Method and system for dynamic link adaptation control of loRa network based on environment perception

Abstract

The application relates to the technical field of Internet of Things communication, in particular to a LoRa network dynamic link adaptive control method and system based on environment sensing, which comprises the following steps: firstly, presetting initial communication parameters for a terminal and collecting data and link information of the terminal; based on building and traffic data on a transmission path, an environment sensing model is constructed to quantize environment influence factors, and a dynamic evaluation matrix is established in combination with performance indexes; based on the matrix, a comprehensive performance index of a current link is calculated, a relative difference is obtained by predicting signal-to-noise ratio change, the system analyzes a performance index change trend, an adaptive algorithm is used to generate optimization suggestions of communication parameters, and performance improvement brought by the optimization suggestions is predicted; by balancing performance improvement and energy consumption and time delay cost required for adjustment, parameter adjustment is only performed when the benefit is greater than the cost; finally, optimization of all terminals is completed within fixed network distribution time, and overall network performance is evaluated; and the application improves the accuracy of LoRa network dynamic link adaptive control.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) communication technology, and in particular to a dynamic link adaptive control method and system for LoRa networks based on environmental awareness. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), LoRa (Long Range) technology, as a core technology of low-power wide-area networks (LPWANs), is widely used in smart cities and other scenarios. However, its existing adaptive data rate algorithms have significant limitations: traditional solutions mainly rely on static signal quality indicators such as signal-to-noise ratio (SNR) and received signal strength for adjustment, making it difficult to quantify and respond to the real-time impact of dynamic factors (such as signal attenuation and Doppler effect) on the link in urban environments, such as building density and traffic flow. This results in lag in parameter adjustment and a decrease in data packet reception rate when the environment changes abruptly. At the same time, existing methods often optimize based on a single indicator, lacking a comprehensive balance between link reliability, network throughput, and terminal power consumption. Furthermore, the parameter adjustment mechanism is rigid, failing to achieve coordinated optimization of spreading factor, power, and bandwidth, and lacking an assessment of the overhead of the adjustment behavior itself. Ultimately, this leads to poor overall network performance in complex and dynamic urban scenarios. Summary of the Invention

[0003] This invention provides a dynamic link adaptive control method and system for LoRa networks based on environment awareness, which solves the problems of inaccurate link quality assessment, delayed parameter adjustment, and poor overall network performance caused by the inability of existing LoRa network adaptive algorithms to sense and respond to real-time environmental changes in complex and dynamic urban environments.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] The first aspect of this invention is to provide an environment-aware dynamic link adaptive control method for LoRa networks, comprising:

[0006] The initial communication parameters of the core physical layer of the LoRa network terminal are preset; the LoRa network terminal sends data to the gateway according to the initial communication parameters, the gateway forwards the successfully received data to the network server, and the network server collects terminal information.

[0007] The distribution of buildings and vehicle traffic conditions along the LoRa network transmission path are obtained. An environmental perception model is constructed based on the building distribution and vehicle traffic conditions. Influence factors are obtained through the environmental perception model. A dynamic environmental perception performance evaluation matrix is ​​constructed based on the influence factors and the performance indicators in the terminal information.

[0008] The comprehensive performance index of the link quality at the current moment is obtained based on the dynamic environment perception performance evaluation matrix; the signal-to-noise ratio data at subsequent moments is predicted by the historical signal-to-noise ratio data in the terminal information; and the relative difference of signal-to-noise ratio is obtained based on the difference between the signal-to-noise ratio data at the subsequent moments and the empirical optimal value of signal-to-noise ratio.

[0009] Based on the comprehensive performance index, the rate of change of the comprehensive performance index at the current moment is obtained; based on the rate of change of the comprehensive performance index, the comprehensive performance index, and the relative difference between the signal-to-noise ratio and the comprehensive performance index, the optimization value of the communication parameters is obtained through a comprehensive adaptive adjustment algorithm; based on the optimization value of the communication parameters, the comprehensive performance index to be optimized is obtained.

[0010] Based on the comprehensive performance index to be optimized and the comprehensive performance index of the link quality at the current moment, the absolute improvement degree of the link quality is obtained; the energy consumption cost and latency cost of the network are obtained, and the overhead evaluation factor to be adjusted is obtained based on the absolute improvement degree of the link quality, energy consumption cost and latency cost; the overhead evaluation factor to be adjusted is used to determine whether the communication parameters should be adjusted to the value to be optimized, so as to complete the optimization of the communication parameters of a single terminal.

[0011] The communication parameters of all terminals are optimized within a preset network configuration time, and the optimized communication parameters of all terminals are output. Then, the overall network performance is evaluated based on the optimized communication parameters of all terminals.

[0012] Further, the step of constructing an environmental perception model based on the building distribution and vehicle traffic conditions; obtaining influencing factors through the environmental perception model; and constructing a dynamic environmental perception performance evaluation matrix based on the influencing factors and performance indicators in the terminal information includes:

[0013] Based on the building distribution and vehicle traffic conditions, the building density index and traffic flow of the transmission path between the terminal and the gateway are obtained; based on the building density index, the transmission path loss index is obtained; a three-dimensional spatial signal attenuation model is constructed based on the transmission path loss index, and the building density influence factor is obtained through the three-dimensional spatial signal attenuation model; a dynamic traffic flow interference model is constructed based on the traffic flow, and the traffic flow influence factor is obtained through the dynamic traffic flow interference model; a dynamic environmental perception performance evaluation matrix is ​​constructed based on the building density influence factor, the traffic flow influence factor, and the performance indicators in the terminal information.

[0014] The environmental perception model includes a three-dimensional spatial signal attenuation model and a dynamic traffic flow interference model; the influencing factors include building density influencing factors and traffic flow influencing factors.

[0015] The traffic flow is the number of vehicles passing through the transmission path per unit time, obtained through real-time monitoring.

[0016] The building density index is specifically expressed by the formula:

[0017]

[0018] In the formula, Indicates the first The projected area of ​​a building along the transmission path. Indicates the first Location weighting factors for each building Indicates the total area of ​​the region. This represents the total number of buildings along the transmission path. Indicates the building density index;

[0019] The transmission path loss index is specifically expressed by the formula:

[0020]

[0021] In the formula, This represents the baseline path loss index in a free-space environment. Represents the mapping coefficients. Indicates the transmission path loss index;

[0022] The three-dimensional spatial signal attenuation model is specifically expressed by the following formula:

[0023]

[0024] In the formula, Indicates distance as Path loss, Indicates reference distance Path loss at the location, Indicates the transmission path loss index. Indicates the distance of the transmission path. Indicates the reference distance. Indicates building obstruction loss. This indicates additional losses due to terrain undulations;

[0025] The building density influence factor is specifically expressed by the formula:

[0026]

[0027] In the formula, Indicates distance as Free space path loss, Indicates the building density influence factor;

[0028] The dynamic traffic flow disturbance model is specifically expressed by the following formula:

[0029]

[0030] In the formula, Indicates the dynamic disturbance intensity of traffic flow. Indicates the intensity of the Doppler effect. Indicates the intensity of reflected interference. Indicates the intensity of multipath interference;

[0031] The intensity of the Doppler effect is specifically expressed by the formula:

[0032]

[0033] In the formula, This represents the average speed of the vehicle. Represents the speed of light. Indicates the LoRa carrier frequency. Indicates traffic flow. Indicates standard vehicle density. Indicates the first fitting coefficient;

[0034] The intensity of reflected interference is specifically expressed by the formula:

[0035]

[0036] In the formula, This represents the second fitting coefficient. Indicates the reflectance coefficient of the vehicle surface material;

[0037] The multipath interference intensity is the measured multipath delay spread data;

[0038] The traffic flow influencing factor is specifically expressed by the formula:

[0039]

[0040] In the formula, Indicates factors affecting traffic flow. Indicates the reference interference intensity;

[0041] A dynamic environmental perception performance evaluation matrix is ​​constructed with environmental features as rows and performance indicators from terminal information as columns. Among them, the environmental features are building density influence factor and traffic flow influence factor; the performance indicators from terminal information are signal-to-noise ratio, received signal strength and data packet reception rate from terminal information.

[0042] Among them, the element values ​​in the dynamic environment perception performance evaluation matrix are obtained by fitting a multiple linear regression function, that is, constructing a multiple linear function between a performance index in the terminal information and the building density influence factor and the traffic flow influence factor.

