Steel wire rope real-time online monitoring method and system based on intelligent sensor

Abstract

The invention discloses a steel wire rope real-time online monitoring method and system based on an intelligent sensor, and relates to the technical field of steel wire rope monitoring. According to the method, a plurality of collection points are arranged on a steel wire rope path, and pretreatment modes such as time synchronous correction, band-pass filtering, tension difference extraction and Z-score standardization are combined; the effective energy level index Elive of the pulse source capable of reflecting the in-vivo pulse energy characteristics of the deep-sea giant tuna is established. A pulsation amplitude item is constructed by adopting a peak-to-peak value P2P, a dispersion item is constructed by using a standard deviation of a real-time tension difference sequence, and a ratio of the pulsation amplitude item to the dispersion item is used as an expression structure of an effective energy level index of a pulsation source, so that the identification capability of the pulsation of a living body on the tension of the steel wire rope can be remarkably improved; effective distinguishing from sea condition disturbance, ship body swing and other pseudo signals is achieved. According to the processing mode, the defect that in the prior art, the non-uniform pulsating type load is difficult to recognize only depending on single-point tension or RMS indexes is overcome.

Description

Technical Field

[0001] This invention relates to the field of wire rope monitoring technology, specifically to a real-time online monitoring method and system for wire ropes based on intelligent sensors. Background Technology

[0002] With the continuous development of marine operation equipment, deep-sea scientific research equipment, and distant-water fishing equipment, electric winch systems, as typical traction and winding mechanisms, are being increasingly widely applied in deep-sea fishing tackle, marine towing systems, and ship lifting equipment. As the scale of deep-sea longline fishing operations continues to expand, the wire rope, as the main load-bearing component of the electric winch system, must withstand multi-source varying loads during operation, including sea state disturbances, winch mechanical drives, and the struggles of large deep-sea tuna. The non-uniform pulsating stress generated by large deep-sea fish is particularly transient, sudden, and irregular. Therefore, during the operation of electric winches in deep-sea fishing tackle, real-time acquisition of the dynamic characteristics of wire rope tension, effective identification of live pulsation patterns, and timely assessment of potential overload risks have become core technical problems that urgently need to be solved for this type of equipment.

[0003] Currently, in existing deep-sea fishing equipment, monitoring the condition of wire ropes mainly relies on single-point tension measurement, winch current monitoring, or visual observation. These methods only reflect macroscopic tension changes and cannot identify intermittent, sudden, and nonlinear pulsating loads caused by the struggles of giant deep-sea tuna. Existing monitoring technologies cannot distinguish between "live pulsating signals" and noise from non-operational sources such as "wave disturbances, hull swaying, and winch mechanical noise," leading to frequent misjudgments or false alarms in practical use. Furthermore, traditional systems lack the ability to detect "local tension differences caused by live dynamics," cannot identify tension differences between the middle section of the wire rope and the winch end, cannot determine the energy level of pulsating signals, and are even less suitable for predicting wire rope fatigue and overload risks. Therefore, existing technologies cannot effectively meet the needs of refined, multi-dimensional, and real-time risk identification of wire rope conditions in deep-sea fishing operations.

[0004] The aforementioned shortcomings primarily stem from the lack of a model foundation for the unique load form of "living, pulsating deep-sea organisms." Existing technologies lack differential analysis of the dynamic tension changes in the wire rope at different locations and a real-time judgment mechanism that couples "living, pulsating energy" with the "instantaneous load state of the winch." Because giant deep-sea tuna generate highly irregular, transient, and strong non-uniform loads during their struggles, these loads are directly transmitted to the winch motor through the wire rope, easily causing instantaneous overload, violent fluctuations in motor current, and rapid accumulation of localized fatigue in the wire rope. Failure to promptly identify such pulsating impacts can lead to wire rope breakage, hook damage, winch overload shutdown, and even serious consequences such as jamming of the winding structure, affecting not only the success rate of fishing but also posing safety hazards to equipment and personnel. Therefore, the lack of timely identification and risk assessment of "living, pulsating loads" is a key technological bottleneck in the field of electric winches for deep-sea fishing tackle. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for real-time online monitoring of steel wire ropes based on intelligent sensors, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps: S1. Set up multiple collection points on the wire rope path of the electric winch to collect pulsation operation data in real time, and transmit the collected winch operation data to the central controller of the electric winch. The central controller preprocesses the winch operation data to obtain the raw monitoring data. S2. Based on the original monitoring data, calculate the effective energy level index Elive of the pulsation source. Based on the effective energy level index Elive of the pulsation source and the preset pulsation reference interval threshold, conduct a preliminary assessment to determine the tension pulsation of the wire rope. S3. When the initial assessment determines that the wire rope tension pulsation is abnormal, the winch risk analysis mechanism is triggered. The winch risk analysis mechanism calculates the comprehensive risk index Rcomb by extracting the load data of the winch motor and combining it with the effective energy level index Elive of the pulsation source. S4. A secondary comparative evaluation is conducted by setting a risk interval threshold and the comprehensive risk index Rcomb, and an adaptive coupling control strategy is executed based on the results of the secondary comparative evaluation.

[0007] Preferably, S1 includes S11; S11. By setting multiple collection points on the electric winch and steel wire rope of the deep-sea fishing tackle, and installing corresponding sensors at each collection point, the collection task is triggered when the steel wire rope extends from the electric winch to 3 meters, the pulsation operation data is collected in real time, and the sensors are wirelessly connected to the central controller of the electric winch via Bluetooth, and the real-time collected pulsation operation data is transmitted to the central controller. The acquisition points include a first tension acquisition point A, a second tension acquisition point B, and a load acquisition point C; The first tension acquisition point A is guided by a wire rope located at the outlet of the electric winch. The second tension acquisition point B is positioned at the middle section of a steel wire rope that is close to the sea surface but not submerged in seawater. Clamped wire rope tension sensors are used at the first tension acquisition point A and the second tension acquisition point B to acquire the first tension TA and the second tension TB in real time. The first tension TA and the second tension TB at each moment are summarized according to the time series to obtain pulsation operation data. The load acquisition point C is set at the drive end of the electric winch, and is in a stationary state by default. A current sensor is installed at the motor of the electric winch, and an encoder is installed at the shaft of the electric winch.

[0008] Preferably, S1 further includes S12; S12. In the central controller, pulsation operation data is received in real time, and the pulsation operation data is preprocessed to obtain raw monitoring data. The preprocessing includes time synchronization correction, noise filtering, real-time tension difference extraction and standardization processing. The original monitoring data includes the real-time tension difference Tdiff sequence of the first tension acquisition point A and the second tension acquisition point B at each moment. The time synchronization correction is achieved by aligning the timestamps of all parameters in the pulsation data collected by all sensors to the same time axis, based on a unified clock maintained inside the central controller. The noise filtering process uses a bandpass filter (FIR) to filter the time-synchronized pulsed running data, eliminating low-frequency drift caused by sea conditions, mid-frequency noise caused by winch vibration, and pulse noise caused by motor current fluctuations. The real-time tension difference extraction is achieved by calculating the difference between the first tension TA and the second tension TB collected from the first tension acquisition point A and the second tension acquisition point B on the same time axis, and outputting the real-time tension difference Tdiff(t) at time t. The standardization process uses the Z-score standardization method to standardize the real-time tension difference Tdiff(t) at time t, thereby eliminating the influence of the unit dimension of the real-time tension difference Tdiff(t) at time t. Then, the real-time tension difference Tdiff at each moment is integrated according to the time series to obtain the real-time tension difference Tdiff sequence.

