A bolt friction coefficient test data reliability verification method and system

Abstract

This invention relates to the field of data processing technology and discloses a method and system for verifying the reliability of bolt friction coefficient test data. The method includes: acquiring bolt friction coefficient test data and operating status information; calibrating to a unified time reference; identifying abnormal data characteristics; searching the operating status information for records of operating status switching; if such records exist, determining whether the data is interfered with, and if so, removing it from the dataset used for reliability verification; for bolt friction coefficient test data not determined to be interfered with, performing consistency checks, fluctuation assessments, and result classification to output a reliability verification conclusion. This invention establishes a precise temporal correlation between test data and interference events in the production environment, enabling proactive diagnosis and tracing of the causes of data anomalies, effectively distinguishing between data fluctuations caused by actual process problems and noise data caused by external interference.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for verifying the reliability of bolt friction coefficient test data. Background Technology

[0002] In the field of fastener production and inspection, bolt friction coefficient testing is a crucial step in evaluating product quality. However, in busy production sites, the stability of the testing environment is difficult to guarantee. Testing equipment is often located near logistics channels or large power equipment. Ground vibrations caused by forklifts, power grid fluctuations caused by the start and stop of large fans, and airflow disturbances can all cause instantaneous or continuous interference to precise friction coefficient measurements. Although traditional inspection procedures record test data, they typically rely solely on inspectors' on-site observations and post-event recollections for these external interference events, lacking precise and synchronous recording. This leads to a core technical problem: when abnormal fluctuations occur in test data, it is impossible to objectively and accurately determine whether the root cause is inherent quality variations in the bolts themselves or external environmental interference. This confusion makes data reliability verification subjective and unreliable, potentially leading to the misclassification of qualified batches as unqualified, or the incorrect inclusion of disturbed abnormal data in statistical analysis, ultimately affecting the accuracy and impartiality of the overall batch product quality evaluation. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention discloses a method and system for verifying the reliability of bolt friction coefficient test data. The aim is to solve the technical problem in existing technologies where the inability to distinguish the true source of fluctuations in test data leads to inaccurate reliability verification conclusions.

[0004] The technical solution of the present invention is as follows: In a first aspect, this invention discloses a method for verifying the reliability of bolt friction coefficient test data, the method comprising: Obtain the test data of the bolt friction coefficient to be verified and the operating status information of the external interference source equipment in the production site, and add a first time identifier to the bolt friction coefficient test data and a second time identifier to the operating status information; The first and second time markers are calibrated to a unified time base. Real-time monitoring of bolt friction coefficient test data to identify abnormal data characteristics whose numerical fluctuations meet preset abnormal conditions; Based on a unified time benchmark, the system searches for whether there are records of switching operation status of external interference source devices within a preset time window corresponding to the abnormal data characteristics. If there is a record of operation status switching, it is determined whether the bolt friction coefficient test data corresponding to the abnormal data characteristics is interfered data. If it is interfered data, the interfered data is removed from the dataset used for reliability verification. For bolt friction coefficient test data that is not determined to be interfered data, consistency check, fluctuation judgment and result classification judgment are performed to output the reliability verification conclusion.

[0005] By establishing a time-synchronized correlation between experimental data and the operating status of external interference sources, this solution can fundamentally solve the problem of misjudgment of reliability caused by the inability to distinguish the source of data fluctuations, achieve accurate identification and isolation of interference data, and significantly improve the objectivity and accuracy of verification conclusions.

[0006] Furthermore, external interference sources include logistics and transportation equipment and power equipment; The steps for obtaining the operating status information of external interference source equipment in the production site include: The real-time location of logistics transportation equipment is obtained through the logistics scheduling system and used as the operating status information of the logistics transportation equipment; The start and stop commands of the power equipment are obtained through the power equipment control system and used as the operating status information of the power equipment.

[0007] Furthermore, prior to the step of calibrating the first time identifier and the second time identifier to a unified time base, the method further includes: Obtain the historical communication logs of the information system to which the external interference source device belongs, and extract the system response delay from the detection of physical action to the generation of operation status information; Determine the time compensation offset for the operating status information based on the system response delay; The second time identifier is corrected using the time compensation offset to obtain the corrected second time identifier; The specific steps for calibrating the first time identifier and the second time identifier to a unified time base are as follows: calibrate the first time identifier and the corrected second time identifier to a unified time base.

[0008] Furthermore, the steps for identifying anomalous data characteristics whose numerical fluctuations meet preset anomaly conditions include: Calculate the standard deviation of the friction coefficient test data of the bolt within the sliding data window. The sliding data window is a set of friction coefficient data points of fixed size arranged continuously according to the acquisition time sequence. When the standard deviation continuously exceeds the preset first fluctuation range threshold and reaches the preset duration, the actual duration of the standard deviation exceeding the threshold is marked as the first abnormal fluctuation period and used as an abnormal data feature. Calculate the difference between the current friction coefficient data point and the previous friction coefficient data point; When the absolute value of the difference exceeds the preset second jump threshold, the time of the current friction coefficient data point is marked as the second abnormal fluctuation point, which is used as an abnormal data feature.

[0009] Furthermore, external interference sources include logistics and transportation equipment and power equipment; The steps for retrieving operational status information to determine whether there are records of operational status switching of external interference source devices within a preset time window corresponding to abnormal data characteristics include: Based on the type of abnormal data characteristics, a corresponding preset time window is determined: if the abnormal data characteristic is a first abnormal fluctuation period, then a first preset time window is determined with the start and end time of the period as the reference and extending forward and backward by a preset duration, respectively, as the corresponding preset time window; if the abnormal data characteristic is a second abnormal fluctuation point, then a second preset time window is determined with the instantaneous moment of the point as the center and extending forward and backward by a preset short duration, respectively, as the corresponding preset time window. When the external interference source device is a logistics transportation device, search the operation status information to see if there is a record of the real-time location of the logistics transportation device entering or leaving the preset range centered on the test area within the first preset time window or the second preset time window. If there is, it is determined that there is an operation status switching record. When the external interference source device is a power device, the system searches the operating status information to see if there is a record of operating status change caused by the power device executing start / stop commands within the first or second preset time window. If such a record exists, it is determined that there is an operating status switching record.

[0010] Furthermore, if there are records of operational status switching, the steps to determine whether the bolt friction coefficient test data corresponding to the abnormal data characteristics are interfered with include: When the abnormal data feature is the first abnormal fluctuation period, the original sampled signal corresponding to the first abnormal fluctuation period is subjected to time-frequency transformation processing to extract the energy distribution feature of the abnormal data feature in the preset feature frequency band. Retrieve a preset interference fingerprint model that matches the device type of the external interference source device. The preset interference fingerprint model records the frequency characteristics of the physical vibration or electromagnetic interference generated by the corresponding device when the operating state is switched. Calculate the waveform similarity between the energy distribution characteristics and the preset interference fingerprint model; When the waveform similarity exceeds the preset similarity threshold, the test data of the bolt friction coefficient corresponding to the abnormal data features is determined to be interfered data. When the abnormal data feature is the second abnormal fluctuation point, if there is a running state switching record within the second preset time window, the bolt friction coefficient test data corresponding to the second abnormal fluctuation point is directly determined to be the disturbed data.

