Steel wire rope whole life cycle safety monitoring and adjusting method and system

Abstract

The invention discloses a steel wire rope full-life-cycle safety monitoring and adjusting method and system, and relates to the technical field of steel wire rope safety monitoring. The method comprises the steps of constructing an operation state scoring model and obtaining a state scoring value of a steel wire rope; dividing the steel wire rope into a plurality of behavior sections, and configuring a corresponding adjustment response mechanism; when the state score value crosses the boundary, entering a buffer state, and judging whether behavior section switching is carried out or not according to the boundary crossing stability of the state score value; scheduling integral value updating is carried out according to the task scheduling behavior, and a risk intervention response mechanism is triggered; and identifying the current life cycle stage of the steel wire rope, constructing a task atlas adaptation mechanism, and matching a suitable task type according to the current behavior section, the scheduling integral value and the life cycle stage of the steel wire rope to perform task allocation. A full-process and dynamic closed-loop steel wire rope service life management and dispatching control system is constructed, and the use value of the steel wire rope is maximized while safety is guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of wire rope safety monitoring, and in particular to a method and system for monitoring and adjusting the safety of wire rope throughout its entire life cycle. Background Technology

[0002] Steel wire rope is a flexible rope made of multiple strands of high-strength steel wires twisted together. Typically made of high-carbon steel, it is an indispensable key component in industry and engineering due to its superior tensile strength, abrasion resistance, and corrosion resistance. From the giant booms of lifting machinery to the main cables of cross-sea bridges, from the vertical transport of elevators to the pressure-resistant cables of deep-sea exploration equipment, steel wire rope, with its unique properties, undertakes the core tasks of transmitting power, supporting heavy loads, or ensuring structural stability in numerous fields. Its operational safety is of decisive significance to the overall safety of equipment and the safety of personnel and property.

[0003] In related technologies, traditional wire rope safety monitoring methods are mostly based on a binary classification of normal / abnormal, with simple scheduling strategies that cannot achieve differentiated responses for different risk levels. Furthermore, they are extremely sensitive to state fluctuations, potentially leading to misjudgments and frequent section switching due to short-term disturbances, affecting stable operation. At the same time, traditional scheduling mechanisms do not consider the cumulative impact of tasks on wire rope lifespan, relying on manual experience or static rules, resulting in a disconnect between scheduling and usage, and failing to adapt to the capacity boundaries of wire ropes under different conditions, thus requiring improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for monitoring and adjusting the safety of wire ropes throughout their entire life cycle, so as to solve the problems mentioned in the background art.

[0005] Firstly, this application provides a method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle, which adopts the following technical solution: Obtain the operating behavior data of the wire rope, construct an operating status scoring model, input the operating behavior data into the operating status scoring model, and obtain the status score value of the wire rope; Based on the state score, the wire rope is divided into multiple behavior segments, and a corresponding adjustment response mechanism is configured for each behavior segment. A behavior segment inertial delay mechanism is introduced. When the state score value goes out of bounds, it enters a buffer state. Whether to switch behavior segments is determined based on the out-of-bounds stability of the state score value. Establish a scheduling points management mechanism, update the scheduling points value of the wire rope according to the task scheduling behavior, and trigger a risk intervention response mechanism based on the updated scheduling points value; A life cycle phase management mechanism for wire ropes is introduced to divide wire ropes into multiple life cycle stages and identify the current life cycle stage of the wire rope. A task graph adaptation mechanism is constructed to match appropriate task types based on the current behavior segment, scheduling integral value, and life cycle stage of the wire rope, and to allocate tasks accordingly.

[0006] Preferably, the steps of acquiring the operating behavior data of the wire rope, constructing an operating status scoring model, and inputting the operating behavior data into the operating status scoring model to obtain the status score value of the wire rope are as follows: Acquire operational behavior data of the wire rope, including structural damage data, mechanical performance parameters, environmental operating condition parameters, and images of external defects; The operational behavior data is preprocessed, and dynamic features are extracted from the processed operational behavior data to generate a multi-dimensional operational behavior feature vector. A running status scoring model is constructed. The multi-dimensional running behavior feature vector is input into the running status scoring model. An attention mechanism is introduced to dynamically adjust the feature vector weights and output the predicted status score value of the wire rope. Obtain the confidence parameter of the predicted state score value, compare the confidence parameter with a preset confidence threshold, and if the confidence parameter exceeds the preset confidence threshold, generate a state score value for the wire rope based on the predicted state score value.

[0007] Preferably, the step of dividing the wire rope into multiple behavior segments based on the state score and configuring a corresponding adjustment response mechanism for each behavior segment specifically includes: Set a threshold range for the operating segment and compare the status score value with the threshold range for the operating segment; If the status score is lower than the threshold range of the operating section, the wire rope is in the normal operating section and can be used to perform routine task scheduling. If the status score is within the threshold range of the operating section, the wire rope is in the warning operating section, triggering the first warning signal, setting the first adjustment response mechanism, limiting the high load task of the wire rope and shortening the maintenance cycle. If the status score exceeds the threshold range of the operating section, the wire rope is in a risky operating section, triggering a second early warning signal, setting a second adjustment response mechanism, prohibiting the wire rope from exceeding the threshold and triggering a pre-replacement mechanism.

[0008] Preferably, a behavior segment inertial delay mechanism is introduced. When the state score value goes out of bounds, the system enters a buffer state. The step of determining whether to switch behavior segments based on the out-of-bounds stability of the state score value is as follows: During the switching of behavior segments, a behavior segment inertial delay mechanism is introduced. When the state score value of the wire rope crosses the boundary of the current behavior segment for the first time, a buffer state is triggered. Within a preset observation time window, the state score value of the wire rope is continuously acquired, and the number of times the state score value of the wire rope falls into the target behavior segment range within the preset observation time window is obtained. Based on the number of boundary violations and the total number of records of the wire rope condition score within the preset observation time window, the ratio of the number of boundary violations to the total number of records is calculated to obtain the boundary violation ratio of the wire rope condition score. Based on the out-of-bounds ratio, the out-of-bounds stability of the state score value is obtained, and it is determined whether to perform behavior segment switching.