[0009] Preferably, S2 includes S21; S21. Based on the real-time tension difference Tdiff sequence at each moment in the original monitoring data, extract the maximum and minimum values ​​in the real-time tension difference Tdiff sequence at each moment to calculate the difference, obtain the pulsation amplitude term, and use the standard deviation of the real-time tension difference Tdiff(t) at time t as the dispersion term to calculate and output the effective energy level index Elive of the pulsation source. The effective energy level index Elive of the pulsation source is calculated and output using the following algorithm formula; In the formula, T represents the total number of real-time tension differences Tdiff within the time frame of the data acquisition task. Let Tdiff represent the average value of the real-time tension difference Tdiff sequence at each time step, max(Tdiff(t)) represents the maximum value of the real-time tension difference Tdiff(t) at time t, and min(Tdiff(t)) represents the minimum value of the real-time tension difference Tdiff(t) at time t.

[0010] Preferably, S2 further includes S22; S22. By statistically analyzing the real-time tension difference (Tdiff) sequence during multiple safe fishing operations, a pulsation reference interval threshold is set. This pulsation reference interval threshold includes the normal reference threshold Elive. safe and suspicious threshold Elive mid ; Wherein: the upper bound of the effective energy level index of the pulsating source obtained under stable mechanical winding state is used as the normal reference threshold. safe The upper limit of the characteristic range of the effective energy level index of the pulsation source that caused slight tension abnormalities in the wire rope but did not pose an overload risk in historical pulsation events was used as the suspicion threshold. mid Based on the real-time acquired effective energy level index Elive of the pulsation source and the pulsation reference interval threshold, a preliminary assessment is made to determine the tension pulsation of the wire rope. Where: when the effective energy level index of the pulsation source Elive ≤ the normal reference threshold Elive safe When this occurs, it is determined to be a normal fluctuation. At this time, the winch continues to work at the currently set speed without triggering any protection strategy. When the normal reference threshold Elive safe <Effective energy level index of pulsating source Elive <Suspicious threshold Elive midIf a mild pulsation is detected, the current speed of the electric winch should be reduced by 5% to prevent the wire rope from being subjected to sudden tension just as the pulsation begins. When the effective energy level index of the pulsation source Eliive ≥ the suspicious threshold Eliive mid When the heartbeat is detected, it is determined to be a violent pulsation, which triggers the winch risk analysis mechanism.

[0011] Preferably, S3 includes S31; S31. After triggering the winch risk analysis mechanism again, start the load acquisition point C, install a current sensor at the end of the winch drive motor to obtain the winch motor current IC at each moment in real time, and install an encoder on the winch shaft to obtain the winch winding speed VD at each moment in real time. The data is then transmitted to the central controller, where it undergoes time synchronization correction, noise filtering, and standardization before being integrated to obtain the load data for the corresponding time. The standardization process involves using the maximum and minimum standardization method based on the maximum motor current of the electric winch and the maximum winding speed of the winch to standardize the current motor current IC and the winding speed VD at each moment, thereby eliminating the influence of unit dimensions. The load data includes the winch motor current IC(t) at time t and the winch winding speed VD(t) at time t.

[0012] Preferably, S3 further includes S32; S32. The statistical upper bound of the effective energy level index Elive of the pulsating source within the historical monitoring window where no overload occurred during historical safe fishing operations is used as the reference value Eref; the rated maximum continuous operating current provided by the winch drive motor manufacturer is used as the rated maximum safe current Imax of the winch; and combined with the load data, the comprehensive risk index Rcomb is calculated and output. The comprehensive risk index Rcomb is calculated and output using the following algorithm formula; The reference value Eref and the rated maximum safe current Imax of the winch are both dimensionless.

[0013] Preferably, S4 includes S41; S41. Set risk interval thresholds based on the historical comprehensive risk index Rcomb, whereby the risk interval thresholds include a risk warning threshold Rwarn and a risk shutdown threshold Rstop; and perform a secondary comparative evaluation based on the real-time calculated comprehensive risk index Rcomb to determine the load risk of the winch motor. The specific evaluation content is as follows: When the comprehensive risk index Rcomb ≤ the risk warning threshold Rwarn, the current stress state of the wire rope is determined to be within the safe range; When the comprehensive risk index Rwarn < comprehensive risk index Rcomb and comprehensive risk index Rcomb < risk shutdown threshold Rstop, the wire rope is determined to be in the risk buffer zone. When the comprehensive risk index Rcomb is greater than or equal to the risk shutdown threshold Rstop, the wire rope is determined to be in the shutdown risk range.

[0014] Preferably, S4 further includes S42; S42. Based on the judgment of the load wind condition of the winch motor, execute the corresponding adaptive coupling control strategy, the details of which are as follows: When it is determined that the current stress state of the wire rope is within the safe range, the winch motor operates at the current winch winding speed VD, continues to monitor, and does not intervene. At the same time, the central controller updates the comprehensive risk index Rcomb every 100ms and then performs a second comparative evaluation. When the wire rope stress state is determined to be within the risk buffer zone, the winch winding speed VD is dynamically decelerated based on the output value of the current comprehensive risk index Rcomb, as follows: When the comprehensive risk index Rcomb exceeds the risk warning threshold Rwarn by 0%-20%, the winding speed is reduced by 3%-6%; When the comprehensive risk index Rcomb exceeds the risk warning threshold Rwarn by 20%-60%, the winding speed is reduced by 6%-18%. When the comprehensive risk index Rcomb exceeds 40%-100% of the risk warning threshold Rwarn, the winding speed is reduced by 18%-30%. When the wire rope is determined to be in the shutdown risk range, the central controller controls the winch motor to issue a dangerous pulsation warning. When the comprehensive risk index Rcomb is greater than the risk shutdown threshold Rstop for 1 second in a row, the winding will be automatically stopped.

[0015] A real-time online monitoring system for steel wire rope based on intelligent sensors includes a central acquisition module, a pulsation effective analysis module, a winch risk analysis module, and an adaptive coupling control module. The central acquisition module collects pulsation operation data in real time by setting multiple acquisition points on the wire rope path of the electric winch, and transmits the acquired winch operation data to the central controller of the electric winch. The central controller then preprocesses the winch operation data to obtain the raw monitoring data. The effective pulsation analysis module calculates the effective energy level index Elive of the pulsation source based on the original monitoring data, and makes a preliminary assessment based on the effective energy level index Elive of the pulsation source and the preset pulsation reference interval threshold to determine the tension pulsation of the wire rope. When the winch risk analysis module determines that the wire rope tension pulsation is abnormal through preliminary assessment, the winch risk analysis mechanism is triggered. The winch risk analysis mechanism calculates the comprehensive risk index Rcomb by extracting the load data of the winch motor and combining it with the effective energy level index Elive of the pulsation source. The adaptive coupling control module performs a secondary comparative evaluation by setting a risk interval threshold and a comprehensive risk index Rcomb, and executes the adaptive coupling control strategy based on the secondary comparative evaluation results.

