Chain production data monitoring and optimization system based on industrial Internet of Things

Abstract

The present invention relates to the field of intelligent manufacturing technology, specifically to a chain production data monitoring and optimization system based on the industrial Internet of Things. The system includes: a stress load monitoring module, a shock wave propagation analysis module, a vibration feature extraction module, a load optimization control module, and a fracture risk warning module. In the present invention, stress data collection is combined with coordinate indexing and gradient calculation to locate the transient stress overload area of ​​the chain plate stamping and the pin shaft heat treatment, and the local extreme value points are dynamically screened to improve the accuracy of abnormality detection. The shock wave propagation speed and attenuation rate are used to quantify the energy accumulation coefficient, and the chain pin shaft and the chain plate vibration abnormality source are associated. The vibration signal is analyzed by short-time Fourier transform to analyze the riveting and assembly characteristic frequencies, and a high-resolution force mode characteristic value is generated to support parameter optimization. The operation data is simulated to construct a stress accumulation curve. The test parameters are dynamically adjusted in combination with the short-term force limit to optimize the load uniformity. The impact energy accumulation value matches the safety threshold to calibrate the load distribution and reduce the risk of fracture.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to a chain production data monitoring and optimization system based on the Industrial Internet of Things. Background Art

[0002] The field of intelligent manufacturing encompasses a digital, networked, and intelligent manufacturing technology system. It aims to enhance the intelligence of manufacturing systems through the deep integration of information technology and manufacturing technologies. Core elements of this technology include intelligent perception, intelligent control, intelligent decision-making, and intelligent collaboration, encompassing the entire process from design, production, management, and service. Intelligent manufacturing leverages technologies such as the Industrial Internet of Things (IIoT), big data, and artificial intelligence to enable real-time monitoring of production equipment, fault prediction, quality optimization, and adaptive control of production processes. Its systematic approach encompasses smart factories, smart production lines, intelligent equipment, and intelligent operations and maintenance. By establishing an efficient, flexible, and green manufacturing model, it aims to improve production efficiency, reduce costs, and optimize resource allocation.

[0003] The chain production data monitoring and optimization system uses industrial IoT technology to collect, analyze, and optimize key data involved in the chain production process to improve production accuracy and stability. This system primarily addresses technical aspects of chain production, including data collection, anomaly detection, parameter optimization, and predictive analysis. Through a sensor network, it acquires data on the chain production line's operating status, machining accuracy, and equipment load. It optimizes production parameters using data modeling and analysis techniques, and conducts trend analysis based on predictive algorithms to adjust machining parameters and optimize production processes. The system uses edge computing technology to preprocess data and reduce data transmission latency. It also integrates cloud computing platforms for data storage and in-depth analysis, forming a data-driven decision-making support mechanism to ensure the efficiency and stability of the chain production process.

[0004] Existing technologies rely on fixed measuring points and static threshold detection, which cannot capture the transient nonlinear stress changes in the stamping and heat treatment processes, and there are blind spots and lags in the identification of overload areas. Traditional vibration analysis uses a static spectrum, which does not combine the propagation speed and attenuation law of shock waves. The ability to quantify the energy accumulation trend is insufficient, and the risk of misjudgment is high. Parameter optimization is based on offline modeling of historical data, lacks the ability to dynamically analyze characteristic frequencies, and is difficult to match real-time working condition changes. The existing early warning mechanism uses a fixed safety threshold, which is not associated with the real-time load distribution status, and the fracture risk assessment deviates significantly from the actual force. For example, when the equipment load suddenly changes, the traditional system may mistakenly trigger a shutdown or miss the detection of local stress mutations due to the failure to quantify the energy accumulation trend. Data transmission relies on centralized processing in the cloud, and insufficient edge preprocessing capabilities lead to the loss of key time series data, limiting the timeliness of optimization control. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a chain production data monitoring and optimization system based on industrial Internet of Things.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: a chain production data monitoring and optimization system based on the Industrial Internet of Things includes:

[0007] The stress load monitoring module places stress sensors in the chain plate stamping area and the pin shaft heat treatment area to collect transient data, determine the measurement point location based on the coordinate index, calculate the stress change rate, use numerical differentiation to find the gradient, screen the local extreme points, and obtain the stress overload area distribution value;

[0008] The shock wave propagation analysis module obtains vibration data of key parts of the chain pin and chain plate based on the stress overload area distribution value, calculates the propagation speed and attenuation rate, compares and analyzes the waveform, and generates a shock wave energy accumulation coefficient;

[0009] The vibration feature extraction module obtains the vibration signal in production based on the shock wave energy accumulation coefficient, distributes the energy using short-time Fourier transform, screens the characteristic frequencies of the chain impact riveting process and the chain tensioning assembly process, and generates the chain force mode characteristic value;

[0010] The load optimization control module constructs a stress accumulation curve based on the chain stress mode characteristic value, calls the chain simulation operation data, calculates the short-term stress limit of the chain, adjusts the chain test parameters, and generates an optimized chain load distribution value;

[0011] The fracture risk warning module obtains shock wave feedback data based on the optimized chain load distribution value, calculates the cumulative impact energy value, determines the load safety threshold, compares the stress state within the threshold range, adjusts the load distribution, and generates a chain fracture risk warning value.

[0012] As a further solution of the present invention, the stress overload area distribution value includes the measuring point position, stress change rate, and local extreme point; the shock wave energy accumulation coefficient is specifically the propagation speed, attenuation rate, and waveform comparison analysis result; the chain force mode characteristic value includes the impact riveting process characteristic frequency, the tensioning assembly process characteristic frequency, and energy distribution; the optimized chain load distribution value specifically refers to the stress accumulation curve, short-term stress limit, and test parameter adjustment plan.

[0013] As a further solution of the present invention, the stress load monitoring module includes:

[0014] The stress data acquisition submodule arranges stress sensors in the chain plate stamping area and the pin shaft heat treatment area to obtain transient data, record stress values ​​at multiple measuring points, and determine the location of the measuring points based on the coordinate index to generate stress distribution data at the measuring points.