[0009] Preferably, the step of obtaining the out-of-bounds stability of the state score value based on the out-of-bounds ratio and determining whether to perform behavior segment switching specifically includes: Based on the aforementioned out-of-bounds ratio, an out-of-bounds ratio threshold is set, and the out-of-bounds ratio is compared with the preset out-of-bounds ratio threshold. If the out-of-bounds ratio does not exceed the preset out-of-bounds ratio threshold, the state score value of the wire rope is unstable and out of bounds. It is determined that the state score value of the wire rope returns to the current behavior segment in the buffer state and exits the buffer state. If the out-of-bounds ratio exceeds the preset out-of-bounds ratio threshold, the state score value of the wire rope is out of bounds and stable. It is determined that the state score value of the wire rope has entered the target behavior segment in the buffer state, and the behavior segment switching is performed.

[0010] Preferably, a scheduling points management mechanism is constructed, which updates the scheduling points value of the wire rope based on task scheduling behavior, and triggers a risk intervention response mechanism based on the updated scheduling points value. Specifically, the steps are as follows: Set an initial scheduling integral value P0 for the wire rope and obtain the task scheduling behavior data of the wire rope. The task scheduling behavior data includes the task load level L, the task execution duration T, and the task scheduling frequency F. The updated scheduling integral value P of the wire rope is calculated using the correlation formula P=P0-(w1×L+w2×T+w3×F), where w1, w2, and w3 are weighting coefficients and none of them are zero. Set a first integration threshold P1 and a second integration threshold P2, and compare the scheduling integration value with the first integration threshold and the second integration threshold respectively; When the scheduling score drops to the first threshold, a risk warning response is triggered, generating a scheduling priority reduction instruction to restrict the allocation of high-load tasks. When the scheduling integral value drops to the second threshold, a mandatory risk intervention response is triggered, a scheduling authority lock command is generated, and an emergency maintenance process is initiated.

[0011] Preferably, a wire rope lifecycle phase management mechanism is introduced, which divides the wire rope into multiple lifecycle stages. The steps for identifying the current lifecycle stage of the wire rope are as follows: A life cycle phase management mechanism for wire ropes is introduced, dividing wire ropes into multiple life cycle stages, including the initial operation stage, stable use stage, fatigue accumulation stage, and critical retirement stage. Acquire the state behavior data of the wire rope, including cumulative running time, fatigue index, and number of wire breakages; When the cumulative running time is less than the first time threshold, the fatigue index is lower than the first fatigue threshold, and the number of wire breaks is zero, the wire rope is determined to be in the initial running stage of its life cycle. When the cumulative running time is between the first time threshold and the second time threshold, the fatigue index is lower than the first fatigue threshold, and the number of broken wires is lower than the first broken wire threshold, the current life cycle stage of the wire rope is determined to be the stable use stage. When the fatigue index is between the first fatigue threshold and the second fatigue threshold, or when the number of wire breaks is between the wire breakage threshold and the second wire breakage threshold, the current life cycle stage of the wire rope is determined to be the fatigue accumulation stage. When the cumulative operating time approaches the rated service life, or the fatigue index exceeds the second fatigue threshold, or the number of broken wires exceeds the second broken wire threshold, the current life cycle stage of the wire rope is determined to be the critical retirement stage.

[0012] Preferably, the steps for constructing a task graph adaptation mechanism, which matches suitable task types based on the current behavior segment, scheduling integral value, and lifecycle stage of the wire rope, and performs task allocation are as follows: Extract the current behavioral segment, scheduling integral value, and life cycle stage of the wire rope to construct a multi-dimensional task matching graph; Based on the task matching map, the compatibility between each task type and the wire rope is calculated using the membership function to obtain the task compatibility parameter. Set a task adaptation threshold, compare the task adaptation parameter with the task adaptation threshold, and obtain the task adaptation result; Based on the task adaptation results, a task allocation request is triggered to allocate the steel wire rope task.

[0013] Secondly, the wire rope full life cycle safety monitoring and adjustment system provided in this application adopts the following technical solution: A wire rope life-cycle safety monitoring and adjustment system includes: The operation status scoring module acquires the operation behavior data of the wire rope, constructs an operation status scoring model, inputs the operation behavior data into the operation status scoring model, and obtains the status score value of the wire rope. The behavior segment division module divides the wire rope into multiple behavior segments based on the state score value and configures a corresponding adjustment response mechanism for each behavior segment. The behavior segment inertial delay module introduces a behavior segment inertial delay mechanism. When the state score value goes out of bounds, it enters a buffer state and determines whether to switch behavior segments based on the out-of-bounds stability of the state score value. The scheduling points management module constructs a scheduling points management mechanism, updates the scheduling points value of the wire rope based on task scheduling behavior, and triggers a risk intervention response mechanism based on the updated scheduling points value. The life cycle phase management module introduces a wire rope life cycle phase management mechanism, which divides the wire rope into multiple life cycle stages and identifies the current life cycle stage of the wire rope. The task graph adaptation module constructs a task graph adaptation mechanism, matching suitable task types and allocating tasks based on the current behavior segment, scheduling integral value, and life cycle stage of the wire rope.

