Interface state monitoring system based on multi-sensor fusion

Abstract

The invention relates to the technical field of state monitoring, in particular to an interface state monitoring system based on multi-sensor fusion, and the system comprises a multi-sensor acquisition module and sensors which form a monitoring node for acquiring interface terminal signals, and performing channel number matching and interference term filtering on vibration amplitude, temperature value and voltage measurement value to obtain multi-sensor interface parameters. According to the power terminal interface monitoring system, the efficiency and the accuracy of the power terminal interface monitoring system are improved by integrating various sensor technologies and comprehensive data analysis. Potential faults can be quickly recognized in the initial stage of problems through anomaly detection and threshold comparison, early warning time is advanced, and therefore maintenance work is more active instead of passive handling. And load balance and resource scheduling of the monitoring nodes are optimized, and through decision support driven by real-time data, the response speed and the processing capability are improved, the fault processing time and the overall operation and maintenance cost are reduced, and the practicability is relatively high.

Description

Technical Field

[0001] The present invention relates to the technical field of condition monitoring, and in particular to an interface condition monitoring system based on multi-sensor fusion. Background Art

[0002] Condition monitoring technology is a field dedicated to continuously or periodically monitoring and analyzing the health of mechanical equipment, electronic systems, and structures in industrial applications. This technology uses various sensors and monitoring devices, such as vibration analyzers, temperature sensors, and pressure sensors, to collect and analyze key performance indicator data. High-current terminal interface condition monitoring systems are specifically designed to monitor the health of high-current connection terminals in power systems.

[0003] However, existing technologies are limited to analyzing a single type of sensor data and lack the comprehensive utilization of multiple data sources. This results in incomplete fault diagnosis and inadequate timeliness and accuracy in responding to complex faults. Furthermore, existing technologies struggle to achieve highly dynamic optimization of resource allocation and task scheduling, particularly in rapidly changing environments like power systems. The lack of effective real-time adjustment mechanisms leads to uneven resource allocation, impacting operational and energy efficiency. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose an interface status monitoring system based on multi-sensor fusion.

[0005] In order to achieve the above objectives, the present invention adopts the following technical solutions: an interface status monitoring system based on multi-sensor fusion includes: The multi-sensor acquisition module, in which sensors form a monitoring node to acquire interface terminal signals, performs channel number matching and interference filtering on vibration amplitude, temperature value, and voltage measurement values to obtain multi-sensor interface parameters, performs consistency verification on the acquisition time mark and acquisition source mark, and records the positioning data of the interface terminal contact position. The vibration amplitude, temperature value, and voltage measurement values in the multi-sensor interface parameters are subjected to boundary verification to obtain valid sensing information; The remote diagnosis module performs threshold comparison and records abnormal differences based on the vibration amplitude, temperature value, and voltage measurement value in the effective sensing information to obtain abnormal monitoring indicators; based on the abnormal monitoring indicators, calculates the temperature value and vibration amplitude and marks the difference range, and combines the accumulated offset of the interface terminal contact position to obtain the contact fault characteristics; A load balancing module compares the CPU usage, memory occupancy, and network bandwidth of the monitoring nodes in real time based on the contact fault characteristics and marks the load level to obtain load distribution parameters; performs group scheduling on the load distribution parameters and evaluates the queue waiting time of the monitoring nodes to obtain resource allocation data; The response mechanism module sorts the task identifiers in the priority queue based on the resource allocation data and records the queuing time and the monitoring node number to obtain a priority determination result.

[0006] Preferably, the steps of acquiring the multi-sensor interface parameters are: Through the sensor acquisition interface terminal signal, the vibration amplitude, temperature value and voltage measurement value are matched with the channel number and the interference item is filtered to obtain the sensor data after preliminary filtering; Based on the preliminarily filtered sensor data, signal cleaning is performed to remove environmental noise and non-correlated frequency components to obtain cleaned sensor data; Based on the sensor data after cleaning, the correlation and consistency between the vibration amplitude, temperature value and voltage measurement value are analyzed to generate the multi-sensor interface parameters.

[0007] Preferably, the steps of obtaining the effective sensing information are: Perform consistency check on the acquisition time mark and the acquisition source mark, and record the positioning data of the interface terminal contact position to obtain the positioning data after consistency check; Based on the consistency-checked positioning data, performing boundary verification on the vibration amplitude, temperature value, and voltage measurement value in the multi-sensor interface parameters to generate boundary-verified sensing data; Based on the sensor data after the boundary verification, the data validity is analyzed and outliers are eliminated to obtain valid sensor information.

[0008] Preferably, the steps for obtaining the abnormal monitoring indicator are: Based on the vibration amplitude, temperature value and voltage measurement value in the effective sensing information, the vibration amplitude, temperature value and voltage measurement value are compared with the preset threshold value to obtain preliminary abnormal difference data; According to the preliminary abnormal difference data, the abnormal score is calculated using the following formula: ; in, Representative Items of sensor data, is the corresponding preset threshold, is the weight, is the exponential parameter, is the number of data items, S is the anomaly score; Based on the abnormality score, data with a score higher than a preset threshold is judged to be abnormal, and an abnormality monitoring indicator is obtained.

[0009] Preferably, the step of obtaining the contact fault characteristics is: Extracting the temperature value and the vibration amplitude from the abnormal monitoring index and combining them with the cumulative offset of the contact position of the interface terminal to obtain the prepared data; Based on the prepared data, the interaction value between temperature and vibration is calculated using the following formula: ; in, represents the temperature variable, represents the vibration amplitude, represents the cumulative offset of the terminal position, and F is the interaction value; Based on the interaction value, it is analyzed whether it exceeds the threshold of the industry standard, and if it exceeds, it is marked as a contact fault feature.

[0010] Preferably, the steps of obtaining the load distribution parameters are: Extracting the CPU usage, memory occupancy, and network bandwidth data of the monitoring node and combining it with the contact fault characteristics to analyze the performance impact of the monitoring node; Score the performance impact of each monitoring node and calculate the performance impact score. The calculation formula is: ; in, Indicates CPU usage. Indicates memory usage. Indicates the network bandwidth, , , are the design upper limits of each resource, and P is the performance impact score; Based on the performance impact score, the load level of each node is marked, and resource allocation is adjusted according to the performance impact score to obtain a load allocation parameter.