[0015] The stress gradient calculation submodule calculates the stress difference between adjacent measuring points based on the stress distribution data of the measuring points, calculates the spatial displacement according to the coordinates of the measuring points, and calculates the stress change rate by numerical differentiation to generate the stress change rate data of the measuring points;

[0016] The stress overload area screening submodule calls the stress change rate data of the measuring point, screens the local extreme value points, and extracts the stress gradient change in the overload area using the formula:

[0017] ;

[0018] The stress distribution value of the overload area is obtained by calculation to obtain the stress overload area distribution value;

[0019] in, Represents the stress difference between adjacent measuring points, Represents the spatial distance between adjacent measuring points, represents the stress gradient weight parameter, Represents the stress value of the measuring point, represents the mean stress value of all measuring points, Represents the stress overload area distribution value, represents the number of stress gradient calculation points, Represents the total number of measurement points, i represents a measurement point, j represents another measurement point related to i in a certain calculation step, and id represents the unique identification of the measurement point.

[0020] As a further solution of the present invention, the shock wave propagation analysis module includes:

[0021] Based on the stress overload area distribution value, the stress data analysis submodule monitors the key parts of the chain pin and chain plate, obtains stress response data, records the data acquisition time and corresponding stress value, and simultaneously arranges the stress data in time series, calculates the time interval and stress change rate between differentiated measurement points, filters abnormal data and performs numerical interpolation processing to obtain a stress time series data set;

[0022] The propagation velocity calculation submodule calls the stress time series data set, calculates the shock wave arrival time difference between the differentiated measuring points, calculates the local propagation velocity based on the measuring point spacing, and performs weighted summation on the propagation velocities of all measuring points to obtain the average propagation velocity using the formula:

[0023] ;

[0024] Obtain the shock wave propagation velocity at multiple measuring points by calculation, and calculate the global propagation velocity to obtain the average shock wave propagation velocity;

[0025] in, represents the average speed of shock wave propagation, Representative The distance between measuring points, Representative The arrival time difference of the shock wave at each measuring point is Represents the standard deviation of the shock wave arrival time difference at all measuring points, Representative The energy loss coefficient of each measuring point, Represents the total number of measurement points, Represents the time difference between measuring points;

[0026] The shock energy analysis submodule uses the average shock wave propagation velocity and, combined with the stress changes in the stress time series data set, calculates the energy loss per unit propagation distance. It also calculates the total shock wave energy loss based on the stress attenuation rate of multiple measuring points and accumulates the energy losses of all measuring points to obtain the shock wave energy accumulation coefficient.

[0027] As a further solution of the present invention, the vibration feature extraction module includes:

[0028] The energy accumulation calculation submodule calculates the shock wave energy distribution of the vibration signal under the differentiated time window based on the shock wave energy accumulation coefficient, extracts the vibration amplitude under multiple windows, and calculates the cumulative energy per unit time. After normalization, it obtains the energy peak distribution under the differentiated process and generates shock wave energy peak data;

[0029] The characteristic frequency screening submodule calls the shock wave energy peak data, distributes the energy using short-time Fourier transform, screens the key frequency bands corresponding to the energy peak, calculates the contribution rate of multiple frequency bands, and removes the frequency components whose contribution rate is lower than the set threshold using the formula:

[0030] ;

[0031] Calculate the characteristic frequency screening value, obtain the characteristic frequency range of the chain impact riveting process and the chain tensioning assembly process, and generate the process characteristic frequency data;

[0032] in, represents the characteristic frequency screening value, Representative The energy value of the frequency band, Representative The center frequency of the frequency band, Representative The vibration signal amplitude of a time window, Representative The length of the time window, Represents the total number of frequency bands, Represents the number of contribution calculation frequency bands, Represents the total number of time windows;

[0033] The force mode extraction submodule calls the process characteristic frequency data, screens the characteristic frequencies of the chain impact riveting process and the chain tensioning assembly process, calculates the spectrum response characteristic values ​​under multiple processes, performs normalization processing, matches the vibration mode category, and establishes the chain force mode characteristic value.

[0034] As a further solution of the present invention, the load optimization control module includes:

[0035] The force parameter analysis submodule calls the chain simulation operation data based on the chain force mode characteristic value, obtains the force data under the differentiated operation state, extracts the force change trend of multiple nodes of the chain, calculates the force mean, fluctuation range and force change rate under the differentiated operation conditions of the chain, and obtains the chain force characteristic parameters;

[0036] The chain short-term stress limit calculation submodule calls the chain stress characteristic parameters and analyzes the stress state of the chain in different time periods using the formula:

[0037] ;

[0038] Calculate the short-term stress limit of the chain, adjust the short-term stress threshold, and generate the short-term stress limit value of the chain;

[0039] in, Represents the current short-term stress limit of the chain, Representatives in the The actual force value within a time period, Represents the average force value, Represents the standard deviation of the force values, Represents the time point of each time period, represents the optimal time point, represents the standard deviation of time, Represents the total number of time periods, Represents the number of short-term force calculation time periods;

[0040] The chain load distribution optimization submodule calls the short-term stress limit value of the chain, adjusts the chain test parameters, compares the matching degree between the differentiated chain stress threshold and the short-term limit stress value, selects the optimal test parameters, and generates the optimized chain load distribution value.

[0041] As a further embodiment of the present invention, the system further comprises:

[0042] The fracture risk warning module obtains shock wave feedback data based on the optimized chain load distribution value, calculates the cumulative impact energy value, determines the load safety threshold, compares the stress state within the threshold range, adjusts the load distribution, and generates a chain fracture risk warning value;

[0043] The chain breakage risk warning value includes the impact energy accumulation value, the load safety threshold, and the stress state comparison result.

