Wireless communication control method and system based on LoRa module

Abstract

The invention discloses a wireless communication control method and system based on a LoRa module, and relates to the technical field of wireless communication, and the method comprises the steps: configuring LoRa module communication parameters; rSSI and SNR monitoring is carried out on a currently used channel, and an optimal spreading factor parameter is evaluated and recommended; multi-channel optimization selection is carried out based on a spreading factor evaluation result, transmission capability evaluation is carried out on a selected channel, and an optimal main channel and an optimal standby channel are determined; analyzing and formulating a hierarchical resource scheduling strategy and a power consumption scheduling strategy based on the communication behavior data of the terminal node, and generating an active control instruction set; executing hierarchical access control and dynamic migration scheduling based on the active control instruction set, and executing a data transmission task; and counting performance indexes of each channel, and adjusting related control parameters to form adaptive optimization closed-loop control. According to the method, the channel adaptability is improved through real-time signal monitoring and spreading factor dynamic optimization, accurate resource configuration is realized based on a hierarchical scheduling strategy of behavior prediction, and the energy efficiency and robustness of the system are remarkably optimized.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a wireless communication control method and system based on a LoRa module. Background Art

[0002] With the rapid development of the Internet of Things (IoT), low-power wide-area network (LPWAN) technology has been widely used in industrial control, smart cities, and other fields. As an important branch of LPWAN technology, LoRa occupies a key position in the field of IoT communications due to its long-distance transmission and low power consumption.

[0003] Currently, LoRa-based wireless communication systems primarily utilize fixed parameter configuration. Communication parameters such as spreading factor and channel bandwidth are often statically set during the network deployment phase. This fixed configuration approach struggles to adapt to complex and changing communication environments, leading to inefficient channel resource utilization and degraded network performance. Furthermore, existing systems lack the ability to analyze and predict terminal node traffic characteristics, making it impossible to optimize resource scheduling based on actual communication needs, impacting the system's overall transmission efficiency. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a wireless communication control method based on the LoRa module, which is used to solve the technical problems of low channel resource utilization efficiency, weak service feature prediction ability, and insufficient network performance optimization in the existing LoRa communication system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a wireless communication control method based on a LoRa module, which comprises: Configure the LoRa module communication parameters and establish a communication channel between the gateway and the terminal node; Monitor the RSSI and SNR of the currently used channel, evaluate and recommend the optimal spreading factor parameters based on the signal quality monitoring results; Implement multi-channel optimization selection based on the spreading factor evaluation results, evaluate the transmission capacity of the selected channels, and determine the optimal primary and backup channels; Formulate hierarchical resource scheduling strategies and power consumption scheduling strategies based on the communication behavior data analysis of terminal nodes, and generate active control instruction sets; Executes hierarchical access control and dynamic migration scheduling based on active control instruction sets, and performs data transmission tasks; The transmission performance indicators and prediction accuracy indicators of each channel are counted, and the relevant control parameters are adjusted based on the statistical results to form an adaptive optimization closed-loop control.

[0007] As a preferred solution of the wireless communication control method based on the LoRa module of the present invention, configuring the communication parameters of the LoRa module includes: Select the operating frequency band from the LoRa standard frequency band based on the geographical distribution of the terminal nodes and the expected communication distance; Set the initial spreading factor based on network capacity requirements and configure the corresponding transmission power and bit rate; Determine the bandwidth parameters based on the channel capacity requirements, and write the operating frequency band, initial spreading factor, and bandwidth parameters into the LoRa modules of the gateway and terminal nodes; Assign device addresses to each terminal node and establish communication channels between the gateway and the terminal nodes; Verify the connectivity of the communication channel through the handshake protocol, start the network synchronization mechanism to establish the monitoring time benchmark, and set the periodic monitoring parameters.

[0008] As a preferred solution of the wireless communication control method based on the LoRa module of the present invention, wherein: evaluating and recommending the optimal spreading factor parameters according to the signal quality monitoring results includes: According to the periodic monitoring parameters and the comparison result between the current network load and the load threshold, the periodic monitoring mode is set and the channel quality monitoring task is started; Based on the monitoring time base, standard test frames are regularly sent to the currently used channel to measure the RSSI and SNR values ​​between the gateway and each terminal node to establish a channel quality database; Analyze the channel quality change trend over multiple consecutive monitoring cycles. When the signal quality drops beyond a preset quality degradation threshold, generate candidate spreading factor parameters and perform preliminary evaluation. The transmission reliability of the candidate spreading factor parameters is verified through the gateway-side algorithm, the optimal spreading factor parameters are determined, and the spreading factor evaluation results are generated.

[0009] As a preferred solution of the wireless communication control method based on the LoRa module of the present invention, wherein: implementing multi-channel optimization selection based on the spreading factor evaluation result includes: Based on the spreading factor evaluation results, the transmission performance parameters of each terminal node on the available channel are calculated and a channel quality score table is established; According to the transmission requirements of the terminal node and the current network load, the channels are matched and screened in combination with the LoRa orthogonality constraint to determine the candidate channel list; Based on the scoring ranking of each candidate channel, the primary channel and the backup channel are selected, and a network channel configuration table is established; Evaluate the transmission performance of the primary channel, calculate channel utilization, signal coverage, and resource conflict rate, and obtain a comprehensive performance score; When the comprehensive performance score of the primary channel is lower than the channel switching trigger threshold, the switching timing is determined by the gateway-side load balancing algorithm, and an instruction to switch to the backup channel is sent to the relevant terminal nodes.