[0011] Preferably, the steps of obtaining the resource allocation data are: Based on the load distribution parameters, the monitoring nodes are divided into multiple service groups, and each group is configured according to resource requirements and performance parameters; According to the configuration, calculate the average waiting time of the monitoring node. The calculation formula is: ; in, represents the resource utilization of the i-th node, is the adjustment coefficient, represents the task processing time of the i-th node, is the total number of nodes, Q is the average waiting time in queue; Based on the average queuing waiting time, resource allocation is adjusted to obtain resource allocation data.

[0012] Preferably, the steps for obtaining the priority determination result are: Based on the resource allocation data, the type and urgency of the tasks to be executed on each node are analyzed, the task identifiers are sorted, and the estimated queue time of each task and the corresponding monitoring node number are recorded to generate a task queue plan; Based on the task queuing plan, priority determination is performed, all tasks and associated monitoring nodes are sorted, and a priority determination result is formed.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: This invention improves the efficiency and accuracy of the power terminal interface monitoring system by integrating multiple sensor technologies and comprehensive data analysis. Anomaly detection and threshold matching can quickly identify potential faults at their earliest stages, providing early warnings and making maintenance more proactive rather than reactive. Load balancing and resource scheduling optimization for monitoring nodes, supported by real-time data-driven decision support, improves response speed and processing capabilities, reduces troubleshooting time, and reduces overall operational and maintenance costs, resulting in high practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 Schematic diagram of the steps for obtaining multi-sensor interface parameters in the present invention; Figure 3 Schematic diagram of the steps for obtaining effective sensing information in the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] See also Figure 1-3 The present invention provides a technical solution: an interface status monitoring system based on multi-sensor fusion includes: The multi-sensor acquisition module, in which sensors form a monitoring node to collect interface terminal signals, performs channel number matching and interference filtering on vibration amplitude, temperature value, and voltage measurement values to obtain multi-sensor interface parameters. It also performs consistency verification on the acquisition time mark and acquisition source mark, records the positioning data of the interface terminal contact position, and performs boundary verification on the vibration amplitude, temperature value, and voltage measurement values in the multi-sensor interface parameters to obtain valid sensing information. The remote diagnosis module compares thresholds based on the vibration amplitude, temperature value, and voltage measurement values in the effective sensing information and records abnormal differences to obtain abnormal monitoring indicators. Based on the abnormal monitoring indicators, the temperature value and vibration amplitude are calculated and the difference range is marked. Combined with the cumulative offset of the interface terminal contact position, the contact fault characteristics are obtained. The load balancing module compares the CPU usage, memory occupancy, and network bandwidth of monitoring nodes in real time based on contact fault characteristics and annotates the load levels to obtain load distribution parameters. It then performs group scheduling on the load distribution parameters and evaluates the queue waiting time of monitoring nodes to obtain resource allocation data. The response mechanism module, based on the resource allocation data, sorts the task identifiers in the priority queue and records the queuing time and monitoring node number to obtain the priority determination result.

[0017] The steps to obtain the multi-sensor interface parameters are as follows: Through the sensor acquisition interface terminal signal, the vibration amplitude, temperature value and voltage measurement value are matched with the channel number and the interference item is filtered to obtain the sensor data after preliminary filtering; Based on the preliminarily filtered sensor data, signal cleaning is performed to remove environmental noise and non-correlated frequency components to obtain cleaned sensor data; Based on the sensor data after cleaning, the correlation and consistency between the vibration amplitude, temperature value and voltage measurement value are analyzed to generate the multi-sensor interface parameters.

[0018] Specifically, first read the interface terminal signal collected by the sensor, and compare it one by one through the channel numbers pre-set for the vibration amplitude, temperature value and voltage measurement value. If the channel number of a piece of data is inconsistent with its actual source, the data is directly discarded. Then, the vibration amplitude, temperature value and voltage measurement value are compared according to the range determined by experience. For example, the vibration amplitude is compared with the range of 0mm to 10mm one by one, the temperature value is compared with the range of 0℃ to 90℃ one by one, and the voltage measurement value is compared with the range of 0V to 24V one by one. The above range is the empirical threshold value obtained based on the equipment design requirements and daily monitoring feedback. If the value of each data falls within the corresponding range, Those outside the range will be marked and temporarily excluded to prevent them from being mistakenly judged as valid records in subsequent analysis. For records whose values are within the specified range but whose channel number marks are abnormal, it is necessary to confirm again whether there is a repeated channel conflict. By comparing them one by one, the data with correct marks and values within a reasonable range are retained, and then these retained records are further filtered for interference items. Interference item filtering mainly measures the collected noise signal characteristics and compares them with the equipment environment noise sample library, identifies and marks the records with consistent noise characteristics for removal, and after channel number comparison and interference item filtering, the remaining records are regarded as preliminary available data and are summarized, and finally the preliminarily filtered sensor data is obtained.

[0019] Based on the sensor data obtained above and after preliminary filtering, the frequency domain analysis method is used to perform signal cleaning, and the retained vibration amplitude, temperature value and voltage measurement value are segmented and processed. The suspicious noise frequency is identified based on the background noise spectrum information obtained by testing in the equipment environment. These suspicious frequencies are compared item by item and removed from the signal components. Then, the low-energy parts that are not related to the main frequency band are eliminated. For example, in actual measurements, it is found that the background noise is concentrated around 50Hz and the stable frequency band of the temperature value is in the extremely low frequency area. This information is collected through multiple equipment operations and sensor data. After confirming the noise frequency, By comparing the energy distribution of the vibration amplitude in the frequency range, if it exceeds the noise energy threshold accumulated in advance based on experience, this part of the spectrum is considered to be environmental noise and is removed from the record. The part where the environmental noise is below this threshold is retained, and at the same time, the short fluctuation segments of the temperature values and voltage measurements that have no correlation with the main frequency band are excluded. Finally, the retained frequency bands are integrated and amplitude smoothing is performed to further reduce local fluctuations, so that all types of data are within a relatively stable frequency range. After merging, the results of these frequency domain filtering and time domain smoothing are regenerated into a data list to obtain the cleaned sensor data.

