A method for evaluating the safety performance of a hoist

Abstract

The present application relates to the technical field of safety monitoring, in particular to a hoist safety performance evaluation method, the method comprising: taking hoist motor current and rope tension as observation parameters, recording data under differentiated working states, identifying dynamic characteristics of the hoist through analyzing the data, and generating dynamic parameter record results. In the present application, dynamic parameter recording allows real-time capture of subtle changes of the hoist under differentiated working states, providing a basis for accurately identifying dynamic characteristics of the equipment, and the application of performance fluctuation indicators further utilizes dynamic recording, identifies fluctuations and deviations outside the normal range through analyzing data changes, provides a basis for judging the severity of fluctuations and taking corresponding adjustment measures, improves the monitoring capability and fault prevention efficiency of the hoist operating state, can identify and take measures in time before potential risks develop into serious faults, reduces the probability of accidents, and improves the safety and efficiency of operations.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology, and in particular to a method for evaluating the safety performance of a winch. Background Technology

[0002] The field of safety monitoring technology focuses on real-time monitoring of equipment and system operation to promptly identify and resolve safety hazards and prevent accidents. In mechanical engineering, especially when dealing with critical lifting equipment such as winches, safety monitoring not only affects the safe operation of the equipment itself but also directly impacts the safety of operators and the surrounding environment. Safety monitoring technology plays a crucial role in the design, use, and maintenance of lifting equipment such as winches. This technology encompasses real-time monitoring of equipment status, performance evaluation, early warning of safety hazards, fault diagnosis, and risk assessment.

[0003] Among them, winch safety performance assessment methods are a key part of the field of safety monitoring technology. Its core lies in comprehensively evaluating the operating status of winches through the integrated application of various technical means, aiming to ensure the safety and reliability of equipment operation. The method's purpose is to provide a scientific basis for equipment maintenance, repair, and operation by accurately assessing the performance and potential risks of winches, thereby reducing the risk of accidents and improving operational efficiency and safety levels. To achieve this effect, winch safety performance assessment relies on advanced monitoring technologies and equipment, such as sensor technology, data analysis software, and machine learning algorithms, which can capture various parameters of the winch during operation in real time, such as load, speed, and temperature, and analyze the data to assess the health status and performance level of the equipment.

[0004] Traditional methods for assessing winch safety performance fall short in real-time data capture and detailed fluctuation analysis. These methods rely on relatively static safety checks and periodic maintenance, lacking the ability to monitor and respond immediately to subtle changes during winch operation. This results in the inability to promptly detect and address performance degradation or minor malfunctions, increasing the risk of accidents. For example, if a slight change in rope tension or abnormal motor current occurs during winch operation, traditional methods cannot capture the signal immediately, missing the optimal opportunity to prevent the fault from escalating. Furthermore, the lack of dynamic characteristic analysis capabilities makes it difficult to accurately locate the problem and develop targeted adjustment measures even after fault diagnosis, impacting the efficiency and timeliness of maintenance and repair. Summary of the Invention

[0005] This application provides a method for assessing the safety performance of winches, addressing the shortcomings of traditional methods in real-time data capture and detailed fluctuation analysis. Traditional methods rely on relatively static safety checks and periodic maintenance, lacking the ability to monitor and respond immediately to subtle changes during winch operation. This results in the inability to promptly detect and address performance degradation or minor malfunctions, increasing the risk of accidents. If slight changes in rope tension or abnormal motor current occur during winch operation, traditional methods cannot capture the signals immediately, missing the optimal opportunity to prevent the malfunction from developing. Furthermore, the lack of dynamic characteristic analysis capabilities makes it difficult to accurately locate problems and formulate targeted adjustment measures even after fault diagnosis, impacting the efficiency and timeliness of maintenance and repair.

[0006] In view of the above problems, this application provides a method for evaluating the safety performance of a winch.

[0007] This application provides a method for evaluating the safety performance of a winch, wherein the method includes the following steps:

[0008] S1: By measuring the winch motor current and rope tension as observation parameters, record data under different working conditions, analyze the data to identify the dynamic characteristics of the winch, screen key dynamic parameters, calculate the dynamic change range, and generate dynamic parameter recording results.