[0010] As a preferred solution of the wireless communication control method based on the LoRa module of the present invention, wherein: formulating a hierarchical resource scheduling strategy and a power consumption scheduling strategy based on the communication behavior data analysis of the terminal node includes: Collect the communication behavior data of the terminal nodes within the preset statistical period, classify the communication behavior data into business models, identify the business types of the terminal nodes and establish a business request pattern library; Establish hierarchical priority mapping relationships based on the business request pattern library; Combining communication behavior data and priority mapping, a hierarchical scoring algorithm is used to calculate the communication scheduling priority of the terminal node; Based on the communication scheduling priority and business request pattern library, a time period prediction method is used to predict the expected active period and data transmission load of the terminal node in the next statistical period; Obtain the configuration parameters of the primary and backup channels, and formulate hierarchical resource scheduling strategies and differentiated power consumption scheduling strategies based on the prediction results and the status of the primary and backup channels.

[0011] As a preferred solution of the wireless communication control method based on the LoRa module of the present invention, wherein: the generating of the active control instruction set includes: Generate a gateway hierarchical scheduling schedule based on expected active periods and communication scheduling priorities, establish a mapping relationship between node priorities and access policies, and formulate a hierarchical access scheduling plan; The gateway hierarchical scheduling schedule, priority-access policy mapping relationship and hierarchical access scheduling scheme are encapsulated as an active control instruction set.

[0012] As a preferred solution of the wireless communication control method based on the LoRa module of the present invention, wherein: executing hierarchical access control based on the active control instruction set includes: Obtain active control instruction sets, set the gateway-side working mode according to the gateway hierarchical scheduling schedule and differentiated power consumption scheduling strategy, and monitor active access requests from terminal nodes; When a terminal node initiates a communication request, its communication scheduling priority is queried according to the node ID, and access control is performed based on the hierarchical resource scheduling strategy and ALOHA protocol; Establish a temporary node-channel binding relationship, perform data transmission tasks, and release channel resources after the transmission is completed; Monitor the operating parameters of the main channel in real time. When an abnormality is detected in the main channel, perform load migration adjustment on the backup channel on the gateway side.

[0013] In a second aspect, the present invention provides a wireless communication control system based on the LoRa module, comprising: LoRa configuration module, used to configure LoRa module communication parameters and establish communication channels between the gateway and terminal nodes; The channel monitoring module is used to monitor the RSSI and SNR of the currently used channel, and evaluate and recommend the optimal spreading factor parameters based on the signal quality monitoring results; The channel selection module is used to implement multi-channel optimization selection based on the spreading factor evaluation results, evaluate the transmission capacity of the selected channels, and determine the optimal primary channel and backup channel; Demand forecasting module, which is used to formulate hierarchical resource scheduling strategies and power consumption scheduling strategies based on the communication behavior data analysis of terminal nodes and generate active control instruction sets; A transmission execution module is used to perform hierarchical access control and dynamic migration scheduling based on an active control instruction set, and to execute data transmission tasks; The performance optimization module is used to collect statistics on the transmission performance indicators and prediction accuracy indicators of each channel, adjust relevant control parameters based on the statistical results, and form an adaptive optimization closed-loop control.

[0014] The present invention improves channel adaptability through real-time RSSI / SNR monitoring and dynamic optimization of the spreading factor. A hierarchical scheduling strategy based on terminal behavior prediction enables precise resource allocation. Multi-channel backup and dynamic migration mechanisms enhance system robustness. Differentiated power consumption scheduling significantly optimizes energy efficiency. Closed-loop control enables continuous adaptive parameter optimization. This overall solution enhances the transmission reliability, resource utilization, fault recovery, and long-term stability of LoRa networks, providing a complete technical solution for intelligent operation and maintenance management of low-power wide-area networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a flow chart of a wireless communication control method based on the LoRa module.

[0017] Figure 2 Generate a flow chart for the active control instruction set of a wireless communication control method based on the LoRa module.

[0018] Figure 3 This is a module connection diagram of a wireless communication control system based on the LoRa module. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0022] Reference Figures 1 to 3 , is an embodiment of the present invention, which provides a wireless communication control method based on the LoRa module, the flow chart is as follows Figure 1 As shown, the following steps are included: S1: Configure the LoRa module communication parameters and establish a communication channel between the gateway and the terminal node.

[0023] Specifically, step S1 includes the following steps: S1.1: Select an operating frequency band from the LoRa standard frequency bands based on the geographical distribution of the terminal nodes and the expected communication distance.

[0024] During specific implementation, the geographic location information of the terminal nodes is collected and the maximum communication distance within the network is calculated. Based on the propagation characteristics of different LoRa frequency bands, the low-frequency band has a long propagation distance but low spectrum efficiency, while the high-frequency band has high spectrum efficiency but a short propagation distance. According to the maximum communication distance requirements, the frequency band that meets the coverage requirements is selected; combined with local spectrum usage regulations, the final operating frequency band is determined.

[0025] S1.2: Set the initial spreading factor based on network capacity requirements and configure the corresponding transmission power and bit rate.

[0026] In one embodiment, the network capacity requirement is evaluated based on the number of terminal nodes and the expected data transmission load; an initial spreading factor is selected within the LoRa spreading factor range in combination with the communication distance requirement and the network capacity requirement; a corresponding transmission power level is configured based on the selected spreading factor and the communication distance requirement, and a coding rate parameter is set according to the data reliability requirement.

[0027] S1.3: Determine the bandwidth parameters according to the channel capacity requirements, and write the operating frequency band, initial spreading factor and bandwidth parameters into the LoRa modules of the gateway and terminal nodes.