[0020] Based on the cleaned sensor data obtained above, we first extract the corresponding records of vibration amplitude, temperature value, and voltage measurement value in the time series. We observe the change relationship between each data item through sliding correlation. We compare the three values at each time point in pairs. For example, we calculate the linear correlation coefficient between vibration amplitude and temperature value, the correlation coefficient between temperature value and voltage measurement value, and the correlation coefficient between vibration amplitude and voltage measurement value in the same time period. If the absolute value of the correlation coefficient of a pair of values exceeds the correlation threshold (such as 0.6) obtained through empirical analysis, it is considered that the two have a significant coupling trend in that time period. To further clarify the consistency, these highly correlated data are grouped and recorded separately. Then, the length of time they remain consistent is calculated based on the difference in changes between adjacent time periods to measure the stability between the data. If multiple consecutive time periods are in the high correlation range, they are recorded as long-term consistency features. Finally, based on the correlation judgment results of these grouped data, a correlation label is assigned to the vibration amplitude, temperature value, and voltage measurement value. Consistency labels are also assigned based on the consistency performance in different time periods. These labels are then aggregated with the values themselves to form a comprehensive multi-sensor information structure. Finally, this comprehensive information structure is summarized and organized to generate the multi-sensor interface parameters.

[0021] The steps to obtain effective sensing information are: Perform consistency check on the acquisition time mark and the acquisition source mark, and record the positioning data of the interface terminal contact position to obtain the positioning data after consistency check; Based on the consistency-checked positioning data, boundary verification is performed on the vibration amplitude, temperature value, and voltage measurement value in the multi-sensor interface parameters to generate boundary-verified sensor data; Based on the sensor data after boundary verification, the data validity is analyzed and outliers are eliminated to obtain valid sensor information.

[0022] Specifically, based on the previously acquired collection time mark and collection source mark, the timestamp carried in each record is compared with the source mark. If it is found that the timestamp deviates too much from the pre-established valid time range, for example, If the difference is less than 10 seconds (this range is an empirical value obtained after multiple tests based on the device's real-time response capability and sensor recording frequency), the record will be marked as having an abnormal source. Records with abnormal sources are then separated from subsequent processing. The source identifier of each record is then compared to see if it matches the registered legal sensor list. This list is a set of legal numbers entered into the system after verification during actual deployment. Any unregistered or conflicting identifiers in the record are excluded. Next, the interface terminal contact position is read and mapped one-to-one with the valid timestamp record. If multiple overlapping positioning information appears at the same time point, only the first one is retained on a first-come, first-served basis and this position is updated as the current device contact point. If the time stamp meets the time range mentioned above and the source identifier can be found in the registered numbers, this record is aggregated with the location coordinates. Finally, a new mapping structure is generated based on all data points that have undergone the above processing and are confirmed to be conflict-free. Each data point is accompanied by the verified time and source information. The corresponding interface terminal contact position is added to the location field of the mapping structure in the form of coordinates or serial numbers, thus obtaining consistency-verified positioning data.

[0023] Based on the previously obtained consistency-calibrated positioning data, boundary verification is performed on the vibration amplitude, temperature value, and voltage measurement value in the multi-sensor interface parameters one by one, and these parameters are compared with the intervals compiled in advance based on the equipment manual and multiple on-site observation records. For example, the vibration amplitude is compared with the 0mm to 10mm interval, the temperature value is compared with the 0℃ to 90℃ interval, and the voltage measurement value is compared with the 0V to 24V interval. These intervals are quantitative data summarized based on the design limits provided by the manufacturer and long-term operation sampling of the equipment. If any parameter exceeds the corresponding interval, the record will be eliminated. If the vibration amplitude, temperature value, and voltage measurement value are all within the corresponding interval, the record will be eliminated. If it is within the interval, it will be marked as a qualified record, and then all qualified records will be summarized into a new data set according to their respective time stamps and contact positions. During the process, if it is found that the temperature value is exactly equal to the critical value of the interval, it will be classified as the boundary column and an additional check will be made to see whether it is still at the critical level within a short time after the record is recorded, so as to determine whether it is a false boundary caused by abnormal jitter. If it is confirmed that it is not jitter, the record will be retained. If it is located near the critical value for many consecutive times, it will be deleted to prevent the frequent fluctuations in the subsequent analysis from affecting the results. Finally, all the vibration amplitudes, temperature values and voltage measurement values that meet the corresponding intervals and pass the above tests will be re-enumerated to generate the sensor data after boundary verification.

[0024] Based on the sensor data obtained after boundary verification, the temporal stability of each record is first tested, and the fluctuation range of the vibration amplitude, temperature value and voltage measurement value at different sampling times is compared one by one. Multiple judgments are made with reference to the thresholds set in advance based on the actual operation of the equipment and historical fault cases. For example, records where the vibration amplitude is continuously higher than the previous average value by more than 1mm in a short period of time are marked. The 1mm threshold here is a fixed upper limit value obtained by analyzing multiple rapid vibration conditions of the equipment. For temperature values, several segments are added in the range of 0℃ to 90℃. Once a record crosses the corresponding range in a short period of time, the threshold value is marked. Obvious jumps in adjacent segments are additionally marked, and these abnormal fluctuations are synchronously compared with the changing trend of the voltage measurement value. By calculating the absolute value of the difference, if the absolute value exceeds the preset reference upper limit, it is considered abnormal. The reference upper limit is mainly obtained by summarizing the numerical differences under high load and no-load operation of the equipment. Data records marked as abnormal will be excluded or retained for manual confirmation. After comprehensive analysis, those records retained that conform to the continuous operation logic of the equipment in terms of time and value distribution are deemed valid. After eliminating the outliers based on the above judgment, the remaining records are merged into a new data set to obtain valid sensing information.