[0011] Furthermore, after determining that the bolt friction coefficient test data corresponding to the abnormal data characteristics are disturbed data, the method also includes: When the operation status switching action of the external interference source device is detected to be completed, the recovery time increment is calculated based on the type of external interference source device and the interference intensity before the action ended. Add transition state markers to the bolt friction coefficient test data collected within the recovery time increment.

[0012] By setting a "recovery time increment" after the interference ends and marking the transitional data, this scheme takes into account the residual effects of the interference, avoids misjudging test data when the system is not yet fully stable as reliable data, and further ensures the purity and reliability of the final dataset.

[0013] Furthermore, the method also includes: Based on the duration since the addition of the transition state marker, confidence weight information is assigned to the test data of the bolt friction coefficient with the transition state marker, and the confidence weight information gradually increases with time. The test data on the friction coefficient of bolts with transition state markings were screened using the following steps, incorporating trust weight information: When the trust weight information is lower than the preset weight threshold, the corresponding bolt friction coefficient test data is determined to be affected by residual interference and moved to the area to be reviewed. When the trust weight information is not lower than the preset weight threshold, the corresponding bolt friction coefficient test data is compared with the reference information under historical stable conditions. When the deviation of the consistency comparison is less than the preset deviation threshold, the corresponding bolt friction coefficient test data is accepted as reliable data and included in the dataset; otherwise, the corresponding bolt friction coefficient test data is moved to the area to be reviewed.

[0014] Furthermore, the steps to remove the corrupted data from the dataset used for reliability verification include: Remove the disturbed data from the main dataset used for batch reliability assessment and store it in a separate disturbed dataset; For data in the interference dataset, a manual review process can be initiated, or a corresponding interference source description label can be added to the interference data in the test report.

[0015] Secondly, the present invention also discloses a reliability verification system for bolt friction coefficient test data, used to perform the method described in any of the foregoing claims, comprising: The data acquisition module is used to acquire the test data of the bolt friction coefficient to be verified and the operating status information of the external interference source equipment in the production site, and to add a first time identifier to the bolt friction coefficient test data and a second time identifier to the operating status information. The time calibration module is used to calibrate the first time identifier and the second time identifier to a unified time reference; The feature recognition module is used to monitor the bolt friction coefficient test data in real time and identify abnormal data features whose numerical fluctuations meet preset abnormal conditions. The associated retrieval module is used to retrieve, based on a unified time benchmark, whether there are records of the switching of the operating status of external interference source devices within a preset time window corresponding to the abnormal data characteristics of the operating status information. The reliability verification module is used to determine whether the bolt friction coefficient test data corresponding to the abnormal data characteristics is interfered data if there is an operation state switching record. If it is interfered data, the interfered data is removed from the dataset used for reliability verification. For bolt friction coefficient test data that is not determined to be interfered data, consistency checks, fluctuation judgments, and result classification judgments are performed to output the reliability verification conclusion.

[0016] By solidifying the method into a system that includes functional modules such as data acquisition, time calibration, feature recognition, correlation retrieval, and reliability verification, this solution provides a materialized blueprint for implementing the above method, enabling the entire verification process to run automatically and unmanned, and transforming complex data analysis logic into stable and reliable industrial applications.

[0017] In summary, this invention provides a method and system for verifying the reliability of bolt friction coefficient test data. The method acquires bolt friction coefficient test data and operational status information of external interference sources, and performs correlation analysis between the two under a unified time reference. This solves the technical problem in existing technologies where it is impossible to accurately distinguish whether data fluctuations are caused by product inherent differences or external environmental interference. This solution can automatically identify abnormal fluctuations in test data and accurately trace back to whether there were any interference events such as the movement of logistics equipment or the start-up and shutdown of power equipment within the time window of the anomaly. If a temporal correspondence exists, the data segment is further determined to be interfered data and removed from the valid dataset. This approach replaces subjective and unreliable manual judgment with objective and automated data correlation, fundamentally improving the accuracy and credibility of data reliability verification. It ensures that quality evaluation based on test data can truly reflect the product's performance, avoids misjudgments caused by environmental factors, and provides a more solid data foundation for production quality control. Attached Figure Description

[0018] Figure 1This is a flowchart illustrating a method for verifying the reliability of bolt friction coefficient test data, as provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of a reliability verification system for bolt friction coefficient test data provided in an embodiment of the present invention.

[0020] Labeling Explanation: 210, Data Acquisition Module; 220, Time Calibration Module; 230, Feature Recognition Module; 240, Association Retrieval Module; 250, Reliability Verification Module. Detailed Implementation

[0021] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of this invention. The components of this invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] In a typical high-strength bolt production workshop, quality inspection is a crucial step. Friction coefficient testing benches are usually located in the inspection area for precise performance testing of randomly selected bolt samples. However, this inspection area is not an isolated laboratory environment; it is often adjacent to busy production logistics channels. When fully loaded Automated Guided Vehicles (AGVs) pass near the testing bench, the resulting ground vibrations are transmitted through the foundation to the testing equipment. While such vibrations may be negligible to humans, they can cause significant interference to precision sensors measuring minute force and torque changes. Similarly, when a high-powered ventilation system or air compressor suddenly starts, the instantaneous voltage drop and electromagnetic radiation can interfere with sensitive electronic components in the testing equipment, leading to abnormal spikes or spikes in the collected data. In traditional workflows, even if inspectors observe these abnormal fluctuations in the data curves, it is difficult to accurately establish a definitive link between each fluctuation and a specific event occurring in the workshop afterward. This leads to a long-standing dilemma: it's impossible to distinguish whether data fluctuations stem from inherent performance variations in the bolt samples themselves, or merely from artifacts caused by external environmental interference. This uncertainty directly impacts the assessment of data reliability and may result in incorrect evaluations of the quality of the entire batch of products.

[0024] Firstly, please see Figure 1 This invention provides a method for verifying the reliability of bolt friction coefficient test data, the method comprising: S1. Obtain the test data of the bolt friction coefficient to be verified and the operating status information of the external interference source equipment in the production site, and add a first time identifier to the bolt friction coefficient test data and a second time identifier to the operating status information. S2. Calibrate the first time marker and the second time marker to a unified time base; S3. Monitor the test data of bolt friction coefficient in real time and identify abnormal data characteristics that meet the preset abnormal conditions. S4. Based on a unified time benchmark, search for whether there are records of switching operation status of external interference source devices within the preset time window corresponding to the abnormal data characteristics of the operation status information. S5. If there is a record of operation status switching, determine whether the bolt friction coefficient test data corresponding to the abnormal data characteristics is interfered data. If it is interfered data, remove the interfered data from the dataset used for reliability verification. For bolt friction coefficient test data that is not determined to be interfered data, perform consistency check, fluctuation judgment and result classification judgment to output the reliability verification conclusion.