[0009] S2: Using the recorded results of the dynamic parameters, monitor the lifting and lowering actions of the winch, compare the changes in data under different working conditions, filter the range of abnormal fluctuations, determine the impact of load changes on the operating status of the winch, and obtain the performance fluctuation index.

[0010] S3: Based on the performance fluctuation index, calculate the severity of the fluctuation, determine whether the preset adjustment standard has been reached, and if so, indicate that parameter adjustment is required, formulate adjustment measures, and obtain a parameter adjustment decision.

[0011] S4: Execute the measures in the parameter adjustment decision, update the settings of the winch control unit, record the adjustment content and execution time, monitor the parameter changes after adjustment, filter the adjustment data, and obtain the operation adjustment execution log;

[0012] S5: Based on the operation adjustment execution log, analyze the fault sequence and impact, filter the fault modes that lead to performance degradation, record the fault conditions, establish a fault trend prediction model, calculate the fault sequence correlation, and establish a fault sequence prediction model.

[0013] S6: Based on the fault sequence prediction results, assess the randomness of the fault, screen high-risk operating conditions, formulate targeted preventive measures and emergency response strategies, record the measures and strategy information, adjust the winch safety management control parameters, and obtain comprehensive risk management measures.

[0014] Preferably, the dynamic parameter recording results include peak motor current, rope tension, and operation time; the performance fluctuation indicators include fluctuation amplitude, number of frequent deviations, and abnormal fluctuation periods; the parameter adjustment decisions include the adjustment value of the lifting speed, the adjustment scheme of the braking configuration, and the optimization range of the control parameters; the operation adjustment execution log includes a description of the adjustment measures, the date and time of execution, and verification data of the adjustment effect; the fault sequence prediction model includes a fault type identification algorithm, key parameters affecting fault development, and fault occurrence time prediction; and the comprehensive risk management measures include a regular maintenance plan, emergency shutdown, fault repair priority, and a spare parts inventory list.

[0015] Preferably, the specific steps for using the dynamic parameter recording results to monitor the hoist's lifting and lowering actions, comparing data changes under different operating conditions, filtering abnormal fluctuation ranges, and determining the impact of load changes on the hoist's operating status to obtain performance fluctuation indicators are as follows:

[0016] S201: Based on the dynamic parameter recording results, monitor the dynamic parameters of the winch under different working conditions, including speed, acceleration and load, record the real-time data of parameters in each lifting and lowering action, and mark and record actions where the parameter values ​​exceed the preset threshold, generating abnormal action recording results;

[0017] S202: Analyze the data in the abnormal action record results, compare the parameter changes under normal and abnormal states, identify the parameters and trends that cause performance deviation, and obtain the deviation parameters and trend analysis results;

[0018] S203: Based on the deviation parameters and trend analysis results, the decision tree classification method is used, employing the DecisionTreeClassifier from the Scikit-learn library, to refine the frequency and degree of influence of the parameters, distinguish the causes of deviation, including operational errors, mechanical failures and external environmental factors, and assess the impact on performance to obtain performance fluctuation indicators.

[0019] Preferably, the decision tree classification method follows the formula:

[0020] ;

[0021] in, For information gain, For dataset entropy, In the feature under conditions conditional entropy, Features In the dataset The proportion in To adjust the coefficient, Features The variability index.

[0022] Preferably, based on the performance fluctuation index, the severity of the fluctuation is calculated to determine whether a preset adjustment standard has been reached. If so, parameter adjustment is indicated, and adjustment measures are formulated. The specific steps for obtaining the parameter adjustment decision are as follows:

[0023] S301: Collect the performance fluctuation indicators, record the equipment operation data in real time, use data comparison analysis to compare the difference between the data and the predetermined adjustment standard, identify fluctuations that exceed the predetermined range, classify and summarize the fluctuations, and generate fluctuation analysis results.

[0024] S302: Based on the fluctuation analysis results, screen parameters that affect performance, including lifting speed and braking configuration. Through simulation experiments, adjust the parameters, monitor the impact on equipment performance, record the performance differences before and after adjustment, summarize and analyze the differences, and obtain a list of adjustment options.