[0025] The steps for obtaining abnormal monitoring indicators are as follows: Based on the vibration amplitude, temperature value and voltage measurement value in the effective sensing information, they are compared with the preset thresholds to obtain preliminary abnormal difference data; According to the preliminary abnormal difference data, the abnormal score is calculated using the following formula: ; in, Representative Items of sensor data, is the corresponding preset threshold, is the weight, is the exponential parameter, is the number of data items, S is the anomaly score; Based on the anomaly score, data with a score higher than a preset threshold is judged as abnormal, and an anomaly monitoring indicator is obtained.

[0026] Specifically, based on the vibration amplitude, temperature value and voltage measurement value recorded in the effective sensing information obtained previously, the data of the previous normal operation stage and high load stage are first extracted from the historical operation log of the equipment, and the statistical results of these data are compared. For example, the vibration amplitude is referenced to the range of 0mm to 10mm, the temperature value is referenced to the range of 0℃ to 90℃, and the voltage measurement value is referenced to the range of 0V to 24V. These value ranges are all from the design limit range provided by the equipment manufacturer and are supplemented by the actual measurement records during hundreds of hours of continuous operation for correction. Then, when performing the comparison, the vibration amplitude, temperature value and voltage measurement value obtained previously are read one by one and compared with the corresponding range The upper and lower limits are compared numerically. If the situation exceeds the range, the specific difference is recorded and classified into the preset threshold exceeding group. If it is within the interval, the difference amplitude is recorded and classified into the normal group. These difference amplitudes are then summarized one by one to form a set of preliminary difference data. Records near the critical value are marked for subsequent detection or on-site comparison reference. If the vibration amplitude is found to exceed the upper limit of the interval by more than 1mm during the process, it will be confirmed again whether it is an occasional interference. If the temperature value continues to approach 90℃ during the high-load period, it will be judged as a high-temperature deviation and an additional record will be made. If the voltage measurement value is close to 24V for many times, its difference will be continuously tracked. Finally, all the difference data are summarized to obtain preliminary abnormal difference data.

[0027] The benefit of this formula is that by simultaneously considering the degree of deviation of vibration amplitude, temperature value, and voltage measurement values from their respective thresholds, and combining the importance weights of different parameters in the overall monitoring system, the deviation of multiple indicators can be comprehensively presented in a single score value.

[0028] The parameter acquisition steps are: select the absolute value of the corresponding sensor data from the preliminary difference data obtained above. For example, if the actual value of the vibration amplitude at a certain moment is 3.2mm and the upper limit of the threshold range is 3.0mm, then It is recorded as 3.2, and the threshold value corresponding to this parameter at the same time is included in the calculation. The vibration amplitude, temperature value and voltage measurement value are all extracted in the same way; The parameters are obtained by referring to the design upper limits of each parameter provided by the equipment manufacturer and taking a weighted average of multiple limit state observations in the operation log to obtain specific thresholds. For example, the vibration amplitude threshold is 3.0mm, the temperature threshold is 85°C, and the voltage measurement threshold is 22V. These thresholds are obtained from long-term monitoring and multiple calibration analyses. The steps for obtaining parameters are as follows: During the prototype testing phase of the equipment, by analyzing the correlation between the failure rate and the deviation degree of a single indicator, and then combining the criticality of each data in the monitoring strategy, a set of weighted value distribution tables are obtained. If the temperature value is too high, it is more likely to cause a failure, then the temperature value corresponding to If the vibration amplitude only causes serious consequences in extreme cases, the vibration amplitude The voltage measurement value may be relatively low. Then take the middle value; The steps for obtaining parameters are: quantitatively evaluate the sensitivity of different parameter deviations in the monitoring system. If the temperature value slightly exceeds the threshold, it may indicate a potential abnormality. A higher index will be taken. If the vibration amplitude needs to exceed the limit significantly before significant consequences occur, then Relatively small, after multiple tests and analyses, each parameter is assigned a corresponding index value; The parameter acquisition steps are as follows: determine the number of monitoring indicators contained in the preliminary difference data obtained previously, for example, if only the vibration amplitude, temperature value and voltage measurement value are monitored, , when expanding other parameters Increased accordingly.

[0029] Calculation process: read the vibration amplitude difference of 3.2mm, temperature value difference of 86℃, and voltage measurement value difference of 23V one by one, and make the difference with their corresponding threshold values of 3.0mm, 85℃, and 22V respectively, and get |3.2-3.0|=0.2, |86-85|=1, and |23-22|=1; bring the above differences into the corresponding weights and indexes, such as the temperature value Take 0.35, Take 2, the vibration amplitude Take 0.25, Take 1.5, the voltage measurement value Take 0.30, Take 1.8 and calculate each term ; Sum and square to get the score S. If there are many numerical sensor indicators, then add up all the items and then square them. For example, after simplifying the calculation in this example, , through actual calculation, we can get a score value of about 1.05.

[0030] The results show that the S value reflects the comprehensive degree of deviation between all monitoring data and their respective thresholds. The larger the S value, the more serious the overall deviation. If the S value is less than 1, it means that most parameters are still in the safe range. If the S value exceeds 2, it is recorded as a significant deviation and attention should be paid to subsequent data to check whether there is any risk.

[0031] The numerical distribution of each record is retrieved from the anomaly score obtained previously. The records are first compared one by one with the threshold set based on multiple on-site observations. This threshold is set based on the numerical range summarized from historical failure statistics and continuous monitoring sampling of the production line. To avoid redundant interference introduced by repeated alarms, a safety margin is added to the critical ranges of temperature, vibration, and voltage. Records with anomaly scores greater than the safety margin are added to the key monitoring list and reviewed together with the data from adjacent time periods. If the anomaly scores within adjacent time periods are continuously large, the sensor data is judged to be high-risk. For records with high temperature anomaly scores, special attention is paid to the current heat dissipation status of the equipment and the temperature curve changes during subsequent sampling are recorded. For records with excessively high vibration anomaly scores, quick inspections are carried out on parts such as bearings and support components. For records with voltage anomaly scores significantly higher than the threshold, the upstream power supply stability is checked and real-time power supply fluctuation data is collected. Finally, all scores above the corresponding threshold are counted to obtain anomaly monitoring indicators.