[0014] In summary, this application includes at least one of the following beneficial technical effects: 1. By systematically collecting key operational behavior parameters of wire ropes and inputting them into a scoring model for quantitative evaluation, a comprehensive and standardized judgment of the wire rope's health status is achieved, avoiding reliance on manual experience or single indicators, thereby improving the accuracy and intelligence level of status assessment. By constructing behavior segments, continuously changing score values ​​are discretized and mapped to operable management states, and differentiated adjustment strategies are configured for each segment, making scheduling decisions and maintenance responses more targeted and refined, thereby improving the utilization efficiency and safety assurance capabilities of the wire rope. By introducing a buffer state, when the score value exceeds the limit, the behavior segment is not immediately switched, but rather allowed to stabilize, effectively suppressing frequent segment switching caused by score fluctuations, improving the robustness and anti-interference ability of system decisions, and enhancing the engineering practicality of the adjustment logic. The impact of task scheduling behavior on the wire rope load is quantified into a scheduling integral, which is continuously accumulated and updated, enabling full-process tracking and monitoring of usage intensity; when the integral value reaches a preset threshold, an intervention mechanism is triggered, thereby achieving soft overload management and risk control without relying on complex sensors, effectively improving the system's risk prevention and control capabilities. Dividing the operating cycle of wire ropes into multiple typical stages and dynamically identifying their current stage status allows for adaptive adjustments to the weights of the scoring model, scheduling rules, and task priorities. This reflects a systematic approach to lifecycle management, improving resource utilization efficiency and system reliability. By establishing a mapping between task types and wire rope state capabilities, personalized and adaptable task allocation is achieved. When the wire rope is in different behavioral segments, lifecycle stages, or integral states, the system can automatically match appropriate tasks, preventing overload or unreasonable scheduling from the outset and enhancing the initiative and intelligence of wire rope lifecycle management. A full-process, adaptive, and dynamically closed-loop wire rope lifecycle management and scheduling control system has been constructed, maximizing the utilization value of wire ropes while ensuring safety.

[0015] 2. An inertial delay mechanism is introduced for behavior segment switching. This prevents the system from immediately switching segments when the state score value exceeds the limit, instead allowing it to enter a buffer state. This improves the anti-interference capability and decision stability of behavior switching, avoiding frequent switching due to sensor fluctuations, occasional interference, or boundary jitter. Continuous state score value data is collected within a defined time window, and a stability trend of the score value is established by combining temporal semantics. This provides a time-based constraint for behavior switching, thereby improving the reliability of system identification. The proportion of the score value in the target segment is quantified by calculating the statistical index of the ratio of the number of out-of-bounds occurrences to the total number of records. This reflects whether the current score value has a sufficiently stable trend. If the out-of-bounds proportion reaches a certain preset threshold, the wire rope can be considered to be trending towards a new behavior segment, thus justifying the behavior switching. The final decision on whether to switch behavior segments is not based on a single score value, but on a comprehensive judgment of the score trend over a period of time. This design improves the robustness and accuracy of system behavior adjustment, avoids adjustment oscillations and misjudged behavior segments, thereby extending the service life of the wire rope and optimizing maintenance strategies.

[0016] 3. By integrating multiple core dimensions of the wire rope's operating status—behavioral segments representing short-term states, scheduling integrals reflecting cumulative task pressure, and lifecycle stages reflecting macro-level usage—a multi-dimensional, quantifiable task adaptation map is constructed. This provides rich state label information for subsequent task allocation, exhibiting good generalization and flexibility. The membership function in fuzzy mathematics handles uncertainties, achieving flexible matching between task types and wire rope states, avoiding rigid adaptation caused by rigid rules. The adaptation degree of the wire rope to different tasks can be accurately calculated based on the load strength and continuity requirements of each task. A task adaptation threshold mechanism is introduced to ensure that tasks are assigned only when the wire rope state allows it, providing threshold protection and risk control capabilities. This prevents the forced allocation of high-intensity tasks under high-risk conditions, thereby reducing the risk of rope breakage. The task adaptation process is decoupled from the scheduling system, with the adaptation result serving as an independent trigger condition, facilitating closed-loop intelligent control of the scheduling system. This not only applies to current task allocation but also serves advanced strategies such as task scheduling and priority adjustment, effectively improving the accuracy and safety control capabilities of task allocation. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the specific steps of an embodiment of a steel wire rope life-cycle safety monitoring and adjustment method according to the present invention.

[0018] Figure 2 This is a schematic diagram of the module connection of an embodiment of a wire rope full life cycle safety monitoring and adjustment system according to the present invention. Detailed Implementation

[0019] The following examples and... Figures 1-2The present invention will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0020] This invention discloses a method for safety monitoring and adjustment of steel wire rope throughout its entire life cycle, specifically including the following steps: Step S1: Obtain the operating behavior data of the wire rope, construct an operating status scoring model, input the operating behavior data into the operating status scoring model, and obtain the status score value of the wire rope. Step S2: Based on the state score, the wire rope is divided into multiple behavior segments, and a corresponding adjustment response mechanism is configured for each behavior segment. Step S3: Introduce a behavior segment inertial delay mechanism. When the state score value goes out of bounds, enter a buffer state. Determine whether to switch behavior segments based on the out-of-bounds stability of the state score value. Step S4: Construct a scheduling points management mechanism, update the scheduling points value of the wire rope according to the task scheduling behavior, and trigger a risk intervention response mechanism based on the updated scheduling points value. Step S5: Introduce a wire rope life cycle phase management mechanism to divide the wire rope into multiple life cycle stages and identify the current life cycle stage of the wire rope. Step S6: Construct a task graph adaptation mechanism, match suitable task types based on the current behavior segment, scheduling integral value, and life cycle stage of the wire rope, and allocate tasks accordingly.