[0044] As a further solution of the present invention, the fracture risk warning module includes:

[0045] The load distribution calculation submodule, based on the optimized chain load distribution value, calls the force conditions of the multi-node chain links, calculates the dynamic load distribution on the chain links, analyzes the load transfer relationship between the differentiated chain links, and adjusts the local load deviation to obtain the optimized chain link load distribution coefficient;

[0046] The impact energy accumulation submodule calls the optimized chain link load distribution coefficient, obtains shock wave feedback data, calculates the distribution of impact energy on the chain link, and analyzes the transmission mode of impact energy on the chain link based on the changing trend of the accumulated impact energy, using the formula:

[0047] ;

[0048] Calculate and obtain the cumulative value of impact energy;

[0049] in, Represents the cumulative value of impact energy, Representative chain link The load at Representative chain link The speed at Representative chain link The quality of Represents the total number of impact energy calculation nodes;

[0050] The fracture risk assessment submodule calls the cumulative impact energy value to determine whether it exceeds the set load safety threshold, compares the stress state within the threshold range, adjusts the chain link load distribution, calculates the overall chain fracture risk level, and obtains the chain fracture risk warning value.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are:

[0052] In the present invention, stress data collection is combined with coordinate indexing and gradient calculation to locate the transient stress overload areas of chain plate stamping and pin heat treatment, and local extreme points are dynamically screened to improve the accuracy of anomaly detection. The shock wave propagation velocity and attenuation rate quantify the energy accumulation coefficient, and accurately associate the abnormal vibration sources of the chain pin and chain plate. The vibration signal is analyzed by short-time Fourier transform to analyze the characteristic frequencies of the riveting and assembly processes, generate high-resolution force mode characteristic values, and support dynamic optimization of parameters. A stress accumulation curve is constructed based on simulated operation data, and the test parameters are dynamically adjusted in combination with the short-term force limit to optimize the load distribution uniformity. The impact energy accumulation value is matched with the safety threshold in real time, the load distribution state is calibrated, and the risk of sudden fracture is reduced. Edge computing preprocessing synchronizes cloud analysis to shorten the closed-loop control response time and improve decision-making efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a system flow chart of the present invention;

[0054] Figure 2 This is a flow chart of the stress load monitoring module of the present invention;

[0055] Figure 3 This is a flow chart of the shock wave propagation analysis module of the present invention;

[0056] Figure 4 This is a flow chart of the vibration feature extraction module of the present invention;

[0057] Figure 5 This is a flow chart of the load optimization control module of the present invention;

[0058] Figure 6 This is a flow chart of the fracture risk warning module of the present invention. DETAILED DESCRIPTION

[0059] 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.

[0060] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0061] Example 1

[0062] See also Figure 1 , the chain production data monitoring and optimization system based on the Industrial Internet of Things includes:

[0063] The stress load monitoring module places stress sensors in the chain plate stamping area and the pin shaft heat treatment area to collect transient data, determine the measurement point location based on the coordinate index, calculate the stress change rate, use numerical differentiation to find the gradient, screen the local extreme points, and obtain the stress overload area distribution value;

[0064] The shock wave propagation analysis module obtains vibration data of key parts of the chain pin and chain plate based on the stress overload area distribution value, calculates the propagation speed and attenuation rate, compares and analyzes the waveform, and generates the shock wave energy accumulation coefficient;

[0065] The vibration feature extraction module obtains the vibration signal in production based on the shock wave energy accumulation coefficient, distributes the energy using short-time Fourier transform, screens the characteristic frequencies of the chain impact riveting process and the chain tensioning assembly process, and generates the characteristic value of the chain force mode;

[0066] The load optimization control module uses the chain simulation operation data to construct a stress accumulation curve based on the chain force mode characteristic value, calculates the chain's short-term force limit, adjusts the chain test parameters, and generates the optimized chain load distribution value;

[0067] The fracture risk warning module obtains shock wave feedback data based on the optimized chain load distribution value, calculates the cumulative impact energy value, determines the load safety threshold, compares the stress state within the threshold range, adjusts the load distribution, and generates a chain fracture risk warning value.

[0068] The stress overload area distribution value includes the measuring point location, stress change rate, and local extreme point. The shock wave energy accumulation coefficient is specifically the propagation speed, attenuation rate, and waveform comparison analysis results. The chain force mode characteristic value includes the characteristic frequency of the impact riveting process, the characteristic frequency of the tensioning assembly process, and the energy distribution. The optimized chain load distribution value specifically refers to the stress accumulation curve, short-term stress limit, and test parameter adjustment plan. The chain breakage risk warning value includes the impact energy accumulation value, load safety threshold, and stress state comparison result.

[0069] See also Figure 2 , stress load monitoring module includes:

[0070] The stress data acquisition submodule arranges stress sensors in the chain plate stamping area and the pin shaft heat treatment area to obtain transient data, record stress values ​​at multiple measuring points, and determine the location of the measuring points based on the coordinate index to generate stress distribution data at the measuring points.

[0071] Stress sensors with appropriate ranges should be selected. In the stamping area, sensors should be installed in locations prone to stress concentration, such as near the action point of the stamping die, at the stress-bearing corners of the chain plate, and in the direct transmission path of the stamping load. In the pin heat treatment area, sensors should be placed in key contact areas after the pin expands due to heat and in areas with large stress gradient changes. Data acquisition uses a high-frequency sampling mode to ensure that data on transient stress changes during impact loads and heat treatment are obtained. After the stress value of each measuring point is recorded, its specific position on the chain plate or pin is calibrated using a coordinate indexing system to generate the stress distribution data of the measuring point. In specific implementation, if the stamping load is applied within 50ms, the sampling frequency can be set to 10kHz to ensure that a complete stress change curve is obtained. For example, during the stamping process, the stress value recorded at the measuring point on the edge of the chain plate may be 120MPa, while the local stress measuring point of the pin after heat treatment may show 80MPa. Through the spatial positioning of these data, stress distribution data for the entire stamping and heat treatment process is generated.

[0072] The stress gradient calculation submodule calculates the stress difference between adjacent measuring points based on the stress distribution data of the measuring points, calculates the spatial displacement according to the coordinates of the measuring points, and calculates the stress change rate using numerical differentiation to generate the stress change rate data of the measuring points;

[0073] If the stress values ​​recorded at adjacent measuring points i and i+1 are 110 MPa and 125 MPa respectively, the corresponding stress difference is 15 MPa. Then the spatial displacement is calculated based on the coordinates of the measuring points. For example, if the distance between the two measuring points Δx = 5 mm, the stress gradient at that position is calculated as MPa / mm. Furthermore, the stress change rate is calculated using the numerical differentiation method. Assuming that the stress value of the measuring point changes with time is recorded as σ(t), for a certain measuring point t1 and t2 (interval Δt = 0.01s), the stress is 90MPa and 95MPa respectively, then the change rate is calculated as MPa / s, and finally the stress change rate data of the measuring point is obtained. Through these calculations, the stress change rate distribution map of the entire area is constructed. The results show that the stress change rate of the measuring point is relatively fast, and there may be a large stress concentration phenomenon in the area. Further analysis can be used to screen potential overload areas.

