A method and system for conditioning industrial vibration monitoring sensor signals for transmission

Abstract

This invention relates to the field of signal conditioning technology, specifically disclosing a conditioning method and system for signal transmission of industrial vibration monitoring sensors. By real-time monitoring of data packet arrival rates and extracting flow characteristics, it can promptly identify burst data and allocate corresponding transmission priorities based on their importance. Based on the allocated priorities, the distribution ratio of available transmission bandwidth is dynamically adjusted to ensure that high-priority data receives sufficient bandwidth resources. Simultaneously, intelligent queue classification and scheduling, combined with an optimal transmission path selection mechanism, significantly improve the transmission efficiency and reliability of burst data. This method effectively reduces the transmission delay of burst data, significantly reduces data packet loss, ensures that important data can be transmitted to the receiving end in a timely and complete manner, and significantly improves the responsiveness and system stability of the entire sensor network in the face of emergencies.

Description

Technical Field

[0001] This invention relates to the field of signal conditioning technology, specifically to a conditioning method and system for signal transmission of industrial vibration monitoring sensors. Background Technology

[0002] In applications such as the Industrial Internet of Things (IIoT), intelligent monitoring, and environmental sensing, sensor networks play a crucial role in the real-time acquisition and transmission of various physical quantity data. Existing sensor signal transmission technologies typically employ fixed bandwidth allocation strategies and simple packet forwarding mechanisms. All sensor nodes collect data according to a preset sampling frequency and send the data packets to the receiving end through a unified transmission channel. Under stable network traffic conditions, this fixed-configuration transmission scheme can meet basic data transmission requirements.

[0003] However, with the continuous expansion of sensor network scale and the increasing complexity of application scenarios, existing technologies face a prominent technical problem: in industrial vibration monitoring scenarios, when sudden data (such as large amounts of data generated by emergency situations like equipment failure alarms or environmental anomaly monitoring) occurs in the sensor network, traditional fixed bandwidth allocation and simple queue scheduling mechanisms cannot effectively distinguish the importance and urgency of the data. This results in high-priority sudden data competing with ordinary data for the same network resources, causing increased latency or even loss of important data transmission, which seriously affects the real-time performance and reliability of system safety monitoring. Summary of the Invention

[0004] The present invention aims to provide a method and system for adjusting the signal transmission of industrial vibration monitoring sensors, which significantly improves the response capability and system stability of the entire sensor network in response to sudden equipment vibration.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for adjusting the signal transmission of an industrial vibration monitoring sensor, comprising:

[0006] The sensor node collects the physical quantities of the vibrating target and converts them into analog signals. Then, the analog signals are converted from analog to digital and formatted into data packets to form digital data packets with timestamps and serial numbers.

[0007] Count the number of digital data packets arriving within each time window, calculate the data packet arrival rate, and extract traffic characteristics;

[0008] The extracted traffic characteristics are compared with the preset normal range to identify burst data and assign transmission priority to burst data.

[0009] Based on the assigned transmission priority, the allocation ratio of available transmission bandwidth is dynamically adjusted, and data packets of different priorities are queued and scheduled according to the adjusted bandwidth resources.

[0010] The system selects the optimal transmission path for data packets of different priorities, performs integrity verification and priority processing on the data packets arriving at the selected transmission path, collects the system operation data corresponding to the processing results, and adjusts the system parameters.

[0011] Preferably, the sensor node acquires the physical quantities of the vibrating target and converts them into analog signals, including:

[0012] The sensor nodes periodically measure the physical quantities of the vibrating target according to a preset sampling frequency, converting the mechanical vibration into a continuous analog voltage signal;

[0013] The analog voltage signal is amplified and filtered to remove high-frequency noise interference, and then converted from analog to digital to quantize it into a discrete digital sample sequence.

[0014] The digital sample sequence is marked with a timestamp to form a data packet with a timestamp and a unique sequence number;

[0015] The system performs quality assessments on data packets, calculates changes in statistical characteristics between consecutive data packets, and identifies sensor malfunctions or signal anomalies.

[0016] Preferably, the steps of counting the number of digital data packets arriving within each time window, calculating the data packet arrival rate, and extracting traffic characteristics include:

[0017] Using a fixed time window as a benchmark, the number of data packets arriving within each time window is counted to form a time series of data packet arrival rates;

[0018] Calculate the mean and standard deviation of the size of consecutive data packets and establish a statistical distribution model of data packet size;

[0019] Calculate the statistical characteristics of the time interval between consecutive data packets, including mean, variance, and skewness index;

[0020] The flow rate indicators are continuously updated and calculated using a sliding window mechanism to extract the rate of change of flow characteristics.

[0021] Preferably, the step of comparing the extracted traffic features with a preset normal range to identify burst data and assign transmission priorities to burst data includes:

[0022] When the flow rate exceeds the preset normal range, it is marked as a potential burst data window. The duration, intensity change trend and correlation with other sensor data of the burst data are analyzed to perform pattern verification.

[0023] Based on the intensity, duration, development trend, and impact on system security of burst data, corresponding transmission priorities are assigned.

[0024] Priority labels are assigned to identified burst data, and packets of different priorities are assigned to different processing queues.

[0025] Preferably, the step of dynamically adjusting the allocation ratio of available transmission bandwidth according to the assigned transmission priority includes:

[0026] The actual transmission capacity and available bandwidth of network links are assessed by combining active detection and passive monitoring.

[0027] Based on the data priority level, a proportional allocation method is used to allocate corresponding bandwidth proportions to data of different priorities;

[0028] Monitor bandwidth usage in real time and reduce bandwidth allocation for low-priority data when network congestion is detected.

[0029] Calculate the overall performance indicators under different bandwidth allocation schemes and select the optimal bandwidth adjustment strategy.

[0030] Preferably, the step of classifying and scheduling data packets of different priorities according to the adjusted bandwidth resources includes:

[0031] Based on the current available bandwidth and data priority distribution, multiple transmission queues are dynamically set up, with each queue corresponding to a different priority level;

[0032] Based on the priority label of the data packet, the data packet is allocated to the corresponding transmission queue, and the queue fill status is checked;

[0033] A scheduling method combining polling and priority is adopted to allocate corresponding service times to queues with different priorities, monitor the status changes of each queue and the data packet transmission progress in real time, and adjust the queue service frequency accordingly.

[0034] Preferably, selecting the optimal transmission path for data packets of different priorities includes:

[0035] A comprehensive evaluation of the available transmission paths in the network is conducted to obtain performance indicators such as latency, packet loss rate, and bandwidth utilization for each path.

[0036] The optimal transmission path is assigned to each data packet based on its priority and the performance characteristics of the path.

[0037] Continuously monitor the operational status of each path, and switch data packets to an alternative path when a significant decline in path performance is detected. Set switching thresholds and delay times to avoid transmission instability caused by frequent path switching.

