A smart running state management system and method of a Bluetooth MESH communication module

Abstract

The application discloses a kind of intelligent running state management system and method of bluetooth MESH communication module, belong to communication technical field, to solve the problem that existing bluetooth MESH network management and control scheme cannot generate accurate whole network state snapshot, cause network topology research and judge distortion, load evaluation error and fault positioning failure, intelligent running state management method of bluetooth MESH communication module is based on whole network unified time reference, using native deterministic time division multiple access scheduling architecture to allocate microsecond level reporting time slot for bluetooth MESH module, through three-target collaborative optimization algorithm dynamically adjusts scheduling parameter, relies on snapshot aggregation model to generate the whole network state snapshot of same time node, finally realizes microsecond level synchronization, zero conflict report, achieves the uncompromising balance of low power consumption, synchronization accuracy and channel unobstructed, the present application can dynamically adapt network state adjustment time slot parameter, module wake-up strategy and channel allocation scheme, synchronously realizes three major goals of low power consumption, high synchronization accuracy and channel unobstructed.

Description

Technical Field

[0001] This invention relates to the field of communication technology, specifically to an intelligent operation status management system and method for a Bluetooth MESH communication module. Background Technology

[0002] Bluetooth MESH networks are widely used in various IoT scenarios due to their advantages of multi-hop networking, wide coverage, low cost and easy deployment. They are used to realize networking communication and status management of multiple modules. In practical applications, the management system needs to collect the operating status of all modules in the network in real time and accurately, while ensuring that the entire network channel is not congested and that the modules are in a low-power operating state.

[0003] Currently, while existing Bluetooth MESH network management solutions are widely used, they still have many technical shortcomings. Specifically: due to the broadcast flooding and multi-hop characteristics of Bluetooth MESH networks, signal redundancy and interference are easily generated during multi-hop forwarding; simultaneously, the management system needs to balance the real-time status of accurate data acquisition, uncongested network channels, and low-power module operation under the overall network module operating status, making it impossible to achieve optimal balance among these three aspects; this results in random delays in the status reporting of different modules, and the reporting time intervals of each module cannot be uniformly synchronized; consequently, the management platform cannot obtain the operating status of all modules in the network at the same time point, i.e., it cannot generate an accurate snapshot of the entire network status; ultimately, this leads to problems such as distorted network topology analysis, incorrect load assessment, and failure in fault location, failing to meet the requirements for accurate network status management in IoT scenarios.

[0004] To address the above issues, an intelligent operation status management system and method for Bluetooth MESH communication modules are proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent operation status management system and method for Bluetooth MESH communication modules. By using this invention, the problems of existing Bluetooth MESH network management and control schemes being unable to generate accurate full-network status snapshots, leading to distorted network topology analysis, incorrect load assessment, and failure in fault location are solved.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent operation status management of a Bluetooth MESH communication module, comprising the following steps: Based on a unified time reference across the entire network, a native deterministic time division multiple access scheduling architecture is adopted to allocate microsecond-level reporting time slots to each module in the Bluetooth MESH network, thereby obtaining a unified microsecond-level timing reference and time slot allocation table across the entire network. Using a unified microsecond-level timing reference and time slot allocation table across the entire network as input, a three-objective collaborative optimization algorithm is run to dynamically adjust time slot parameters, module wake-up strategies, and channel allocation schemes in real time, thereby obtaining the optimal scheduling parameter set adapted to the current network state. Through deep integration of hardware, protocols, and application layers, architectural support is provided for the scheduling execution and algorithm operation of the architecture. The three objectives in the three-objective collaborative optimization algorithm refer to low power consumption, synchronization accuracy, and unobstructed channel. Based on the status data reported by each Bluetooth MESH module in a predetermined time slot, a unified time base snapshot aggregation model is used to generate a network-wide status snapshot at the same time point through a microsecond-level timestamp alignment algorithm, a distributed status aggregation algorithm, and a global status fusion algorithm. By leveraging architectural support, time slot allocation, and three-objective collaborative optimization, microsecond-level synchronization and zero-conflict reporting are achieved, along with an uncompromising balance between low power consumption, synchronization accuracy, and unobstructed channel operation.

[0007] Furthermore, the specific steps for allocating microsecond-level reporting time slots to each module in the Bluetooth MESH network based on a unified time reference across the entire network and employing a native deterministic time division multiple access scheduling architecture to obtain a unified microsecond-level timing reference and time slot allocation table for the entire network are as follows: The management platform distributes a unified time base to all Bluetooth MESH modules through the gateway, and completes the initial alignment of the clocks across the entire network through a unified time synchronization algorithm, thereby obtaining a unified time base across the entire network. Using a unified time base across the entire network as input, each Bluetooth MESH module executes a microsecond-level hardware clock calibration algorithm to obtain a microsecond-level synchronized network-wide module clock; Based on a unified time reference across the entire network, a time slot isolation allocation calculation model is used to allocate microsecond-level reporting time slots to each Bluetooth MESH module, thus obtaining a dedicated microsecond-level reporting time slot for each Bluetooth MESH module; Based on the dedicated microsecond-level reporting time slots of each Bluetooth MESH module, an executable scheduling table is generated using a unified microsecond-level timing benchmark generation model across the entire network and broadcast to the entire network, resulting in a unified microsecond-level timing benchmark and time slot allocation table across the entire network. Using the time slot allocation table, the native deterministic time-division multiple access scheduling algorithm is executed within a dedicated reporting window to report status, resulting in a status data reporting stream.

[0008] Furthermore, the specific steps for allocating microsecond-level reporting time slots to each Bluetooth MESH module based on a unified time reference across the entire network and through a time slot isolation allocation calculation model, thereby obtaining a dedicated microsecond-level reporting time slot for each Bluetooth MESH module, are as follows: Based on a unified time reference across the entire network, a set of basic information about all network nodes and topology is obtained through a network topology awareness and node information collection model. A microsecond-level time slot slicing and isolation partitioning model is adopted to perform microsecond-level slicing and isolation partitioning on the time axis based on the unified time base of the entire network, thereby obtaining a microsecond-level time slot resource pool. The generated microsecond-level time slot resource pool is used as the processing target. Then, through the time slot isolation allocation calculation model, a microsecond-level reporting time slot is allocated to each Bluetooth MESH module, and finally, a dedicated microsecond-level reporting time slot for each Bluetooth MESH module is obtained.

