A highway infrastructure monitoring device wireless networking communication control method and system

Abstract

The application relates to the technical field of wireless networking communication control of highway infrastructure monitoring equipment, and discloses a wireless networking communication control method and system of highway infrastructure monitoring equipment. When an alarm data packet is detected by the monitoring equipment, the alarm data packet is split into emergency abstract information and detailed main data according to the information emergency degree and data volume size, and the transmission parameter with high anti-interference capability is preferentially used to send the emergency abstract information, so that the problem of high alarm data transmission failure rate and serious delay when a physical event (such as a heavy vehicle passing) is used as a strong electromagnetic interference source is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of wireless networking communication control technology for highway infrastructure monitoring equipment, and in particular to a wireless networking communication control method and system for highway infrastructure monitoring equipment. Background Technology

[0002] In the long-term operation and maintenance of highway infrastructure, wireless network communication transmission and control plays a crucial role. Existing wireless network systems for highway infrastructure monitoring equipment were designed from the outset with a mapping rule between data packet size and frame structure based on mainstream sensor types and expected network traffic patterns. This rule aims to efficiently encapsulate data packets of different sizes into the most suitable wireless frames to minimize protocol overhead and ensure efficient and stable communication under normal operating conditions, such as during periodic transmission of status data like road surface temperature and vibration frequency.

[0003] When a sudden event occurs, such as an overloaded truck crossing a bridge or minor geological activity, the sensor is immediately triggered into a high-frequency sampling mode to capture the detailed dynamic response of the structure under impact. The data packets generated in this mode are very large, containing dense waveform information. Existing control methods recognize this large data packet and adjust the communication frame structure accordingly to a high-capacity mode. For example, allocating a longer communication time slot to the sensor node or using a frame format that supports a larger payload ensures that this important large data packet can be transmitted completely in one go. This control method, which adjusts the frame structure solely based on the data packet size, is effective in theoretical models or environments with minimal interference.

[0004] However, the actual highway environment is far more complex than ideal. Highways, especially busy main roads, are themselves strong sources of electromagnetic interference. Vehicles, particularly large trucks, buses, and the increasing number of new energy vehicles, generate instantaneous and intense electromagnetic radiation during operation. This interference is not a stable background noise, but rather intermittent, strong interference pulses closely related to traffic flow and vehicle type.

[0005] The root of the problem lies in the fact that the original control method only established a one-way logic: the size of the application layer's data packets determines the frame structure of the communication layer. It ignores the inherent connection between the physical environment and the communication environment. Summary of the Invention

[0006] This invention provides a wireless networking communication control method and system for highway infrastructure monitoring equipment, aiming to solve the problems of high alarm data transmission failure rate, severe delay, increased energy consumption, and reduced reliability and timeliness of the monitoring system when the existing wireless networking communication control method for highway infrastructure monitoring equipment fails to fully consider the characteristics of physical events as strong electromagnetic interference sources when facing the transmission of large data packets triggered by physical events.

[0007] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a wireless networking communication control method for highway infrastructure monitoring equipment, comprising:

[0008] When the monitoring device detects an alarm data packet, it splits the alarm data packet into an emergency summary and detailed main data according to the urgency and data size of the alarm data packet. The alarm data packet is generated by a physical event.

[0009] The emergency summary information is sent preferentially using transmission parameters with strong anti-interference capabilities;

[0010] Continuously monitor the environmental parameters of the physical event, and determine the timing of the dissipation of communication channel interference based on the changing trends of the environmental parameters;

[0011] The detailed main data is sent at the appropriate time according to the fading time, and the large payload transmission parameters adapted to the current channel conditions are used during the transmission.

[0012] Preferably, the step of continuously monitoring the environmental parameters of the physical event and determining the timing of the dissipation of communication channel interference based on the changing trends of the environmental parameters includes:

[0013] After sending the emergency summary information, the environmental parameters of the physical event are continuously monitored, and radio environment information is periodically obtained through the wireless communication module.

[0014] Measure the strength indication value and signal-to-noise ratio of the received signal in the current wireless channel and perform correlation analysis;

[0015] When the vibration signal envelope drops below a preset threshold, and the intensity indication value and the signal-to-noise ratio reach a preset channel recovery standard, it is determined that the communication channel interference has subsided.

[0016] When the vibration signal envelope has decreased, and the intensity indicator value or the signal-to-noise ratio indicates poor channel quality, continue to wait.

[0017] Preferably, the step of continuously monitoring the environmental parameters of the physical event after sending the emergency summary information and periodically acquiring radio environment information through the wireless communication module includes:

[0018] When the wireless communication module performs radio environment sensing, it performs received signal strength scanning detection on different frequency bands or channels to obtain the radio environment information.

[0019] The receiver gain and sampling frequency are adaptively adjusted according to the dynamic changes in the current channel environment.

[0020] When the intensity indication value is detected to be close to the upper or lower limit of the measurement, the receiving gain is adjusted;

[0021] Based on the instantaneous rate of change of the channel noise ratio, the sampling frequency of the signal-to-noise ratio is dynamically adjusted to capture fluctuations in channel quality;

[0022] The vibration signal intensity collected by the vibration sensor is cross-validated with the measurement results of the radio environment parameters.

[0023] When the vibration signal intensity is at a high level, the measurement results of the intensity indication value and the signal-to-noise ratio are weighted.

[0024] Preferably, the step of adaptively adjusting the receiver gain and sampling frequency according to the dynamic changes in the current channel environment includes:

[0025] When measuring the intensity indication value and the signal-to-noise ratio, the instantaneous amplitude change rate of the vibration signal collected by the vibration sensor is analyzed in real time.

[0026] When the instantaneous amplitude change rate exceeds a preset rapid change threshold, the response speed of the receiving gain adjustment is increased, and the sampling frequency of the signal-to-noise ratio is increased.

[0027] The length of the measurement window is dynamically adjusted based on the measurement results of the intensity indication value and the signal-to-noise ratio.

[0028] When a large fluctuation in the intensity indication value is detected within a short period of time, the measurement window length is shortened to capture the instantaneous peak or trough of the interference.

[0029] When the intensity indication value changes gradually, the measurement window length is extended to reduce measurement noise.

[0030] Preferably, the cross-validation of the vibration signal intensity collected by the vibration sensor with the measurement results of the radio environment parameters includes:

[0031] When the vibration signal collected by the vibration sensor is received, frequency domain analysis is performed on the vibration signal to identify the dominant frequency components and duration of the vibration signal;

[0032] Based on the dominant frequency component and the duration, different types of physical events are obtained by matching them with the features of preset different physical event types.

[0033] The intensity indication value and the signal-to-noise ratio are compared with the electromagnetic interference characteristic parameter set corresponding to the different types of physical events;

[0034] The measurement results of the radio environment parameters are weighted based on the comparison results.

[0035] Preferably, the weighting process for the measurement results of the radio environment parameters based on the comparison results includes:

[0036] Real-time monitoring of the instantaneous rate of change of the intensity indication value and the signal-to-noise ratio;

[0037] When the instantaneous rate of change exceeds the preset rapid change threshold, the weighting factor is adjusted, and a weighting function with a fast response speed is adopted to quickly adapt to the change in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set.

[0038] When the instantaneous rate of change is lower than the preset steady change threshold, a weighting function with a slow response speed is used to smooth the adjustment of the weighting factor;

[0039] Based on the adjusted weighting factor, the intensity indication value and the signal-to-noise ratio are weighted to obtain the weighted radio environment parameter measurement results and to evaluate the channel quality.

[0040] Preferably, when the instantaneous rate of change exceeds a preset rapid change threshold, the weighting factor is adjusted, and a weighting function with a fast response speed is adopted to quickly adapt to changes in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set, including:

[0041] When the instantaneous rate of change exceeds a preset rapid change threshold, the interference source identification program is activated. The interference source identification program is used to perform rapid scanning on different frequency bands or channels to identify currently active interference sources.

[0042] Based on the frequency band, intensity, and variation characteristics of the identified dominant interference source, the corresponding interference type is matched from the preset interference source feature library;

[0043] The corresponding weighting factor is invoked based on the matched interference type, and the adjustment strategy and weighting function are also invoked. The weighting factor is adjusted according to the invoked adjustment strategy and weighting function to quickly adapt to changes in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set.

[0044] Preferably, when the instantaneous rate of change is lower than a preset steady-state change threshold, a weighting function with a slow response speed is used to smooth the adjustment of the weighting factor, including:

[0045] When the instantaneous rate of change is lower than the preset steady change threshold, the background noise identification program is started. The background noise identification program is used to perform long-term low-speed scanning on different frequency bands or channels.

[0046] Analyze the received signal strength indication value, signal-to-noise ratio and spectral characteristics of each frequency band or channel to identify persistent, low-intensity and wide-band background noise;

[0047] Match the corresponding background noise type from a preset background noise feature library based on the background noise;

[0048] Based on the matched background noise type, the corresponding weighting function is called;

[0049] During continuous operation of the identification program, the intensity indication value and signal-to-noise ratio are periodically compared with the identified background noise features. When the measured value is higher than the background noise level, it is determined to be actual interference and the weighting factor is adjusted accordingly.