[0074] The stress overload area screening submodule calls the stress change rate data of the measuring point, screens the local extreme points, and extracts the stress gradient change in the overload area using the formula:

[0075] ;

[0076] The stress distribution value of the overload area is obtained by calculation to obtain the stress overload area distribution value;

[0077] in, Represents the stress difference between adjacent measuring points, Represents the spatial distance between adjacent measuring points, represents the stress gradient weight parameter, Represents the stress value of the measuring point, represents the mean stress value of all measuring points, Represents the stress overload area distribution value, represents the number of stress gradient calculation points, Represents the total number of measurement points, i represents a measurement point, j represents another measurement point related to i in a certain calculation step, and id represents the unique identification of the measurement point.

[0078] For example, if the stress change rate data measured in the stamping area of ​​a chain plate is {500, 450, 300, 700, 250} MPa / s, the local extreme point may be 700 MPa / s. When calculating the stress gradient change in the overload area, the formula is used:

[0079] ;

[0080] Assuming that the stress difference between adjacent measuring points is {15, 20, 18, 25} MPa, and the spacing between adjacent measuring points is Δx = {5, 4, 6, 5} mm, the calculated stress gradients are {3, 5, 3, 5} MPa / mm, respectively. Assuming the corresponding weights w = {1, 1.2, 1, 1.1}, the weighted sum is calculated as follows:

[0081] ;

[0082] Then calculate the stress mean of all measuring points. Assuming that the stress values ​​of the measuring points are {100, 120, 110, 130} MPa, the mean is:

[0083] ;

[0084] Calculate the mean square error:

[0085] ;

[0086] Finally, the stress overload area distribution value is obtained:

[0087] ;

[0088] The results indicate that the stress overload distribution value in this area is high, exceeding the set overload identification threshold of 35. Therefore, this area can be judged as a stress overload area, which means that there may be a risk of stress concentration or material fatigue in this area. Subsequent structural analysis or experimental verification can be combined to further confirm whether this area requires structural optimization or material strengthening.

[0089] Table 1 Stress measurement point data

[0090]

[0091] As shown in Table 1, the data of the measuring points record the stress values ​​at different positions and calculate the stress gradient between adjacent measuring points for subsequent stress gradient calculation and overload area screening.

[0092] See also Figure 3 , the shock wave propagation analysis module includes:

[0093] The stress data analysis submodule monitors the key parts of the chain pins and chain plates based on the stress overload area distribution value, obtains stress response data, records the data acquisition time and corresponding stress value, and simultaneously arranges the stress data in a time series, calculates the time interval and stress change rate between differentiated measurement points, filters abnormal data, and performs numerical interpolation processing to obtain a stress time series data set;

[0094] Measuring points are set in the structural monitoring system, with a fixed spacing of 5 mm. Stress response data of each measuring point is recorded by stress sensors under the action of shock waves. The stress data storage format includes the measuring point number, stress value (unit: MPa), and data acquisition time (unit: ms). An example of data recording is shown in Table 2. During data processing, the stress values ​​are first arranged in time series, and the time difference between adjacent measuring points is calculated. For example, if the first measuring point records a stress of 5 MPa at 0.5 ms and the second measuring point records a stress of 4.8 MPa at 0.7 ms, the time interval is 0.2 ms. The stress change rate between the measuring points is further calculated, that is, the stress difference divided by the time interval, for example MPa / ms. When the stress change rate at a measuring point exceeds the set threshold of ±3 MPa / ms, the data at that measuring point is considered abnormal and corrected using numerical interpolation. For example, if the stress at measuring point A is 4.6 MPa and the stress at measuring point C is 4.4 MPa, the stress at interpolated point B is set to 4.5 MPa. The resulting stress time series data set includes the stress values ​​and corresponding time information for each measuring point, as detailed in Table 2.

[0095] Table 2 Monitoring point stress time series data

[0096]

[0097] Table 2 lists the stress data distribution among the measuring points. The data have been arranged in time series and have been processed by abnormal data elimination and interpolation to ensure data integrity and accuracy.

[0098] The propagation velocity calculation submodule calls the stress time series data set, calculates the shock wave arrival time difference between differentiated measuring points, calculates the local propagation velocity based on the measuring point spacing, and performs weighted summation of the propagation velocities of all measuring points to obtain the average propagation velocity using the formula:

[0099] ;

[0100] Obtain the shock wave propagation velocity at multiple measuring points by calculation, and calculate the global propagation velocity to obtain the average shock wave propagation velocity;

[0101] in, represents the average speed of shock wave propagation, Representative The distance between measuring points, Representative The arrival time difference of the shock wave at each measuring point is Represents the standard deviation of the shock wave arrival time difference at all measuring points, Representative The energy loss coefficient of each measuring point, Represents the total number of measurement points, Represents the time difference between measuring points;

[0102] First, calculate the arrival time difference of the shock wave at each two adjacent measuring points. For example, if the recording time at measuring point 1 is 0.5ms and the recording time at measuring point 2 is 0.7ms, then the time difference is ms. Calculate the propagation velocity for all measurement points by dividing the time difference by the distance between adjacent measurement points (5mm), for example mm / ms. The average propagation velocity is calculated by weighted summing the velocities of all measuring points. The calculation formula is as follows:

[0103] ;

[0104] in, The distance between measuring points is 5mm. is the time difference of the measurement points, standard deviation Calculate the standard deviation of all time differences, energy loss coefficient Set to 0.02, the total number of measurement points . Calculate the propagation velocity at each measuring point:

[0105] ;

[0106] Standard Deviation The calculation is as follows:

[0107] ;

[0108] Bring in data , mean ,have to:

[0109] ;

[0110] Compute the weighted sum:

[0111] ;

[0112] ;

[0113] The results show that the average propagation speed of the shock wave under the current measurement point distribution is 7.07 mm / ms, indicating that the shock wave maintains a stable propagation speed during the propagation process and there is no obvious energy attenuation or abnormal propagation phenomenon.