[0038] Preferably, the integrity verification and priority processing of data packets arriving via the selected transmission path includes:

[0039] Continuously listen to the network port and read the header information of incoming data packets, including source address, destination address, packet length, and priority label;

[0040] By checking the checksum and sequence number continuity information of the data packets, it can be determined whether the data packets have been damaged or lost during transmission.

[0041] Data packets are classified and processed according to their priority labels, with high-priority burst data being processed and stored first.

[0042] Verify the processing results by checking the temporal continuity of the data, the rationality of the values, and the consistency with historical data.

[0043] Preferably, the process of collecting and processing the system operation data corresponding to the results and adjusting the system parameters includes:

[0044] Collect operational data from each stage, from sensor acquisition, traffic monitoring, burst identification, bandwidth adjustment, queue management, path selection to data reception;

[0045] By comparing actual operating indicators with preset target values, identify performance bottlenecks and optimization opportunities in the system, and formulate optimization measures and parameter adjustment plans, including adjusting traffic monitoring thresholds, modifying priority determination rules, and reconfiguring bandwidth allocation ratios.

[0046] A gradual approach is used to adjust parameters, and an observation period is set to monitor the operation after adjustment and evaluate the optimization effect.

[0047] On the other hand, the present invention proposes an adjustment system for signal transmission of industrial vibration monitoring sensors, comprising:

[0048] The sensor signal acquisition and initial processing unit is used to acquire the physical quantities of the vibration target and convert them into analog signals, perform analog-to-digital conversion and data packet formatting on the analog signals, and form digital data packets with timestamps and serial numbers.

[0049] The real-time data traffic monitoring and feature extraction unit is used to count the number of digital data packets arriving in each time window, calculate the data packet arrival rate, and extract traffic features.

[0050] The burst data identification and priority determination unit is used to compare the extracted traffic characteristics with the preset normal range, identify burst data, and assign transmission priority to burst data.

[0051] The transmission bandwidth dynamic adjustment unit is used to dynamically adjust the allocation ratio of available transmission bandwidth according to the assigned transmission priority.

[0052] The data transmission queue optimization unit is used to classify and schedule data packets of different priorities according to the adjusted bandwidth resources.

[0053] The transmission path selection and switching unit is used to select the optimal transmission path for data packets of different priorities.

[0054] The receiving end data processing and verification unit is used to perform integrity verification and priority processing on the data packets arriving through the selected transmission path;

[0055] The system status feedback and adaptive optimization unit is used to collect system operation data corresponding to the processing results and adjust system parameters.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0057] This invention, by real-time monitoring of data packet arrival rates and extracting traffic characteristics, can promptly identify burst data and allocate corresponding transmission priorities based on their importance. It dynamically adjusts the allocation ratio of available transmission bandwidth based on the assigned priorities, ensuring that high-priority data receives sufficient bandwidth resources. Simultaneously, it employs intelligent queue classification and scheduling, combined with an optimal transmission path selection mechanism, significantly improving the transmission efficiency and reliability of burst data. This method effectively reduces transmission latency for burst data, significantly minimizes data packet loss, and ensures that important data is transmitted to the receiving end in a timely and complete manner, significantly enhancing the responsiveness and system stability of the entire sensor network in the face of emergencies. Attached Figure Description

[0058] Figure 1 This is a flowchart of the adjustment method for sensor signal transmission according to the present invention;

[0059] Figure 2 This is a block diagram of the sensor signal transmission adjustment system of the present invention. Detailed Implementation

[0060] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0061] like Figure 1 As shown, this invention proposes a method for adjusting the signal transmission of industrial vibration monitoring sensors. This method effectively reduces the transmission delay of high-frequency vibration anomalies and sudden fault alarm data generated during the operation of large equipment, significantly reduces data packet loss caused by network congestion, and ensures that the core vibration waveforms and alarm information reflecting the health status of the equipment can be transmitted to the receiving end in a timely and complete manner. This significantly improves the response capability and system stability of the entire sensor network in the face of sudden situations such as severe equipment vibration or early fault symptoms. Specifically, it includes:

[0062] The sensor node collects the physical quantity of the vibrating target and converts it into an analog signal. Then, it performs analog-to-digital conversion and data packet formatting on the analog signal to form a digital data packet with a timestamp and sequence number. Specifically, the sensor node periodically measures the physical quantity of the vibrating target according to a preset sampling frequency, converting the mechanical vibration into a continuous analog voltage signal; the analog voltage signal is amplified and filtered to remove high-frequency noise interference, and then analog-to-digital conversion is performed to quantize it into a discrete digital sample sequence; the digital sample sequence is marked with a timestamp to form a data packet with a timestamp and a unique sequence number; the data packet quality is evaluated, the statistical characteristic changes between consecutive data packets are calculated, and sensor faults or signal anomalies are identified.

[0063] By periodically measuring and preprocessing signals, high-frequency noise interference is effectively suppressed, significantly improving the purity and signal-to-noise ratio of the original signal. The introduction of timestamp and unique serial number mechanisms ensures the timing accuracy and integrity of data transmission, providing a reliable foundation for subsequent traffic analysis. Combined with quality assessment and fault identification based on statistical feature changes, the sensor status can be perceived in real time, and hardware faults or signal anomalies can be detected and warned in a timely manner, thereby greatly improving the reliability of data acquisition and the stability of system operation.

[0064] The process involves counting the number of digital data packets arriving within each time window, calculating the data packet arrival rate, and extracting traffic characteristics. Specifically, this includes: using a fixed time window as a baseline, counting the number of data packets arriving within each time window to form a time series of data packet arrival rates; calculating the mean and standard deviation of consecutive data packet sizes to establish a statistical distribution model of data packet sizes; calculating the statistical characteristics of the time interval between consecutive data packet arrivals, including mean, variance, and skewness indices; and continuously updating and calculating traffic indicators using a sliding window mechanism to extract the rate of change of traffic characteristics.

[0065] By constructing a multidimensional traffic feature model, the dynamic distribution pattern and sudden change trend of network traffic can be accurately depicted. Combined with the sliding window mechanism to capture the traffic change rate in real time, it realizes the keen perception and early identification of sudden data streams. This provides accurate data support for subsequent differentiated bandwidth allocation and priority scheduling, effectively avoids the response delay caused by the lag in feature extraction in traditional methods, and significantly improves the system's adaptability to complex network environments.

[0066] The extracted traffic characteristics are compared with a preset normal range to identify burst data and assign transmission priorities to the burst data. Specifically, this includes: marking potential burst data windows when traffic indicators exceed the preset normal range; analyzing the duration, intensity trend, and correlation with other sensor data of the burst data to perform pattern verification; assigning corresponding transmission priorities based on the intensity, duration, development trend, and impact on system security of the burst data; assigning priority labels to the identified burst data and allocating data packets of different priorities to different processing queues.