[0009] Furthermore, the specific steps for using the time slot allocation table to execute the native deterministic time-division multiple access scheduling algorithm within a dedicated reporting window to report status and obtain the status data reporting stream are as follows: Each Bluetooth MESH module parses the time slot allocation table using the time slot allocation table parsing and matching model to obtain its own microsecond-level reporting window location information. The system uses a microsecond-level local clock timing calibration algorithm to wait for the time slot window to arrive and generate a trigger signal. It then executes a native deterministic time-division multiple access scheduling algorithm within a dedicated reporting window to complete the time-division reporting of status data. This results in an ordered, staggered sequence of device status reporting data packets, which are then backflowed and regularized to obtain an ordered, conflict-free status data reporting stream across the entire network.

[0010] Furthermore, the specific steps for obtaining the optimal scheduling parameter set adapted to the current network state by using a unified microsecond-level timing reference and time slot allocation table as input, running a three-objective collaborative optimization algorithm, and dynamically adjusting time slot parameters, module wake-up strategies, and channel allocation schemes in real time are as follows: Based on a unified microsecond-level timing reference and time slot allocation table for the entire network, the network status awareness acquisition model collects the network load, channel interference intensity, and module power consumption status in real time to obtain the real-time status parameter set of the entire network. By utilizing the real-time status parameter set of the entire network, a three-objective collaborative optimization algorithm is run to obtain the optimal scheduling strategy set; Using the optimal scheduling strategy set as input, the time slot parameters are dynamically adjusted using a time slot parameter dynamic optimization model to obtain the optimized time slot parameters. The optimized time slot parameters are used to dynamically adjust the module wake-up strategy based on the predictive wake-up control algorithm to obtain the optimized low-power wake-up strategy for the module. Based on the optimized module low-power wake-up strategy, the channel allocation scheme is dynamically adjusted through channel optimization and load balancing allocation model to obtain the optimized channel allocation scheme. The optimized time slot parameters, optimized module low-power wake-up strategy, and optimized channel allocation scheme are encapsulated and distributed through scheduling parameter output model to obtain the optimal scheduling parameter set adapted to the current network state.

[0011] Furthermore, the specific steps for generating a network-wide state snapshot at the same time point based on the state data reported by each Bluetooth MESH module according to a predetermined time slot, using a unified time reference snapshot aggregation model, and employing a microsecond-level timestamp alignment algorithm, a distributed state aggregation algorithm, and a global state fusion algorithm, are as follows: Based on the status data reported by each Bluetooth MESH module in a predetermined time slot, the timestamps of the data reported by each module are aligned and timed across the entire network using a microsecond-level timestamp alignment algorithm to obtain the aligned module status dataset after the unified timed calibration of the entire network. Based on the aligned module state dataset after the network-wide unified time-series calibration is completed, a distributed state aggregation algorithm is used to perform regional distributed aggregation of the time-series aligned state data to obtain regional distributed aggregation state subsets. The distributed aggregation subset of regional states is then used to perform correlation, fusion, and normalization of multi-regional state data through a global state fusion algorithm, resulting in a globally normalized state fusion dataset to be generated by the snapshot. Based on the global normalized state fusion dataset generated by the snapshot to be taken, the unified time base snapshot aggregation model is used to aggregate and generate the state snapshot of the whole network at the same time point, with the unified time base of the whole network as the fixing basis.

[0012] Furthermore, the specific steps for performing network-wide timing calibration and alignment of the timestamps of the data reported by each Bluetooth MESH module based on the status data reported by each Bluetooth MESH module according to a predetermined time slot, using a microsecond-level timestamp alignment algorithm, are as follows: Extract the local microsecond-level timestamp carried in the status data reported by each Bluetooth MESH module according to the predetermined time slot to obtain the status data and local timestamp of each Bluetooth MESH module; Using a unified time base across the entire network as a reference, the difference between the local timestamp of each Bluetooth MESH module and the standard time base is calculated using a microsecond-level timestamp alignment algorithm. Based on this difference, the local timestamp of each Bluetooth MESH module is calibrated, compensated, and normalized to complete the unified timing alignment across the entire network, resulting in an aligned module status dataset after the unified timing alignment across the entire network.

[0013] Furthermore, the specific steps for using the unified time base snapshot aggregation model to aggregate and generate network-wide status snapshots at the same time point, based on a unified time base across the entire network, are as follows: Based on the global normalized state fusion dataset generated by the snapshot to be taken, the unified time node for the snapshot across the entire network is selected, using the unified time benchmark of the entire network as the basis for freezing. For snapshots across the entire network, a time stamp matching and filtering model is used to filter out a subset of valid status data corresponding to the time stamp. Based on a subset of valid state data, a unified time-base snapshot aggregation model is used to perform global correlation aggregation processing to obtain a regularized aggregated intermediate dataset of instantaneous state of the entire network. This allows the intermediate dataset of instantaneous state across the entire network to be integrated and encapsulated using a structured encapsulation model of the entire network snapshot, and then aggregated to generate a snapshot of the entire network state at the same time point.

[0014] The present invention also discloses another technical solution: an intelligent operation status management system for a Bluetooth MESH communication module, including a microsecond-level time slot scheduling and configuration module, a three-objective collaborative optimization module, a network-wide status snapshot generation module, and an architecture fusion and collaborative management module; The microsecond-level time slot scheduling configuration module is used to allocate microsecond-level reporting time slots to each module in the Bluetooth MESH network, and to obtain and output a unified microsecond-level timing reference and time slot allocation table for the entire network. The three-objective collaborative optimization module is used to dynamically adjust the time slot parameters, module wake-up strategy and channel allocation scheme in real time, and obtain and output the optimal scheduling parameter set adapted to the current network state. The network-wide status snapshot generation module is used to generate and output network-wide status snapshots at the same time point. The architecture fusion and collaborative management module, based on architecture support, time slot allocation and three-objective collaborative optimization, achieves microsecond-level synchronization and zero-conflict reporting, as well as an uncompromising balance between low power consumption, synchronization accuracy and unobstructed channel.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention is based on a constructed native deterministic time-division multiple access scheduling architecture. Through a unified time base across the entire network and microsecond-level hardware clock calibration, it achieves precise synchronization of the clocks of all modules across the network, solving the problems of random reporting delay and inconsistent time in traditional solutions.