[0050] Preferably, the analysis of the received signal strength indication value, signal-to-noise ratio, and spectral characteristics of each frequency band or channel to identify persistent, low-intensity, and wide-bandwidth background noise includes:

[0051] During low-speed scanning, time-series analysis is performed on the received signal strength indication value, signal-to-noise ratio, and spectral characteristic data of each frequency band or channel to identify low-intensity signal components with periodic or quasi-periodic changes.

[0052] Based on the location information and timestamp of the monitoring device, the expected range of background noise spectrum characteristics within the current location and time period is obtained from a preset noise feature library;

[0053] The spectral characteristics acquired in real time and analyzed through time series are compared with the expected range of background noise spectral characteristics.

[0054] When the match is found and the intensity of the real-time spectral characteristic is at a low level, the real-time spectral characteristic is identified as background noise.

[0055] When the real-time spectrum characteristics exhibit periodic or quasi-periodic changes, and the intensity of the real-time spectrum characteristics is low but deviates from the expected range of background noise spectrum characteristics, the real-time spectrum characteristics are identified as periodic low-intensity interference sources.

[0056] When the real-time spectrum characteristics exhibit a non-periodic, transient signal with an intensity higher than the background noise in a specific frequency band or channel, the real-time spectrum characteristics are identified as actual interference.

[0057] The channel quality is assessed based on the identified background noise, the periodic low-intensity interference source, or the actual interference.

[0058] Secondly, the present invention provides a wireless networking communication control system for highway infrastructure monitoring equipment, comprising:

[0059] The detection end is used to split the alarm data packet into emergency summary information and detailed main data according to the urgency and data size of the alarm data packet when the monitoring device detects the alarm data packet. The alarm data packet is generated by a physical event.

[0060] The judgment end is used to prioritize sending the emergency summary information using transmission parameters with strong anti-interference capabilities; continuously monitor the environmental parameters of the physical event, and determine the timing of the dissipation of communication channel interference based on the changing trend of the environmental parameters;

[0061] The output terminal is used to send the detailed main data at the appropriate time according to the fading time, and to use high-payload transmission parameters adapted to the current channel conditions when sending.

[0062] This application discloses a wireless networking communication control method and system for highway infrastructure monitoring equipment. When the monitoring equipment detects an alarm data packet, it splits it into an emergency summary and detailed main data based on the urgency and size of the information. The emergency summary is then prioritized for transmission using parameters with strong anti-interference capabilities. This effectively solves the problem of high alarm data transmission failure rate and severe delay when physical events (such as the passage of heavy vehicles) act as strong electromagnetic interference sources. Furthermore, the method continuously monitors environmental parameters (such as vibration signals) related to physical events and determines the timing of communication channel interference dissipation based on the changing trends of these parameters. Detailed main data is then transmitted opportunely when channel conditions recover, using high-load transmission parameters adapted to the current channel conditions. This "urgent first, then mild, and opportunistic" strategy fully leverages the inherent correlation between physical events and electromagnetic interference, transforming passive response into proactive prediction. It avoids performing the most time-consuming and least tolerant transmission tasks under the worst channel conditions, significantly improving the success rate and timeliness of alarm information transmission, reducing the number of retransmissions and energy consumption, thereby enhancing the reliability and timeliness of the entire monitoring system. It overcomes the shortcomings of existing technologies that only adjust the frame structure based on the data packet size while ignoring the correlation between the physical and communication environments. Attached Figure Description

[0063] Figure 1This is a flowchart of a wireless networking communication control method for highway infrastructure monitoring equipment provided in an embodiment of the present invention;

[0064] Figure 2 This is a flowchart of another wireless networking communication control method for highway infrastructure monitoring equipment provided in an embodiment of the present invention;

[0065] Figure 3 This is a flowchart of a method for obtaining radio environment information through a wireless communication module according to an embodiment of the present invention;

[0066] Figure 4 This is a schematic diagram of the structure of a wireless networking communication control system for highway infrastructure monitoring equipment provided in an embodiment of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Reference Figure 1 The present invention provides a flowchart of a wireless networking communication control method for highway infrastructure monitoring equipment, comprising the following steps:

[0069] S1, When the monitoring device detects an alarm data packet, it splits the alarm data packet into emergency summary information and detailed main data according to the urgency and data size of the alarm data packet. The alarm data packet is generated by a physical event.

[0070] S2, the emergency summary information is sent preferentially using transmission parameters with strong anti-interference capabilities;

[0071] S3, continuously monitor the environmental parameters of the physical event, and determine the timing of the dissipation of communication channel interference based on the changing trend of the environmental parameters;

[0072] S4. Based on the fading time, the detailed main data is sent at an opportune time, and the large payload transmission parameters adapted to the current channel conditions are used during the transmission.

[0073] The method proposed in this application is mainly applied to wireless networking communication scenarios for highway infrastructure monitoring equipment. The "alarm data packet" refers to the data set generated by the monitoring equipment when it detects an abnormal physical event (such as bridge vibration, road surface settlement, etc.). It contains detailed information about the event and is used to notify the monitoring center to take appropriate measures. The "emergency summary information" is a simplified, highest-priority part of the alarm data packet, typically containing key information such as event type, occurrence time, location, and initial severity, aiming to quickly convey the core alarm information with minimal data volume. The "detailed main data" is the remaining part of the alarm data packet, containing richer details, such as high-frequency sampling waveforms and multi-sensor linkage data, for subsequent in-depth analysis and decision-making. "Physical event" refers to an abnormal phenomenon occurring on highway infrastructure that can be perceived by the monitoring equipment, such as heavy vehicle passage, earthquakes, and structural fatigue. "Transmission parameters" include, but are not limited to, modulation method, coding rate, transmission power, and retransmission mechanism. The selection of these parameters directly affects the anti-interference capability and transmission efficiency of data transmission. "Communication channel interference" refers to the phenomenon of signal transmission quality degradation during wireless communication due to external electromagnetic environment or internal equipment noise. "Elimination timing" refers to the moment when communication channel interference weakens or disappears, and the channel quality recovers to a level suitable for large-load data transmission.

[0074] In practical implementation, when monitoring equipment detects an alarm data packet, it first needs to analyze the packet. Specifically, the breakdown of the alarm data packet can be performed by the processor inside the monitoring equipment. The assessment of the urgency of the information can be based on a preset event type priority list; for example, structural damage alarms have a higher urgency than environmental parameter anomaly alarms. The data volume is directly measured by the number of bytes in the data packet. The emergency summary information usually contains core elements such as event type, time of occurrence, and location, and its data volume is designed to be minimized to ensure rapid transmission. The detailed main data includes the raw sensor data and high-frequency sampling data at the time of the event, and the data volume is relatively large. For example, for an alarm data packet triggered by severe bridge vibration, its emergency summary information may only contain "Bridge X, severe vibration occurred, time Y, preliminary judgment is that an overloaded vehicle passed," while the detailed main data includes the raw high-frequency sampling data and strain data from the vibration sensors.

[0075] Subsequently, the emergency summary information is transmitted preferentially using transmission parameters with strong anti-interference capabilities. These parameters may include, but are not limited to: low-order modulation schemes (such as Binary Phase Shift Keying (BPSK) or Quadrature Phase Shift Keying (QPSK), highly redundant forward error correction coding, increased transmit power, or the use of spread spectrum communication technology. For example, the system can preset a dedicated transmission configuration for emergency summary information, which can still provide a high transmission success rate even when channel quality is poor. In some embodiments, specific communication frequency bands or channels can also be selected, which are assessed as having less interference in the current environment.

[0076] Simultaneously, the environmental parameters of the physical event are continuously monitored, and the timing of the communication channel interference's dissipation is determined based on the changing trends of these parameters. In practical applications, the environmental parameters of the physical event can be physical quantities directly related to the event. For example, when the physical event is the passage of a heavy vehicle, the environmental parameters could be the vibration signal intensity collected by a vibration sensor, the ambient noise level collected by an acoustic sensor, etc. The monitoring equipment continuously collects these environmental parameters through its built-in sensors and analyzes them in real time. For example, by observing the changing trend of the vibration signal amplitude over time, it can be inferred whether the physical source causing the vibration (such as a vehicle) has moved away or its influence has weakened. When the changing trend of the environmental parameters indicates that the influence of the physical interference source is weakening or has disappeared, it can be preliminarily determined that the communication channel interference may subsequently subside.

[0077] Finally, the detailed main data is transmitted at the opportune time of the interference's dissipation, using high-payload transmission parameters adapted to the current channel conditions. Once the dissipation time of the communication channel interference is determined, the system will seize this favorable opportunity to transmit the detailed main data. At this time, high-payload transmission parameters adapted to the current channel conditions can be used, for example, switching to a higher-order modulation scheme (such as 16QAM or 64QAM), reducing the redundancy of forward error correction coding, or using larger data packet lengths and longer communication time slots to maximize data throughput and transmission efficiency.

