A method and system for node digital silent scheduling based on mapping protocol interaction time slot allocation

Abstract

This application discloses a node digital silence scheduling method and system for mapping protocol interaction time slot allocation, relating to the field of wireless network protocol optimization technology. The method includes: acquiring multi-dimensional operational status data of each node in the current scheduling period, such as mapping service load, channel environment awareness, and neighbor status monitoring; and performing coupled prediction of mapping load and interference intensity within a future prediction window; calculating the target digital silence ratio of each node based on the predicted mapping request load, mapping response load, and interference intensity, thereby determining the request time slot demand, response time slot demand, and digital silence time slot demand in the next scheduling period; and jointly allocating the three types of time slots under the constraint of the total number of time slots, generating a target time slot scheduling table, and broadcasting it to each node. This improves time slot resource utilization, reduces invalid channel occupation and node energy consumption, and enhances the scheduling adaptability of mapping interaction services in dynamic interference environments.

Description

Technical Field

[0001] This application relates to the field of wireless network protocol optimization technology, and in particular to a node digital silent scheduling method and system for mapping protocol interaction time slot allocation. Background Technology

[0002] In wireless communication systems such as wireless sensor networks, drone swarm communication, and industrial IoT, multiple nodes typically need to periodically exchange request and response data for services such as state mapping, task coordination, or control feedback. These interactions not only focus on successful data transmission but also on the timing matching between requests and responses. The time slot scheduling and channel access control mechanisms handled by the MAC layer directly affect the throughput, communication latency, and node power consumption during inter-node interactions, making them a crucial technical aspect for ensuring the stable operation of such wireless networks.

[0003] Currently, channel access and time slot scheduling in wireless networks typically employ fixed time slot allocation, random contention access, or a hybrid mechanism combining both. Fixed time slot allocation can reduce collisions between nodes to some extent and facilitate nodes entering a low-power state during non-communication periods. However, its time slot structure usually relies on preset rules, and when service load, node queues, or interaction frequency change, some time slots may become idle while some nodes queue. Random contention access can improve the flexibility of node access, but in situations with a large number of nodes, high service concurrency, or strong channel interference, backoff waiting and idle listening times may increase, leading to uncertain access latency and increased energy consumption.

[0004] To mitigate the impact of heterogeneous system coexistence, sudden interference, or high-priority services on the communication of ordinary nodes, some network protocols set a quiet period within the scheduling cycle. This allows some nodes to suspend transmission for a specific time, reducing channel occupancy or making way for other services. While this type of quiet management mechanism can improve channel conflict and interference mitigation to some extent, in actual operation, the quiet range, quiet duration, and the ratio between request and response time slots often rely on fixed configurations or simple operational statistics, making it difficult to fully reflect the differences in service load, response pressure, interference exposure, and energy consumption among different nodes.

[0005] Therefore, in wireless communication scenarios where service load, node size, and channel interference can all change dynamically, existing time slot scheduling and silent management methods still have certain limitations in adaptability. On the one hand, fixed or semi-fixed time slot structures are difficult to fully match the changing communication needs of interactive services at different times; on the other hand, coarse-grained silent configurations may also affect the effective utilization of communication resources and make it difficult for the system to achieve a stable balance between throughput, latency, and energy consumption. Summary of the Invention

[0006] This application provides a node digital silent scheduling method, system, storage medium, computer program product, and electronic device for mapping protocol interaction time slot allocation, which at least solves the problem in the prior art that wireless communication networks are difficult to balance resource utilization efficiency, interaction delay stability, and node energy consumption control under dynamic service and interference environments.

[0007] In a first aspect, embodiments of this application provide a node digital silence scheduling method for mapping protocol interaction time slot allocation. The method includes: acquiring multi-dimensional operational status data collected by each node in a wireless communication network during the current scheduling period; the multi-dimensional operational status data includes at least the node's own mapping service load data, channel environment awareness data of the channel it is in, and neighbor status monitoring data, wherein the mapping service load data includes mapping request load data and mapping response load data; based on the multi-dimensional operational status data, performing coupled prediction of the mapping load and interference intensity of each node within a future prediction window to obtain the predicted mapping load value and interference intensity value of each node; wherein the predicted mapping load value includes the predicted mapping request load value and the predicted mapping response load value; calculating the target digital silence ratio of each node based on the predicted mapping load value and the predicted interference intensity value of each node; the target digital silence ratio is used to characterize the time slot proportion of the corresponding node in the digital silence state in the next scheduling period; based on the predicted mapping request load value of each node... The predicted load of the mapped response and the target digital silence ratio are used to determine the request time slot demand, response time slot demand, and digital silence time slot demand of each node in the next scheduling cycle, respectively. The response time slot demand is determined based on preset mapping protocol request-response association constraints, and the digital silence time slot demand is determined based on a preset total number of time slots in the next scheduling cycle. Under the constraint of the total number of time slots in the next scheduling cycle, the request time slot demand, response time slot demand, and digital silence time slot demand are jointly allocated to obtain the number of request time slots, response time slots, and digital silence time slots allocated to each node in the next scheduling cycle, as well as the corresponding time slot positions. A target time slot scheduling table is constructed based on the allocation results. The target time slot scheduling table is broadcast to each node to instruct the corresponding node to stop radio frequency transmission and enter a digital silence state within the digital silence time slot positions indicated by the target time slot scheduling table, and to perform data interaction for the mapped service within the request time slot positions and response time slot positions indicated by the target time slot scheduling table.

[0008] Secondly, embodiments of this application provide a node digital silence scheduling system for mapping protocol interaction time slot allocation. The system includes: a multi-dimensional operation status acquisition unit, used to acquire multi-dimensional operation status data collected by each node in the wireless communication network during the current scheduling period; the multi-dimensional operation status data includes at least the node's own mapping service load data, channel environment awareness data of the channel it is in, and neighbor status monitoring data, the mapping service load data including mapping request load data and mapping response load data; a mapping interference coupling prediction unit, used to perform coupling prediction of the mapping load and interference intensity of each node in the future prediction window based on the multi-dimensional operation status data, to obtain the mapping load prediction value and interference intensity prediction value of each node; wherein, the mapping load prediction value includes the mapping request load prediction value and the mapping response load prediction value; a target silence ratio calculation unit, used to calculate the target digital silence ratio of each node based on the mapping load prediction value and the interference intensity prediction value of each node; the target digital silence ratio is used to characterize the time slot proportion of the corresponding node in the digital silence state in the next scheduling period; and a time slot demand determination unit, used to determine the demand based on the time slots of each node. The predicted mapping request load, the predicted mapping response load, and the target digital silence ratio are used to determine the request slot demand, response slot demand, and digital silence slot demand for each node in the next scheduling cycle, respectively. The response slot demand is determined based on preset mapping protocol request-response association constraints, and the digital silence slot demand is determined based on a preset total number of slots in the next scheduling cycle. A slot scheduling table construction unit is used to, under the constraint of the total number of slots in the next scheduling cycle, determine the request slot demand, response slot demand, and target digital silence ratio. The demand for digital silence time slots is jointly allocated to obtain the number of request time slots, response time slots, digital silence time slots, and corresponding time slot positions allocated to each node in the next scheduling cycle, and a target time slot scheduling table is constructed based on the allocation results. The time slot scheduling table broadcasting unit is used to broadcast the target time slot scheduling table to each node to instruct the corresponding node to stop radio frequency transmission and enter the digital silence state within the digital silence time slot positions indicated by the target time slot scheduling table, and to perform data interaction of the mapping service within the request time slot positions and response time slot positions indicated by the target time slot scheduling table.

[0009] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the node digital silent scheduling method for mapping protocol interaction time slot allocation according to any embodiment of the present application.

[0010] Fourthly, embodiments of this application provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the node digital silent scheduling method for mapping protocol interaction time slot allocation according to any embodiment of this application.

[0011] Fifthly, embodiments of this application provide a computer program product, including a computer program / instructions, which, when executed by a processor, implements the steps of the node digital silent scheduling method for mapping protocol interaction time slot allocation according to any embodiment of this application.

[0012] The node digital silent scheduling method and system for mapping protocol interaction time slot allocation provided in this application can achieve at least the following technical effects:

[0013] (1) By using the node's own mapping request load, mapping response load, channel environment awareness information, and neighbor status monitoring information as a unified operational state basis, and coupling the mapping load and interference intensity prediction within the future prediction window, the scheduling basis can simultaneously reflect the node's interactive service pressure and channel occupancy risk. Since the mapping protocol interactive service itself has the temporal characteristics of interrelated requests and responses, predicting the request load and response load separately can more accurately identify the request initiation requirements and response carrying requirements of different nodes in the next scheduling cycle; further combining the interference intensity prediction results to calculate the target digital silence ratio can make the digital silence control correspond to the node's service requirements, interference exposure level, and channel occupancy status. Thus, the system can adjust the communication activity level of different nodes in advance before the scheduling cycle arrives, reduce unnecessary channel occupancy and invalid monitoring, and improve the adaptability of silence control to service fluctuations and channel status changes.

[0014] (2) Based on the predicted values ​​of the mapping request load, the predicted values ​​of the mapping response load, and the target digital silence ratio, the demand for request time slots, response time slots, and digital silence time slots are determined respectively, and jointly allocated under the constraint of the total number of time slots in the next scheduling cycle, so that the request time slots, response time slots, and digital silence time slots are determined collaboratively within the same resource budget. Since the demand for response time slots is limited by the request-response association constraint of the mapping protocol, the response resources and the request interaction relationship can be kept matched, reducing the risk of waiting, idling, or interaction interruption caused by the imbalance of time slot configuration between the request side and the response side; at the same time, the demand for digital silence time slots is determined according to the preset total number of time slots in the next scheduling cycle and participates in the joint allocation, so that silence control is not isolated from the service interaction resources, but forms a complete periodic time slot structure together with the request time slots and response time slots. Thus, the system can take into account the requirements of interactive service continuity, channel conflict suppression, and low power operation of nodes under limited time slot resources.

[0015] This technical solution combines the determination of the digital silence ratio with the mapping service load, request-response relationship, and channel interference status, and further generates an executable target time slot scheduling table through unified time slot joint allocation. As a result, nodes can complete the mapping service interaction within the allocated request and response time slots and stop radio frequency transmission within the digital silence time slot. This reduces invalid channel occupation and node energy consumption while ensuring request-response timing matching, improves time slot resource utilization efficiency within the scheduling cycle, and enhances the scheduling stability of the wireless communication network under dynamic service load and dynamic interference environments. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart is shown as an example of a node digital silent scheduling method for mapping protocol interaction time slot allocation according to an embodiment of this application;

[0018] Figure 2 A flowchart illustrating an example of determining the predicted mapped load and interference intensity of each node in a method according to an embodiment of this application is shown.

[0019] Figure 3 This paper presents an example of a method for jointly allocating time slot requirements and constructing a target time slot scheduling table according to an embodiment of the present application.

[0020] Figure 4 A schematic diagram illustrating the system operation mechanism of an example of a node digital silent scheduling method based on the mapping protocol interaction time slot allocation according to an embodiment of this application is shown.

[0021] Figure 5 A schematic diagram of a comparative experimental simulation showing the network throughput and robustness of different methods under sudden disturbances is presented.

[0022] Figure 6 A schematic diagram of comparative experimental simulation results of different methods in terms of cumulative distribution boundary of interaction delay and tail delay suppression is shown.

[0023] Figure 7 A comparative simulation diagram showing the results of different methods in controlling the trade-off between signaling overhead and node power consumption is presented.

[0024] Figure 8A structural block diagram of an example of a node digital silent scheduling system with mapping protocol interaction time slot allocation according to an embodiment of this application is shown. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be noted that in terms of multi-node service interaction and access scheduling optimization, current technologies have explored distributed time slot adjustment, centralized collision avoidance, and dynamic resource allocation. For example, MD-MAC (Multi-hop Desynchronization MAC) schemes coordinate the scheduling cycle structure or channel resources under multi-hop topologies through inter-node desynchronization mechanisms to improve slot utilization and scheduling convergence issues under fixed time slot structures. In scenarios such as dynamic formation communication, some schemes introduce RB-CCA (Random Backoff with Centralized Collision Avoidance) and graph coloring, frequency domain resource allocation, and other mechanisms to improve node access efficiency and resource reuse capabilities. These methods typically focus more on collision avoidance, throughput improvement, or access fairness, and their time slot configuration logic is mostly centered around general data transmission. For service scenarios with differences in interaction stages, there are still cases where the adaptation is not detailed enough.