[0114] The shock energy analysis submodule uses the average propagation velocity of the shock wave and combines it with the stress changes in the stress time series data set to calculate the energy loss per unit propagation distance. At the same time, the total energy loss of the shock wave is calculated based on the stress attenuation rate of multiple measuring points, and the energy loss of all measuring points is accumulated to obtain the shock wave energy accumulation coefficient.

[0115] Based on the average speed of propagation mm / ms, and the energy loss is calculated by combining the stress time series data. First, the energy loss per unit propagation distance is calculated as follows:

[0116] ;

[0117] For example, the stress change from measuring point 1 to measuring point 2 MPa, then the energy loss per unit propagation distance is:

[0118] ;

[0119] Calculate all measurement points:

[0120] ;

[0121] Total shock wave energy loss:

[0122] ;

[0123] The cumulative energy loss coefficient is calculated as follows:

[0124] ;

[0125] The results show that the shock wave will suffer energy loss during its propagation due to the influence of the medium, and the final cumulative loss is 0.8 MPa, which means that the shock wave gradually attenuates during the contact between the chain pin and the chain plate, and its impact on the subsequent impact effect gradually decreases. It can also serve as an important basis for subsequent energy compensation and shock wave optimization.

[0126] See also Figure 4 , the vibration feature extraction module includes:

[0127] The energy accumulation calculation submodule calculates the shock wave energy distribution of the vibration signal under the differentiated time window based on the shock wave energy accumulation coefficient, extracts the vibration amplitude under multiple windows, and calculates the cumulative energy per unit time. After normalization, it obtains the energy peak distribution under the differentiated process and generates shock wave energy peak data;

[0128] First, the collected vibration signal is segmented and the length of each time window is set to 0.05 seconds. The instantaneous energy is calculated based on the time domain vibration amplitude. The specific calculation method is to sum the amplitude of the vibration signal in the time window after square. For example, in the window time If the vibration signal amplitude data is , then the instantaneous energy of the window is:

[0129] ;

[0130] By analogy, after calculating all windows, the instantaneous energy of each time window is accumulated to form the cumulative energy per unit time, and then normalized so that the energy values ​​under different processes are within the range of [0,1]. The normalization adopts the maximum and minimum normalization method, that is:

[0131] ;

[0132] Assuming that the minimum energy value of the vibration signal is 0.05 and the maximum energy value is 1.2, the energy value of a window of 0.1724 is normalized to:

[0133] ;

[0134] After processing all windows, the energy peak distribution is obtained, and the energy peak characteristics of each process are identified, ultimately forming the shock wave energy peak data. This result shows that the energy distribution of the vibration signal varies in different time windows. The impact energy changes in different processes can be classified using the normalized energy peak data, providing basic data for subsequent characteristic frequency screening.

[0135] The characteristic frequency screening submodule calls the shock wave energy peak data, uses short-time Fourier transform to distribute energy, screens the key frequency bands corresponding to the energy peak, calculates the contribution rate of multiple frequency bands, and removes the frequency components whose contribution rate is lower than the set threshold. The formula is:

[0136] ;

[0137] Calculate the characteristic frequency screening value, obtain the characteristic frequency range of the chain impact riveting process and the chain tensioning assembly process, and generate the process characteristic frequency data;

[0138] in, represents the characteristic frequency screening value, Representative The energy value of the frequency band, Representative The center frequency of the frequency band, Representative The vibration signal amplitude of a time window, Representative The length of the time window, Represents the total number of frequency bands, Represents the number of contribution calculation frequency bands, Represents the total number of time windows;

[0139] Set the window length to 256 points and the step length to 128 points, calculate the frequency energy distribution of each time period, and filter out the key frequency bands corresponding to the energy peaks. For example, within a certain time window, the calculated energy peaks appear in the 300Hz, 450Hz, and 700Hz frequency bands, and the corresponding energy values ​​are , then its contribution rate is calculated as follows:

[0140] ;

[0141] For the contribution rate of 300Hz:

[0142] ;

[0143] Contribution rate of 450Hz:

[0144] ;

[0145] Contribution rate of 700Hz:

[0146] ;

[0147] The contribution rate threshold is set to 0.2, and frequencies below this value are eliminated. Therefore, 700Hz is removed, leaving only 300Hz and 450Hz. Then the final characteristic frequency screening value is calculated:

[0148] ;

[0149] If the amplitude of the vibration signal is , the window time is 0.05 seconds, then:

[0150] ;

[0151] Bring in the filtered frequency values:

[0152] ;

[0153] Finally, the characteristic frequency ranges for the chain impact riveting and chain tensioning assembly processes were obtained, forming process characteristic frequency data. This result shows that the characteristic frequency screening value can reflect the main energy distribution of the vibration signal. The calculated value of 11286 indicates that in the riveting and tensioning processes, the main energy is concentrated at 300Hz and 450Hz, and the contribution rate of the vibration signal meets the screening threshold, further proving that the process has stable vibration characteristics and providing reliable characteristic frequency data for subsequent force pattern extraction.

[0154] The force mode extraction submodule calls the process characteristic frequency data, filters the characteristic frequencies of the chain impact riveting process and the chain tensioning assembly process, calculates the spectral response characteristic values ​​under multiple processes, performs normalization processing, matches the vibration mode category, and establishes the chain force mode characteristic value.

[0155] For example, in the impact riveting process, the characteristic frequencies are mainly distributed at 300Hz and 450Hz, while in the tensioning assembly process, they are mainly distributed at 250Hz and 500Hz. In order to perform pattern matching and calculate the spectral response characteristic values, the normalized calculation method is adopted:

[0156] ;

[0157] Assuming that the characteristic frequency value of a certain test is 350Hz, after normalization:

[0158] ;

[0159] All characteristic frequency data is calculated accordingly, and vibration mode classification is performed. The test data is matched with existing modes, ultimately establishing the chain force mode characteristic value data. The results show that the characteristic frequency ranges under different processes can be normalized using the spectral response eigenvalues. The value of 0.4 indicates that the characteristic frequency of the test signal is closer to the riveting process and has a high degree of match with the characteristics in the process database. Therefore, it can be determined that the test signal is more likely to come from the impact riveting process, providing a basis for process status identification.