[0067] By introducing a multi-dimensional pattern verification mechanism, misjudgments caused by instantaneous network fluctuations are effectively avoided, significantly improving the accuracy of identifying sudden data. Based on a comprehensive evaluation model of data strength, persistence, and system security impact, a fine-grained dynamic allocation of transmission priorities is achieved, ensuring that critical and urgent data receives the highest level of protection. Coupled with differentiated queue classification processing, resource scheduling strategies are optimized from the source, significantly reducing the transmission latency and packet loss risk of high-priority data, and ensuring the system's core business response capabilities under abnormal operating conditions.

[0068] Based on the assigned transmission priority, the allocation ratio of available transmission bandwidth is dynamically adjusted. Specifically, this includes: evaluating the actual transmission capacity and available bandwidth of the network link through a combination of active probing and passive monitoring; allocating corresponding bandwidth ratios to data of different priorities using a proportional allocation method based on data priority levels; monitoring bandwidth usage in real time and reducing bandwidth allocation for low-priority data when network congestion is detected; calculating the comprehensive performance indicators under different bandwidth allocation schemes and selecting the optimal bandwidth adjustment strategy.

[0069] By integrating active detection and passive monitoring, the system achieves accurate perception of the actual capabilities of network links, ensuring that bandwidth allocation decisions are based on real-time and reliable network conditions. A priority-based dynamic proportional allocation strategy is adopted to ensure sufficient transmission resources for high-priority bursty data while flexibly suppressing bandwidth consumption by low-priority data, effectively alleviating network congestion. Combined with optimal strategy selection based on comprehensive performance indicators, the system significantly improves bandwidth resource utilization efficiency, maximizing overall network throughput and stability while ensuring low-latency and low-packet-loss transmission of critical data.

[0070] Based on the adjusted bandwidth resources, data packets of different priorities are classified and scheduled. Specifically, this includes: dynamically setting up multiple transmission queues based on the current available bandwidth and data priority distribution, with each queue corresponding to a different priority level; allocating data packets to the corresponding transmission queues according to their priority tags and checking the queue's fill status; using a scheduling method combining polling and priority to allocate corresponding service times to queues of different priorities, monitoring the status changes of each queue and the transmission progress of data packets in real time, and adjusting the queue service frequency.

[0071] By constructing dynamic queues and mapping multi-level tags, the system achieves refined hierarchical management of data streams, ensuring that high-priority burst data receives priority transmission channels. Combined with real-time monitoring and adaptive frequency adjustment, the system can flexibly cope with network load fluctuations, significantly improving the throughput efficiency and service stability of the system in high-concurrency scenarios.

[0072] Select the optimal transmission path for data packets of different priorities; specifically, this includes: comprehensively evaluating the available transmission paths in the network and obtaining performance indicators such as latency, packet loss rate, and bandwidth utilization of each path; assigning the optimal transmission path to each data packet based on the priority of the data packet and the performance characteristics of the path; continuously monitoring the operating status of each path, and switching the data packet to the backup path when a significant decline in path performance is detected, setting switching thresholds and delay times to avoid transmission instability caused by frequent path switching.

[0073] By employing a multi-dimensional path performance evaluation and priority matching mechanism, the system achieves precise routing of critical data on low-latency, high-reliability links, significantly improving transmission success rates. The introduction of an adaptive switching strategy with threshold and latency anti-jitter effectively avoids frequent oscillations caused by network jitter, ensuring the continuity of long-connection services. While guaranteeing an exceptional experience for high-priority services, the solution optimizes the overall network load distribution, greatly enhancing overall resilience and fault tolerance in complex network environments.

[0074] The system performs integrity checks and priority processing on data packets arriving via the selected transmission path. Specifically, this includes: continuously monitoring the network port and reading the header information of arriving data packets, including the source address, destination address, packet length, and priority label; determining whether data packets have been corrupted or lost during transmission by checking the checksum and sequence number continuity information; classifying data packets according to their priority labels, with high-priority bursts of data processed and stored first; and verifying the processing results by checking the temporal continuity, numerical rationality, and consistency with historical data.

[0075] By using real-time header parsing and multi-dimensional verification mechanisms (checksum, sequence number), damaged or lost packets in transmission are accurately identified and filtered, significantly improving data integrity and reliability. Based on priority labeling, a differentiated processing strategy ensures that critical burst data receives priority response with zero waiting time, greatly reducing the latency of core business operations. Combined with triple verification of time continuity, numerical rationality, and historical consistency, abnormal data pollution is effectively intercepted, ensuring the accuracy of system decision-making and the robustness of business logic.

[0076] Collect system operation data corresponding to the processing results and adjust system parameters; specifically, this includes: collecting operation data from each stage from sensor acquisition, traffic monitoring, burst identification, bandwidth adjustment, queue management, path selection to data reception; comparing actual operation indicators with preset target values, identifying performance bottlenecks and optimization space in the system, and formulating optimization measures and parameter adjustment plans, including adjusting traffic monitoring thresholds, modifying priority determination rules, and reconfiguring bandwidth allocation ratios; implementing parameter adjustments using a gradual approach, setting an observation period to monitor the operation status after adjustment, and evaluating the optimization effect.

[0077] Through closed-loop data feedback across the entire link, the system's performance bottlenecks can be accurately located and adaptively optimized, ensuring that network parameters always match dynamic business needs. The gradual adjustment strategy combined with the observation period verification mechanism effectively avoids the risk of system turbulence caused by radical changes and ensures stable operation.

[0078] On the other hand, the present invention proposes a conditioning system for signal transmission of industrial vibration monitoring sensors, such as... Figure 2 As shown, it includes:

[0079] The sensor signal acquisition and initial processing unit is used to acquire the physical quantities of the vibration target and convert them into analog signals, perform analog-to-digital conversion and data packet formatting on the analog signals, and form digital data packets with timestamps and serial numbers.

[0080] The real-time data traffic monitoring and feature extraction unit is used to count the number of digital data packets arriving in each time window, calculate the data packet arrival rate, and extract traffic features.

[0081] The burst data identification and priority determination unit is used to compare the extracted traffic characteristics with the preset normal range, identify burst data, and assign transmission priority to burst data.

[0082] The transmission bandwidth dynamic adjustment unit is used to dynamically adjust the allocation ratio of available transmission bandwidth according to the assigned transmission priority.

[0083] The data transmission queue optimization unit is used to classify and schedule data packets of different priorities according to the adjusted bandwidth resources.

[0084] The transmission path selection and switching unit is used to select the optimal transmission path for data packets of different priorities.

[0085] The receiving end data processing and verification unit is used to perform integrity verification and priority processing on the data packets arriving through the selected transmission path;

[0086] The system status feedback and adaptive optimization unit is used to collect system operation data corresponding to the processing results and adjust system parameters.