[0028] During specific implementation, the channel bandwidth is determined based on the data transmission rate requirements and spectrum efficiency requirements; the determined working frequency band, initial spreading factor and bandwidth parameters are written into the gateway LoRa module through the configuration interface, and the same parameters are sent to the LoRa module of each terminal node through the network configuration message.

[0029] S1.4: Assign a device address to each terminal node and establish a communication channel between the gateway and the terminal node.

[0030] Furthermore, a hierarchical address allocation strategy is adopted to generate a unique device address based on the functional type and geographical location of the terminal node; a mapping relationship between the device address and the node identifier is established and stored in a device address allocation table; an address allocation instruction is sent to each terminal node, and the terminal node receives and stores the allocated device address and then replies with a confirmation message, and a logical communication channel is established between the gateway and each terminal node based on the allocated device address.

[0031] S1.5: Verify the connectivity of the communication channel through the handshake protocol, start the network synchronization mechanism to establish the monitoring time benchmark, and set the periodic monitoring parameters.

[0032] During specific implementation, the gateway and each terminal node execute a handshake protocol and verify the connectivity of the communication channel through request-response message interaction; the network time synchronization mechanism is started, the gateway regularly broadcasts time synchronization messages, each terminal node receives the synchronization message and calibrates the local clock to establish a unified monitoring time benchmark for the entire network; according to the geographical distribution density of terminal nodes in the network, the complexity of the communication environment and the real-time requirements of the business, the corresponding periodic monitoring parameters are set.

[0033] Preferably, the present invention combines geographical distribution analysis, network capacity assessment and parameter collaborative optimization through a demand-oriented LoRa communication parameter configuration method, thereby realizing intelligent matching configuration of working frequency band, spreading factor, transmission power and bandwidth parameters, so that the communication parameters can simultaneously meet the comprehensive requirements of coverage, network capacity and transmission reliability; at the same time, through the integrated application of hierarchical address allocation, handshake protocol verification and time synchronization mechanism, a complete network initialization verification system is established, ensuring the reliability of network establishment and the consistency of the time reference of each node, laying a stable foundation for subsequent dynamic monitoring and adaptive optimization.

[0034] S2: Monitor the RSSI and SNR of the currently used channel, evaluate and recommend the optimal spreading factor parameters based on the signal quality monitoring results.

[0035] Specifically, step S2 includes the following steps: S2.1: According to the periodic monitoring parameters and the comparison result between the current network load and the load threshold, the periodic monitoring mode is set and the channel quality monitoring task is started.

[0036] In one embodiment, a baseline monitoring cycle parameter and a comparison result between the current network load and the load threshold are obtained; based on the monitoring parameters and the load comparison result, a corresponding monitoring mode is selected, and a corresponding monitoring time interval is determined: when the network load exceeds the load threshold, a high-frequency monitoring mode is selected; when the network load is lower than the load threshold, a low-frequency monitoring mode is selected; when the network load is equal to the load threshold, a standard monitoring mode is selected; the monitoring mode configuration information is sent to the terminal node, and the corresponding monitoring cycle parameters are set.

[0037] The load threshold is determined based on the theoretical channel capacity and the actual network scale, and 70%-80% of the theoretical channel capacity is taken as the load threshold.

[0038] S2.2: Based on the monitoring time base, standard test frames are regularly sent to the currently used channel to measure the RSSI value and SNR value between the gateway and each terminal node to establish a channel quality database.

[0039] In specific implementation, the gateway uses the monitoring time interval as a benchmark and sends a standard test frame to each terminal node within the preset monitoring period. After receiving the frame, the terminal node replies with a response frame according to the ALOHA mechanism. The gateway measures the RSSI and SNR values ​​based on the received response frame; the measurement data is stored in the channel quality database to establish a long-term monitoring record. The data structure includes node identification, timestamp, RSSI value, SNR value, etc. The database adopts a circular storage mechanism to retain historical data of the most recent multiple monitoring cycles.

[0040] S2.3: Analyze the channel quality change trend over multiple consecutive monitoring periods. When the signal quality drops beyond a preset quality degradation threshold, generate candidate spreading factor parameters and perform preliminary evaluation.

[0041] Specifically, the RSSI and SNR values ​​of each terminal node for N consecutive monitoring cycles (N is 3-5) are extracted from the channel quality database to calculate the signal quality parameter change rate; the signal quality parameter change rate is compared with the preset quality attenuation threshold to identify the target terminal node that needs to adjust the spreading factor; based on the current spreading factor and communication requirements of the target terminal node, a candidate spreading factor parameter set is generated; based on the channel capacity theory and LoRa modulation characteristics, the transmission performance of the candidate spreading factor parameter set is preliminarily evaluated, and a list of parameters to be verified is screened.

[0042] The preset quality attenuation threshold is pre-configured according to the terminal node type and communication distance requirements.

[0043] S2.4: Verify the transmission reliability of the candidate spreading factor parameters through the gateway-side algorithm, determine the optimal spreading factor parameters and generate a spreading factor evaluation result.

[0044] Furthermore, a list of parameters to be verified and the corresponding target terminal node information is obtained, and the current RSSI value and SNR value of each target terminal node are extracted as a verification benchmark; through the gateway-side algorithm, the theoretical transmission distance and signal coverage range of each spreading factor parameter to be verified are calculated to evaluate the transmission reliability; combined with the current network load and terminal node communication requirements, the impact of each spreading factor parameter to be verified on the overall network performance is evaluated; based on key indicators such as transmission reliability, network capacity and transmission efficiency, the optimal spreading factor parameter is determined from the list of parameters to be verified.