[0078] The wireless networking communication control method for highway infrastructure monitoring equipment proposed in this application aims to solve the problem in existing technologies where, when a physical event triggers the generation of alarm data packets, the physical event source itself may also become a source of communication interference, leading to a high failure rate in the transmission of critical alarm data. The core innovation of this application lies in moving away from solely relying on data packet size to adjust communication strategies. Instead, it correlates and analyzes the environmental parameters of physical events with the communication channel conditions, achieving intelligent and adaptive communication strategies.

[0079] Unlike traditional methods that adjust the communication frame structure solely based on data packet size, this application significantly improves the timeliness and reliability of alarm information by introducing techniques such as alarm packet splitting, priority transmission of urgent summary information, monitoring of physical event environmental parameters, and opportunistic transmission of detailed main data. Specifically, by intelligently splitting alarm packets and prioritizing the transmission of urgent summary information, the rapid and reliable transmission of the most critical information is ensured even in interference environments. Simultaneously, by continuously monitoring the environmental parameters of physical events to proactively predict the dissipation of communication channel interference, the system can efficiently transmit large amounts of detailed main data when channel conditions are favorable. This optimizes resource utilization efficiency, reduces the risk of transmission failure due to poor channel quality, and enhances the overall reliability of the monitoring system.

[0080] In some of the embodiments described above in this application, when the monitoring device detects an alarm data packet, it is proposed to split the alarm data packet into emergency summary information and detailed main data according to the urgency and data size of the alarm data packet. The emergency summary information is sent first using transmission parameters with strong anti-interference capabilities. At the same time, the environmental parameters of the physical event are continuously monitored, and the timing of the dissipation of communication channel interference is determined based on the changing trend of the environmental parameters. Then, the detailed main data is sent at the appropriate time according to the dissipation timing.

[0081] For details, please refer to Figure 2 Step S3 includes:

[0082] S31, after sending the emergency summary information, continuously monitor the environmental parameters of the physical event and periodically obtain radio environment information through the wireless communication module;

[0083] S32, Measure the strength indication value and signal-to-noise ratio of the received signal in the current wireless channel and perform correlation analysis;

[0084] S33, when the vibration signal envelope drops below the preset threshold and the intensity indication value of the received signal and the signal-to-noise ratio reach the preset channel recovery standard, it is determined that the communication channel interference has subsided.

[0085] S34. When the vibration signal envelope has decreased and the received signal strength indicator or signal-to-noise ratio indicates poor channel quality, continue to wait.

[0086] Specifically, after sending the emergency summary information, the system will initiate continuous monitoring of the physical event's environmental parameters. These environmental parameters can be understood as external environmental quantities related to the physical event that triggered the alarm data packet, such as vibration intensity, sound pressure, temperature, and humidity caused by vehicle passage, structural vibration, or wind. The wireless communication module refers to the hardware unit in the monitoring equipment responsible for wireless signal transmission, reception, and processing. It can periodically sense and acquire radio environment information, such as channel occupancy, interference levels, and background noise.

[0087] Received Signal Strength Indication (RSSI) measures the power of the received signal, while Signal-to-Noise Ratio (SNR) reflects the strength of the signal relative to noise. Both are key parameters for evaluating the quality of a wireless channel. Correlation analysis involves comprehensively considering both RSSI and SNR to provide a more holistic assessment of channel conditions.

[0088] The vibration signal envelope refers to the instantaneous amplitude change trend of the vibration signal generated by a physical event (such as a vehicle crossing a bridge). Its drop below a preset threshold usually indicates that the impact of the physical event is weakening or has ended. The preset channel recovery criterion is a combination of pre-defined received signal strength indication values ​​and signal-to-noise ratios. When these conditions are met, it indicates that the channel has recovered from interference to an acceptable transmission state.

[0089] This application's solution, by immediately initiating continuous monitoring of physical event environmental parameters after the transmission of emergency summary information, and combining this with radio environment information periodically acquired by the wireless communication module, enables real-time and dynamic understanding of channel conditions. By measuring the received signal strength indication value and signal-to-noise ratio and performing correlation analysis, the quality of the current wireless channel can be quantitatively assessed. Furthermore, the decrease in the vibration signal envelope of the physical event is used as a preliminary criterion for judging interference fading, and combined with the achievement of preset channel recovery standards by the received signal strength indication value and signal-to-noise ratio, forming a multi-dimensional and more reliable judgment mechanism. When the vibration signal envelope decreases but the wireless channel quality remains poor, the system will choose to continue waiting, avoiding blindly transmitting large payload data when channel conditions are immature, thereby effectively avoiding the risk of data transmission failure.

[0090] Through the above technical solution, this application can more accurately and reliably determine the timing of communication channel interference dissipation. This multi-parameter fusion judgment mechanism, especially by incorporating the characteristics of the physical event itself (such as the envelope of vibration signals), makes the system's perception of channel conditions more refined and comprehensive, significantly improving the accuracy of the judgment. Therefore, it can effectively avoid sending detailed main data while the channel is still interfered with, thereby reducing data loss rate and retransmission overhead, and ensuring the success rate and efficiency of detailed main data transmission. Simultaneously, through the "continue waiting" strategy, the system can fully utilize the optimal transmission conditions after channel recovery, providing a more stable guarantee for subsequent high-payload data transmission.

[0091] In some of the embodiments described above in this application, although it is proposed to continuously monitor the environmental parameters of physical events after sending emergency summary information and periodically acquire radio environment information through the wireless communication module, in practical applications, the radio environment may be affected by drastic dynamic changes in the physical event itself or external factors, which may cause the simple periodic acquisition of radio environment information to fail to accurately reflect the channel quality, thereby affecting the accuracy and efficiency of judging the timing of communication channel interference dissipation.

[0092] In this regard, refer to Figure 3 S31 includes:

[0093] S311, When the wireless communication module performs radio environment sensing, it performs received signal strength scanning detection on different frequency bands or channels to obtain radio environment information.

[0094] S312 adaptively adjusts the receiving gain and sampling frequency according to the dynamic changes in the current channel environment;

[0095] S313, when the intensity indication value is detected to be close to the upper or lower limit of the measurement, adjust the receiving gain;

[0096] S314, dynamically adjusts the sampling frequency of the signal-to-noise ratio according to the instantaneous rate of change of channel noise in order to capture fluctuations in channel quality;

[0097] S315 combines the vibration signal intensity collected by the vibration sensor with the measurement results of radio environmental parameters for cross-validation.

[0098] S316, when the vibration signal intensity is at a high level, the measurement results of the intensity indication value and the signal-to-noise ratio are weighted.

[0099] Specifically, when a wireless communication module performs radio environment sensing, it can obtain more comprehensive and accurate radio environment information by scanning and detecting the received signal strength on different frequency bands or channels. The purpose of this multi-band or multi-channel scanning and detection is to avoid the deviation of the overall channel quality judgment caused by local interference or anomalies on a single frequency band or channel, thereby obtaining a more representative channel condition.

[0100] The system adaptively adjusts the receiver gain and sampling frequency based on dynamic changes in the current channel environment to ensure high-quality measurement data in various complex radio environments. Specifically, the receiver gain is adjusted when the intensity indication value approaches the upper or lower limit of the measurement. For example, if the received signal is too strong, causing the intensity indication value to approach the upper limit of the measurement, the receiver gain is reduced to prevent signal saturation distortion; conversely, if the received signal is too weak, causing the intensity indication value to approach the lower limit of the measurement, the receiver gain is increased to ensure that weak signals can also be effectively detected.

[0101] Furthermore, the sampling frequency of the signal-to-noise ratio is dynamically adjusted according to the instantaneous rate of change of channel noise to capture fluctuations in channel quality. For example, when channel noise changes drastically, the sampling frequency is increased to promptly capture rapid declines or recovery in channel quality; when channel noise changes gradually, the sampling frequency can be appropriately reduced to conserve system resources.

[0102] In practical applications, cross-validation is performed by combining the vibration signal intensity collected by vibration sensors with the measurement results of radio environment parameters. The purpose is to correlate the direct impact of physical events with changes in the radio environment. For example, when physical events (such as vehicle collisions or structural vibrations) occur, they are usually accompanied by specific vibration signals, which may directly or indirectly cause interference in the radio environment. Through cross-validation, it is possible to more accurately determine whether changes in the radio environment are related to physical events.

[0103] Furthermore, when the vibration signal intensity is at a high level, the measurement results of the intensity indication value and the signal-to-noise ratio are weighted. The purpose is to give higher weight to the radio environment measurement results when the physical event has a significant impact on the radio environment, so as to more accurately reflect the channel interference caused by the physical event.

[0104] Through the above technical solution, this application can significantly improve the accuracy and reliability of radio environmental information acquired by highway infrastructure monitoring equipment in complex dynamic environments. Adaptive adjustment of measurement parameters ensures the validity and accuracy of measurement data, while the cross-validation mechanism with vibration signals enables the system to more intelligently distinguish between interference caused by physical events and other environmental noise, thereby more accurately determining the timing of communication channel interference dissipation. This not only optimizes the opportune transmission strategy for detailed main data, reducing data transmission delays and retransmissions, but also improves the robustness and efficiency of the entire wireless network communication control method, ensuring timely and reliable transmission of alarm information at critical moments.