[0021] In practical applications, by systematically collecting key operational behavior parameters of the wire rope and inputting them into a scoring model for quantitative evaluation, a comprehensive and standardized judgment of the wire rope's health status is achieved, avoiding reliance on manual experience or single indicators, thereby improving the accuracy and intelligence level of status assessment. By constructing behavior segments, continuously changing score values ​​are discretized and mapped into operable management states, and differentiated adjustment strategies are configured for each segment, making scheduling decisions and maintenance responses more targeted and refined, thereby improving the utilization efficiency and safety assurance capabilities of the wire rope. By introducing a buffer state, when the score value exceeds the limit, the behavior segment is not immediately switched, but rather allowed to stabilize, effectively suppressing frequent segment switching caused by score jitter, improving the robustness and anti-interference ability of system decisions, and enhancing the engineering practicality of the adjustment logic. The impact of task scheduling behavior on the wire rope load is quantified into a scheduling integral, which is continuously accumulated and updated, enabling full-process tracking and monitoring of usage intensity; when the integral value reaches a preset threshold, an intervention mechanism is triggered, thereby achieving soft overload management and risk control without relying on complex sensors, effectively improving the system's risk prevention and control capabilities. Dividing the operating cycle of wire ropes into multiple typical stages and dynamically identifying their current stage status allows for adaptive adjustments to the weights of the scoring model, scheduling rules, and task priorities. This reflects a systematic approach to lifecycle management, improving resource utilization efficiency and system reliability. By establishing a mapping between task types and wire rope state capabilities, personalized and adaptable task allocation is achieved. When the wire rope is in different behavioral segments, lifecycle stages, or integral states, the system can automatically match appropriate tasks, preventing overload or unreasonable scheduling from the outset and enhancing the initiative and intelligence of wire rope lifecycle management.

[0022] The steps for acquiring the operational behavior data of the wire rope, constructing an operational status scoring model, and inputting the operational behavior data into the operational status scoring model to obtain the status score value of the wire rope are as follows: Step S11: Obtain the operating behavior data of the wire rope, which includes structural damage data, mechanical performance parameters, environmental operating condition parameters, and images of appearance defects. Step S12: Preprocess the running behavior data, extract dynamic features from the processed running behavior data, and generate a multi-dimensional running behavior feature vector. Step S13: Construct an operation status scoring model. Input the multi-dimensional operation behavior feature vector into the operation status scoring model, introduce an attention mechanism to dynamically adjust the feature vector weights, and output the predicted status score value of the wire rope. Step S14: Obtain the confidence parameter of the predicted state score value, compare the confidence parameter with a preset confidence threshold, and if the confidence parameter exceeds the preset confidence threshold, generate the state score value of the wire rope based on the predicted state score value.

[0023] In practical applications, acquiring operational behavior data of wire ropes enables a comprehensive understanding of their overall operational behavior, going beyond single stress or fatigue indicators. Introducing unstructured data such as images of external defects expands the data dimension, showcasing the multimodal fusion characteristics of monitoring and providing a high-dimensional, reliable input foundation for subsequent evaluation. Preprocessing operations such as filtering, standardization, and missing value handling improve data quality. Combined with dynamic feature extraction algorithms, such as temporal aggregation and sliding windows, highly correlated features reflecting the changing trends of the wire rope's state can be effectively extracted. The generated feature vectors comprehensively reflect the state evolution trajectory of the wire rope under complex operating environments, providing high-quality modeling data for state scoring. By constructing state scoring models, such as deep neural networks, support vector machines, or ensemble regression models, the high-dimensional input features are modeled to assess the current health status of the wire rope. Introducing an attention mechanism dynamically assigns higher weights to key features based on different scenarios or operating cycles, enabling the model to adapt, improving prediction accuracy and generalization ability, and significantly enhancing system intelligence. The introduction of a prediction confidence assessment mechanism enhances the credibility management capability of the model's output results. When the confidence level of the score is lower than the threshold, compensation can be made by prompting retesting, integrating historical data, or manual review. This can effectively avoid misjudgments caused by model uncertainty, enhance the stability and controllability of prediction results, and improve the robustness and reliability of the system.

[0024] Based on the state score, the wire rope is divided into multiple behavior segments, and a corresponding adjustment response mechanism is configured for each behavior segment. The specific steps are as follows: Step S21: Set the operating segment threshold range and compare the status score value with the operating segment threshold range; Step S22: If the status score is lower than the threshold range of the operating section, the wire rope is in the normal operating section and can be used to perform routine task scheduling. Step S23: If the status score value is within the threshold range of the operating section, the wire rope is in the warning operating section, triggering the first warning signal, setting the first adjustment response mechanism, limiting the high load task of the wire rope and shortening the maintenance cycle. Step S24: If the status score exceeds the threshold range of the operating section, the wire rope is in a risky operating section, triggering a second early warning signal, setting a second adjustment response mechanism, prohibiting the wire rope from exceeding the threshold and triggering a pre-replacement.

[0025] In practical applications, the status score is compared with the threshold range of the operating section. By setting a boundary threshold for the status score, continuous scoring results can be transformed into discrete behavioral sections, thus forming a standardized and easily executable adjustment logic system, providing a boundary identification basis for subsequent adjustment responses. When the status score is below the threshold range of the operating section, the wire rope is considered to be operating healthily, scheduling restrictions are relaxed, wire rope utilization efficiency is improved, and the normal progress of task load allocation is ensured, which helps to maximize resource utilization and avoid waste of wire rope resources due to conservative strategies. When the status score falls within the threshold range of the operating section, an early warning buffer section is set to realize early identification and response intervention for the deterioration of the wire rope's operating status. By limiting high-load tasks and shortening maintenance cycles, performance degradation can be delayed and the fatigue accumulation rate can be reduced, demonstrating the system's forward-looking control capability over fatigue progression trends. When the status score exceeds the threshold range of the operating section, a second adjustment response mechanism is set, which is the highest level of intervention in the wire rope status management. This reflects that the system has the ability to identify and control faults in advance. By prohibiting high-risk tasks and triggering advance replacement strategies, it can prevent potential accidents such as breakage and rope jamming, and significantly improve operational safety and reliability.