[0045] The expected performance improvement and channel matching degree under the optimal spreading factor parameters are further calculated to generate adjustment suggestions. The optimal spreading factor parameters, performance expectations, and adjustment suggestions are encapsulated to generate a spreading factor evaluation result. When the determined optimal spreading factor parameters are different from the current configuration, the optimal spreading factor parameters are sent to the relevant terminal nodes through the network configuration message and the gateway configuration is updated.

[0046] Preferably, the present invention realizes dynamic adaptive optimization of the spreading factor by constructing a channel quality assessment system based on RSSI and SNR monitoring. This solution can timely perceive changes in the channel environment and automatically generate optimal spreading factor parameters through adaptive monitoring frequency adjustment and continuous multi-cycle signal quality trend analysis, significantly improving the network's adaptability to environmental changes, improving the data transmission success rate and overall communication quality, and realizing the technical upgrade of the LoRa network from static parameter configuration to dynamic intelligent adjustment.

[0047] S3: Implement multi-channel optimization selection based on the spreading factor evaluation results, evaluate the transmission capacity of the selected channels, and determine the optimal primary channel and backup channel.

[0048] Specifically, step S3 includes the following steps: S3.1: Based on the spreading factor evaluation results, calculate the transmission performance parameters of each terminal node on the available channel and establish a channel quality score table.

[0049] In one embodiment, all channel frequencies in the current frequency band are scanned, channels with severe interference and channels prohibited by regulations are excluded, and a list of channel frequencies to be evaluated is generated; the optimal spreading factor value currently applied by each terminal node and related transmission performance benchmark data are extracted from the spreading factor evaluation results; for each channel to be evaluated, the path loss value, expected received signal strength RSSI value, and expected signal-to-noise ratio SNR value of each terminal node are calculated; the theoretical data transmission rate of the channel to be evaluated is calculated by combining the LoRa modulation and demodulation characteristics and the optimal spreading factor parameters of each terminal node, and the current load of each channel to be evaluated is counted; the RSSI value, SNR value, data transmission rate, and load condition of the channel to be evaluated are comprehensively scored according to preset weights to generate a channel quality score table, with the preset weights set to 25%, 35%, 25%, and 15%.

[0050] S3.2: Based on the transmission requirements of the terminal node and the current network load, the channels are matched and screened in combination with the LoRa orthogonality constraint to determine the candidate channel list.

[0051] Furthermore, according to the channel quality score table, available channels with scores exceeding a preset channel quality score threshold are screened out, and the preset channel quality score threshold is determined based on network coverage requirements, business service levels and historical benchmarks of channel performance; the data transmission requirements of each terminal node in the network are counted, including business type, data volume and latency requirements, and classified according to the urgency of the requirements; the real-time load status of each available channel in the current network is obtained, including the proportion of channel occupancy time and the number of active terminal nodes; based on the orthogonal characteristics of LoRa spreading factors, the theoretical concurrent capacity of each available channel under the current spreading factor configuration is calculated; the transmission requirements of each terminal node are matched with the channel performance to form a channel priority recommendation scheme; the performance indicators of each channel under the current load are verified to determine the final candidate channel list.

[0052] S3.3: Based on the ranking of the candidate channels, the channel with the highest comprehensive score is selected as the primary channel, and the channel with the second highest score and good complementarity with the primary channel is selected as the backup channel, and a network channel configuration table is established.

[0053] The network channel configuration table includes: operating parameter configurations of the primary and backup channels, channel priority recommendation schemes, related orthogonality constraint information, and basic performance parameters of the primary and backup channels.

[0054] S3.4: Evaluate the transmission performance of the primary channel, calculate the channel utilization, signal coverage, and resource conflict rate, and obtain a comprehensive performance score.

[0055] During specific implementation, the channel utilization is calculated based on the actual transmission time of the main channel; the network signal coverage is calculated based on the RSSI value of the terminal node; the number of statistical transmission conflicts is counted to obtain the resource conflict rate; the above performance indicators are comprehensively scored using the weighted summation method to evaluate the actual transmission performance of the main channel; a performance evaluation cycle is set, and the main channel is continuously monitored and scored within each cycle.

[0056] S3.5: When the comprehensive performance score of the primary channel is lower than the channel switching trigger threshold, the gateway-side load balancing algorithm is used to determine the switching timing, and an instruction to switch to the backup channel is sent to the relevant terminal nodes via downlink data frames, and the channel switching status is recorded.

[0057] It should be noted that the channel switching trigger threshold is set according to the network initialization parameters and application scenario requirements; the gateway-side load balancing algorithm determines the optimal switching time based on the transmission requirements of the terminal node and the performance of the backup channel; when the performance of the main channel continues to deteriorate, the switching process to the backup channel is executed and the network channel configuration table is updated.

[0058] Preferably, the present invention realizes the intelligent configuration of the main channel and the backup channel by constructing a multi-channel optimization selection mechanism based on quantitative scoring. This scheme combines the orthogonality constraint of the spreading factor for channel matching, which significantly improves the concurrent transmission capacity and spectrum utilization efficiency of the network; at the same time, through the dynamic switching mechanism of the main and backup channels, the system's fault tolerance and service continuity are improved, and the overall utilization rate and operational stability of network resources are improved while ensuring the quality of communication.

[0059] S4: Based on the communication behavior data analysis of the terminal nodes, a hierarchical resource scheduling strategy and a power consumption scheduling strategy are formulated to generate an active control instruction set.

[0060] Specifically, the active control instruction set generates a flow chart as follows: Figure 2 As shown, the following steps are included: S4.1: Collect the communication behavior data of the terminal nodes within the preset statistical period, classify the communication behavior data into business models, identify the business types of the terminal nodes and establish a business request model library.