[0027] In low-power multi-hop networks, some studies have proposed on-demand scheduling mechanisms like SCoRe (Scheduling Commands and Responses), which temporarily configure transmission and feedback time slots during specific service processes to reduce internal conflicts between command propagation and response feedback. This approach can improve feedback reliability in multi-hop networks to some extent, but it primarily focuses on the internal command execution and feedback collection processes, and its comprehensive adaptation to external interference coexistence, silent avoidance, and differences in the operating states of different nodes is relatively limited.

[0028] In scenarios where heterogeneous wireless systems coexist, current technologies have also proposed interference avoidance methods based on silence periods. For example, BB (Blank Burst) mechanisms set specific silence indicators in the scheduling cycle or control information, causing controlled network nodes to suspend transmission for a specified time, thereby providing communication opportunities for other systems or high-priority services. While these mechanisms can reduce cross-system interference to some extent, silence configurations typically focus more on overall control and do not adequately consider the differences in service states and communication needs among nodes within the controlled network, potentially affecting the effective utilization of communication resources.

[0029] Furthermore, regarding the dissemination of silence information and the management of silence periods, some solutions employ QP IE (Quiet Period Information Element) to carry silence-related control content in beacon messages and improve the consistency of silence arrangements through information forwarding between nodes. Other approaches utilize SPME / SPIE (Silent Period Management Entity / Silent Period Interpretation Entity) mechanisms to attempt dynamic management of silence periods. While these methods enhance the dissemination and coordination capabilities of silence control information, their focus remains primarily on the dissemination, synchronization, or periodic configuration of silence commands, with limited capacity for joint adaptation to changes in node services, interference, and operational status.

[0030] In summary, current technologies have made improvements in multi-hop time slot allocation, command response scheduling, heterogeneous system silencing, and silencing information propagation. However, the focus of different technical approaches is relatively scattered. In wireless communication scenarios where interactive service traffic, node states, and external interference may all change dynamically, the relevant scheduling methods may still suffer from problems such as insufficient matching between time slot configuration and service requirements, coarse-grained silencing control, and insufficient utilization of multi-dimensional operating states.

[0031] It should be understood that the above description of the relevant technologies is intended only to help the public better understand the inventive spirit and motivation of this application, and is not intended to limit this application. Furthermore, the technical solutions described in the above-mentioned relevant technologies are not prior art, and may also be undisclosed technical solutions, such as those under research or in the laboratory stage.

[0032] The technical solutions in this application, including the collection, storage, use, processing, transmission, provision, and disclosure of users' personal information, comply with relevant laws and regulations and do not violate public order and good morals.

[0033] Figure 1A flowchart illustrating an example of a node digital silent scheduling method for mapping protocol interaction time slot allocation according to an embodiment of this application is shown.

[0034] Regarding the execution entity of the method in the embodiments of this application, it can be any controller or processor with computing or processing capabilities, such as a wireless network time slot scheduling platform controller deployed in a wireless sensor network gateway, a drone formation communication management node, or an industrial IoT edge node. It implements the method of the embodiments of this application by running programs or instructions stored in a storage medium. In some examples, it can be integrated and configured in a network device, edge device, electronic device, or terminal through software, hardware, or a combination of software and hardware, and the types of network devices, edge devices, electronic devices, or terminals can be diverse.

[0035] like Figure 1 As shown, in step S110, multi-dimensional operational status data collected by each node in the wireless communication network during the current scheduling period is obtained. Here, the multi-dimensional operational status data includes at least the node's own mapping service load data, channel environment awareness data of the channel it is on, and neighbor status monitoring data. The mapping service load data includes mapping request load data and mapping response load data.

[0036] Here, the wireless communication network can be a wireless sensor network, an industrial IoT network, a drone swarm communication network, a smart manufacturing field communication network, or other wireless communication networks that employ periodic time-slot scheduling. The current scheduling cycle can be understood as a periodic time unit in which the network performs state awareness, resource assessment, time-slot allocation, and scheduling issuance. Each node can be a sensor node, actuator node, mobile platform communication node, drone communication node, or a subordinate terminal node of an edge gateway. A network coordinator node can be set up in the network, or the coordination function can be undertaken by a gateway base station, cluster head node, or edge control node with scheduling capabilities.

[0037] Specifically, the mapping service load data is used to characterize the service pressure on nodes during the mapping protocol interaction process. For example, mapping protocol interaction can be a request and response interaction between nodes regarding services such as status queries, control feedback, address or function mapping, and task status mapping. For instance, in an industrial IoT scenario, a controller can periodically send status query requests to sensor nodes or actuator nodes, and the nodes subsequently return response data such as temperature, pressure, vibration, or execution status; in a drone swarm communication scenario, the master node can send a formation status synchronization request to swarm members, and each drone node returns response information such as position, speed, attitude, or remaining energy; in a wireless sensor network, the aggregation node can send a polling request to regional nodes, and the nodes return corresponding mapping response data based on the collected results. Since such services typically have a temporal correspondence between requests and responses, this embodiment further divides the mapping service load data into mapping request load data and mapping response load data to subsequently determine request time slot requirements and response time slot requirements respectively.

[0038] Channel environment awareness data reflects noise, sudden interference, external system occupancy, or localized communication congestion in the channel where a node resides. For example, in industrial settings, Wi-Fi devices, Bluetooth devices, mobile terminals, or other wireless control devices may share similar frequency bands with low-power sensing nodes, leading to sudden interference within localized periods. In drone swarm scenarios, swarm movement, changes in obstruction, and variations in the external electromagnetic environment can also cause channel quality fluctuations. Neighbor state monitoring data reflects information such as neighbor nodes' time slot occupancy, queue pressure, or silent status. For instance, if a node detects that a neighbor node is occupying a large number of request time slots in a recent scheduling cycle, or that a neighbor node is experiencing high response queue pressure, it indicates a high level of competition in its surrounding local communication area.

[0039] By collecting the aforementioned multi-dimensional operational status data, the system no longer makes scheduling decisions based solely on a single local queue length or a single channel measurement. Instead, it can characterize the current operating environment of a node from three dimensions: service load, physical channel, and neighbor coordination status.

[0040] In step S120, based on multidimensional operational status data, the mapped load and interference intensity of each node within the future prediction window are coupled and predicted to obtain the predicted values ​​of the mapped load and interference intensity for each node. The predicted values ​​of the mapped load include the predicted values ​​of the mapped request load and the predicted values ​​of the mapped response load.

[0041] Here, the future prediction window refers to the prediction time range extending forward from the current scheduling cycle. It can correspond to the next scheduling cycle or cover multiple consecutive subsequent scheduling cycles. After obtaining multi-dimensional operational status data, the system does not directly use the instantaneous state at the current moment as the sole basis for allocating time slots in the next cycle. Instead, it predicts possible changes in mapped service load and interference intensity within the future prediction window to improve the foresight of the scheduling results.

[0042] Specifically, the mapping load and channel interference status may have a correlated impact in actual wireless communication environments. For example, in industrial IoT scenarios, if a device area is affected by short-term strong interference from nearby wireless devices, the response data that a node could complete in the current cycle may be delayed or retransmitted, thus increasing the response load in the next cycle. In UAV swarm communication scenarios, when the link quality deteriorates due to changes in distance or attitude occlusion of some nodes in the swarm, state synchronization requests and retransmission responses may occur in clusters within a local time window. In sensor network scenarios, if multiple nodes respond to the polling request of the aggregation node simultaneously, it may also increase the local contention level and affect the subsequent interference status.

[0043] In this way, the system can obtain the predicted interference intensity of each node within the future prediction window, as well as the predicted mapping load to characterize the subsequent mapping service requirements. Furthermore, the predicted mapping load is divided into predicted mapping request load and predicted mapping response load. For example, for industrial nodes primarily polled by the controller, the predicted mapping request load reflects the scale of query or control requests that may arrive in the next cycle, while the predicted mapping response load reflects the scale of status data or acknowledgment data that the node needs to return. For UAV formation nodes, the predicted mapping request load reflects the pressure of formation synchronization requests or task update requests, while the predicted mapping response load reflects the pressure of member nodes returning pose status or task acknowledgment information. This allows the scheduling process to anticipate changes in service load and channel environment trends, thereby optimizing time slot configuration mismatches caused by mixed request and response estimations and reducing time slot configuration lags or resource mismatches caused by relying solely on the current instantaneous state.

[0044] In step S130, the target digital silence ratio of each node is calculated based on the predicted value of the mapped load and the predicted value of the interference intensity of each node. The target digital silence ratio is used to characterize the proportion of time slots in which the corresponding node is in digital silence in the next scheduling cycle.

[0045] Here, digital silence refers to a node ceasing radio frequency transmission within a corresponding time slot, thereby reducing the occupancy of the shared wireless channel and the impact on the communication of other nodes. The target digital silence ratio is a proportional parameter used to guide the digital silence time slot arrangement in the next scheduling cycle. It reflects the relative degree to which a node should be scheduled to enter digital silence in subsequent scheduling cycles. This ratio can be determined based on the node's predicted interference intensity and predicted mapped load, rather than solely based on a fixed silence period or a unified network-wide silence command.

[0046] In practice, the system can calculate the target digital silence ratio for each node. For nodes with strong predicted interference and relatively low predicted mapping load, their target digital silence ratio can be increased to reduce invalid transmissions and unnecessary channel contention in the next scheduling cycle. For example, in an industrial setting, if a node is near a strong interference source and only has a few non-emergency reporting tasks in the next cycle, its digital silence ratio can be appropriately increased to reduce active transmissions in time slots with strong interference. For nodes with high predicted mapping request or response pressure, their target digital silence ratio can be relatively reduced to preserve necessary data interaction opportunities. For example, in a UAV formation, if a node undertakes critical attitude synchronization or path status feedback tasks, even with some environmental interference, sufficient request or response time slots need to be reserved to maintain formation coordination.

[0047] It should be noted that the target digital silence ratio is not equivalent to simply shutting down node communication functions. Instead, it transforms silence control from a fixed-period or uniform-range control method into a node-level proportional control parameter. This takes into account both the limitations imposed by channel interference on communication behavior and the transmission opportunities required by the mapped service load. In some cases, nodes can cease active radio frequency transmission during digital silence slots, but still retain necessary synchronization, reception control information, or low-power listening capabilities. This reduces unnecessary channel occupancy without disrupting the overall network scheduling and synchronization.

[0048] In step S140, based on the predicted mapping request load, predicted mapping response load, and target digital silence ratio of each node, the request slot demand, response slot demand, and digital silence slot demand of each node in the next scheduling cycle are determined respectively. Here, the response slot demand is determined based on preset mapping protocol request-response association constraints, and the digital silence slot demand is determined based on the preset total number of slots in the next scheduling cycle.

[0049] It should be understood that the request slot requirement represents the slot resources needed by a node to initiate a mapping request in the next scheduling cycle; the response slot requirement represents the slot resources needed by a node to return a mapping response in the next scheduling cycle; and the digital silence slot requirement represents the slot resources needed by a node to enter a digital silence state in the next scheduling cycle. These three types of slot requirements together constitute the demand input for subsequent joint slot allocation.

[0050] In some implementations, the request slot demand can be determined based on the predicted mapping request load, while the response slot demand can be determined based on the predicted mapping response load combined with the mapping protocol request-response association constraints. The mapping protocol request-response association constraints characterize the correspondence, proportional relationship, or timing matching relationship between mapping requests and responses. For example, in industrial control polling operations, a single query request typically requires at least one status acknowledgment or data response; in UAV formation status synchronization operations, a single synchronization request may require multiple member nodes to return their respective poses or task states in subsequent response phases; in low-power sensor networks, a single area polling request may trigger multiple sensor nodes to return collected data within their allocated response slots. Therefore, the response slot demand cannot be determined solely based on the current response buffer size; it should also be determined in conjunction with the request triggering scale and protocol interaction relationships.

[0051] The demand for digital silence time slots can be determined based on the target digital silence ratio and the preset total number of time slots in the next scheduling cycle. For example, if the number of available time slots in the next scheduling cycle is a fixed value, the higher the target digital silence ratio, the greater the demand for digital silence time slots corresponding to that node; conversely, the lower the target digital silence ratio, the smaller the demand for digital silence time slots corresponding to that node. This process converts continuous or proportional silence control parameters into discrete time slot demands that can be scheduled by the MAC layer.

[0052] Therefore, the predicted business requirements, interference states, and silence control requirements are uniformly expressed as time slot requirements that can be handled by the MAC layer. This helps to avoid scheduling fragmentation caused by the independent calculation of request time slots, response time slots, and silence time slots, and enables subsequent joint allocation to handle business interaction requirements and digital silence requirements simultaneously on the same resource scale.

[0053] In step S150, under the constraint of the total number of time slots in the next scheduling cycle, the demand for request time slots, the demand for response time slots, and the demand for digital silent time slots are jointly allocated to each node in the next scheduling cycle to obtain the number of request time slots, the number of response time slots, the number of digital silent time slots, and the corresponding time slot positions. The target time slot scheduling table is then constructed based on the allocation results.