[0043] Further, the comprehensive performance index of the link quality at the current moment is obtained based on the dynamic environment perception performance evaluation matrix; the signal-to-noise ratio (SNR) data at subsequent moments is predicted using historical SNR data from the terminal information; and the relative difference in SNR is obtained based on the difference between the SNR data at subsequent moments and the empirically optimal SNR value, including:

[0044] The comprehensive performance index is specifically expressed by the formula:

[0045]

[0046] In the formula, Indicates the first Dynamic weights of environmental features Indicates the first The weight of various performance indicators Indicates the normalized i-th The values ​​of various performance indicators The first element in the dynamic environment perception performance evaluation matrix represents the... Line number The element values ​​of the column, This indicates the number of all performance metrics. This represents the number of all environmental features. This represents the comprehensive performance index; the dynamic weights of all environmental features are determined using fuzzy hierarchical analysis; the weights of all performance indicators are determined using a combination of expert scoring and CRITIC objective weighting.

[0047] The network server continuously collects and stores the historical signal-to-noise ratio (SNR) data of the terminals, forming a time series, denoted as the SNR sequence; based on the SNR sequence, the SNR data for subsequent time moments is predicted using the ordinary kriging method.

[0048] Based on the predicted signal-to-noise ratio (SNR) data for subsequent time moments and the empirically optimal SNR value under the same environmental conditions, the relative SNR difference is obtained; specifically, the relative SNR difference is expressed by the formula:

[0049]

[0050] In the formula, This represents the relative difference in signal-to-noise ratio. Indicates the subsequent Predicted signal-to-noise ratio data at each time point. This represents the empirically optimal signal-to-noise ratio under the same environmental conditions. It is the absolute value symbol. This represents the preset parameter factor.

[0051] Further, the process involves obtaining the rate of change of the comprehensive performance index at the current moment based on the comprehensive performance index; obtaining the optimization value of the communication parameters through a comprehensive adaptive adjustment algorithm based on the rate of change of the comprehensive performance index, the comprehensive performance index, and the relative difference between the signal-to-noise ratio and the comprehensive performance index; and obtaining the comprehensive performance index to be optimized based on the optimization value of the communication parameters, including:

[0052] A reference duration is preset, and the reference duration before each moment is used as the time window for each moment. The comprehensive performance index corresponding to the moments on both sides of each time window is obtained. Based on the difference between the comprehensive performance index corresponding to the moments on both sides of each time window, the rate of change of the comprehensive performance index of each time window is obtained.

[0053] Among them, the rate of change of the comprehensive performance index for each time window is the ratio of the difference in the comprehensive performance index at the two moments on both sides of each time window to the duration of each time window;

[0054] The specific process of the comprehensive adaptive adjustment algorithm is as follows:

[0055] Obtain the current time window; if the rate of change of the comprehensive performance index within the current time window exceeds the performance change rate threshold... When the overall performance index change rate is less than or equal to the performance change rate threshold, the system will trigger an emergency adjustment mechanism. And the difference between the comprehensive performance index corresponding to the three previous time points is less than or equal to 1. If this occurs, the system triggers the fuzzy decision tree global optimization mechanism; among which, the performance change rate threshold... and This is the default value;

[0056] When the system triggers the emergency adjustment mechanism, the core physical layer communication parameters of the LoRa terminal are adjusted to obtain the adjusted core physical layer communication parameters; when the system triggers the fuzzy decision tree global optimization mechanism, the core physical layer communication parameters of the LoRa terminal are globally optimized to obtain the globally optimized core physical layer communication parameters.

[0057] Among them, the core physical layer communication parameters of LoRa terminals include spreading factor, transmit power and modulation bandwidth;

[0058] Specifically, when the system triggers the emergency adjustment mechanism, the core physical layer communication parameters of the LoRa terminal are adjusted to obtain the adjusted core physical layer communication parameters; the specific adjustment process is as follows:

[0059] The overall performance index at the current moment and At this point, it represents an extreme case; under extreme conditions, the spreading factor SF is adjusted to 12, the transmit power TP is maximized, and the modulation bandwidth BW is adjusted to 125kHz; when the overall performance index at the current moment... and At this time, it is a severe situation; under severe conditions, the spreading factor SF is adjusted to 10, the transmit power TP is increased by 3dB, and the modulation bandwidth BW is adjusted to 125kHz; under other conditions, the communication parameters are not adjusted.

[0060] in, , , and All are preset values;

[0061] Specifically, when the system triggers the fuzzy decision tree global optimization mechanism, it performs global optimization on the core physical layer communication parameters of the LoRa terminal to obtain the globally optimized core physical layer communication parameters, including:

[0062] The comprehensive performance index, relative difference in signal-to-noise ratio, building density influence factor, and traffic flow influence factor are used as the feature parameters input to the fuzzy decision tree; the spreading factor SF, transmit power TP, and modulation bandwidth BW are used as the feature parameters output to the fuzzy decision tree.

[0063] The input feature parameters are divided into three fuzzy levels: the comprehensive performance index is divided into poor, medium, and good; the relative difference in signal-to-noise ratio is divided into small, medium, and large; the building density influence factor is divided into low, medium, and high; and the traffic flow influence factor is divided into small, medium, and large. The output feature parameters are also divided into three fuzzy levels: the spreading factor is divided into low, medium, and high; the transmit power is divided into low, medium, and high; and the modulation bandwidth is divided into narrow bandwidth, medium bandwidth, and wide bandwidth. These output feature parameters are used as the optimization values ​​for the communication parameters.

[0064] Based on the values ​​of the communication parameters to be optimized, the overall performance index to be optimized is recalculated and obtained.

[0065] Furthermore, the absolute improvement in link quality is obtained based on the overall performance index to be optimized and the overall performance index of the link quality at the current moment. This absolute improvement in link quality is specifically expressed by the following formula:

[0066]

[0067] In the formula, This represents the overall performance index that needs to be optimized. This represents the overall performance index of the link quality at the current moment. It indicates the absolute degree of improvement in link quality.

[0068] Further, the step of obtaining the overhead evaluation factor to be adjusted based on the absolute improvement in link quality, energy consumption cost, and latency cost; and determining whether to adjust the communication parameters to the value to be optimized using the overhead evaluation factor to be adjusted, includes:

[0069] Energy consumption costs are standardized to obtain standardized energy consumption costs; specifically, the standardized energy consumption cost is expressed by the following formula:

[0070]

[0071] In the formula, Indicates energy consumption cost, Indicates reference energy consumption cost. Indicates standardized energy consumption cost;

[0072] The latency cost is standardized to obtain a standardized latency cost; specifically, the standardized latency cost is expressed by the following formula:

[0073]

[0074] In the formula, Indicates latency cost, Indicates the reference delay cost. Indicates the standardized latency cost;

[0075] The total adjustment cost is obtained by weighting the standardized energy consumption cost and the standardized latency cost; the total adjustment cost is specifically expressed by the formula:

[0076]

[0077] In the formula, Indicates energy consumption weight. Indicates the delay weight. This represents the total adjustment cost; among which, and All are preset values;

[0078] The specific formula for the cost evaluation factor to be adjusted is as follows:

[0079]

[0080] In the formula, Indicates the absolute degree of improvement in link quality. This indicates the expense evaluation factor to be adjusted;

[0081] When the cost evaluation factor to be adjusted is greater than or equal to the preset cost threshold, the communication parameters are adjusted to the value to be optimized; when the cost evaluation factor to be adjusted is less than the preset cost threshold, the communication parameters are not adjusted.