[0061] In one embodiment, a preset statistical period and communication behavior data collection dimensions are set according to the device type and application scenario of the terminal node, and the collection dimensions include data packet size, transmission frequency, communication time interval, channel occupancy time and data transmission direction; the communication behavior of each terminal node is passively recorded through the gateway, and when the terminal node actively initiates communication, the corresponding data is collected to generate an original communication behavior data set; the original communication behavior data set is preprocessed, and the normal sleep state and abnormal state judgment rules are defined according to the terminal node type, and the abnormal data is eliminated to form a standardized time series data sample; based on the time series data sample, a sliding time window analysis method is used to extract the communication behavior time series characteristics of the terminal node, and the time series characteristics include the communication active period, data transmission peak period, service burst frequency and load change pattern.

[0062] Furthermore, based on the extracted timing characteristics, a rule-based classification method based on transmission frequency and time interval is used to identify the service patterns of terminal nodes, classifying them into three categories: periodic, bursty, and mixed. A clustering algorithm is then used to segment and optimize service patterns based on network scale. Based on the service pattern classification results, a parameter template containing key characteristic parameters and weight coefficients is generated, forming a service request pattern library with an adaptive update mechanism. By constructing a service pattern classification mechanism based on timing characteristic analysis and an adaptively updated service request pattern library, the precise identification and pattern-based management of terminal node service behaviors are achieved, providing an accurate data foundation for subsequent hierarchical resource scheduling and power consumption optimization.

[0063] In addition, the method for determining the preset statistical period includes: determining the benchmark period based on the communication frequency baseline of the terminal node, and the benchmark period is set to 2-5 times the average communication interval; adjusting based on the network load situation, and extending the statistical period when the number of network nodes exceeds the network capacity design benchmark value; setting the period upper limit according to the storage capacity limit.

[0064] Preferably, for terminal nodes with periodic services, the preset statistical period is 3-10 times of its service period; for terminal nodes with sudden services, the preset statistical period is 1-6 hours; for terminal nodes with mixed services, the preset statistical period is 2-12 hours.

[0065] S4.2: Establish a hierarchical priority mapping relationship based on the business request pattern library.

[0066] In one embodiment, characteristic parameters of each service mode type are extracted from a service request mode library; the service modes are prioritized and quantitatively scored based on indicators such as data timeliness requirements, transmission frequency, power consumption sensitivity, and service quality level; the service modes are divided into three priority levels of high, medium, and low according to the scoring results, and a mapping relationship between the service modes and the priority levels is established; when the channel quality changes significantly, the priority level is dynamically adjusted according to the current network status.

[0067] S4.3: Combining the communication behavior data and priority mapping relationship, a hierarchical scoring algorithm is used to calculate the communication scheduling priority of the terminal node.

[0068] Furthermore, a basic scheduling score is calculated based on each terminal node's current communication behavior data, combined with its corresponding business model and priority level. This basic scheduling score is dynamically revised based on network load and competition with nodes of the same priority level. A hierarchical scoring algorithm is used to schedule and sort terminal nodes, generating a communication scheduling priority list. By constructing a hierarchical priority mapping mechanism with multi-dimensional quantitative scoring, the dynamic calculation and intelligent sorting of terminal node scheduling priorities are achieved, improving the accuracy and real-time adaptability of network resource scheduling.

[0069] S4.4: Based on the communication scheduling priority and service request pattern library, a time period prediction method is used to predict the expected active period and data transmission load of the terminal node in the next statistical period.

[0070] During specific implementation, the historical communication behavior time series characteristics of the terminal nodes are extracted from the business request pattern library; a prediction accuracy evaluation mechanism is established to monitor the accuracy performance of each prediction method in real time; a moving average prediction method based on periodic laws is adopted for periodic business patterns, a probability distribution prediction method based on historical statistics is adopted for bursty business patterns, and a combined prediction method is adopted for hybrid business patterns; based on the prediction accuracy evaluation results, the parameter weights and time window sizes of each prediction method are dynamically adjusted; combined with the communication scheduling priority of the terminal node, the credibility and scheduling timing of the prediction results are adjusted based on the priority weight, and the expected active period and data transmission load of the terminal node in the next statistical period are determined.

[0071] Furthermore, for periodic business models: based on the historical communication cycle and time interval stability, the weighted moving average prediction method is used to predict the active period of the next statistical period, and the transmission load is predicted in combination with the statistical characteristics of the data packet size; for bursty business models: based on the business burst frequency and peak period distribution, the probability distribution prediction method is used to calculate the active probability and transmission load of each period in the next statistical period; for mixed business models: the periodic component and the burst component are predicted separately and then weighted combined, and the weight coefficient is determined based on the contribution ratio of the periodic component to the burst component in the historical data.

[0072] Preferably, when the historical data of the terminal node is insufficient, a reference node with similar business characteristic parameters is selected from the business request pattern library, and the pattern migration method is used to obtain the initial prediction parameters; a prediction accuracy feedback mechanism is established, and the weight coefficients and time window parameters of each prediction method are dynamically updated according to the deviation between the actual communication behavior and the prediction result, forming an adaptive learning optimization process.

[0073] Furthermore, a prediction accuracy threshold is set. When the accuracy of a prediction method falls below the threshold, a parameter recalibration process is automatically triggered. In the event of significant changes in the network environment, a rapid adaptation mechanism is activated, shortening the statistical window of historical data and improving the responsiveness of each prediction method to environmental changes. By building a differentiated prediction mechanism based on business model classification, accurate predictions of terminal node active periods and transmission loads are achieved. Combined with adaptive learning optimization, prediction accuracy is significantly improved, providing reliable data support for the formulation of hierarchical resource scheduling strategies.

