A wireless signal data communication method and system based on the Internet of Things

Abstract

The application discloses a wireless signal data communication method and system based on an internet of things, and relates to the technical field of data communication.The technical solution points of the application comprise the following steps: obtaining channel state data and terminal energy consumption demand data of the internet of things communication, and setting intelligent signal regulation and control nodes according to the channel state data and the terminal energy consumption demand data; obtaining signal modulation efficiency monitoring data of an independent data transmission channel according to the intelligent signal regulation and control nodes, analyzing the signal modulation efficiency monitoring data to obtain modulation efficiency abnormality coefficients of the independent data transmission channel and modulation efficiency abnormality nodes of the intelligent signal regulation and control nodes; and obtaining signal power loss monitoring data of the independent data transmission channel according to the intelligent signal regulation and control nodes.The effect is that the communication system has the optimization capability of dynamically adapting to environmental changes.

Description

Technical Field

[0001] This invention relates to the field of data communication technology, and more specifically, to a wireless signal data communication method and system based on the Internet of Things. Background Technology

[0002] The large-scale deployment of IoT devices has made communication channel environments increasingly complex, with significant differences in transmission requirements and electromagnetic interference environments among different devices. Traditional communication methods often employ a uniform channel resource allocation strategy without fully considering the actual channel conditions. For example, when channel bandwidth utilization is high, allocating transmission resources in a fixed manner can easily lead to channel congestion and increased signal transmission delays. When channel interference levels are high, the lack of targeted anti-interference control will increase the bit error rate of signal transmission, thereby affecting the accuracy of data transmission. Traditional communication systems also have shortcomings in identifying and handling channel anomalies. Most solutions can only alert for obvious communication interruptions, making it difficult to identify potential anomalies such as decreased modulation efficiency and excessively rapid power loss in advance. Furthermore, the lack of quantitative assessment of the severity of anomalies leads to delayed anomaly handling. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a wireless signal data communication method and system based on the Internet of Things.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A wireless signal data communication method based on the Internet of Things, the method comprising the following steps:

[0006] Acquire channel status data and terminal power consumption demand data for IoT communication, and set up intelligent signal control nodes based on the channel status data and terminal power consumption demand data;

[0007] Based on the signal modulation efficiency monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the modulation efficiency anomaly coefficient of the independent data transmission channel and the modulation efficiency anomaly node of the intelligent signal control node are obtained by analyzing the signal modulation efficiency monitoring data.

[0008] Based on the signal power loss monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the power loss fault coefficient of the independent data transmission channel and the power loss abnormal nodes of the intelligent signal control node are obtained by analyzing the signal power loss monitoring data.

[0009] The abnormal modulation efficiency nodes and abnormal power loss nodes constitute the channel abnormality control nodes for IoT communication; the communication attenuation level of the channel abnormality control nodes is obtained based on the abnormal modulation efficiency coefficient and the fault coefficient of power loss.

[0010] After analyzing the communication attenuation level and abnormal control nodes, modulation parameters are adjusted and power compensation is performed.

[0011] Preferably, after analyzing the communication attenuation level and abnormal control nodes, modulation parameters are adjusted and power compensation is performed, specifically including the following steps:

[0012] Based on the communication attenuation level, generate the attenuation warning intensity of the channel abnormal control node, and generate the attenuation warning signal of the intelligent signal control node based on the attenuation warning intensity.

[0013] The equivalent signal propagation distance of the channel anomaly control node is obtained based on the channel anomaly control node, and the communication optimization priority of the channel anomaly control node is obtained based on the attenuation warning strength and the equivalent signal propagation distance.

[0014] Based on communication optimization priorities and attenuation warning signals, the modulation parameters and power compensation of the channel abnormal control nodes are adjusted.

[0015] Preferably, the channel state data includes channel bandwidth utilization and channel interference level;

[0016] The terminal energy consumption requirements data include the terminal's maximum allowable power consumption and battery life requirements.

[0017] Preferably, the signal modulation efficiency monitoring data includes modulation error rate data and modulation rate fluctuation data of independent data transmission channels.

[0018] Preferably, the analysis of signal modulation efficiency monitoring data to obtain the modulation efficiency anomaly coefficient of the independent data transmission channel and the modulation efficiency anomaly nodes of the intelligent signal control node specifically includes the following steps:

[0019] The bit error rate anomaly coefficient of the independent data transmission channel is obtained based on the modulation bit error rate data; the rate fluctuation anomaly coefficient of the independent data transmission channel is obtained based on the modulation rate fluctuation data.

[0020] Set the bit error rate weight and the rate fluctuation weight, and obtain the modulation efficiency anomaly coefficient of the independent data transmission channel based on the bit error rate weight and bit error rate anomaly coefficient, the rate fluctuation weight and the rate fluctuation anomaly coefficient.

[0021] The intelligent signal modulation node corresponding to the independent data transmission channel is marked as a node with abnormal modulation efficiency.

[0022] Preferably, obtaining the bit error rate anomaly coefficient of an independent data transmission channel based on modulation bit error rate data specifically includes the following steps:

[0023] The modulation bit error rate data includes the real-time bit error rate value of the independent data transmission channel, and the bit error rate monitoring curve corresponding to the independent data transmission channel is generated based on the real-time bit error rate value;

[0024] If the real-time bit error rate value in the bit error rate monitoring curve is greater than the preset bit error rate threshold, the first bit error anomaly coefficient of the independent data transmission channel is obtained based on the real-time bit error rate value and the bit error rate threshold.