[0025] S303: Based on the list of adjustment options, formulate modification plans for the lifting speed and braking configuration, implement adjustment measures, test the effect of the adjustment plan in the control environment, and determine that the adjustment measures can improve equipment performance through comparative analysis of test results, and obtain parameter adjustment decisions.

[0026] Preferably, the specific steps for establishing a fault sequence prediction model based on the operation adjustment execution log, analyzing the fault sequence and impact, recording the fault modes and conditions that lead to performance degradation, are as follows:

[0027] S501: Based on the operation adjustment execution log, collect data, analyze the change records in the log one by one, including changes in operation parameters and adjustments in environmental conditions, record the time of each performance degradation and the environmental parameters before and after, identify the initial factors that cause performance degradation, and generate a set of initial fault signals.

[0028] S502: Based on the initial fault signal set, time series analysis is performed using an autoregressive integral moving average model to sort the fault events, classify the fault events according to time sequence and degree of impact, refine the development stages of the fault events, including from initial signal identification to fault expansion and impact on performance, record the fault development process, and obtain fault development path analysis results.

[0029] S503: Using the fault development path analysis results, by analyzing fault modes and development conditions, predict the fault sequence that will occur in the future time period and its impact on performance, analyze the existing fault data, and obtain the fault sequence prediction results.

[0030] Preferably, the autoregressive integral moving average model is based on the formula:

[0031] ;

[0032] Calculate time-series data of fault events;

[0033] in, For time points The observed values, and For autoregressive parameters, and For moving average parameters, For error terms, For constant terms, To adjust the weights seasonally, The seasonal component from the previous point in time. Adjusting weights for trends The trend component at the previous time point. Adjusting weights for external influences These are external influencing factors at the previous point in time.

[0034] Preferably, based on the failure sequence prediction results, the randomness of the failure is assessed, targeted preventive measures and emergency response strategies are formulated, and the information on the measures and strategies is recorded to obtain comprehensive risk management measures. The specific steps are as follows:

[0035] S601: Based on the fault sequence prediction results, by analyzing each predicted fault type, including mechanical wear and electrical faults, examining the environmental factors and operating conditions of the fault occurrence, evaluating the probability of occurrence of each fault, recording the fault type, occurrence conditions and predicted probability, and generating a fault probability table.

[0036] S602: Based on the aforementioned failure probability table, develop preventive measures and emergency response plans, including optimizing maintenance frequency and enhancing employee safety training, specifying implementation steps, responsible persons, and timelines for each measure, and constructing a prevention and emergency implementation plan;

[0037] S603: Based on the aforementioned prevention and emergency response implementation plan, record the implementation details of the prevention measures and emergency response strategies, including the purpose of the measures, the implementation timeline, the assigned responsible persons and expected assessments, to form a comprehensive risk management measure.

[0038] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0039] By applying dynamic parameter recording and performance fluctuation indicators, dynamic parameter recording allows for the real-time capture of subtle changes in the winch under differentiated operating conditions, providing a basis for accurately identifying the dynamic characteristics of the equipment. The application of performance fluctuation indicators further utilizes dynamic recording to identify fluctuations and deviations outside the normal range by analyzing data changes. This provides a basis for judging the severity of fluctuations and taking corresponding adjustment measures, improving the monitoring capability of the winch's operating status and the efficiency of fault prevention. It can identify and take measures in a timely manner before potential risks develop into serious faults, reducing the probability of accidents and improving the safety and efficiency of operations.

[0040] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0041] Figure 1 This invention presents a schematic diagram of the overall process for evaluating the safety performance of a winch.

[0042] Figure 2 A schematic diagram of the specific process S1 of the present invention for a method for evaluating the safety performance of a winch;

[0043] Figure 3 The following is a schematic diagram of the specific process of S2 in the present invention for a method for evaluating the safety performance of a winch;

[0044] Figure 4 This invention provides a schematic diagram of the specific process S3 for a method to evaluate the safety performance of a winch.

[0045] Figure 5 This invention provides a schematic diagram of the specific process of S4 in a method for evaluating the safety performance of a winch.

[0046] Figure 6 A schematic diagram of the specific process S5 of the present invention for a method for evaluating the safety performance of a winch;

[0047] Figure 7 The present invention provides a schematic diagram of the specific process S6 of a method for evaluating the safety performance of a winch. Detailed Implementation

[0048] This application provides a method for evaluating the safety performance of a winch.