[0074] S4.5: Obtain configuration parameters of the primary and backup channels, and formulate a hierarchical resource scheduling strategy and a differentiated power consumption scheduling strategy based on the prediction results and the status of the primary and backup channels.

[0075] During specific implementation, the working parameters of the primary and backup channels are obtained from the network channel configuration table; the terminal nodes are divided into three priority levels: high, medium and low based on the communication scheduling priority list; based on the expected active period of the terminal nodes and the data transmission load prediction results, combined with the current primary and backup channel status, a hierarchical resource scheduling strategy is formulated: high-priority layer nodes give priority to allocating primary channel resources and configure smaller backoff parameters, medium-priority layer nodes are dynamically scheduled between the primary and backup channels and use medium backoff parameters, and low-priority layer nodes give priority to using backup channels and set larger backoff parameters; based on the LoRaWAN protocol framework, the resource scheduling strategy is implemented by adjusting the receiving window timing and backoff parameters; differentiated power consumption scheduling strategies are set according to the business characteristics and expected active period of nodes at each level.

[0076] Furthermore, a differentiated power consumption scheduling mechanism is set up based on the priority level of the node, and fine-grained control of power consumption is achieved by adjusting the monitoring frequency, the duration of the receiving window and the sleep period; high-priority level nodes adopt a shorter monitoring period to ensure timely response, medium-priority level nodes dynamically adjust power consumption based on expected active periods, and low-priority level nodes maximize the sleep time to reduce power consumption while ensuring communication connection requirements.

[0077] Preferably, by establishing a hierarchical resource scheduling and differentiated power consumption management mechanism based on priority levels, high-quality resource guarantee for high-priority services and power consumption optimization for low-priority services are achieved, significantly improving the LoRa network's service quality hierarchical guarantee capability and overall energy efficiency level in multiple service scenarios.

[0078] S4.6: Generate a gateway hierarchical scheduling schedule based on the expected active period and communication scheduling priority, establish a mapping relationship between node priority and access strategy, and formulate a hierarchical access scheduling scheme compatible with the LoRaWAN protocol.

[0079] Specifically, based on the expected active periods and communication scheduling priorities of terminal nodes, a hierarchical allocation proposal for multi-channel resources is formulated; a hierarchical scheduling schedule is established, and differentiated communication timing recommendations are pushed to nodes of different priorities through the LoRaWAN downlink message mechanism; a mapping relationship between node priority and access strategy is established, and based on the LoRaWAN protocol framework, downlink scheduling instructions are used to guide terminal nodes to adopt corresponding sending timing and transmission parameter configurations.

[0080] Furthermore, hierarchical guidance thresholds are set based on the channel utilization monitoring results. When channel congestion is detected, delay or channel switching suggestions are sent to medium and low priority nodes first. A differentiated guidance mechanism based on priority is adopted to provide personalized communication scheduling suggestions for nodes at different levels through downlink messages. A priority channel mechanism is reserved for emergency services to ensure that the basic communication needs of key nodes are guaranteed.

[0081] Preferably, by building a hierarchical access scheduling solution compatible with the LoRaWAN protocol, network resource utilization is improved and communication conflicts are reduced. Different priority services receive differentiated service quality guarantees, significantly improving the overall throughput and operational stability of the network in multi-service mixed scenarios.

[0082] S4.7: Encapsulate the gateway hierarchical scheduling schedule, priority-access policy mapping relationship, and hierarchical access scheduling scheme into an active control instruction set.

[0083] It should be noted that encapsulation is achieved through a structured data format, and scheduling parameters are organized according to priority levels based on the LoRaWAN protocol; the validity period of the instruction is set, and the issuance of instructions is reasonably arranged according to the downlink communication restrictions.

[0084] Preferably, by building a hierarchical scheduling system based on terminal communication behavior analysis, the LoRa network has achieved a transformation from static resource allocation to prediction-driven dynamic scheduling. This system combines differentiated prediction algorithms, adaptive learning mechanisms and hierarchical resource scheduling, significantly improving network resource utilization efficiency, service quality assurance capabilities and overall energy efficiency in multiple business scenarios, and providing a complete technical solution for the intelligent management of low-power wide area networks.

[0085] S5: Execute hierarchical access control and dynamic migration scheduling based on the active control instruction set, and perform data transmission tasks.

[0086] Specifically, step S5 includes the following steps: S5.1: Obtain the active control instruction set, set the gateway side working mode according to the gateway hierarchical scheduling schedule and differentiated power consumption scheduling strategy, and monitor the active access request of the terminal node.

[0087] In one embodiment, the gateway parses the active control instruction set and configures a hierarchical access scheduling scheme; sets the gateway's monitoring mechanism according to the scheduling scheme and configures differentiated receiving window parameters according to priority; and adopts a multi-channel parallel monitoring mechanism to simultaneously monitor access requests on the primary and backup channels.

[0088] Furthermore, the gateway dynamically adjusts receiving parameters based on the load distribution during the expected active period; when a node access request is detected, authentication and access control are performed based on the priority-access policy mapping relationship; load evaluation is performed on access requests that exceed the expected period, and dynamic adjustment of the hierarchical resource scheduling policy is triggered when necessary.

[0089] S5.2: When a terminal node initiates a communication request, its communication scheduling priority is queried according to the node identifier, and access control is performed based on the hierarchical resource scheduling strategy and the ALOHA protocol.

[0090] Furthermore, differentiated backoff parameters and receive window parameters are configured based on node priority: high-priority nodes use smaller backoff parameters, medium-priority nodes use medium backoff parameters, and low-priority nodes use larger backoff parameters. When resource contention arises, priority control is achieved through backoff parameter differentiation. Through priority-access policy mapping and differentiated backoff parameter configuration, differentiated access control is achieved for nodes of different priority levels.