[0054] Here, the total number of available time slots in the next scheduling cycle is usually limited, while the sum of the time slot requests, response time slot requests, and digital silence time slot requests of multiple nodes may exceed this total number of time slots. Therefore, under the constraint of the total number of time slots in the next scheduling cycle, it is necessary to coordinate and jointly allocate the three types of time slot requests of each node in a unified manner, so that the limited time slot resources can achieve a reasonable balance between business interaction and digital silence control.

[0055] During the joint allocation process, the system uses the request time slot demand, response time slot demand, and digital silent time slot demand of each node as the allocation basis, combined with the mapping protocol request-response association constraints and the total time slot capacity of the next scheduling cycle, to determine the actual number of request time slots, response time slots, and digital silent time slots allocated to different nodes. For example, for nodes with high predicted response pressure, corresponding response time slots can be configured for them while meeting the total time slot capacity; for nodes with high predicted interference and light service load, corresponding digital silent time slots can be configured; for nodes with concentrated request services, corresponding request time slots can be configured based on the predicted request load value. Therefore, the joint allocation process does not simply maximize the number of communication time slots, nor does it simply increase the number of silent time slots, but rather coordinates the request, response, and silent time slots together.

[0056] Furthermore, after determining the number of time slots allocated to each node, the system maps the allocation results onto the time slot time axis of the next scheduling cycle, determining the corresponding request time slot position, response time slot position, and digital silence time slot position for each node, and generating a target time slot scheduling table based on the node identifier, time slot type, and time slot position. For example, within a scheduling cycle, a node can send a mapping request in several time slots at the beginning, return a mapping response in a specified time slot in the middle or end, and enter a digital silence state in a time slot position with high interference or where communication is not required. For different nodes, the target time slot scheduling table can indicate their request, response, and silence time slot positions in the next scheduling cycle, enabling the node side to directly execute according to the scheduling table.

[0057] Therefore, the three types of time slot requirements are transformed into actual allocation results that satisfy the total time slot constraint, and further transformed into a target time slot scheduling table with time domain location indication. Compared with the method of only giving the number of time slots for each node, the scheduling table including time slot location enables nodes to know when to send a request, when to return a response, and when to enter a digital silent state, thereby improving the executability of the scheduling results and the coordination of multiple nodes.

[0058] In step S160, the target time slot schedule is broadcast to each node to instruct the corresponding node to stop radio frequency transmission and enter digital silence state within the digital silence time slot position indicated by the target time slot schedule, and to perform data interaction of the mapping service within the request time slot position and response time slot position indicated by the target time slot schedule.

[0059] Here, after generating the target time slot scheduling table, the network coordinator node or the control node responsible for scheduling broadcasts the target time slot scheduling table to all nodes via control frames, control beacons, or other scheduling notification messages. Upon receiving the target time slot scheduling table, each node can resolve the corresponding request time slot location, response time slot location, and digital silent time slot location based on its own node identifier, and then execute the corresponding communication actions according to the scheduling table in the next scheduling cycle.

[0060] During the digital silence time slot, the corresponding node ceases radio frequency transmission and enters a digital silence state. It should be noted that the digital silence state is used to restrict nodes from actively transmitting radio frequency data during that time slot, and does not necessarily require nodes to shut down all radio frequency-related hardware; in some implementations, nodes can still maintain necessary time synchronization capabilities or control information reception capabilities according to system configuration. During the request time slot, the node executes mapping request data transmission; during the response time slot, the node executes mapping response data transmission, thereby completing the data interaction for the mapping service.

[0061] For example, in an Industrial Internet of Things (IIoT) scenario, a sensor node can receive or initiate mapping requests related to status queries within a request time slot, upload collected data or status confirmation information within a response time slot, and stop actively transmitting within its assigned digital silent time slot to reduce frequency band competition with other industrial wireless devices. In a drone swarm communication scenario, member nodes can send status update requests within a specified request time slot according to a scheduling table, return the pose or task status required for swarm coordination within a response time slot, and stop radio frequency transmission within a silent time slot that does not affect formation synchronization to reduce unnecessary air interface occupation.

[0062] Therefore, the aforementioned state acquisition, coupling prediction, digital silence ratio calculation, and time slot joint allocation results are implemented as actual execution actions on the node side. The target time slot scheduling table enables each node to execute request, response, or digital silence behavior within a defined time slot location, thereby reducing the uncertainty caused by random competition and improving the time slot utilization efficiency and operational stability of multi-node mapping interaction services under dynamic load and dynamic interference environments.

[0063] Regarding the implementation details of obtaining multi-dimensional operational status data in step S110, in some examples of embodiments of this application, firstly, for each node, within the current scheduling period, the number of newly arrived request data packets and the number of response data packets in the pending-send buffer queue are counted through the mapping protocol queue manager to obtain mapping request load data and mapping response load data respectively. The mapping request load data can be used to characterize the mapping request service pressure that the node needs to initiate within the current scheduling period, and the mapping response load data can be used to characterize the service pressure that the node needs to return mapping responses within the current scheduling period. Since the request and response services in industrial IoT, wireless sensor networks, or UAV swarm communication networks may differ in terms of generation timing, data direction, and real-time requirements, counting them separately is beneficial for accurately depicting the bidirectional service status of the node during the mapping protocol interaction process.

[0064] Furthermore, based on the duration of the current scheduling period, the system quantifies and extracts the mapping request arrival rate of a node within the current scheduling period, and uses the mapping request load data, mapping response load data, and mapping request arrival rate as the node's own mapping service load data. The mapping request arrival rate reflects the rate at which mapping requests enter the node queue per unit time; it not only represents the current number of requests but also the arrival intensity of the request service within the current scheduling period. In some examples, the mapping request arrival rate can be determined using the following formula:

[0065] Equation (1)

[0066] In the formula, Represents a node In the current scheduling cycle Mapping request arrival rate within the region; Represents a node In the current scheduling cycle The number of newly received mapping request packets; This indicates the duration of the current scheduling period. This quantization method converts the request arrival status of a node within the current scheduling period into a rate-type parameter that can be used for scheduling decisions.

[0067] Then, the background noise floor power of the current channel is sampled by the radio frequency energy detection module of the node, and the energy peak value exceeding the preset coexistence interference threshold of the received signal strength indication value is captured within a preset time window. This is used to statistically analyze the occurrence frequency, cumulative duration, and interference duty cycle of external sudden interference events. The background noise floor power, occurrence frequency, cumulative duration, and interference duty cycle are aggregated into channel environment perception data. Background noise floor power can be used to characterize the basic noise level of the channel, occurrence frequency can be used to characterize the density of sudden interference events, and cumulative duration and interference duty cycle can be used to characterize the degree of disturbance to the channel in the time dimension. The above data can be combined to form channel environment perception data.

[0068] In some examples, the interference duty cycle can be determined using the following formula:

[0069] Equation (2)

[0070] In the formula, Represents a node In the current scheduling cycle Interference duty cycle obtained from internal statistics; Indicates the preset time window The number of external sudden interference events detected internally; Indicates the first Duration of the sudden external interference event; This represents the preset time window used for statistical analysis of interference events. The interference duty cycle can be set to zero if no external sudden interference events are detected. This parameter allows discrete sudden interference events to be converted into a quantitative indicator reflecting the proportion of time the channel is disturbed.

[0071] Subsequently, based on the node's listening interface, the system listens for control beacons broadcast by neighboring nodes within a preset shared listening time slot. It then extracts the neighboring nodes' historical time slot occupancy ratio, remaining queue depth, and historical digital silent ratio from these control beacons as neighbor status monitoring data. The historical time slot occupancy ratio characterizes the neighboring nodes' use of shared time slot resources in a recent scheduling period; the remaining queue depth characterizes the remaining service pressure on the neighboring nodes; and the historical digital silent ratio characterizes the silent state of the neighboring nodes in a recent scheduling period. By acquiring this neighbor status monitoring data, nodes or network coordinators can identify resource occupancy status and potential competition relationships within a local neighborhood.

[0072] Subsequently, the mapped service load data, channel environment awareness data, and neighbor status monitoring data are associated and encapsulated according to node identifiers and current scheduling cycle identifiers to obtain multi-dimensional operational status data collected by each node within the current scheduling cycle. Node identifiers are used to distinguish different data sources, and the current scheduling cycle identifier is used to indicate the time period to which each type of data belongs. Through this association and encapsulation, service-side data, channel-side data, and neighbor-side data collected by the same node within the same scheduling cycle can form a unified data record, facilitating the network coordinator to synchronize the operational status of different nodes.

[0073] In some examples, to reduce reporting overhead, the joint state change of the mapped service load data and channel environment awareness data relative to the previous scheduling period can be calculated. When the joint state change exceeds the preset sparse reporting threshold, differential compression encoding is performed on the locally cached multidimensional running state data, and the compressed data is uploaded to the network coordinator node.

[0074] Specifically, differential compression coding can encode the differences between the current multidimensional operating status data and the data reported in the previous scheduling cycle, thereby reducing the amount of duplicate data transmitted.

[0075] To improve the stability of joint state change calculation, in some examples, the mapped service load data can be aggregated into a comprehensive service load index for the current scheduling period, and the channel environment awareness data can be aggregated into a comprehensive environment awareness index for the current scheduling period. Then, the relative changes of both compared to the previous scheduling period can be calculated. The joint state change can be calculated using the following formula:

[0076] Equation (3)

[0077] In the formula, Represents a node In the current scheduling cycle The change in the joint state within; Represents a node Comprehensive business load metrics in the previous scheduling cycle; Represents a node The comprehensive environmental perception indicators of the previous scheduling cycle; This indicates the change in the overall business load index of the current scheduling period relative to the previous scheduling period. This indicates the change in the comprehensive environmental perception index of the current scheduling cycle relative to the previous scheduling cycle. and These represent the preset business-side weights and environment-side weights, respectively. and These represent the preset minimum constants used to prevent denominator anomalies.

[0078] If the following conditions are met:

[0079] Equation (4)

[0080] Then determine the node The operating status changes significantly compared to the previous scheduling cycle, triggering differential compression encoding and reporting processes; among them, This represents the preset sparse reporting threshold.

[0081] Through the embodiments of this application, multidimensional operational status data can simultaneously reflect the node's own request-response service pressure, the interference status of its channel, and the resource consumption of neighboring nodes, enabling the network coordinator to obtain unified status input with time and node identifiers. Furthermore, by controlling the timing of data reporting through joint state change and sparse reporting thresholds, critical operational information can be reported promptly when state changes are significant, while redundant reporting is reduced when state changes are minor. This lowers node-side signaling overhead and uplink bandwidth consumption, thereby improving the effectiveness of multidimensional status acquisition and the availability of network scheduling input data.

[0082] Figure 2 A flowchart illustrating an example of the operation of determining the predicted mapped load and the predicted interference intensity of each node in a method according to an embodiment of this application is shown.

[0083] like Figure 2 As shown, in step S210, the background noise power, frequency of occurrence of external sudden interference events, cumulative duration and interference duty cycle in the channel environment perception data are extracted from the multi-dimensional operating status data, as well as the mapping request load data, mapping response load data and mapping request arrival rate in the mapping service load data.

[0084] Since the aforementioned data originates from physical layer channel measurements, MAC layer queue statistics, and service arrival statistics, and their physical dimensions and numerical ranges are inconsistent, the extracted data can be normalized. A pre-defined feature weighted fusion mechanism can then be used to obtain normalized current interference intensity indicators, normalized current mapped load indicators, and normalized current request arrival rates. For example, extreme value normalization, Z-score normalization, or other normalization methods can be used to map various features to a unified dimensionless representation space, thus avoiding weight shifts caused by features with different dimensions directly participating in model calculations.

[0085] In some examples, nodes In the current scheduling cycle Normalized current interference intensity index It can be determined using the following formula:

[0086] Equation (5)

[0087] In the formula, , , , Representing nodes respectively In the current scheduling cycle The background noise floor power after internal normalization, the frequency of occurrence of external sudden interference events, the cumulative duration and interference duty cycle; , , , These are the preset feature fusion weights for the corresponding dimensions. In some cases, each preset feature fusion weight can be a non-negative value and can be configured according to the interference characteristics of different wireless network scenarios.

[0088] Similarly, the mapping request load data and mapping response load data can be normalized and fused to obtain a normalized current mapping load metric. For example, it can be determined using the following formula:

[0089] Equation (6)

[0090] In the formula, Represents a node In the current scheduling cycle Normalized mapping request payload data, Represents a node In the current scheduling cycle Normalized mapping response load data, and These are preset fusion weights for request load and response load, respectively. The normalized mapping request arrival rate of a node can be used as the normalized current request arrival rate. Through the above normalization and weighted fusion processing, the multi-dimensional channel state and service state can be converted into three core state indicators: interference intensity, mapping load, and request arrival rate. This reduces the input complexity of the prediction model and improves the comparability of different types of features participating in the prediction calculation.