[0016] This invention provides a method and system for real-time online monitoring of steel wire ropes based on intelligent sensors. It has the following beneficial effects: (1) This method establishes the effective energy level index Elive, which reflects the energy characteristics of the live pulsation of deep-sea giant tuna, by setting multiple acquisition points along the wire rope path and combining preprocessing methods such as time synchronization correction, bandpass filtering, tension difference extraction, and Z-score standardization. By constructing the pulsation amplitude term using peak-to-peak P2P and the dispersion term using the standard deviation of the real-time tension difference sequence, and then using the ratio of the two as the expression structure of the effective energy level index of the pulsation source, this invention can significantly improve the ability to identify the influence of live pulsation on wire rope tension, and effectively distinguish it from pseudo-signals such as sea state disturbance, ship swaying, and winch structure vibration. This processing method not only overcomes the shortcomings of existing technologies that rely solely on single-point tension or RMS index to identify non-uniform pulsation loads, but also accurately captures the transient impact energy changes of live pulsation, thereby achieving real-time, accurate, and low-misjudgment identification of the tension pulsation state of the wire rope.

[0017] (2) Based on the effective energy level index Elive of the pulsation source, this method introduces the load data of the winch motor obtained by the current sensor and encoder, and constructs a comprehensive risk index Rcomb based on the improved Euclidean distance model, so that the stress state of the wire rope can be constrained and evaluated by two dimensions: "living pulsation energy" and "winch driving force state". This invention integrates Elive with the standardized winch motor current IC in a dimensionless manner, so that the load source coupling relationship that was not considered in the traditional wire rope monitoring can be quantitatively expressed. Based on the partition judgment of the risk warning threshold Rwarn and the risk shutdown threshold Rstop, this invention can dynamically execute different levels of adaptive coupling control strategies such as speed reduction adjustment, intermittent winding, and fatigue suppression according to the change of the comprehensive risk index Rcomb, thereby effectively reducing instantaneous impact, reducing the risk of winch overload and suppressing the fatigue accumulation of the wire rope, and significantly improving the operational safety and control intelligence level of the deep-sea fishing electric winch.

[0018] (3) This method dynamically links the effective energy level index Elive of the pulsation source with the comprehensive risk index Rcomb, triggering a load risk analysis mechanism in real time when the wire rope experiences violent live pulsation. This allows the invention to not only identify the true source of the non-uniform pulsating load of deep-sea giant tuna, but also to perform coupled risk assessment based on multi-dimensional data such as pulsation intensity, winch load, and motor current changes, and immediately execute the corresponding level of adaptive coupled control strategy. Through this collaborative chain of "first identifying live dynamics, then coupling load analysis to finally executing control," a real-time dynamic safety management system that traditional technologies cannot achieve is formed. Compared with existing fishing tackle systems that rely on single-point tension alarms or fixed deceleration strategies, this invention can intervene in time before the impact caused by the fish's explosive struggle is transmitted to the winch, fundamentally reducing the probability of high-risk events such as instantaneous wire rope breakage, winch overload, and winding structure jamming, and significantly improving the success rate of deep-sea fishing operations and the service life of equipment. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of a real-time online monitoring method for steel wire rope based on intelligent sensors according to the present invention. Figure 2 This is a schematic diagram of the process of a real-time online monitoring system for steel wire rope based on intelligent sensors according to the present invention; Figure 3 Illustrations showing the data acquisition points and their hardware structure. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 Please see Figure 1 This invention provides a real-time online monitoring method for steel wire ropes based on intelligent sensors. To achieve the above objectives, this invention is implemented through the following technical solution, including the following steps: S1. Set up multiple collection points on the wire rope path of the electric winch to collect pulsation operation data in real time, and transmit the collected winch operation data to the central controller of the electric winch. The central controller preprocesses the winch operation data to obtain the raw monitoring data. S2. Based on the original monitoring data, calculate the effective energy level index Elive of the pulsation source. Based on the effective energy level index Elive of the pulsation source and the preset pulsation reference interval threshold, conduct a preliminary assessment to determine the tension pulsation of the wire rope. S3. When the initial assessment determines that the wire rope tension pulsation is abnormal, the winch risk analysis mechanism is triggered. The winch risk analysis mechanism extracts the load data of the winch motor and calculates the comprehensive risk index Rcomb by combining the effective energy level index Elive of the pulsation source. S4. A secondary comparative evaluation is conducted by setting a risk interval threshold and the comprehensive risk index Rcomb, and an adaptive coupling control strategy is executed based on the results of the secondary comparative evaluation.

[0022] In this embodiment, the method sets up multiple acquisition points along the wire rope path and collects pulsation data in real time. This allows the central controller to synchronously obtain the actual propagation of deep-sea fish tension at different locations. The purpose is to promptly identify non-uniform pulsating stress caused by the instantaneous struggle of deep-sea giant tuna. If only the tension at a single point on the wire rope is relied upon, it is easy to misjudge waves, ship swaying, or winch vibration as fish pulsation, leading to the risk of incorrect intervention or missed detection. Therefore, multi-point acquisition and real-time transmission are used to obtain dynamic parameters that are closest to the actual force source. The effective energy level index Elive of the pulsation source is calculated based on the preprocessed raw monitoring data, which can directly quantify the energy effectiveness of fish pulsation. This is because the pulsation of deep-sea fish has typical peak impact characteristics, while ordinary sea state disturbances often have low amplitude and high dispersion. Without joint analysis of peak-to-peak value and standard deviation, it is impossible to distinguish between real pulsation and background noise. The role of this index is to identify the rapid rise in pulsation intensity in advance, avoiding the wire rope from being subjected to initial energy impact without protection. When the effective energy level index Elive of the pulsation source reaches an abnormal state, the winch risk analysis mechanism is triggered. This mechanism extracts winch motor load data and couples it with Elive to calculate the comprehensive risk index Rcomb. The reason for introducing motor load when pulsation is abnormal is that pulsation intensity alone is insufficient to represent the breakage risk; what truly causes wire rope breakage is the combined effect of a simultaneous increase in "pulsation energy" and "winch driving force." Without coupling analysis, situations may arise where pulsation is mild but the winch is overloaded, or pulsation is severe but the winch load is normal, leading to incorrect control strategy triggering. This implementation uses Rcomb to provide stronger protection when both risk sources increase simultaneously; its physical meaning is to determine whether the system has entered a dangerous combined stress state. Finally, the system performs a secondary comparison and evaluation using the comprehensive risk index Rcomb and the risk interval threshold, and executes the corresponding adaptive coupling control strategy to ensure that the wire rope receives the most suitable protection measures in different risk intervals. For example, when Rcomb just exceeds the warning threshold, only a slight deceleration is performed to weaken the initial pulsation energy; while when it approaches the shutdown threshold, a significant deceleration and intermittent winding are implemented to give the wire rope a "buffering" capacity, preventing the instantaneous impact from being directly transmitted to the winch. This is because the explosive struggle of giant deep-sea tuna is a millisecond-level, non-linear impact. If the system continues to wind at a fixed speed, the instantaneous stress in the wire rope may instantly jump to the breakage range. Through the above implementation process, this invention can capture the actual pulsation source at the initial stage of pulsation and couple it with the winch load for analysis, then implement different degrees of speed adjustment and buffering actions according to the risk level. The final effects include: reducing the instantaneous impact peak of the wire rope, improving the winch's overload resistance, reducing the wire rope fatigue accumulation rate, significantly reducing the risk of rope breakage, jamming, and equipment overload, and improving the overall safety and success rate of deep-sea fishing operations.