[0091] S5.3: Establish a temporary node-channel binding relationship, perform the data transmission task, and release the channel resources after the transmission is completed.

[0092] During specific implementation, transmission parameters are allocated to access nodes, including channel frequency, spreading factor and transmission power; the data transmission process is monitored and transmission performance indicators are recorded; channel resources are released in a timely manner after the transmission is completed and the channel availability status is updated; and node access records are maintained for subsequent scheduling optimization.

[0093] S5.4: Monitor the operating parameters of the primary channel in real time. When an abnormality is detected in the primary channel, perform load migration adjustment on the backup channel on the gateway side.

[0094] Specifically, the system continuously monitors the signal quality, load level, and transmission success rate of the primary channel. When the primary channel performance falls below the channel switching trigger threshold, it triggers the backup channel switching mechanism. Load migration is performed according to node priority to ensure the transmission stability of high-priority services. After the migration is complete, the network channel configuration table is updated. Based on load distribution prediction and real-time monitoring, adaptive allocation and adjustment of gateway-side resources are achieved.

[0095] Optimally, by building a hierarchical access control and dynamic migration scheduling system based on an active control instruction set, the LoRa network has transitioned from passive response access to intelligent predictive scheduling. This step, combined with priority-driven access strategies, differentiated backoff control, and multi-channel dynamic migration mechanisms, significantly improves access success rates, resource allocation efficiency, and transmission stability in multi-priority service scenarios, providing a complete technical solution for differentiated service management in low-power wide-area networks.

[0096] S6: Count the transmission performance indicators and prediction accuracy indicators of each channel, adjust the relevant control parameters based on the statistical results, and form an adaptive optimization closed-loop control.

[0097] Specifically, channel transmission performance indicators are collected, including channel utilization, signal quality indicators (RSSI value and SNR value), transmission success rate and latency level; the accuracy of service predictions is evaluated, and the matching degree between expected active periods and actual communication behaviors, as well as load prediction deviations, is calculated; the effectiveness of hierarchical access control is evaluated, and the access success rate and resource occupancy ratio of each priority level are counted; based on performance statistical results, the channel switching trigger threshold, prediction model parameters and hierarchical resource scheduling strategy are dynamically adjusted; a performance feedback mechanism is established, and the optimized control parameters are updated to the network channel configuration table to achieve continuous optimization of network operation efficiency.

[0098] Advantageously, the present invention achieves a comprehensive shift from static parameter configuration to prediction-driven adaptive scheduling by constructing an intelligent wireless communication control system based on the LoRa module. This system combines adaptive channel quality monitoring, dynamic multi-channel optimization and selection, terminal behavior prediction and analysis, hierarchical access control with dynamic migration scheduling, and a closed-loop optimization mechanism to systematically address the resource allocation efficiency, service quality assurance, and energy consumption control issues of LoRa networks in complex environments.

[0099] This embodiment also provides a wireless communication control system based on the LoRa module. The module connection diagram is as follows: Figure 3 Shown, including: LoRa configuration module, used to configure LoRa module communication parameters and establish communication channels between the gateway and terminal nodes; The channel monitoring module is used to monitor the RSSI and SNR of the currently used channel, and evaluate and recommend the optimal spreading factor parameters based on the signal quality monitoring results; The channel selection module is used to implement multi-channel optimization selection based on the spreading factor evaluation results, evaluate the transmission capacity of the selected channels, and determine the optimal primary channel and backup channel; Demand forecasting module, which is used to formulate hierarchical resource scheduling strategies and power consumption scheduling strategies based on the communication behavior data analysis of terminal nodes and generate active control instruction sets; A transmission execution module is used to perform hierarchical access control and dynamic migration scheduling based on an active control instruction set, and to execute data transmission tasks; The performance optimization module is used to collect statistics on the transmission performance indicators and prediction accuracy indicators of each channel, adjust relevant control parameters based on the statistical results, and form an adaptive optimization closed-loop control.

[0100] In summary, this invention improves channel adaptability through real-time RSSI / SNR monitoring and dynamic optimization of the spreading factor. A hierarchical scheduling strategy based on terminal behavior prediction enables precise resource allocation. Multi-channel backup and dynamic migration mechanisms enhance system robustness. Differentiated power consumption scheduling significantly optimizes energy efficiency, and closed-loop control enables continuous adaptive parameter optimization. This overall solution enhances the transmission reliability, resource utilization, fault recovery, and long-term stability of LoRa networks, providing a complete technical solution for intelligent operation and maintenance management of low-power wide-area networks.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wireless communication control method based on LoRa module, characterized in that: include, Configure the LoRa module communication parameters and establish a communication channel between the gateway and the terminal node; Monitor the RSSI and SNR of the currently used channel, evaluate and recommend the optimal spreading factor parameters based on the signal quality monitoring results; Implement multi-channel optimization selection based on the spreading factor evaluation results, evaluate the transmission capacity of the selected channels, and determine the optimal primary and backup channels; Formulate hierarchical resource scheduling strategies and power consumption scheduling strategies based on the communication behavior data analysis of terminal nodes, and generate active control instruction sets; Executes hierarchical access control and dynamic migration scheduling based on active control instruction sets, and performs data transmission tasks; The transmission performance indicators and prediction accuracy indicators of each channel are counted, and the relevant control parameters are adjusted based on the statistical results to form an adaptive optimization closed-loop control.