[0160] Table 3: Peak shock wave energy data

[0161]

[0162] As shown in Table 3, the vibration signal amplitudes, calculated instantaneous energy values, and normalized energy values ​​for multiple time windows were recorded. This data was used for subsequent characteristic frequency screening and force pattern extraction analysis. The results show that the vibration signal energy exhibits a regular distribution in different time windows. Spectral analysis can further determine the characteristic frequency distribution, laying the foundation for matching the chain's force patterns.

[0163] See also Figure 5, the load optimization control module includes:

[0164] The force parameter analysis submodule uses the chain force mode characteristic value, calls the chain simulation operation data, obtains the force data under the differentiated operation state, extracts the force change trend of multiple nodes of the chain, calculates the force mean value, fluctuation range and force change rate under the differentiated operation conditions of the chain, and obtains the chain force characteristic parameters;

[0165] First, call the simulation data of the chain under different operating conditions and analyze the stress conditions in each time period. For the chain stress data under different operating conditions, first divide it into multiple time periods according to the time series. For example, with a time interval of 0.5 seconds, the chain stress value in each time period is The force data of all time periods are recorded and stored in the data table, and then the average is calculated. and standard deviation , that is, calculate the arithmetic mean of each force value and the degree of deviation of the force value in each time period relative to the average value. Furthermore, based on the force changes of multiple nodes in each time period, the force fluctuation trend is extracted. The specific method is to calculate the force change rate of each node between two consecutive time periods, that is, ,in Indicates the time interval, by counting the force change rate of each node in all time periods, the force change trend of the entire chain under different working conditions is obtained. After the data is sorted, by setting the force mean With its fluctuation range, that is Multiples of (such as ), screen out abnormal force values, and simultaneously calculate the maximum force change rate to analyze the chain's dynamic force characteristics. For example, under a certain operating condition, the force mean is 800N and the standard deviation is 50N. The force fluctuation range can be set to 700N-900N. If the force value in a certain time period exceeds this range, such as 950N, it is considered that the chain may have been in a sudden force state during this time period. At this time, the force change rate of the adjacent time periods before and after is extracted. If the change rate exceeds the set threshold (for example, 500N / s), it is identified as a force mutation point, and the force characteristic parameters are then summarized. These results show that the force fluctuation characteristics of the chain in different time periods can be quantified by calculating the mean and standard deviation, and that analysis based on the force change rate can effectively identify abnormal force conditions, providing key data support for the subsequent calculation of the chain's short-term force limit.

[0166] The chain short-term stress limit calculation submodule calls the chain stress characteristic parameters and analyzes the stress state of the chain in different time periods using the formula:

[0167] ;

[0168] Calculate the short-term stress limit of the chain, adjust the short-term stress threshold, and generate the short-term stress limit value of the chain;

[0169] in, Represents the current short-term stress limit of the chain, Representatives in the The actual force value within a time period, Represents the average force value, Represents the standard deviation of the force values, Represents the time point of each time period, represents the optimal time point, represents the standard deviation of time, Represents the total number of time periods, Represents the number of short-term force calculation time periods;

[0170] First, based on the chain stress characteristic parameters obtained above, determine the calculation range of the short-term stress limit, and then call the formula:

[0171] ;

[0172] in, Representatives in the The force value of a time period, for example, a set of force data is selected from the above calculation , mean force , standard deviation , for the time point , assuming that the optimal force point in a certain period of time Set to 2.5s, time standard deviation , calculate the force limit value at a certain moment :

[0173] 1. Calculate the normalized force difference in each time period:

[0174] ;

[0175] The calculation result is .

[0176] 2. Calculate the time weight item:

[0177] ;

[0178] For example, the time period corresponds to They are [2.1, 2.3, 2.5, 2.7, 2.9] seconds respectively, and their exponential weights are calculated:

[0179] ;

[0180] 3. Calculate the final short-term stress limit:

[0181] ;

[0182] ;

[0183] This result shows that the calculated The value is used to measure the maximum force range that the chain can withstand in a short period of time. The value determines whether the chain needs to adjust the force threshold under a specific operating state to ensure that the chain's force state is reasonable and reduce the risk of sudden force changes.

[0184] The chain load distribution optimization submodule calls the short-term force limit value of the chain, adjusts the chain test parameters, compares the matching degree between the differentiated chain force threshold and the short-term limit force value, selects the optimal test parameters, and generates the optimized chain load distribution value.

[0185] First, call the short-term force limit of the chain calculated above , set the initial force threshold of each operating condition. For example, if the chain force threshold is set to 850N, the short-term force limit is , then calculate its threshold correction In the subsequent chain load optimization process, for different test parameters, such as chain tension, drive wheel torque, lubrication status, etc., adjust the test parameters one by one, for example, reduce the drive wheel torque by 10Nm, or adjust the chain tension to the set optimization range (such as 600N-700N), and then compare the stress conditions of the chain after each parameter adjustment, according to the short-term stress limit value. The rationality of the force distribution is calculated, and the optimal test parameters are selected through multiple rounds of optimization, ultimately generating the optimized chain load distribution value. This result shows that by optimizing the load distribution, the chain's operating state can be ensured within the short-term limit force range, reducing the impact of overload or sudden force changes on chain performance, thereby improving the chain's service life and operational stability.