[0049] Application Overview

[0050] Existing technologies for assessing the safety performance of winches suffer from shortcomings in real-time data capture and detailed fluctuation analysis. Traditional methods rely on relatively static safety checks and periodic maintenance, lacking the ability to monitor and respond immediately to subtle changes during winch operation. This results in the inability to promptly detect and address performance degradation or minor malfunctions, increasing the risk of accidents. If slight changes in rope tension or abnormal motor current occur during winch operation, traditional methods cannot capture the signals immediately, missing the optimal opportunity to prevent the malfunction from developing. The lack of dynamic characteristic analysis capabilities also makes it difficult to accurately locate problems and develop targeted adjustment measures even after fault diagnosis, impacting the efficiency and timeliness of maintenance and repair.

[0051] To address the aforementioned technical problems, the overall approach of the technical solution provided in this application is as follows:

[0052] like Figure 1 As shown, this application provides a method for evaluating the safety performance of a winch, wherein the method includes the following steps:

[0053] S1: By measuring the winch motor current and rope tension as observation parameters, record data under different working conditions, analyze the data to identify the dynamic characteristics of the winch, screen key dynamic parameters, calculate the dynamic change range, and generate dynamic parameter recording results.

[0054] S2: Using dynamic parameters to record results, monitor the hoist's lifting and lowering actions, compare data changes under different working conditions, filter abnormal fluctuation ranges, determine the impact of load changes on the hoist's operating status, and obtain performance fluctuation indicators.

[0055] S3: Based on the performance fluctuation index, calculate the severity of the fluctuation, determine whether the preset adjustment standard has been reached, and if so, indicate that parameter adjustment is required, formulate adjustment measures, and obtain parameter adjustment decision;

[0056] S4: Implement measures in the parameter adjustment decision, update the settings of the winch control unit, record the adjustment content and execution time, monitor parameter changes after adjustment, filter adjustment data, and obtain the operation adjustment execution log;

[0057] S5: Based on the operation adjustment execution log, analyze the failure sequence and impact, filter failure modes that lead to performance degradation, record failure conditions, establish a failure trend prediction model, calculate the correlation of failure sequences, and establish a failure sequence prediction model.

[0058] S6: Based on the failure sequence prediction results, assess the randomness of the failure, screen high-risk operating conditions, formulate targeted preventive measures and emergency response strategies, record the measures and strategy information, adjust the winch safety management control parameters, and obtain comprehensive risk management measures.

[0059] The dynamic parameter recording results include peak motor current, rope tension, and operation time; performance fluctuation indicators include fluctuation amplitude, number of frequent deviations, and abnormal fluctuation periods; parameter adjustment decisions include adjustment values ​​for hoisting speed, adjustment schemes for braking configuration, and optimization ranges for control parameters; operation adjustment execution logs include descriptions of adjustment measures, execution dates and times, and verification data of adjustment effects; fault sequence prediction models include fault type identification algorithms, key parameters affecting fault development, and fault occurrence time predictions; and comprehensive risk management measures include regular maintenance plans, emergency shutdowns, fault repair priorities, and a spare parts inventory list.

[0060] Specifically, such as Figure 2 As shown, by measuring the winch motor current and rope tension as observation parameters, recording data under different operating conditions, and analyzing the data to identify the dynamic characteristics of the winch, the specific steps for generating dynamic parameter recording results are as follows:

[0061] S101: The execution flow of measuring the motor current and rope tension of the winch as observation parameters, real-time acquisition of motor current and rope tension data of the winch under different loads and speeds, and real-time transmission of data to the data collection platform to generate the raw dataset is as follows.

[0062] Sub-step S101, based on the winch's operating status, employs a real-time data acquisition method. Sensors are used to measure motor current and rope tension as key observation parameters. Sensors are configured to monitor motor current and rope tension in real time, ensuring accurate data capture. The data is then transmitted in real-time to the data collection platform via the data acquisition module. During data transmission, the MQTT protocol is used to ensure reliability and real-time performance. Data received by the data collection platform is immediately stored in the raw dataset, which is subsequently used for further data processing and analysis. The focus is on ensuring real-time data acquisition and accurate transmission to generate the raw dataset.