2. The wireless communication control method based on the LoRa module as claimed in claim 1, wherein: The configuration of LoRa module communication parameters includes: Select the operating frequency band from the LoRa standard frequency band based on the geographical distribution of the terminal nodes and the expected communication distance; Set the initial spreading factor based on network capacity requirements and configure the corresponding transmission power and bit rate; Determine the bandwidth parameters based on the channel capacity requirements, and write the operating frequency band, initial spreading factor, and bandwidth parameters into the LoRa modules of the gateway and terminal nodes; Assign device addresses to each terminal node and establish communication channels between the gateway and the terminal nodes; Verify the connectivity of the communication channel through the handshake protocol, start the network synchronization mechanism to establish the monitoring time benchmark, and set the periodic monitoring parameters.

3. The wireless communication control method based on the LoRa module as claimed in claim 1, wherein: The evaluating and recommending the optimal spreading factor parameters according to the signal quality monitoring results includes: According to the periodic monitoring parameters and the comparison result between the current network load and the load threshold, the periodic monitoring mode is set and the channel quality monitoring task is started; Based on the monitoring time base, standard test frames are regularly sent to the currently used channel to measure the RSSI and SNR values ​​between the gateway and each terminal node to establish a channel quality database; Analyze the channel quality change trend over multiple consecutive monitoring cycles. When the signal quality drops beyond a preset quality degradation threshold, generate candidate spreading factor parameters and perform preliminary evaluation. The transmission reliability of the candidate spreading factor parameters is verified through the gateway-side algorithm, the optimal spreading factor parameters are determined, and the spreading factor evaluation results are generated.

4. The wireless communication control method based on the LoRa module as claimed in claim 1, wherein: The implementing multi-channel optimization selection based on the spreading factor evaluation result includes: Based on the spreading factor evaluation results, the transmission performance parameters of each terminal node on the available channel are calculated and a channel quality score table is established; According to the transmission requirements of the terminal node and the current network load, the channels are matched and screened in combination with the LoRa orthogonality constraint to determine the candidate channel list; Based on the scoring ranking of each candidate channel, the primary channel and the backup channel are selected, and a network channel configuration table is established; Evaluate the transmission performance of the primary channel, calculate channel utilization, signal coverage, and resource conflict rate, and obtain a comprehensive performance score; When the comprehensive performance score of the primary channel is lower than the channel switching trigger threshold, the switching timing is determined by the gateway-side load balancing algorithm, and an instruction to switch to the backup channel is sent to the relevant terminal nodes.

5. The wireless communication control method based on the LoRa module as claimed in claim 1, wherein: The formulation of a hierarchical resource scheduling strategy and a power consumption scheduling strategy based on the communication behavior data analysis of the terminal nodes includes: Collecting communication behavior data of terminal nodes within a preset statistical period, classifying the communication behavior data into business models, identifying the business types of terminal nodes and establishing a business request model library; Establish hierarchical priority mapping relationships based on the business request pattern library; Combining communication behavior data and priority mapping, a hierarchical scoring algorithm is used to calculate the communication scheduling priority of the terminal node; Based on the communication scheduling priority and the service request pattern library, a time period prediction method is used to predict the expected active time period and data transmission load of the terminal node in the next statistical period; Obtain the configuration parameters of the primary and backup channels, and formulate hierarchical resource scheduling strategies and differentiated power consumption scheduling strategies based on the prediction results and the status of the primary and backup channels.

6. The wireless communication control method based on the LoRa module as claimed in claim 1, wherein: Generating the active control instruction set includes: Generate a gateway hierarchical scheduling schedule based on expected active periods and communication scheduling priorities, establish a mapping relationship between node priorities and access policies, and formulate a hierarchical access scheduling plan; The gateway hierarchical scheduling schedule, priority-access policy mapping relationship and hierarchical access scheduling scheme are encapsulated as an active control instruction set.

7. The wireless communication control method based on the LoRa module as claimed in claim 1, wherein: The performing of hierarchical access control based on the active control instruction set includes: Obtain active control instruction sets, set the gateway-side working mode according to the gateway hierarchical scheduling schedule and differentiated power consumption scheduling strategy, and monitor active access requests from terminal nodes; When a terminal node initiates a communication request, its communication scheduling priority is queried according to the node ID, and access control is performed based on the hierarchical resource scheduling strategy and ALOHA protocol; Establish a temporary node-channel binding relationship, perform data transmission tasks, and release channel resources after the transmission is completed; Monitor the operating parameters of the main channel in real time. When an abnormality is detected in the main channel, perform load migration adjustment on the backup channel on the gateway side.

8. A wireless communication control system based on a LoRa module, based on the wireless communication control method based on a LoRa module according to any one of claims 1 to 7, characterized in that: include, LoRa configuration module, used to configure LoRa module communication parameters and establish communication channels between the gateway and terminal nodes; The channel monitoring module is used to monitor the RSSI and SNR of the currently used channel, and evaluate and recommend the optimal spreading factor parameters based on the signal quality monitoring results; The channel selection module is used to implement multi-channel optimization selection based on the spreading factor evaluation results, evaluate the transmission capacity of the selected channels, and determine the optimal primary channel and backup channel; Demand forecasting module, which is used to formulate hierarchical resource scheduling strategies and power consumption scheduling strategies based on the communication behavior data analysis of terminal nodes and generate active control instruction sets; A transmission execution module is used to perform hierarchical access control and dynamic migration scheduling based on an active control instruction set, and to execute data transmission tasks; The performance optimization module is used to collect statistics on the transmission performance indicators and prediction accuracy indicators of each channel, adjust relevant control parameters based on the statistical results, and form an adaptive optimization closed-loop control.

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