[0023] Example 2 Please see Figure 1 and Figure 3 Specifically: S1 includes S11; S11. By setting multiple collection points on the electric winch and steel wire rope of the deep-sea fishing tackle, and installing corresponding sensors at each collection point, the collection task is triggered when the steel wire rope extends from the electric winch to 3 meters, the pulsation operation data is collected in real time, and the sensors are wirelessly connected to the central controller of the electric winch via Bluetooth, and the real-time collected pulsation operation data is transmitted to the central controller. The data acquisition points include the first tension acquisition point A, the second tension acquisition point B, and the load acquisition point C; The first tension acquisition point A is located at the wire rope guide position set at the outlet of the electric winch; The second tension sampling point B is located at a guide position in the middle of a steel wire rope that is close to the sea surface but not submerged in seawater. Clamped wire rope tension sensors are used at the first tension acquisition point A and the second tension acquisition point B to acquire the first tension TA and the second tension TB in real time. The first tension TA and the second tension TB at each moment are summarized according to the time series to obtain pulsation operation data. The load acquisition point C is set at the drive end of the electric winch, which is in a stationary state by default. A current sensor is installed at the motor of the electric winch, and an encoder is installed at the shaft of the electric winch.

[0024] S1 also includes S12; S12. In the central controller, the pulsation operation data is received in real time, and the pulsation operation data is preprocessed to obtain the raw monitoring data. The preprocessing includes time synchronization correction, noise filtering, real-time tension difference extraction and standardization processing. The raw monitoring data includes the real-time tension difference Tdiff sequence at each moment of the first tension acquisition point A and the second tension acquisition point B; Time synchronization correction aligns all parameters in the pulsation data collected by all sensors to the same time axis by using a unified clock maintained inside the central controller. Noise filtering uses a bandpass filter (FIR) to filter the time-synchronized pulsed running data, eliminating low-frequency drift caused by sea conditions, mid-frequency noise caused by winch vibration, and pulse noise caused by motor current jumps. Real-time tension difference extraction is achieved by calculating the difference between the first tension TA and the second tension TB collected from the first tension acquisition point A and the second tension acquisition point B on the same time axis, and outputting the real-time tension difference Tdiff(t) at time t. The standardization process uses the Z-score standardization method to standardize the real-time tension difference Tdiff(t) at time t, thereby eliminating the influence of the unit dimension of the real-time tension difference Tdiff(t) at time t. Then, the real-time tension difference Tdiff at each moment is integrated according to the time series to obtain the real-time tension difference Tdiff sequence.

[0025] In this embodiment, the method sets a first tension acquisition point A and a second tension acquisition point B at the outlet of the electric winch and the middle section of the wire rope, respectively. The purpose is to obtain the true difference characteristics of the fish's pulsation as it is transmitted along the wire rope. If only a single point is set at the winch end, the waves, hull swaying, and mechanical vibration of the winch itself will mix with the fish's pulsation, making it impossible for the system to determine the true source of force. However, by forming a "distance difference" through two acquisition points several meters apart, the propagation attenuation of the live pulsation at different positions on the wire rope can be directly reflected, physically isolating background disturbance signals. This structural design can avoid common problems in traditional systems such as "misinterpreting sea conditions as pulsation" or "false alarms triggered by vibration noise." Each acquisition point maintains real-time communication with the central controller via wireless Bluetooth, so that the pulsation data is sent to the control end in milliseconds. However, the significance of this is not only in transmission, but also in that the acquisition only begins after the 3-meter longline condition is triggered. This is because the initial impact of giant deep-sea tuna often occurs after the hook has sunk a certain distance. If data is collected as soon as the wire rope leaves the winch, transient noise and startup jitter from system power-on will be included in the analysis, which will actually impair the recognition accuracy. Therefore, setting a trigger distance can avoid erroneous acquisition of system disturbances. The preprocessing steps in the central controller must include time synchronization correction, bandpass filtering, and Z-score normalization because multi-point data are not naturally comparable. For example, if there is a timestamp offset of tens of milliseconds between two sensors, it will directly lead to "misaligned signals" in deep-sea fish scenarios where the pulsation signal is a sudden peak, causing false fluctuations in the tension difference Tdiff calculated by the system. By unifying the time axis, it can be ensured that the data from each sensor belongs to the same physical moment. Bandpass filtering is used to eliminate low-frequency fluctuations caused by sea state, mid-frequency noise caused by winch structure vibration, and pulse interference generated by motor jumps. Otherwise, these noises will be mistaken for pulsation peaks, directly amplifying the risk of misjudgment. Z-score standardization unifies the values ​​from different sensors and sampling times into dimensionless parameters, ensuring that the subsequently calculated tension difference sequence Tdiff is unaffected by absolute numerical scales and reflects only the "strength of the pulsation itself." Through these steps, this implementation achieves a key effect: separating deep-sea live animal pulsations from complex marine environmental noise and forming a high-quality, real-time tension difference sequence suitable for subsequent energy level determination. This avoids false triggering caused by noise in traditional systems and ensures that pulsation analysis is based on reliable data, providing the necessary physical accuracy and signal stability for subsequent pulsation energy calculation and risk index analysis, thereby significantly improving the accuracy and reliability of online wire rope monitoring.