[0026] A behavior segment inertial delay mechanism is introduced. When the state score value goes out of bounds, it enters a buffer state. The step of determining whether to switch behavior segments based on the out-of-bounds stability of the state score value is as follows: Step S31: During the switching of behavior segments, an inertial delay mechanism for behavior segments is introduced. When the state score value of the wire rope crosses the boundary of the current behavior segment for the first time, a buffer state is triggered. Step S32: Within a preset observation time window, continuously acquire the state score value of the wire rope to obtain the number of times the state score value of the wire rope falls into the target behavior segment range within the preset observation time window. Step S33: Based on the number of boundary crossings and the total number of records of the wire rope status score within the preset observation time window, calculate the ratio of the number of boundary crossings to the total number of records to obtain the boundary crossing ratio of the wire rope status score. Step S34: Based on the out-of-bounds ratio, obtain the out-of-bounds stability of the state score value, and determine whether to perform behavior segment switching.

[0027] In practical applications, an inertial delay mechanism is introduced for behavior segment switching. This prevents the system from immediately switching segments when the state score value exceeds the limit, instead allowing it to enter a buffer state. This improves the anti-interference capability and decision stability of behavior switching, avoiding frequent switching due to sensor fluctuations, occasional interference, or boundary jitter. Continuous state score value data is collected within a limited time window, and a stability trend of the score value is established by combining temporal semantics. This provides a time-based constraint for behavior switching, thereby improving the reliability of system identification. The proportion of the score value in the target segment is quantified by calculating the statistical index of the ratio of the number of out-of-bounds occurrences to the total number of records. This reflects whether the current score value has a sufficiently stable trend. If the out-of-bounds proportion reaches a certain preset threshold, the wire rope can be considered to be trending towards a new behavior segment, thus justifying the behavior switching. The final decision on whether to switch behavior segments is not based on a single score value, but on a comprehensive judgment of the score trend over a period of time. This design improves the robustness and accuracy of system behavior adjustment, avoids adjustment oscillations and misjudged behavior segments, thereby extending the service life of the wire rope and optimizing maintenance strategies.

[0028] The step of determining whether to perform behavior segment switching based on the out-of-bounds ratio, obtaining the out-of-bounds stability of the state score value, is as follows: Step S341: Based on the out-of-bounds ratio, set an out-of-bounds ratio threshold and compare the out-of-bounds ratio with the preset out-of-bounds ratio threshold. Step S342: If the out-of-bounds ratio does not exceed the preset out-of-bounds ratio threshold, the state score value of the wire rope is unstable and out of bounds. It is determined that the state score value of the wire rope returns to the current behavior segment in the buffer state and exits the buffer state. Step S343: If the out-of-bounds ratio exceeds the preset out-of-bounds ratio threshold, the state score value of the wire rope is out of bounds and stable. It is determined that the state score value of the wire rope has entered the target behavior segment in the buffer state, and the behavior segment switching is performed.

[0029] In practical applications, by setting an out-of-bounds percentage threshold, the system establishes a clear and quantifiable judgment standard, enabling objective evaluation of the state score's performance in the buffer state. This helps the system make stability judgments about the scoring trend, improving the system's controllability and reliability. When the score only occasionally exceeds the limit or exhibits strong fluctuations, or when the percentage of the score remaining in the target behavior segment is insufficient, the system maintains the current behavior segment unchanged. This effectively avoids unnecessary behavior switching caused by occasional interference, sensor errors, or short-term anomalies, thereby enhancing the system's anti-disturbance capability and scoring stability. When the percentage of the score in the target behavior segment reaches the threshold, it indicates a clear and persistent state transition trend. The system can reliably determine that the state has deviated from its original behavior state and execute a behavior segment switch. While ensuring safety redundancy, this also avoids excessive system lag affecting response, improving the sensitivity and real-time performance of wire rope management, and effectively enhancing the intelligence and practicality of the wire rope full lifecycle state management system.

[0030] The steps for constructing a scheduling points management mechanism, updating the scheduling points value of the wire rope based on task scheduling behavior, and triggering a risk intervention response mechanism based on the updated scheduling points value are as follows: Step S41: Set an initial scheduling integral value P0 for the wire rope and obtain the task scheduling behavior data of the wire rope. The task scheduling behavior data includes the task load level L, the task execution duration T, and the task scheduling frequency F. Step S42: The updated scheduling integral value P of the wire rope is calculated using the correlation formula P=P0-(w1×L+w2×T+w3×F), where w1, w2, and w3 are weight coefficients and none of them are zero. Step S43: Set the first integration threshold P1 and the second integration threshold P2, and compare the scheduling integration value with the first integration threshold and the second integration threshold respectively; Step S44: When the scheduling integral value drops to the first threshold of integral, a risk warning response is triggered, a scheduling priority reduction instruction is generated, and the allocation of high-load tasks is restricted. Step S45: When the scheduling integral value drops to the second integral threshold, a mandatory risk intervention response is triggered, a scheduling authority lock command is generated, and an emergency maintenance process is initiated.

[0031] In practical applications, an initialization and data input mechanism for the scheduling integral model is established. By setting a unified initial integral value P0 and introducing three elements of scheduling behavior—load level L, task duration T, and scheduling frequency F—a data foundation is provided for subsequent integral evolution, facilitating the cumulative identification of task pressure and historical tracking of operational intensity. By comprehensively considering different dimensions of scheduling behavior through correlation formulas and adjusting the influence of each factor using weighting factors w1, w2, and w3, a quantitative reflection of the risk of wire rope usage due to task load is achieved, contributing to the construction of a behavior-driven risk identification system. The setting of tiered thresholds introduces a risk level management mechanism into the system, subdividing the wire rope's usage status into three stages: normal, warning, and high-risk. This allows for layered intervention, effectively preventing sudden damage caused by excessive scheduling and improving the foresight and accuracy of risk control. When the scheduling integral value drops to the first integral threshold, a risk warning response is triggered, indicating that the wire rope has entered a moderate risk state. The system will proactively intervene in the scheduling task type, limiting the continued allocation of high-load tasks to reduce the load intensity of the wire rope, achieving a task avoidance-based risk control mechanism and extending the usable lifespan of the wire rope. When the scheduling integral value drops to the second threshold, a mandatory risk intervention response is triggered, indicating that the wire rope is in a high-risk state. The system directly prohibits scheduling operations and initiates maintenance procedures to ensure that no further task pressure is applied to the wire rope, effectively avoiding major accidents caused by wire rope fatigue or loss of control of hidden dangers, and improving the safety closed-loop management capability of the scheduling system.