[0032] The steps to obtain contact fault characteristics are: The temperature value and vibration amplitude are extracted from the abnormal monitoring indicators and combined with the cumulative offset of the interface terminal contact position to obtain the prepared data; Based on the prepared data, the interaction value between temperature and vibration is calculated using the following formula: ; in, represents the temperature variable, represents the vibration amplitude, represents the cumulative offset of the terminal position, and F is the interaction value; Based on the interaction value, it is analyzed whether it exceeds the threshold of the industry standard. If it exceeds, it is marked as a contact fault feature.

[0033] Specifically, select the temperature value and vibration amplitude from the abnormal monitoring indicators obtained above, and query the actual displacement record of the contact position of the interface terminal at the same time. Match these three parts of information one by one and combine them in chronological order. In the process, first compare the temperature value with the range of 0℃ to 90℃, and the vibration amplitude with the range of 0mm to 10mm. The cumulative offset of the contact position is usually taken from 0mm to 5mm. The above range is derived from the design upper limit of the equipment and the actual data collected from multiple on-site tests. If a record shows a temperature value far exceeding 90℃ or a vibration amplitude continuously exceeding 10mm, it is necessary to mark it immediately and reconfirm whether the source time is correct. If the cumulative offset of the contact position deviates from 5m If the displacement is greater than 1 mm, the wear condition of the corresponding mechanical parts needs to be compared to determine whether a large-scale displacement occurs. All records within the above range are listed as valid, and then the temperature value and vibration amplitude of each record are confirmed one by one to see whether they remain stable in adjacent time periods. During this period, check whether the cumulative offset has increased significantly. If the displacement increment reaches more than 1 mm in a short period of time, the historical operation status of the equipment is retrieved again, and then the paired data sequences of temperature, vibration and cumulative offset are merged according to the timestamp. The paired data are indexed and refined tags are used to distinguish continuity from instantaneous jumps. The entries marked as normal continuity are merged into a preliminary set, and finally summarized to form a "prepared data" sequence.

[0034] The formula is beneficial in that it measures the potential coupling between temperature and vibration by combining the difference between the temperature variation and the vibration amplitude, and using an exponential adjustment term caused by the cumulative offset of the terminal position.

[0035] The parameter acquisition steps are as follows: Based on the temperature values obtained above, the sensor records the temperature every 5 minutes during the operation of the equipment, and compares it under normal operation and high load operation conditions, so as to select a set of stable and reliable data for calculation. For example, if a record shows the temperature is 64.2℃, then ; The steps for obtaining the parameters are: select the vibration amplitudes that are synchronously obtained in the same time period. The corresponding entry is checked and confirmed to be consistent with the design range of 0mm to 10mm. If the record shows that the vibration amplitude is 3.6mm, then ; The steps for obtaining the parameters are as follows: perform multiple position detections on the same interface terminal during long-term use, record the mechanical wear and thermal expansion and contraction during each operating cycle through equipment condition monitoring, and summarize these data to obtain the cumulative offset of the terminal position. For example, after cumulative statistics, a record is 1.5mm, then .

[0036] Calculation process: Calculate the absolute difference: , subtract the vibration amplitude of 3.6 from the temperature variable of 64.2 in the current example to get 60.6, and take the absolute value of this value, which is still 60.6; calculate the exponent: , we can first take the approximate value 7.78, and then let Approximately ; Calculate the denominator: ,in ,Right now , so the denominator is approximately ; Comprehensive calculation: .

[0037] The results show that when the interaction value When the value is in the hundreds or thousands place, it means that the difference between the temperature and the vibration amplitude is large and the correction caused by the cumulative offset of the terminal position is relatively small. At this time, the coupling degree is more significant. If in the same record Maintaining a value in the thousands indicates a strong correlation between temperature and vibration, and also means that the cumulative offset of the terminal position is relatively insufficient to offset the coupling effect between the two.

[0038] Based on the interaction value F obtained previously, the F value in each record is first read one by one and compared with the industry standard threshold. This threshold is usually formulated based on a combination of industry-recognized safety guidelines and the specifications and recommendations provided by the equipment manufacturer. The specific limit is determined by combining the risk performance brought about by a large gap between the temperature value and the vibration amplitude in the long-term operation data of the equipment. For example, in some industries, an F value between 500 and 1000 is considered to be in the high-risk range, and an industry standard threshold of 1000 is set to determine whether it is in the extreme area. After reading all records, each one is checked. When the interaction value F is greater than 1000, it is recorded as a significant deviation and a special investigation is carried out. Those items recorded as significant deviations are further compared with the terminal position change trend. If it is found that the terminal position has not changed significantly in the recent period but the F value remains in the high range, the record will be marked in the contact fault feature list. Finally, all records that meet the above conditions are summarized to form a contact fault feature list.

[0039] The steps to obtain load distribution parameters are: Extract the CPU usage, memory usage, and network bandwidth data of the monitoring node, and analyze the performance impact of the monitoring node based on the contact fault characteristics; Score the performance impact of each monitoring node and calculate the performance impact score. The calculation formula is: ; in, Indicates CPU usage. Indicates memory usage. Indicates the network bandwidth, , , are the design upper limits of each resource, and P is the performance impact score; Based on the performance impact score, the load level of each node is marked, and resource allocation is adjusted according to the performance impact score to obtain the load distribution parameters.