[0105] In some preferred embodiments, a highway bridge monitoring device triggers the generation of an alarm data packet when it detects severe vibrations on the bridge deck (e.g., caused by a heavy vehicle speeding or a minor collision). After sending an emergency summary message, the device initiates continuous monitoring of environmental parameters related to the physical event. Specifically, the wireless communication module actively scans and detects received signal strength on multiple preset wireless frequency bands (e.g., 2.4 GHz, 5.8 GHz ISM bands, and specific licensed bands) to obtain comprehensive radio environment information. For example, if the intensity indication value of a certain frequency band suddenly spikes to near the receiver's measurement limit (e.g., -20 dBm), the system immediately reduces the receive gain of that frequency band to avoid signal saturation and ensure the accuracy of the measurement. Simultaneously, if the vibration sensor detects a continuous, high-intensity vibration signal, the system determines that the current channel noise may be significantly affected by the physical event. In this case, the sampling frequency of the signal-to-noise ratio is dynamically increased, for example, from 10 times per second to 50 times per second, to more precisely capture instantaneous fluctuations in channel quality.

[0106] Furthermore, the system correlates the vibration signal intensity collected by the vibration sensor with the real-time intensity indication value and signal-to-noise ratio. When the vibration signal intensity is high, such as exceeding a preset vibration threshold, the system weights the currently measured intensity indication value and signal-to-noise ratio, assigning them higher weight to emphasize channel changes caused by physical events. This allows for a more accurate assessment of whether the channel has recovered from interference caused by physical events. In this way, the device can more accurately determine when channel interference subsides, thus enabling it to send detailed core data at the appropriate time, ensuring the reliability and efficiency of data transmission.

[0107] In some embodiments described above, this application proposes a scheme to adaptively adjust the receiver gain and sampling frequency based on dynamic changes in the current channel environment. However, in practical applications, especially when interference caused by physical events is characterized by rapid and instantaneous changes, adjusting solely based on the instantaneous rate of change of signal strength or channel noise may fail to adequately capture the instantaneous characteristics of the interference, resulting in insufficient response speed and accuracy of channel quality assessment. This may prevent the system from adjusting transmission parameters in a timely manner when interference changes drastically, thereby affecting the reliable transmission of detailed core data.

[0108] In response, this application further proposes the aforementioned adaptive adjustment of receive gain and sampling frequency based on dynamic changes in the current channel environment, including:

[0109] When measuring the intensity indication value and the signal-to-noise ratio, the instantaneous amplitude change rate of the vibration signal collected by the vibration sensor is analyzed in real time.

[0110] When the instantaneous amplitude change rate exceeds a preset rapid change threshold, the response speed of the receiving gain adjustment is increased, and the sampling frequency of the signal-to-noise ratio is increased.

[0111] The length of the measurement window is dynamically adjusted based on the measurement results of the intensity indication value and the signal-to-noise ratio.

[0112] When a large fluctuation in the intensity indication value is detected within a short period of time, the measurement window length is shortened to capture the instantaneous peak or trough of the interference.

[0113] When the intensity indication value changes gradually, the measurement window length is extended to reduce measurement noise.

[0114] Specifically, real-time analysis of the instantaneous amplitude change rate of vibration signals acquired by vibration sensors refers to calculating the change in vibration amplitude per unit time by continuously sampling and differentially analyzing the signal output by the vibration sensor. This instantaneous amplitude change rate can serve as a direct indicator of the severity of a physical event. For example, when a heavy vehicle crosses a bridge, the instantaneous amplitude change rate of the vibration signal will increase significantly.

[0115] Specifically, when the instantaneous amplitude change rate exceeds a preset rapid change threshold, the system is configured to improve the response speed of the receive gain adjustment. This means that the receive gain adjustment algorithm will respond more quickly to changes in signal strength to avoid signal saturation or excessively low signal. Simultaneously, the sampling frequency of the signal-to-noise ratio (SNR) is increased to collect SNR data more densely, thereby capturing rapid fluctuations in channel quality and ensuring more refined channel state information is obtained even under severe interference.

[0116] In practical applications, dynamically adjusting the measurement window length based on the intensity indication value and signal-to-noise ratio (SNR) measurement results means that the system no longer uses a fixed measurement period, but flexibly adjusts the data sampling duration according to real-time monitored changes in radio environment parameters. For example, when a large fluctuation in the intensity indication value is detected within a short period, this usually indicates a momentary strong interference or rapid dissipation of interference. In this case, the measurement window length is shortened to more accurately capture these momentary peaks or troughs, preventing the loss of critical information due to averaging. Conversely, when the intensity indication value changes gradually, it indicates a relatively stable channel environment. In this case, the measurement window length is extended to perform averaging over a longer period, thereby effectively reducing measurement noise and improving the accuracy and stability of the measurement results.

[0117] This application's solution combines the instantaneous amplitude change rate of vibration signals with a measurement and adjustment strategy for radio environment parameters, enabling more accurate judgment of the timing of channel interference decay. When a physical event (such as a vehicle passing by) causes severe vibration, its instantaneous amplitude change rate increases rapidly, which is usually accompanied by an increase in electromagnetic interference. By analyzing this change rate in real time, the system can predict whether the channel environment is about to or is undergoing drastic changes, thereby proactively improving the response speed of receiver gain adjustment and the sampling frequency of the signal-to-noise ratio. This proactive adjustment allows the system to capture the instantaneous state of channel quality more quickly and precisely when interference changes rapidly, avoiding the lag or information loss caused by traditional fixed parameter measurements. In addition, the mechanism of dynamically adjusting the measurement window length allows the system to focus on instantaneous changes when the channel fluctuates violently, and to improve measurement accuracy through long-term averaging when the channel is stable, thereby comprehensively improving the adaptability and measurement accuracy to complex and variable channel environments.

[0118] Through the above technical solution, this application can significantly improve the accuracy and real-time performance of communication channel quality assessment for highway infrastructure monitoring equipment in complex electromagnetic environments. Especially when interference caused by physical events is instantaneous and severe, this solution can achieve rapid adaptive adjustment of the receiving gain and sampling frequency through real-time analysis of the instantaneous amplitude change rate of vibration signals, effectively avoiding misjudgments of channel status due to measurement lag or improper parameters. Furthermore, the mechanism for dynamically adjusting the measurement window length allows the system to flexibly capture the instantaneous characteristics of interference or smooth measurement noise according to channel fluctuations, further improving the robustness of channel quality assessment. As a result, detailed main data can be transmitted using larger payload transmission parameters more adapted to the current channel conditions at a more accurate channel recovery time, thereby improving the efficiency and reliability of data transmission.

[0119] In some preferred embodiments, a specific example is given below. Suppose that on a highway bridge, monitoring equipment is continuously monitoring the structural condition of the bridge.

[0120] When a convoy of heavy trucks crosses a bridge at high speed, vibration sensors capture intense vibration signals with extremely high instantaneous amplitude changes. The system immediately recognizes that this instantaneous amplitude change rate exceeds a preset rapid change threshold and rapidly increases the response speed of the wireless communication module's receiver gain adjustment, while simultaneously increasing the sampling frequency of the signal-to-noise ratio (SNR). For example, the receiver gain adjustment period might be shortened from the usual 100 milliseconds to 20 milliseconds, and the SNR sampling frequency might increase from 10 times per second to 50 times per second. Simultaneously, because electromagnetic interference caused by the passing heavy trucks can lead to significant fluctuations in the intensity indication value within a short period, the system dynamically shortens the measurement window length, for example, from 5 seconds to 1 second, to accurately capture the instantaneous peaks and troughs of the interference. As the convoy departs and the instantaneous amplitude change rate of the vibration signal gradually decreases and flattens, the system correspondingly extends the measurement window length, for example, restoring it to 5 seconds or longer, to reduce measurement noise through long-term averaging, ensuring stable and accurate channel quality assessment results during channel recovery.

[0121] In some of the embodiments described above in this application, a cross-validation method is proposed that combines the vibration signal intensity collected by the vibration sensor with the measurement results of radio environment parameters. However, in the implementation process, if the vibration signal characteristics generated by different physical events and their corresponding electromagnetic interference modes are not effectively distinguished, the judgment of communication channel interference may not be accurate enough, thereby affecting the timing strategy for sending detailed main data.

[0122] In response, this application further proposes a step for cross-validating the vibration signal intensity collected by the vibration sensor with the measurement results of the radio environment parameters, including:

[0123] When the vibration signal collected by the vibration sensor is received, frequency domain analysis is performed on the vibration signal to identify the dominant frequency components and duration of the vibration signal;

[0124] Based on the dominant frequency component and the duration, different types of physical events are obtained by matching them with the features of preset different physical event types.

[0125] The intensity indication value and the signal-to-noise ratio are compared with the electromagnetic interference characteristic parameter set corresponding to the different types of physical events;

[0126] The measurement results of the radio environment parameters are weighted based on the comparison results.

[0127] Specifically, when a vibration signal collected by a vibration sensor is received, frequency domain analysis is first performed on the signal. Frequency domain analysis refers to converting the time-domain vibration signal into a frequency-domain spectrum using mathematical methods such as Fourier transform, thereby identifying the dominant frequency components and their duration. The dominant frequency components can be understood as the frequency points or frequency ranges where vibration energy is most concentrated, while the duration represents the length of time from the start to the end of the vibration event. The purpose is to extract key information that characterizes the physical event from the complex vibration signal.