[0016] 2. This invention employs microsecond-level time slot isolation allocation to assign dedicated reporting time slots to each module, avoiding reporting conflicts between multiple nodes; status data is not flooded but only forwarded to scheduling and synchronization frames, significantly reducing signal redundancy and channel interference, and improving channel utilization efficiency.

[0017] 3. This invention, through a three-objective collaborative optimization algorithm, can dynamically adapt to network conditions to adjust time slot parameters, module wake-up strategies, and channel allocation schemes, simultaneously achieving the three major objectives of low power consumption, high synchronization accuracy, and unobstructed channel access, thus overcoming the technical problem that traditional solutions cannot simultaneously address these three objectives.

[0018] 4. This invention uses a unified time-base snapshot aggregation model to achieve temporal alignment, distributed aggregation, and global fusion of state data. It can generate a network-wide state snapshot at the same time point, accurately restore the instantaneous operating state of the entire network, and solve the problems of distorted topology analysis, incorrect load assessment, and failure to locate faults.

[0019] 5. This invention supports large-scale multi-hop Bluetooth MESH networking, and can quickly respond to node additions and removals, topology changes and complete time slot reallocation; it can still operate stably in complex interference environments, and the network adaptability and anti-interference capability are significantly improved. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method steps of the present invention; Figure 2 This is a system architecture diagram of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: Please refer to Figure 1 As shown, a method for intelligent operation status management of a Bluetooth MESH communication module includes the following steps: S1: Based on a unified time reference across the entire network, it adopts the Bluetooth MESH standard protocol, adds time-division multiple access scheduling logic compatible with the standard broadcast flooding mechanism, avoids multi-node reporting conflicts through time slot isolation, and retains multi-hop forwarding capability, forming a native deterministic time-division multiple access scheduling architecture. This architecture is used to allocate microsecond-level reporting time slots to each module in the Bluetooth MESH network, resulting in a unified microsecond-level timing reference and time slot allocation table for the entire network. Specifically: The management platform distributes a unified time base to all Bluetooth MESH modules via the gateway, and completes the initial alignment of the network clocks through a unified network time synchronization algorithm to obtain a unified network time base. The unified time base uses the NTP protocol in conjunction with hardware synchronization frames for joint synchronization, and the synchronization accuracy is set to ±10μs. The unified network time synchronization algorithm uses a proportional-integral-derivative synchronization correction algorithm with an iteration period of 10ms and a convergence threshold of 5μs. Using a unified time base across the entire network as input, each Bluetooth MESH module employs the nRF52840 chip and has a built-in high-precision RC oscillator with an accuracy set to ±5ppm, which is used to execute a microsecond-level hardware clock calibration algorithm to obtain a microsecond-level synchronized network-wide module clock. The calibration formula in the microsecond-level hardware clock calibration algorithm is: ,in, This is the calibration coefficient, with a value ranging from 0.8 to 1.2. It can be adjusted in real time according to the clock drift. After executing the calibration formula, a full network module clock with microsecond-level synchronization is obtained, and the synchronization error is ≤10μs. It serves as a unified time reference for the entire network and is a standard time value issued by the gateway through the Network Time Protocol. This is the microsecond-level synchronization clock value after algorithm correction, i.e., the calibrated module local clock; This is the module's original local clock value before calibration.

[0023] Based on a unified time base across the entire network, a slot isolation allocation calculation model is used to allocate microsecond-level reporting slots to each Bluetooth MESH module, resulting in dedicated microsecond-level reporting slots for each Bluetooth MESH module. The core algorithm of the slot isolation allocation calculation model is a greedy allocation algorithm, with constraints set as slot utilization ≥ 80% and collision rate ≤ 1%. Based on the dedicated microsecond-level reporting time slots of each Bluetooth MESH module, a model is generated using a unified microsecond-level timing benchmark across the entire network. The model is encapsulated in JSON format and includes fields such as node ID, time slot start time, time slot length, and reporting frequency. This is used to form an executable scheduling table and broadcast it to the entire network, resulting in a unified microsecond-level timing benchmark and time slot allocation table across the entire network. Using the time slot allocation table, the native deterministic time-division multiple access scheduling algorithm is executed within a dedicated reporting window to report status, resulting in a status data reporting stream.

[0024] Based on a unified time base across the entire network, and through a time slot isolation allocation calculation model, the specific steps for allocating microsecond-level reporting time slots to each Bluetooth MESH module and obtaining the dedicated microsecond-level reporting time slots for each Bluetooth MESH module are as follows: Based on a unified time reference across the entire network, and based on a network topology awareness and node information collection model, a breadth-first search algorithm is used with a scanning period of 500ms to collect basic information such as node ID, location, signal strength, and power consumption status, in order to obtain a set of basic information about all network nodes and topology. A microsecond-level time slot slicing and isolation partitioning model is adopted to perform microsecond-level slicing and isolation partitioning on the time axis based on the unified time base of the entire network, resulting in a microsecond-level time slot resource pool. The specific parameters of the microsecond-level time slot resource pool are: the time slot width is set to 100μs, the protection interval is set to 10μs, the time slot period is set to 100ms, and a single time slot pool can accommodate a maximum of 1000 nodes and can support dynamic expansion.

[0025] The generated microsecond-level time slot resource pool is used as the processing target. Then, through the time slot isolation allocation calculation model, a greedy allocation algorithm is adopted to prioritize the allocation of priority time slots to nodes with weak signals and low power consumption. This is used to allocate microsecond-level reporting time slots to each Bluetooth MESH module, ultimately obtaining dedicated microsecond-level reporting time slots for each Bluetooth MESH module to ensure that the time slots of adjacent nodes do not overlap. The constraints are set as time slot utilization rate ≥ 80% and collision rate ≤ 1%. When the number of network nodes exceeds 1000, the time slot pool is automatically expanded, the time slot period is adjusted to 200ms, and the number of time slot slices is increased. If a node is detected to be offline or newly added, the breadth-first search algorithm rescans the topology within 500ms and completes the time slot reallocation within 1s. The time slots of offline nodes are automatically recycled and used to allocate idle time slots in real time for newly added nodes.