[0091] In step S220, a mapping-interference coupling prediction model is constructed and run. This model includes an interference prediction branch and a load prediction branch. The interference prediction branch calculates the predicted interference intensity for the next scheduling period based on the environmental noise floor bias, the autoregressive effect of historical interference, the linear effect of historical mapped load, and the nonlinear cross-coupling effect between mapped load and external interference. The load prediction branch calculates the predicted overall mapped load for the next scheduling period based on the base load level, the autoregressive effect of historical load, and the request arrival rate-driven effect.

[0092] Specifically, in high-concurrency wireless communication networks, there may be a correlation between channel interference status and node service load. For example, external burst interference may reduce the effective transmission success rate of nodes, causing request or response data to back up in the queue; when node service load increases, channel occupancy and retransmission probability may also increase accordingly. Therefore, embodiments of this application introduce nonlinear cross-coupling effects into the prediction model, enabling the model to express the superimposed effect of both the mapping load and external interference on the interference prediction results.

[0093] In some examples, the mapping-interference coupled prediction model can calculate the predicted interference intensity for the next scheduling period using the following coupled evolution formula. and overall mapped load forecast values :

[0094] Equation (7)

[0095] Equation (8)

[0096] In the formula, For nodes In the current scheduling cycle The normalized current interference intensity index; For nodes In the current scheduling cycle Normalized current mapped load metrics; For nodes In the current scheduling cycle Normalized current request arrival rate; For nodes In the next scheduling cycle The predicted value of the interference intensity; For nodes In the next scheduling cycle The overall mapped load forecast value; , , These are the interference prediction weight parameters, used to characterize the basic bias, the autoregressive effect of historical interference, and the linear effect of historical mapping load on interference prediction, respectively. The coupling weight parameter is a nonlinear cross-product term used to characterize the superposition effect between the mapped load and external disturbances. , , These are load evolution prediction parameters, used to characterize the base load level, the autoregressive effect of historical load, and the request arrival rate-driven effect, respectively. and This is a predicted perturbation term used to characterize random perturbations that are not fully represented by the current input features.

[0097] In equations (7) and (8) above, , and All are normalized indices, therefore the nonlinear cross-product term It has a dimensionless meaning and can be used to characterize the coupling effect when the interference level and the mapped load change simultaneously. Through this prediction method, the system can simultaneously characterize the persistence of channel interference, the impact of service load on interference, and the driving effect of request arrival rate on service load changes in the same prediction model, so that the interference prediction value and the overall mapped load prediction value have a consistent data source and time base. In some examples, if the interference intensity prediction value or the overall mapped load prediction value calculated by equation (7) or equation (8) exceeds the preset effective range, non-negative amplitude limiting processing or normalized projection processing is performed on them to keep the interference intensity prediction value and the overall mapped load prediction value within the preset effective value range, and the processed result is used for the calculation of the target digital silence ratio.

[0098] In step S230, the historical interaction distribution coefficient is determined based on the actual business distribution data of the nodes in the historical time series, and the overall mapping load prediction value is split into the mapping request load prediction value and the mapping response load prediction value based on the historical interaction distribution coefficient. The mapping request load prediction value and the mapping response load prediction value are used as the mapping load prediction value of each node.

[0099] In some implementations, nodes can be counted within a preset historical sliding window. The actual mapping request volume and the actual mapping response volume are calculated, and the historical interaction distribution coefficient is determined based on the ratio between the two. In some examples, the historical interaction distribution coefficient can be determined according to the following formula:

[0100] Equation (9)

[0101] In the formula, Represents a node In historical scheduling cycles The actual mapping request volume within; Represents a node In historical scheduling cycle The actual mapping response volume within; Indicates the preset length of the history sliding window; This represents a preset minimum constant used to prevent denominator anomalies. Historical interaction distribution coefficient. It can be used to characterize the proportion of mapping request load relative to the total mapping load in the node's historical business.

[0102] After determining the historical interaction distribution coefficients, the overall mapped load forecast can be broken down into mapped request load forecast and mapped response load forecast using the following formula:

[0103] Equation (10)

[0104] Equation (11)

[0105] In the formula, For nodes In the next scheduling cycle The predicted value of the mapped request load; For nodes In the next scheduling cycle The mapped response load prediction value; For nodes In the next scheduling cycle The overall mapped load forecast value. Through this splitting process, a single overall load forecast result can be converted into two load forecast results: one for the request side and one for the response side, allowing request and response services to participate separately in subsequent time slot demand determination.

[0106] In step S240, based on the prediction residual between the actual observed value and the historical prediction value in the previous prediction window, the weight parameters used to calculate the predicted value of the disturbance intensity and the predicted value of the overall mapping load are updated online using a local weighted regression algorithm combined with a recursive least squares method with a forgetting factor.

[0107] In practice, the wireless environment and service patterns of a node may change over time, so fixed prediction parameters may not be able to continuously adapt to the channel and service states during long-term operation. Therefore, the interference prediction weight parameters and load evolution prediction parameters can be updated based on the residual between the actual observations and predictions over a recent period. (The interference prediction weight parameter vector is then used as the basis for this update.) For example, updates can be performed using the following objective function:

[0108] Equation (12)

[0109] In the formula, The weighted residual objective function represents the interference prediction branch; This is a forgetting factor used to cause the residual weights of earlier historical samples to decay over time. This represents the local sample weights in locally weighted regression, used to characterize the similarity between historical sample states and the current state; Represents a node In historical scheduling cycle The actual observed value of the interference intensity; Represents the predicted weight parameter vector based on disturbances. The calculated historical disturbance prediction values.

[0110] Similarly, for the load evolution prediction parameter vector The update can be performed using the following objective function:

[0111] Equation (13)

[0112] In the formula, This represents the weighted residual objective function for the load forecasting branch; Represents a node In historical scheduling cycle The true overall mapped load observations; Represents the parameter vector predicted by load evolution. The calculated historical overall mapped load forecast value. Based on the above objective function, the recursive least squares method can be used to... and Online recursive updates are performed to gradually correct the predicted parameters based on actual observations.

[0113] This application's embodiments enable the transformation of channel and service states with different dimensions into normalized core indicators, and the joint prediction of interference intensity and overall mapping load for the next scheduling cycle based on interference prediction and load prediction branches. Simultaneously, by using historical interaction distribution coefficients, the overall mapping load is split into mapping request load prediction values ​​and mapping response load prediction values, allowing the prediction results to reflect the differences between the request and response sides in mapping protocol interactions. Furthermore, online parameter updates with forgetting factors and local sample weights can reduce the risk of mismatch between prediction parameters and the current operating environment, improving the adaptability of prediction results to time-varying channels and dynamic service loads.

[0114] Regarding the implementation details of calculating the target digital silence ratio of each node in step S130, in some examples of embodiments of this application, firstly, for each node, the relative ratio between the predicted value of interference intensity and the predicted value of overall mapped load is calculated, and a minimal constant is introduced to smooth and correct the relative ratio to prevent denominator anomalies, thus obtaining the relative index of interference load.

[0115] Specifically, whether a node enters a digital silence state should not be determined solely by the absolute magnitude of the predicted interference intensity. For a given node, if its predicted interference intensity is high, but there is also a high mapping request load or mapping response load, the node may still need to retain some communication opportunities. If its predicted mapping load is low, even if the interference intensity is at a moderate level, the digital silence ratio can be appropriately increased to reduce invalid transmissions and channel occupancy. Therefore, this embodiment utilizes the relative relationship between the predicted interference intensity and the predicted overall mapping load to characterize the degree to which a node is affected by predicted interference under current service pressure. Furthermore, by introducing a preset minimum constant into the denominator, calculation anomalies can be avoided when the predicted overall mapping load is zero or close to zero, thereby improving the stability of the ratio calculation process.

[0116] Subsequently, using the silence sensitivity coefficient as a linear weighting coefficient, combined with the nonlinear adjustment index used to adjust the change amplitude of the initial digital silence ratio under strong interference conditions, the relative index of interference load is mapped by a power function to generate the initial digital silence ratio of the corresponding node in the next scheduling cycle.

[0117] In this process, the silence sensitivity coefficient is used to adjust the overall influence of the relative index of interference load on the initial digital silence ratio, while the nonlinear adjustment index is used to change the relationship between this relative index and the initial digital silence ratio. When the nonlinear adjustment index is large, an increase in the relative index of interference load will cause the initial digital silence ratio to increase more sensitively; when the nonlinear adjustment index is small, the change in the initial digital silence ratio with the relative index of interference load is relatively gradual. Therefore, the response strength of the node's digital silence ratio can be configured according to the real-time requirements, anti-interference requirements, or energy consumption control requirements of different wireless communication scenarios.

[0118] Subsequently, a preset inertial smoothing factor is used to weight and fuse the initial digital silence ratio with the historical digital silence ratio of the corresponding node in the current scheduling period, in order to suppress frequent jumps in the target digital silence ratio between adjacent scheduling periods. Sudden interference, short-term blockage, or transient service changes in the wireless channel may cause significant fluctuations in the initial digital silence ratio within adjacent scheduling periods. If this initial digital silence ratio is directly used as the target digital silence ratio, nodes may repeatedly switch between high and low silence ratios, affecting the stability of time slot scheduling. Therefore, this embodiment introduces the historical digital silence ratio through an inertial smoothing factor, so that the target digital silence ratio reflects the current prediction result while maintaining temporal continuity.

[0119] Furthermore, by using preset upper and lower limit amplitude rules, the weighted fusion result is constrained to within an effective proportion range, generating the target digital silence ratio for the corresponding node. Since the digital silence ratio characterizes the proportion of time slots in which a node will be in a digital silence state during the next scheduling cycle, its effective value range should be between 0 and 1. Through amplitude limiting, the nonlinear mapping or smooth fusion result can be prevented from exceeding the physical meaning range of the proportion parameter, allowing the output target digital silence ratio to be directly used for determining time slot demand.

[0120] For example, the target digital silent ratio Calculated using the following formula:

[0121] Equation (14)

[0122] Equation (15)

[0123] In the formula, For nodes The initial digital silence ratio for the next scheduling cycle; Used to characterize the relative index of disturbance load; This is the silence sensitivity coefficient, used to adjust the weight of the influence of the relative exponent of interference load on the initial digital silence ratio. It is a non-linear adjustment index, and ; It is the inertial smoothing factor, and This is used to suppress frequent jumps in the target digital silent ratio between adjacent scheduling cycles; To prevent the denominator from being zero, a pre-defined minimum constant is used; and The function is used to limit the target number's silent ratio to a valid range of values ​​in the range [0, 1].

[0124] The above calculation process can be divided into two stages: initial scale generation and smoothing / limiting. In the initial scale generation stage, the relative relationship between the predicted interference intensity and the predicted overall mapped load determines the degree of interference constraint on nodes under the current predicted state. The amplitude and sensitivity of the initial digital silence scale are adjusted using a silence sensitivity coefficient and a nonlinear adjustment index. In the smoothing / limiting stage, the initial digital silence scale is weighted and fused with the historical digital silence scale using an inertial smoothing factor. The fusion result is then constrained within an effective scale range to obtain the final target digital silence scale that can be used for scheduling.

[0125] Through the embodiments of this application, differentiated target digital silence ratios can be generated based on the predicted interference state and predicted service load state of the nodes. This ratio can reflect the node's need to reduce radio frequency transmission under strong interference conditions, and can also avoid excessively increasing the silence ratio when the service load is high. At the same time, inertial smoothing and limiting processing improve the continuity and effectiveness of the ratio output, giving the target digital silence ratio a clear physical meaning, and can be stably used to determine the digital silence time slot demand in the next scheduling cycle.

[0126] Figure 3 A flowchart illustrating an example of joint allocation of time slot requirements and construction of a target time slot scheduling table according to an embodiment of this application is shown.

[0127] like Figure 3 As shown, in step S310, a weighted multi-objective scheduling objective function is constructed to comprehensively optimize network throughput benefits, queuing delay costs, and silent execution deviation.

[0128] Here, the weighted multi-objective scheduling objective function takes the marginal diminishing utility gain corresponding to communication throughput as a positive gain term, the linear queuing delay penalty caused by digital silence and positively correlated with the predicted value of the mapping request load as a first negative cost term, and the quadratic deviation penalty of the actual number of digital silence time slots deviating from the digital silence time slot demand as a second negative cost term.