[0040] Specifically, the CPU usage, memory occupancy and network bandwidth data of the monitoring node are extracted. First, the real-time CPU usage percentage is read one by one from the system monitoring log and compared with the standard range in the equipment design manual. For example, 0% to 100% is used as the reference interval. For memory usage, the upper limit is determined by the total amount of device memory and recorded one by one in combination with the average load level during long-term use. For network bandwidth data, the comparison range of 0Mbps to 1000Mbps is set according to the type of connected physical interface and the peak description provided by the manufacturer. Then, the list of suspicious nodes obtained from the contact fault characteristics is combined and the CPU, memory and bandwidth performance of each node in the same period are checked one by one. If the CPU usage is found to be If the rate is higher than 80% or the memory usage continuously exceeds 80%, it will be included in the continuous load growth record for cross-analysis with the contact fault characteristics. If the network bandwidth usage is close to the gigabit level at one time, it is necessary to check the specific business type of the node, and then determine whether there is overlap with the anomaly pointed out by the contact fault characteristics. After summarizing all records, mark whether each node has a sharp increase in CPU, continuous high memory or bandwidth excess within the same monitoring interval, and arrange them in chronological order to obtain a preliminary performance impact list. Combined with the contact fault characteristics mentioned above, those nodes suspected of being affected by the fault or with a high correlation with the fault are recorded, and their CPU, memory and bandwidth data are summarized into a set of comparison data for reference in subsequent scoring.

[0041] The benefit of the formula is that by introducing a comprehensive description of CPU usage, memory occupancy, and network bandwidth, the impact of different resource usage on the overall load of the monitoring node can be comprehensively reflected in the same score value.

[0042] The steps to obtain the parameters are: regularly collect CPU usage on the monitoring node, and make statistics on the records every minute based on 0% to 100%, and then average or extract the peak value of the CPU data of all nodes in a day to form several CPU usage sampling values. For example, a node collects the CPU usage during the high load period. %; The parameters are obtained by referring to the total capacity of the node's CPU physical cores and logical cores and normalizing them to the manufacturer's nominal 100% usage percentage, for example, determining %; The steps for obtaining the parameters are as follows: obtain the memory usage of the node in the same collection cycle, and determine the value of each record by referring to the node physical memory specifications and real-time allocation. For example, if the memory usage of a node is 4GB and the total physical memory is 16GB, it can be considered %; The steps to obtain the parameters are as follows: register according to the total memory capacity of the device and the maximum available memory that can be actually allocated by the system. For example, if the total amount is 16GB, %, used to maintain consistency with the previous value; The steps to obtain the parameters are: periodically monitor the real-time occupancy of the network bandwidth and compare it with the gigabit network interface to which the node is connected, record the maximum upstream or downstream traffic, and if the node monitors that the occupied bandwidth is 500Mbps, then ; The steps to obtain the parameters are: when the network connection has a peak bandwidth of 1000Mbps, record Mbps, which is convenient for normalizing the actual bandwidth usage value.

[0043] Calculation process: Calculation And add 1: Take %, %,but ,get ; Take the logarithm: ;calculate :Pick %, %,but , ; Multiply the two together: ;calculate :Pick Mbps, Mbps, then , ; Sum: , recorded as .

[0044] This result shows that: when performance affects the score When the value is greater than 1, it indicates that the overall level of node resource usage has entered a relatively high state. If it continues to rise to above 2, it can be regarded as a high-load node. If it is lower than 1, it means that resource usage is still in a low-load range.

[0045] Based on the performance impact scores obtained previously, the scores of each monitoring node are first read in sequence and compared with the actual device operation records in chronological order. During this process, if a node's score is consistently above 1.0 across multiple sampling periods, it is considered to be overloaded. If the score exceeds 2.0, it is considered to be highly loaded. The specific values of the node's CPU utilization, memory utilization, and network bandwidth utilization are summarized and compared one by one to see if they are correlated with the contact fault characteristics identified previously. If the correlation is high, these nodes are added to the optimization list and additional resource monitoring frequency is allocated. If the node score increases significantly between different time periods, the cause of the increase in utilization is further analyzed. If the CPU utilization rate jumps too quickly, the process queue status of the node is checked. If the bandwidth utilization index increases, the throughput curve of the connected device is checked. The scores of all verified nodes are summarized and bundled with the node ID to identify the distribution of resource pressure within the same monitoring period. Finally, scheduling is carried out based on this distribution to generate load distribution parameters.

[0046] The steps to obtain resource allocation data are: Based on the load distribution parameters, the monitoring nodes are divided into multiple service groups, and each group is configured according to resource requirements and performance parameters; According to the configuration, calculate the average waiting time of the monitoring node. The calculation formula is: ; in, represents the resource utilization of the i-th node, is the adjustment coefficient, represents the task processing time of the i-th node, is the total number of nodes, Q is the average waiting time in queue; Based on the average queue waiting time, resource allocation is adjusted to obtain resource allocation data.

[0047] Specifically, based on the load distribution parameters obtained previously, the resource requirements and performance parameters of the monitoring nodes are sorted out. First, the CPU, memory, network and other resource statistics recorded by all nodes in daily operation and high-load scenarios are collected, and these data are compared with the original hardware specifications of the nodes. The CPU part can be calibrated in the range of 0% to 100% with reference to the actual deployment situation, the memory can be recorded according to the occupancy ratio of the physical capacity, and the interface bandwidth range from 0Mbps to the upper limit Mbps is used as a reference for the network. Then, combined with the usual workload scale of the node and the faults or potential fault signs identified previously, the nodes are roughly divided into three preliminary categories: high request pressure, medium load and light load. Then the nodes of the same category are grouped and summarized. For example, if high request pressure If the load nodes are mainly concentrated in the case where the network bandwidth usage often exceeds 80%, these nodes are assigned to a service group. If the memory usage and CPU usage are continuously in the medium range, they are assigned to another service group. If the three resources rarely fluctuate significantly, these nodes are placed in the light-load service group, and each service group is preliminarily configured based on the specific number of nodes and the average resource usage. If some nodes are overloaded on a certain resource but other resources perform smoothly during the process, these nodes will be recorded separately for further subdivision in subsequent grouping corrections, and eventually multiple service groups corresponding to different load levels and resource requirements are formed. Each group determines the preliminary performance parameter range after referring to the hardware capabilities and task volume distribution of the monitoring node, and finally each group is configured according to resource requirements and performance parameters.