[0128] The process involves matching the identified dominant frequency components and their durations with pre-defined features for different physical event types. These pre-defined physical event type features can be stored in a feature library. For example, a passing vehicle might correspond to specific low-frequency vibrations and a short duration, while an earthquake or heavy machinery operation might correspond to a different frequency range and a longer duration. Through this matching process, the specific type of the currently occurring physical event can be accurately determined, such as whether it is a heavy vehicle passing by, construction activity, or other natural phenomenon.

[0129] In practical applications, the intensity indication values ​​and signal-to-noise ratios obtained above are compared with the electromagnetic interference (EMI) characteristic parameter sets corresponding to the different types of physical events. These EMI characteristic parameter sets are pre-established to describe typical EMI patterns that may occur when different physical events occur. For example, the passage of certain types of vehicles may be accompanied by radio frequency noise in specific frequency bands, while some industrial equipment may generate broadband interference. By comparing the real-time measured radio environment parameters with these pre-defined EMI characteristics, it is possible to assess whether the anomalies in the current radio environment match the identified physical event types.

[0130] Furthermore, the measurement results of the radio environment parameters are weighted based on the comparison results. The purpose of the weighting is to adjust the reliability or importance of the radio environment measurement results according to the degree of agreement between the physical event and the electromagnetic interference characteristics. For example, if the physical event type highly matches the electromagnetic interference characteristics, the radio environment measurement results can be given a higher weight, indicating that it is more likely to be the real interference caused by that physical event; conversely, if the agreement is low, the weight may be reduced to reduce misjudgment.

[0131] Through the above technical solution, this application can significantly improve the accuracy and reliability of communication channel interference judgment by highway infrastructure monitoring equipment in complex electromagnetic environments. Compared with simply correlating vibration signals with radio environment information, this solution introduces frequency domain analysis of vibration signals, physical event type matching, and comparison with electromagnetic interference characteristic parameter sets, enabling the system to gain a deeper understanding of the source and nature of interference. This effectively distinguishes interference caused by different physical events, avoiding misjudging irrelevant noise or sporadic signals as critical interference, thereby reducing the false alarm rate. Furthermore, the weighted processing based on the comparison results makes the evaluation of radio environment measurement results more intelligent and adaptive, ensuring more accurate judgment of the timing of communication channel interference decay under different physical event scenarios, thus optimizing the transmission strategy of detailed main data and improving the efficiency and reliability of data transmission.

[0132] In some preferred embodiments, a specific example is given below. Suppose a monitoring device deployed on a highway bridge collects a set of vibration signals through its vibration sensors. The device first performs a Fast Fourier Transform (FFT) on the vibration signals for frequency domain analysis, identifying that its dominant frequency components are concentrated in the 50Hz-80Hz range and last for approximately 5 seconds. The system matches these features with a pre-defined physical event feature database and finds that this highly matches the characteristics of "a heavy truck passing by".

[0133] Simultaneously, the wireless communication module measured a significant decrease in both the current channel strength indicator (SMI) and signal-to-noise ratio (SNR). The system then compared these radio environment measurements with a set of electromagnetic interference (EMI) characteristic parameters corresponding to a "heavy truck passing" event. This set of parameters may include transient broadband noise occurring within a specific frequency band. If the comparison shows that the current radio environment's degradation pattern highly matches the expected EMI characteristics when a heavy truck passes, the system assigns higher weights to the SMI and SNR measurements based on this high degree of agreement, thus increasing confidence that the current channel quality degradation is genuine interference caused by the heavy truck's passage. Conversely, if the vibration signal characteristics do not match the EMI characteristics—for example, the vibration signal indicates a light vehicle passing, but the radio environment shows strong broadband interference—the system reduces the weights and may initiate further interference source identification procedures to avoid misjudgment.

[0134] In some embodiments described above in this application, the accuracy of channel quality assessment is improved by comparing the intensity indication value and the signal-to-noise ratio with a set of electromagnetic interference characteristic parameters corresponding to different types of physical events, and by weighting the measurement results of radio environment parameters based on the comparison results. However, in practical applications, the electromagnetic interference environment is often dynamically changing. If the weighting method cannot respond to such changes in a timely manner, it may lead to a lag or inaccuracy in the channel quality assessment, thereby affecting the efficiency and reliability of timely transmission of detailed main data.

[0135] In response, this application further proposes a method for weighting the measurement results of radio environment parameters based on the comparison results, which includes:

[0136] Real-time monitoring of the instantaneous rate of change of the intensity indication value and the signal-to-noise ratio;

[0137] When the instantaneous rate of change exceeds the preset rapid change threshold, the weighting factor is adjusted, and a weighting function with a fast response speed is adopted to quickly adapt to the change in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set.

[0138] When the instantaneous rate of change is lower than the preset steady change threshold, a weighting function with a slow response speed is used to smooth the adjustment of the weighting factor;

[0139] Based on the adjusted weighting factor, the intensity indication value and the signal-to-noise ratio are weighted to obtain the weighted radio environment parameter measurement results and to evaluate the channel quality.

[0140] Specifically, real-time monitoring of the instantaneous change rate of the received signal strength indicator (RSI) and signal-to-noise ratio (SNR) refers to the system continuously acquiring the RSI and SNR of the current wireless channel and calculating the rate of change of these parameters over extremely short time intervals. This aims to capture the dynamic characteristics of the radio environment, such as the appearance or disappearance of sudden interference and the slow changes in background noise.

[0141] When the instantaneous rate of change exceeds a preset rapid change threshold, it indicates that the radio environment is undergoing drastic changes, such as the sudden appearance of a strong interference source. In this case, to quickly adapt to such changes, the system adjusts the weighting factors and adopts a fast-response weighting function. A fast-response weighting function can be understood as a function that can quickly assign higher weights to the latest measurement data, or whose adjustment curve is steeper. This allows for rapid updates to the assessment of the fit between the measurement results and the electromagnetic interference characteristic parameter set, avoiding delays in subsequent communication decisions.

[0142] Furthermore, when the instantaneous rate of change is below a preset steady-state change threshold, it indicates that the radio environment is relatively stable or only slowly changing, such as persistent background noise. In this case, to improve the stability of the assessment and reduce the impact of measurement noise, the system employs a weighting function with a slow response time to smooth the adjustment of the weighting factors. A slow-response weighting function typically considers measurement data over a longer period, or its adjustment curve is flatter, thus making the adjustment of the weighting factors more stable, avoiding overreaction to small fluctuations, and ensuring the reliability of the channel quality assessment.

[0143] Therefore, based on the adjusted weighting factor, the intensity indication value and the signal-to-noise ratio are weighted to obtain the weighted radio environment parameter measurement results, and the channel quality is evaluated based on these results. This dynamically adjusted weighting mechanism enables the channel quality assessment to more accurately reflect the true state of the current radio environment, providing a reliable basis for subsequent detailed main data transmission.

[0144] Through the above technical solution, this application can adaptively adjust the weighting processing method of radio environment information measurement results according to the dynamic changes in the radio environment. Specifically, when facing sudden and rapidly changing electromagnetic interference, the system can quickly respond and adjust the weighting factors, thereby quickly and accurately identifying the type and intensity of interference, avoiding delays or failures in the transmission of detailed main data due to assessment lag. Under relatively stable environmental conditions, by smoothing the adjustment of the weighting factors, the influence of measurement noise can be effectively suppressed, improving the stability and reliability of channel quality assessment. This dynamic adaptive weighting processing mechanism significantly enhances the communication control capabilities of highway infrastructure monitoring equipment in complex and ever-changing wireless environments, ensuring timely and reliable transmission of alarm data, especially in critical alarm information transmission scenarios, where its value is even more prominent.

[0145] In some preferred embodiments, it is assumed that the highway infrastructure monitoring equipment is operating in a relatively stable wireless environment, where the instantaneous rate of change of the intensity indication value and the signal-to-noise ratio is low, below a preset stable change threshold. The system will use a slow-response weighting function, such as a smoothing function based on a long-term average, to adjust the weighting factors. This approach can effectively filter out random noise in the environment, making the channel quality assessment results more stable and reliable, and avoiding frequent adjustments to the communication strategy due to minor fluctuations.

[0146] For example, when a large engineering vehicle suddenly passes near the monitoring equipment, its engine or onboard electronic equipment may generate instantaneous and strong electromagnetic interference. At this time, the monitored intensity indication value and signal-to-noise ratio will fluctuate drastically, with the instantaneous rate of change rapidly exceeding a preset rapid change threshold. The solution in this application immediately adjusts the weighting factors and employs a fast-response weighting function, such as an exponential decay function, to quickly increase the weight of the current measurement results and rapidly adapt to such sudden interference. Through this rapid response mechanism, the system can promptly identify the sharp deterioration of channel quality, thereby more accurately determining the timing of the communication channel interference dissipation, avoiding the transmission of detailed main data under strong interference, and ensuring the success rate of data transmission.