[0082] Furthermore, the optimization of communication parameters for all terminals within a preset network configuration time, outputting the optimized communication parameters for all terminals, and then evaluating the overall network performance using the optimized communication parameters for all terminals, includes:

[0083] Obtain the overall network performance index before optimization and the overall network performance index after optimization;

[0084] The comprehensive network performance index is specifically expressed by the formula:

[0085]

[0086] In the formula, This indicates the performance evaluation metrics for coverage. This represents a performance metric for network throughput. Indicates communication reliability evaluation index, Indicates the coverage performance weight. Indicates the network throughput performance weight. Indicates communication reliability weights. This represents the overall network performance index;

[0087] The coverage performance evaluation index is specifically expressed by the following formula:

[0088]

[0089] In the formula, This represents the average signal strength received by the gateway across all terminals. This indicates the additional losses incurred by the outdoor environment relative to the ideal environment. This indicates the additional signal attenuation caused by building density. This represents the total number of all terminals;

[0090] The network throughput performance evaluation index is specifically expressed by the following formula:

[0091]

[0092] In the formula, Indicates the first Data transmission rate of each terminal Indicates the first The data packet error rate of each terminal affected by the environment. This represents the maximum throughput under ideal conditions. This represents the total number of all terminals; Indicates the first The current actual throughput of each terminal This represents the current actual throughput of all terminals;

[0093] The communication reliability evaluation index is specifically expressed by the following formula:

[0094]

[0095] In the formula, Indicates the SF switching frequency. Indicates the sensitivity coefficient to environmental fluctuations. This represents an exponential function with the natural constant as its base. Indicates the data packet reception rate. Indicates communication reliability evaluation indicators;

[0096] The ratio between the optimized overall network performance index and the unoptimized overall network performance index is calculated and denoted as the evaluation ratio. When the evaluation ratio is greater than 1, it indicates that the optimization of communication parameters has improved the LoRa network performance.

[0097] A second aspect of the present invention is to provide an environment-aware LoRa network dynamic link adaptive control system, comprising:

[0098] Initial parameter configuration and information statistics module: used to preset the initial communication parameters of the core physical layer of LoRa network terminals; LoRa network terminals send data to the gateway according to the initial communication parameters, the gateway forwards the successfully received data to the network server, and the network server collects terminal information;

[0099] Environmental perception and model building module: used to obtain the building distribution and vehicle traffic conditions along the LoRa network transmission path, build an environmental perception model based on the building distribution and vehicle traffic conditions, obtain influencing factors through the environmental perception model, and build a dynamic environmental perception performance evaluation matrix based on the influencing factors and the performance indicators in the terminal information;

[0100] Link quality assessment feature quantification module: used to obtain the comprehensive performance index of the link quality at the current moment based on the dynamic environment perception performance evaluation matrix; predict the signal-to-noise ratio data at subsequent moments through historical signal-to-noise ratio data in terminal information, and obtain the relative difference of signal-to-noise ratio based on the difference between the signal-to-noise ratio data at the subsequent moments and the empirical optimal value of signal-to-noise ratio.

[0101] Intelligent parameter decision module: used to obtain the rate of change of the comprehensive performance index at the current moment based on the comprehensive performance index; to obtain the value to be optimized for the communication parameters through a comprehensive adaptive adjustment algorithm based on the rate of change of the comprehensive performance index, the comprehensive performance index and the relative difference between the signal and noise ratio; and to obtain the comprehensive performance index to be optimized based on the value to be optimized for the communication parameters.

[0102] The overhead assessment and execution module is used to obtain the absolute improvement degree of link quality based on the comprehensive performance index to be optimized and the comprehensive performance index of the link quality at the current moment; obtain the network's energy consumption cost and latency cost; obtain the overhead evaluation factor to be adjusted based on the absolute improvement degree of link quality, energy consumption cost, and latency cost; and determine whether to adjust the communication parameters to the value to be optimized based on the overhead evaluation factor to complete the optimization of the communication parameters of a single terminal.

[0103] Global Optimization and Network Performance Evaluation Module: This module optimizes the communication parameters of all terminals within a preset network configuration time, outputs the optimized communication parameters of all terminals, and then evaluates the overall network performance based on the optimized communication parameters of all terminals.

[0104] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the environment-aware LoRa network dynamic link adaptive control method.

[0105] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the environment-aware LoRa network dynamic link adaptive control method.

[0106] Compared with existing technologies, the beneficial effects of this invention are: It presets the initial communication parameters of the core physical layer of the LoRa network terminal; the LoRa network terminal sends data to the gateway according to the initial communication parameters, and the gateway forwards the successfully received data to the network server, which then collects terminal information; it establishes a communication baseline to provide initial data reference for subsequent dynamic evaluation and optimization; it acquires the building distribution and vehicle traffic conditions along the LoRa network transmission path, and constructs an environmental perception model based on these conditions; it obtains influencing factors through the environmental perception model; and it constructs a dynamic environmental perception performance evaluation system based on the influencing factors and performance indicators from the terminal information. The system employs a performance evaluation matrix to quantify dynamic environmental interference, establishing a crucial environmental correlation model for accurate link quality assessment. Based on the dynamic environmental perception performance evaluation matrix, it obtains the comprehensive performance index of the link quality at the current moment. By using historical signal-to-noise ratio (SNR) data from terminal information, it predicts the SNR data for subsequent moments, and obtains the relative SNR difference based on the difference between the subsequent SNR data and the empirically optimal SNR value. This enables a comprehensive and forward-looking assessment of link quality, not only diagnosing the current state but also predicting performance trends. Based on the comprehensive performance index, it obtains the rate of change of the comprehensive performance index at the current moment. Finally, it calculates the relative SNR difference based on the rate of change of the comprehensive performance index, the comprehensive performance index, and the relative SNR difference. The algorithm uses a comprehensive adaptive adjustment method to obtain the desired values ​​for communication parameters. Based on these values, it calculates the desired comprehensive performance index. Depending on the urgency of the link status, it intelligently selects a fast or fine-grained optimization path to ensure accurate and timely parameter adjustments. Using the desired comprehensive performance index and the current link quality index, it determines the absolute improvement in link quality. It also acquires the network's energy and latency costs, and uses these factors to determine the required overhead evaluation factor. Finally, it uses this overhead evaluation factor to decide whether to adjust the communication parameters to the desired values, thus completing the process for a single terminal. The system optimizes the communication parameters of all terminals; introduces cost-benefit analysis to avoid ineffective or counterproductive adjustments, and achieves intelligent allocation of network resources; optimizes the communication parameters of all terminals within a preset network configuration time, outputs the optimized communication parameters of all terminals, and then evaluates the overall network performance based on the optimized communication parameters of all terminals to ensure that all terminals have completed optimization, and verifies the overall performance improvement effect of the dynamic adaptive strategy from a global network perspective; it solves the problems of inaccurate link quality assessment, delayed parameter adjustment, and poor overall network performance caused by the inability of existing LoRa network adaptive algorithms to perceive and respond to real-time environmental changes in complex and dynamic urban environments. Attached Figure Description

[0107] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0108] Figure 1 A flowchart illustrating the steps of the LoRa network dynamic link adaptive control method based on environment awareness is provided for this invention.

[0109] Figure 2 A schematic diagram of the module flow of the LoRa network dynamic link adaptive control system based on environment awareness is provided for this invention.

[0110] Figure 3 This is a schematic diagram of dynamic weight allocation for link quality assessment based on environment awareness.

[0111] Figure 4 A schematic diagram showing the distribution of the empirical optimal SNR value over 24 hours for a single terminal under different spreading factors;

[0112] Figure 5 The graph shows the SNR prediction results for a single terminal under different traffic flows.

[0113] Figure 6 This is a comparison chart of the data packet reception rates between the single-terminal adaptive adjustment scheme and the standard adaptive data rate scheme. Detailed Implementation

[0114] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0115] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0116] To address the problems existing in the background technology, a dynamic link adaptive control method and system for LoRa networks based on environment awareness has been developed, which has significant practical implications.

[0117] like Figure 1 As shown, the first aspect of the present invention is to provide an environment-aware dynamic link adaptive control method for LoRa networks, comprising the following steps:

[0118] Step S1: Preset the initial communication parameters of the core physical layer of the LoRa network terminal; the LoRa network terminal sends data to the gateway according to the initial communication parameters, the gateway forwards the successfully received data to the network server, and the network server collects terminal information.

[0119] The terminal information includes the data content collected and transmitted by the terminal, the signal-to-noise ratio, the received signal strength, and the data packet reception rate.

[0120] In this embodiment, data is collected at 10-second intervals. However, the specific time interval for collection is not limited in this embodiment and can be determined by the implementer according to the specific circumstances.

[0121] At this point, data collection is complete.

[0122] Step S2: Obtain the building distribution and vehicle traffic conditions along the LoRa network transmission path; construct an environmental perception model based on the building distribution and vehicle traffic conditions; obtain influencing factors through the environmental perception model; construct a dynamic environmental perception performance evaluation matrix based on the influencing factors and the performance indicators in the terminal information.

[0123] Specifically, the network server collects building density index and traffic flow data along the transmission path between the terminal and the gateway.