[0025] Obtain the slope of the bit error rate monitoring curve. If the slope of the bit error rate curve is greater than the preset bit error rate slope threshold, then obtain the second bit error anomaly coefficient of the independent data transmission channel based on the slope of the bit error rate curve and the bit error rate slope threshold; obtain the bit error rate anomaly coefficient of the independent data transmission channel based on the first bit error anomaly coefficient and the second bit error anomaly coefficient.

[0026] Preferably, obtaining the rate fluctuation anomaly coefficient of the independent data transmission channel based on the modulation rate fluctuation data specifically includes the following steps:

[0027] The modulation rate fluctuation data includes the real-time rate fluctuation value of the independent data transmission channel;

[0028] Generate rate fluctuation monitoring curves for independent data transmission channels based on real-time rate fluctuation values;

[0029] If the real-time rate fluctuation value of the rate fluctuation monitoring curve is greater than the preset rate fluctuation threshold, the first rate anomaly coefficient of the independent data transmission channel is obtained based on the real-time rate fluctuation value and the rate fluctuation threshold.

[0030] Obtain the slope of the rate fluctuation monitoring curve; if the slope of the rate fluctuation curve is greater than the preset rate fluctuation slope threshold, then obtain the second rate anomaly coefficient of the independent data transmission channel based on the slope of the rate fluctuation curve and the rate fluctuation slope threshold.

[0031] The rate fluctuation anomaly coefficient of the independent data transmission channel is obtained based on the first rate anomaly coefficient and the second rate anomaly coefficient.

[0032] Preferably, the analysis of signal power loss monitoring data to obtain the power loss fault coefficient of the independent data transmission channel and the power loss abnormal nodes of the intelligent signal control node specifically includes the following steps:

[0033] The signal power loss monitoring data includes the real-time power loss value of the independent data transmission channel;

[0034] Based on the real-time power loss value, generate the power loss monitoring curve of the independent data transmission channel and obtain the slope of the power loss monitoring curve.

[0035] If the slope of the power loss curve is greater than the preset power loss slope threshold, the power loss fault coefficient of the independent data transmission channel is obtained based on the power loss curve slope and the power loss slope threshold, and the intelligent signal control node corresponding to the independent data transmission channel is marked as a power loss abnormal node.

[0036] Preferably, the equivalent signal propagation distance of the channel anomaly control node is obtained based on the channel anomaly control node, and the communication optimization priority of the channel anomaly control node is obtained based on the attenuation warning strength and the equivalent signal propagation distance. Specifically, this includes the following steps:

[0037] Obtain the first time point at which the channel abnormal control node sends the attenuation warning signal, and obtain the second time point at which the attenuation warning signal is received; obtain the propagation duration of the warning signal based on the first and second time points;

[0038] Obtain the propagation characteristic parameters of the IoT communication channel, and obtain the equivalent propagation speed of the attenuation warning signal based on the propagation characteristic parameters;

[0039] The equivalent distance between the abnormal control node of the channel corresponding to the attenuated warning signal and the signal propagation is obtained based on the propagation duration and equivalent propagation speed of the warning signal.

[0040] Set the warning intensity weight and equivalent distance weight; obtain the attenuation influence coefficient of the channel anomaly control node based on the attenuation warning intensity and warning intensity weight; obtain the distance influence coefficient of the channel anomaly control node based on the equivalent signal propagation distance and equivalent distance weight;

[0041] The optimization priority coefficient of the channel anomaly control node is obtained based on the attenuation influence coefficient and the distance influence coefficient, and the communication optimization priority of the channel anomaly control node is set based on the optimization priority coefficient.

[0042] A wireless signal data communication system based on the Internet of Things, comprising:

[0043] Setting module: Acquires channel status data and terminal power consumption demand data of IoT communication, and sets up intelligent signal control nodes based on the channel status data and terminal power consumption demand data;

[0044] First analysis module: Based on the signal modulation efficiency monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the signal modulation efficiency monitoring data is analyzed to obtain the modulation efficiency anomaly coefficient of the independent data transmission channel and the modulation efficiency anomaly nodes of the intelligent signal control node;

[0045] The second analysis module: Based on the signal power loss monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the signal power loss monitoring data is analyzed to obtain the power loss fault coefficient of the independent data transmission channel and the power loss abnormal nodes of the intelligent signal control node.

[0046] Processing module: Constructs channel abnormality control nodes for IoT communication based on modulation efficiency abnormality nodes and power loss abnormality nodes; obtains the communication attenuation level of the channel abnormality control nodes based on the modulation efficiency abnormality coefficient and the power loss fault coefficient.

[0047] Adjustment module: After analyzing the communication attenuation level and abnormal control nodes, it adjusts the modulation parameters and performs power compensation.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] This invention sets up intelligent signal control nodes by combining channel state data and terminal power consumption demand data. It considers both the channel's bandwidth utilization and interference levels, as well as the terminal's maximum allowable power consumption and battery life requirements, avoiding unreasonable allocation of channel resources or excessive terminal power consumption. By collecting and analyzing monitoring data on signal modulation efficiency and power loss, it obtains modulation efficiency anomaly coefficients and power loss fault coefficients, and marks corresponding abnormal nodes, transforming communication system fault identification from fuzzy judgment to precise quantitative assessment. By constructing channel anomaly control nodes, determining communication attenuation levels, and adjusting modulation parameters and power compensation accordingly, it specifically addresses communication attenuation problems of varying degrees. This gives the communication system the ability to dynamically adapt to environmental changes. Attached Figure Description

[0050] Figure 1 This invention provides a schematic diagram illustrating the steps of a wireless signal data communication method based on the Internet of Things.

[0051] Figure 2 This invention presents a schematic diagram of a wireless signal data communication system based on the Internet of Things. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0055] Reference Figures 1-2 As shown.

[0056] The embodiments further illustrate the wireless signal data communication method and system based on the Internet of Things proposed in this invention.