[0087] Furthermore, the aforementioned units, during execution, are also used to implement other steps of the aforementioned adjustment method for signal transmission of an industrial vibration monitoring sensor, as follows:

[0088] Step 1: Sensor signal acquisition and initial processing:

[0089] In scenarios involving sudden data acquisition, such as vibration monitoring, sensor nodes continuously capture physical quantities generated by environmental changes and convert them into electrical signals. These signals contain crucial information about the equipment's operating status. Sensor signal acquisition and initial processing are the fundamental steps in transforming raw physical quantities into transmittable digital signals, providing the original data source for subsequent data flow sensing and transmission regulation.

[0090] Step 1.1: The continuous acquisition of sensor signals begins with the simultaneous operation of multiple distributed sensor nodes. Each node periodically measures the physical quantity of the vibrating target at a preset sampling frequency. In vibration monitoring applications, accelerometers capture minute displacement changes on the equipment surface at a frequency of thousands of times per second, converting mechanical vibration into continuous analog voltage signals. These analog signals undergo preliminary amplification and filtering through the signal conditioning circuitry within the sensor to remove high-frequency noise interference and retain useful vibration characteristic components. The acquired analog signals then enter the analog-to-digital conversion stage, where they are quantized into a discrete digital sample sequence, with each sample value precisely recording the vibration intensity at a specific moment.

[0091] Step 1.2: The acquired digital sample sequences need to undergo preliminary data processing and formatting to facilitate subsequent transmission and analysis. Each sensor node marks consecutive digital samples with timestamps, forming data packets with time information. The timestamp accuracy reaches the microsecond level, ensuring the time synchronization of data collected by different sensor nodes. The size of the data packet is dynamically determined based on the sensor type and sampling frequency, typically containing hundreds to thousands of consecutive samples. During data packet formation, the system assigns a unique sequence number to each data packet, facilitating data integrity verification and loss detection at the receiving end.

[0092] Step 1.3: The preliminary processing stage also includes quality assessment and anomaly detection of the collected data. The system identifies potential sensor malfunctions or signal anomalies by calculating changes in the statistical characteristics between consecutive data packets. For example, when the mean or variance of multiple consecutive data packets significantly deviates from the normal range, the system marks the data packet as suspicious. This quality assessment not only improves the reliability of subsequent transmitted data but also provides a reference for identifying sudden data bursts. In vibration monitoring scenarios, data collected under normal operating conditions typically exhibits stable statistical characteristics, while equipment malfunctions or abnormal vibrations can cause drastic changes in data characteristics; these changes are a key indicator of sudden data bursts.

[0093] Step 1.4: After initial processing, data packets are sent to a local buffer queue awaiting transmission. The buffer queue employs a first-in, first-out (FIFO) management method to ensure data is arranged in chronological order of acquisition. The queue capacity is dynamically configured based on the sensor's sampling rate and network transmission capabilities, typically accommodating several seconds to tens of seconds of acquired data. During caching, the system continuously monitors the queue's fill status, triggering an early warning mechanism when the queue approaches full capacity. This early warning information provides crucial status references for subsequent traffic monitoring and transmission adjustment. Simultaneously, each data packet in the buffer queue maintains its complete metadata information, including timestamps, sequence numbers, and quality markers, which play a critical role in subsequent transmission.

[0094] Step 2: Real-time monitoring and feature extraction of data flow:

[0095] Based on the sensor data packets collected and preliminarily processed in the first step, the real-time data traffic monitoring and feature extraction process begins to continuously observe and analyze the network transmission status. This process accurately captures dynamic changes during data transmission through multi-dimensional traffic indicator calculations, especially changes in traffic characteristics when sudden data spikes occur, providing a quantitative basis for subsequent burst identification and transmission adjustment.

[0096] Step 2.1: The primary task of traffic monitoring is to calculate the data packet arrival rate per unit time, which is the most basic indicator reflecting network load. The system uses a fixed time window as a benchmark, counting the number of data packets arriving within each window to form a time series of data packet arrival rates. The length of the time window is set according to the real-time requirements of the application scenario, typically on the order of milliseconds to seconds. In vibration monitoring scenarios, the data packet arrival rate is relatively stable under normal operating conditions. However, when abnormal vibrations occur in the equipment, the sensor increases its sampling frequency or generates more valid data, leading to a significant increase in the data packet arrival rate. This rate change is one of the important clues for identifying bursty data.

[0097] Step 2.2: In addition to the data packet arrival rate, the system also needs to monitor the distribution characteristics of data packet size. Different types of sensor data packets may have different size characteristics, and burst data is often accompanied by abnormal changes in data packet size. The system establishes a statistical distribution model of data packet size by calculating the mean and standard deviation of the size of consecutive data packets. When the size of a newly arriving data packet deviates significantly from the normal distribution range, the system records this abnormal event. In vibration monitoring applications, the data packet size generated during normal equipment operation is relatively uniform, while abnormal vibration may cause drastic fluctuations in data packet size. This fluctuation characteristic provides additional judgment criteria for the identification of burst data.

[0098] Step 2.3: During traffic monitoring, the system also needs to analyze the time interval distribution between data packets. Under stable transmission conditions, the time interval between data packet arrivals usually exhibits a certain regularity, while bursts of data can lead to a significant shortening of the time interval. The system establishes a baseline model of the time interval by calculating the statistical characteristics of the arrival time intervals of consecutive data packets, including indicators such as mean, variance, and skewness. When a significant change in the statistical characteristics of the time interval is detected, the system triggers a corresponding early warning mechanism. This time interval analysis method can effectively identify burst patterns in the data stream, even if the data packet arrival rate does not change significantly.

[0099] Step 2.4: Based on the traffic monitoring results from the above multiple dimensions, the system needs to perform feature extraction and comprehensive analysis to form a comprehensive assessment of the current network transmission status. The feature extraction process includes operations such as calculating the rate of change of traffic indicators, detecting anomalies, and identifying mode conversion. The system will compare the extracted features with a preset benchmark model to determine whether the current traffic status deviates from the normal range. When multiple traffic indicators show abnormal changes simultaneously, the system will increase the confidence level of burst data. In vibration monitoring scenarios, burst data usually manifests as a sharp increase in the arrival rate of data packets, abnormal fluctuations in data packet size, and a significant shortening of time intervals. Comprehensive analysis of these characteristics can improve the accuracy of burst data identification.

[0100] During traffic monitoring, the system uses a sliding window mechanism to continuously update and calculate traffic metrics. Let the packet arrival rate within the current time window be... The window length is T, and the number of data packets arriving within the window is... The formula for calculating the data packet arrival rate is:

[0101] ;

[0102] in, This represents the data packet arrival rate within the current time window t (unit: packets / second). This formula represents the total number of data packets arriving within a time window T, where T represents the length of the time window (in seconds). It is used to calculate the data packet arrival rate in real time, providing a quantitative basis for identifying bursty data.