[0124] Traffic flow refers to the number of vehicles passing through the transmission path per unit time, obtained through real-time monitoring.

[0125] The building density index is specifically expressed by the formula:

[0126]

[0127] In the formula, Indicates the first The projected area of ​​a building along the transmission path. Indicates the first The location weighting factor of a building (is an empirical parameter based on communication principles, which is related to the relative position of the building in the communication path). Indicates the total area of ​​the region. This represents the total number of buildings along the transmission path. This indicates the building density index.

[0128] The projected area of ​​a building along the transmission path is calculated automatically by accessing the city's building GIS database (containing the building's location, outline, and height) and using the system. The total area of ​​the region is determined and obtained by drawing a buffer zone on an electronic map, centered on the transmission path between the terminal and the gateway, based on wireless signal propagation models (such as the Fresnel zone principle). The width of this buffer zone is calculated from the signal wavelength and communication distance, and its total area is the "total area" referred to in the formula.

[0129] The transmission path loss index is obtained by linearly mapping the building density index; the transmission path loss index is specifically expressed by the formula:

[0130]

[0131] In the formula, This represents the baseline path loss index in a free-space environment. Indicates the building density index. Represents the mapping coefficients. This represents the transmission path loss index.

[0132] In this embodiment, the baseline path loss index in the free space environment is set to 2.7 based on experience, and the mapping coefficient is set to 0.6 based on experience; however, no specific limitations are made in this embodiment, and the implementer can determine them according to the specific circumstances.

[0133] It should be noted that, in order to transform the two core factors of dynamic changes in the urban environment—building obstruction and traffic interference—from qualitative descriptions to quantitative calculations, the system constructs signal attenuation and traffic flow interference models. This allows for the precise quantification of the signal attenuation caused by building density, as well as the combined effects of Doppler effects and reflection interference caused by traffic flow. This transforms abstract environmental dynamics into calculable "environmental impact factors," providing a crucial data foundation for subsequent accurate assessment of link quality and intelligent adjustment of communication parameters. This completely solves the core deficiency of traditional methods in their inability to perceive and respond to dynamic environmental changes.

[0134] Furthermore, by constructing a three-dimensional spatial signal attenuation model and a dynamic traffic flow interference model, environmental factors such as building density and traffic flow are quantified into specific signal attenuation and interference factors, thereby accurately assessing the impact of the environment on the communication link. Furthermore, by constructing a dynamic environmental perception performance evaluation matrix, a quantitative mapping relationship between environmental parameters and communication performance indicators is established, forming a correlation model between environmental perception and communication quality. The fundamental purpose of this operational logic is to overcome the limitations of traditional methods that rely solely on historical signal indicators. By quantifying the dynamic impact of the environment in real time, it provides accurate environmental perception data support for link quality assessment and parameter adjustment, ultimately achieving intelligent optimization and reliable assurance of LoRa network communication quality in complex urban environments.

[0135] Specifically, the three-dimensional spatial signal attenuation model is expressed as follows:

[0136]

[0137] In the formula, Indicates distance as Path loss (in decibels, dB). Indicates reference distance Path loss at the location, Indicates the transmission path loss index. Indicates the distance of the transmission path. Indicates the reference distance. Indicates building obstruction loss. This indicates additional losses due to terrain undulations.

[0138] Building shading loss is obtained through an empirical lookup table method. Based on the building material (e.g., reinforced concrete, glass) and relative location, the loss is retrieved from a database established beforehand through actual measurements or ray-tracing simulations. Terrain undulation-related loss is calculated using an empirical model. Based on digital elevation model data between the terminal and the gateway, a terrain profile is calculated and substituted into empirical formulas (e.g., the Egli model, the Longley-Rice model) to arrive at the loss.

[0139] Based on the distance The path loss and free space path loss are used to obtain the building density influence factor; the building density influence factor is specifically expressed by the formula:

[0140]

[0141] In the formula, Indicates distance as Path loss, Indicates distance as Free space path loss, This indicates the building density influence factor.

[0142] The dynamic traffic flow disturbance model is specifically represented as follows:

[0143]

[0144] In the formula, Indicates the dynamic disturbance intensity of traffic flow. Indicates the intensity of the Doppler effect. Indicates the intensity of reflected interference. This indicates the intensity of multipath interference.

[0145] The intensity of the Doppler effect is specifically expressed by the formula:

[0146]

[0147] In the formula, This represents the average speed of the vehicle. Represents the speed of light. This indicates the LoRa carrier frequency (a known network parameter, 475MHz). Indicates traffic flow. This represents the standard vehicle density (20 vehicles / minute in this embodiment, but not specifically limited in this embodiment). This represents the first fitting coefficient (obtained through regression analysis of measured data using spectral analysis). This indicates the intensity of the Doppler effect.

[0148] The intensity of reflected interference is specifically expressed by the formula:

[0149]

[0150] In the formula, Indicates traffic flow. Indicates standard vehicle density. This represents the second fitting coefficient. This represents the reflectance coefficient of the vehicle surface material (an empirical value, specifically 0.6, but not specifically limited in this embodiment). This indicates the intensity of reflected interference.

[0151] The multipath interference intensity is 1.8 dB, based on measured multipath delay spread data, with a typical value of 1.8 dB in urban environments.

[0152] Based on the dynamic disturbance intensity and baseline disturbance intensity of traffic flow, the traffic flow influencing factor is obtained; the traffic flow influencing factor is specifically expressed by the formula:

[0153]

[0154] In the formula, Indicates factors affecting traffic flow. This indicates the reference interference intensity.

[0155] A dynamic environmental perception performance evaluation matrix is ​​constructed with environmental features as rows and performance indicators in terminal information as columns. Among them, the environmental features are building density influence factor and traffic flow influence factor; the performance indicators in terminal information are signal-to-noise ratio, received signal strength and data packet reception rate in terminal information.

[0156] Among them, the element values ​​in the dynamic environment perception performance evaluation matrix are obtained by fitting a multiple linear regression function, that is, constructing a multiple linear function between a performance index in the terminal information and the building density influence factor and the traffic flow influence factor.

[0157] Step S3: Obtain the comprehensive performance index of the link quality at the current moment based on the dynamic environment perception performance evaluation matrix; predict the signal-to-noise ratio data at subsequent moments through historical signal-to-noise ratio data in the terminal information, and obtain the relative difference of signal-to-noise ratio based on the difference between the signal-to-noise ratio data at the subsequent moments and the empirical optimal value of signal-to-noise ratio.

[0158] Specifically, the comprehensive performance index of the link quality at the current moment is obtained based on the element values ​​in the dynamic environment perception performance evaluation matrix; the comprehensive performance index of the link quality at the current moment is specifically expressed by the formula:

[0159]

[0160] In the formula, Indicates the first Dynamic weights of environmental features Indicates the first The weight of various performance indicators Indicates the normalized i-th The values ​​of various performance indicators The first element in the dynamic environment perception performance evaluation matrix represents the... Line number The element values ​​of the column, This indicates the number of all performance metrics. This represents the number of all environmental features. This represents the comprehensive performance index. The dynamic weights of all environmental features are determined using the fuzzy hierarchical analysis method; the weights of all performance indicators are determined using an expert scoring method combined with the CRITIC (Criterion Impact Correlations through Intercriteria Correlation) objective weighting method. The fuzzy hierarchical analysis method, expert scoring method, and CRITIC objective weighting method are all well-known techniques and will not be elaborated upon here. A schematic diagram of the dynamic weight allocation for link quality assessment based on environmental awareness is shown below. Figure 3 As shown.

[0161] The network server continuously collects and stores the historical SNR (Signal-to-Noise Ratio) data of the terminals, forming a time series, denoted as the SNR sequence. Based on the SNR sequence, the SNR data of subsequent time moments is predicted using the ordinary kriging method. The ordinary kriging method is a well-known technique and will not be described in detail here.

[0162] Based on the predicted signal-to-noise ratio (SNR) data for subsequent time moments and the empirically optimal SNR value under the same environmental conditions, the relative SNR difference is obtained; specifically, the relative SNR difference is expressed by the formula:

[0163]

[0164] In the formula, This represents the relative difference in signal-to-noise ratio. Indicates the subsequent Predicted signal-to-noise ratio data at each time point. This represents the empirically optimal signal-to-noise ratio under the same environmental conditions. It is the absolute value symbol. This represents a preset parameter factor (used to prevent the denominator from being zero). In this embodiment, the preset parameter factor... In this embodiment, the preset parameter factor is... No specific limitations are set; implementers can determine the appropriate approach based on their specific circumstances. The diagram illustrating the distribution of the empirically optimal SNR value over 24 hours for a single terminal under different spreading factors is shown below. Figure 4 As shown in the figure. The SNR prediction results for a single terminal under different traffic flows are shown in the figure below. Figure 5 As shown.