[0147] In response, this application further proposes adjusting the weighting factor and using a fast-response weighting function when the instantaneous rate of change exceeds a preset rapid change threshold to quickly adapt to changes in the fit between the measurement results and the electromagnetic interference characteristic parameter set, including:

[0148] When the instantaneous rate of change exceeds a preset rapid change threshold, the interference source identification program is activated. The interference source identification program is used to perform rapid scanning on different frequency bands or channels to identify currently active interference sources.

[0149] Based on the frequency band, intensity, and variation characteristics of the identified dominant interference source, the corresponding interference type is matched from the preset interference source feature library;

[0150] The corresponding weighting factor is invoked based on the matched interference type, and the adjustment strategy and weighting function are also invoked. The weighting factor is adjusted according to the invoked adjustment strategy and weighting function to quickly adapt to changes in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set.

[0151] Specifically, the interference source identification program refers to a software module or hardware circuit specifically designed for detecting and classifying transient or persistent interference signals in a radio environment. Its working principle involves rapidly scanning the spectrum and analyzing signals across multiple preset or dynamically selected frequency bands or channels to capture and locate electromagnetic interference sources present in the current environment. For example, the program can utilize Fast Fourier Transform (FFT) to perform frequency domain analysis on the received signal, thereby identifying characteristics such as the signal's center frequency, bandwidth, and power spectral density.

[0152] The identified dominant interference source's frequency band, intensity, and variation characteristics can be understood as the result of multi-dimensional analysis of the detected interference signal. Frequency band refers to the frequency range occupied by the interference signal; intensity refers to the power of the interference signal, such as the received signal strength indicator or signal-to-noise ratio; variation characteristics include the interference signal's duration, periodicity, instantaneous peak value, fading mode, and other dynamic features. These characteristics collectively constitute the interference source's "fingerprint," used for subsequent classification and matching.

[0153] In practical applications, a pre-set interference source feature library is a database that stores typical frequency bands, intensities, and variation patterns of various known electromagnetic interference sources (e.g., vehicle ignition systems, industrial motors, high-voltage transmission lines, specific wireless communication devices, etc.). When the characteristics of the identified dominant interference source closely match a certain entry in the library, the corresponding interference type can be matched. For example, if short-duration, high-intensity, wide-bandwidth impulse noise is detected, and its frequency band matches the characteristics of a vehicle ignition system, it can be identified as vehicle interference.

[0154] Furthermore, the system invokes the corresponding weighting factor based on the matched interference type, along with the adjustment strategy and weighting function. This means that different weighting factor adjustment algorithms and mathematical functions are pre-defined for different types of interference. For example, for persistent narrowband interference, a smooth and continuously decaying weighting function might be used; while for transient wideband impulse interference, a fast-responding weighting function that recovers quickly after the interference ends might be used. These strategies and functions aim to minimize or mitigate the negative impact of specific interferences on channel quality assessment.

[0155] Through the above technical solution, this application overcomes the evaluation bias that may arise from traditional methods that only make general and rapid adjustments when facing complex and ever-changing electromagnetic interference. By introducing interference source identification and type matching mechanisms, the adjustment of weighting factors is no longer blind but highly targeted and adaptable. This significantly improves the accuracy of correcting radio environment parameter measurement results under transient strong interference environments, thereby ensuring the robustness and reliability of channel quality assessment. Therefore, in wireless network communication for highway infrastructure monitoring equipment, even in the face of sudden, high-intensity electromagnetic interference, the channel condition can be quickly and accurately determined, providing a more reliable decision-making basis for the timely transmission of subsequent detailed main data, effectively improving communication stability and data transmission success rate.

[0156] In some preferred embodiments, assuming the highway infrastructure monitoring equipment is deployed alongside a highway, when a large truck passes at high speed, its ignition system and onboard electronics may generate instantaneous, high-intensity electromagnetic interference, causing rapid and significant fluctuations in the received signal strength indication and signal-to-noise ratio of the wireless channel. At this time, the system detects that the instantaneous rate of change exceeds a preset rapid change threshold and immediately initiates an interference source identification program. This program performs a rapid scan across different frequency bands, analyzing the frequency band (e.g., concentrated in a specific frequency range), intensity (e.g., high instantaneous peak value), and variation characteristics (e.g., short duration, pulse-like). By matching with a preset interference source feature library, the system identifies the interference source as "vehicle ignition interference." Based on this identification result, the system invokes a weighting factor adjustment strategy and weighting function specifically designed for vehicle ignition interference. For example, this strategy might include assigning lower weights to measurements in specific frequency bands during the interference duration and employing a weighting function capable of rapidly attenuating instantaneous peak values. In this way, even under strong vehicle interference, the system can quickly and accurately adjust the weighting factor to obtain more realistic radio environment parameter measurement results, avoid misjudging channel quality due to transient interference, and ensure that detailed main data can be transmitted in a timely and reliable manner after the interference subsides.

[0157] In some embodiments described above, when the instantaneous rate of change of radio environment parameters is lower than a preset steady-state change threshold, a weighting function with a slow response time is used to smooth the adjustment of the weighting factors. However, in practical applications, the "steady" state of the channel environment may not be completely free of interference, but rather contain persistent, low-intensity background noise. If this background noise is not effectively identified and distinguished from actual channel interference, the assessment of channel quality may be inaccurate, thus affecting the optimization of subsequent data transmission parameters. To address this, this application further proposes a more refined weighting factor adjustment strategy by introducing a background noise identification procedure to more accurately assess channel quality.

[0158] When the instantaneous rate of change is lower than the preset steady-state change threshold, a weighting function with a slow response speed is used to smooth the adjustment of the weighting factor, including:

[0159] When the instantaneous rate of change is lower than the preset steady change threshold, the background noise identification program is started. The background noise identification program is used to perform long-term low-speed scanning on different frequency bands or channels.

[0160] Analyze the received signal strength indication value, signal-to-noise ratio and spectral characteristics of each frequency band or channel to identify persistent, low-intensity and wide-band background noise;

[0161] Match the corresponding background noise type from a preset background noise feature library based on the background noise;

[0162] Based on the matched background noise type, the corresponding weighting function is called;

[0163] During continuous operation of the identification program, the intensity indication value and signal-to-noise ratio are periodically compared with the identified background noise features. When the measured value is higher than the background noise level, it is determined to be actual interference and the weighting factor is adjusted accordingly.

[0164] Specifically, the background noise identification program is designed to start when the channel environment is relatively stable. Its main task is to distinguish between inherent background noise and potential actual interference in the environment. This program acquires comprehensive radio environment information by performing long-term, low-speed scans across multiple frequency bands or channels. The purpose of the low-speed scan is to collect data for as long as possible without affecting normal communication, in order to perform more in-depth statistical analysis of the signals.

[0165] During the scan, received signal strength indicators, signal-to-noise ratios, and spectral characteristics are collected for each frequency band or channel. This data is then used to identify persistent, low-intensity signal components with a wide spectral range, which are typically considered background noise. For example, background noise may include weak signals from long-distance wireless devices, natural electromagnetic radiation, or inherent noise from the devices themselves.

[0166] Furthermore, the identified background noise is matched against a pre-defined background noise feature library. This library stores information such as typical spectral characteristics, intensity range, and duration of different types of background noise. Through matching, the type of background noise in the current environment can be determined, such as electromagnetic noise in an urban environment, radiated noise from industrial equipment, or radio frequency noise from nature. Once the background noise type is identified, the system invokes the corresponding weighting function. These weighting functions are pre-designed to reasonably weaken or compensate for the impact of background noise when evaluating channel quality, thereby making the channel quality assessment results closer to the actual available channel capacity.

[0167] Furthermore, during the continuous operation of the background noise identification program, the system periodically compares the real-time measured received signal strength indication value and signal-to-noise ratio with the identified background noise features. The purpose of this comparison is to dynamically monitor the channel environment. Once the real-time measurement value is significantly higher than the background noise level, it indicates that there may be new or enhanced actual interference. At this time, the system will determine it as actual interference and adjust the weighting factor accordingly to respond more sensitively to changes in channel quality.

[0168] Through the above technical solution, this application can significantly improve the accuracy of communication channel quality assessment when the channel environment changes gradually. Compared with smoothing using only a weighting function with a slow response speed, this solution introduces a background noise identification program, which can effectively distinguish between inherent background noise in the environment and actual channel interference. This allows the adjustment of the weighting factor to more accurately reflect the true availability of the channel, avoiding misjudgments or assessment biases caused by background noise. Therefore, in low-rate-of-change environments, the system can perform channel quality assessment more stably and reliably, providing a more accurate judgment of channel conditions for the subsequent timely transmission of detailed main data, thereby optimizing data transmission efficiency and reliability.

[0169] In some preferred embodiments, it is assumed that the highway infrastructure monitoring equipment is deployed under a busy urban overpass. In addition to vibration signals from passing vehicles, this area is constantly exposed to low-intensity electromagnetic radiation from the urban power grid, surrounding factory equipment, and broadcast television signals, constituting typical background noise. When the monitoring equipment detects a physical event (such as abnormal vibration of the bridge structure) and sends an emergency summary message, the vibration signal envelope gradually decreases, and the instantaneous rate of change of radio environmental parameters also decreases, falling below a preset stable change threshold. At this point, the system initiates a background noise identification procedure.