[0186] See also Figure 6 , the fracture risk warning module includes:

[0187] The load distribution calculation submodule uses the optimized chain load distribution value to call the force conditions of multi-node chain links, calculate the dynamic load distribution on the chain links, analyze the load transfer relationship between differentiated chain links, and adjust the local load deviation to obtain the optimized chain link load distribution coefficient;

[0188] In actual applications, when the chain is under load, the load on each link is different. Especially under different working conditions, these links may experience different load fluctuations. For example, when the chain is changing speed, the stress state of the link may vary greatly due to the change in torque. Therefore, during the execution of this module, it is first necessary to conduct a detailed mechanical analysis of the chain under the current load state, through finite element analysis or direct measurement of the stress conditions of each node on the link (including vertical force, tangential force, etc.), and input these data into the calculation model to further calculate the dynamic load distribution of each link. This process requires multi-level parameter correction and load calculation based on the physical properties of the link, the speed of movement, and the external load on the chain, so as to ensure that the load deviation of each link is reduced as much as possible, so that the chain is more stable in actual use. In the specific implementation process, it is assumed that the initial loads of the links are 、 、 The load distribution coefficient of the chain link is calculated by calculating the change trend of these loads during the chain movement, and optimizing the chain load characteristics so that the final chain link load distribution coefficient approaches the equilibrium state. In this process, methods such as dynamically adjusting the chain link connection angle and using different materials to enhance the load-bearing capacity can be adopted to optimize the load distribution. Finally, the optimized chain link load distribution coefficient is obtained by calculation, such as the coefficient 、 、 Etc. This optimization result can be used for subsequent impact energy analysis and directly affect the fatigue life and fracture risk assessment of the chain.

[0189] The impact energy accumulation submodule calls the optimized chain link load distribution coefficient, obtains shock wave feedback data, calculates the distribution of impact energy on the chain link, and analyzes the transmission mode of impact energy on the chain link based on the changing trend of the accumulated impact energy. The formula is:

[0190] ;

[0191] Calculate and obtain the cumulative value of impact energy;

[0192] in, Represents the cumulative value of impact energy, Representative chain link The load at Representative chain link The speed at Representative chain link The quality of Represents the total number of impact energy calculation nodes;

[0193] In actual applications, the impact energy of the chain often comes from sudden external load changes, such as when the equipment starts or stops, the chain will encounter instantaneous impact. In this module, it is first necessary to obtain the shock wave feedback data that the chain may encounter during operation through multiple measurements. This data can be obtained through strain gauges or pressure sensors. In actual calculations, it is assumed that each node of the chain is subjected to different impact loads. Assume that the impact load of the chain link at a certain moment is , the speed is , the quality is , at this time through the formula:

[0194] ;

[0195] The impact energy can be calculated. For example, assuming there are 3 nodes to calculate, and the given load, velocity and mass are: , , ; , , ; , , , then calculate the impact energy of each node one by one:

[0196] ;

[0197] ;

[0198] ;

[0199] The total impact energy is:

[0200] ;

[0201] Based on this accumulated impact energy, the next step is to analyze the changing trend of the impact energy and provide a basis for subsequent load adjustment and fracture risk assessment based on the changing trend.

[0202] The fracture risk assessment submodule calls the cumulative impact energy value to determine whether it exceeds the set load safety threshold, compares the stress state within the threshold range, adjusts the chain link load distribution, calculates the overall chain fracture risk level, and obtains the chain fracture risk warning value.

[0203] In practice, setting a reasonable load safety threshold is crucial to ensuring the lifespan of a chain. This threshold can be based on empirical data, historical testing, or simulation experiments. For example, assuming a safety threshold of 3000J, an impact energy exceeding this threshold may present a high risk of fracture. Therefore, when the impact energy reaches 3642.26J, a risk assessment mechanism should be activated to analyze the stress conditions at each link in the chain. By adjusting the load distribution among the links (e.g., reducing the load on heavier links and increasing the load on lighter links), the chain's overall energy transfer is more even, reducing the risk of overload. Furthermore, the chain's fracture risk level can be calculated using a series of test data and mathematical models. For example, an impact energy greater than 3000J is assigned a "high" risk level, greater than 2500J to "medium," and less than 2500J to "low." In this case, due to the cumulative impact energy of 3642.26J, the final assessment result is "high," triggering the early warning system to alert the operator to inspect or maintain the chain.

[0204] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. Chain production data monitoring and optimization system based on industrial Internet of Things, characterized by: The system comprises: The stress load monitoring module places stress sensors in the chain plate stamping area and the pin shaft heat treatment area to collect transient data, determine the measurement point location based on the coordinate index, calculate the stress change rate, use numerical differentiation to find the gradient, screen the local extreme points, and obtain the stress overload area distribution value; The shock wave propagation analysis module obtains vibration data of key parts of the chain pin and chain plate based on the stress overload area distribution value, calculates the propagation speed and attenuation rate, compares and analyzes the waveform, and generates a shock wave energy accumulation coefficient; The vibration feature extraction module obtains the vibration signal in production based on the shock wave energy accumulation coefficient, distributes the energy using short-time Fourier transform, screens the characteristic frequencies of the chain impact riveting process and the chain tensioning assembly process, and generates the chain force mode characteristic value; The load optimization control module constructs a stress accumulation curve based on the chain stress mode characteristic value, calls the chain simulation operation data, calculates the short-term stress limit of the chain, adjusts the chain test parameters, and generates an optimized chain load distribution value; The load optimization control module includes: The force parameter analysis submodule calls the chain simulation operation data based on the chain force mode characteristic value, obtains the force data under the differentiated operation state, extracts the force change trend of multiple nodes of the chain, calculates the force mean, fluctuation range and force change rate under the differentiated operation conditions of the chain, and obtains the chain force characteristic parameters; The chain short-term stress limit calculation submodule calls the chain stress characteristic parameters and analyzes the stress state of the chain in different time periods using the formula: ； Calculate the short-term stress limit of the chain, adjust the short-term stress threshold, and generate the short-term stress limit value of the chain; in, Represents the current short-term stress limit of the chain, Representatives in the The actual force value within a time period, represents the average force value, Represents the standard deviation of the force values, Represents the time point of each time period, represents the optimal time point, represents the standard deviation of time, Represents the total number of time periods, Represents the number of short-term force calculation time periods; The chain load distribution optimization submodule calls the short-term stress limit value of the chain, adjusts the chain test parameters, compares the matching degree between the differentiated chain stress threshold and the short-term limit stress value, selects the optimal test parameters, and generates the optimized chain load distribution value.

2. The chain production data monitoring and optimization system based on industrial Internet of Things according to claim 1 is characterized in that: The stress overload area distribution value includes the measuring point position, stress change rate, and local extreme point. The shock wave energy accumulation coefficient specifically refers to the propagation speed, attenuation rate, and waveform comparison analysis result. The chain force mode characteristic value includes the impact riveting process characteristic frequency, the tensioning assembly process characteristic frequency, and energy distribution. The optimized chain load distribution value specifically refers to the stress accumulation curve, short-term stress limit, and test parameter adjustment plan.