[0165] Step S4: Obtain the rate of change of the comprehensive performance index at the current moment based on the comprehensive performance index; obtain the value to be optimized for the communication parameters through a comprehensive adaptive adjustment algorithm based on the rate of change of the comprehensive performance index, the comprehensive performance index and the relative difference between the signal and noise ratio; obtain the comprehensive performance index to be optimized based on the value to be optimized for the communication parameters.

[0166] Specifically, a reference duration is preset, and the reference duration before each moment is used as the time window for each moment. The comprehensive performance index corresponding to the moments on both sides of each time window is obtained. Based on the difference between the comprehensive performance index corresponding to the moments on both sides of each time window, the rate of change of the comprehensive performance index of each time window is obtained.

[0167] The rate of change of the comprehensive performance index for each time window is the ratio of the difference in the comprehensive performance index at the two ends of each time window to the duration of each time window. In this embodiment, the reference duration is 60 seconds. However, the reference duration is not specifically limited in this embodiment and can be determined by the implementer according to the specific circumstances.

[0168] The specific process of the comprehensive adaptive adjustment algorithm is as follows:

[0169] Obtain the current time window; if the rate of change of the comprehensive performance index within the current time window exceeds the performance change rate threshold... When the overall performance index change rate is less than or equal to the performance change rate threshold, the system will trigger an emergency adjustment mechanism. And the difference between the comprehensive performance index corresponding to the three previous time points is less than or equal to 1. If this occurs, the system triggers the fuzzy decision tree global optimization mechanism; among which, the performance change rate threshold... and This is a preset value; where, in this embodiment, the performance change rate threshold is... , In this embodiment, the performance change rate threshold is specified. and No specific restrictions are imposed; implementers can decide based on the specific circumstances.

[0170] When the system triggers the emergency adjustment mechanism, the core physical layer communication parameters of the LoRa terminal are adjusted to obtain the adjusted core physical layer communication parameters; when the system triggers the fuzzy decision tree global optimization mechanism, the core physical layer communication parameters of the LoRa terminal are globally optimized to obtain the globally optimized core physical layer communication parameters.

[0171] Among them, the core physical layer communication parameters of LoRa terminals include spreading factor, transmit power and modulation bandwidth;

[0172] Specifically, when the system triggers the emergency adjustment mechanism, the core physical layer communication parameters of the LoRa terminal are adjusted to obtain the adjusted core physical layer communication parameters; the specific adjustment process is as follows:

[0173] The overall performance index at the current moment and At this point, it represents an extreme case; under extreme conditions, the spreading factor SF is adjusted to 12, the transmit power TP is maximized, and the modulation bandwidth BW is adjusted to 125kHz; when the overall performance index at the current moment... and At this time, it is a severe situation; under severe conditions, the spreading factor SF is adjusted to 10, the transmit power TP is increased by 3dB, and the modulation bandwidth BW is adjusted to 125kHz; under other conditions, the communication parameters are not adjusted.

[0174] in, , , and All are preset values;

[0175] Specifically, when the system triggers the fuzzy decision tree global optimization mechanism, it performs global optimization on the core physical layer communication parameters of the LoRa terminal to obtain the globally optimized core physical layer communication parameters, including:

[0176] The comprehensive performance index, relative difference in signal-to-noise ratio, building density influence factor, and traffic flow influence factor are used as the feature parameters input to the fuzzy decision tree; the spreading factor SF, transmit power TP, and modulation bandwidth BW are used as the feature parameters output to the fuzzy decision tree.

[0177] The input feature parameters are divided into three fuzzy levels: the comprehensive performance index is divided into poor, medium, and good; the relative difference in signal-to-noise ratio is divided into small, medium, and large; the building density influence factor is divided into low, medium, and high; and the traffic flow influence factor is divided into small, medium, and large. The output feature parameters are also divided into three fuzzy levels: the spreading factor is divided into low, medium, and high; the transmit power is divided into low, medium, and high; and the modulation bandwidth is divided into narrow bandwidth, medium bandwidth, and wide bandwidth. These output feature parameters are used as the optimization values ​​for the communication parameters.

[0178] Based on the values ​​of the communication parameters to be optimized, the overall performance index to be optimized is recalculated and obtained.

[0179] Step S5: Based on the comprehensive performance index to be optimized and the comprehensive performance index of the link quality at the current moment, obtain the absolute improvement degree of the link quality; obtain the network energy consumption cost and latency cost, and obtain the overhead evaluation factor to be adjusted based on the absolute improvement degree of the link quality, energy consumption cost and latency cost; use the overhead evaluation factor to determine whether to adjust the communication parameters to the value to be optimized, thereby completing the optimization of the communication parameters of a single terminal.

[0180] Specifically, based on the current comprehensive performance index of the link quality and the comprehensive performance index to be optimized, the absolute improvement degree of the link quality is obtained; whereby the absolute improvement degree of the link quality is expressed by the formula:

[0181]

[0182] In the formula, This represents the overall performance index that needs to be optimized. This represents the overall performance index of the link quality at the current moment. It indicates the absolute degree of improvement in link quality.

[0183] It should be noted that when the microprocessor and RF chip inside the terminal reconfigure the spreading factor, bandwidth and power, additional computing and logic power consumption will be generated (parameter switching power consumption); and after switching parameters, the terminal may need to resynchronize or handshake with the network server, which requires sending and receiving signals and consumes energy (signal reconstruction power consumption); therefore, it is also necessary to analyze the energy consumption cost in conjunction with the terminal.

[0184] Specifically, the energy cost (the sum of parameter switching energy consumption and signal reconstruction energy consumption) is obtained based on typical values ​​from the chip datasheet and with reference to laboratory test data or empirical values ​​from network servers.

[0185] It should be noted that after the terminal switches parameters, it needs to re-establish synchronization with the gateway. During this process, communication will be temporarily interrupted, resulting in latency (network synchronization latency). After the network server sends new parameters, there is the time required to wait for the terminal to confirm and reply (handshake confirmation latency). Therefore, it is also necessary to analyze the latency costs in conjunction with the process.

[0186] Specifically, latency costs (the sum of network synchronization latency and handshake confirmation latency) can be obtained through network performance testing tools or protocol analysis tools.

[0187] Energy consumption costs are standardized to obtain standardized energy consumption costs; the standardized energy consumption cost is specifically expressed by the formula:

[0188]

[0189] In the formula, Indicates energy consumption cost, Indicates reference energy consumption cost. This represents the standardized energy consumption cost. In this embodiment, the reference energy consumption cost is 0.1 joules, and no specific limit is imposed on the reference energy consumption cost in this embodiment.

[0190] The latency cost is standardized to obtain the standardized latency cost; the standardized latency cost is specifically expressed by the formula:

[0191]

[0192] In the formula, Indicates latency cost, Indicates the reference delay cost. This represents the standardized latency cost. In this embodiment, the reference latency cost is 10 seconds, and no specific limit is set for the reference latency cost in this embodiment.

[0193] The total adjustment cost is obtained by weighting the standardized energy consumption cost and the standardized latency cost; the total adjustment cost is specifically expressed by the formula:

[0194]

[0195] In the formula, Indicates standardized energy consumption cost, Indicates the standardized latency cost. Indicates energy consumption weight. Indicates the delay weight. This represents the total adjustment cost; among which, and All are preset values.

[0196] In this embodiment , In this embodiment, for and No specific restrictions are imposed; implementers can decide based on the specific circumstances.

[0197] Based on the total adjustment cost and the absolute improvement in link quality, the evaluation factor of the overhead to be adjusted is obtained; the specific formula for the evaluation factor of the overhead to be adjusted is as follows:

[0198]

[0199] In the formula, Indicates the absolute degree of improvement in link quality. This represents the total adjustment cost. This indicates the cost evaluation factor that needs to be adjusted.