[0025] Specifically, external interference sources refer to various devices in the production site whose operating state switching can affect the bolt friction coefficient test process through physical means (vibration, shock, temperature change, airflow disturbance) or electromagnetic means (electromagnetic radiation, power grid fluctuations). These devices are not part of the test system themselves, but their activities overlap with the test environment.

[0026] A unified time standard refers to a high-precision time reference standard that is consistently followed throughout the entire verification system. Since the bolt friction coefficient test data acquisition system and the information system recording the operating status of external interference source equipment may be two independent systems, their respective system clocks may deviate. Establishing a unified time standard, such as synchronizing the clocks of all relevant systems via a Network Time Protocol (NTP) server, is fundamental to ensuring the accuracy of subsequent event correlation analysis. All time signatures will be converted or calibrated to this common standard.

[0027] Abnormal data characteristics refer to specific fluctuation patterns in bolt friction coefficient test data streams that differ from the normal stable state. This does not refer to the high or low value of the data itself, but rather to the behavioral characteristics of the data over time, such as drastic jumps in a short period of time, or continuous fluctuations exceeding the normal range over a certain period of time.

[0028] A preset time window is a time interval set around a identified anomalous data feature. This interval is set to focus on searching for external interference events that may have caused the anomaly within this time range.

[0029] Interference-affected data refers to bolt friction coefficient test data whose abnormal characteristics have been confirmed to be correlated with the switching records of external interference source equipment operating states occurring within the corresponding time window. This data is considered to fail to accurately reflect the frictional performance of the bolt itself, and is largely contaminated by external factors.

[0030] The overall process of this method will be described in detail below.

[0031] The core of this method lies in establishing a temporal correlation between experimental data and potential interference events. First, information needs to be acquired from two sources simultaneously. On one hand, real-time experimental data streams are obtained from the bolt friction coefficient testing equipment. This data is typically collected by torque and angle sensors, and a series of timestamped friction coefficient values ​​are calculated. For example, the data format could be a data table containing columns such as: timestamp (e.g., 2023-10-26 10:30:15.123), batch number, sample number, real-time torque, real-time angle, and calculated friction coefficient. The timestamp here serves as the first time identifier. On the other hand, it is necessary to access various information systems within the factory to obtain operational status information of equipment defined as external interference sources. For example, records of equipment start-up, shutdown, position movement, and mode switching can be obtained from the factory's Manufacturing Execution System (MES) or equipment control system (such as SCADA). These records also bear timestamps, serving as the second time identifier.

[0032] After acquiring the two data streams, their respective time signatures are calibrated to a unified time base. In a basic implementation, it can be assumed that all systems are synchronized with a central time server via an NTP service. In this case, the calibration process may simply be a format conversion and time zone unification operation to ensure that, for example, "10:30:15.123" represents exactly the same time in both data sources.

[0033] Next, the bolt friction coefficient test data stream, calibrated for time, is monitored in real time to identify abnormal data characteristics. In a specific embodiment, this invention uses a simple, efficient, and engineering-adaptable dual-threshold rule to identify instantaneous abrupt changes and long-term persistent fluctuations, respectively. The specific determination method is as follows: This scheme first identifies instantaneous abnormal fluctuation points: a single sampling period can be set to T1 (e.g., 10ms), and the instantaneous change in friction coefficient between adjacent sampling nodes is calculated in real time. If the change in friction coefficient exceeds a preset jump threshold (e.g., 0.05) within a single sampling period, the data point at that moment is immediately marked as an instantaneous abnormal fluctuation point. Simultaneously, to identify longer-lasting, non-instantaneous steady-state disturbances, this scheme adds short-term volatility monitoring logic: a sliding statistical window with a duration of T2 (e.g., 1 second) is maintained in real time, continuously collecting all friction coefficient data points collected within the most recent T2, and the standard deviation of the data within this window is calculated in real time to characterize data stability. If the standard deviation calculated by the sliding window continuously exceeds a preset stability threshold (e.g., 0.02) and the duration reaches a preset duration T3 (e.g., 3 seconds), the data within this continuous period is determined not to meet the experimental steady-state requirements, and the complete T3 time period is marked as an abnormal fluctuation period.

[0034] This type of persistent abnormal fluctuation, unlike instantaneous random jumps, is usually caused by continuous external environmental interference. Typical scenarios include: stable airflow disturbances caused by the continuous operation of ventilation equipment near the test station, continuous micro-vibrations caused by material transport vehicles parking for extended periods in workshop aisles, weak electromagnetic interference from the continuous operation of surrounding large electromechanical equipment, and small ground vibrations. Such interference does not cause instantaneous large jumps in data, but it leads to long-term data fluctuations and deterioration in stability. By using a dual identification mechanism combining instantaneous rate of change threshold judgment with continuous judgment of sliding window standard deviation, this solution can simultaneously cover two types of abnormal scenarios: sudden short-term interference and steady-state continuous interference, accurately distinguishing between bolt performance fluctuations and abnormal data characteristics caused by external equipment and the environment.

[0035] Once abnormal data characteristics are identified, such as a momentary abnormal jump at time point T_abnormal, or a period of continuous abnormal fluctuation, a preset time window is generated based on the abnormal moment or the center of the abnormal period. For example, a window extending 5 seconds before and after can be set, i.e., [T_abnormal-5s, T_abnormal+5s]. Then, the operating status information records of all external interference source devices within this time window under the same time reference are retrieved to determine whether any device status switching timestamps fall within the current retrieval interval.

[0036] If a record of the operational status change is retrieved within this time window—for example, a record showing "AGV_03 entered the inspection area at time T_event"—and T_event is within the interval [T_abnormal-5s, T_abnormal+5s], then a preliminary correlation between the two is established. At this point, data near T_abnormal is identified as disturbed data. This disturbed data will be removed from the main dataset, for example, moved to a separate database table or marked as invalid, and will no longer participate in subsequent routine statistical analyses of the reliability of this batch of bolts, such as calculating the mean and standard deviation.

[0037] Data that is not identified as disturbed will proceed to the next stage of the standard verification process, including consistency checks and fluctuation assessments, before a reliability conclusion is finally output. The specific process is as follows: First, a data consistency check is performed. Based on the mechanical characteristics of bolt tightening and the test standard thresholds, the system verifies the temporal matching and physical logic consistency of parameters such as torque, axial force, rotation angle, and friction coefficient in the valid data sequence. It removes a small number of abnormal data that are not related to external equipment interference but have inherent acquisition biases, ensuring that all data to be checked conforms to the inherent mechanical change law of bolt tightening.

[0038] Secondly, the data fluctuation situation is assessed. By using a preset sliding statistical window to calculate the mean, standard deviation, dispersion, and other statistical characteristic parameters of the effective data set, the stationarity, dispersion level, and repeatability of the entire experimental data are evaluated. It is determined whether the data fluctuation is within the normal fluctuation range allowed by industry standards and enterprise processes, and whether slight normal data fluctuations are distinguished from abnormal fluctuations.