[0129] In some implementations, the weighted multi-objective scheduling objective function can be expressed as follows:

[0130] Equation (16)

[0131] In the formula, This represents the overall scheduling utility value. This represents the total number of nodes participating in scheduling within the network. , and They are to be assigned to the nodes respectively The number of request slots, response slots, and digital silent slots; This indicates the diminishing marginal utility benefit corresponding to communication throughput; This represents the penalty for queuing delay caused by digital silence, where The latency sensitivity weight is positively correlated with the predicted value of the mapping request load; For nodes Digital silent time slot demand; This represents the secondary deviation penalty for the actual number of allocated digital silent time slots deviating from the required number of digital silent time slots. The preset total number of time slots for the next scheduling cycle; , , These are preset adjustment weights for throughput, latency, and silent matching dimensions, respectively.

[0132] In the objective function of equation (16), the communication throughput benefit term is in logarithmic form, representing the diminishing marginal return on communication gains for nodes as the number of request and response time slots increases. This setting avoids excessive bias in the scheduling process towards a few nodes with higher communication gains, resulting in a more balanced allocation of time slot resources among different nodes. The queuing delay penalty term represents the impact of increased digital silent time slots on node service waiting time, and is weighted by delay sensitivity. This reflects the sensitivity of different nodes to latency in terms of mapping request load. For nodes with a large predicted mapping request load, the queuing cost caused by digital silence is correspondingly higher, thus enabling the scheduling process to take the service pressure of that node into account when allocating digital silence time slots.

[0133] The silence bias penalty term is used to constrain the actual number of allocated digital silence slots to be close to the digital silence slot demand. Using a quadratic bias form, smaller silence biases correspond to smaller penalties, while larger biases correspond to higher penalties, thereby enhancing the consistency between the silence allocation results and the aforementioned digital silence demand. Through the combination of the above positive gain and negative cost terms, this objective function can form a unified evaluation metric among communication throughput, queuing delay, and digital silence matching, enabling comparisons of different types of slot allocation results under the same objective function.

[0134] In step S320, a multi-dimensional scheduling constraint set is obtained. Here, the multi-dimensional scheduling constraint set includes: a total number of time slots constraint, which limits the sum of the number of request time slots, response time slots, and digital silent time slots allocated to each node in the entire network to no more than the preset total number of time slots for the next scheduling cycle; a request-response association constraint, which limits the number of response time slots allocated to each node to no less than the theoretical lower limit determined based on its number of request time slots and the preset response coefficient; a requirement upper limit constraint, which limits the number of request time slots and response time slots to no more than the corresponding requirement amount; and a time slot non-negative integer constraint, which limits the allocation amount of each time slot to a non-negative integer.

[0135] For example, a multidimensional scheduling constraint set can be expressed by the following formula:

[0136] Equation (17)

[0137] In the formula, The response coefficients are determined based on the pre-defined mapping protocol request-response association constraints. and They are nodes The request time slot requirement and response time slot requirement; It represents the set of non-negative integers.

[0138] In the constraint set as shown in Equation (17), the total number of time slots constraint is used to ensure that the total number of request time slots, response time slots, and digital silence time slots obtained by all nodes does not exceed the time slot capacity of the next scheduling cycle. The request-response association constraint is used to ensure that the interaction relationship between the number of response time slots and the number of request time slots conforms to the mapping protocol. For example, in confirmation-type mapping interaction services, a request may require at least one response or confirmation message, so the response coefficient can be used to determine the interaction relationship. This sets the minimum configuration requirement for the number of response time slots relative to the number of request time slots. This response coefficient can be preset based on the request-response ratio, confirmation requirements, or business reliability level of the specific mapping protocol.

[0139] The upper limit constraint on demand is used to prevent nodes from being allocated request or response time slots exceeding their predicted demand, thereby reducing idle or invalid time slot configurations. The non-negative integer constraint on time slots ensures that the allocation results correspond to discrete time slots in the actual scheduling cycle, avoiding negative time slots or unexecutable decimal time slots. Through these constraints, the solution range of the scheduling objective function is limited to a feasible region that satisfies time slot capacity, protocol interaction relationships, upper limit of service demand, and discrete time slot requirements.

[0140] In step S330, under the constraints of the multidimensional scheduling constraint set, an augmented scheduling objective function is constructed by introducing Lagrange multiplier vectors, and the augmented scheduling objective function is solved by the projection gradient iteration method. The goal is to maximize the comprehensive scheduling utility value corresponding to the weighted multi-objective scheduling objective function, and output the number of request slots, response slots and digital silent slots of each node that meet the convergence condition.

[0141] Here, since the multidimensional scheduling constraint set includes not only the global total number of time slots, but also the request-response correlation constraints and demand upper limit constraints of each node, the constraints can be introduced into the objective function using Lagrange multiplier vectors to form an augmented scheduling objective function that is easy to solve iteratively. For example, the time slot allocation variables can be continuously relaxed first, and the update results can be restricted to the feasible region through projection operations during the iteration process.

[0142] In some examples, for nodes The number of requested time slots can be updated using the following projection gradient iteration method:

[0143] Equation (18)

[0144] In the formula, Indicates the number of iterations; Indicates the first Node at the next iteration The number of request time slots; Indicates the first Node at the next iteration The number of request time slots; The preset iteration step size; Represent the augmented scheduling objective function; This represents the gradient of the augmented scheduling objective function with respect to the number of requested time slots; Indicates to feasible region Projection operators.

[0145] Among them, feasible region The total number of time slots can be determined by constraints on the total number of time slots, request-response correlation, upper limit of demand, and non-negative integer constraints on time slots. Projection operations may include truncating update values ​​that exceed the upper or lower limits, and integerizing the results after continuous relaxation. The same or similar iterative update and projection processing methods can also be used for the number of response time slots and the number of digital silent time slots. By performing projection processing after each iteration, the iteration results can always remain within the feasible range defined by the scheduling constraint set.

[0146] When the preset convergence condition is met, the iteration can be stopped, and the number of request slots, response slots, and digital silent slots for each node can be output. The preset convergence condition may include a change in the overall scheduling utility value between two consecutive iterations that is less than a preset threshold, a change in the allocation variables for each slot that is less than a preset threshold, or the number of iterations reaching a preset maximum number of iterations. Through the above solution process, the allocation result of the number of slots satisfying the scheduling objective can be obtained under multi-dimensional constraints, giving the number of request, response, and digital silent slots for each node a clear source of optimization and a basis for constraint.

[0147] In step S340, the number of request time slots, response time slots, and digital silent time slots of each node are mapped to the time slot time axis of the next scheduling cycle to determine the corresponding request time slot position, response time slot position, and digital silent time slot position of each node. Based on the request time slot position, response time slot position, digital silent time slot position, and corresponding node identifier, a target time slot scheduling table is generated.

[0148] It should be understood that step S330 outputs the number of different types of time slots obtained by each node, while the node also needs to know the specific position of each type of time slot in the next scheduling cycle when performing scheduling. Therefore, according to the preset time slot arrangement rules, the number of request time slots, response time slots, and digital silent time slots can be mapped to the time slot time axis of the next scheduling cycle. The time slot arrangement rules can be determined based on the network protocol structure, request-response interaction timing, node priority, or channel occupancy status.

[0149] In some implementations, request and response time slots can be arranged according to the interaction order of the mapped services, with the request time slot preceding the corresponding response time slot. Alternatively, request, response, and digital silence time slots for different nodes can be interleaved based on node identifiers, neighbor relationships, or interference status to reduce the probability of multiple nodes transmitting simultaneously in adjacent time slots within the same local area. For nodes assigned digital silence time slots, their corresponding digital silence time slot positions can be marked in the target time slot scheduling table, causing the node to stop radio frequency transmission within the corresponding time slot.

[0150] Specifically, the target time slot scheduling table may include fields such as node identifier, time slot type, time slot location index, and scheduling period identifier. The time slot type may include request time slots, response time slots, and digital silence time slots; the time slot location index indicates the position of the corresponding time slot in the time slot time axis of the next scheduling period. Through this tabular scheduling information, each node can obtain the corresponding time slot configuration based on its own node identifier and perform request sending, response sending, or digital silence operations according to the target time slot scheduling table in the next scheduling period.

[0151] This application's embodiments enable the conversion of request time slot requirements, response time slot requirements, and digital silent time slot requirements into executable time slot allocation results, even when the total number of time slots is limited. The throughput gain, queuing delay cost, and silent execution deviation in the objective function jointly constrain the allocation direction. The scheduling constraint set limits the capacity and protocol boundaries of the allocation results. Iterative solution using projected gradients keeps the allocation variables within a feasible range, and time slot time axis mapping further converts the number of time slots into time slot locations that can be executed by nodes. This improves the executability of the time slot allocation results and allows request, response, and digital silent time slots to be coordinated and configured within the same scheduling cycle.

[0152] In some examples of embodiments of this application, before the number of time slots in step S340 is mapped to the time slot time axis of the next scheduling cycle, a secondary fine-tuning operation of the allocation results under fairness and energy consumption constraints can also be performed.

[0153] First, based on the number of request time slots and response time slots of each node, the total number of business interaction time slots used to characterize the resource occupancy of node business interaction is determined. Then, assuming there is an effective allocation of business interaction time slots across the entire network, the system-level fairness of the current allocation scheme is calculated according to the Jain fairness index.

[0154] For example, node The total number of business interaction time slots can be expressed as In order to satisfy In this case, the system-level fairness of the current allocation scheme It can be calculated using the Jain Fairness Index formula:

[0155] Equation (19)

[0156] In the formula, For nodes The total number of business interaction time slots; For nodes The number of request time slots; For nodes The number of response time slots; The total number of nodes participating in the scheduling; For system-level fairness. Assuming there are valid business interaction time slots allocated across the entire network, The value of can be used to characterize the degree of balance in the distribution of business interaction time slots among nodes. The closer the value is to 1, the more balanced the resource sharing of business interaction among nodes.

[0157] In this embodiment, the sum of the request time slots and the response time slots is used as the node's service interaction resource occupancy because both request and response time slots occupy radio interface resources and correspond to the node's actual radio frequency transmission or reception requirements. By introducing... The judgment conditions can avoid calculating fairness when no business interaction time slots are allocated across the entire network, thereby avoiding denominator anomalies in the Jain fairness index calculation.

[0158] Then, at the system-level fairness When the battery level is below the preset fair threshold, the remaining battery power of each node is obtained, and based on the deviation of the total number of business interaction time slots of each node from the average number of business interaction time slots of the entire network, each node is divided into high-occupancy nodes and low-occupancy nodes.

[0159] For example, the average number of business interaction time slots across the entire network It can be determined using the following formula:

[0160] Equation (20)

[0161] In the formula, This represents the average number of business interaction time slots across the entire network. In some examples, the total number of business interaction time slots can be greater than... The node is identified as the high-occupancy node, and the total number of business interaction time slots is less than The node is identified as a low-occupancy node. Alternatively, a preset deviation tolerance range can be set in actual implementation, allowing a node to be considered a low-occupancy node only when its total number of business interaction time slots is relatively low. Only when the deviation exceeds the tolerance range will the node be classified as a high-occupancy node or a low-occupancy node, thereby avoiding unnecessary secondary fine-tuning due to minor differences.

[0162] Subsequently, under the condition of satisfying the mapping protocol request-response association constraints, the associated pairing combination of request time slots and response time slots is used as the minimum replacement unit, and the business interaction time slot quota of high-occupancy nodes is transferred to low-occupancy nodes through an iterative exchange algorithm.

[0163] In this embodiment of the application, the associated pairing combination refers to a set of business interaction time slots that satisfy the request-response association constraint conditions of the mapping protocol. The combination may include at least one request time slot and a response time slot that matches the request time slot. The number of response time slots is determined according to the preset response coefficient or the request-response association constraint conditions. Using the associated pairing combination as the smallest replacement unit can avoid destroying the interaction integrity of the mapping protocol by only transferring the request time slot or only transferring the response time slot.

[0164] In practice, the iterative exchange algorithm can select transferable associated pairings from high-occupancy nodes and transfer the corresponding business interaction time slot quotas to low-occupancy nodes. After each transfer, the system-level fairness can be recalculated, and it can be determined whether the system-level fairness has reached a preset fairness threshold. If it has not reached the threshold, the next round of transfer continues; if it has, the transfer stops. In this way, the fairness of the initial allocation results can be corrected without violating the request-response association constraints.

[0165] During the execution of the iterative exchange algorithm, the eligibility of nodes to receive compensation is restricted based on the current remaining battery power. Under the premise of satisfying the mapping protocol request-response association constraint conditions and the existence of adjustable association pairings, at least one association pairing is selected as the pairing to be adjusted from the request and response time slots already allocated to nodes with power below the preset power safety threshold. The pairing to be adjusted is then adjusted to a digital silent time slot.