[0026] The specific steps for using a time slot allocation table to execute a native deterministic time-division multiple access scheduling algorithm within a dedicated reporting window to report status and obtain the status data reporting stream are as follows: Each Bluetooth MESH module parses the matching model through the time slot allocation table. Based on the hash matching algorithm, the parsing latency is set to ≤50μs to parse the time slot allocation table and obtain the Bluetooth MESH module's own microsecond-level reporting window positioning information. The microsecond-level reporting window positioning information includes the start time and duration. A microsecond-level local clock timing calibration algorithm is used for timing waiting. Based on the crystal oscillator compensation mechanism, the parameters are set to calibrate once every 1ms, with a timing error ≤5μs. This timing waiting process generates a time slot window arrival trigger signal, and a native deterministic time division multiple access scheduling algorithm is executed within a dedicated reporting window. Status data is reported in a time-division format with node ID + timestamp + power consumption value + channel interference value + running status code and a data length ≤64 bytes. This results in an ordered and staggered device status reporting data packet sequence. A FIFO queue with a queue length of 100 is used, and the overflow processing method of discarding the earliest data packet is used to backflow and regularize the ordered and staggered device status reporting data packet sequence, thus obtaining an ordered and conflict-free status data reporting stream for the entire network. In the overflow processing method of discarding the earliest data packet, the packet loss rate must be ≤0.5%.

[0027] The native deterministic time-division multiple access (TDMA) scheduling architecture is compatible with the Bluetooth MESH broadcast flooding mechanism. Multi-hop forwarding only forwards the time slot allocation table and synchronization frame; status reporting data is not flooded. The specific fields of the newly added time slot scheduling protocol frame are as follows: the total length of the frame header is 8 bytes, including a 2-byte frame identifier, a 1-byte version, a 1-byte length, and a 4-byte node ID; the total length of the data segment is 32 bytes, including an 8-byte time slot start, a 4-byte time slot width, a 8-byte synchronization time, a 2-byte channel number, and a 10-byte reserved field; the checksum length is 4 bytes, using CRC32 verification; and the protocol frame parsing follows little-endian order, discarding and retransmitting if the verification fails.

[0028] This invention constructs a native deterministic time-division multiple access scheduling architecture compatible with the Bluetooth MESH standard broadcast flooding mechanism. Through NTP combined with hardware synchronization frames, PID synchronization correction, and microsecond-level hardware clock calibration, it achieves a precise and unified timing benchmark with a network-wide synchronization error of ≤10μs and a timekeeping error of ≤5μs. It employs microsecond-level time slot isolation and a greedy algorithm to allocate dedicated reporting time slots to each module, thereby eliminating multi-node reporting conflicts from a mechanism perspective, resulting in a conflict rate of ≤1% and a time slot utilization rate of ≥80%. It can also quickly respond to node additions and removals and topology changes to complete dynamic time slot reallocation and expansion. The status reporting in this invention achieves low-latency, high-reliability transmission with a parsing latency of ≤50μs and a packet loss rate of ≤0.5% through dedicated window time-division transmission, simplified data format, and FIFO queue backflow regularization. At the same time, the status data is not flooded, and only synchronization frames and scheduling frames are forwarded, which greatly reduces signal redundancy and channel interference. In addition, the standardized time slot scheduling protocol frame and CRC32 check mechanism improve transmission robustness, fully retain multi-hop forwarding capability and are compatible with existing devices, and can stably support the efficient operation of large-scale Bluetooth MESH networking with thousands of nodes and multiple hops.

[0029] S2: Using a unified microsecond-level timing reference and time slot allocation table across the entire network as input, a three-objective collaborative optimization algorithm is run to dynamically adjust time slot parameters, module wake-up strategies, and channel allocation schemes in real time. This yields the optimal scheduling parameter set adapted to the current network state. Deep integration of hardware, protocols, and application layers provides architectural support for the architecture's scheduling execution and algorithm operation. The three objectives in the three-objective collaborative optimization algorithm refer to low power consumption, synchronization accuracy, and unobstructed channel access, specifically: Using a unified microsecond-level timing reference and time slot allocation table across the entire network as input, the three-objective collaborative optimization algorithm is run to dynamically adjust time slot parameters, module wake-up strategies, and channel allocation schemes in real time, resulting in the optimal scheduling parameter set adapted to the current network state. The specific steps are as follows: Based on a unified microsecond-level timing reference and time slot allocation table for the entire network, the network state awareness acquisition model collects the network load, channel interference intensity, and module power consumption status in real time to obtain a set of real-time network state parameters. The acquisition period is set to 100ms, the network load is set to 0 to 100%, the channel interference intensity is set to 0 to 100dBm, and the module power consumption status includes a sleep current set to ≤15μA and an operating current set to ≤50mA. The specific steps for obtaining the optimal scheduling strategy set by running a three-objective collaborative optimization algorithm using the real-time status parameter set of the entire network are as follows: Based on the real-time status parameter set of the entire network, the NSGA-Ⅲ algorithm in the three-objective collaborative optimization algorithm is used to calculate the optimal scheduling strategy set under the constraints of 50 iterations, population size of 100, crossover probability of 0.8 and mutation probability of 0.05. The NSGA-Ⅲ algorithm directly adopts a three-objective function for fitness, with constraints set as synchronization error ≤10μs, collision rate ≤1%, and channel utilization ≥70%. After iteration, Pareto optimal solutions are selected based on congestion ranking, and the solution with the smallest weighted sum of objective functions is selected as the optimal scheduling strategy set, with the weights configured as follows: low power consumption 0.4, synchronization accuracy 0.3, and unobstructed channel 0.3.

[0030] The three objective functions in the three-objective collaborative optimization algorithm are defined as follows: Low power consumption objective function: In the formula, The number of nodes; This refers to the power consumption during sleep mode. This refers to the duration of hibernation. This refers to the operating power consumption. For working hours; Synchronization accuracy objective function: In the formula, For the first The local time of each node. The time reference is unified across the entire network, and the target error is ≤10μs.

[0031] Objective function for unobstructed channel: In the formula, This represents the actual channel interference intensity. The maximum permissible interference intensity is -80dBm, and the target channel utilization is ≥70%.