[0039] Finally, the results are categorized and judged, and reliability verification conclusions are output. Combining the data consistency verification results and data fluctuation characteristics, the reliability of the test data is graded: if the data is logically consistent and the fluctuation indicators are all within the preset acceptable threshold range, the test data for the friction coefficient of this batch of bolts is deemed reliable and valid; if the overall data consistency is acceptable, but local fluctuations slightly exceed the tolerance, the data is deemed basically reliable and local deviation characteristics are marked, which can be used as a reference for process optimization; if there are multiple logical deviations, severe overall dispersion, and no external interference attribution, the test data reliability is deemed unacceptable, and it is recommended to conduct a new sampling test. Based on the above grading judgment results, the final standardized test data reliability verification conclusion with data characteristics, deviation descriptions, and reliability levels is output, providing accurate data support for bolt batch quality judgment and assembly process optimization.

[0040] Through the above process, this method can separate false anomaly data caused by sudden changes in the external environment from the data that truly reflects product quality, avoiding the interference of these contaminated data on the final quality assessment conclusion, thereby significantly improving the objectivity and accuracy of the verification results.

[0041] In a specific application scenario, the external interference source device can be specific. For example, it can include logistics and transportation equipment and power equipment.

[0042] The steps for obtaining the operating status information of external interference source equipment in the production site can specifically include: The real-time location of logistics transportation equipment is obtained through the logistics scheduling system and used as the operating status information of the logistics transportation equipment; The start and stop commands of the power equipment are obtained through the power equipment control system and used as the operating status information of the power equipment.

[0043] The technical solution of this invention is further refined in this regard. In the aforementioned factory environment, a typical example of logistics and transportation equipment is the Automated Guided Vehicle (AGV). The factory's logistics scheduling system, typically a functional module of the Manufacturing Execution System (MES), tracks the location of each AGV in real time. This tracking can be achieved through various technologies, such as site positioning using magnetic strips or QR codes laid on the ground, or triangulation via Wi-Fi or 5G networks. When an AGV enters or leaves a pre-defined virtual geofence area, for example, a circular area with a radius of 10 meters centered on a test bench is defined as an inspection zone, the logistics scheduling system generates an event log, which may be formatted as: [Timestamp: 2023-10-26 10:30:14.500, Device ID: AGV_03, Event: Enter, Area: Inspection Zone]. This method obtains this location change information as the operating status information of the logistics and transportation equipment through an application programming interface (API) or by directly reading database logs.

[0044] Similarly, a typical example of power equipment is a large air compressor that powers pneumatic tools throughout the workshop. This type of equipment is typically controlled by a programmable logic controller (PLC) and monitored by a Supervisory Control and Data Acquisition (SCADA) system. When the operator presses the start button, or the system automatically starts the compressor based on a preset pressure threshold, the SCADA system records an operation command or status change log, possibly in the format: [Timestamp: 2023-10-26 11:15:02.300, Device ID: AirCompressor_01, Status: Started]. This method subscribes to or polls these start / stop command information from the SCADA system via the OPC UA protocol or other industrial communication protocols, using this information as the operating status information of the power equipment. In this way, abstract external interference sources are concretized into clearly monitorable objects, and the acquisition of operating status information is implemented through specific information systems and data interfaces, greatly enhancing the feasibility and automation of the solution.

[0045] However, simply aligning the timestamps of different systems to a common benchmark may not be accurate enough. This is because there is an unavoidable delay—the system response delay—between the occurrence of a physical event, sensor detection, and recording of the event by the information system. To address this issue, this invention proposes a further optimization scheme.

[0046] Prior to the step of calibrating the first time identifier and the second time identifier to a unified time base, the method further includes: Obtain the historical communication logs of the information system to which the external interference source device belongs, and extract the system response delay from the detection of physical action to the generation of operation status information; Determine the time compensation offset for the operating status information based on the system response delay; The second time identifier is corrected using the time compensation offset to obtain the corrected second time identifier; The specific steps for calibrating the first time identifier and the second time identifier to a unified time base are as follows: calibrate the first time identifier and the corrected second time identifier to a unified time base.

[0047] Specifically, taking the AGV entering the inspection area as an example, the physical fact is that the AGV's front end crosses the area boundary line at time T_physical. However, the position sensor on the AGV may need a few milliseconds to confirm its position, and then send the data to the server via a wireless network. The logistics scheduling system software on the server processes this information and writes it to the database, ultimately recording a timestamp of T_log. This time difference between T_physical and T_log is the system response latency. Without compensation, subsequent correlation analysis will be based on an incorrect time point.

[0048] To determine this latency, calibration can be performed during system deployment or periodic maintenance. For example, a high-precision infrared photoelectric switch can be installed at the entrance of the inspection area. When the AGV body cuts off the beam, an independent timer, strictly synchronized with the NTP server, records the physical trigger moment T_physical. Simultaneously, the system monitors the logistics scheduling system's database, waiting for the corresponding AGV's entry into the inspection area log entry to appear, and records its timestamp T_log. By repeating this process multiple times, the average or statistical distribution of the latency can be calculated. For example, after 100 tests, the average latency was found to be 280 milliseconds. Therefore, the time compensation offset for the logistics scheduling system is determined to be -280 milliseconds.

[0049] In actual operation, when a record with a timestamp of "10:30:14.500" is obtained from the logistics scheduling system, this offset is automatically applied to calculate the corrected timestamp of the event as "10:30:14.220". This corrected timestamp is considered to be closer to the actual time of the physical event. Similarly, for the start-up of an air compressor, its specific time compensation offset can be determined by measuring the time difference between the electrical signal from the PLC issuing the start command and the SCADA system recording the state change. By using this method of personalized time delay compensation for information systems from different interference sources, the time accuracy of event correlation is greatly improved, laying a solid foundation for subsequent accurate judgment.

[0050] This invention also provides a more specific and robust method for accurately identifying anomalous data features from continuous experimental data, rather than simply relying on threshold judgment.

[0051] The steps for identifying anomalous data features whose numerical fluctuations meet preset anomaly conditions include: Calculate the standard deviation of the friction coefficient test data of the bolt within the sliding data window. The sliding data window is a set of friction coefficient data points of fixed size arranged continuously according to the acquisition time sequence. When the standard deviation continuously exceeds the preset first fluctuation range threshold and reaches the preset duration, the actual duration of the standard deviation exceeding the threshold is marked as the first abnormal fluctuation period and used as an abnormal data feature. Calculate the difference between the current friction coefficient data point and the previous friction coefficient data point; When the absolute value of the difference exceeds the preset second jump threshold, the time of the current friction coefficient data point is marked as the second abnormal fluctuation point, which is used as an abnormal data feature.

[0052] Two complementary anomaly detection logics are proposed here, targeting two different types of interference.