[0032] A lifecycle phase management mechanism for wire ropes is introduced, dividing the wire rope into multiple lifecycle stages. The steps for identifying the current lifecycle stage of the wire rope are as follows: Step S51: Introduce a wire rope life cycle phase management mechanism to divide the wire rope into multiple life cycle stages, including the initial operation stage, stable use stage, fatigue accumulation stage, and critical retirement stage. Step S52: Obtain the state behavior data of the wire rope, including the cumulative running time, fatigue index, and number of wire breaks; Step S53: When the cumulative running time is less than the first time threshold, the fatigue index is lower than the first fatigue threshold, and the number of wire breaks is zero, the current life cycle stage of the wire rope is determined to be the initial running stage. Step S54: When the cumulative running time is between the first time threshold and the second time threshold, the fatigue index is lower than the first fatigue threshold, and the number of wire breaks is lower than the first wire breakage threshold, then the current life cycle stage of the wire rope is determined to be the stable use stage. Step S55: When the fatigue index is between the first fatigue threshold and the second fatigue threshold, or when the number of wire breaks is between the wire breakage threshold and the second wire breakage threshold, the current life cycle stage of the wire rope is determined to be the fatigue accumulation stage. Step S56: When the cumulative running time is close to the rated service life, or the fatigue index is higher than the second fatigue threshold, or the number of broken wires exceeds the second broken wire threshold, the current life cycle stage of the wire rope is determined to be the critical retirement stage.

[0033] In practical applications, a phased framework was established for the full life-cycle management of wire ropes. The complex continuous use state is divided into four stages with typical characteristics: initial operation, stable use, fatigue accumulation, and critical retirement. This enables structured modeling of state evolution, facilitating subsequent targeted health assessments and scheduling strategy development. Wire rope state behavior data is acquired, including cumulative runtime, fatigue index, and number of wire breaks, covering time (runtime), damage (fatigue index), and safety (number of wire breaks). This provides multi-source data support for life-cycle identification, enhancing the model's accuracy and adaptability. The initial operation stage is identified based on combined rules, indicating the wire rope is in an "early healthy" state, which helps in formulating routine scheduling strategies and maintaining relatively lenient scheduling permissions. The stable use stage reflects that the wire rope is in a healthy and stable range, capable of executing standard task assignments, and is the main scheduling stage, where scheduling strategies can maintain high utilization. The fatigue accumulation stage indicates the early stage of fatigue, suggesting potential risk accumulation in the wire rope, requiring a gradual tightening of task scheduling intensity and an increase in the frequency of state monitoring. By identifying critical decommissioning stages and high-risk conditions, the system should automatically lock scheduling permissions and trigger a replacement mechanism to prevent rope breakage accidents caused by exceeding usage limits and ensure operational safety. This significantly improves the predictability, economy, and safety assurance capabilities of wire rope usage, making it suitable for operational safety management and resource efficiency optimization in high-risk environments.

[0034] The steps for constructing a task graph adaptation mechanism, which matches suitable task types based on the current behavior segment, scheduling integral value, and lifecycle stage of the wire rope, and assigns tasks are as follows: Step S61: Extract the current behavior segment, scheduling integral value, and life cycle stage of the wire rope to construct a multi-dimensional task matching graph; Step S62: Based on the task matching map, calculate the compatibility between each task type and the wire rope using the membership function to obtain the task compatibility parameter. Step S63: Set a task adaptation threshold, compare the task adaptation parameter with the task adaptation threshold, and obtain the task adaptation result; Step S64: Based on the task adaptation result, trigger a task allocation request and perform task allocation for the wire rope.

[0035] In practical applications, by integrating multiple core dimensions of the wire rope's operating state—behavioral segments representing short-term states, scheduling integrals reflecting cumulative task pressure, and lifecycle stages reflecting macro-level usage—a multi-dimensional, quantifiable task adaptation map is constructed. This provides rich state label information for subsequent task allocation, exhibiting good generalization and flexibility. The membership function in fuzzy mathematics handles uncertainties, achieving flexible matching between task types and wire rope states, avoiding rigid adaptation caused by rigid rules. The adaptation degree of the wire rope to different tasks can be accurately calculated based on the load strength and continuity requirements of each task. A task adaptation threshold mechanism is introduced to ensure that tasks are assigned only when the wire rope state allows it, providing threshold protection and risk control capabilities. This prevents the forced allocation of high-intensity tasks under high-risk conditions, thereby reducing the risk of rope breakage. Decoupling the task adaptation process from the scheduling system, with the adaptation result as an independent trigger condition, facilitates closed-loop intelligent control of the scheduling system. This can be used not only for current task allocation but also for advanced strategies such as task scheduling and priority adjustment, effectively improving the accuracy and safety control capabilities of task allocation.