[0200] When the cost evaluation factor to be adjusted is greater than or equal to the preset cost threshold, the adjustment is considered "worthwhile." The system approves and executes the parameter adjustment, then the communication parameters are adjusted to the value to be optimized.

[0201] When the cost evaluation factor to be adjusted is less than the preset cost threshold, it indicates that the cost of adjustment outweighs the benefits, making it "not worth the effort." The system abandons this adjustment, and the communication parameters are not adjusted; the terminal continues to use the original parameters. In this embodiment, the preset cost threshold is 1. However, this preset cost threshold is not specifically limited in this embodiment; the implementer can determine it according to specific circumstances. The comparison chart of data packet reception rates between the single-terminal adaptive adjustment scheme and the standard adaptive data rate scheme is shown below. Figure 6 As shown.

[0202] Step S6: Optimize the communication parameters of all terminals within the preset network configuration time, output the optimized communication parameters of all terminals, and then evaluate the overall network performance based on the optimized communication parameters of all terminals.

[0203] Specifically, obtain the overall network performance index before optimization and the overall network performance index after optimization;

[0204] The comprehensive network performance index is specifically expressed by the formula:

[0205]

[0206] In the formula, This indicates the performance evaluation metrics for coverage. This represents a performance metric for network throughput. Indicates communication reliability evaluation index, Indicates the coverage performance weight. Indicates the network throughput performance weight. Indicates communication reliability weights. This represents the overall network performance index;

[0207] The coverage performance evaluation index is specifically expressed by the following formula:

[0208]

[0209] In the formula, This represents the average signal strength received by the gateway across all terminals. This indicates the additional losses incurred by the outdoor environment relative to the ideal environment. This indicates the additional signal attenuation caused by building density. This represents the total number of all terminals;

[0210] The network throughput performance evaluation index is specifically expressed by the following formula:

[0211]

[0212] In the formula, Indicates the first Data transmission rate of each terminal Indicates the first The data packet error rate of each terminal affected by the environment. This represents the maximum throughput under ideal conditions. This represents the total number of all terminals; Indicates the first The current actual throughput of each terminal This represents the current actual throughput of all terminals;

[0213] The communication reliability evaluation index is specifically expressed by the following formula:

[0214]

[0215] In the formula, Indicates the SF switching frequency. Indicates the sensitivity coefficient to environmental fluctuations. This represents an exponential function with the natural constant as its base. Indicates the data packet reception rate. Indicates communication reliability evaluation indicators;

[0216] The ratio between the optimized overall network performance index and the unoptimized overall network performance index is calculated and denoted as the evaluation ratio. When the evaluation ratio is greater than 1, it indicates that the optimization of communication parameters has improved the LoRa network performance.

[0217] The comprehensive network performance index quantifies the overall performance improvement of the entire LoRa network after dynamic environment awareness optimization.

[0218] This invention not only enables fine-tuning of communication parameters for individual terminals, but also realizes a complete, data-driven network deployment and optimization closed loop, providing operators with clear performance verification criteria.

[0219] This concludes the embodiment.

[0220] like Figure 2 As shown, a second aspect of the present invention is to provide an environment-aware LoRa network dynamic link adaptive control system, comprising:

[0221] Initial parameter configuration and information statistics module 101: used to preset the initial communication parameters of the core physical layer of the LoRa network terminal; the LoRa network terminal sends data to the gateway according to the initial communication parameters, the gateway forwards the successfully received data to the network server, and the network server collects terminal information.

[0222] Environment perception and model building module 102: used to obtain the building distribution and vehicle traffic conditions on the LoRa network transmission path, build an environment perception model based on the building distribution and vehicle traffic conditions; obtain influencing factors through the environment perception model; and build a dynamic environment perception performance evaluation matrix based on the influencing factors and the performance indicators in the terminal information.

[0223] Link quality assessment feature quantification module 103: used to obtain the comprehensive performance index of the link quality at the current moment based on the dynamic environment perception performance evaluation matrix; predict the signal-to-noise ratio data at subsequent moments through historical signal-to-noise ratio data in terminal information; and obtain the relative difference of signal-to-noise ratio based on the difference between the signal-to-noise ratio data at the subsequent moments and the empirical optimal value of signal-to-noise ratio.

[0224] Intelligent parameter decision module 104: used to obtain the rate of change of the comprehensive performance index at the current moment based on the comprehensive performance index; to obtain the value to be optimized for the communication parameters through a comprehensive adaptive adjustment algorithm based on the rate of change of the comprehensive performance index, the comprehensive performance index and the relative difference between the signal and noise ratio; and to obtain the comprehensive performance index to be optimized based on the value to be optimized for the communication parameters.

[0225] The overhead assessment and execution module 105 is used to obtain the absolute improvement degree of link quality based on the comprehensive performance index to be optimized and the comprehensive performance index of the link quality at the current moment; obtain the network's energy consumption cost and latency cost; obtain the overhead evaluation factor to be adjusted based on the absolute improvement degree of link quality, energy consumption cost, and latency cost; and determine whether to adjust the communication parameters to the value to be optimized based on the overhead evaluation factor to complete the optimization of the communication parameters of a single terminal.

[0226] Global Optimization and Network Performance Evaluation Module 106: This module optimizes the communication parameters of all terminals within a preset network configuration time, outputs the optimized communication parameters of all terminals, and then evaluates the overall network performance based on the optimized communication parameters of all terminals.

[0227] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement an environment-aware LoRa network dynamic link adaptive control method.

[0228] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements an environment-aware LoRa network dynamic link adaptive control method.