[0048] The benefit of this formula is that by considering the sum of the squares of node resource utilization, the total number of nodes, and the exponential decay effect of task processing time in the same expression, it can more intuitively measure the overall average queue waiting time.

[0049] The steps for obtaining parameters are as follows: monitor the comprehensive utilization of CPU, memory, and bandwidth on each node at the hourly or minute level, and normalize them to the range of 0 to 1 based on the monitoring data obtained above. If the CPU utilization of a node during the observation period is about 70%, the memory utilization is about 60%, and the bandwidth utilization is about 30%, the three items can be averaged or the maximum value can be taken to form a comprehensive resource utilization, for example, the comprehensive utilization can be recorded as ; The steps to obtain the parameters are as follows: in the long-term historical records of the system, the effects of multiple scheduling adjustments are counted and the change in the average queue waiting time before and after each adjustment is recorded. Then, the correlation coefficients are weighted and accumulated to obtain a curve that can reflect the scheduling sensitivity. The average value of the curve is selected as the adjustment coefficient. And keep it in a reasonable range, such as ; The steps for obtaining the parameters are as follows: retrieve the task processing time data on each node, and use the average processing time of the node when executing regular requests or high-load services as a measure. If the historical average processing time for the i-th node to execute the same type of task is 2 seconds, it is recorded as If a node has many long-time requests, they will be weighted during statistics, striving to reflect the actual average processing time of the node; The parameter acquisition step is: determine the total number of nodes included in the calculation of the average queue waiting time according to the number of nodes after the previous grouping is completed. For example, after grouping, a service group contains n=6 nodes.

[0050] Calculation process: calculate For example, there are 6 nodes in total, and their comprehensive resource utilization rates are 0.53, 0.62, 0.40, 0.78, 0.55, and 0.66 respectively. The squares of each node are added together to get ; Divide by n=6 to get ; calculate ,If the average processing time of the six nodes is 2 seconds, 3 seconds, 2 seconds, 4 seconds, 2 seconds, and 2 seconds, the total is 15 seconds; ,and then ; Multiplying 0.36197 by 0.7408 gives ; Square the result: , recorded as Second; The results show that the average waiting time in queue is about 0.52 seconds. When Q is less than 1 second, it means that the overall queue in the system is short. If Q exceeds 2 seconds, it can be considered that the queue has increased significantly, and the resource allocation or scheduling strategy should be adjusted again.

[0051] Based on the average queue wait time data obtained previously, nodes with significantly high or continuously increasing queue wait times are recorded. Resource quotas for these nodes are then reviewed during scheduling. First, the CPU priority, memory limit, and network bandwidth allocated to each node in the previous configuration are read. Each node is then individually determined to see if there has been frequent, short-term resource contention within the current monitoring period. If a node's queue wait time has repeatedly exceeded 1 second during the past monitoring period, this indicates a potential performance bottleneck. The system then checks whether this matches its memory utilization, CPU utilization, and network bandwidth metrics. If all three resources are experiencing high loads, consider reducing the number of queued requests for that node or shifting some tasks to other, less-loaded nodes. The adjustment results for all nodes are then consolidated and recorded to further confirm whether the planned load balancing range has been achieved. For example, in some production scenarios, CPU utilization should be controlled at around 60%, memory utilization at around 70%, and network bandwidth utilization should not be maintained above 80% for extended periods. Finally, the adjusted resource information for each node is aggregated to generate resource allocation data.

[0052] The steps for obtaining the priority determination result are: Based on resource allocation data, the type and urgency of tasks to be executed on each node are analyzed, task identifiers are sorted, and the estimated queue time of each task and the corresponding monitoring node number are recorded to generate a task queue plan. Based on the task queuing plan, priority determination is performed, and all tasks and associated monitoring nodes are sorted to form a priority determination result.

[0053] Specifically, based on the resource allocation data obtained previously, the types and urgency of the tasks to be executed on each node are compared. First, the node list is checked and the category information and identification of each task to be processed are read from the task scheduling record. Combined with the data accumulated in the historical operation of the equipment, a preliminary urgency level is assigned to each task. For example, the equipment safety monitoring task that needs to be completed within a limited time is set to high urgency, the routine monitoring task is set to medium urgency, and the auxiliary data collection task is set to low urgency. Then, the node load information generated previously is read to obtain the current CPU usage, memory usage, and network bandwidth usage of each node. If it is found that the resource usage of a node is higher than the empirical range, for example, the CPU usage is higher than 80% or the memory usage exceeds 70%, then the task execution at the node needs to be postponed or queued. These postponement or queuing time estimates are recorded, and the urgency of different tasks in the same node can be broken down into multiple levels according to the high, medium and low levels set previously. If a high-urgency monitoring task and a medium-urgency routine task are on the same node, the high-urgency task will be placed in the front waiting queue, and the estimated queuing time for each task will be calculated based on the node load condition. If the resource utilization performance of a node is continuously high and there are many tasks to be processed, the estimated queuing time for each task will be lengthened. When recording, the delay time can be calculated one by one and accumulated into the overall queuing statistics. The monitoring node number is then added to each task so that the node position can be quickly identified during subsequent jump scheduling. Finally, these data are organized into a comprehensive structure of task identification and urgency, estimated queuing time and monitoring node number to generate a task queuing plan.

[0054] Based on the task queuing plan obtained above, all tasks and their associated monitoring nodes are sorted as a whole. First, the urgency level of each task recorded in the queuing plan is read one by one. If the urgency of two tasks is the same, their estimated queuing times are further compared. If the current queue of the node where one of the tasks falls is longer or the resource occupancy rate is higher than the predetermined standard, a lower priority is assigned to the task. At the same time, its queuing time is compared with the overall scheduling strategy to see if it exceeds the field experience threshold, such as 5 seconds or 10 seconds. This experience threshold is based on the average acceptable queue time actually counted in multiple previous equipment linkage scheduling. The queue upper limit is set according to the law that the waiting time of tasks increases rapidly under high load conditions. If the urgency of the task exceeds the threshold, the weight in the global sorting is automatically increased. After traversing and sorting this comprehensive sorting result, the priority is marked one by one and the corresponding nodes are marked. When the sorting of all tasks is completed, whether there are special cases with high urgency but long queue time is reviewed. If so, adjustments are made and consistency is verified with the generated node load control data to avoid conflicts between resource allocation and task priority. Finally, the sorting lists of all tasks and monitoring nodes are confirmed and summarized to form the priority determination result.