3. The chain production data monitoring and optimization system based on industrial Internet of Things according to claim 2 is characterized in that: The stress load monitoring module includes: The stress data acquisition submodule arranges stress sensors in the chain plate stamping area and the pin shaft heat treatment area to obtain transient data, record stress values ​​at multiple measuring points, and determine the location of the measuring points based on the coordinate index to generate stress distribution data at the measuring points. The stress gradient calculation submodule calculates the stress difference between adjacent measuring points based on the stress distribution data of the measuring points, calculates the spatial displacement according to the coordinates of the measuring points, and calculates the stress change rate by numerical differentiation to generate the stress change rate data of the measuring points; The stress overload area screening submodule calls the stress change rate data of the measuring point, screens the local extreme value points, and extracts the stress gradient change in the overload area using the formula: ； The stress distribution value of the overload area is obtained by calculation to obtain the stress overload area distribution value; in, Represents the stress difference between adjacent measuring points, Represents the spatial distance between adjacent measuring points, represents the stress gradient weight parameter, Represents the stress value of the measuring point, represents the mean stress value of all measuring points, Represents the stress overload area distribution value, represents the number of stress gradient calculation points, Represents the total number of measurement points, i represents a measurement point, j represents another measurement point related to i in a certain calculation step, and id represents the unique identification of the measurement point.

4. The chain production data monitoring and optimization system based on industrial Internet of Things according to claim 3 is characterized in that: The shock wave propagation analysis module includes: Based on the stress overload area distribution value, the stress data analysis submodule monitors the key parts of the chain pin and chain plate, obtains stress response data, records the data acquisition time and corresponding stress value, and simultaneously arranges the stress data in time series, calculates the time interval and stress change rate between differentiated measurement points, filters abnormal data and performs numerical interpolation processing to obtain a stress time series data set; The propagation velocity calculation submodule calls the stress time series data set, calculates the shock wave arrival time difference between the differentiated measuring points, calculates the local propagation velocity based on the measuring point spacing, and performs weighted summation on the propagation velocities of all measuring points to obtain the average propagation velocity using the formula: ； Obtain the shock wave propagation velocity at multiple measuring points by calculation, and calculate the global propagation velocity to obtain the average shock wave propagation velocity; in, represents the average speed of shock wave propagation, Representative The distance between measuring points, Representative The arrival time difference of the shock wave at each measuring point is Represents the standard deviation of the shock wave arrival time difference at all measuring points, Representative The energy loss coefficient of each measuring point, Represents the total number of measurement points, Represents the time difference between measuring points; The shock energy analysis submodule uses the average shock wave propagation velocity and, combined with the stress changes in the stress time series data set, calculates the energy loss per unit propagation distance. It also calculates the total shock wave energy loss based on the stress attenuation rate of multiple measuring points and accumulates the energy losses of all measuring points to obtain the shock wave energy accumulation coefficient.

5. The chain production data monitoring and optimization system based on industrial Internet of Things according to claim 4 is characterized in that: The vibration feature extraction module includes: The energy accumulation calculation submodule calculates the shock wave energy distribution of the vibration signal under the differentiated time window based on the shock wave energy accumulation coefficient, extracts the vibration amplitude under multiple windows, and calculates the cumulative energy per unit time. After normalization, it obtains the energy peak distribution under the differentiated process and generates shock wave energy peak data; The characteristic frequency screening submodule calls the shock wave energy peak data, distributes the energy using short-time Fourier transform, screens the key frequency bands corresponding to the energy peak, calculates the contribution rate of multiple frequency bands, and removes the frequency components whose contribution rate is lower than the set threshold using the formula: ； Calculate the characteristic frequency screening value, obtain the characteristic frequency range of the chain impact riveting process and the chain tensioning assembly process, and generate the process characteristic frequency data; in, represents the characteristic frequency screening value, Representative The energy value of the frequency band, Representative The center frequency of the frequency band, Representative The vibration signal amplitude of a time window, Representative The length of the time window, Represents the total number of frequency bands, Represents the number of contribution calculation frequency bands, Represents the total number of time windows, i represents the measurement point number, frequency band number, or time window number, and E represents the energy value of a frequency band; The force mode extraction submodule calls the process characteristic frequency data, screens the characteristic frequencies of the chain impact riveting process and the chain tensioning assembly process, calculates the spectrum response characteristic values ​​under multiple processes, performs normalization processing, matches the vibration mode category, and establishes the chain force mode characteristic value.

6. The chain production data monitoring and optimization system based on industrial Internet of Things according to claim 5 is characterized in that: The system further comprises: The fracture risk warning module obtains shock wave feedback data based on the optimized chain load distribution value, calculates the cumulative impact energy value, determines the load safety threshold, compares the stress state within the threshold range, adjusts the load distribution, and generates a chain fracture risk warning value; The chain breakage risk warning value includes the impact energy accumulation value, the load safety threshold, and the stress state comparison result.

7. The chain production data monitoring and optimization system based on industrial Internet of Things according to claim 6 is characterized in that: The fracture risk warning module includes: The load distribution calculation submodule, based on the optimized chain load distribution value, calls the force conditions of the multi-node chain links, calculates the dynamic load distribution on the chain links, analyzes the load transfer relationship between the differentiated chain links, and adjusts the local load deviation to obtain the optimized chain link load distribution coefficient; The impact energy accumulation submodule calls the optimized chain link load distribution coefficient, obtains shock wave feedback data, calculates the distribution of impact energy on the chain link, and analyzes the transmission mode of impact energy on the chain link based on the changing trend of the accumulated impact energy, using the formula: ； Calculate and obtain the cumulative value of impact energy; in, Represents the cumulative value of impact energy, Representative chain link The load at Representative chain link The speed at Representative chain link The quality of Represents the total number of impact energy calculation nodes; The fracture risk assessment submodule calls the cumulative impact energy value to determine whether it exceeds the set load safety threshold, compares the stress state within the threshold range, adjusts the chain link load distribution, calculates the overall chain fracture risk level, and obtains the chain fracture risk warning value.

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