[0063] S102: The execution flow of arranging the current values ​​and tension data in the original dataset according to the time series, separating the data of normal and abnormal working states by identifying the data mutation points, and constructing the working state database is as follows;

[0064] Substep S102 arranges the current and tension data in the original dataset using time series analysis. Anomaly detection algorithms are used to identify abrupt changes in the data, thus separating data from those in normal and abnormal operating states. A statistical anomaly detection method is employed to identify anomalies by analyzing the deviation of data points from the overall trend of the time series. By setting an anomaly detection threshold, data points deviating from the average level to a certain extent are marked as anomalies. The aim is to classify the data into normal and abnormal operating states for more targeted analysis and processing, and to build an operating state database.

[0065] S103: Based on the working status database, analyze the relationship between current and tension, reveal the dynamic characteristics of winch operation, and obtain the execution flow of dynamic parameter recording results as follows;

[0066] Sub-step S103, based on the operating status database, employs correlation analysis to explore the relationship between current and tension, using scatter plots and correlation coefficient calculations to analyze the strength of their linear relationship. Through comprehensive analysis of current and tension data, the operating characteristics of the winch under different working conditions are revealed, and the analysis helps to understand the interaction between current and tension and its impact on winch performance. Even under abnormal operating conditions, this relationship analysis can reveal potential failure modes or the causes of performance degradation. The analysis process not only relies on mathematical data processing but also incorporates practical working experience to interpret the results and obtain dynamic parameter records.

[0067] Specifically, such as Figure 3 As shown, by using dynamic parameter recording results to monitor the hoist's lifting and lowering actions, comparing data changes under different operating conditions, filtering abnormal fluctuation ranges, and determining the impact of load changes on the hoist's operating status, the specific steps to obtain performance fluctuation indicators are as follows:

[0068] S201: Based on the dynamic parameter recording results, monitor the dynamic parameters of the winch under different working conditions, including speed, acceleration and load, record the real-time data of parameters in each lifting and lowering action, and mark and record actions with parameter values ​​exceeding the preset threshold. The execution process for generating abnormal action recording results is as follows:

[0069] The S201 sub-step, based on the dynamic parameter recording results of the winch under differentiated working conditions, employs a time series analysis method. It utilizes Python's Pandas library to process speed, acceleration, and load data, and uses a sliding window technique to calculate the moving average of each parameter to smooth short-term fluctuations. The standard deviation is calculated using the stats module in the SciPy library to determine the data fluctuation range. A threshold is set at the moving average plus or minus two standard deviations. Real-time parameter data for each lifting and lowering action is analyzed, and actions where parameter values ​​exceed preset thresholds are marked and recorded, generating abnormal action record results.

[0070] S202: Analyze the data in the abnormal action record results, compare the parameter changes under normal and abnormal states, identify the parameters and trends that cause performance deviation, and obtain the deviation parameters and trend analysis results. The execution process is as follows:

[0071] The S202 sub-step employs a machine learning algorithm, specifically a Support Vector Machine (SVM), implemented using the Scikit-learn library. It analyzes the data from the abnormal action recording results, uses MinMaxScaler for data normalization to ensure that the values ​​of each parameter are on the same order of magnitude, uses the parameter data under normal conditions as the training set, and the data under abnormal conditions as the test set. By comparing the parameter changes under normal and abnormal conditions through the SVM model, it identifies the parameters and trends that cause performance deviations, and obtains the deviation parameters and trend analysis results.

[0072] S203: Based on the deviation parameters and trend analysis results, refine the frequency and degree of influence of the parameters, distinguish the causes of deviation, including operational errors, mechanical failures and external environmental factors, and assess the impact on performance. The execution process of the performance fluctuation index is as follows.

[0073] The S203 sub-step utilizes the decision tree classification method, employing the DecisionTreeClassifier from the Scikit-learn library, to refine the deviation parameters and trend analysis results. It defines the causes of deviation as three categories: operational errors, mechanical failures, and external environmental factors. Based on the frequency and degree of influence of the parameters, it uses feature vectors and information gain as the splitting criterion to train the model. The impact of each deviation cause on performance is evaluated based on the decision tree model to obtain the performance fluctuation index.