[0170] The program performs long-term low-speed scans across multiple commonly used communication frequency bands, such as 900MHz, 1.8GHz, and 2.4GHz, continuously collecting received signal strength indicators, signal-to-noise ratios, and spectral data. By analyzing this data, the system identifies persistent signal components with strengths between -90dBm and -80dBm and broad spectral distributions within specific frequency bands. These signals highly match the "urban electromagnetic environment noise" type in a pre-defined urban background noise feature library. Based on this, the system classifies these signals as background noise and invokes a weighting function specifically designed for urban electromagnetic environment noise.

[0171] During the continuous operation of the background noise identification program, the system compares the real-time measured received signal strength indication value and signal-to-noise ratio with the identified background noise characteristics every 5 minutes. For example, if the received signal strength indication value of a certain frequency band suddenly rises from -85dBm (background noise level) to -70dBm and remains so for a period of time, the system will determine that this is higher than the background noise level and there may be a new actual interference source (such as temporary electromagnetic radiation from nearby construction equipment). The system will then adjust the weighting factor accordingly to more sensitively reflect this change, thereby ensuring that actual channel interference can be accurately captured even in background noise environments.

[0172] In some embodiments described above, a background noise identification procedure is proposed to perform long-term low-speed scans on different frequency bands or channels, and analyze the received signal strength indication value, signal-to-noise ratio, and spectral characteristics of each frequency band or channel to identify persistent, low-intensity, and wide-bandwidth background noise. However, in practical applications, the complexity of the radio environment means that simply identifying background noise may not be sufficient to comprehensively assess channel quality. Further differentiation between background noise, periodic low-intensity interference sources, and actual interference is needed to more accurately determine channel conditions.

[0173] In this regard, this application further proposes the following steps for analyzing the received signal strength indication value, signal-to-noise ratio, and spectral characteristics of each frequency band or channel to identify persistent, low-intensity, and wide-bandwidth background noise:

[0174] During low-speed scanning, time-series analysis is performed on the received signal strength indication value, signal-to-noise ratio, and spectral characteristic data of each frequency band or channel to identify low-intensity signal components with periodic or quasi-periodic changes.

[0175] Combining the location information and timestamp of the monitoring device, the expected range of background noise spectrum characteristics within the current location and time period is obtained from a preset geographic location-time-background noise feature library;

[0176] The spectral characteristics acquired in real time and analyzed through time series are compared with the expected range of background noise spectral characteristics.

[0177] When the match is found and the intensity of the real-time spectral characteristic is at a low level, the real-time spectral characteristic is identified as background noise.

[0178] When the real-time spectrum characteristics exhibit periodic or quasi-periodic changes, and the intensity of the real-time spectrum characteristics is low but deviates from the expected background noise spectrum characteristics range, the real-time spectrum characteristics are identified as periodic low-intensity interference sources.

[0179] When the real-time spectrum characteristics exhibit a non-periodic, transient signal with an intensity higher than the background noise in a specific frequency band or channel, the real-time spectrum characteristics are identified as actual interference.

[0180] The channel quality is assessed based on the identified background noise, the periodic low-intensity interference source, or the actual interference.

[0181] Specifically, during low-speed scanning, time-series analysis is performed on the received signal strength indication (RSI), signal-to-noise ratio (SNR), and spectral characteristics data of each frequency band or channel. The aim is to reveal the regularity of signal changes over time, thereby identifying low-intensity signal components with periodic or quasi-periodic variations. Time-series analysis can employ methods such as Fourier transform, wavelet analysis, or autocorrelation analysis to extract features such as signal frequency, amplitude, and phase. The RSI measures the signal power received by the wireless receiver, the SNR is the ratio of signal power to noise power, and the spectral characteristics data reflect the energy distribution of the signal at different frequencies.

[0182] Furthermore, to improve the accuracy of background noise identification, this application combines the location information and timestamp of the monitoring equipment to obtain the expected range of background noise spectrum characteristics for the current location and time period from a pre-defined geographic location-time-background noise feature library. The geographic location-time-background noise feature library can be established in advance through long-term monitoring and data accumulation, and includes typical background noise spectrum characteristics for different geographical regions and time periods (e.g., daytime, nighttime, different seasons). In this way, a benchmark can be provided for real-time measurements, making background noise identification more targeted.

[0183] Subsequently, the real-time acquired and time-series analyzed spectral characteristics are compared with the expected range of background noise spectral characteristics. The comparison process can employ techniques such as correlation analysis, Euclidean distance calculation, or pattern matching. When the real-time spectral characteristics match the expected range of background noise spectral characteristics, and the intensity of the real-time spectral characteristics is at a low level, it is identified as background noise. This indicates that the current channel environment is in a relatively stable background noise state.

[0184] In a preferred embodiment, when the real-time spectral characteristics exhibit periodic or quasi-periodic changes, and although their intensity is low, they deviate from the expected range of background noise spectral characteristics, then the real-time spectral characteristics are identified as a periodic low-intensity interference source. Such interference sources may originate from low-power devices that operate periodically, such as certain industrial sensors or environmental monitoring equipment. Although their intensity is low, their periodic characteristics may have a cumulative impact on communication.

[0185] Furthermore, when real-time spectral characteristics exhibit non-periodic, transient signals with intensity higher than background noise in a specific frequency band or channel, these real-time spectral characteristics are identified as actual interference. This interference is typically caused by sudden events or high-power interference sources, such as passing vehicles, the start of large machinery, or illegal radio transmissions, which have a more significant and direct impact on communication channels.

[0186] This application's solution, by introducing time series analysis, combining geographic location-time information, and detailed spectral characteristic comparison, enables refined classification of signal components in complex radio environments. Specifically, time series analysis helps capture the dynamic changes and periodic characteristics of signals, thereby distinguishing between stable background noise and periodic interference. The introduction of a geographic location-time-background noise feature database provides a geographical and temporal reference benchmark for background noise identification, making the identification results more consistent with the actual environment. By comparing real-time spectral characteristics with the expected range and making multi-dimensional judgments based on intensity and variation patterns, background noise, periodic low-intensity interference sources, and actual interference can be effectively distinguished. This avoids the misjudgment of treating all low-intensity signals as background noise and also avoids ignoring periodic low-intensity interference, thus providing a more accurate and detailed basis for subsequent channel quality assessment and transmission parameter adjustment.

[0187] The above technical solutions enable more refined perception and classification of the wireless channel environment, improving the accuracy and robustness of background noise identification. By distinguishing between background noise, periodic low-intensity interference sources, and actual interference, a more comprehensive understanding of the channel conditions can be achieved, providing more accurate data support for subsequent communication strategy formulation. This helps optimize the selection of transmission parameters, improves the reliability and efficiency of data transmission, especially in application scenarios such as highway infrastructure monitoring where high communication stability is required. It can effectively avoid communication interruptions or data loss caused by channel misjudgment, thereby enhancing the intelligence and adaptability of the entire wireless network communication control method.

[0188] Reference Figure 4 This invention provides a wireless networking communication control system for highway infrastructure monitoring equipment, comprising:

[0189] The detection end is used to split the alarm data packet into emergency summary information and detailed main data according to the urgency and data size of the alarm data packet when the monitoring device detects the alarm data packet. The alarm data packet is generated by a physical event.

[0190] The judgment end is used to prioritize sending the emergency summary information using transmission parameters with strong anti-interference capabilities; continuously monitor the environmental parameters of the physical event, and determine the timing of the dissipation of communication channel interference based on the changing trend of the environmental parameters;

[0191] The output terminal is used to send the detailed main data at the appropriate time according to the fading time, and to use high-payload transmission parameters adapted to the current channel conditions when sending.

[0192] It should be noted that the wireless networking communication control system for highway infrastructure monitoring equipment provided in this embodiment of the invention is used to execute all the process steps of the wireless networking communication control method for highway infrastructure monitoring equipment in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0193] This invention also provides a terminal device. The terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described in the embodiments of the wireless networking communication control method for highway infrastructure monitoring equipment, for example... Figure 1 Step S1 is shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above system embodiments.