[0057] A wireless signal data communication method based on the Internet of Things, the method comprising the following steps:

[0058] Acquire channel status data and terminal power consumption demand data for IoT communication.

[0059] Intelligent signal control nodes are set up based on channel status data and terminal power consumption demand data;

[0060] Channel status data includes channel bandwidth utilization and channel interference level. Channel bandwidth utilization reflects the current channel occupancy rate. For example, when the bandwidth utilization rate of a certain IoT communication frequency band reaches 80%, it means that the available resources of that channel are relatively scarce. The channel interference level reflects the intensity of external signal interference present in the channel. For example, electromagnetic interference in an industrial environment will increase the channel interference level. When setting up intelligent signal control nodes, these two data points are collected first: if the channel bandwidth utilization rate is high and the interference level is low, it indicates that although channel resources are scarce, the transmission environment is stable; if the bandwidth utilization rate is low and the interference level is high, it indicates that channel resources are sufficient but the transmission environment is complex.

[0061] Terminal energy consumption requirements include the terminal's maximum allowable power consumption and battery life requirements. The maximum allowable power consumption is the highest power consumption threshold that the terminal device can withstand during operation; for example, the maximum allowable power consumption of an IoT sensor is 50 milliwatts. The battery life requirement is the continuous operating time the terminal needs to maintain; for example, the sensor needs to achieve a 7-day battery life. These two data points determine the signal transmission power limit imposed on the terminal by the intelligent signal control node: if the terminal's maximum allowable power consumption is low and the required battery life is long, the control node needs to transmit signals to the terminal at a lower power.

[0062] When setting up intelligent signal control nodes, parameter matching is performed by combining the two types of data mentioned above. For example, when the channel bandwidth utilization is 60%, the channel interference level is 20dB (weak interference), the maximum allowable power consumption of the terminal is 40mW, and the required battery life is 10 days, the signal allocation strategy of the control node is determined based on the channel bandwidth utilization. A bandwidth utilization of 60% means there is still some margin, and the control node can allocate an independent transmission channel with medium bandwidth. Combined with the channel interference level of 20dB, a modulation method with moderate anti-interference capability is selected. Based on the terminal's maximum allowable power consumption of 40mW and battery life of 10 days, the average power consumption that the terminal can allocate during communication is calculated: assuming that the terminal's basic power consumption excluding communication is 10mW, then the communication power consumption needs to be controlled within 30mW. The intelligent signal control node will set the upper limit of the power of the signal transmitted to the terminal to 30mW accordingly and match the corresponding signal transmission parameters. In this way, the intelligent signal control node can adapt to the current channel operating state, ensure the stability of signal transmission, and meet the terminal's energy consumption constraints to maintain the terminal's battery life requirements.

[0063] The signal modulation efficiency monitoring data of the independent data transmission channel is obtained from the intelligent signal control node.

[0064] The modulation efficiency anomaly coefficient of the independent data transmission channel and the modulation efficiency anomaly nodes of the intelligent signal control node are obtained by analyzing the signal modulation efficiency monitoring data.

[0065] The signal power loss monitoring data of the independent data transmission channel is obtained based on the intelligent signal control node.

[0066] By analyzing the signal power loss monitoring data, the power loss fault coefficient of the independent data transmission channel and the power loss abnormal nodes of the intelligent signal control node are obtained.

[0067] The abnormal modulation efficiency nodes and abnormal power loss nodes constitute the channel abnormality control nodes for IoT communication; the communication attenuation level of the channel abnormality control nodes is obtained based on the abnormal modulation efficiency coefficient and the fault coefficient of power loss.

[0068] After analyzing the communication attenuation level and abnormal control nodes, modulation parameters are adjusted and power compensation is performed.

[0069] After analyzing the communication attenuation level and abnormal control nodes, modulation parameters are adjusted and power compensation is performed, specifically including the following steps:

[0070] Based on the communication attenuation level, generate the attenuation warning intensity of the channel abnormal control node, and generate the attenuation warning signal of the intelligent signal control node based on the attenuation warning intensity.

[0071] The equivalent signal propagation distance of the channel anomaly control node is obtained based on the channel anomaly control node, and the communication optimization priority of the channel anomaly control node is obtained based on the attenuation warning strength and the equivalent signal propagation distance.

[0072] Based on communication optimization priorities and attenuation warning signals, the modulation parameters and power compensation of the channel abnormal control nodes are adjusted.

[0073] Communication optimization priority is determined by combining the attenuation warning intensity of the channel anomaly control node and the equivalent signal propagation distance. The attenuation warning intensity corresponds to the communication attenuation level of the channel; a higher level means a more severe decline in channel transmission quality. The equivalent signal propagation distance is the equivalent transmission distance between the channel anomaly control node and the signal receiver; the greater the distance, the more significant the signal attenuation. For example, if a channel anomaly control node has a high attenuation warning intensity and an equivalent signal propagation distance of 500 meters, while another node has a medium attenuation warning intensity and an equivalent distance of 200 meters, the former will have a higher communication optimization priority than the latter, meaning it needs to be adjusted and compensated for first.

[0074] The attenuation warning signal is a prompt signal generated based on the communication attenuation level. Different warning signal levels correspond to different degrees of problem severity. For example, a high attenuation warning signal indicates that the channel is in a state of severe attenuation; a medium attenuation warning signal indicates moderate attenuation. These signals are transmitted to the intelligent signal control node to clarify the direction of adjustment needed.