[0036] A wire rope life-cycle safety monitoring and adjustment system, which applies the above-described wire rope life-cycle safety monitoring and adjustment method, includes: The operation status scoring module acquires the operation behavior data of the wire rope, constructs an operation status scoring model, inputs the operation behavior data into the operation status scoring model, and obtains the status score value of the wire rope. The behavior segment division module divides the wire rope into multiple behavior segments based on the state score value and configures a corresponding adjustment response mechanism for each behavior segment. The behavior segment inertial delay module introduces a behavior segment inertial delay mechanism. When the state score value goes out of bounds, it enters a buffer state and determines whether to switch behavior segments based on the out-of-bounds stability of the state score value. The scheduling points management module constructs a scheduling points management mechanism, updates the scheduling points value of the wire rope based on task scheduling behavior, and triggers a risk intervention response mechanism based on the updated scheduling points value. The life cycle phase management module introduces a wire rope life cycle phase management mechanism, which divides the wire rope into multiple life cycle stages and identifies the current life cycle stage of the wire rope. The task graph adaptation module constructs a task graph adaptation mechanism, matching suitable task types and allocating tasks based on the current behavior segment, scheduling integral value, and life cycle stage of the wire rope.

[0037] In practical applications, the operational status scoring module dynamically senses and scores the wire rope's operational behavior data, such as structural damage, mechanical parameters, environmental factors, and appearance images, outputting a quantified status score value. This provides the system with a unified health evaluation benchmark, enabling real-time monitoring and intelligent judgment of the wire rope's status, breaking away from the traditional model relying on manual inspection and experience-based judgment. The behavior segment division module divides the wire rope into multiple behavior segments, such as normal, warning, and risk zones, based on the status score value, and configures corresponding adjustment and response mechanisms. This achieves hierarchical management and differentiated responses of operational status, improving the accuracy of scheduling strategies and risk control capabilities, and avoiding the allocation of high-risk tasks under unsuitable conditions. The behavior segment inertial delay module introduces a buffer state when the status score value exceeds the limit, monitoring the stability of the out-of-limit movement and determining whether a true switch to the target behavior segment has occurred. This effectively suppresses frequent switching caused by status jitter, enhancing system robustness and stability, and avoiding misjudgments and over-response. The scheduling integral management module records and updates the scheduling integral value of the wire rope, dynamically assessing its consumption level by considering factors such as task load, duration, and frequency, and triggering different levels of risk response mechanisms. This achieves a closed-loop feedback mechanism between operational history and current scheduling behavior, emphasizing a traceable and risk-trackable operational philosophy, effectively supporting task planning optimization and safety early warning. The lifecycle phase management module identifies the wire rope's lifecycle stage, such as initial operation, stable use, fatigue accumulation, and critical retirement, using this as a crucial input parameter for system operation. Introducing the lifecycle management concept shifts from static state assessment to dynamic stage control, enhancing the scientific rigor and foresight of scheduling strategies. The task graph adaptation module integrates the wire rope's current behavior segment, scheduling integral value, and lifecycle stage, using a multi-dimensional matching graph to adapt to suitable task types and guide task allocation. This improves the matching accuracy between tasks and equipment states, enabling rope-specific task scheduling, reducing the risk of sudden failures and rope breaks, and ensuring operational safety and resource allocation efficiency.

[0038] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle, characterized in that, Includes the following steps: Obtain the operating behavior data of the wire rope, construct an operating status scoring model, input the operating behavior data into the operating status scoring model, and obtain the status score value of the wire rope; Based on the state score, the wire rope is divided into multiple behavior segments, and a corresponding adjustment response mechanism is configured for each behavior segment. A behavior segment inertial delay mechanism is introduced. When the state score value goes out of bounds, it enters a buffer state. Whether to switch behavior segments is determined based on the out-of-bounds stability of the state score value. Establish a scheduling points management mechanism, update the scheduling points value of the wire rope according to the task scheduling behavior, and trigger a risk intervention response mechanism based on the updated scheduling points value; A life cycle phase management mechanism for wire ropes is introduced to divide wire ropes into multiple life cycle stages and identify the current life cycle stage of the wire rope. A task graph adaptation mechanism is constructed to match appropriate task types based on the current behavior segment, scheduling integral value, and life cycle stage of the wire rope, and to allocate tasks accordingly.

2. The method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle according to claim 1, characterized in that, The steps of acquiring the operating behavior data of the wire rope, constructing an operating status scoring model, and inputting the operating behavior data into the operating status scoring model to obtain the status score value of the wire rope are as follows: Acquire operational behavior data of the wire rope, including structural damage data, mechanical performance parameters, environmental operating condition parameters, and images of external defects; The operational behavior data is preprocessed, and dynamic features are extracted from the processed operational behavior data to generate a multi-dimensional operational behavior feature vector. A running status scoring model is constructed. The multi-dimensional running behavior feature vector is input into the running status scoring model. An attention mechanism is introduced to dynamically adjust the feature vector weights and output the predicted status score value of the wire rope. Obtain the confidence parameter of the predicted state score value, compare the confidence parameter with a preset confidence threshold, and if the confidence parameter exceeds the preset confidence threshold, generate a state score value for the wire rope based on the predicted state score value.

3. The method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle according to claim 2, characterized in that, The step of dividing the wire rope into multiple behavior segments based on the state score and configuring a corresponding adjustment response mechanism for each behavior segment is as follows: Set a threshold range for the operating segment and compare the status score value with the threshold range for the operating segment; If the status score is lower than the threshold range of the operating section, the wire rope is in the normal operating section and can be used to perform routine task scheduling. If the status score is within the threshold range of the operating section, the wire rope is in the warning operating section, triggering the first warning signal, setting the first adjustment response mechanism, limiting the high load task of the wire rope and shortening the maintenance cycle. If the status score exceeds the threshold range of the operating section, the wire rope is in a risky operating section, triggering a second early warning signal, setting a second adjustment response mechanism, prohibiting the wire rope from exceeding the threshold and triggering a pre-replacement mechanism.