[0166] Specifically, when the remaining battery power of a node is lower than a preset power safety threshold, the node can be restricted from being used as a compensation receiving node to avoid increasing its energy consumption burden due to receiving additional service interaction time slots. For the service interaction time slots already allocated to the low-power node, if there are adjustable associated pairings, at least one associated pairing can be selected, provided that the mapping protocol request-response association constraints are met, and the request and response time slots in the pairing to be adjusted can be adjusted to digital silent time slots. Accordingly, the number of request and response time slots of the node can be reduced according to the number of request and response time slots included in the selected pairing to be adjusted, and the number of digital silent time slots can be increased according to the number of adjusted service time slots to maintain the consistency of the total number of time slots.

[0167] This allows the system to consider the remaining energy state of nodes simultaneously during the fairness fine-tuning process. For nodes with low energy levels, the system no longer compensates them for service slots solely based on fairness metrics. Instead, it limits their ability to undertake more communication tasks based on their energy consumption status and converts adjustable service slots into digital silent slots. This reduces the active communication burden on low-energy nodes in the next scheduling cycle and ensures that the allocation results after the secondary fine-tuning are constrained by both fairness and energy consumption status.

[0168] Furthermore, when the system-level fairness is not lower than the preset fairness threshold, or when the preset iteration stopping condition is met, the iterative exchange algorithm is stopped, and the number of request time slots for each node after the second fine-tuning is calculated. Number of response time slots and the number of digital silent time slots This serves as the final allocation result of the time slot time axis mapped to the next scheduling cycle.

[0169] In some examples, preset iteration stopping conditions may include reaching a preset maximum number of iterations, the absence of transferable associated pairings, the possibility that continuing to transfer would violate the mapping protocol request-response association constraints, or the possibility that continuing to transfer would cause the node's energy consumption constraints to be unmet. By setting system-level fairness thresholds and iteration stopping conditions, unbounded operation of the secondary fine-tuning process can be avoided, and the output results can still satisfy the request-response association constraints, non-negative integer constraints, and total time slot number constraints.

[0170] This application's embodiments enable the correction of differences in resource occupancy for service interactions between nodes based on the initial time slot allocation results, incorporating the limitation of the node's current remaining battery power during the correction process. The Jain fairness index measures the degree of balanced distribution of service interaction time slots among nodes, the associated pairing combination maintains the coordination between request and response time slots, and the power safety threshold limits nodes with low power from accepting additional service time slots or maintaining excessively high service interaction burdens. Therefore, the final allocation result exhibits better coordination between fairness, request-response association integrity, and node energy consumption status.

[0171] In some examples of embodiments of this application, after the target time slot scheduling table is broadcast to each node in step S160, and after each node completes the data interaction for the mapping service in the next scheduling cycle, the system can also execute the parameter adaptive feedback closed-loop step.

[0172] First, collect the actual execution statistics of each node in the next scheduling cycle. The actual execution statistics include the physical layer measured posterior interference intensity, actual mapped load observation, actual service throughput, and measured queuing delay.

[0173] In some implementations, the network coordinator node can receive actual execution statistics from each node after the data interaction of the next scheduling cycle has ended, via control channels, status return frames, or periodic status reporting messages. Specifically, the posterior interference intensity can be obtained by the node during the execution cycle through radio frequency sampling or channel energy detection; the actual mapping load observation can be obtained by the mapping protocol queue manager based on the actual arriving or processed request and response data packets; the actual traffic throughput can be used to characterize the amount of data interaction successfully completed within the scheduling cycle; and the measured queuing delay can be used to characterize the waiting time for service data from entering the queue to completing transmission or response. By collecting these actual execution statistics, the true operating status after scheduling execution can be obtained, which can be used to evaluate the deviation between the predicted results and scheduling parameters and the actual environment.

[0174] Subsequently, the interference prediction error between the posterior interference intensity and the corresponding predicted interference intensity, and the load prediction error between the actual mapped load observation and the corresponding overall mapped load prediction are calculated. Specifically, for nodes... Nodes can During the scheduling period The posterior interference intensity obtained from the internal measurements is compared with the previously predicted interference intensity to obtain the interference prediction error; and the node... During the scheduling period The observed actual mapped load is compared with the previously predicted total mapped load to obtain the load prediction error, which reflects the degree of deviation of the prediction model from the current operating environment.

[0175] Then, based on the actual business throughput of the entire network, the measured queuing delay, and the actual digital silent ratio calculated from the number of digital silent time slots allocated to each node, the performance loss of the current scheduling scheme relative to the preset multi-dimensional performance evaluation benchmark is evaluated.

[0176] Specifically, the preset multi-dimensional performance evaluation benchmarks can include throughput reference values, queuing delay reference values, and digital silence ratio reference ranges. They can also be configured as other benchmark indicators that characterize service quality and energy consumption status, depending on different application scenarios. In some examples, the performance penalty can be determined comprehensively based on the degree to which the actual throughput is lower than the throughput reference value, the degree to which the measured queuing delay is higher than the delay reference value, and the degree to which the actual digital silence ratio deviates from the reference range.

[0177] For example, performance loss It can be expressed in the following form:

[0178] Equation (21)

[0179] In the formula, This represents the actual service throughput within the current scheduling period; This indicates the preset throughput reference value; This represents the measured queuing delay within the current scheduling cycle; Indicates the preset delay reference value; This represents the actual digital silence ratio calculated based on the number of digital silence time slots allocated to each node. This indicates the preset digital silent ratio reference value; , , These represent the weights for throughput, latency, and quietness ratio, respectively. , , These are preset minimum constants used to prevent abnormal denominators.

[0180] It should be understood that the expression of the above formula (21) is only used as an example. In actual implementation, other performance loss calculation methods that can characterize the degree of deviation of throughput, latency and silent state can also be used.

[0181] Then, a comprehensive loss function is constructed, which is composed of interference prediction error, load prediction error and performance loss degree weighted together.

[0182] In some implementations, the comprehensive loss function It can be constructed using the following formula:

[0183] Equation (22)

[0184] In the formula, This represents the overall loss function value; This represents the number of nodes participating in the statistics. For nodes During the scheduling period Measured posterior interference strength of the inner physical layer; For nodes The corresponding predicted interference intensity value; For nodes During the scheduling period Actual mapped load observations within; For nodes The corresponding overall mapped load forecast value; This refers to the performance penalty. , , These are the weighting coefficients corresponding to interference prediction error, load prediction error, and performance loss, respectively. Through this comprehensive loss function, prediction deviation and scheduling execution effect can be unified into a single evaluation metric, facilitating adjustments to the silent ratio calculation parameters.

[0185] Next, the negative gradient direction of the comprehensive loss function relative to the silence sensitivity coefficient and nonlinear adjustment index used to calculate the initial digital silence ratio is extracted, and the silence sensitivity coefficient and nonlinear adjustment index are updated adaptively with a preset learning rate.

[0186] For example, the silent sensitivity coefficient and nonlinear adjustment index Adaptive updates can be performed using the following formula:

[0187] Equation (23)

[0188] Equation (24)

[0189] In the formula, This represents the updated silent sensitivity coefficient; This represents the updated nonlinear adjustment index; and These represent the silent sensitivity coefficient and the nonlinear adjustment index before the update, respectively; and These are the preset learning rates for the corresponding parameters; and These are the partial derivatives of the comprehensive loss function with respect to the corresponding parameters.

[0190] In some implementations, if the comprehensive loss function is relative to and The relationship can be represented by the silent ratio calculation process after continuous relaxation, and the corresponding gradient can be obtained by chain rule differentiation. If the target time slot allocation process involves integerization or discretization operations and is inconvenient to directly differentiate, the negative gradient direction can also be estimated based on continuous relaxation variables, approximate surrogate functions, or finite difference methods. Therefore, the silent sensitivity coefficient and nonlinear adjustment index can be gradually corrected based on the error feedback generated by the actual execution statistical indicators.

[0191] Furthermore, boundary projection limiting processing is performed on the updated silent sensitivity coefficient and nonlinear adjustment index to keep them within their respective preset value ranges, and the processed silent sensitivity coefficient is then... and nonlinear adjustment index Used for calculating the target digital silent ratio in subsequent scheduling cycles.

[0192] In some examples, boundary projection limiting can be achieved using the following formula:

[0193] Equation (25)

[0194] Equation (26)

[0195] In the formula, and These are the preset lower and upper bounds of the silent sensitivity coefficient, respectively; and These are the preset lower and upper bounds of the nonlinear adjustment index, respectively. It can be set to a value greater than zero to ensure that the nonlinear adjustment exponent meets the validity requirements of the power function mapping. Boundary projection limiting can prevent parameters from exceeding the preset effective range after updating, reducing the risk of numerical out-of-bounds errors during gradient updates.

[0196] This application's embodiments utilize actual performance statistics to correct the calculation parameters for the target digital silence ratio. Interference prediction error and load prediction error are used to reflect the deviation between the prediction results and the actual observation state. The performance loss degree is used to reflect the deviation of throughput, queuing delay, and the actual digital silence ratio from the evaluation benchmark. A comprehensive loss function is used to uniformly measure the above deviations. Based on parameter updates in the negative gradient direction and boundary projection limiting processing, the silence sensitivity coefficient and nonlinear adjustment index can be gradually adjusted within a preset value range, thereby improving the adaptability of the target digital silence ratio calculation process to time-varying channel states and dynamic traffic loads.

[0197] Figure 4 This paper illustrates a schematic diagram of the system operation mechanism of an example of a node digital silent scheduling method based on the mapping protocol interaction time slot allocation according to an embodiment of this application.

[0198] like Figure 4 As shown, the system operation mechanism can include logical components such as multi-dimensional data acquisition, coupled prediction and silent ratio calculation, time slot joint scheduling, and feedback learning. The multi-dimensional data acquisition component can include mapping queue monitoring, channel energy detection, and neighbor status monitoring. Mapping queue monitoring is used to acquire the node's own mapping request load data and mapping response load data; channel energy detection is used to acquire the current channel noise, energy peak, or interference occupancy status; and neighbor status monitoring is used to acquire the time slot occupancy, queue status, or historical digital silent status of neighboring nodes. These data collectively constitute the node's multi-dimensional operational status data and serve as input for subsequent prediction and scheduling processing.

[0199] In the core prediction and silence ratio calculation section, multi-dimensional operational status data is input into the MICM (Mapping-Interference Coupling Model) coupled prediction model. The MICM model uses node service load and channel environment status to perform coupled prediction of mapping load and interference intensity within a future prediction window, obtaining predicted values ​​for both. The prediction results are then input into the IWASR (Interference-Weighted Adaptive Silence Ratio) adaptive silence ratio calculation module. The IWASR model calculates the target digital silence ratio for each node in the next scheduling cycle based on the predicted mapping load and interference intensity values.

[0200] In the joint time slot scheduling section, the WMOL (Weighted Multi-Objective Lagrangian) time slot scheduler determines the demand for request time slots, response time slots, and digital silence time slots based on the predicted mapping request load, predicted mapping response load, and target digital silence ratio of each node, and performs joint allocation under the constraint of the total number of time slots in the next scheduling cycle. The fairness constraint manager can perform constraint correction or secondary fine-tuning on the allocation results based on the fairness of the distribution of service interaction time slots between nodes and the energy status of the nodes. Based on the joint scheduling results, the system outputs the service and silent time slot allocation results and generates a digital silent scheduling table to instruct each node to perform corresponding actions within the corresponding request time slot, response time slot, and digital silent time slot. The feedback learning module is used to receive actual execution statistics such as posterior interference intensity, actual throughput, actual mapped load observations, and queuing delay after scheduling execution, and to update the prediction parameters of the MICM coupled prediction model and / or the silent ratio calculation parameters of the IWASR adaptive silent ratio calculation module to improve the system's adaptability to dynamic wireless environments and changes in service load.

[0201] To objectively verify the effectiveness and boundary performance of the node digital silent scheduling method with mapping protocol interaction time slot allocation provided in this application embodiment under dynamic heterogeneous interference, a system-level evaluation can be performed based on the NS-3 network simulation platform. Specifically, the simulation scenario can be set as an Industrial Internet of Things (IIoT) environment within a 100×100 square meter area, with the network consisting of one coordinator node and 50 randomly distributed child nodes. Node communication is based on the IEEE 802.15.4 physical layer standard (using the 2.4 GHz band, with a transmission rate of 250 kbps). To simulate sudden cross-system interference in a real environment, a sudden interference source based on a Markov chain is introduced into the simulation environment to simulate coexisting large-volume Wi-Fi data streams, and the interference intensity is set to dynamically jump between -90 dBm and -60 dBm.