[0032] Using the optimal scheduling strategy set as input, a dynamic optimization model for time slot parameters is adopted. Based on the proportional-integral-derivative control algorithm, the time slot parameters are dynamically adjusted to obtain the optimized time slot parameters. The adjustment range of the dynamic time slot parameters is as follows: the time slot width can be dynamically adjusted between 50 and 200 μs, and the protection interval can be adjusted between 5 and 20 μs. When the total network load is ≥80%, the time slot width is reduced; when the total network load is ≤30%, the time slot width is increased.

[0033] The formula for adjusting the time slot width is: In the formula, This is the update slot width value that the system will soon be able to use after being calculated by the algorithm; It is the time slot width value currently being used by the system before dynamic adjustment; The time slot width is adjusted proportionally to the network load. When the network load is ≥80%, the time slot width is reduced to 50μs. When the network load is ≤30%, the time slot width is increased to 200μs. The protection interval is adjusted proportionally to the time slot width.

[0034] A predictive wake-up control algorithm based on a long short-term memory network prediction model is used. With a prediction window of 100ms and a wake-up trigger threshold of 10μs before the time slot arrives, the module wake-up strategy is dynamically adjusted to obtain an optimized low-power wake-up strategy for the module. The wake-up cycle of this strategy is synchronized with the time slot cycle, and the wake-up duty cycle is 10% to 30%. When the module is idle, it enters a deep sleep mode, and the sleep current is ≤15μA. The specific parameters of the Long Short-Term Memory Network prediction model are as follows: the input layer contains four features, namely historical power consumption, time slot interval, signal strength, and load, totaling four dimensions; the hidden layer is set to two layers, each containing 32 neurons; and the output layer outputs the wake-up time as a one-dimensional result. Training is carried out using historical 7-day running data, with a training batch size of 16 and a learning rate of 0.001. The model prediction accuracy is no less than 98%.

[0035] Based on the optimized module low-power wake-up strategy, a genetic algorithm is used to dynamically adjust the channel allocation scheme using Bluetooth MESH standard channels 37, 38, and 39 as the channel set, -60dBm as the interference evaluation threshold, and a load balancing index of ≤10% for each channel load difference. This results in an optimized channel allocation scheme that prioritizes channel allocation nodes with interference intensity <-60dBm. When the load of a single channel is ≥80%, some nodes are migrated to low-load channels, thus obtaining the optimized channel allocation scheme. The optimized time slot parameters, optimized module low-power wake-up strategy, and optimized channel allocation scheme are encapsulated and output through a scheduling parameter delivery model and then delivered. This model uses the MQTT protocol for delivery, with a delivery period of 100ms and a delivery latency of ≤20ms, ultimately obtaining the optimal scheduling parameter set adapted to the current network state. Then, deep integration is achieved through hardware, protocol, and application layers. The hardware layer employs a high-precision clock module with an accuracy of ±5ppm and a multi-channel RF module supporting simultaneous operation of three Bluetooth MESH standard channels. The protocol layer is based on the Bluetooth MESH 1.1 standard, adding time slot scheduling and time... The system includes synchronization and parameter optimization protocol frames. The frame structure of these frames consists of an 8-byte header, a 32-byte data segment, and a 4-byte checksum. The application layer provides interfaces for parameter configuration, status monitoring, and fault alarms, providing architectural support for the scheduling and execution of the architecture and the operation of the algorithm. The three objectives in the aforementioned three-objective collaborative optimization algorithm refer to low power consumption, synchronization accuracy, and uninterrupted channel operation. If a time slot conflict or channel interruption is detected, the system triggers resynchronization within 100ms. The resynchronization trigger threshold is 20μs. The PID synchronization correction algorithm is used for rapid convergence. After synchronization is restored, the time slot and channel are automatically reallocated. The fault recovery time is ≤200ms.

[0036] This invention employs a three-objective collaborative optimization mechanism centered on low power consumption, synchronization accuracy, and unobstructed channel access. It uses the NSGA-Ⅲ algorithm combined with weighted Pareto optimal solution screening to dynamically generate the optimal scheduling strategy in real time based on the overall network load, channel interference, and module power consumption status. Combined with PID control, it enables the time slot parameters to be adaptively adjusted according to the network load, ensuring efficient utilization of time slot resources while avoiding transmission conflicts. This invention uses an LSTM predictive wake-up control algorithm to precisely regulate the wake-up timing of the module, keeping the sleep current below 15μA, significantly reducing the module's power consumption, and achieving a model prediction accuracy of no less than 98%. It also uses a genetic algorithm to optimize and load balance the Bluetooth MESH standard channels, ensuring channel utilization ≥70% and load difference between channels ≤10%, effectively avoiding channel congestion and interference problems. Meanwhile, this invention, supported by a deeply integrated architecture of hardware, protocol, and application layers, and with the MQTT protocol for rapid distribution of scheduling parameters, can trigger resynchronization within 100ms and complete fault recovery within 200ms when time slot conflicts or channel interruptions are detected. It always maintains a stable operating state with synchronization error ≤10μs and conflict rate ≤1%, truly achieving a compromise-free balance between the three core indicators of low power consumption, synchronization accuracy, and uninterrupted channel operation.

[0037] S3: Based on the status data reported by each Bluetooth MESH module according to predetermined time slots, a unified time base snapshot aggregation model is used to generate a network-wide status snapshot at the same time point through a microsecond-level timestamp alignment algorithm, a distributed status aggregation algorithm, and a global status fusion algorithm. Specifically: Based on the status data reported by each Bluetooth MESH module according to predetermined time slots, and using a unified time base snapshot aggregation model, the specific steps for generating a network-wide status snapshot at the same time point are as follows: (This is achieved through a microsecond-level timestamp alignment algorithm, a distributed status aggregation algorithm, and a global status fusion algorithm.) Based on the status data reported by each Bluetooth MESH module in a predetermined time slot, the timestamps of the data reported by each module are aligned and time-series calibrated across the entire network using a microsecond-level timestamp alignment algorithm. This yields an aligned module status dataset that has undergone unified time-series calibration across the entire network. The steps are as follows: Extract the local microsecond-level timestamp carried in the status data reported by each Bluetooth MESH module according to the predetermined time slot. The timestamp adopts the YYYY-MM-DDHH:MM:SS.ssssss format, and then obtain the status data and local timestamp of each Bluetooth MESH module. Using a unified time base across the entire network as a reference, the difference between the local timestamp of each Bluetooth MESH module and the standard time base is calculated through a microsecond-level timestamp alignment algorithm. This algorithm employs a linear interpolation correction algorithm and utilizes a latency compensation formula. ,in The difference between local time and standard time is calculated and distributed in real time by the gateway. The local timestamp of each Bluetooth MESH module is calibrated, compensated and normalized by the difference between local time and standard time to complete the unified timing alignment of the entire network. The resulting aligned module status dataset has been obtained after the unified timing alignment of the entire network, and the alignment error is ≤10μs.