[0103] Step 3: Identification and Priority Determination of Burst Data

[0104] After the second step of real-time data traffic monitoring and feature extraction, the burst data identification and priority determination process begins to conduct in-depth analysis and judgment of the detected traffic anomalies. This process accurately identifies genuine burst data events through multi-layered screening and verification mechanisms, and assigns appropriate transmission priorities based on the importance and urgency of the burst data, ensuring that critical alarm signals are processed promptly.

[0105] Step 3.1: The initial stage of burst data identification involves threshold comparison and anomaly detection of the traffic features extracted in step two. The system compares the current traffic indicators with preset normal ranges. When one or more indicators exceed the normal range, the system marks that time period as a potential burst data window. Threshold settings need to comprehensively consider the characteristics of the application scenario and the statistical characteristics of historical data, avoiding excessive false alarms while ensuring that no genuine burst events are missed. In vibration monitoring scenarios, the traffic indicators during normal equipment operation are usually relatively stable, while abnormal vibrations cause significant changes in the traffic indicators. This change pattern provides important evidence for burst data identification.

[0106] Step 3.2: After initially identifying potential burst data windows, the system needs to perform further pattern verification and confirmation. The verification process includes analyzing the duration, intensity trend, and correlation with other sensor data of the burst data. Short-duration flow anomalies may simply be occasional noise interference, while longer-duration anomalies with gradually increasing intensity are more likely to be genuine burst data. The system determines the development trend of the burst data by calculating the rate of change of flow indicators within the burst data window. In vibration monitoring applications, equipment failures often lead to a continuous increase in vibration intensity; this trend helps distinguish genuine burst data from occasional flow fluctuations.

[0107] Step 3.3: After confirming the burst data, the system needs to assign corresponding transmission priorities based on the characteristics and importance of the burst data. Priority determination is based on a comprehensive evaluation of multiple factors, including the intensity, duration, development trend, and impact on system safety of the burst data. In vibration monitoring scenarios, burst data with high intensity, long duration, and an increasing trend usually indicates serious equipment failure and requires the highest transmission priority. The system adopts a hierarchical priority system, dividing burst data into multiple levels, each corresponding to a different transmission processing strategy. This hierarchical processing mechanism ensures that the most urgent alarm signals are transmitted and processed first.

[0108] Step 3.4: After priority determination, the system needs to associate the determination result with the original data packets to guide subsequent transmission adjustments. Each data packet identified as burst data is marked with a corresponding priority tag, which plays a crucial guiding role in data transmission. The system also allocates different processing queues for data packets of different priorities, with high-priority data packets placed in a dedicated fast processing channel. In vibration monitoring applications, the highest priority alarm signals are immediately sent to the transmission queue, while lower-priority routine monitoring data is processed according to the normal transmission rhythm. This differentiated processing method ensures that critical alarm signals are not overwhelmed by the regular data stream.

[0109] During the burst data identification process, the system employs an exponentially weighted moving average method to smooth the flow rate indicator, thereby reducing the impact of random fluctuations on the identification results. Let the flow rate indicator value at the current moment be... The smoothed index value is The smoothing coefficient is The smoothing calculation formula is:

[0110] ;

[0111] in, This represents the smoothed flow rate indicator value at the current moment. This represents the original flow rate value at the current moment. This represents the smoothed flow rate indicator value from the previous time step. This is the smoothing coefficient (ranging from 0 to 1). This formula is used to smooth flow indicators and improve the stability of burst data identification.

[0112] Step 4: Dynamic adjustment of transmission bandwidth:

[0113] Based on the identification and prioritization results of burst data in the third step, the dynamic bandwidth adjustment process begins to adjust the allocation strategy of available transmission resources in real time according to the current network conditions and data priorities. This process, through a flexible bandwidth allocation mechanism, ensures timely transmission of high-priority data while making reasonable use of network resources, avoiding network congestion and transmission delays caused by burst data.

[0114] Step 4.1: The initial stage of bandwidth adjustment involves assessing and measuring the current available network bandwidth. The system obtains the actual transmission capacity of the network link through a combination of active probing and passive monitoring. Active probing methods include sending test data packets and measuring round-trip time, while passive monitoring methods infer network conditions by analyzing acknowledgment information of transmitted data. In vibration monitoring scenarios, network bandwidth may dynamically change due to the impact of other application data transmissions, thus requiring continuous monitoring of the trend of available bandwidth changes. The system establishes a historical record of bandwidth usage to provide a reference for bandwidth adjustment decisions.

[0115] Step 4.2: Based on the assessment of currently available bandwidth, the system needs to formulate a bandwidth allocation strategy according to the data priority determined in Step 3. High-priority burst data will be allocated more bandwidth resources to ensure its rapid transmission to the receiving end. The system adopts a proportional allocation method, allocating corresponding bandwidth proportions to data of different priorities according to their priority levels. In vibration monitoring applications, the highest priority alarm signals may be allocated more than 50% of the available bandwidth, while regular monitoring data shares the remaining bandwidth resources. This differentiated bandwidth allocation strategy ensures the transmission quality of critical data.

[0116] Step 4.3: After the bandwidth allocation strategy is formulated, the system needs to monitor bandwidth usage in real time and dynamically adjust it according to changes in network conditions. When network congestion or excessively high bandwidth utilization is detected, the system will appropriately reduce the bandwidth allocation for low-priority data to free up more transmission space for high-priority data. Conversely, when network conditions are good and bandwidth resources are sufficient, the system can appropriately increase the bandwidth allocation for low-priority data to improve overall transmission efficiency. This dynamic adjustment mechanism can adapt to changes in the network environment and maintain the stability and efficiency of the transmission system. In vibration monitoring scenarios, the occurrence of sudden data bursts is often brief, and the system needs to promptly restore the normal bandwidth allocation strategy after the burst data transmission is completed.

[0117] Step 4.4: During bandwidth adjustment, the system also needs to consider the balance between transmission latency and data integrity. While excessive bandwidth allocation can reduce transmission latency, it may lead to wasted network resources and increased transmission costs. The system calculates the comprehensive performance indicators under different bandwidth allocation schemes and selects the optimal adjustment strategy. In vibration monitoring applications, the timeliness of alarm signals is crucial. Therefore, the system prioritizes low-latency transmission of high-priority data while maintaining the normal transmission rhythm of routine data as much as possible. This balancing strategy ensures that the system can guarantee the timely transmission of critical signals when dealing with sudden data spikes without excessively impacting routine monitoring.