[0053] The first method is based on the standard deviation of a sliding data window, used to identify persistent, vibration-like disturbances. A sliding data window can be understood as a first-in, first-out (FIFO) data queue. For example, the window size is set to 100 data points. Whenever a new friction coefficient value is collected, it is placed at the end of the window, while the oldest data point at the front is discarded. The system recalculates the standard deviation of these 100 data points after each window update. During normal, stable testing, the fluctuation of the friction coefficient value is small, so the standard deviation will remain at a low level, for example, below 0.003. When an AGV passes nearby, the persistent micro-vibrations on the ground it causes are transmitted to the test bench, causing the friction coefficient reading to fluctuate wildly for a short period. At this time, the dispersion of the 100 data points within the sliding window will increase significantly, and the calculated standard deviation may jump to 0.01. A first fluctuation range threshold can be preset, such as 0.008. When the calculated standard deviation exceeds 0.008, timing begins. If this threshold-exceeding state persists for a preset period of time, such as more than 0.5 seconds, the data during this period is determined to be subject to continuous interference, and the start and end times of this standard deviation exceeding the threshold are marked as a first abnormal fluctuation period. This method is very effective in capturing continuous interference events such as passing vehicles or equipment resonance.

[0054] The second method is based on the difference between adjacent data points, used to identify transient, pulse-like interference. This method is very simple and direct: calculate the absolute value of the difference between the currently acquired friction coefficient value and the previously acquired value. Under normal circumstances, this difference is very small. However, if a strong electromagnetic pulse is generated due to a nearby welding machine arcing or a high-power motor starting, interfering with the sensor's signal amplification circuit, it may cause one or more isolated, abnormally high spikes or glitches in the data stream. In this case, the difference between the current data point and the previous data point will become very large instantaneously. For example, a second jump threshold can be preset, such as 0.03. When the calculated absolute value of the difference exceeds 0.03, the time of the current data point is immediately marked as a second abnormal fluctuation point. This method is very sensitive to capturing sudden interference events such as power grid surges and electrostatic discharges.

[0055] By combining these two identification logics, the system can more comprehensively capture the characteristics of different types of abnormal data, providing more reliable input for subsequent correlation analysis.

[0056] After identifying the characteristics of abnormal data, how to efficiently and accurately retrieve them from the operational status information of the interference source is also a problem that needs to be refined.

[0057] External interference sources include logistics and transportation equipment and power equipment; The steps for retrieving operational status information to determine whether there are records of operational status switching of external interference source devices within a preset time window corresponding to abnormal data characteristics include: Based on the type of abnormal data characteristics, a corresponding preset time window is determined: if the abnormal data characteristic is a first abnormal fluctuation period, then a first preset time window is determined with the start and end time of the period as the reference and extending forward and backward by a preset duration, respectively, as the corresponding preset time window; if the abnormal data characteristic is a second abnormal fluctuation point, then a second preset time window is determined with the instantaneous moment of the point as the center and extending forward and backward by a preset short duration, respectively, as the corresponding preset time window. When the external interference source device is a logistics transportation device, search the operation status information to see if there is a record of the real-time location of the logistics transportation device entering or leaving the preset range centered on the test area within the first preset time window or the second preset time window. If there is, it is determined that there is an operation status switching record. When the external interference source device is a power device, the system searches the operating status information to see if there is a record of operating status change caused by the power device executing start / stop commands within the first or second preset time window. If such a record exists, it is determined that there is an operating status switching record.

[0058] The core idea of ​​this step is to match the retrieval strategy with the type of abnormal features, thereby achieving intelligent and refined association.

[0059] When a first abnormal fluctuation period is identified, such as from 10:30:15.000 to 10:30:18.500, this indicates a persistent anomaly. The cause of this anomaly, such as an AGV entering the area, may occur before the anomaly is detected, and its effects, such as residual vibrations on the ground after the AGV leaves, may not completely disappear until the anomaly ends. Therefore, a wider initial preset time window is generated. For example, based on the start and end times of this period, it is extended forward and backward by 3 seconds each. Thus, the final search window becomes [10:30:12.000, 10:30:21.500]. This longer time window increases the probability of capturing associated persistent events (such as vehicle passage).

[0060] Conversely, when a second anomalous fluctuation point is identified, such as a spike at 11:15:02.550, this indicates a transient anomaly. The cause of this anomaly, such as the electromagnetic pulse of a motor starting, often coincides highly with the anomaly point in time. Therefore, a very narrow second preset time window is generated. For example, it extends only 0.5 seconds forward and backward from the anomaly point. The final search window becomes [11:15:02.050, 11:15:03.050]. This short window can very accurately pinpoint sudden events (such as equipment start / stop commands) that are closely coupled in time to the spike pulse, while avoiding the incorrect association of other unrelated events.

[0061] After determining a suitable retrieval time window, the search begins in the corresponding interference source information logs. If the anomaly type is persistent, i.e., the first abnormal fluctuation period, the focus is on searching the status logs of logistics and transportation equipment to find records of AGVs entering or leaving the test area within the first preset time window. If the anomaly type is transient, i.e., the second abnormal fluctuation point, the focus is on searching the status logs of power equipment to find records of start-up or stop commands for large motors or compressors within the second preset time window. Once a matching type of operating status switching record is found within the corresponding time window, a strong temporal correlation between the abnormal data and the external interference event is confirmed. This approach of dynamically adjusting the retrieval strategy based on anomaly characteristic types not only improves the efficiency of correlation analysis but also significantly enhances its accuracy.

[0062] After confirming the temporal correlation between the experimental data anomalies and external interference events, a more in-depth question arises: is this temporal overlap a definitive causal relationship or merely a coincidence? To address this issue and avoid misjudging coincidental events as interference, this invention further provides a method for in-depth verification based on physical characteristics.

[0063] If there is a record of operation status switching, the steps to determine whether the bolt friction coefficient test data corresponding to the abnormal data characteristics is disturbed data include: When the abnormal data feature is the first abnormal fluctuation period, the original sampled signal corresponding to the first abnormal fluctuation period is subjected to time-frequency transformation processing to extract the energy distribution feature of the abnormal data feature in the preset feature frequency band. Retrieve a preset interference fingerprint model that matches the device type of the external interference source device. The preset interference fingerprint model records the frequency characteristics of the physical vibration or electromagnetic interference generated by the corresponding device when the operating state is switched. Calculate the waveform similarity between the energy distribution characteristics and the preset interference fingerprint model; When the waveform similarity exceeds the preset similarity threshold, the test data of the bolt friction coefficient corresponding to the abnormal data features is determined to be interfered data. When the abnormal data feature is the second abnormal fluctuation point, if there is a running state switching record within the second preset time window, the bolt friction coefficient test data corresponding to the second abnormal fluctuation point is directly determined to be the disturbed data.

[0064] This step introduces the concept of interfering fingerprints, expanding the judgment criteria from a single time dimension to a matching dimension of physical features.

