personal Computer, UMPC), a netbook or a personal digital assistant (Personal Digital Assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (Personal Computer, PC), etc., which is not specifically limited in the embodiment of the present application.

[0123] The node time slot allocation device for burst services provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented in the embodiment of the node time slot allocation method for burst services are not described here.

[0124] The present application also provides a computer device, such as Figure 3 As shown, the computer device includes a processor 301 and a memory 302. The memory 302 stores programs or instructions that can be run on the processor 301. When the program or instructions are executed by the processor 301, the various steps of the node time slot allocation method for the above-mentioned burst service are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0125] It should be noted that the computer device in the embodiment of the present application includes the above-mentioned mobile computer device and non-mobile computer device.

[0126] The memory 302 can be used to store software programs and various data. The memory 302 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 302 may include a volatile memory or a non-volatile memory, or the memory 302 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 302 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0127] The processor 301 may include one or more processing units; optionally, the processor 301 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 301.

[0128] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the node time slot allocation method embodiment for the above-mentioned burst service is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0129] An embodiment of the present application also provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the node time slot allocation method embodiment for the above-mentioned burst service, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0130] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0131] An embodiment of the present application also provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the node time slot allocation method embodiment for burst services as described above, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0132] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0133] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for allocating node time slots for burst services, characterized in that: The method comprises: Dividing the node time slots in the frame structure into control time slots and data time slots, and dividing the data time slots into fixed time slots and shared time slots; Acquire actual business flow data, build a parallel LSTM neural network to predict the actual business flow data, and obtain a third business volume prediction value; the parallel LSTM neural network includes a first LSTM neural network and a second LSTM neural network, and the first LSTM neural network and the second LSTM neural network run in parallel; The third traffic volume prediction value is used to calculate the fixed time slot required by the node, and the formula is as follows: in, Indicates the fixed time slot required by the node, A indicates Class A service, Indicates the fixed time slot required for node A class service, Indicates the total time slots required for Class A services, Indicates the number of continuous frames of Class A services. represents the third traffic volume prediction value, which is obtained from the prediction algorithm result, α represents the node traffic volume burst parameter, α is calculated based on the third traffic volume prediction value The value is determined by the size of SC, which represents the service capacity of a single time slot, delay, which represents the delay requirement, and flame. length Indicates the frame length, the single time slot transmission service capacity SC, delay requirement delay and frame length flame length is the default value; The frame structure is adjusted based on the required fixed time slot, the required fixed time slot is evenly distributed in the frame structure, and the locally existing fixed time slot is obtained, the locally existing fixed time slot is a pre-allocated time slot, and the pre-allocated time slot is a preset value evenly distributed in the frame structure, and the required fixed time slot is compared with the locally existing fixed time slot. If the required fixed time slot is larger than the locally existing fixed time slot, the number of time slots is increased, the increased time slot is marked as a pre-occupied state, and a pre-occupied broadcast is performed. After other nodes receive the pre-occupied broadcast, they mark the increased time slot as a fixed time slot; if the required fixed time slot is smaller than the locally existing fixed time slot, the number of time slots is reduced, the reduced time slot is marked as an idle state, and an idle broadcast is performed. After other nodes receive the idle broadcast, they mark the reduced time slot as a shared time slot.

2. The node time slot allocation method for burst services according to claim 1, characterized in that: The required fixed time slot Subject to the maximum value Max, after calculating the required fixed time slot of the node based on the third traffic volume prediction value, the required fixed time slot The constraints are as follows:

3. The node time slot allocation method for burst services according to claim 1, characterized in that: In the parallel LSTM neural network, the first LSTM neural network is a main predictor, which is used to process the actual business flow data x(t) to obtain a first business volume prediction value, and the second LSTM neural network is used as an auxiliary predictor, which simultaneously receives the actual business flow data x(t) and the simulated emergency event sequence y(t) as input to obtain a second business volume prediction value, and the second LSTM neural network is used to compare the first business volume prediction value with the second business volume prediction value to obtain a comparison result, and identify the occurrence time of the emergency event according to the comparison result; During the training phase, the first LSTM neural network and the second LSTM neural network use the same actual business traffic data x(t) and simulated emergency event sequence y(t) as input, the first LSTM neural network and the second LSTM neural network have the same weights, and share internal states with each other, wherein the internal states include cell states and hidden states.

4. The node time slot allocation method for burst services as claimed in claim 3, characterized in that: The first LSTM neural network includes a state register and a result register. At time t-1, the first LSTM neural network stores the cell state c1(t-1) and the hidden state h1(t-1) in the state register, takes the actual business flow data x(t-1) at time t-1 as input, and outputs the first business volume prediction value at time t And store it in the result register.