[0229] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0230] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0231] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0232] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for dynamic link adaptation control of LoRa network based on environment perception, characterized in that, The application relates to a method for optimizing communication parameters of a LoRa network terminal. The method comprises the following steps: acquiring initial communication parameters of a core physical layer of a preset LoRa network terminal; sending data to a gateway according to the initial communication parameters, forwarding successfully received data to a network server, and counting terminal information by the network server; acquiring building distribution and vehicle traffic conditions on a LoRa network transmission path, constructing an environment perception model according to the building distribution and the vehicle traffic conditions, obtaining an influence factor through the environment perception model, and constructing a dynamic environment perception performance evaluation matrix according to the influence factor and a performance index in the terminal information; the environment perception model comprises a three-dimensional space signal attenuation model and a dynamic traffic flow interference model, the influence factor comprises a building density influence factor and a traffic flow influence factor, the environment perception model is constructed according to the building distribution and the vehicle traffic conditions, the influence factor is obtained through the environment perception model, and the method comprises the following steps: obtaining a building density index and a traffic flow of a transmission path between a terminal and a gateway according to the building distribution and the vehicle traffic conditions, wherein the traffic flow is the number of vehicles passing through the transmission path per unit time obtained by real-time monitoring; wherein, represents a reference path loss exponent in a free space environment, represents a mapping coefficient, represents a transmission path loss exponent; represents a projected area of the th building on the transmission path, represents a position weight factor of the th building, represents a total area of the region, represents a total number of all buildings on the transmission path, represents a building density index; obtaining a transmission path loss index according to the building density index, and the method comprises the following steps: In the formula, represents a path loss at a distance of represents a path loss at a reference distance represents a transmission path loss exponent, represents a distance of a transmission path, represents a reference distance, represents a building block loss, represents a terrain undulation additional loss;​​ constructing a three-dimensional space signal attenuation model according to the transmission path loss index, and the method comprises the following steps: wherein represents a free space path loss of distance represents a building density impact factor;​ obtaining the building density influence factor through the three-dimensional space signal attenuation model, and the method comprises the following steps: In the formula, a dynamic interference intensity representing traffic flow, a Doppler effect intensity, a reflection interference intensity, a multipath interference intensity; wherein the multipath interference intensity is measured multipath delay spread data; and the Doppler effect intensity is specifically represented by the formula: wherein, denotes the average speed of the vehicles, denotes the speed of light, denotes the LoRa carrier frequency, denotes the traffic flow, denotes the standard vehicle density, denotes the first fitting coefficient; constructing a dynamic traffic flow interference model according to the traffic flow, obtaining the traffic flow influence factor through the dynamic traffic flow interference model, and the dynamic traffic flow interference model is specifically expressed by a formula as follows: wherein represents a second fitting coefficient, represents a reflection coefficient of the surface material of the vehicle; the reflection interference intensity is specifically expressed by a formula as follows: In the formula, denotes the traffic flow influence factor, denotes the reference interference intensity; the traffic flow influence factor is specifically expressed by a formula as follows: the dynamic environment perception performance evaluation matrix is constructed according to the influence factor and the performance index in the terminal information, and the dynamic environment perception performance evaluation matrix is a multiple linear function among one performance index in the terminal information, the building density influence factor and the traffic flow influence factor; obtaining a comprehensive performance index of link quality at a current moment based on the dynamic environment perception performance evaluation matrix, predicting signal-to-noise ratio data at a subsequent moment through historical signal-to-noise ratio data in the terminal information, and obtaining a signal-to-noise ratio relative difference value according to a difference between the signal-to-noise ratio data at the subsequent moment and an experienced optimal signal-to-noise ratio value; obtaining a comprehensive performance index change rate at the current moment according to the comprehensive performance index, obtaining a to-be-optimized value of the communication parameter through a comprehensive self-adaptive adjustment algorithm according to the comprehensive performance index change rate, the comprehensive performance index and the signal-to-noise ratio relative difference value, and obtaining a to-be-optimized comprehensive performance index according to the to-be-optimized value of the communication parameter; obtaining an absolute improvement degree of the link quality according to the to-be-optimized comprehensive performance index and the comprehensive performance index of the link quality at the current moment, obtaining a to-be-adjusted overhead evaluation factor according to the absolute improvement degree of the link quality, an energy consumption cost and a time delay cost, and judging whether the communication parameter is adjusted to the to-be-optimized value through the to-be-adjusted overhead evaluation factor, so as to complete optimization of the communication parameter of a single terminal. The communication parameters of all terminals are optimized within a preset network deployment time, and the optimized communication parameters of all terminals are output, and then the overall network performance is evaluated through the optimized communication parameters of all terminals. 2.The dynamic link adaptation control method based on environment-aware LoRa network according to claim 1, characterized in that, The dynamic environment perception performance evaluation matrix is constructed according to the influence factors and the performance indicators in the terminal information, and includes: The dynamic environment perception performance evaluation matrix is constructed according to the building density influence factor, the traffic flow influence factor and the performance indicators in the terminal information. The dynamic environment perception performance evaluation matrix is constructed with environmental characteristics as rows and performance indicators in the terminal information as columns; wherein the environmental characteristics are the building density influence factor and the traffic flow influence factor; and the performance indicators in the terminal information are the signal-to-noise ratio, the received signal strength and the data packet reception rate in the terminal information. The element values in the dynamic environment perception performance evaluation matrix are obtained through a multiple linear regression function fitting. 3.The method of claim 2, wherein, The comprehensive performance index of the current time link quality is obtained based on the dynamic environment perception performance evaluation matrix; the signal-to-noise ratio data at the subsequent time is predicted through the historical signal-to-noise ratio data in the terminal information, and the signal-to-noise ratio relative difference value is obtained according to the difference between the signal-to-noise ratio data at the subsequent time and the signal-to-noise ratio empirical optimal value, including: The comprehensive performance index is specifically expressed by the formula: In the formula, represents the dynamic weight of the first environmental feature, represents the weight of the first performance index, represents the value of the first performance index after normalization, represents the element value of the first row and the first column in the dynamic environment perception performance evaluation matrix, represents the number of all performance indexes, represents the number of all environmental features, represents the comprehensive performance index; wherein the dynamic weight of all environmental features is determined by a fuzzy analytic hierarchy process; the weight of all performance indexes is determined by an expert scoring method combined with a CRITIC objective weight method. The network server continuously collects and stores the historical signal-to-noise ratio data of the terminal to form a time sequence, which is denoted as a signal-to-noise ratio sequence; and the signal-to-noise ratio data at the subsequent time is predicted through the ordinary Kriging method according to the signal-to-noise ratio sequence; The signal-to-noise ratio relative difference value is obtained according to the predicted signal-to-noise ratio data at the subsequent time and the signal-to-noise ratio empirical optimal value under the same environmental condition; wherein the signal-to-noise ratio relative difference value is specifically expressed by the formula: In the formula, represents the relative difference of signal-to-noise ratio, represents the predicted signal-to-noise ratio data of the subsequent time point, represents the empirical optimal value of signal-to-noise ratio under the same environmental condition, is an absolute value symbol, represents a preset parameter factor.

4. The dynamic link adaptation control method based on environment perception for LoRa network according to claim 3, characterized in that, The comprehensive performance index change rate at the current time is obtained according to the comprehensive performance index; and the to-be-optimized value of the communication parameter is obtained through a comprehensive adaptive adjustment algorithm according to the comprehensive performance index change rate, the comprehensive performance index and the signal-to-noise ratio relative difference value; The to-be-optimized comprehensive performance index is obtained according to the to-be-optimized value of the communication parameter, including: A reference time length is preset, and the reference time length before each time is taken as a time window of each time to obtain the comprehensive performance indexes corresponding to the time on both sides of each time window; and the comprehensive performance index change rate of each time window is obtained according to the difference between the comprehensive performance indexes corresponding to the time on both sides of each time window. The comprehensive performance index change rate of each time window is the difference between the comprehensive performance indexes corresponding to the time on both sides of each time window divided by the time length of each time window. The specific process of the comprehensive adaptive adjustment algorithm is as follows: acquire a time window of the current moment, when the comprehensive performance index change rate of the time window of the current moment is greater than a performance change rate threshold , the system triggers an emergency adjustment mechanism; when the comprehensive performance index change rate of the current moment is less than or equal to the performance change rate threshold , and the difference between the comprehensive performance indexes corresponding to the three moments before the current moment is less than or equal to , the system triggers a fuzzy decision tree global optimization mechanism; wherein the performance change rate threshold and are preset values; When the system triggers the emergency adjustment mechanism, the core physical layer communication parameters of the LoRa terminal are adjusted to obtain the adjusted core physical layer communication parameters; and when the system triggers the fuzzy decision tree global optimization mechanism, the core physical layer communication parameters of the LoRa terminal are globally optimized to obtain the globally optimized core physical layer communication parameters. The core physical layer communication parameters of the LoRa terminal include the spreading factor, the transmission power and the modulation bandwidth. Wherein, when the system triggers the emergency adjustment mechanism, the core physical layer communication parameters of the LoRa terminal are adjusted, and the adjusted core physical layer communication parameters are obtained; the specific adjustment process is: when the comprehensive performance index of the current moment and this is an extreme case; when in the extreme case, the spreading factor SF is adjusted to 12, the transmitting power TP is adjusted to the maximum, and the modulation bandwidth BW is adjusted to 125 kHz; when the comprehensive performance index of the current moment and this is a poor case; when in the poor case, the spreading factor SF is adjusted to 10, the transmitting power TP is increased by 3 dB, and the modulation bandwidth BW is adjusted to 125 kHz; wherein, in other cases, the communication parameters are not adjusted; wherein, , , and are preset values; Wherein, when the system triggers the fuzzy decision tree global optimization mechanism, the core physical layer communication parameters of the LoRa terminal are globally optimized, and the globally optimized core physical layer communication parameters are obtained; including: The comprehensive performance index, the signal-to-noise ratio relative difference, the building density influence factor and the traffic flow influence factor are taken as the characteristic parameters of the fuzzy decision tree input; the spreading factor SF, the transmission power TP and the modulation bandwidth BW are taken as the characteristic parameters of the fuzzy decision tree output; Wherein, the input characteristic parameters are divided into three fuzzy levels, specifically: the comprehensive performance index is divided into poor, medium and good, the signal-to-noise ratio relative difference is divided into small, medium and large, the building density influence factor is divided into low, medium and high, and the traffic flow influence factor is divided into small, medium and large; wherein, the output characteristic parameters are divided into three fuzzy levels, specifically: the spreading factor is divided into low, medium and high, the transmission power is divided into low, medium and high, and the modulation bandwidth is divided into narrow bandwidth, medium bandwidth and wide bandwidth; wherein, the output characteristic parameters are taken as the optimization values of the communication parameters; According to the optimization values of the communication parameters, the optimization of the comprehensive performance index is recalculated.