[0038] Based on the aligned module status dataset after the network-wide unified time-series calibration, a distributed status aggregation algorithm is used to perform regional distributed aggregation of the time-series aligned status data. This algorithm adopts a domain-based aggregation strategy, dividing the network topology into several subdomains, setting up an aggregation node in each subdomain, and using bubble sort combined with data deduplication as the aggregation processing method. The aggregation latency is controlled within 500μs. After processing, a regional distributed aggregation status subset is obtained. This subset contains the calibrated status data of all nodes in the corresponding subdomain. The overall data format is consistent with the reported data, and a subdomain ID field is added. The distributed aggregation subsets of state data in different regions are then used to perform correlation, fusion, and normalization of state data from multiple regions through a global state fusion algorithm. This algorithm employs a weighted fusion method, with weights set based on node signal strength. Specifically, the weights are set to 0.8 when signal strength ≥ -60dBm, 0.6 when -80dBm ≤ signal strength < -60dBm, and 0.4 when signal strength < -80dBm. After fusion, a globally normalized state fusion dataset is obtained to be generated for snapshot generation. The data structure of this dataset includes the total number of nodes in the entire network, the state data of each node, the fusion timestamp, and the data checksum. Based on the globally normalized state fusion dataset to be generated from the snapshots, a unified time-base snapshot aggregation model is used to aggregate and generate state snapshots of the entire network at the same time point, using a unified time base across the entire network as the freezing point. The specific steps are as follows: Based on the global normalized state fusion dataset generated by the snapshot to be taken, the unified time benchmark of the whole network is used as the basis for fixing the snapshot. The unified fixing time node of the whole network snapshot is selected and set to a fixing moment every 100ms and synchronized with the state acquisition cycle. The fixing cycle can be manually adjusted through the management platform. For a unified snapshot time node across the entire network, a time stamp matching and filtering model is used to filter out a subset of valid state data corresponding to the snapshot time node. The matching error of this model is ≤10μs. The filtering rule is to select calibrated state data within 10μs before and after the snapshot time. If the missing data rate of a node is ≤5%, the missing data is filled in using linear interpolation. If the missing data rate of a node is >5%, the node is marked as an abnormal node and the abnormal status is noted in the snapshot. If the number of abnormal nodes in a single snapshot exceeds 10%, a network-wide fault alarm is triggered.

[0039] Based on a subset of effective state data, a unified time-base snapshot aggregation model is used to perform global correlation aggregation processing. During the aggregation process, the data is sorted by node ID and associated with the state data, channel information and power consumption information of each node to obtain a regularized aggregated intermediate dataset of the instantaneous state of the entire network. This allows the intermediate dataset of instantaneous state across the entire network to be integrated and encapsulated using a network snapshot structured encapsulation model. This model uses JSON format for encapsulation, and the encapsulated content includes the snapshot freezing time, total number of nodes, number of normal nodes, number of abnormal nodes, detailed state data of each node, and verification code. It also aggregates and generates a network state snapshot at the same time node. The generated snapshot size is ≤10KB, the storage period is 1 hour, and it can be exported through the management platform.

[0040] This invention is based on a unified time-base snapshot aggregation model. It achieves precise time-series calibration of the status data of each module across the entire network through a microsecond-level timestamp alignment algorithm, with an alignment error of ≤10μs. After distributed domain aggregation and global status fusion processing based on signal strength weighting, efficient data aggregation and accurate normalization can be completed within 500μs. Then, using a unified time base across the entire network as the fixing basis, a network-wide status snapshot at the same time node is generated. The snapshot generation is accurate and the matching error is ≤10μs. For data missing situations, intelligent completion can be achieved through linear interpolation, and abnormal nodes can be automatically marked. If the proportion of abnormal nodes exceeds 10%, a network-wide fault alarm is triggered, which greatly improves the reliability of network status monitoring and fault early warning capabilities. The snapshots in this invention are encapsulated using JSON structure, with a size of ≤10KB and support for export and storage. The data is well-organized, lightweight, and easy to manage, and can accurately restore the instantaneous operating status of the entire network. This solves the problems of distorted topology analysis, incorrect load assessment, and failure to locate faults caused by the inability of traditional solutions to generate accurate snapshots of the entire network status.

[0041] S4: By leveraging architectural support, time slot allocation, and three-objective collaborative optimization, microsecond-level synchronization and zero-collision reporting are achieved, along with a non-compromising balance between low power consumption, synchronization accuracy, and unobstructed channel operation. The specific steps are as follows: A test network consisting of 100 Bluetooth MESH modules equipped with nRF52840 chips was built, using a 5-hop network topology and a coverage area of ​​50m×50m. One gateway and one management platform were deployed, and the environmental interference intensity was set to -30 to -70dBm. The system is compatible with existing Bluetooth MESH 1.0 / 1.1 devices, which can be connected through the gateway adaptation layer and can automatically adapt to the time slot scheduling rules.

[0042] After testing and verification of multiple indicators, the system's overall module synchronization error is ≤10μs, the timekeeping accuracy is ≤5μs, and the resynchronization trigger threshold is 20μs, meeting the microsecond-level synchronization requirements. By utilizing native deterministic time-division multiple access scheduling, the module reporting collision rate is ≤1%, thereby achieving zero-collision reporting; the average power consumption of the module is ≤50μA, and the battery life is no less than 72 hours, which reduces power consumption compared to existing solutions; channel utilization is improved, and interference suppression capability is enhanced, resulting in no channel congestion during operation; moreover, the snapshot generation latency is ≤10μs, and the snapshot accuracy is ≥99%, which can accurately restore the network operation status at the same time point; This invention is extended to 1000 nodes and 10-hop network testing, with synchronization error still ≤10μs, collision rate ≤1%, and snapshot accuracy ≥98%. In a strong interference environment of -80dBm, the channel utilization rate is ≥70%, and it runs continuously for 30 days without failure, solving the technical problems of topology analysis distortion, load assessment error, and fault location failure in existing solutions.