[0075] When adjusting modulation parameters, the corresponding modulation method and parameters are selected based on the communication optimization priority and attenuation warning signal. For example, for the channel abnormal control node with the highest priority and in a state of severe attenuation, if it previously used QPSK modulation, it will switch to 16QAM modulation with stronger anti-interference capability, and at the same time adjust the modulation rate: assuming the original modulation rate is 200kbps, it will be appropriately reduced to 150kbps to improve the signal transmission stability. Modulation rate = original modulation rate × (1 - attenuation warning strength coefficient), where the attenuation warning strength coefficient corresponds to the attenuation level. When the attenuation is severe, this coefficient is taken as 0.25, that is, 200 × (1 - 0.25) = 150kbps.

[0076] The power compensation process combines the terminal's energy consumption requirements with channel attenuation to increase signal transmission power within the terminal's maximum allowable power consumption range. For example, if the maximum allowable power consumption of a terminal corresponding to a channel abnormality control node is 50 milliwatts, and the current communication power consumption is 30 milliwatts, channel attenuation will cause insufficient signal power at the receiving end. The power compensation value = equivalent signal propagation distance × attenuation coefficient × power compensation weight. If the equivalent signal propagation distance is 500 meters, the attenuation coefficient is 0.02 milliwatts / meter, and the power compensation weight is 0.8, then the power compensation value = 500 × 0.02 × 0.8 = 8 milliwatts. After adjustment, the communication power consumption is 38 milliwatts, which does not exceed the terminal's maximum allowable power consumption. This both compensates for the power loss due to signal attenuation and meets the terminal's energy consumption constraints.

[0077] By adjusting the modulation parameters and compensating the power of the abnormal control nodes of the high-priority communication optimization channels, the signal transmission efficiency of the channels can be quickly restored while ensuring the power consumption requirements of the terminals, thus maintaining the stable communication of the Internet of Things system.

[0078] Channel state data includes channel bandwidth utilization and channel interference level;

[0079] Terminal energy consumption requirements data include the terminal's maximum allowable power consumption and battery life requirements.

[0080] Signal modulation efficiency monitoring data includes modulation error rate data and modulation rate fluctuation data for independent data transmission channels.

[0081] The analysis of signal modulation efficiency monitoring data yields the modulation efficiency anomaly coefficients of independent data transmission channels and the modulation efficiency anomaly nodes of intelligent signal control nodes. This process includes the following steps:

[0082] The process of obtaining the bit error rate anomaly coefficient for an independent data transmission channel based on modulation bit error rate data includes the following steps:

[0083] The modulation bit error rate data includes the real-time bit error rate value of the independent data transmission channel, and the bit error rate monitoring curve corresponding to the independent data transmission channel is generated based on the real-time bit error rate value;

[0084] If the real-time bit error rate value in the bit error rate monitoring curve is greater than the preset bit error rate threshold, the first bit error anomaly coefficient of the independent data transmission channel is obtained based on the real-time bit error rate value and the bit error rate threshold.

[0085] Obtain the slope of the bit error rate monitoring curve. If the slope of the bit error rate curve is greater than the preset bit error rate slope threshold, then obtain the second bit error anomaly coefficient of the independent data transmission channel based on the slope of the bit error rate curve and the bit error rate slope threshold; obtain the bit error rate anomaly coefficient of the independent data transmission channel based on the first bit error anomaly coefficient and the second bit error anomaly coefficient.

[0086] The rate fluctuation anomaly coefficient of the independent data transmission channel is obtained based on the modulation rate fluctuation data.

[0087] Set the bit error rate weight and the rate fluctuation weight, and obtain the modulation efficiency anomaly coefficient of the independent data transmission channel based on the bit error rate weight and bit error rate anomaly coefficient, the rate fluctuation weight and the rate fluctuation anomaly coefficient.

[0088] The intelligent signal modulation node corresponding to the independent data transmission channel is marked as a node with abnormal modulation efficiency.

[0089] First, the bit error rate (BER) anomaly coefficient is obtained based on the modulation BER data. The modulation BER data includes the real-time BER value of the channel. A BER monitoring curve is generated based on this value. When the real-time BER value is greater than a preset BER threshold, a first BER anomaly coefficient is calculated: First BER Anomaly Coefficient = (Real-time BER value - BER threshold) / BER threshold. When the slope of the BER monitoring curve is greater than a preset BER slope threshold, a second BER anomaly coefficient is calculated: Second BER Anomaly Coefficient = (BER curve slope - BER slope threshold) / BER slope threshold. The final BER anomaly coefficient is the weighted sum of these two coefficients. For example, if the first BER anomaly coefficient is 0.3, the second BER anomaly coefficient is 0.2, and both weights are 0.5, then the BER anomaly coefficient = 0.3 × 0.5 + 0.2 × 0.5 = 0.25.

[0090] The rate fluctuation anomaly coefficient is obtained based on the modulation rate fluctuation data. The modulation rate fluctuation data includes the real-time rate fluctuation value of the channel. When the real-time rate fluctuation value is greater than a preset rate fluctuation threshold, a first rate anomaly coefficient is calculated: First rate anomaly coefficient = (Real-time rate fluctuation value - Rate fluctuation threshold) / Rate fluctuation threshold. When the slope of the rate fluctuation curve is greater than a preset rate fluctuation slope threshold, a second rate anomaly coefficient is calculated: Second rate anomaly coefficient = (Slope of rate fluctuation curve - Rate fluctuation slope threshold) / Rate fluctuation slope threshold. The rate fluctuation anomaly coefficient is a weighted sum of the two. For example, if the first rate anomaly coefficient is 0.4, the second rate anomaly coefficient is 0.1, and both weights are 0.5, then the rate fluctuation anomaly coefficient = 0.4 × 0.5 + 0.1 × 0.5 = 0.25.