5. The node time slot allocation method for burst services as claimed in claim 4, characterized in that: The second LSTM neural network includes a buffer, and the second LSTM neural network copies the cell state c1(t-1) and hidden state h1(t-1) stored in the state register of the first LSTM neural network as its internal state, and combines the actual business flow data x(t-1) at time t-1 and the simulated emergency event sequence y(t-1) at time t-1 as input to form an input vector x(t-1)=[x(t-1)y(t-1)]. When the input vector x(t-1) is formed, the second LSTM neural network completes forward propagation, and the input vector x(t-1) is used to predict the second business volume prediction value at time t. The prediction result is stored in the buffer, while retaining the second LSTM The internal states c2(t) and h2(t) of the neural network when it completes forward propagation and the input simulated emergency event sequence y(t-1) are stored in the buffer.

6. The method for allocating node time slots for burst services as claimed in claim 5, characterized in that: At time t, the first LSTM neural network and the second LSTM neural network perform predictions independently, and the first LSTM neural network predicts the first business volume prediction value at time t+1 based on the actual business flow data x(t). The second LSTM neural network predicts the second traffic volume forecast value at time t+1 based on the actual traffic flow data x(t) and the simulated emergency event sequence y(t). The first traffic volume forecast value and the second traffic volume prediction value Compare and judge whether there is a sudden change in the actual business traffic. The formula is as follows: and If the above formula is satisfied, it is determined that a mutation occurs; if the above formula is not satisfied, it is determined that no mutation occurs, wherein TH1 and TH2 are preset thresholds.

7. The node time slot allocation method for burst services as claimed in claim 6, characterized in that: The determining whether a sudden change occurs in the actual service traffic includes: If it is determined that a mutation occurs, the internal states c2(t) and h2(t) of the second LSTM neural network stored in the buffer are updated to the state register of the first LSTM neural network, and the simulated emergency event sequence y(t) is used as one of the inputs of the first LSTM neural network. The third traffic volume forecast value at time t+1 is generated in the first LSTM neural network in combination with x(t), y(t) and the internal states c2(t) and h2(t). The third traffic volume forecast value is used to correct the first traffic volume forecast value And output to the node, after the update is completed, the new internal states c1(t) and h1(t) of the first LSTM neural network are stored in the state register; If it is determined that no mutation occurs, the step at the time t-1 is repeated.

8. A node time slot allocation device for burst services, characterized in that: The device comprises: A time slot division module, configured to divide the node time slots in the frame structure into control time slots and data time slots, and divide the data time slots into fixed time slots and shared time slots; A business prediction module is configured to obtain actual business flow data, construct a parallel LSTM neural network to predict the actual business flow data, and obtain a third business volume prediction value; the parallel LSTM neural network includes a first LSTM neural network and a second LSTM neural network, and the first LSTM neural network and the second LSTM neural network run in parallel; The time slot calculation module is configured to calculate the fixed time slot required by the node based on the third traffic volume prediction value, and the formula is as follows: in, Indicates the fixed time slot required by the node, A indicates Class A service, Indicates the fixed time slot required for node A class service, Indicates the total time slots required for Class A services, Indicates the number of continuous frames of Class A services. represents the third traffic volume prediction value, which is obtained from the prediction algorithm result, α represents the node traffic volume burst parameter, α is calculated based on the third traffic volume prediction value The value is determined by the size of SC, which represents the service capacity of a single time slot, delay, which represents the delay requirement, and flame. length Indicates the frame length, the single time slot transmission service capacity SC, delay requirement delay and frame length flame length is the default value; A time slot allocation module is configured to adjust the frame structure based on the required fixed time slot, evenly distribute the required fixed time slot in the frame structure, obtain the locally existing fixed time slot, the locally existing fixed time slot is a pre-allocated time slot, and the pre-allocated time slot is a preset value evenly distributed in the frame structure, compare the required fixed time slot with the locally existing fixed time slot, if the required fixed time slot is larger than the locally existing fixed time slot, increase the number of time slots, mark the increased time slot as a pre-occupied state, and perform a pre-occupied broadcast, after other nodes receive the pre-occupied broadcast, mark the increased time slot as a fixed time slot; if the required fixed time slot is smaller than the locally existing fixed time slot, reduce the number of time slots, mark the reduced time slot as an idle state, and perform an idle broadcast, after other nodes receive the idle broadcast, mark the reduced time slot as a shared time slot.

9. A node time slot allocation system for burst services, the system comprising a processor and a memory, wherein a computer program is stored in the memory, characterized in that: The computer program is loaded and executed by the processor to implement the node time slot allocation method for burst services as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, wherein a computer program is stored in the storage medium, characterized in that: When the computer program is executed by a processor, it is used to implement the node time slot allocation method for burst services as described in any one of claims 1 to 7.

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