[0202] Meanwhile, to fully demonstrate the technical advantages of the method in the embodiments of this application, three classic schemes were selected as comparative baselines in the simulation evaluation: the first is the static TDMA (Time Division Multiple Access) scheme, which adopts a fixed-length superframe structure and a fixed request-response time slot ratio, and does not have an interference silencing mechanism; the second is the CSMA / CA (Carrier Sense Multiple Access with Collision Avoidance) scheme, which is a standard distributed random backoff mechanism that mainly relies on carrier sensing to avoid collisions; the third is the global silencing BB-MAC (Blank Burst Medium Access Control) scheme based on the Blank Burst algorithm, which broadcasts a control frame when the coordinator detects strong interference, causing all nodes in the network to synchronously enter a sleep silencing state. By comparing the running results of the method in the embodiments of this application with the above three baseline schemes in the same simulation environment, the comprehensive effectiveness of this application in ensuring throughput and reducing queuing latency can be verified.

[0203] Figure 5 This diagram illustrates a comparative simulation of network throughput and robustness under burst interference using different methods. The horizontal axis represents the interference duty cycle of the external burst interference, ranging from 10% to 70%; the vertical axis represents the normalized throughput of the network. Figure 5The paper compares the normalized throughput performance of the method in the embodiments of this application, namely MPITS-DS (Mapping Protocol Interaction Time-Slot based Digital SilenceScheduling), with the CSMA / CA scheme and the global silent scheme based on Blank Burst, namely BB-MAC, under different external interference duty cycles using line graphs with error bars.

[0204] like Figure 5 As shown, when the interference duty cycle is low, both the CSMA / CA scheme and the method in this embodiment can maintain a high normalized throughput. However, as the interference duty cycle gradually increases, the throughput of the CSMA / CA scheme decreases significantly. This is because under strong burst interference conditions, carrier sensing and random backoff processes are easily affected by channel occupancy and collision retransmission, resulting in fewer opportunities for effective service interaction. The BB-MAC scheme, due to its large-scale silence control, maintains a relatively low normalized throughput overall, indicating that while unified silence can reduce some collisions, it may also cause weakly interfered nodes to lose available transmission opportunities, thus limiting the overall network throughput performance.

[0205] In contrast, the MPITS-DS method in this application exhibits a more gradual decrease in throughput as interference duty cycle increases, maintaining a high normalized throughput in the medium-to-high interference range. This result is consistent with the scheduling logic in this application, which uses coupled prediction based on mapped load and interference intensity, and calculates the target digital silence ratio based on node differences. By configuring corresponding digital silence time slots for nodes with strong interference or low communication benefits, while reserving request and response time slots for nodes that still have effective interaction needs, the method in this application can maintain good service interaction capabilities under sudden interference conditions. Furthermore, Figure 5 The error bars in the figure show that the throughput fluctuation of the method in this application embodiment is generally within a small range under high interference duty cycle, indicating that it has good throughput stability and time slot scheduling robustness under dynamic interference conditions.

[0206] Figure 6 This diagram illustrates a comparative simulation of different methods in terms of the cumulative distribution boundary of interaction delay and tail delay suppression. The horizontal axis represents the end-to-end delay in milliseconds (ms) on a logarithmic scale; the vertical axis represents the cumulative probability of the end-to-end delay. Figure 6 By using the cumulative distribution function curve, the differences in end-to-end delay distribution between the MPITS-DS method of this application embodiment and the static TDMA scheme and the CSMA / CA scheme are compared.

[0207] like Figure 6 As shown, the CDF curve of the static TDMA scheme rises in a step-like manner, with a long tail in the high-latency range, indicating that the fixed time slot allocation is prone to generating long waiting times in scenarios of sudden requests or response backlogs. The CSMA / CA scheme has a certain cumulative probability in the lower latency range, but as node concurrency and interference increase, its curve still extends towards higher latency, indicating that random contention and backoff waiting increase the uncertainty of end-to-end latency.

[0208] In contrast, the CDF curve of the MPITS-DS method in this application embodiment rises faster and reaches a cumulative probability of 95% within approximately 80ms, indicating that most of its interactive services can be completed within a shorter latency range. This result is consistent with the scheduling logic in this application embodiment, which calculates the digital silence ratio based on mapping load and interference intensity prediction and considers queuing delay costs in joint time slot allocation. By reserving necessary request and response time slots for high-load nodes and arranging digital silence for interfered or low-yield communication periods, the method in this application embodiment can reduce the probability of long-tail latency and improve latency stability during mapping protocol interaction.

[0209] Figure 7 A comparative simulation diagram illustrating the trade-off between control signaling overhead and node power consumption for different methods is presented in a dual-axis graph. The left vertical axis and bar chart represent the MAC layer control signaling overhead, expressed in bytes per superframe. The right vertical axis and line chart represent the average node power consumption, expressed in joules per 1KB of successfully transmitted data. The horizontal axis shows the performance of the static TDMA scheme, the MPITS-DS method of this application embodiment in the initial stage, and the performance of the MPITS-DS method of this application embodiment after feedback learning convergence.

[0210] like Figure 7 As shown, compared to the static TDMA scheme, the MPITS-DS method in this embodiment requires multi-dimensional operational status data reporting and the distribution of scheduling information including request slots, response slots, and digital silence slot configurations. Therefore, the MAC layer control signaling overhead increases, approximately 25% in the example shown in the figure. This result indicates that while achieving stronger dynamic scheduling capabilities, the method in this embodiment introduces certain control information overhead. Furthermore, the figure also shows that the feedback learning convergence time is approximately 2 to 3 superframes, indicating that the parameter adaptive feedback processing in this embodiment can adjust the silence ratio calculation parameters or prediction parameters within several scheduling cycles.

[0211] From the perspective of average node energy consumption, the MPITS-DS method in this application already achieves lower average node energy consumption in the initial stage than the static TDMA scheme, and further decreases after feedback learning convergence. This is consistent with the processing logic in this application, which corrects scheduling parameters based on actual execution statistics and reduces invalid RF transmissions and conflict retransmissions through digital silent time slots. Based on unit energy consumption transmission efficiency calculations, the example in the figure shows an energy efficiency improvement of approximately 18%; from the perspective of unit data transmission energy consumption, this manifests as a decrease in average node energy consumption. Therefore, the method in this application can improve node energy consumption performance at the cost of certain control signaling overhead, achieving a good trade-off between control overhead and energy efficiency.

[0212] In summary, this application addresses the problems of coarse-grained silence control, insufficient adaptability of time slot allocation, and inadequate utilization of service load and channel interference states in current MAC layer time slot scheduling and digital silence schemes. It provides a node digital silence scheduling method based on time slot allocation through mapping protocol interaction. This method introduces a mapping-interference coupling prediction model and an adaptive silence ratio calculation process to jointly predict interference intensity and mapped service load within the future prediction window, thereby determining the target digital silence ratio for different nodes. Simultaneously, a weighted multi-objective scheduling objective function incorporates network throughput gains, queuing delay costs, and silence execution deviations into a unified scheduling evaluation, and corrects the allocation results based on fairness and energy consumption constraints to improve the coordination and energy consumption adaptability of resource allocation among nodes. Furthermore, through a parameter adaptive feedback closed-loop step, relevant prediction parameters or silence ratio calculation parameters are updated based on actual execution statistical indicators, enabling the system to continuously adjust scheduling parameters according to dynamic wireless environment and service load changes.

[0213] Simulation results show that, under heterogeneous burst interference scenarios, the method of this application can maintain a high normalized throughput and reduce the long-tail phenomenon in the end-to-end delay distribution when the interference duty cycle increases; under the example simulation conditions, its 95th percentile delay is controlled within approximately 80 ms. Furthermore, the method of this application achieves lower average node energy consumption by increasing certain control signaling overhead and introducing a feedback learning convergence process; calculated based on unit energy consumption transmission efficiency, the example results show an energy efficiency improvement of approximately 18%. Therefore, the method of this application can achieve a good comprehensive balance between throughput, latency, energy consumption, and control overhead, and is suitable for wireless communication scenarios with dynamic loads and heterogeneous interference, such as industrial IoT, drone swarms, and smart manufacturing.

[0214] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of combined actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Secondly, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application. In the above embodiments, the descriptions of each embodiment have their own emphasis; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0215] Figure 8 A structural block diagram of an example of a node digital silent scheduling system with mapping protocol interaction time slot allocation according to an embodiment of this application is shown.

[0216] like Figure 8 As shown, the node digital silent scheduling system 800 with mapping protocol interaction time slot allocation includes a multi-dimensional operation status acquisition unit 810, a mapping interference coupling prediction unit 820, a target silent ratio calculation unit 830, a time slot demand determination unit 840, a time slot scheduling table construction unit 850, and a time slot scheduling table broadcasting unit 860.

[0217] The multi-dimensional operation status acquisition unit 810 is used to acquire multi-dimensional operation status data collected by each node in the wireless communication network during the current scheduling period; the multi-dimensional operation status data includes at least the node's own mapping service load data, the channel environment awareness data of the channel it is in, and the neighbor status monitoring data, and the mapping service load data includes mapping request load data and mapping response load data.

[0218] The mapping interference coupling prediction unit 820 is used to perform coupled prediction of the mapping load and interference intensity of each node within the future prediction window based on the multi-dimensional operating status data, so as to obtain the mapping load prediction value and interference intensity prediction value of each node; wherein, the mapping load prediction value includes the mapping request load prediction value and the mapping response load prediction value.

[0219] The target silence ratio calculation unit 830 is used to calculate the target digital silence ratio of each node based on the predicted value of the mapped load and the predicted value of the interference intensity of each node; the target digital silence ratio is used to characterize the proportion of time slots in which the corresponding node is in digital silence in the next scheduling cycle.

[0220] The time slot demand determination unit 840 is used to determine the request time slot demand, response time slot demand, and digital silence time slot demand of each node in the next scheduling cycle based on the mapping request load prediction value, the mapping response load prediction value, and the target digital silence ratio of each node; wherein, the response time slot demand is determined based on a preset mapping protocol request-response association constraint condition, and the digital silence time slot demand is determined based on a preset total number of time slots in the next scheduling cycle.

[0221] The time slot scheduling table construction unit 850 is used to jointly allocate the request time slot demand, the response time slot demand, and the digital silent time slot demand under the constraint of the total number of time slots in the next scheduling period, so as to obtain the number of request time slots, the number of response time slots, the number of digital silent time slots allocated to each node in the next scheduling period and the corresponding time slot positions, and construct the target time slot scheduling table according to the allocation results.

[0222] The time slot scheduling table broadcasting unit 860 is used to broadcast the target time slot scheduling table to each node, so as to instruct the corresponding node to stop radio frequency transmission and enter the digital silence state within the digital silence time slot position indicated by the target time slot scheduling table, and to perform data interaction of the mapping service within the request time slot position and response time slot position indicated by the target time slot scheduling table.

[0223] In some embodiments, this application provides a non-volatile computer-readable storage medium storing one or more programs including execution instructions. The execution instructions can be read and executed by electronic devices (including but not limited to computers, servers, or network devices) to perform the steps of the node digital silent scheduling method for mapping protocol interaction time slot allocation described above in this application.

[0224] In some embodiments, this application also provides a computer program product, the computer program product including a computer program stored on a non-volatile computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the steps of the node digital silent scheduling method for the interaction time slot allocation of any of the above mapping protocols.

[0225] In some embodiments, this application also provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of a node digital silent scheduling method for mapping protocol interaction time slot allocation.

[0226] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0227] The electronic devices in this application can exist in various forms, including but not limited to: mobile communication devices, ultra-mobile personal computer devices, portable entertainment devices, or other airborne electronic devices with data interaction functions.