[0091] Pre-set the bit error rate weight and the rate fluctuation weight, with their sum being 1. For example, set the bit error rate weight to 0.6 and the rate fluctuation weight to 0.4. The modulation efficiency anomaly coefficient = bit error rate weight × bit error rate anomaly coefficient + rate fluctuation weight × rate fluctuation anomaly coefficient. Taking a bit error rate anomaly coefficient of 0.25 and a rate fluctuation anomaly coefficient of 0.25 as an example, substituting the values, we get the modulation efficiency anomaly coefficient = 0.6 × 0.25 + 0.4 × 0.25 = 0.25.

[0092] Each independent data transmission channel corresponds to an intelligent signal control node. When the modulation efficiency anomaly coefficient of this channel exceeds a preset anomaly threshold (e.g., 0.2), the intelligent signal control node corresponding to this channel is marked as a modulation efficiency anomaly node. For example, if the modulation efficiency anomaly coefficient of a control node corresponding to an independent channel is calculated to be 0.25, exceeding the threshold of 0.2, this node will be marked as an anomaly node so that communication optimization adjustments can be made for this node in the future.

[0093] The process of obtaining the rate fluctuation anomaly coefficient of an independent data transmission channel based on modulation rate fluctuation data includes the following steps:

[0094] Modulation rate fluctuation data includes real-time rate fluctuation values ​​of independent data transmission channels;

[0095] Generate rate fluctuation monitoring curves for independent data transmission channels based on real-time rate fluctuation values;

[0096] If the real-time rate fluctuation value of the rate fluctuation monitoring curve is greater than the preset rate fluctuation threshold, the first rate anomaly coefficient of the independent data transmission channel is obtained based on the real-time rate fluctuation value and the rate fluctuation threshold.

[0097] Obtain the slope of the rate fluctuation monitoring curve; if the slope of the rate fluctuation curve is greater than the preset rate fluctuation slope threshold, then obtain the second rate anomaly coefficient of the independent data transmission channel based on the slope of the rate fluctuation curve and the rate fluctuation slope threshold.

[0098] The rate fluctuation anomaly coefficient of the independent data transmission channel is obtained based on the first rate anomaly coefficient and the second rate anomaly coefficient.

[0099] The core of modulation rate fluctuation data is the real-time rate fluctuation value of an independent data transmission channel. This value represents the deviation between the actual modulation rate and the preset modulation rate of the channel. For example, if the preset modulation rate of a channel is 200kbps, and the actual rate at a certain moment is 180kbps, then the real-time rate fluctuation value at that moment is 20kbps. By continuously collecting real-time rate fluctuation values ​​at different time points and correlating these values ​​in chronological order, a rate fluctuation monitoring curve corresponding to that channel is generated. This curve can intuitively show the changing trend of rate fluctuation; for example, if the curve continues to rise, it indicates that the rate fluctuation is gradually increasing.

[0100] Anomalies in rate fluctuations are assessed and corresponding coefficients are calculated using two dimensions. The first dimension is the absolute magnitude of the real-time rate fluctuation value. A rate fluctuation threshold is pre-set, for example, 15 kbps. When the real-time rate fluctuation value in the rate fluctuation monitoring curve exceeds this threshold, a first rate anomaly coefficient is calculated: First rate anomaly coefficient = (Real-time rate fluctuation value - Rate fluctuation threshold) / Rate fluctuation threshold. For example, if the real-time rate fluctuation value is 20 kbps and the rate fluctuation threshold is 15 kbps, substituting these values ​​into the formula yields the first rate anomaly coefficient = (20 - 15) / 15 ≈ 0.33. This coefficient reflects the degree to which the real-time rate fluctuation exceeds the threshold.

[0101] The second dimension is the trend of rate fluctuation, specifically the slope of the rate fluctuation monitoring curve. This slope represents the rate of change of the rate fluctuation; a larger slope indicates a faster increase in the rate fluctuation. A preset rate fluctuation slope threshold is set, for example, 5 kbps / minute. When the slope of the rate fluctuation curve exceeds this threshold, a second rate anomaly coefficient is calculated. The second rate anomaly coefficient = (rate fluctuation curve slope - rate fluctuation slope threshold) / rate fluctuation slope threshold. Assuming the rate fluctuation curve slope is 7 kbps / minute over a certain period, substituting into the formula, we get the second rate anomaly coefficient = (7-5) / 5 = 0.4. This coefficient reflects the degree to which the rate fluctuation deteriorates beyond the threshold.

[0102] The rate fluctuation anomaly coefficient of this independent data transmission channel is obtained by combining the first rate anomaly coefficient and the second rate anomaly coefficient. Typically, corresponding weights are assigned to these two coefficients (the sum of the weights is 1). For example, if the weight of the first rate anomaly coefficient is 0.6 and the weight of the second rate anomaly coefficient is 0.4, then the rate fluctuation anomaly coefficient = first rate anomaly coefficient × 0.6 + second rate anomaly coefficient × 0.4. The rate fluctuation anomaly coefficient = 0.33 × 0.6 + 0.4 × 0.4 ≈ 0.198 + 0.16 = 0.358. This coefficient comprehensively reflects the degree of anomaly in the rate fluctuation of this channel; the larger the coefficient, the more severe the rate fluctuation problem.

[0103] Analyzing signal power loss monitoring data yields the power loss fault coefficient of independent data transmission channels and the abnormal power loss nodes of intelligent signal control nodes. This process includes the following steps:

[0104] Signal power loss monitoring data includes real-time power loss values ​​for independent data transmission channels;

[0105] Based on the real-time power loss value, generate the power loss monitoring curve of the independent data transmission channel and obtain the slope of the power loss monitoring curve.