Claims

1. The interface status monitoring system based on multi-sensor fusion is characterized by: The system comprises: The multi-sensor acquisition module, in which sensors form a monitoring node to acquire interface terminal signals, performs channel number matching and interference filtering on vibration amplitude, temperature value, and voltage measurement values to obtain multi-sensor interface parameters, performs consistency verification on the acquisition time mark and acquisition source mark, and records the positioning data of the interface terminal contact position. The vibration amplitude, temperature value, and voltage measurement values in the multi-sensor interface parameters are subjected to boundary verification to obtain valid sensing information; The remote diagnosis module performs threshold comparison and records abnormal differences based on the vibration amplitude, temperature value, and voltage measurement value in the effective sensing information to obtain abnormal monitoring indicators; based on the abnormal monitoring indicators, calculates the temperature value and vibration amplitude and marks the difference range, and combines the accumulated offset of the interface terminal contact position to obtain the contact fault characteristics; A load balancing module compares the CPU usage, memory occupancy, and network bandwidth of the monitoring nodes in real time based on the contact fault characteristics and marks the load level to obtain load distribution parameters; performs group scheduling on the load distribution parameters and evaluates the queue waiting time of the monitoring nodes to obtain resource allocation data; The response mechanism module sorts the task identifiers in the priority queue based on the resource allocation data and records the queuing time and the monitoring node number to obtain a priority determination result.

2. The interface status monitoring system based on multi-sensor fusion according to claim 1 is characterized in that: The steps for obtaining the multi-sensor interface parameters are as follows: Through the sensor acquisition interface terminal signal, the vibration amplitude, temperature value and voltage measurement value are matched with the channel number and the interference item is filtered to obtain the sensor data after preliminary filtering; Based on the preliminarily filtered sensor data, signal cleaning is performed to remove environmental noise and non-correlated frequency components to obtain cleaned sensor data; Based on the sensor data after cleaning, the correlation and consistency between the vibration amplitude, temperature value and voltage measurement value are analyzed to generate the multi-sensor interface parameters.

3. The interface status monitoring system based on multi-sensor fusion according to claim 1, characterized in that: The steps for obtaining the effective sensing information are: Perform consistency check on the acquisition time mark and the acquisition source mark, and record the positioning data of the interface terminal contact position to obtain the positioning data after consistency check; Based on the consistency-checked positioning data, performing boundary verification on the vibration amplitude, temperature value, and voltage measurement value in the multi-sensor interface parameters to generate boundary-verified sensing data; Based on the sensor data after the boundary verification, the data validity is analyzed and outliers are eliminated to obtain valid sensor information.

4. The interface status monitoring system based on multi-sensor fusion according to claim 1, characterized in that: The steps for obtaining the abnormal monitoring indicator are as follows: Based on the vibration amplitude, temperature value and voltage measurement value in the effective sensing information, the vibration amplitude, temperature value and voltage measurement value are compared with the preset threshold value to obtain preliminary abnormal difference data; According to the preliminary abnormal difference data, the abnormal score is calculated using the following formula: ； in, Representative Items of sensor data, is the corresponding preset threshold, is the weight, is the exponential parameter, is the number of data items, S is the anomaly score; Based on the abnormality score, data with a score higher than a preset threshold is judged to be abnormal, and an abnormality monitoring indicator is obtained.

5. The interface status monitoring system based on multi-sensor fusion according to claim 1, characterized in that: The steps for obtaining the contact fault characteristics are: Extracting the temperature value and the vibration amplitude from the abnormal monitoring index and combining them with the cumulative offset of the contact position of the interface terminal to obtain the prepared data; Based on the prepared data, the interaction value between temperature and vibration is calculated using the following formula: ； in, represents the temperature variable, represents the vibration amplitude, represents the cumulative offset of the terminal position, and F is the interaction value; Based on the interaction value, it is analyzed whether it exceeds the threshold of the industry standard, and if it exceeds, it is marked as a contact fault feature.

6. The interface status monitoring system based on multi-sensor fusion according to claim 1, characterized in that: The steps for obtaining the load distribution parameters are as follows: Extracting the CPU usage, memory occupancy, and network bandwidth data of the monitoring node and combining it with the contact fault characteristics to analyze the performance impact of the monitoring node; Score the performance impact of each monitoring node and calculate the performance impact score. The calculation formula is: ； in, Indicates CPU usage. Indicates memory usage. Indicates the network bandwidth, , , are the design upper limits of each resource, and P is the performance impact score; Based on the performance impact score, the load level of each node is marked, and resource allocation is adjusted according to the performance impact score to obtain a load allocation parameter.

7. The interface status monitoring system based on multi-sensor fusion according to claim 1, characterized in that: The steps for obtaining the resource allocation data are: Based on the load distribution parameters, the monitoring nodes are divided into multiple service groups, and each group is configured according to resource requirements and performance parameters; According to the configuration, calculate the average waiting time of the monitoring node. The calculation formula is: ； in, represents the resource utilization of the i-th node, is the adjustment coefficient, represents the task processing time of the i-th node, is the total number of nodes, Q is the average waiting time in queue; Based on the average queuing waiting time, resource allocation is adjusted to obtain resource allocation data.

8. The interface status monitoring system based on multi-sensor fusion according to claim 1, characterized in that: The steps for obtaining the priority determination result are: Based on the resource allocation data, the type and urgency of the tasks to be executed on each node are analyzed, the task identifiers are sorted, and the estimated queue time of each task and the corresponding monitoring node number are recorded to generate a task queue plan; Based on the task queuing plan, priority determination is performed, all tasks and associated monitoring nodes are sorted, and a priority determination result is formed.

Images