[0065] For the initial period of sustained abnormal fluctuations, such as vibrations caused by the passage of an AGV, it's not enough to simply match the time. The system retrieves the raw high-frequency sampled signal collected by a torque sensor or an accelerometer mounted on the test bench during that abnormal period. Then, time-frequency transformation processing is performed on this signal. One specific implementation uses Short-Time Fourier Transform (STFT) to decompose the signal into a series of time-varying spectrograms, thus clearly showing which frequencies the signal energy is primarily distributed at each moment the abnormality occurs.

[0066] Simultaneously, interference fingerprint models of various external interference sources are pre-stored. This model is established through calibration tests of the interference sources in a controlled environment. For example, to establish the vibration fingerprint of an AGV, engineers repeatedly pass a specific model of AGV near a test bench at different speeds, recording the vibration signals generated using high-precision sensors. Spectral analysis of these signals reveals that the motor and transmission system of this AGV primarily generates a significant energy peak between 25 Hz and 40 Hz during operation. This characteristic spectrum, including frequency and corresponding energy intensity, is then solidified as the interference fingerprint model of that AGV model and stored in the database. Similarly, the electromagnetic interference generated when a large air compressor starts up may exhibit a fingerprint showing a transient energy enhancement at the 50 Hz power frequency and its harmonic frequencies.

[0067] After extracting the energy distribution characteristics of the current abnormal data period, the system retrieves the associated device within the time window, such as the interference fingerprint model of AGV_03, from the database. Then, it quantifies the degree of matching by calculating the waveform similarity between the two spectrograms, for example, using a cosine similarity algorithm or a cross-correlation function. If the calculated similarity score exceeds a preset similarity threshold, such as 0.85, the system makes a high-confidence judgment: the abnormal fluctuations in this experimental data are indeed caused by the vibration of this AGV, and therefore it is determined to be interfered data.

[0068] For transient second anomalous fluctuations, such as a sharp pulse, if it happens to occur within an extremely narrow time window, and within this window a clear event capable of generating pulse interference is recorded, such as the closing of a large contactor, then this precise temporal correspondence itself constitutes very strong evidence. In this case, the probability of coincidence is extremely low. Therefore, to improve processing efficiency, the system is configured to directly determine the data point as interfered data, eliminating the need for complex fingerprint comparison.

[0069] After determining that the data is interfered with, the present invention also considers the aftereffects of the interference event, i.e. the system recovery process.

[0070] After determining that the bolt friction coefficient test data corresponding to the abnormal data characteristics are disturbed data, the method further includes: When the operation status switching action of the external interference source device is detected to be completed, the recovery time increment is calculated based on the type of external interference source device and the interference intensity before the action ended. Add transition state markers to the bolt friction coefficient test data collected within the recovery time increment.

[0071] The significance of this step lies in acknowledging the inertia of physical systems. For example, when an AGV has left the inspection area, the logistics scheduling system records the departure event, but the minute vibrations on the ground and test bench do not stop instantly; instead, there is a gradual decay process. Similarly, after experiencing a large load shock, the power grid also needs time to recover to a fully stable state. Ignoring this recovery process may lead to data that is still unstable during the recovery period being mistakenly considered reliable data.

[0072] Therefore, the system starts a timer after detecting the end of an interference event, such as when the AGV leaves the area or the compressor completes startup and enters stable operation. The duration of this timer, i.e., the recovery time increment, is dynamically calculated. The calculation is based on two main factors: the type of interference source and the intensity of the interference. A basic recovery time schedule can be preset in the system; for example, the basic recovery time for the AGV is 2 seconds, and the basic recovery time for the air compressor startup is 3 seconds. Furthermore, the system will adjust according to the intensity of the interference. The interference intensity can be quantified by the peak value of the standard deviation of the friction coefficient within the abnormal period: extract all friction coefficient sampling data from the abnormal interval, calculate the sample standard deviation, and select the maximum standard deviation within the interval as the quantified value of the interference intensity; pre-configure the baseline recovery time, intensity threshold, and unit compensation coefficient corresponding to various interference sources; when the peak standard deviation exceeds the intensity threshold, an additional compensation time is calculated based on the deviation magnitude; the baseline recovery time is superimposed with the compensation time to obtain the recovery time increment; this increment is used to define the transition period after the interference ends, and transition state markers are uniformly added to the data collected within this period. For example, if the peak standard deviation exceeds a high threshold, indicating severe interference, the system will add an additional amount, such as 1.5 seconds, to the base recovery time. The final calculated recovery time increment, such as 3.5 seconds, is used to define a transition state period. All bolt friction coefficient test data collected during this period will be automatically marked with a transition state tag by the system.

[0073] Instead of simply discarding all the data that has been marked with transition state tags, the system employs a more refined filtering strategy to maximize the use of the data while ensuring reliability.

[0074] The method also includes: Based on the duration since the addition of the transition state marker, confidence weight information is assigned to the test data of the bolt friction coefficient with the transition state marker, and the confidence weight information gradually increases with time. The test data on the friction coefficient of bolts with transition state markings were screened using the following steps, incorporating trust weight information: When the trust weight information is lower than the preset weight threshold, the corresponding bolt friction coefficient test data is determined to be affected by residual interference and moved to the area to be reviewed. When the trust weight information is not lower than the preset weight threshold, the corresponding bolt friction coefficient test data is compared with the reference information under historical stable conditions. When the deviation of the consistency comparison is less than the preset deviation threshold, the corresponding bolt friction coefficient test data is accepted as reliable data and included in the dataset; otherwise, the corresponding bolt friction coefficient test data is moved to the area to be reviewed.

[0075] This step introduces a dynamic trust weight concept to the transitional data. The design logic for this weight is: the longer the time remaining since the end of the interference, the more stable the system recovery, and the higher the data reliability. In a specific embodiment, the trust weight can be designed as a function that linearly increases from 0 to 1. Assuming the total recovery time increment is T_total, and the transition time elapsed after the interference ends is t, the real-time trust weight W is calculated as: W = t / T_total. When t = 0, i.e., immediately after the interference ends, W = 0, and the data reliability is the lowest; when t = T_total, i.e., at the end of the recovery period, W = 1, the experimental system has essentially fully recovered to a steady state, and the data reliability is the highest. For example, assuming the calculated recovery time increment is 3 seconds, then the data collected at 0.3 seconds after the interference ends has a trust weight of 0.1; the data collected at 1.5 seconds has a trust weight of 0.5; and at the end of 3 seconds, the weight reaches 1.

[0076] The system sets a preset weight threshold, such as 0.4. For all data points with a trust weight below 0.4, the system considers them to be still significantly affected by residual interference and have insufficient reliability. Therefore, they are moved to a separate area for review by engineers for subsequent manual analysis, but are not directly used for batch reliability judgment.

[0077] For data points with a trust weight of 0.4 or higher, the system considers them potentially reliable and performs further verification. The verification method involves comparing their consistency with reference information from historical steady-state conditions. This reference information can be the average friction coefficient obtained from multiple tests of the bolt model under ideal conditions (without any interference). If the deviation of the current transitional data point from the reference average is less than a preset deviation threshold, for example, no more than 5%, the system determines that the data point has overcome the influence of residual interference and is truly reliable. It is then included in the master dataset for reliability verification; otherwise, it is moved to a separate area awaiting review for subsequent manual analysis by engineers. Through this weighted screening mechanism, this method achieves refined management of recovery period data, avoiding a one-size-fits-all approach that wastes resources.