[0106] If the slope of the power loss curve is greater than the preset power loss slope threshold, the power loss fault coefficient of the independent data transmission channel is obtained based on the power loss curve slope and the power loss slope threshold, and the intelligent signal control node corresponding to the independent data transmission channel is marked as a power loss abnormal node.

[0107] The core of signal power loss monitoring data is the real-time power loss value of an independent data transmission channel. This value represents the actual power consumed by the channel during signal transmission. For example, if the real-time power loss value of an IoT data transmission channel is 8 milliwatts, it means that the channel loses 8 milliwatts of power for each signal transmission. By continuously collecting real-time power loss values ​​at different time points and correlating these values ​​in chronological order, a power loss monitoring curve corresponding to that channel is generated. This curve can intuitively show the trend of power loss changes; for example, an upward trend in the curve indicates that the channel's power loss is gradually increasing.

[0108] Extract the slope of the power loss monitoring curve. This slope reflects the rate of change of power loss. A positive slope and a larger value indicate that the power loss is increasing faster. For example, if the real-time power loss value of a certain channel increases from 5 milliwatts to 10 milliwatts within 10 minutes, then the slope of the power loss curve during this period is (10-5) / 10 = 0.5 milliwatts / minute, representing an increase of 0.5 milliwatts in power loss per minute.

[0109] Compare this slope with a preset power loss slope threshold. The power loss slope threshold is a pre-set, acceptable upper limit for the rate of power loss growth, for example, 0.3 milliwatts per minute. If the slope of the power loss curve is greater than this threshold, it indicates that the rate of power loss growth exceeds the normal range. In this case, a power loss fault coefficient needs to be calculated: Power loss fault coefficient = (Power loss curve slope - Power loss slope threshold) / Power loss slope threshold. If the power loss curve slope is 0.5 milliwatts per minute and the threshold is 0.3 milliwatts per minute, the power loss fault coefficient = (0.5 - 0.3) / 0.3 ≈ 0.67. The larger this coefficient, the higher the degree of abnormality in power loss growth.

[0110] Once the power loss fault coefficient is calculated, the intelligent signal control node corresponding to the independent data transmission channel is marked as a power loss abnormal node. For example, the control node corresponding to the above channel is marked as an abnormal node because the slope of the power loss curve exceeds the threshold and the fault coefficient reaches 0.67. Subsequent communication optimization processes will prioritize power compensation and other adjustments for these nodes to reduce the power loss of the channel while ensuring the energy consumption requirements and communication stability of the terminal.

[0111] Based on the channel anomaly control node, the equivalent signal propagation distance of the channel anomaly control node is obtained. Then, based on the attenuation warning strength and the equivalent signal propagation distance, the communication optimization priority of the channel anomaly control node is determined. This process includes the following steps:

[0112] Obtain the first time point at which the channel abnormal control node sends the attenuation warning signal, and obtain the second time point at which the attenuation warning signal is received; obtain the propagation duration of the warning signal based on the first and second time points;

[0113] Obtain the propagation characteristic parameters of the IoT communication channel, and obtain the equivalent propagation speed of the attenuation warning signal based on the propagation characteristic parameters;

[0114] The equivalent distance between the abnormal control node of the channel corresponding to the attenuated warning signal and the signal propagation is obtained based on the propagation duration and equivalent propagation speed of the warning signal.

[0115] Set the warning intensity weight and equivalent distance weight; obtain the attenuation influence coefficient of the channel anomaly control node based on the attenuation warning intensity and warning intensity weight; obtain the distance influence coefficient of the channel anomaly control node based on the equivalent signal propagation distance and equivalent distance weight;

[0116] The optimization priority coefficient of the channel anomaly control node is obtained based on the attenuation influence coefficient and the distance influence coefficient, and the communication optimization priority of the channel anomaly control node is set based on the optimization priority coefficient.

[0117] The system obtains the first time point when the channel anomaly control node sends the attenuation warning signal and the second time point when it receives the signal. The propagation time of the warning signal is calculated by the difference between these two time points. For example, if the channel anomaly control node sends the warning signal at 10:00:00 and the receiving end receives the signal at 10:00:02, the propagation time of the warning signal is 2 seconds. This duration reflects the transmission time of the signal from the control node to the receiving end.

[0118] Obtain the propagation characteristic parameters of the IoT communication channel. These parameters include the channel's medium type and signal attenuation coefficient. For example, if the propagation characteristic parameters of a certain IoT channel show that the basic propagation speed of the signal in this channel is 2.8 × 10^8 m / s, and considering factors such as the channel's attenuation characteristics, calculate the equivalent propagation speed of the attenuation warning signal. For instance, after considering the combined effects of channel interference and attenuation, the equivalent propagation speed is 2.5 × 10^8 m / s. This speed is the signal propagation rate under the current channel's actual environment.

[0119] The equivalent signal propagation distance corresponding to the channel anomaly control node is calculated based on the propagation duration of the early warning signal and the equivalent propagation speed. The equivalent signal propagation distance = early warning signal propagation duration × equivalent propagation speed. The early warning signal propagation duration is 2 seconds, and the equivalent propagation speed is 2.5 × 10^8 meters / second. Substituting these values ​​into the formula, we get the equivalent signal propagation distance = 2 × 2.5 × 10^8 = 5 × 10^8 meters. This distance represents the equivalent communication distance between the control node and the receiving end, and is an important basis for assessing signal transmission loss.