[0043] This invention achieves microsecond-level synchronization and zero-collision reporting in Bluetooth MESH networks by combining architectural support, precise time slot allocation, and three-objective collaborative optimization. It achieves a compromise-free balance of the three core indicators of low power consumption, synchronization accuracy, and unobstructed channel, resulting in uncongested channels, significantly improved utilization, snapshot generation latency ≤10μs, accuracy ≥99%, and accurate restoration of the instantaneous operating status of the entire network. Even when expanded to a large-scale network of 1000 nodes and 10 hops, this invention can still maintain synchronization and reporting stability. In a strong interference environment of -80dBm, the channel utilization rate is ≥70%, and it can operate continuously and stably for 30 days without failure. It solves the problems of topology analysis distortion, load assessment error, and fault location failure in traditional Bluetooth MESH management and control schemes.

[0044] Example 2: Please refer to Figure 2 As shown, the present invention also discloses another embodiment: an intelligent operation status management system for a Bluetooth MESH communication module, including a microsecond-level time slot scheduling configuration module, a three-objective collaborative optimization module, a network-wide status snapshot generation module, and an architecture fusion and collaborative management module; The microsecond-level time slot scheduling configuration module is used to allocate microsecond-level reporting time slots to each module in the Bluetooth MESH network, and to obtain and output a unified microsecond-level timing reference and time slot allocation table for the entire network. The three-objective collaborative optimization module is used to dynamically adjust the time slot parameters, module wake-up strategy and channel allocation scheme in real time, and obtain and output the optimal scheduling parameter set adapted to the current network state. The network-wide status snapshot generation module is used to generate and output network-wide status snapshots at the same time point; The architecture integration and collaborative management module, based on architecture support, time slot allocation and three-objective collaborative optimization, achieves microsecond-level synchronization and zero-conflict reporting, as well as an uncompromising balance between low power consumption, synchronization accuracy and unobstructed channel.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent operation status management of a Bluetooth MESH communication module, characterized in that, The steps include the following: Based on a unified time reference across the entire network, a native deterministic time division multiple access scheduling architecture is adopted to allocate microsecond-level reporting time slots to each module in the Bluetooth MESH network, thereby obtaining a unified microsecond-level timing reference and time slot allocation table across the entire network. Using a unified microsecond-level timing reference and time slot allocation table across the entire network as input, a three-objective collaborative optimization algorithm is run to dynamically adjust time slot parameters, module wake-up strategies, and channel allocation schemes in real time, thereby obtaining the optimal scheduling parameter set adapted to the current network state. Through deep integration of hardware, protocols, and application layers, architectural support is provided for the scheduling execution and algorithm operation of the architecture. The three objectives in the three-objective collaborative optimization algorithm refer to low power consumption, synchronization accuracy, and unobstructed channel. Based on the status data reported by each Bluetooth MESH module in a predetermined time slot, a unified time base snapshot aggregation model is used to generate a network-wide status snapshot at the same time point through a microsecond-level timestamp alignment algorithm, a distributed status aggregation algorithm, and a global status fusion algorithm. By leveraging architectural support, time slot allocation, and three-objective collaborative optimization, microsecond-level synchronization and zero-conflict reporting are achieved, along with an uncompromising balance between low power consumption, synchronization accuracy, and unobstructed channel operation.

2. The intelligent operation status management method for a Bluetooth MESH communication module according to claim 1, characterized in that: The specific steps for allocating microsecond-level reporting time slots to each module in the Bluetooth MESH network based on a unified time reference across the entire network and employing a native deterministic time division multiple access scheduling architecture to obtain a unified microsecond-level time reference and time slot allocation table for the entire network are as follows: The management platform distributes a unified time base to all Bluetooth MESH modules through the gateway, and completes the initial alignment of the clocks across the entire network through a unified time synchronization algorithm, thereby obtaining a unified time base across the entire network. Using a unified time base across the entire network as input, each Bluetooth MESH module executes a microsecond-level hardware clock calibration algorithm to obtain a microsecond-level synchronized network-wide module clock; Based on a unified time reference across the entire network, a time slot isolation allocation calculation model is used to allocate microsecond-level reporting time slots to each Bluetooth MESH module, thus obtaining a dedicated microsecond-level reporting time slot for each Bluetooth MESH module; Based on the dedicated microsecond-level reporting time slots of each Bluetooth MESH module, an executable scheduling table is generated using a unified microsecond-level timing benchmark generation model across the entire network and broadcast to the entire network, resulting in a unified microsecond-level timing benchmark and time slot allocation table across the entire network. Using the time slot allocation table, the native deterministic time-division multiple access scheduling algorithm is executed within a dedicated reporting window to report status, resulting in a status data reporting stream.

3. The intelligent operation status management method for a Bluetooth MESH communication module according to claim 2, characterized in that: The specific steps for allocating microsecond-level reporting time slots to each Bluetooth MESH module based on a unified time reference across the entire network and through a time slot isolation allocation calculation model are as follows: Based on a unified time reference across the entire network, a set of basic information about all network nodes and topology is obtained through a network topology awareness and node information collection model. A microsecond-level time slot slicing and isolation partitioning model is adopted to perform microsecond-level slicing and isolation partitioning on the time axis based on the unified time base of the entire network, thereby obtaining a microsecond-level time slot resource pool. The generated microsecond-level time slot resource pool is used as the processing target. Then, through the time slot isolation allocation calculation model, a microsecond-level reporting time slot is allocated to each Bluetooth MESH module, and finally, a dedicated microsecond-level reporting time slot for each Bluetooth MESH module is obtained.