[0194] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0195] The terminal device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of terminal devices and do not constitute a limitation on the terminal device. It may include more or fewer components than described above, or a combination of certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0196] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0197] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0198] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0199] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0200] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A wireless networking communication control method for highway infrastructure monitoring equipment, characterized in that, The method includes: When the monitoring device detects an alarm data packet, it splits the alarm data packet into an emergency summary and detailed main data according to the urgency and data size of the alarm data packet. The alarm data packet is generated by a physical event. The emergency summary information is sent preferentially using transmission parameters with strong anti-interference capabilities; Continuously monitor the environmental parameters of the physical event, and determine the timing of the dissipation of communication channel interference based on the changing trends of the environmental parameters; The detailed main data is sent at the appropriate time according to the fading timing, and high-payload transmission parameters adapted to the current channel conditions are used during transmission. The continuous monitoring of environmental parameters of the physical event, and the determination of the timing for the dissipation of communication channel interference based on the changing trends of the environmental parameters, includes: After sending the emergency summary information, the environmental parameters of the physical event are continuously monitored, and radio environmental parameters are periodically obtained through the wireless communication module. Measure the strength indication value and signal-to-noise ratio of the received signal in the current wireless channel and perform correlation analysis; Vibration signals are collected by a vibration sensor, and a vibration signal envelope is generated. When the vibration signal envelope drops below a preset threshold, and the intensity indication value and the signal-to-noise ratio reach a preset channel recovery standard, it is determined that the communication channel interference has subsided. When the vibration signal envelope has decreased, and the intensity indicator value or the signal-to-noise ratio indicates poor channel quality, continue to wait; After sending the emergency summary information, the step of continuously monitoring the environmental parameters of the physical event and periodically acquiring radio environmental parameters through the wireless communication module includes: When the wireless communication module performs radio environment sensing, it performs received signal strength scanning detection on different frequency bands or channels to obtain the radio environment parameters. The receiver gain and sampling frequency are adaptively adjusted according to the dynamic changes in the current channel environment. When the intensity indication value is detected to be close to the upper or lower limit of the measurement, the receiving gain is adjusted; The sampling frequency of the signal-to-noise ratio is dynamically adjusted based on the instantaneous rate of change of the channel noise ratio in order to capture fluctuations in channel quality. The vibration signal intensity collected by the vibration sensor is cross-validated with the measurement results of the radio environment parameters. When the vibration signal intensity is at a high level, the measurement results of the intensity indication value and the signal-to-noise ratio are weighted.

2. The wireless networking communication control method for highway infrastructure monitoring equipment according to claim 1, characterized in that, The adaptive adjustment of the receiver gain and sampling frequency based on dynamic changes in the current channel environment includes: When measuring the intensity indication value and the signal-to-noise ratio, the instantaneous amplitude change rate of the vibration signal collected by the vibration sensor is analyzed in real time. When the instantaneous amplitude change rate exceeds a preset rapid change threshold, the response speed of the receiving gain adjustment is increased, and the sampling frequency of the signal-to-noise ratio is increased. The length of the measurement window is dynamically adjusted based on the measurement results of the intensity indication value and the signal-to-noise ratio. When a large fluctuation in the intensity indication value is detected within a short period of time, the measurement window length is shortened to capture the instantaneous peak or trough of the interference. When the intensity indication value changes gradually, the measurement window length is extended to reduce measurement noise.

3. The wireless networking communication control method for highway infrastructure monitoring equipment according to claim 1, characterized in that, The cross-validation of the vibration signal intensity collected by the vibration sensor with the measurement results of the radio environment parameters includes: When the vibration signal collected by the vibration sensor is received, frequency domain analysis is performed on the vibration signal to identify the dominant frequency components and duration of the vibration signal; Based on the dominant frequency component and the duration, different types of physical events are obtained by matching them with the features of preset different physical event types. The intensity indication value and the signal-to-noise ratio are compared with the electromagnetic interference characteristic parameter set corresponding to the different types of physical events; The measurement results of the radio environment parameters are weighted based on the comparison results.

4. The wireless networking communication control method for highway infrastructure monitoring equipment according to claim 3, characterized in that, The weighting process for the measurement results of the radio environment parameters based on the comparison results includes: Real-time monitoring of the instantaneous rate of change of the intensity indication value and the signal-to-noise ratio; When the instantaneous rate of change exceeds the preset rapid change threshold, the weighting factor is adjusted, and a weighting function with a fast response speed is adopted to quickly adapt to the change in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set. When the instantaneous rate of change is lower than the preset steady change threshold, a weighting function with a slow response speed is used to smooth the adjustment of the weighting factor; Based on the adjusted weighting factor, the intensity indication value and the signal-to-noise ratio are weighted to obtain the weighted radio environment parameter measurement results and to evaluate the channel quality.

5. The wireless networking communication control method for highway infrastructure monitoring equipment according to claim 4, characterized in that, When the instantaneous rate of change exceeds a preset rapid change threshold, the weighting factor is adjusted, and a weighting function with a fast response speed is adopted to quickly adapt to changes in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set, including: When the instantaneous rate of change exceeds a preset rapid change threshold, the interference source identification program is activated. The interference source identification program is used to perform rapid scanning on different frequency bands or channels to identify currently active interference sources. Based on the frequency band, intensity, and variation characteristics of the identified dominant interference source, the corresponding interference type is matched from the preset interference source feature library; The corresponding weighting factor is invoked based on the matched interference type, and the adjustment strategy and weighting function are also invoked. The weighting factor is adjusted according to the invoked adjustment strategy and weighting function to quickly adapt to changes in the degree of agreement between the measurement results and the electromagnetic interference characteristic parameter set.

6. The wireless networking communication control method for highway infrastructure monitoring equipment according to claim 4, characterized in that, When the instantaneous rate of change is lower than a preset steady-state change threshold, a weighting function with a slow response speed is used to smooth the adjustment of the weighting factor, including: When the instantaneous rate of change is lower than the preset steady change threshold, the background noise identification program is started. The background noise identification program is used to perform long-term low-speed scanning on different frequency bands or channels. Analyze the received signal strength indication value, signal-to-noise ratio and spectral characteristics of each frequency band or channel to identify persistent, low-intensity and wide-band background noise; Match the corresponding background noise type from a preset background noise feature library based on the background noise; Based on the matched background noise type, the corresponding weighting function is called; During the continuous operation of the background noise identification program, the intensity indication value and the signal-to-noise ratio are periodically compared with the identified background noise features. When the measured value is higher than the background noise level, it is judged as actual interference and the weighting factor is adjusted accordingly.

7. A wireless networking communication control method for highway infrastructure monitoring equipment according to claim 6, characterized in that, The analysis of received signal strength indication values, signal-to-noise ratios, and spectral characteristics of each frequency band or channel identifies persistent, low-intensity, and wide-bandwidth background noise, including: During low-speed scanning, time-series analysis is performed on the received signal strength indication value, signal-to-noise ratio, and spectral characteristic data of each frequency band or channel to identify low-intensity signal components with periodic or quasi-periodic changes. Based on the location information and timestamp of the monitoring device, the expected range of background noise spectrum characteristics within the current location and time period is obtained from a preset noise feature library; The spectral characteristics acquired in real time and analyzed through time series are compared with the expected range of background noise spectral characteristics. When the match is found and the intensity of the real-time spectral characteristics is at a low level, the real-time spectral characteristics are identified as background noise. When the real-time spectrum characteristics exhibit periodic or quasi-periodic changes, and the intensity of the real-time spectrum characteristics is low but deviates from the expected range of background noise spectrum characteristics, the real-time spectrum characteristics are identified as periodic low-intensity interference sources. When the real-time spectrum characteristics exhibit a non-periodic, transient signal with an intensity higher than the background noise in a specific frequency band or channel, the real-time spectrum characteristics are identified as actual interference. The channel quality is assessed based on the identified background noise, the periodic low-intensity interference source, or the actual interference.

8. A wireless networking communication control system for highway infrastructure monitoring equipment, characterized in that, The system includes: The detection end is used to split the alarm data packet into emergency summary information and detailed main data according to the urgency and data size of the alarm data packet when the monitoring device detects the alarm data packet. The alarm data packet is generated by a physical event. The judgment end is used to prioritize sending the emergency summary information using transmission parameters with strong anti-interference capabilities; continuously monitor the environmental parameters of the physical event, and determine the timing of the dissipation of communication channel interference based on the changing trend of the environmental parameters; The output terminal is used to send the detailed main data at the appropriate time according to the fading time, and to use high-payload transmission parameters adapted to the current channel conditions when sending the data. The continuous monitoring of environmental parameters of the physical event, and the determination of the timing for the dissipation of communication channel interference based on the changing trends of the environmental parameters, includes: After sending the emergency summary information, the environmental parameters of the physical event are continuously monitored, and radio environmental parameters are periodically obtained through the wireless communication module. Measure the strength indication value and signal-to-noise ratio of the received signal in the current wireless channel and perform correlation analysis; Vibration signals are collected by a vibration sensor, and a vibration signal envelope is generated. When the vibration signal envelope drops below a preset threshold, and the intensity indication value and the signal-to-noise ratio reach a preset channel recovery standard, it is determined that the communication channel interference has subsided. When the vibration signal envelope has decreased, and the intensity indicator value or the signal-to-noise ratio indicates poor channel quality, continue to wait; After sending the emergency summary information, the step of continuously monitoring the environmental parameters of the physical event and periodically acquiring radio environmental parameters through the wireless communication module includes: When the wireless communication module performs radio environment sensing, it performs received signal strength scanning detection on different frequency bands or channels to obtain the radio environment parameters. The receiver gain and sampling frequency are adaptively adjusted according to the dynamic changes in the current channel environment. When the intensity indication value is detected to be close to the upper or lower limit of the measurement, the receiving gain is adjusted; The sampling frequency of the signal-to-noise ratio is dynamically adjusted based on the instantaneous rate of change of the channel noise ratio in order to capture fluctuations in channel quality. The vibration signal intensity collected by the vibration sensor is cross-validated with the measurement results of the radio environment parameters. When the vibration signal intensity is at a high level, the measurement results of the intensity indication value and the signal-to-noise ratio are weighted.

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