[0074] The decision tree classification method follows the formula:

[0075] ;

[0076] in, For information gain, For dataset entropy, In the feature under conditions conditional entropy, Features In the dataset The proportion in To adjust the coefficient, Features The variability index.

[0077] The execution process is as follows;

[0078] Compute dataset entropy , representing the uncertainty of the entire dataset, for each feature Calculate its conditional entropy That is, given features Post-dataset uncertainty, calculating features In the dataset The proportion in This is used to weight the contribution of each conditional entropy, introducing a variability index. , measuring characteristics The degree of variation in the dataset improves the model's sensitivity to uneven feature distribution through parameters. Adjusting variability index Confirm the extent of the impact on the final information gain. The specific value can be determined through methods such as cross-validation to achieve optimal model performance. Combining the newly introduced parameters with the original information gain calculation method, the improved formula not only considers the reduction in feature uncertainty but also the variability of feature distribution, thereby improving the accuracy of classification and the robustness of the model.

[0079] Specifically, such as Figure 4 As shown, based on the performance fluctuation index, the severity of the fluctuation is calculated to determine whether the preset adjustment standard has been reached. If it has, parameter adjustment is indicated, and adjustment measures are formulated. The specific steps for parameter adjustment decision-making are as follows:

[0080] S301: Collect performance fluctuation indicators, record equipment operation data through real-time monitoring, use data comparison analysis to compare the differences between the data and the predetermined adjustment standard, identify fluctuations that exceed the predetermined range, classify and summarize the fluctuations, and generate fluctuation analysis results. The execution process is as follows:

[0081] The S301 sub-step collects performance fluctuation indicators, uses time series analysis methods, employs Python combined with the Pandas library for data processing, implements real-time monitoring of equipment operation data, uses the NumPy library for numerical comparison analysis of the data with comparison operations based on set thresholds, uses the matplotlib library to generate fluctuation charts, marks fluctuation data exceeding the predetermined range, uses the SciPy library to perform cluster analysis on the marked data, classifies the fluctuations through clustering algorithms, summarizes the fluctuation data for each category, uses JupyterNotebook as the development environment to record the analysis process, and generates fluctuation analysis results.

[0082] S302: Based on the fluctuation analysis results, screen the parameters that affect performance, including lifting speed and braking configuration. Through simulation experiments, adjust the parameters, monitor the impact on equipment performance, record the performance differences before and after adjustment, and summarize and analyze the differences to obtain the list of adjustment options. The execution process is as follows:

[0083] Substep S302, based on the fluctuation analysis results, filters parameters affecting performance and uses a genetic algorithm. Python and the DEAP library are used to optimize the lifting speed and braking configuration parameters. The genetic algorithm population size is set to 100, the crossover rate to 0.7, and the mutation rate to 0.2. Simulation experiments are conducted, and the fitness value of each iteration is recorded during parameter adjustment. A performance change trend chart is generated using the matplotlib library. The optimal and average performance values ​​are summarized and analyzed across all iterations. By comparing the iteration results, a list of adjustment options is generated.

[0084] S303: Based on the adjustment option list, formulate modification plans for lifting speed and braking configuration, implement adjustment measures, test the effect of the adjustment plan in the control environment, and determine that the adjustment measures can improve equipment performance through comparative analysis of test results. The execution process for obtaining parameter adjustment decisions is as follows;

[0085] Substep S303, based on the adjustment option list, formulates modification plans for the lifting speed and braking configuration, implements adjustment measures, and uses a simulated annealing algorithm. It employs Python and the SciPy library to globally optimize the parameters, setting the initial annealing temperature to 1000, the cooling rate to 0.95, and the stop temperature to 1. The simulated annealing algorithm searches for the global optimum, records the results of each annealing process, and uses the matplotlib library to generate effect test plots of the adjustment plan. By comparing and analyzing the parameter optimization trends in the effect test plots, it determines the parameter selection for the adjustment measures and generates a parameter adjustment decision.