[0118] During the dynamic adjustment of transmission bandwidth, the system employs a weighted fair queue algorithm to allocate bandwidth to data of different priorities. Let the bandwidth allocation ratio for the i-th priority data be . The weighting coefficient for this priority is If the total number of priority levels is n, then the bandwidth allocation calculation formula is:

[0119] ;

[0120] in, This represents the proportion of bandwidth allocated to the i-th priority data. Let represent the weight coefficient for the i-th priority level, and n represent the total number of priorities. This formula is used to calculate the bandwidth allocation ratio for data of different priorities, thereby achieving a reasonable allocation of bandwidth resources.

[0121] Step 5: Optimize the data transmission queue:

[0122] Following the dynamic bandwidth adjustment results completed in step four, the data transmission queue optimization process begins to organize and schedule waiting data packets in an orderly manner. This process uses intelligent queue management methods to ensure that high-priority burst data receives priority for transmission, while maintaining the orderly transmission of regular data, thus preventing data packet chaos and loss during transmission.

[0123] Step 5.1: The initial step in queue optimization is to reconfigure the queue structure based on the bandwidth resources adjusted in Step 4. The system dynamically sets up multiple transmission queues according to the current available bandwidth and data priority distribution, with each queue corresponding to a different priority level. High-priority queues typically have smaller capacities but higher service frequencies to ensure rapid throughput of burst data. In vibration monitoring scenarios, the highest priority alarm signals are placed in a dedicated fast queue, whose service interval is much shorter than that of regular data queues. This hierarchical queue structure provides differentiated transmission channels for data of different priorities.

[0124] Step 5.2: After queue configuration, the system needs to classify and enqueue the data packets in the cache. Each data packet is assigned to the corresponding transmission queue according to the priority tag marked in step three. During the enqueueing process, the system checks the current fill status of the queues. When a queue is close to full, an alert is triggered, and the service strategies of other queues may be adjusted. In vibration monitoring applications, high-priority queues fill rapidly when sudden data occurs. The system needs to adjust the queue service frequency in a timely manner to ensure that this important data is not lost due to queue overflow. This dynamic enqueueing management mechanism ensures the orderliness and integrity of data transmission.

[0125] Step 5.3: After data packets are enqueued, the system needs to determine the scheduling order and time arrangement for queue services. The scheduling strategy is dynamically adjusted based on queue priority and current network conditions, with higher-priority queues receiving more service opportunities and shorter service intervals. The system employs a scheduling method combining round-robin and priority scheduling to ensure that high-priority data is transmitted first while preventing low-priority data from being unserviced for extended periods. In vibration monitoring scenarios, when sudden data bursts occur continuously, the system will appropriately increase the service weight of high-priority queues to ensure continuous transmission of alarm signals. This flexible scheduling strategy balances the transmission needs of data with different priorities.

[0126] Step 5.4: During the queue service process, the system also needs to monitor the status changes of each queue and the transmission progress of data packets in real time. When a serious data packet backlog is detected in a queue, the system will adjust the service frequency of that queue or reallocate bandwidth resources. Simultaneously, the system will record the enqueue time and dequeue time of each data packet, calculate the average waiting time and maximum waiting time of the queue, and provide a reference for subsequent queue optimization. In vibration monitoring applications, after a burst of data transmission is completed, the system will gradually restore the normal service ratio of each queue to avoid continuous impact on regular monitoring data. This continuous monitoring and adjustment ensures the stable operation of the queue system.

[0127] During queue service scheduling, the system uses a weighted round-robin method to allocate services to queues of different priorities. Let the number of times the i-th priority queue receives service in one round of scheduling be . The priority weight of this queue is The total number of services served in the scheduling rounds is The formula for calculating queue service allocation is:

[0128] ;

[0129] in, This represents the number of times the i-th priority queue receives service in the current scheduling round. This represents the weight value of the i-th priority queue. This formula represents the total number of service cycles in the scheduling rounds, where n represents the total number of queues. It is used to calculate the service allocation for each priority queue, enabling efficient scheduling of queue resources.

[0130] Step 6: Transmission Path Selection and Switching

[0131] Based on the optimized data transmission queue from step five, the transmission path selection and switching process begins to choose the most suitable network transmission path for data packets of different priorities. This process, through multi-path evaluation and dynamic switching mechanisms, ensures that high-priority data can be transmitted quickly through the highest-quality path, while fully utilizing redundant path resources in the network to improve the overall reliability and efficiency of transmission.

[0132] Step 6.1: The initial stage of path selection involves a comprehensive evaluation and performance measurement of available transmission paths in the network. The system acquires real-time transmission quality metrics for each path, including latency, packet loss rate, and bandwidth utilization, through a combination of active detection and historical data. In vibration monitoring scenarios, the network environment may include various transmission methods such as wired and wireless connections, each with its specific performance characteristics and applicable scenarios. The system establishes a performance profile for each path, recording its performance under different time periods and load conditions, providing comprehensive reference information for path selection.

[0133] Step 6.2: After completing the path performance evaluation, the system needs to allocate the most suitable transmission path to each data packet based on its priority and the path's performance characteristics. High-priority burst data will be preferentially assigned to the path with the lowest latency and best stability, ensuring its fast and reliable arrival at the receiving end. The system employs a multi-dimensional path scoring method, comprehensively considering various performance indicators of the path to calculate a comprehensive score for each path. In vibration monitoring applications, alarm signals are typically assigned to high-reliability paths such as wired connections, while routine monitoring data can use lower-cost paths such as wireless connections. This differentiated path allocation strategy optimizes the utilization efficiency of network resources.

[0134] Step 6.3: After path allocation is completed, the system needs to continuously monitor the operating status and performance changes of each path. When a significant performance degradation or failure is detected on a certain path, the system will promptly switch the data packets on that path to the backup path. The switching process must ensure the orderly transmission and integrity of data packets to avoid data loss or out-of-order delivery due to path switching. In vibration monitoring scenarios, wireless connection paths may experience performance fluctuations due to environmental interference; the system needs to quickly detect such changes and execute path switching. This real-time path monitoring and switching mechanism improves the robustness and reliability of the transmission system.

[0135] Step 6.4: During path switching, the system also needs to consider the smoothness of the switching and its impact on transmission quality. Frequent path switching may lead to transmission instability and additional overhead; therefore, the system needs to set reasonable switching thresholds and delay times. The system will only perform a switching operation when the path performance change exceeds the preset threshold and persists for a certain period of time. In vibration monitoring applications, the system records the cause and effect of each path switching, providing empirical data for subsequent path selection strategy optimization. This cautious switching strategy balances the relationship between path optimization and transmission stability.

[0136] During the transmission path selection process, the system uses a comprehensive scoring method to evaluate and rank each path. Let the comprehensive score of the k-th path be... The standardized values ​​of the various performance indicators of this path are The weighting coefficient of the corresponding indicator is If the total number of performance indicators is m, then the formula for calculating the overall path score is:

[0137] ;

[0138] in, This represents the overall score of the k-th path. This represents the standardized value (ranging from 0 to 1) of the i-th performance metric for the k-th path. Let represent the weighting coefficient of the i-th performance indicator, and m represent the total number of performance indicators. This formula is used to calculate the comprehensive score of each path, providing a quantitative basis for path selection.