[0078] Finally, for those data that are ultimately determined to be interfered with, the present invention clarifies the processing and handling procedures.

[0079] The steps to remove corrupted data from the dataset used for reliability verification include: Remove the disturbed data from the main dataset used for batch reliability assessment and store it in a separate disturbed dataset; For data in the interference dataset, a manual review process can be initiated, or a corresponding interference source description label can be added to the interference data in the test report.

[0080] The stripping operation is not simply deletion. Specifically, the system will completely remove these data points identified as being disturbed, along with all their associated metadata, including the original values, timestamps, identified anomaly types, associated interference source device IDs, and similarity scores from interference fingerprint comparisons, from the main dataset and archive them in a dedicated interference dataset.

[0081] This interference dataset has significant follow-up value. On one hand, the system can be configured to automatically trigger a manual review process. For example, when a new record is added to the interference dataset, the system will automatically send an email or a notification on the company's internal instant messaging platform, indicating that an interference event has occurred and including a link to the relevant data. Engineers can click the link to view detailed information and analyze the causes and impacts of the interference.

[0082] On the other hand, this information can be used to generate more transparent and insightful test reports. In the final output test report for this batch of bolts, in addition to the usual statistical results such as mean and standard deviation, the system can automatically add a note in the footnotes section, such as: "During this test, 5 sets of data from 10:30:15 to 10:30:18 were excluded from the statistical analysis due to vibration interference caused by the passing of AGV_03 (fingerprint similarity 0.91)." Such a label not only explains the basis of the data processing to the report reader, giving them more confidence in the conclusions, but also, over time, these interference records can become important data support for improving production site management, optimizing equipment layout, or upgrading the testing environment. For example, if the report shows that a certain test bench is frequently disturbed, the manager can consider installing a vibration isolation platform or adjusting the AGV's travel path.

[0083] Secondly, see Figure 2 The present invention also provides a reliability verification system for bolt friction coefficient test data, for performing any of the foregoing methods, comprising: The data acquisition module 210 is used to acquire the test data of the bolt friction coefficient to be verified and the operating status information of the external interference source equipment in the production site, and to add a first time identifier to the bolt friction coefficient test data and a second time identifier to the operating status information. Time calibration module 220 is used to calibrate the first time identifier and the second time identifier to a unified time reference; The feature recognition module 230 is used to monitor the bolt friction coefficient test data in real time and identify abnormal data features whose numerical fluctuations meet preset abnormal conditions. The associated retrieval module 240 is used to retrieve, based on a unified time reference, whether there are records of the switching of the operating status of external interference source devices within a preset time window corresponding to the abnormal data characteristics of the operating status information. The reliability verification module 250 is used to determine whether the bolt friction coefficient test data corresponding to the abnormal data characteristics is interfered data if there is an operation state switching record. If it is interfered data, the interfered data is removed from the dataset used for reliability verification. For bolt friction coefficient test data that is not determined to be interfered data, consistency check, fluctuation judgment and result classification judgment are performed to output the reliability verification conclusion.

[0084] The system can be deployed on an industrial control computer or server, and the functions of its various modules can be implemented by software programs.

[0085] The data acquisition module 210 connects to the outside world through various interfaces. For example, it can receive raw data packets from the bolt friction coefficient testing machine controller in real time via a serial port or Ethernet interface, parse information such as torque and rotation angle, and calculate the friction coefficient value. Simultaneously, through another network interface, it retrieves AGV location information and air compressor start / stop records from the factory's logistics scheduling system and SCADA system via API calls, database queries, or message queue subscriptions. This module assigns a high-precision timestamp to each piece of acquired data.

[0086] The time calibration module 220 has a built-in or connected NTP client that periodically synchronizes with the factory's internal time server to ensure it has an accurate system time. When the data acquisition module 210 receives two data streams with their own time identifiers, the time calibration module 220 executes the aforementioned time compensation and calibration logic. It queries a preset response delay compensation table, corrects the second time identifiers from different information systems, and then converts all time identifiers to a standard format such as Coordinated Universal Time (UTC) to ensure that all events are compared on the same timeline.

[0087] The feature recognition module 230 continuously analyzes the bolt friction coefficient test data stream processed by the time calibration module 220. This module internally implements two anomaly detection algorithms based on the sliding window standard deviation and the absolute value of the difference between adjacent points. Module parameters, such as window size, fluctuation range threshold, duration threshold, and jump threshold, can be set and adjusted through a configuration file. Once an abnormal data feature that meets the conditions is detected, it immediately packages the feature type, occurrence time, and other information and passes it to the association retrieval module 240.

[0088] After receiving an abnormal event from the feature recognition module 230, the association retrieval module 240 dynamically generates an optimal retrieval time window based on the type of abnormality (persistent or transient). Then, it uses this time window as a query condition to efficiently search the external interference source device operating status information database, which has also been processed by the time calibration module 220. Its task is to quickly identify whether there are any predefined device state switching records within this time window that might have caused the abnormality.

[0089] The reliability verification module 250 initiates a deep verification process when the correlation retrieval module 240 reports finding a temporally matching interference event. It invokes time-frequency analysis and fingerprint comparison algorithms to confirm the physical characteristics of persistent anomalies. After making a final determination that the data is interfered with, it performs a data stripping operation, moving the contaminated data into the interference dataset. Simultaneously, it handles the transitional data after the interference ends, filtering and accepting it using a trust weight model. For all clean data not determined to be interfered with, this module invokes conventional quality statistical analysis procedures to perform consistency checks and fluctuation assessments, ultimately generating and outputting a reliability verification conclusion report containing detailed explanations.

[0090] Through the collaborative work of these functional modules, the system can automate and intelligently complete the entire reliability verification process of bolt friction coefficient test data, freeing inspectors from tedious and subjective data identification work, and providing accuracy and reliability far exceeding that of manual judgment.

[0091] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for verifying the reliability of bolt friction coefficient test data, characterized in that, The method includes: Obtain the test data of the bolt friction coefficient to be verified and the operating status information of the external interference source equipment in the production site, and add a first time identifier to the bolt friction coefficient test data and a second time identifier to the operating status information; The first time identifier and the second time identifier are calibrated to a unified time base; The test data of the bolt friction coefficient are monitored in real time to identify abnormal data characteristics whose numerical fluctuations meet preset abnormal conditions; Based on the unified time reference, the system retrieves whether the operating status information contains a record of the switching of the operating status of the external interference source device within the preset time window corresponding to the abnormal data characteristics. If the operation state switching record exists, it is determined whether the bolt friction coefficient test data corresponding to the abnormal data feature is interfered data. If it is interfered data, the interfered data is removed from the dataset used for reliability verification. For bolt friction coefficient test data that is not determined to be interfered data, consistency check, fluctuation judgment and result classification judgment are performed to output the reliability verification conclusion.