[0086] Specifically, such as Figure 5As shown, the specific steps for implementing the parameter adjustment decision, updating the winch control configuration settings, recording the adjustment content and execution time, monitoring the adjustment effect, and obtaining the operation adjustment execution log are as follows:

[0087] S401: Based on parameter adjustment decisions, by reviewing the current operating performance indicators of the winch, the parameters that need to be adjusted are determined, including adjusting the lifting speed to the current load value, inputting the new parameter values ​​into the control configuration, recording the adjusted parameter name, new value and adjustment reason, and generating the parameter adjustment details. The execution process is as follows:

[0088] The S401 sub-step, based on the current operating performance indicators of the winch, employs a decision analysis method implemented through Python programming. It utilizes the NumPy library to perform numerical analysis on the relationship between the current load and the lifting speed, determining a new parameter value to adjust the lifting speed to the current load value. The new parameter value, parameter name, and adjustment reason are written to the control configuration file using Python's csv module, ensuring the data format is CSV for subsequent processing. The adjusted parameter name, new value, and adjustment reason are recorded, generating a parameter adjustment detail.

[0089] S402: Input the new parameter values ​​shown in the parameter adjustment details in the winch control configuration, record the time of each parameter update, track the timing of the adjustment operation, and form the following execution flow of adjustment operation and time recording results;

[0090] In sub-step S402, the new parameter values ​​shown in the parameter adjustment details are input into the winch control configuration. A file operation method is used, which opens the control configuration file in write mode using Python's open function, uses the datetime module to obtain the current timestamp, records the time of each parameter update, and uses the write method to write the new parameter values ​​and corresponding update times to the file. This allows for tracking the timing of adjustment operations and forming a record of adjustment operations and times.

[0091] S403: Based on the adjustment operation and time recording results, the operation monitoring tool records the performance of the winch after the parameter update, focuses on the changes in lifting speed, descent smoothness and response time, records performance changes, and forms the operation adjustment execution log. The execution process is as follows:

[0092] The S403 sub-step, based on the adjustment operation and time recording results, utilizes a logging tool, specifically Python's logging module, setting the log level to INFO, to record the winch performance after parameter updates. It monitors winch operation programmatically, focusing on changes in lifting speed, descent smoothness, and response time, recording performance change data using the logging.info method and storing it in a text file. This recording method facilitates subsequent performance analysis and auditing, forming an operation adjustment execution log.

[0093] Specifically, such as Figure 6 As shown, the specific steps for establishing a fault sequence prediction model based on operation adjustment execution logs, analyzing fault sequence and impact, recording fault modes and conditions leading to performance degradation, are as follows:

[0094] S501: Based on the operation adjustment execution log, data is collected, and the change records in the log are analyzed one by one, including changes in operation parameters and adjustments to environmental conditions. The time of each performance degradation and the environmental parameters before and after are recorded, the initial factors that cause performance degradation are identified, and the execution flow of generating the fault initial signal set is as follows.

[0095] The S501 sub-step is based on the operation adjustment execution log. It employs log analysis technology, using Python combined with the Pandas library and the re regular expression library for data collection. Each change record in the log is analyzed, and regular expressions are used to identify key information, including changes in operation parameters and adjustments to environmental conditions. The timing of each performance degradation and the environmental parameters before and after it are recorded. A logistic regression model, combined with the Scikit-learn library, is used to analyze the causes of performance degradation, identifying the initial factors leading to the degradation. By setting the logistic regression model parameters, such as max_iter to 1000 and solver to liblinear, the model is trained to identify the initial factors causing the performance degradation. Based on the training results, a set of initial fault signals is generated.

[0096] S502: Based on the initial fault signal set, time series analysis is performed using an autoregressive integral moving average model to sort fault events, classify fault events according to time sequence and degree of impact, refine the development stages of fault events, including from initial signal identification to fault expansion and impact on performance, record the fault development process, and obtain the fault development path analysis results. The execution flow is as follows:

[0097] The S502 sub-step, based on the initial fault signal set, utilizes time series analysis methods, employing Python and the Statsmodels library to sort fault events, classifying them according to time sequence and impact, refining the development stages of fault events, including from initial signal identification to fault expansion and performance impact, recording the fault development process, and using an autoregressive integral moving average model to analyze the fault events. The parameters of the ARIMA model are set, such as p = 2, d = 1, and q = 2, to train the model on the fault development process. The fault development path analysis results are obtained through the training results.