[0139] Step 7: Data Processing and Verification at the Receiving End

[0140] Following the data transmission path selection and switching completed in step six, the receiving end's data processing and verification process begins, receiving, parsing, and performing quality checks on the arriving data packets. This process, through a rigorous data verification mechanism and an orderly processing flow, ensures the integrity and accuracy of the transmitted data, promptly detects and handles any anomalies that may occur during transmission, and provides a reliable data foundation for subsequent data analysis and applications.

[0141] Step 7.1: The initial stage of data reception involves capturing and preliminarily parsing data packets arriving at the network interface. The receiving system continuously listens to the network port, and when a data packet arrives, it immediately reads the packet header information, including metadata such as source address, destination address, packet length, and priority tag. In vibration monitoring scenarios, the receiving end needs to process data streams from multiple sensor nodes simultaneously, each potentially arriving via different transmission paths. The system establishes an independent receive buffer for each data stream to avoid mutual interference between different data streams. This orderly receive management mechanism ensures the systematic and efficient data processing.

[0142] Step 7.2: After data packet capture, the system performs integrity verification and error detection. By checking the data packet's checksum, sequence number continuity, and other information, the system determines whether the data packet was damaged or lost during transmission. When a damaged data packet is detected, the system marks it as invalid and requests retransmission. In vibration monitoring applications, data integrity is crucial for subsequent analysis results, especially since the accuracy of alarm signals directly affects equipment safety. The system records the verification results and processing status of each data packet, providing data support for transmission quality assessment. This rigorous verification mechanism ensures the reliability of received data.

[0143] Step 7.3: Data packets that pass integrity verification are sent to the corresponding processing queue and classified according to their priority tags. High-priority burst data is processed and stored first, ensuring that alarm signals can be promptly transmitted to monitoring personnel. The system employs a multi-level buffering strategy, allocating different processing resources and storage space to data of different priorities. In vibration monitoring scenarios, the alarm signal will immediately trigger a notification mechanism upon arrival, and relevant data will be saved to a dedicated alarm database. This differentiated processing strategy ensures timely response and proper preservation of important data.

[0144] Step 7.4: After data processing is complete, the system needs to verify and record the results. The verification process includes checking the data's temporal continuity, numerical reasonableness, and consistency with historical data. When abnormal data is detected, the system will perform secondary verification or mark it as suspicious data. Simultaneously, the system will generate detailed data processing logs, recording information such as the reception time, processing status, and storage location of each data packet. In vibration monitoring applications, this log information provides crucial information for subsequent data traceability and problem investigation. This comprehensive verification and recording mechanism improves the transparency and traceability of data processing.

[0145] During data integrity verification, the system employs a cyclic redundancy check (CRC) method to detect errors in data packets. Let the content of the data packet be D, the generated checksum be C, and the check polynomial be G. Then the formula for calculating the checksum is:

[0146] C=DmodG;

[0147] Where C represents the calculated checksum (a fixed-length binary number), D represents the original content of the data packet (represented as a binary polynomial), and G represents the preset checksum polynomial (a standard CRC polynomial). This formula is used to generate the checksum of the data packet, enabling error detection during data transmission.

[0148] Step 8: System Status Feedback and Adaptive Optimization

[0149] Based on the data processing and verification results completed in step seven, the system status feedback and adaptive optimization process begins to comprehensively evaluate and continuously improve the entire transmission regulation system. This process collects operational data and performance indicators from each stage, analyzes the overall system performance and existing problems, and dynamically adjusts various parameters and strategies to enable the system to better adapt to constantly changing application environments and needs.

[0150] Step 8.1: The initial stage of status feedback involves collecting operational data and performance metrics from all aspects of the system. The system collects key metrics including processing latency, resource utilization, error rate, and throughput from various stages, including sensor acquisition, traffic monitoring, burst identification, bandwidth adjustment, queue management, path selection, and data reception. In vibration monitoring scenarios, this data reflects the system's overall performance in responding to burst data, providing a quantitative basis for subsequent optimization decisions. The system establishes a unified data collection framework to ensure data format consistency and time synchronization across all stages. This comprehensive data collection mechanism provides a solid foundation for system evaluation.

[0151] Step 8.2: The collected operational data needs to be comprehensively analyzed and its performance evaluated. The system identifies performance bottlenecks and optimization potential by comparing actual operational indicators with preset target values. The analysis process includes calculating the statistical characteristics of various indicators, identifying abnormal patterns, and assessing the rationality of resource allocation. In vibration monitoring applications, the system pays particular attention to the timeliness and accuracy of handling sudden data, analyzing whether there are any delays or omissions in alarm signals. Simultaneously, the system evaluates the stability of regular data transmission to ensure that optimization measures do not negatively impact normal monitoring. This in-depth analysis and evaluation provides a scientific basis for optimization decisions.

[0152] Step 8.3: Based on the performance evaluation results, the system needs to formulate corresponding optimization measures and parameter adjustment schemes. Optimization measures may include adjusting traffic monitoring thresholds, modifying priority determination rules, reconfiguring bandwidth allocation ratios, and optimizing queue service strategies. In vibration monitoring scenarios, if certain types of burst data are not identified in a timely manner, the system will appropriately lower the corresponding identification thresholds. If high-priority queues are frequently found to be backlogged, the system will increase the service weight of those queues or expand their capacity. These targeted optimization measures can effectively improve the overall performance of the system.

[0153] Step 8.4: After the optimization plan is formulated, the system needs to be gradually implemented and its effects continuously monitored. Parameter adjustments are usually made gradually to avoid system instability caused by a large-scale change at once. The system will set an observation period during which the operation after adjustment will be closely monitored to evaluate whether the optimization effect has achieved the expected goals. In vibration monitoring applications, the system will record the performance changes before and after each parameter adjustment and establish an evaluation file of the optimization effect. When the adjustment effect is found to be unsatisfactory, the system will roll back or further optimize in a timely manner. This continuous feedback and optimization cycle enables the system to continuously adapt to environmental changes and maintain optimal operating conditions.

[0154] During the system performance evaluation process, the system employs a comprehensive performance index method to quantitatively evaluate its overall operational status. Let the comprehensive system performance index be PI, and the standardized values ​​of each performance indicator be... The weighting coefficient of the corresponding indicator is If the total number of performance indicators is n, then the formula for calculating the comprehensive performance index is:

[0155] ;

[0156] PI represents the overall performance index of the system (ranging from 0 to 1). This represents the standardized value of the i-th performance metric (ranging from 0 to 1). Let represent the weight coefficient of the i-th performance indicator, and n represent the total number of performance indicators. This formula is used to calculate the overall performance index of the system, providing a quantitative reference for optimization decisions.