5. The method of claim 1, wherein, According to the optimization values of the communication parameters, the optimization of the comprehensive performance index is recalculated. wherein denotes the overall performance index to be optimized, denotes the overall performance index of the link quality at the current time instant, denotes the absolute improvement degree of the link quality. 6.The method of claim 1, wherein, The absolute improvement degree of the link quality is obtained according to the optimization values of the comprehensive performance index and the comprehensive performance index of the current link quality, and the absolute improvement degree of the link quality is specifically expressed by the formula: The adjustment cost evaluation factor is obtained according to the absolute improvement degree of the link quality, the energy consumption cost and the delay cost; Whether the communication parameter is adjusted to the optimization value is determined by the adjustment cost evaluation factor, including: wherein represents the energy cost, represents the reference energy cost, represents the normalized energy cost; The energy consumption cost is standardized to obtain the standardized energy consumption cost; wherein, the standardized energy consumption cost is specifically expressed by the formula: In the formula, denotes the delay cost, denotes the reference delay cost, denotes the normalized delay cost; The delay cost is standardized to obtain the standardized delay cost; wherein, the standardized delay cost is specifically expressed by the formula: In the formula, represents the energy consumption weight, represents the time delay weight, represents the total adjustment cost; wherein, and are preset values; The total adjustment cost is obtained by weighting the standardized energy consumption cost and the standardized delay cost; wherein, the total adjustment cost is specifically expressed by the formula: wherein represents the absolute degree of improvement of the link quality, represents the overhead evaluation factor to be adjusted; The adjustment cost evaluation factor is specifically expressed by the formula: 7.The dynamic link adaptation control method based on environment-aware LoRa network according to claim 1, characterized in that, When the adjustment cost evaluation factor is greater than or equal to the preset cost threshold, the communication parameter is adjusted to the optimization value; when the adjustment cost evaluation factor is less than the preset cost threshold, the communication parameter is not adjusted. The communication parameters of all terminals are optimized within the preset network configuration time, and the optimized communication parameters of all terminals are output, and then the overall network performance is evaluated by the optimized communication parameters of all terminals, including: The comprehensive network performance index before optimization and the comprehensive network performance index after optimization are obtained; In the formula, denotes a coverage performance evaluation index, denotes a network throughput performance evaluation index, denotes a communication reliability evaluation index, denotes a coverage performance weight, denotes a network throughput performance weight, denotes a communication reliability weight, denotes a comprehensive network performance index; The comprehensive network performance index is specifically expressed by the formula: wherein represents the average signal strength received by the gateway for all terminals, represents the additional loss of the outdoor environment relative to the ideal environment, represents the additional signal attenuation caused by the building density, represents the total number of all terminals; Wherein, the coverage performance evaluation index is specifically expressed by the formula: wherein, denotes the data transmission rate of the terminal, denotes the data packet error rate of the terminal affected by the environment, denotes the maximum throughput in an ideal environment, denotes the total number of all terminals; denotes the current actual throughput of the terminal, denotes the current actual throughput of all terminals; Wherein, the network throughput performance evaluation index is specifically expressed by the formula: Wherein, the communication reliability evaluation index is specifically expressed by the formula: In the formula, represents the SF switching frequency, represents the environmental fluctuation sensitivity coefficient, represents an exponential function with a natural constant as the base, represents the data packet reception rate, represents the communication reliability evaluation index; A ratio between the optimized comprehensive network performance index and the comprehensive network performance index before optimization is calculated, denoted as an evaluation ratio; when the evaluation ratio is greater than 1, it indicates that the optimization of the communication parameters improves the LoRa network performance.

8. A dynamic link adaptation control system for LoRa network based on environment perception, characterized in that, The method comprises the following steps: An initial parameter configuration and information statistics module is configured to preset initial communication parameters of a core physical layer of a LoRa network terminal; The LoRa network terminal transmits data to a gateway according to the initial communication parameters, the gateway forwards successfully received data to a network server, and the network server counts terminal information; An environment perception and model construction module is configured to obtain a building distribution and a vehicle traffic condition on a transmission path of the LoRa network, construct an environment perception model according to the building distribution and the vehicle traffic condition, obtain an influence factor through the environment perception model, and construct a dynamic environment perception performance evaluation matrix according to the influence factor and performance indicators in the terminal information; The environment perception model comprises a three-dimensional space signal attenuation model and a dynamic traffic flow interference model, and the influence factor comprises a building density influence factor and a traffic flow influence factor; the environment perception model is constructed according to the building distribution and the vehicle traffic condition, and the influence factor is obtained through the environment perception model, which comprises the following steps: A building density index and a traffic flow of a transmission path between a terminal and a gateway are obtained according to the building distribution and the vehicle traffic condition; the traffic flow is the number of vehicles passing through the transmission path per unit time obtained through real-time monitoring; A transmission path loss index is obtained according to the building density index; specifically, the transmission path loss index is obtained according to the building density index and a three-dimensional space signal attenuation model; wherein represents a reference path loss exponent in a free space environment, represents a mapping coefficient, represents a transmission path loss exponent; represents a projected area of the th building on the transmission path, represents a position weight factor of the th building, represents a total area of the region, represents a total number of all buildings on the transmission path, represents a building density exponent; The three-dimensional space signal attenuation model is constructed according to the transmission path loss index; specifically, the three-dimensional space signal attenuation model is constructed according to the transmission path loss index and a three-dimensional space signal attenuation model; In the formula, represents a path loss at a distance of represents a path loss at a reference distance of represents a transmission path loss exponent, represents a distance of a transmission path, represents a reference distance, represents a building block loss, represents a terrain undulation additional loss;​​ The building density influence factor is obtained through the three-dimensional space signal attenuation model; specifically, the building density influence factor is obtained through the three-dimensional space signal attenuation model and a three-dimensional space signal attenuation model; wherein represents a free space path loss of distance represents a building density impact factor;​ A dynamic traffic flow interference model is constructed according to the traffic flow, and the traffic flow influence factor is obtained through the dynamic traffic flow interference model; the dynamic traffic flow interference model is specifically represented by a formula as follows: In the formula, a dynamic interference intensity representing traffic flow, a Doppler effect intensity, a reflection interference intensity, a multipath interference intensity; wherein the multipath interference intensity is measured multipath delay spread data; and the Doppler effect intensity is specifically represented by the formula: wherein, denotes the average speed of the vehicles, denotes the speed of light, denotes the LoRa carrier frequency, denotes the traffic flow, denotes the standard vehicle density, denotes the first fitting coefficient; The reflection interference intensity is specifically represented by a formula as follows: wherein represents a second fitting coefficient, represents a reflection coefficient of the surface material of the vehicle; The traffic flow influence factor is specifically represented by a formula as follows: In the formula, denotes the traffic flow influence factor, denotes the reference interference intensity; The dynamic environment perception performance evaluation matrix is constructed according to the influence factor and the performance indicators in the terminal information, which is a multiple linear function between one performance indicator in the terminal information, the building density influence factor and the traffic flow influence factor; A link quality evaluation feature quantization module is configured to obtain a comprehensive performance index of link quality at a current moment based on the dynamic environment perception performance evaluation matrix, predict subsequent signal-to-noise ratio data based on historical signal-to-noise ratio data in the terminal information, and obtain a signal-to-noise ratio relative difference value according to a difference between the subsequent signal-to-noise ratio data and an experienced optimal signal-to-noise ratio value; An intelligent parameter decision module is configured to obtain a comprehensive performance index change rate at the current moment according to the comprehensive performance index, obtain a to-be-optimized value of the communication parameter through a comprehensive adaptive adjustment algorithm according to the comprehensive performance index change rate, the comprehensive performance index and the signal-to-noise ratio relative difference value, and obtain a to-be-optimized comprehensive performance index according to the to-be-optimized value of the communication parameter. The adjustment overhead evaluation and execution module is configured to obtain an absolute improvement degree of the link quality according to the comprehensive performance index to be optimized and the comprehensive performance index of the link quality at the current moment, obtain the energy consumption cost and the delay cost of the network, and obtain an adjustment overhead evaluation factor according to the absolute improvement degree of the link quality, the energy consumption cost and the delay cost; and determine whether to adjust the communication parameter to the to-be-optimized value by using the adjustment overhead evaluation factor, so as to complete the optimization of the communication parameter of the single terminal. The global optimization and network performance evaluation module is configured to optimize the communication parameters of all terminals within a preset network deployment time, output the optimized communication parameters of all terminals, and then evaluate the overall performance of the network by using the optimized communication parameters of all terminals.

9. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the LoRa network dynamic link adaptive control method based on environment perception according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the LoRa network dynamic link adaptive control method based on environment perception according to any one of claims 1-7.

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