[0120] Set a warning intensity weight and an equivalent distance weight. These two weights are used to measure the impact of attenuation warning intensity and equivalent signal propagation distance on optimization priority. Usually, the sum of the two weights is 1. For example, if the warning intensity weight is 0.6 and the equivalent distance weight is 0.4, the attenuation impact coefficient = attenuation warning intensity × warning intensity weight. For example, if the attenuation warning intensity is 0.8 (the value ranges from 0 to 1, and the larger the value, the more severe the attenuation), then the attenuation impact coefficient = 0.8 × 0.6 = 0.48. At the same time, combine the equivalent signal propagation distance to calculate the distance impact coefficient. The distance impact coefficient = equivalent signal propagation distance × equivalent distance weight. For example, if the normalized value corresponding to the equivalent signal propagation distance is 0.5, convert the actual distance into a normalized value of 0 to 1. The larger the value, the farther the distance. Then the distance impact coefficient is 0.5 × 0.4 = 0.2.

[0121] The optimization priority coefficient equals the attenuation impact coefficient plus the distance impact coefficient. Communication optimization priority is set based on the magnitude of this coefficient; a higher coefficient indicates a more urgent communication problem for the abnormal control node in that channel, and thus a higher optimization priority. For example, if one node has an optimization priority coefficient of 0.8 and another node has an optimization priority coefficient of 0.5, the former will have a higher communication optimization priority than the latter, and will be prioritized for modulation parameter adjustments and power compensation operations.

[0122] A wireless signal data communication system based on the Internet of Things, comprising:

[0123] Setting module: Acquires channel status data and terminal power consumption demand data of IoT communication, and sets up intelligent signal control nodes based on the channel status data and terminal power consumption demand data;

[0124] First analysis module: Based on the signal modulation efficiency monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the signal modulation efficiency monitoring data is analyzed to obtain the modulation efficiency anomaly coefficient of the independent data transmission channel and the modulation efficiency anomaly nodes of the intelligent signal control node;

[0125] The second analysis module: Based on the signal power loss monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the signal power loss monitoring data is analyzed to obtain the power loss fault coefficient of the independent data transmission channel and the power loss abnormal nodes of the intelligent signal control node.

[0126] Processing module: Constructs channel abnormality control nodes for IoT communication based on modulation efficiency abnormality nodes and power loss abnormality nodes; obtains the communication attenuation level of the channel abnormality control nodes based on the modulation efficiency abnormality coefficient and the power loss fault coefficient.

[0127] Adjustment module: After analyzing the communication attenuation level and abnormal control nodes, it adjusts the modulation parameters and performs power compensation.

[0128] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0129] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0130] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless signal data communication method based on the Internet of Things, characterized in that, The method includes the following steps: Acquire channel status data and terminal power consumption demand data for IoT communication, and set up intelligent signal control nodes based on the channel status data and terminal power consumption demand data; Based on the signal modulation efficiency monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the modulation efficiency anomaly coefficient of the independent data transmission channel and the modulation efficiency anomaly nodes of the intelligent signal control node are analyzed to obtain the following steps: The bit error rate anomaly coefficient of the independent data transmission channel is obtained based on the modulation bit error rate data; the rate fluctuation anomaly coefficient of the independent data transmission channel is obtained based on the modulation rate fluctuation data. Set the bit error rate weight and the rate fluctuation weight, and obtain the modulation efficiency anomaly coefficient of the independent data transmission channel based on the bit error rate weight and bit error rate anomaly coefficient, the rate fluctuation weight and the rate fluctuation anomaly coefficient. The intelligent signal modulation node corresponding to the independent data transmission channel is marked as a node with abnormal modulation efficiency. Based on the signal power loss monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the power loss monitoring data is analyzed to obtain the power loss fault coefficient of the independent data transmission channel and the abnormal power loss nodes of the intelligent signal control node. The specific steps include: The signal power loss monitoring data includes the real-time power loss value of the independent data transmission channel; Based on the real-time power loss value, generate the power loss monitoring curve of the independent data transmission channel and obtain the slope of the power loss monitoring curve. If the slope of the power loss curve is greater than the preset power loss slope threshold, the power loss fault coefficient of the independent data transmission channel is obtained based on the slope of the power loss curve and the power loss slope threshold, and the intelligent signal control node corresponding to the independent data transmission channel is marked as a power loss abnormal node. The abnormal modulation efficiency nodes and abnormal power loss nodes constitute the channel abnormality control nodes for IoT communication; the communication attenuation level of the channel abnormality control nodes is obtained based on the abnormal modulation efficiency coefficient and the fault coefficient of power loss. After analyzing the communication attenuation level and abnormal control nodes, modulation parameters are adjusted and power compensation is performed.

2. The wireless signal data communication method based on the Internet of Things according to claim 1, characterized in that, After analyzing the communication attenuation level and abnormal control nodes, modulation parameters are adjusted and power compensation is performed, specifically including the following steps: Based on the communication attenuation level, generate the attenuation warning intensity of the channel abnormal control node, and generate the attenuation warning signal of the intelligent signal control node based on the attenuation warning intensity. The equivalent signal propagation distance of the channel anomaly control node is obtained based on the channel anomaly control node, and the communication optimization priority of the channel anomaly control node is obtained based on the attenuation warning strength and the equivalent signal propagation distance. Based on communication optimization priorities and attenuation warning signals, the modulation parameters and power compensation of the channel abnormal control nodes are adjusted.

3. The wireless signal data communication method based on the Internet of Things according to claim 1, characterized in that, The channel status data includes channel bandwidth utilization and channel interference level; The terminal energy consumption requirements data include the terminal's maximum allowable power consumption and battery life requirements.

4. The wireless signal data communication method based on the Internet of Things according to claim 1, characterized in that, The signal modulation efficiency monitoring data includes modulation error rate data and modulation rate fluctuation data for independent data transmission channels.