[0026] Example 3 Please see Figure 1 Specifically: S2 includes S21; S21. Based on the real-time tension difference Tdiff sequence at each moment in the original monitoring data, extract the maximum and minimum values ​​in the real-time tension difference Tdiff sequence at each moment to calculate the difference, obtain the pulsation amplitude term, and use the standard deviation of the real-time tension difference Tdiff(t) at time t as the dispersion term to calculate and output the effective energy level index Elive of the pulsation source, and quantitatively analyze the significance and energy efficiency of the propagation of the live pulsation of the deep-sea giant tuna in the steel wire rope. The effective energy level index Elive of the pulsation source is calculated and output using the following algorithm formula; In the formula, T represents the total number of real-time tension differences Tdiff within the time frame of the data acquisition task. Let Tdiff represent the average value of the real-time tension difference Tdiff sequence at each time step, max(Tdiff(t)) represents the maximum value of the real-time tension difference Tdiff(t) at time t, and min(Tdiff(t)) represents the minimum value of the real-time tension difference Tdiff(t) at time t. The numerator of this formula represents the pulsation amplitude term, and the denominator represents the dispersion term; The pulsation amplitude term originates from the "peak-to-peak (P2P)" concept commonly used in physics and signal processing, and is used to describe the instantaneous intensity of a signal, reflecting the maximum energy of vibration or disturbance. The dispersion term originates from classical statistics and is used to measure the degree of dispersion of data. The standard deviation is used to describe: the strength of fluctuations, data stability, and the uniformity of vibrations. The reason for using the ratio is that if only the peak-to-peak value is taken, it is easy to misinterpret "waves + mast swaying" as a pulsating signal. If only the standard deviation is taken: the peak impact characteristics of the pulsation cannot be captured; Therefore, the physical meaning of the ratio is: if the numerator is large and the denominator is moderate, the strong pulsation (real signal) is amplified; if the numerator is small and the denominator is large, the noise (pseudo signal) is greatly suppressed.

[0027] S2 also includes S22; S22. By statistically analyzing the real-time tension difference (Tdiff) sequence during multiple safe fishing operations, a pulsation reference interval threshold is set. The pulsation reference interval threshold includes the normal reference threshold Elive. safe and suspicious threshold Elive mid ; Wherein: the upper bound of the effective energy level index of the pulsating source obtained under stable mechanical winding state is used as the normal reference threshold. safe The upper limit of the characteristic range of the effective energy level index of the pulsation source that caused slight tension abnormalities in the wire rope but did not pose an overload risk in historical pulsation events was used as the suspicion threshold. midBased on the real-time acquired effective energy level index Elive of the pulsation source and the pulsation reference interval threshold, a preliminary assessment is made to determine the tension pulsation of the wire rope. Where: when the effective energy level index of the pulsation source Elive ≤ the normal reference threshold Elive safe When the disturbance is normal, it is determined to be a minor fluctuation, indicating that the disturbance to the winch input from sea conditions, ship rolling, etc. is weak. At this time, the winch continues to work at the current set speed and does not trigger any protection strategy. When the normal reference threshold Elive safe <Effective energy level index of pulsating source Elive <Suspicious threshold Elive mid When the pulsation is determined to be mild, it means that the pulsation energy has not yet reached a dangerous level. It is the early or middle stage of the tuna's struggle. The load on the wire rope fluctuates slightly and is not enough to cause fatigue accumulation or rope breakage. At this time, the current speed of the electric winch is reduced by 5% to avoid the wire rope being suddenly pulled up when the pulsation just begins to rise. When the effective energy level index of the pulsation source Eliive ≥ the suspicious threshold Eliive mid When the fish is violently throbbing, the winch risk analysis mechanism is triggered, indicating that the fish is struggling explosively and the steel wire rope cannot fully absorb the impact. The winch drive system is subjected to a violent short-term load, which can easily lead to: steel wire rope breakage, hook damage, winch overload, and winding structure jamming.

[0028] In this embodiment, the method extracts the peak-to-peak value and calculates the standard deviation of the real-time tension difference Tdiff at each moment. Its purpose is to independently identify the instantaneous impact intensity of the live throbbing of a giant deep-sea tuna from the overall tension change. If only the original tension sequence is used to judge the tension of the wire rope, wave disturbances, hull swaying, and winch vibrations often produce similar amplitude fluctuations in the low-frequency range, easily leading to misjudgment. The peak-to-peak value accurately reflects the maximum instantaneous energy of the throbbing, while the standard deviation is used to measure whether this energy belongs to a continuous disturbance. The combination of these two forms the effective energy level index Elive of the throbbing source. Its true physical meaning is: **the more obvious the live throbbing, the larger the peak-to-peak value and the more reasonable the dispersion, resulting in a significantly higher ratio; the stronger the noise, the larger the dispersion, which in turn lowers the ratio, thus filtering out false signals.** Therefore, this index enables the system to distinguish between "real fish throbbing" and "background noise disturbance" with millisecond-level accuracy. Furthermore, by statistically analyzing Elive in multiple safe fishing operations, a normal reference threshold Elive is set. safe and suspicious threshold Elive midThe purpose is to establish a quantifiable risk classification for pulsations of different intensities. Without a threshold reference, the system cannot determine whether the pulsation is in the initial disturbance, mild struggle, or explosive impact stage. For example, deep-sea tuna produce tension waveforms with small amplitude but increased frequency in the early stages of struggle. If the system does not distinguish between these, it will frequently trigger interventions in a risk-free state, reducing operational efficiency. Through threshold classification, the system can distinguish between normal sea state fluctuations, mild pulsations, and severe pulsations, giving the control logic a clear boundary. Finally, by comparing the threshold with Elive in real time, this implementation can reduce the winch speed in time during the early stages of pulsation, preventing the wire rope from being suddenly tightened before the pulsation energy is fully released, thus avoiding stress concentration caused by instantaneous traction. When Elive reaches the severe pulsation threshold, triggering the risk analysis mechanism can prevent the explosive impact from being directly transmitted to the winch motor, avoiding wire rope breakage, hook damage, or winding structure jamming due to short-term high load. Through the above implementation, this invention achieves a core effect: early detection of deep-sea live animal movements, early reduction of impact energy, and early prevention of overload risk. Compared to traditional methods that only monitor tension or current, this invention significantly improves the accuracy, sensitivity, and real-time response capability of dynamic safety monitoring of wire ropes.

[0029] Example 4 Please see Figure 1 Specifically: S3 includes S31; S31. After triggering the winch risk analysis mechanism again, start the load acquisition point C, install a current sensor at the end of the winch drive motor to obtain the winch motor current IC at each moment in real time, and install an encoder on the winch shaft to obtain the winch winding speed VD at each moment in real time. The data is then transmitted to the central controller, where it undergoes time synchronization correction, noise filtering, and standardization before being integrated to obtain the load data for the corresponding time. The standardization process involves using the maximum and minimum standardization method based on the maximum motor current of the electric winch and the maximum winding speed of the winch to standardize the current motor current IC and the winding speed VD at each moment, thereby eliminating the influence of unit dimensions. The load data includes the winch motor current IC(t) at time t and the winch winding speed VD(t) at time t.