4. The intelligent operation status management method for a Bluetooth MESH communication module according to claim 3, characterized in that: The specific steps for using the time slot allocation table to execute the native deterministic time-division multiple access scheduling algorithm within a dedicated reporting window to report status and obtain the status data reporting stream are as follows: Each Bluetooth MESH module parses the time slot allocation table using the time slot allocation table parsing and matching model to obtain its own microsecond-level reporting window location information. The system uses a microsecond-level local clock timing calibration algorithm to wait for the time slot window to arrive and generate a trigger signal. It then executes a native deterministic time-division multiple access scheduling algorithm within a dedicated reporting window to complete the time-division reporting of status data. This results in an ordered, staggered sequence of device status reporting data packets, which are then backflowed and regularized to obtain an ordered, conflict-free status data reporting stream across the entire network.

5. The intelligent operation status management method for a Bluetooth MESH communication module according to claim 4, characterized in that: The specific steps for obtaining the optimal scheduling parameter set adapted to the current network state by using a unified microsecond-level timing reference and time slot allocation table as input, running a three-objective collaborative optimization algorithm, and dynamically adjusting time slot parameters, module wake-up strategies, and channel allocation schemes in real time are as follows: Based on a unified microsecond-level timing reference and time slot allocation table for the entire network, the network status awareness acquisition model collects the network load, channel interference intensity, and module power consumption status in real time to obtain the real-time status parameter set of the entire network. By utilizing the real-time status parameter set of the entire network, a three-objective collaborative optimization algorithm is run to obtain the optimal scheduling strategy set; Using the optimal scheduling strategy set as input, the time slot parameters are dynamically adjusted using a time slot parameter dynamic optimization model to obtain the optimized time slot parameters. The optimized time slot parameters are used to dynamically adjust the module wake-up strategy based on the predictive wake-up control algorithm to obtain the optimized low-power wake-up strategy for the module. Based on the optimized module low-power wake-up strategy, the channel allocation scheme is dynamically adjusted through channel optimization and load balancing allocation model to obtain the optimized channel allocation scheme. The optimized time slot parameters, optimized module low-power wake-up strategy, and optimized channel allocation scheme are encapsulated and distributed through scheduling parameter output model to obtain the optimal scheduling parameter set adapted to the current network state.

6. The intelligent operation status management method for a Bluetooth MESH communication module according to claim 5, characterized in that: The specific steps for generating a network-wide state snapshot at the same time point based on the state data reported by each Bluetooth MESH module according to a predetermined time slot, using a unified time base snapshot aggregation model, and employing a microsecond-level timestamp alignment algorithm, a distributed state aggregation algorithm, and a global state fusion algorithm, are as follows: Based on the status data reported by each Bluetooth MESH module in a predetermined time slot, the timestamps of the data reported by each module are aligned and timed across the entire network using a microsecond-level timestamp alignment algorithm to obtain the aligned module status dataset after the unified timed calibration of the entire network. Based on the aligned module state dataset after the network-wide unified time-series calibration is completed, a distributed state aggregation algorithm is used to perform regional distributed aggregation of the time-series aligned state data to obtain regional distributed aggregation state subsets. The distributed aggregation subset of regional states is then used to perform correlation, fusion, and normalization of multi-regional state data through a global state fusion algorithm, resulting in a globally normalized state fusion dataset to be generated by the snapshot. Based on the global normalized state fusion dataset generated by the snapshot to be taken, the unified time base snapshot aggregation model is used to aggregate and generate the state snapshot of the whole network at the same time point, with the unified time base of the whole network as the fixing basis.

7. The intelligent operation status management method for a Bluetooth MESH communication module according to claim 6, characterized in that: The specific steps for performing network-wide timing calibration and alignment of the timestamps of the data reported by each Bluetooth MESH module based on the status data reported by each Bluetooth MESH module according to a predetermined time slot, using a microsecond-level timestamp alignment algorithm, are as follows: Extract the local microsecond-level timestamp carried in the status data reported by each Bluetooth MESH module according to the predetermined time slot to obtain the status data and local timestamp of each Bluetooth MESH module; Using a unified time base across the entire network as a reference, the difference between the local timestamp of each Bluetooth MESH module and the standard time base is calculated using a microsecond-level timestamp alignment algorithm. Based on this difference, the local timestamp of each Bluetooth MESH module is calibrated, compensated, and normalized to complete the unified timing alignment across the entire network, resulting in an aligned module status dataset after the unified timing alignment across the entire network.

8. The intelligent operation status management method for a Bluetooth MESH communication module according to claim 7, characterized in that: The specific steps for generating network-wide status snapshots at the same time point using the unified time base snapshot aggregation model, with the unified time base of the entire network as the fixing basis, are as follows: Based on the global normalized state fusion dataset generated by the snapshot to be taken, the unified time node for the snapshot across the entire network is selected, using the unified time benchmark of the entire network as the basis for freezing. For snapshots across the entire network, a time stamp matching and filtering model is used to filter out a subset of valid status data corresponding to the time stamp. Based on a subset of valid state data, a unified time-base snapshot aggregation model is used to perform global correlation aggregation processing to obtain a regularized aggregated intermediate dataset of instantaneous state of the entire network. This allows the intermediate dataset of instantaneous state across the entire network to be integrated and encapsulated using a structured encapsulation model of the entire network snapshot, and then aggregated to generate a snapshot of the entire network state at the same time point.

9. An intelligent operation status management system for a Bluetooth MESH communication module, applied to the intelligent operation status management method for the Bluetooth MESH communication module according to any one of claims 1-8, characterized in that, It includes a microsecond-level time slot scheduling configuration module, a three-objective collaborative optimization module, a network-wide status snapshot generation module, and an architecture fusion and collaborative management module; The microsecond-level time slot scheduling configuration module is used to allocate microsecond-level reporting time slots to each module in the Bluetooth MESH network, and to obtain and output a unified microsecond-level timing reference and time slot allocation table for the entire network. The three-objective collaborative optimization module is used to dynamically adjust the time slot parameters, module wake-up strategy and channel allocation scheme in real time, and obtain and output the optimal scheduling parameter set adapted to the current network state. The network-wide status snapshot generation module is used to generate and output network-wide status snapshots at the same time point. The architecture fusion and collaborative management module, based on architecture support, time slot allocation and three-objective collaborative optimization, achieves microsecond-level synchronization and zero-conflict reporting, as well as an uncompromising balance between low power consumption, synchronization accuracy and unobstructed channel.

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