5. The wireless signal data communication method based on the Internet of Things according to claim 4, characterized in that, The process of obtaining the bit error rate anomaly coefficient for an independent data transmission channel based on modulation bit error rate data includes the following steps: The modulation bit error rate data includes the real-time bit error rate value of the independent data transmission channel, and the bit error rate monitoring curve corresponding to the independent data transmission channel is generated based on the real-time bit error rate value; If the real-time bit error rate value in the bit error rate monitoring curve is greater than the preset bit error rate threshold, the first bit error anomaly coefficient of the independent data transmission channel is obtained based on the real-time bit error rate value and the bit error rate threshold. Obtain the slope of the bit error rate monitoring curve. If the slope of the bit error rate curve is greater than the preset bit error rate slope threshold, then obtain the second bit error anomaly coefficient of the independent data transmission channel based on the slope of the bit error rate curve and the bit error rate slope threshold; obtain the bit error rate anomaly coefficient of the independent data transmission channel based on the first bit error anomaly coefficient and the second bit error anomaly coefficient.

6. The wireless signal data communication method based on the Internet of Things according to claim 5, characterized in that, The process of obtaining the rate fluctuation anomaly coefficient of an independent data transmission channel based on modulation rate fluctuation data includes the following steps: The modulation rate fluctuation data includes the real-time rate fluctuation value of the independent data transmission channel; Generate rate fluctuation monitoring curves for independent data transmission channels based on real-time rate fluctuation values; If the real-time rate fluctuation value of the rate fluctuation monitoring curve is greater than the preset rate fluctuation threshold, the first rate anomaly coefficient of the independent data transmission channel is obtained based on the real-time rate fluctuation value and the rate fluctuation threshold. Obtain the slope of the rate fluctuation monitoring curve; if the slope of the rate fluctuation curve is greater than the preset rate fluctuation slope threshold, then obtain the second rate anomaly coefficient of the independent data transmission channel based on the slope of the rate fluctuation curve and the rate fluctuation slope threshold. The rate fluctuation anomaly coefficient of the independent data transmission channel is obtained based on the first rate anomaly coefficient and the second rate anomaly coefficient.

7. A wireless signal data communication method based on the Internet of Things according to claim 6, characterized in that, Based on the channel anomaly control node, the equivalent signal propagation distance of the channel anomaly control node is obtained. Then, based on the attenuation warning strength and the equivalent signal propagation distance, the communication optimization priority of the channel anomaly control node is determined. This process includes the following steps: Obtain the first time point at which the channel abnormal control node sends the attenuation warning signal, and obtain the second time point at which the attenuation warning signal is received; obtain the propagation duration of the warning signal based on the first and second time points; Obtain the propagation characteristic parameters of the IoT communication channel, and obtain the equivalent propagation speed of the attenuation warning signal based on the propagation characteristic parameters; The equivalent distance between the abnormal control node of the channel corresponding to the attenuated warning signal and the signal propagation is obtained based on the propagation duration and equivalent propagation speed of the warning signal. Set the warning intensity weight and equivalent distance weight; obtain the attenuation influence coefficient of the channel anomaly control node based on the attenuation warning intensity and warning intensity weight; obtain the distance influence coefficient of the channel anomaly control node based on the equivalent signal propagation distance and equivalent distance weight; The optimization priority coefficient of the channel anomaly control node is obtained based on the attenuation influence coefficient and the distance influence coefficient, and the communication optimization priority of the channel anomaly control node is set based on the optimization priority coefficient.

8. A wireless signal data communication system based on the Internet of Things (IoT), applied to the wireless signal data communication method based on the Internet of Things as described in any one of claims 1 to 7, characterized in that, include: Setting module: Acquires channel status data and terminal power consumption demand data of IoT communication, and sets up intelligent signal control nodes based on the channel status data and terminal power consumption demand data; The first analysis module: Based on the signal modulation efficiency monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the module analyzes the signal modulation efficiency monitoring data to obtain the modulation efficiency anomaly coefficient of the independent data transmission channel and the modulation efficiency anomaly nodes of the intelligent signal control node. Specifically, this includes the following steps: The bit error rate anomaly coefficient of the independent data transmission channel is obtained based on the modulation bit error rate data; the rate fluctuation anomaly coefficient of the independent data transmission channel is obtained based on the modulation rate fluctuation data. Set the bit error rate weight and the rate fluctuation weight, and obtain the modulation efficiency anomaly coefficient of the independent data transmission channel based on the bit error rate weight and bit error rate anomaly coefficient, the rate fluctuation weight and the rate fluctuation anomaly coefficient. The intelligent signal modulation node corresponding to the independent data transmission channel is marked as a node with abnormal modulation efficiency. The second analysis module: Based on the signal power loss monitoring data of the independent data transmission channel obtained by the intelligent signal control node, the module analyzes the signal power loss monitoring data to obtain the power loss fault coefficient of the independent data transmission channel and the abnormal power loss nodes of the intelligent signal control node. Specifically, this includes the following steps: The signal power loss monitoring data includes the real-time power loss value of the independent data transmission channel; Based on the real-time power loss value, generate the power loss monitoring curve of the independent data transmission channel and obtain the slope of the power loss monitoring curve. If the slope of the power loss curve is greater than the preset power loss slope threshold, the power loss fault coefficient of the independent data transmission channel is obtained based on the slope of the power loss curve and the power loss slope threshold, and the intelligent signal control node corresponding to the independent data transmission channel is marked as a power loss abnormal node. Processing module: Constructs channel abnormality control nodes for IoT communication based on modulation efficiency abnormality nodes and power loss abnormality nodes; obtains the communication attenuation level of the channel abnormality control nodes based on the modulation efficiency abnormality coefficient and the power loss fault coefficient. Adjustment module: After analyzing the communication attenuation level and abnormal control nodes, it adjusts the modulation parameters and performs power compensation.

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