[0030] S3 also includes S32; S32. The statistical upper bound of the effective energy level index Elive of the pulsating source within the historical monitoring window where no overload occurred during historical safe fishing operations is used as the reference value Eref; the rated maximum continuous operating current provided by the winch drive motor manufacturer is used as the rated maximum safe current Imax of the winch; and combined with the load data, the comprehensive risk index Rcomb is calculated and output. The comprehensive risk index Rcomb is calculated and output using the following algorithm formula; The reference value Eref and the rated maximum safe current Imax of the winch are both dimensionless. This formula originates from the Euclidean distance model in multi-factor risk assessment, which is widely used in engineering safety index, multivariate quantitative evaluation, comprehensive assessment of mechanical load, and statistical distance measurement. This formula does not simply use Euclidean distance, but makes two key innovations. Traditional wire rope monitoring only detects tension, peak tension, and tension RMS, and has never jointly evaluated the energy level Elive of the living pulsating source with the instantaneous current IC(t) of the winch. This application proposes a coupled risk model of the two for the first time. Dimensional consistency analysis shows that the real-time tension difference Tdiff is a dimensionless parameter, therefore the effective energy level index Elive of the pulsation source is also a dimensionless parameter. The winch motor current IC is standardized and is also a dimensionless parameter, therefore the comprehensive risk index Rcomb is also a dimensionless parameter.

[0031] In this embodiment, the method initiates load acquisition point C after triggering the winch risk analysis mechanism. Its core purpose is to monitor the actual load changes on the winch motor side in real time. If the system only monitors the wire rope tension without introducing the motor current IC(t) and winding speed VD(t), it cannot identify whether the winch is already in a high-load operating range. For example, when a giant deep-sea tuna struggles violently, although the wire rope tension can reflect the external impact, the increase in load inside the winch motor is also an important factor leading to overload or jamming. Therefore, it is necessary to collect this data synchronously as soon as abnormal pulsation occurs.

[0032] After transmitting the winch motor current and winding speed to the central controller, time synchronization correction and noise filtering are performed to eliminate pulse noise caused by changes in the motor magnetic field, driver commutation jumps, and shaft micro-vibrations. Without this processing, a large number of high-frequency disturbances from non-load sources would be mixed into the current sequence, causing the system to mistakenly believe that the winch load has increased sharply, thus triggering an erroneous protection strategy. The purpose of maximum and minimum standardization is to transform physical parameters of different dimensions and ranges into dimensionless evaluation values, so that subsequent coupling calculations with the effective energy level index Elive of the pulsation source have a unified scale, ensuring the stability and comparability of the risk index model. Based on this, by using the monitoring window where no overload occurred in historical safe fishing operations as the reference value Eref, and the rated maximum motor current provided by the manufacturer as the rated maximum safe current Imax of the winch, a real, stable, and industrially meaningful risk benchmark can be formed. Without introducing historical window data and the upper limit of the rated current, the system will lack the boundary recognition of "normal load" and "dangerous load," thus failing to accurately identify the upward trend of compound risks. For example, a large peak pulse in deep-sea fish while the winch motor is still under light load does not constitute an overload risk. However, moderate pulse but motor current approaching its rated limit can easily lead to motor damage. By introducing Eref and Imax for dimensionless fusion, the "dual load relationship" in the real physical scenario can be mathematically restored. Finally, through the comprehensive risk index Rcomb constructed based on the Euclidean distance model, this implementation can achieve simultaneous evaluation of "pulse intensity + winch load," avoiding misjudgments or omissions caused by traditional systems focusing on only one dimension. Its true physical meaning is that only when the fish's pulse energy and the winch load increase simultaneously is the wire rope truly in a high-risk breakage zone; therefore, a composite index must be used for judgment. This implementation process enables the system to take timely deceleration, buffering, or shutdown measures before the overload risk occurs, effectively avoiding sudden wire rope breakage, hook damage, or jamming of the winding structure, thereby significantly improving the overall safety and equipment lifespan of deep-sea fishing operations.

[0033] Example 5 Please see Figure 1 Specifically: S4 includes S41; S41. Set risk interval thresholds based on the historical comprehensive risk index Rcomb. These thresholds include a risk warning threshold Rwarn and a risk shutdown threshold Rstop. Specifically: the upper bound of the stable interval of the comprehensive risk index Rcomb during historical safe operation periods is used as the risk warning threshold Rwarn; the lower bound of the comprehensive risk index Rcomb corresponding to events such as high load impact on the wire rope or a sharp increase in winch motor current is used as the risk shutdown threshold Rstop. A secondary comparative evaluation is then performed based on the real-time calculated comprehensive risk index Rcomb to determine the load risk of the winch motor. The specific evaluation content is as follows: When the comprehensive risk index Rcomb ≤ the risk warning threshold Rwarn, the current stress state of the wire rope is determined to be within the safe range; When the comprehensive risk index Rwarn < comprehensive risk index Rcomb and comprehensive risk index Rcomb < risk shutdown threshold Rstop, the wire rope is determined to be in the risk buffer zone. When the comprehensive risk index Rcomb is greater than or equal to the risk shutdown threshold Rstop, the wire rope is determined to be in the shutdown risk range.

[0034] S4 also includes S42; S42. Based on the judgment of the load wind condition of the winch motor, execute the corresponding adaptive coupling control strategy, the details of which are as follows: When it is determined that the current stress state of the wire rope is within the safe range, the winch motor operates at the current winch winding speed VD, continues to monitor, and does not intervene. At the same time, the central controller updates the comprehensive risk index Rcomb every 100ms and then performs a second comparative evaluation. When the wire rope stress state is determined to be within the risk buffer zone, the winch winding speed VD is dynamically decelerated based on the output value of the current comprehensive risk index Rcomb, as follows: When the comprehensive risk index Rcomb exceeds the risk warning threshold Rwarn by 0%-20%, the winding speed is reduced by 3%-6%; When the comprehensive risk index Rcomb exceeds the risk warning threshold Rwarn by 20%-60%, the winding speed is reduced by 6%-18%. When the comprehensive risk index Rcomb exceeds 40%-100% of the risk warning threshold Rwarn, the winding speed is reduced by 18%-30%. The 30% reduction limit is based on the following: it does not affect the normal power output of the winch and can significantly improve the ability to absorb transient shocks; When the wire rope is determined to be in the shutdown risk range, the central controller controls the winch motor to issue a dangerous pulsation warning. When the comprehensive risk index Rcomb is greater than the risk shutdown threshold Rstop for 1 second in a row, the winding will be automatically stopped.