4. The method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle according to claim 1, characterized in that, The introduced behavior segment inertial delay mechanism, which enters a buffer state when the state score value goes out of bounds, and determines whether to switch behavior segments based on the out-of-bounds stability of the state score value, specifically includes the following steps: During the switching of behavior segments, a behavior segment inertial delay mechanism is introduced. When the state score value of the wire rope crosses the boundary of the current behavior segment for the first time, a buffer state is triggered. Within a preset observation time window, the state score value of the wire rope is continuously acquired, and the number of times the state score value of the wire rope falls into the target behavior segment range within the preset observation time window is obtained. Based on the number of boundary violations and the total number of records of the wire rope condition score within the preset observation time window, the ratio of the number of boundary violations to the total number of records is calculated to obtain the boundary violation ratio of the wire rope condition score. Based on the out-of-bounds ratio, the out-of-bounds stability of the state score value is obtained, and it is determined whether to perform behavior segment switching.

5. The method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle according to claim 4, characterized in that, The step of obtaining the out-of-bounds stability of the state score value based on the out-of-bounds ratio and determining whether to perform behavior segment switching is as follows: Based on the aforementioned out-of-bounds ratio, an out-of-bounds ratio threshold is set, and the out-of-bounds ratio is compared with the preset out-of-bounds ratio threshold. If the out-of-bounds ratio does not exceed the preset out-of-bounds ratio threshold, the state score value of the wire rope is unstable and out of bounds. It is determined that the state score value of the wire rope returns to the current behavior segment in the buffer state and exits the buffer state. If the out-of-bounds ratio exceeds the preset out-of-bounds ratio threshold, the state score value of the wire rope is out of bounds and stable. It is determined that the state score value of the wire rope has entered the target behavior segment in the buffer state, and the behavior segment switching is performed.

6. The method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle according to claim 1, characterized in that, The steps of constructing a scheduling points management mechanism, updating the scheduling points value of the wire rope based on task scheduling behavior, and triggering a risk intervention response mechanism based on the updated scheduling points value are as follows: Set an initial scheduling integral value P0 for the wire rope and obtain the task scheduling behavior data of the wire rope. The task scheduling behavior data includes the task load level L, the task execution duration T, and the task scheduling frequency F. The updated scheduling integral value P of the wire rope is calculated using the correlation formula P=P0-(w1×L+w2×T+w3×F), where w1, w2, and w3 are weighting coefficients and none of them are zero. Set a first integration threshold P1 and a second integration threshold P2, and compare the scheduling integration value with the first integration threshold and the second integration threshold respectively; When the scheduling score drops to the first threshold, a risk warning response is triggered, generating a scheduling priority reduction instruction to restrict the allocation of high-load tasks. When the scheduling integral value drops to the second threshold, a mandatory risk intervention response is triggered, a scheduling authority lock command is generated, and an emergency maintenance process is initiated.

7. The method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle according to claim 1, characterized in that, The steps of introducing a wire rope lifecycle phase management mechanism, which divides the wire rope into multiple lifecycle stages and identifies the current lifecycle stage of the wire rope, are as follows: A life cycle phase management mechanism for wire ropes is introduced, dividing wire ropes into multiple life cycle stages, including the initial operation stage, stable use stage, fatigue accumulation stage, and critical retirement stage. Acquire the state behavior data of the wire rope, including cumulative running time, fatigue index, and number of wire breakages; When the cumulative running time is less than the first time threshold, the fatigue index is lower than the first fatigue threshold, and the number of wire breaks is zero, the wire rope is determined to be in the initial running stage of its life cycle. When the cumulative running time is between the first time threshold and the second time threshold, the fatigue index is lower than the first fatigue threshold, and the number of broken wires is lower than the first broken wire threshold, the current life cycle stage of the wire rope is determined to be the stable use stage. When the fatigue index is between the first fatigue threshold and the second fatigue threshold, or when the number of wire breaks is between the wire breakage threshold and the second wire breakage threshold, the current life cycle stage of the wire rope is determined to be the fatigue accumulation stage. When the cumulative operating time approaches the rated service life, or the fatigue index exceeds the second fatigue threshold, or the number of broken wires exceeds the second broken wire threshold, the current life cycle stage of the wire rope is determined to be the critical retirement stage.

8. The method for monitoring and adjusting the safety of a steel wire rope throughout its entire life cycle according to claim 7, characterized in that, The task graph adaptation mechanism, which matches the current behavior segment, scheduling integral value, and lifecycle stage of the wire rope with suitable task types for task allocation, specifically includes the following steps: Extract the current behavioral segment, scheduling integral value, and life cycle stage of the wire rope to construct a multi-dimensional task matching graph; Based on the task matching map, the compatibility between each task type and the wire rope is calculated using the membership function to obtain the task compatibility parameter. Set a task adaptation threshold, compare the task adaptation parameter with the task adaptation threshold, and obtain the task adaptation result; Based on the task adaptation results, a task allocation request is triggered to allocate the steel wire rope task.

9. A wire rope full life cycle safety monitoring and adjustment system, characterized in that, The method for monitoring and adjusting the safety of a wire rope throughout its entire life cycle, as described in any one of claims 1-8, includes: The operation status scoring module acquires the operation behavior data of the wire rope, constructs an operation status scoring model, inputs the operation behavior data into the operation status scoring model, and obtains the status score value of the wire rope. The behavior segment division module divides the wire rope into multiple behavior segments based on the state score value and configures a corresponding adjustment response mechanism for each behavior segment. The behavior segment inertial delay module introduces a behavior segment inertial delay mechanism. When the state score value goes out of bounds, it enters a buffer state and determines whether to switch behavior segments based on the out-of-bounds stability of the state score value. The scheduling points management module constructs a scheduling points management mechanism, updates the scheduling points value of the wire rope based on task scheduling behavior, and triggers a risk intervention response mechanism based on the updated scheduling points value. The life cycle phase management module introduces a wire rope life cycle phase management mechanism, which divides the wire rope into multiple life cycle stages and identifies the current life cycle stage of the wire rope. The task graph adaptation module constructs a task graph adaptation mechanism, matching suitable task types and allocating tasks based on the current behavior segment, scheduling integral value, and life cycle stage of the wire rope.

Images