[0157] Through the orderly execution and iterative optimization of the above steps, this sensor signal transmission and adjustment system can achieve rapid response and efficient transmission in sudden data acquisition scenarios, ensuring that critical alarm signals are not overwhelmed by the regular data stream, and providing reliable transmission guarantees for applications such as vibration monitoring. The various process steps form a close logical connection and data flow, constituting a complete adaptive adjustment system.

[0158] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for adjusting the signal transmission of an industrial vibration monitoring sensor, characterized in that, include: The sensor node collects the physical quantities of the vibrating target and converts them into analog signals. Then, the analog signals are converted from analog to digital and formatted into data packets to form digital data packets with timestamps and serial numbers. Count the number of digital data packets arriving within each time window, calculate the data packet arrival rate, and extract traffic characteristics; The extracted traffic characteristics are compared with the preset normal range to identify burst data and assign transmission priority to burst data. Based on the assigned transmission priority, the allocation ratio of available transmission bandwidth is dynamically adjusted, and data packets of different priorities are queued and scheduled according to the adjusted bandwidth resources. The system selects the optimal transmission path for data packets of different priorities, performs integrity verification and priority processing on the data packets arriving at the selected transmission path, collects the system operation data corresponding to the processing results, and adjusts the system parameters.

2. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The sensor node collects the physical quantities of the vibrating target and converts them into analog signals, including: The sensor nodes periodically measure the physical quantities of the vibrating target according to a preset sampling frequency, converting the mechanical vibration into a continuous analog voltage signal; The analog voltage signal is amplified and filtered to remove high-frequency noise interference, and then converted from analog to digital to quantize it into a discrete digital sample sequence. The digital sample sequence is marked with a timestamp to form a data packet with a timestamp and a unique sequence number; The system performs quality assessments on data packets, calculates changes in statistical characteristics between consecutive data packets, and identifies sensor malfunctions or signal anomalies.

3. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The process of counting the number of digital data packets arriving within each time window, calculating the data packet arrival rate, and extracting traffic characteristics includes: Using a fixed time window as a benchmark, the number of data packets arriving within each time window is counted to form a time series of data packet arrival rates; Calculate the mean and standard deviation of the size of consecutive data packets and establish a statistical distribution model of data packet size; Calculate the statistical characteristics of the time interval between consecutive data packets, including mean, variance, and skewness index; The flow rate indicators are continuously updated and calculated using a sliding window mechanism to extract the rate of change of flow characteristics.

4. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The step of comparing the extracted traffic features with a preset normal range to identify burst data and assign transmission priorities to burst data includes: When the flow rate exceeds the preset normal range, it is marked as a potential burst data window. The duration, intensity change trend and correlation with other sensor data of the burst data are analyzed to perform pattern verification. Based on the intensity, duration, development trend, and impact on system security of burst data, corresponding transmission priorities are assigned. Priority labels are assigned to identified burst data, and packets of different priorities are assigned to different processing queues.

5. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The step of dynamically adjusting the allocation ratio of available transmission bandwidth according to the assigned transmission priority includes: The actual transmission capacity and available bandwidth of network links are assessed by combining active detection and passive monitoring. Based on the data priority level, a proportional allocation method is used to allocate corresponding bandwidth proportions to data of different priorities; Monitor bandwidth usage in real time and reduce bandwidth allocation for low-priority data when network congestion is detected. Calculate the overall performance indicators under different bandwidth allocation schemes and select the optimal bandwidth adjustment strategy.

6. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The process of classifying and scheduling data packets of different priorities according to the adjusted bandwidth resources includes: Based on the current available bandwidth and data priority distribution, multiple transmission queues are dynamically set up, with each queue corresponding to a different priority level; Based on the priority label of the data packet, the data packet is allocated to the corresponding transmission queue, and the queue fill status is checked; A scheduling method combining polling and priority is adopted to allocate corresponding service times to queues with different priorities, monitor the status changes of each queue and the data packet transmission progress in real time, and adjust the queue service frequency accordingly.

7. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The selection of the optimal transmission path for data packets of different priorities includes: A comprehensive evaluation of the available transmission paths in the network is conducted to obtain performance indicators such as latency, packet loss rate, and bandwidth utilization for each path. The optimal transmission path is assigned to each data packet based on its priority and the performance characteristics of the path. Continuously monitor the operational status of each path, and switch data packets to an alternative path when a significant decline in path performance is detected. Set switching thresholds and delay times to avoid transmission instability caused by frequent path switching.

8. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The integrity verification and priority processing of data packets arriving via the selected transmission path includes: Continuously listen to the network port and read the header information of incoming data packets, including source address, destination address, packet length, and priority label; By checking the checksum and sequence number continuity information of the data packets, it can be determined whether the data packets have been damaged or lost during transmission. Data packets are classified and processed according to their priority labels, with high-priority burst data being processed and stored first. Verify the processing results by checking the temporal continuity of the data, the rationality of the values, and the consistency with historical data.

9. The adjustment method for signal transmission of an industrial vibration monitoring sensor according to claim 1, characterized in that, The collection and processing of system operation data corresponding to the system parameters and the adjustment of system parameters include: Collect operational data from each stage, from sensor acquisition, traffic monitoring, burst identification, bandwidth adjustment, queue management, path selection to data reception; By comparing actual operating indicators with preset target values, identify performance bottlenecks and optimization opportunities in the system, and formulate optimization measures and parameter adjustment plans, including adjusting traffic monitoring thresholds, modifying priority determination rules, and reconfiguring bandwidth allocation ratios. A gradual approach is used to adjust parameters, and an observation period is set to monitor the operation after adjustment and evaluate the optimization effect.

10. A sensor signal transmission conditioning system for implementing the method as described in any one of claims 1-9, characterized in that, include: The sensor signal acquisition and initial processing unit is used to acquire the physical quantities of the vibration target and convert them into analog signals, perform analog-to-digital conversion and data packet formatting on the analog signals, and form digital data packets with timestamps and serial numbers. The real-time data traffic monitoring and feature extraction unit is used to count the number of digital data packets arriving in each time window, calculate the data packet arrival rate, and extract traffic features. The burst data identification and priority determination unit is used to compare the extracted traffic characteristics with the preset normal range, identify burst data, and assign transmission priority to burst data. The transmission bandwidth dynamic adjustment unit is used to dynamically adjust the allocation ratio of available transmission bandwidth according to the assigned transmission priority. The data transmission queue optimization unit is used to classify and schedule data packets of different priorities according to the adjusted bandwidth resources. The transmission path selection and switching unit is used to select the optimal transmission path for data packets of different priorities. The receiving end data processing and verification unit is used to perform integrity verification and priority processing on the data packets arriving through the selected transmission path; The system status feedback and adaptive optimization unit is used to collect system operation data corresponding to the processing results and adjust system parameters.

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