[0035] In this embodiment, the method statistically analyzes the historical comprehensive risk index Rcomb and sets risk warning thresholds Rwarn and Rstop, aiming to establish clear risk level boundaries for winch operation. Without these two thresholds, the system cannot determine whether the current risk is in a "controllable fluctuation" or "approaching rope breakage danger." For example, deep-sea tuna typically exhibit brief but increasingly frequent tension fluctuations before violently struggling. If this stage is not identified in advance and the winch continues to wind at high speed, the subsequent explosive impact will instantly superimpose on the wire rope, easily leading to accelerated fatigue accumulation or instantaneous breakage. Therefore, Rwarn is used to detect rising risk trends in advance, while Rstop is used to identify dangerous states that push the wire rope into its extreme stress range. A secondary comparative evaluation based on the real-time calculated comprehensive risk index Rcomb is performed to implement completely different control strategies under different risk levels, thereby ensuring that intervention actions are neither delayed nor excessive. No intervention is implemented when Rcomb is below Rwarn to ensure operational efficiency and prevent frequent speed reductions in the winch due to minor sea state disturbances. Dynamic deceleration begins when Rcomb falls into the buffer zone to reduce impact energy transmission at the initial stage of pulsation intensification, providing the wire rope with a "stress relief window" and preventing stress concentration caused by direct tension. Immediate shutdown when Rcomb reaches Rstop is to prevent the cumulative risk of "pulsation energy + winch load" from escalating, preventing the wire rope from entering a critical stress state that threatens breakage. In the adaptive coupling control strategy, by correlating the percentage of Rcomb exceeding Rwarn with the reduction rate of the winch winding speed, a linear adjustment method with stronger control is achieved as the risk increases. For example, when the percentage exceeding Rwarn is still within a small range, only a 3%-6% speed reduction is implemented to maintain sufficient power and avoid false triggering of protection; while when the percentage exceeding Rwarn is as high as 80%-100%, reducing the speed to 70%-82% of the original speed can significantly weaken the pulsation impact. This design avoids the sudden surge in stress on the winch caused by the explosive struggles of deep-sea fish, effectively preventing breakage accidents due to the peak stress in the wire rope. Ultimately, this implementation allows the winch to respond with varying degrees of force at different risk stages: maintaining efficiency in the safe zone, actively absorbing impact in the buffer zone, and immediately cutting off power in the shutdown zone. The true physical significance of this process lies in transforming the previously unpredictable explosive pulsation of living organisms into a controllable, adjustable, and preventable stress state through quantification, thresholding, and graded response mechanisms. Compared to the traditional approach of passively shutting down only after observing an anomaly, this implementation significantly improves: the wire rope's impact suppression capability, the winch's overload protection capability, and the overall safety and reliability of deep-sea fishing operations.

[0036] Example 6 Please see Figure 1 and Figure 2A real-time online monitoring system for steel wire rope based on intelligent sensors includes a central acquisition module, a pulsation effective analysis module, a winch risk analysis module, and an adaptive coupling control module. The central acquisition module collects pulsation operation data in real time by setting multiple acquisition points on the wire rope path of the electric winch, and transmits the acquired winch operation data to the central controller of the electric winch. The central controller then preprocesses the winch operation data to obtain the raw monitoring data. The effective pulsation analysis module calculates the effective energy level index (Elive) of the pulsation source based on the original monitoring data. It then performs a preliminary assessment based on the Elive index and the preset pulsation reference interval threshold to determine the tension pulsation status of the wire rope. When the winch risk analysis module determines that the wire rope tension pulsation is abnormal through preliminary assessment, the winch risk analysis mechanism is triggered. The winch risk analysis mechanism calculates the comprehensive risk index Rcomb by extracting the load data of the winch motor and combining it with the effective energy level index Elive of the pulsation source. The adaptive coupling control module performs a secondary comparative evaluation by setting a risk interval threshold and a comprehensive risk index Rcomb, and executes the adaptive coupling control strategy based on the results of the secondary comparative evaluation.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for real-time online monitoring of steel wire ropes based on intelligent sensors, characterized in that: Includes the following steps: S1. Set up multiple collection points on the wire rope path of the electric winch to collect pulsation operation data in real time, and transmit the collected winch operation data to the central controller of the electric winch. The central controller preprocesses the winch operation data to obtain the raw monitoring data. S2. Based on the original monitoring data, calculate the effective energy level index Elive of the pulsation source. Based on the effective energy level index Elive of the pulsation source and the preset pulsation reference interval threshold, conduct a preliminary assessment to determine the tension pulsation of the wire rope. S3. When the initial assessment determines that the wire rope tension pulsation is abnormal, the winch risk analysis mechanism is triggered. The winch risk analysis mechanism calculates the comprehensive risk index Rcomb by extracting the load data of the winch motor and combining it with the effective energy level index Elive of the pulsation source. S4. A secondary comparative evaluation is conducted by setting a risk interval threshold and the comprehensive risk index Rcomb, and an adaptive coupling control strategy is executed based on the results of the secondary comparative evaluation.

2. The method for real-time online monitoring of steel wire rope based on intelligent sensors according to claim 1, characterized in that: S1 includes S11; S11. By setting multiple collection points on the electric winch and steel wire rope of the deep-sea fishing tackle, and installing corresponding sensors at each collection point, the collection task is triggered when the steel wire rope extends from the electric winch to 3 meters, the pulsation operation data is collected in real time, and the sensors are wirelessly connected to the central controller of the electric winch via Bluetooth, and the real-time collected pulsation operation data is transmitted to the central controller. The acquisition points include a first tension acquisition point A, a second tension acquisition point B, and a load acquisition point C; The first tension acquisition point A is guided by a wire rope located at the outlet of the electric winch. The second tension acquisition point B is positioned at the middle section of a steel wire rope that is close to the sea surface but not submerged in seawater. Clamped wire rope tension sensors are used at the first tension acquisition point A and the second tension acquisition point B to acquire the first tension TA and the second tension TB in real time. The first tension TA and the second tension TB at each moment are summarized according to the time series to obtain pulsation operation data. The load acquisition point C is set at the drive end of the electric winch, and is in a stationary state by default. A current sensor is installed at the motor of the electric winch, and an encoder is installed at the shaft of the electric winch.

3. The method for real-time online monitoring of steel wire rope based on intelligent sensors according to claim 2, characterized in that: S1 further includes S12; S12. In the central controller, pulsation operation data is received in real time, and the pulsation operation data is preprocessed to obtain raw monitoring data. The preprocessing includes time synchronization correction, noise filtering, real-time tension difference extraction and standardization processing. The original monitoring data includes the real-time tension difference Tdiff sequence of the first tension acquisition point A and the second tension acquisition point B at each moment. The time synchronization correction is achieved by aligning the timestamps of all parameters in the pulsation data collected by all sensors to the same time axis, based on a unified clock maintained inside the central controller. The noise filtering process uses a bandpass filter (FIR) to filter the time-synchronized and corrected pulsation data. Eliminate low-frequency drift caused by sea conditions, medium-frequency noise caused by winch vibration, and pulse noise caused by motor current fluctuations; The real-time tension difference extraction is achieved by calculating the difference between the first tension TA and the second tension TB collected from the first tension acquisition point A and the second tension acquisition point B on the same time axis, and outputting the real-time tension difference Tdiff(t) at time t. The standardization process uses the Z-score standardization method to standardize the real-time tension difference Tdiff(t) at time t, thereby eliminating the influence of the unit dimension of the real-time tension difference Tdiff(t) at time t. Then, the real-time tension difference Tdiff at each moment is integrated according to the time series to obtain the real-time tension difference Tdiff sequence.

4. The method for real-time online monitoring of steel wire rope based on intelligent sensors according to claim 3, characterized in that: S2 includes S21; S21. Based on the real-time tension difference Tdiff sequence at each moment in the original monitoring data, extract the maximum and minimum values ​​in the real-time tension difference Tdiff sequence at each moment to calculate the difference, obtain the pulsation amplitude term, and use the standard deviation of the real-time tension difference Tdiff(t) at time t as the dispersion term to calculate and output the effective energy level index Elive of the pulsation source. The effective energy level index Elive of the pulsation source is calculated and output using the following algorithm formula; In the formula, T represents the total number of real-time tension differences Tdiff within the time frame of the data acquisition task. Let Tdiff represent the average value of the real-time tension difference Tdiff sequence at each time step, max(Tdiff(t)) represents the maximum value of the real-time tension difference Tdiff(t) at time t, and min(Tdiff(t)) represents the minimum value of the real-time tension difference Tdiff(t) at time t.