2. The method for verifying the reliability of bolt friction coefficient test data according to claim 1, characterized in that, The external interference source equipment includes logistics and transportation equipment and power equipment; The steps for obtaining the operating status information of external interference source equipment in the production site include: The real-time location of the logistics transportation equipment is obtained through the logistics scheduling system and used as the operating status information of the logistics transportation equipment. The start / stop commands of the power equipment are obtained through the power equipment control system and used as the operating status information of the power equipment.

3. The method for verifying the reliability of bolt friction coefficient test data according to claim 1, characterized in that, Before the step of calibrating the first time identifier and the second time identifier to a unified time base, the method further includes: Obtain the historical communication logs of the information system to which the external interference source device belongs, and extract the system response delay of the information system from detecting the physical action to generating the operating status information; Determine the time compensation offset for the operating status information based on the system response delay; The second time identifier is corrected using the time compensation offset to obtain the corrected second time identifier; The step of calibrating the first time identifier and the second time identifier to a unified time base specifically involves calibrating the first time identifier and the corrected second time identifier to a unified time base.

4. The method for verifying the reliability of bolt friction coefficient test data according to claim 1, characterized in that, The step of identifying abnormal data features whose numerical fluctuations meet preset abnormal conditions includes: Calculate the standard deviation of the friction coefficient test data of the bolt within the sliding data window, wherein the sliding data window is a set of friction coefficient data points of fixed size and arranged continuously according to the acquisition time sequence; When the standard deviation continuously exceeds a preset first fluctuation range threshold and reaches a preset duration, the actual duration of the standard deviation exceeding the threshold is marked as the first abnormal fluctuation period and used as the abnormal data feature. Calculate the difference between the current friction coefficient data point and the previous friction coefficient data point; When the absolute value of the difference exceeds the preset second jump threshold, the time of the current friction coefficient data point is marked as the second abnormal fluctuation point, which is used as the abnormal data feature.

5. The method for verifying the reliability of bolt friction coefficient test data according to claim 4, characterized in that, The external interference source equipment includes logistics and transportation equipment and power equipment; The step of retrieving whether the operating status information contains a record of the external interference source device switching within a preset time window corresponding to the abnormal data feature includes: Based on the type of the abnormal data feature, a corresponding preset time window is determined: if the abnormal data feature is the first abnormal fluctuation period, then a first preset time window extending forward and backward by a preset duration based on the start and end time of the period is determined as the corresponding preset time window; if the abnormal data feature is the second abnormal fluctuation point, then a second preset time window extending forward and backward by a preset short duration based on the instantaneous moment of the point is determined as the corresponding preset time window. When the external interference source device is the logistics transportation equipment, retrieve from the operation status information whether there is a record of the real-time location of the logistics transportation equipment entering or leaving a preset range centered on the test area within the first preset time window or the second preset time window. If there is, it is determined that there is an operation status switching record. When the external interference source device is the power device, the system searches the operating status information to see if there is a record of operating status change caused by the power device executing a start / stop command within the first preset time window or the second preset time window. If there is, it is determined that the operating status switching record exists.

6. The method for verifying the reliability of bolt friction coefficient test data according to claim 5, characterized in that, The step of determining whether the bolt friction coefficient test data corresponding to the abnormal data characteristics is interfered data if the operation state switching record exists includes: When the abnormal data feature is a first abnormal fluctuation period, the original sampled signal corresponding to the first abnormal fluctuation period is subjected to time-frequency transformation processing to extract the energy distribution feature of the abnormal data feature in a preset feature frequency band. Retrieve a preset interference fingerprint model that matches the device type of the external interference source device, wherein the preset interference fingerprint model records the frequency characteristics of physical vibration or electromagnetic interference generated by the corresponding device when switching operating states. Calculate the waveform similarity between the energy distribution characteristics and the preset interference fingerprint model; When the waveform similarity exceeds a preset similarity threshold, the bolt friction coefficient test data corresponding to the abnormal data feature is determined to be interfered data. When the abnormal data feature is the second abnormal fluctuation point, if the running state switching record exists within the second preset time window, the bolt friction coefficient test data corresponding to the second abnormal fluctuation point is directly determined to be interfered data.

7. The method for verifying the reliability of bolt friction coefficient test data according to claim 6, characterized in that, After determining that the bolt friction coefficient test data corresponding to the abnormal data characteristics is interfered data, the method further includes: When the operation state switching action of the external interference source device is detected to be over, the recovery time increment is calculated based on the type of the external interference source device and the interference intensity before the action was over. Add a transition state marker to the bolt friction coefficient test data collected within the recovery time increment.

8. The method for verifying the reliability of bolt friction coefficient test data according to claim 7, characterized in that, The method also includes: Based on the time elapsed since the addition of the transition state marker, confidence weight information is assigned to the bolt friction coefficient test data with the transition state marker, and the confidence weight information gradually increases with time. Based on the aforementioned trust weight information, the bolt friction coefficient test data with the transition state marker are screened through the following steps: When the trust weight information is lower than the preset weight threshold, the corresponding bolt friction coefficient test data is determined to be affected by residual interference and moved to the area to be reviewed. When the trust weight information is not lower than the preset weight threshold, the corresponding bolt friction coefficient test data is compared with the reference information under historical stable conditions. When the deviation of the consistency comparison is less than the preset deviation threshold, the corresponding bolt friction coefficient test data is accepted as reliable data and included in the dataset; otherwise, the corresponding bolt friction coefficient test data is moved to the area to be reviewed.

9. The method for verifying the reliability of bolt friction coefficient test data according to claim 1, characterized in that, The step of stripping the interfered data from the dataset used for reliability verification includes: The interfered data is removed from the main dataset used for batch reliability assessment and stored in a separate interference dataset; The data in the interference dataset will be subject to a manual review process, or a corresponding interference source description label will be added to the interfered data in the test report.

10. A reliability verification system for bolt friction coefficient test data, used to execute the method described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to acquire the test data of the bolt friction coefficient to be verified and the operating status information of the external interference source equipment in the production site, and to add a first time identifier to the bolt friction coefficient test data and a second time identifier to the operating status information. The time calibration module is used to calibrate the first time identifier and the second time identifier to a unified time reference; The feature recognition module is used to monitor the bolt friction coefficient test data in real time and identify abnormal data features whose numerical fluctuations meet preset abnormal conditions. The associated retrieval module is used to retrieve, based on the unified time benchmark, whether the operating status information exists within a preset time window corresponding to the abnormal data characteristics, and whether there is an operating status switching record of the external interference source device. The reliability verification module is used to determine whether the bolt friction coefficient test data corresponding to the abnormal data characteristics is interfered data if the operation state switching record exists. If it is interfered data, the interfered data is removed from the dataset used for reliability verification. For bolt friction coefficient test data that is not determined to be interfered data, consistency check, fluctuation judgment and result classification judgment are performed to output the reliability verification conclusion.

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