[0228] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0229] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A node digital silent scheduling method based on the time slot allocation of a mapping protocol interaction, characterized in that, The method includes: The system acquires multi-dimensional operational status data collected by each node in the wireless communication network during the current scheduling period. The multi-dimensional operational status data includes at least the node's own mapping service load data, channel environment awareness data of the channel it is on, and neighbor status monitoring data. The mapping service load data includes mapping request load data and mapping response load data. Based on the multidimensional operating status data, the mapped load and interference intensity of each node within the future prediction window are coupled and predicted to obtain the predicted values ​​of the mapped load and interference intensity of each node; wherein, the predicted value of the mapped load includes the predicted value of the mapped request load and the predicted value of the mapped response load. Based on the predicted mapped load and the predicted interference intensity of each node, the target digital silence ratio of each node is calculated; the target digital silence ratio is used to characterize the proportion of time slots in which the corresponding node is in digital silence in the next scheduling cycle. Based on the predicted mapping request load, the predicted mapping response load, and the target digital silence ratio of each node, the request time slot demand, response time slot demand, and digital silence time slot demand of each node in the next scheduling cycle are determined respectively; wherein, the response time slot demand is determined based on the preset mapping protocol request-response association constraint, and the digital silence time slot demand is determined based on the preset total number of time slots in the next scheduling cycle. Under the constraint of the total number of time slots in the next scheduling cycle, the demand for request time slots, the demand for response time slots, and the demand for digital silent time slots are jointly allocated to obtain the number of request time slots, the number of response time slots, the number of digital silent time slots allocated to each node in the next scheduling cycle, as well as the corresponding time slot positions. A target time slot scheduling table is then constructed based on the allocation results. The target time slot schedule is broadcast to each node to instruct the corresponding node to stop radio frequency transmission and enter digital silence state within the digital silence time slot position indicated by the target time slot schedule, and to perform data interaction of the mapping service within the request time slot position and response time slot position indicated by the target time slot schedule; The step of calculating the target digital silence ratio of each node based on the predicted mapped load value and the predicted interference intensity value of each node includes: For each node, the relative ratio between the predicted interference intensity and the predicted overall mapped load is calculated, and a minimal constant is introduced to smooth and correct the relative ratio to prevent denominator anomalies, thus obtaining the relative index of interference load. Using the silence sensitivity coefficient as a linear weighting coefficient, combined with a nonlinear adjustment index for adjusting the change amplitude of the initial digital silence ratio under strong interference conditions, the interference load relative index is mapped by a power function to generate the initial digital silence ratio of the corresponding node in the next scheduling cycle. The initial digital silence ratio and the historical digital silence ratio of the corresponding node in the current scheduling cycle are weighted and fused using a preset inertial smoothing factor in order to suppress frequent jumps in the target digital silence ratio between adjacent scheduling cycles. By using preset upper and lower limit amplitude rules, the weighted fusion result is constrained to the effective ratio range, generating the target digital silent ratio of the corresponding node.

2. The method according to claim 1, characterized in that, The acquisition of multi-dimensional operational status data collected by each node in the wireless communication network during the current scheduling period includes: For each node, within the current scheduling period, the number of newly arrived request packets and the number of response packets in the pending-send buffer queue are counted through the mapping protocol queue manager to obtain mapping request load data and mapping response load data respectively; and based on the duration of the current scheduling period, the mapping request arrival rate of the node within the current scheduling period is quantitatively extracted; the mapping request load data, the mapping response load data, and the mapping request arrival rate are used as the node's own mapping service load data; The radio frequency energy detection module of the node samples the background noise power of the current channel and captures the energy peak value of the received signal strength indication value exceeding the preset coexistence interference threshold within a preset time window. This is used to statistically analyze the occurrence frequency, cumulative duration and interference duty cycle of external sudden interference events. The background noise power, occurrence frequency, cumulative duration and interference duty cycle are then aggregated into channel environment perception data. Based on the listening interface of the node, the control beacons broadcast by the neighboring nodes are listened to within the preset shared listening time slot, and the historical time slot occupancy ratio, remaining queue depth and historical digital silence ratio of the neighboring nodes are parsed and extracted from the control beacons as neighbor status listening data. The mapping service load data, the channel environment awareness data, and the neighbor status monitoring data are associated and encapsulated according to the node identifier and the current scheduling cycle identifier to obtain the multi-dimensional operating status data collected by each node in the current scheduling cycle. The joint state change of the mapped service load data and the channel environment awareness data relative to the previous scheduling period is calculated. When it is determined that the joint state change exceeds the preset sparse reporting threshold, differential compression encoding is performed on the locally cached multidimensional operating state data, and the compressed data is uploaded to the network coordinator node.

3. The method according to claim 2, characterized in that, Based on the multidimensional operating status data, the mapped load and interference intensity of each node within the future prediction window are coupled and predicted to obtain the predicted values ​​of the mapped load and interference intensity of each node, including: The background noise floor power, frequency of occurrence, cumulative duration and interference duty cycle of external sudden interference events are extracted from the multidimensional operational status data, as well as the mapping request load data, mapping response load data and mapping request arrival rate from the mapping service load data. The extracted data are normalized and normalized current interference intensity index, normalized current mapping load index and normalized current request arrival rate are obtained through a preset feature weighted fusion mechanism. A mapping-interference coupling prediction model is constructed and run. The mapping-interference coupling prediction model includes an interference prediction branch and a load prediction branch. The interference prediction branch calculates the predicted interference intensity for the next scheduling period based on the environmental noise floor bias, the autoregressive effect of historical interference, the linear effect of historical mapped load, and the nonlinear cross-coupling effect between mapped load and external interference. The load prediction branch calculates the predicted overall mapped load for the next scheduling period based on the base load level, the autoregressive effect of historical load, and the request arrival rate-driven effect. Based on the actual business distribution data of the nodes in the historical time series, the historical interaction distribution coefficient is determined, and the overall mapping load prediction value is split into the mapping request load prediction value and the mapping response load prediction value based on the historical interaction distribution coefficient. The mapping request load prediction value and the mapping response load prediction value are used as the mapping load prediction value of each node. Based on the prediction residual between the actual observed value and the historical predicted value in the previous prediction window, a local weighted regression algorithm combined with a recursive least squares method with a forgetting factor is used to iteratively update the weight parameters used to calculate the predicted value of the disturbance intensity and the predicted value of the overall mapping load online.

4. The method according to claim 1, characterized in that, Under the constraint of the total number of time slots in the next scheduling cycle, the requested time slot demand, the response time slot demand, and the digital silent time slot demand are jointly allocated to obtain the number of requested time slots, the number of response time slots, the number of digital silent time slots allocated to each node in the next scheduling cycle, and the corresponding time slot positions. A target time slot scheduling table is then constructed based on the allocation results, including: A weighted multi-objective scheduling objective function is constructed to comprehensively optimize network throughput benefits, queuing delay costs, and silent execution deviations. The weighted multi-objective scheduling objective function takes the marginal diminishing utility benefit corresponding to communication throughput as a positive gain term, takes the linear queuing delay penalty caused by digital silence and positively correlated with the predicted value of the mapping request load as a first negative cost term, and takes the quadratic deviation penalty of the actual number of digital silence time slots deviating from the digital silence time slot demand as a second negative cost term. Obtain a multi-dimensional scheduling constraint set, which includes: a total number of time slots constraint that the sum of the number of request time slots, the number of response time slots and the number of digital silent time slots allocated to each node in the entire network does not exceed the preset total number of time slots in the next scheduling cycle; a request-response association constraint that limits the number of response time slots allocated to each node to not be lower than the theoretical lower limit determined based on its number of request time slots and the preset response coefficient; a requirement upper limit constraint that limits the number of request time slots and the number of response time slots to not exceed the corresponding requirement amount; and a time slot non-negative integer constraint that limits the allocation amount of each time slot to a non-negative integer. Under the constraints of the multidimensional scheduling constraint set, an augmented scheduling objective function is constructed by introducing Lagrange multiplier vectors, and the augmented scheduling objective function is solved by the projection gradient iteration method. The goal is to maximize the comprehensive scheduling utility value corresponding to the weighted multi-objective scheduling objective function, and output the number of request slots, response slots and digital silence slots of each node that meet the convergence condition. The number of request time slots, the number of response time slots, and the number of digital silent time slots of each node are mapped to the time slot time axis of the next scheduling cycle to determine the request time slot position, response time slot position, and digital silent time slot position corresponding to each node. Based on the request time slot position, the response time slot position, the digital silent time slot position, and the corresponding node identifier, a target time slot scheduling table is generated.

5. The method according to claim 4, characterized in that, Before mapping the number of request time slots, the number of response time slots, and the number of digital silent time slots of each output node to the time slot time axis of the next scheduling cycle, the method further includes performing a secondary fine-tuning operation on the allocation results under fairness and energy consumption constraints. The secondary fine-tuning operation specifically includes: The total number of business interaction time slots used to characterize the resource occupancy of each node is determined based on the number of request time slots and response time slots of each node. Under the condition that there is an effective allocation of business interaction time slots in the whole network, the system-level fairness of the current allocation scheme is calculated according to the Jain fairness index. When the system-level fairness is lower than the preset fairness threshold, the current remaining battery power of each node is obtained, and based on the deviation of the total number of business interaction time slots of each node from the average number of business interaction time slots of the entire network, each node is divided into high-occupancy nodes and low-occupancy nodes. Under the condition that the mapping protocol request-response association constraint is met, the associated pairing combination of request time slots and response time slots is used as the minimum replacement unit, and a partial service interaction time slot quota of the high-occupancy node is transferred to the low-occupancy node through an iterative exchange algorithm. During the execution of the iterative exchange algorithm, the eligibility of nodes to receive compensation is restricted based on the current remaining battery power. Under the premise of satisfying the mapping protocol request-response association constraint conditions and the existence of adjustable association pairings, at least one of the association pairings is selected as the pairing to be adjusted from the request and response time slots allocated to nodes below the preset power safety threshold, and the pairing to be adjusted is adjusted to a digital silent time slot. When the system-level fairness is not lower than the preset fairness threshold, or when the preset iteration stop condition is reached, the iterative exchange algorithm is stopped, and the number of request time slots, response time slots, and digital silent time slots of each node after the second fine-tuning are used as the final allocation result of the time slot time axis mapped to the next scheduling cycle.

6. The method according to claim 3, characterized in that, After broadcasting the target time slot scheduling table to each node, and after each node completes the data interaction for the mapping service in the next scheduling cycle, the method further includes an adaptive feedback closed-loop step for execution parameters, which includes: Collect the actual execution statistics of each node in the next scheduling cycle. The actual execution statistics include the physical layer measured posterior interference intensity, actual mapped load observation, actual service throughput, and measured queuing delay. Calculate the interference prediction error between the posterior interference intensity and the corresponding predicted interference intensity, and the load prediction error between the actual mapped load observation and the corresponding predicted overall mapped load. Based on the actual business throughput of the entire network, the measured queuing delay, and the actual digital silent ratio calculated from the number of digital silent time slots allocated to each node, the performance loss of the current scheduling scheme relative to the preset multi-dimensional performance evaluation benchmark is evaluated. Construct a comprehensive loss function that is weighted by the interference prediction error, the load prediction error, and the performance loss. Extract the negative gradient direction of the comprehensive loss function relative to the silence sensitivity coefficient and the nonlinear adjustment index used to calculate the initial digital silence ratio, and adaptively update the silence sensitivity coefficient and the nonlinear adjustment index by combining the preset learning rate. Boundary projection limiting processing is performed on the updated silence sensitivity coefficient and the nonlinear adjustment index to keep the silence sensitivity coefficient and the nonlinear adjustment index within their respective preset value ranges, and the processed silence sensitivity coefficient and the nonlinear adjustment index are used for the calculation of the target digital silence ratio in subsequent scheduling cycles.

7. A node digital silent scheduling system with mapping protocol interaction time slot allocation, characterized in that, The system is used to implement the method as described in any one of claims 1-6; the system comprises: A multi-dimensional operation status acquisition unit is used to acquire multi-dimensional operation status data collected by each node in the wireless communication network during the current scheduling period. The multi-dimensional operation status data includes at least the node's own mapping service load data, the channel environment awareness data of the channel it is on, and the neighbor status monitoring data. The mapping service load data includes mapping request load data and mapping response load data. The mapping interference coupling prediction unit is used to perform coupled prediction of the mapping load and interference intensity of each node within a future prediction window based on the multidimensional operating status data, so as to obtain the mapping load prediction value and interference intensity prediction value of each node; wherein, the mapping load prediction value includes the mapping request load prediction value and the mapping response load prediction value. The target silence ratio calculation unit is used to calculate the target digital silence ratio of each node based on the mapped load prediction value and the interference intensity prediction value of each node; the target digital silence ratio is used to characterize the proportion of time slots in which the corresponding node is in digital silence state in the next scheduling cycle. The time slot demand determination unit is used to determine the request time slot demand, response time slot demand, and digital silence time slot demand of each node in the next scheduling cycle based on the mapping request load prediction value, the mapping response load prediction value, and the target digital silence ratio of each node, respectively; wherein, the response time slot demand is determined based on the preset mapping protocol request-response association constraint condition, and the digital silence time slot demand is determined based on the preset total number of time slots in the next scheduling cycle; The time slot scheduling table construction unit is used to jointly allocate the request time slot demand, the response time slot demand, and the digital silent time slot demand under the constraint of the total number of time slots in the next scheduling period, so as to obtain the number of request time slots, the number of response time slots, the number of digital silent time slots allocated to each node in the next scheduling period and the corresponding time slot positions, and construct the target time slot scheduling table according to the allocation results. The time slot scheduling table broadcasting unit is used to broadcast the target time slot scheduling table to each node, so as to instruct the corresponding node to stop radio frequency transmission and enter the digital silent state within the digital silent time slot position indicated by the target time slot scheduling table, and to perform data interaction of the mapping service within the request time slot position and response time slot position indicated by the target time slot scheduling table.

8. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the method as described in any one of claims 1-6.

9. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-6.

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