5. The method for real-time online monitoring of steel wire rope based on intelligent sensors according to claim 4, characterized in that: S2 further includes S22; S22. By statistically analyzing the real-time tension difference (Tdiff) sequence during multiple safe fishing operations, a pulsation reference interval threshold is set. This pulsation reference interval threshold includes the normal reference threshold Elive. safe and suspicious threshold Elive mid ; Wherein: the upper bound of the effective energy level index of the pulsating source obtained under stable mechanical winding state is used as the normal reference threshold. safe The upper limit of the characteristic range of the effective energy level index of the pulsation source that caused slight tension abnormalities in the wire rope but did not pose an overload risk in historical pulsation events was used as the suspicion threshold. mid Based on the real-time acquired effective energy level index Elive of the pulsation source and the pulsation reference interval threshold, a preliminary assessment is made to determine the tension pulsation of the wire rope. Where: when the effective energy level index of the pulsation source Elive ≤ the normal reference threshold Elive safe When this occurs, it is determined to be a normal fluctuation. At this time, the winch continues to work at the currently set speed without triggering any protection strategy. When the normal reference threshold Elive safe <Effective energy level index of pulsating source Elive <Suspicious threshold Elive mid If a mild pulsation is detected, the current speed of the electric winch should be reduced by 5%. When the effective energy level index of the pulsation source Eliive ≥ the suspicious threshold Eliive mid When the heartbeat is detected, it is determined to be a violent pulsation, which triggers the winch risk analysis mechanism.

6. The method for real-time online monitoring of steel wire rope based on intelligent sensors according to claim 1, characterized in that: S3 includes S31; S31. After triggering the winch risk analysis mechanism again, start the load acquisition point C, install a current sensor at the end of the winch drive motor to obtain the winch motor current IC at each moment in real time, and install an encoder on the winch shaft to obtain the winch winding speed VD at each moment in real time. The data is then transmitted to the central controller, where it undergoes time synchronization correction, noise filtering, and standardization before being integrated to obtain the load data for the corresponding time. The standardization process involves using the maximum and minimum standardization method based on the maximum motor current of the electric winch and the maximum winding speed of the winch to standardize the current motor current IC and the winding speed VD at each moment, thereby eliminating the influence of unit dimensions. The load data includes the winch motor current IC(t) at time t and the winch winding speed VD(t) at time t.

7. A real-time online monitoring method for steel wire rope based on intelligent sensors according to claim 6, characterized in that: S3 further includes S32; S32. The statistical upper bound of the effective energy level index Elive of the pulsating source within the historical monitoring window where no overload occurred during historical safe fishing operations is used as the reference value Eref; the rated maximum continuous operating current provided by the winch drive motor manufacturer is used as the rated maximum safe current Imax of the winch; and combined with the load data, the comprehensive risk index Rcomb is calculated and output. The comprehensive risk index Rcomb is calculated and output using the following algorithm formula; The reference value Eref and the rated maximum safe current Imax of the winch are both dimensionless.

8. A method for real-time online monitoring of steel wire rope based on intelligent sensors according to claim 6, characterized in that: S4 includes S41; S41. Set risk interval thresholds based on the historical comprehensive risk index Rcomb, whereby the risk interval thresholds include a risk warning threshold Rwarn and a risk shutdown threshold Rstop; and perform a secondary comparative evaluation based on the real-time calculated comprehensive risk index Rcomb to determine the load risk of the winch motor. The specific evaluation content is as follows: When the comprehensive risk index Rcomb ≤ the risk warning threshold Rwarn, the current stress state of the wire rope is determined to be within the safe range; When the comprehensive risk index Rwarn < comprehensive risk index Rcomb and comprehensive risk index Rcomb < risk shutdown threshold Rstop, the wire rope is determined to be in the risk buffer zone. When the comprehensive risk index Rcomb is greater than or equal to the risk shutdown threshold Rstop, the wire rope is determined to be in the shutdown risk range.

9. A method for real-time online monitoring of steel wire rope based on intelligent sensors according to claim 8, characterized in that: S4 further includes S42; S42. Based on the judgment of the load wind condition of the winch motor, execute the corresponding adaptive coupling control strategy, the details of which are as follows: When it is determined that the current stress state of the wire rope is within the safe range, the winch motor operates at the current winch winding speed VD, continues to monitor, and does not intervene. At the same time, the central controller updates the comprehensive risk index Rcomb every 100ms and then performs a second comparative evaluation. When the wire rope stress state is determined to be within the risk buffer zone, the winch winding speed VD is dynamically decelerated based on the output value of the current comprehensive risk index Rcomb, as follows: When the comprehensive risk index Rcomb exceeds the risk warning threshold Rwarn by 0%-20%, the winding speed is reduced by 3%-6%; When the comprehensive risk index Rcomb exceeds the risk warning threshold Rwarn by 20%-60%, the winding speed is reduced by 6%-18%. When the comprehensive risk index Rcomb exceeds 40%-100% of the risk warning threshold Rwarn, the winding speed is reduced by 18%-30%. When the wire rope is determined to be in the shutdown risk range, the central controller controls the winch motor to issue a dangerous pulsation warning. When the comprehensive risk index Rcomb is greater than the risk shutdown threshold Rstop for 1 second in a row, the winding will be automatically stopped.

10. A real-time online monitoring system for steel wire rope based on intelligent sensors, applied to the real-time online monitoring method for steel wire rope based on intelligent sensors as described in any one of claims 1-9, characterized in that: It includes a central acquisition module, a pulse effective analysis module, a winch risk analysis module, and an adaptive coupling control module; The central acquisition module collects pulsation operation data in real time by setting multiple acquisition points on the wire rope path of the electric winch, and transmits the acquired winch operation data to the central controller of the electric winch. The central controller then preprocesses the winch operation data to obtain the raw monitoring data. The effective pulsation analysis module calculates the effective energy level index Elive of the pulsation source based on the original monitoring data, and makes a preliminary assessment based on the effective energy level index Elive of the pulsation source and the preset pulsation reference interval threshold to determine the tension pulsation of the wire rope. When the winch risk analysis module determines that the wire rope tension pulsation is abnormal through preliminary assessment, the winch risk analysis mechanism is triggered. The winch risk analysis mechanism calculates the comprehensive risk index Rcomb by extracting the load data of the winch motor and combining it with the effective energy level index Elive of the pulsation source. The adaptive coupling control module performs a secondary comparative evaluation by setting a risk interval threshold and a comprehensive risk index Rcomb, and executes the adaptive coupling control strategy based on the secondary comparative evaluation results.

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