[0098] The autoregressive integral moving average model follows the formula:

[0099] ;

[0100] Calculate time-series data of fault events;

[0101] in, For time points The observed values, and For autoregressive parameters, and For moving average parameters, For error terms, For constant terms, To adjust the weights seasonally, The seasonal component from the previous point in time. Adjusting weights for trends The trend component at the previous time point. Adjusting weights for external influences These are external influencing factors at the previous point in time.

[0102] The execution process is as follows;

[0103] The model adjusts the weights seasonally. and seasonal ingredients To consider the seasonal variations in time series, adjust the weights based on trends. and trend components To consider the impact of long-term trends, adjust the weights based on external influences. and external influencing factors To consider the impact of external events on time series, the model incorporates autoregressive parameters. and and moving average parameters and To analyze the dynamic relationships within the time series and ensure the error term The randomness of the constant term To reflect the baseline level of the model. Determine the weighting coefficients. , and The specific steps include cross-validation using historical data and optimizing parameter values ​​by minimizing prediction error.

[0104] S503: Utilizing the results of fault development path analysis, by analyzing fault modes and development conditions, the execution flow for predicting fault sequences and their impact on performance within a future time period is as follows: Analyze existing fault data to obtain fault sequence prediction results.

[0105] The S503 sub-step utilizes the fault development path analysis results. By analyzing fault modes and development conditions, it employs machine learning methods, using Python and the Scikit-learn library to predict fault sequences. It analyzes existing fault data and uses the random forest algorithm to predict fault sequences and their impact on performance in future time periods. Random forest parameters are set, such as n_estimators = 100 and max_depth = 10. The model is trained on the fault data, and the fault sequence prediction results are obtained based on the training results.

[0106] Specifically, such as Figure 7 As shown, based on the failure sequence prediction results, the randomness of the failure is assessed, targeted preventive measures and emergency response strategies are formulated, and the information on the measures and strategies is recorded. The specific steps for obtaining comprehensive risk management measures are as follows:

[0107] S601: Based on the fault sequence prediction results, the following execution flow is used to analyze each predicted fault type, including mechanical wear and electrical faults, examine the environmental factors and operating conditions of the fault occurrence, evaluate the probability of occurrence of each fault, record the fault type, occurrence conditions and predicted probability, and generate a fault probability table.

[0108] The S601 sub-step, based on the fault sequence prediction results, employs statistical probability analysis. Using the stats module in Python's SciPy library, it performs probability analysis on each predicted fault type. Logistic regression models are used to analyze the relationship between mechanical wear and electrical faults and environmental factors and operating conditions. The probability of each fault occurring under given environmental factors and operating conditions is calculated. A DataFrame is created using the Pandas library to record the fault type, occurrence conditions, and predicted probability. A probability distribution plot is generated using the matplotlib library to visually display the occurrence probability of each fault type, resulting in a fault probability table.

[0109] S602: Based on the failure probability table, develop preventive measures and emergency response plans, including optimizing maintenance frequency and enhancing employee safety training. Specify the implementation steps, responsible persons, and timelines for each measure. The execution flow of the prevention and emergency response plan is as follows.

[0110] Sub-step S602, based on the failure probability table, uses a project management tool, specifically Microsoft Project, to develop preventative measures and emergency response plans. Based on failure types and predicted probabilities, a Gantt chart is used to represent the schedule for maintenance frequency, operational procedure optimization, and employee safety training. Execution steps, responsible persons, and specific start and end dates are assigned to each measure. Through Microsoft Project's resource allocation function, the task allocation for each responsible person is clearly defined, resource utilization is optimized, and a preventative and emergency execution plan is constructed.

[0111] S603: Based on the prevention and emergency response implementation plan, record the implementation details of the prevention measures and emergency response strategies, including the purpose of the measures, the implementation timeline, the assigned responsible persons and expected assessments, and form the following comprehensive risk management measures implementation process;

[0112] The S603 sub-step, based on the prevention and emergency response implementation plan, utilizes performance tracking and management tools, specifically Oracle Primavera, to record the implementation details of developing preventive measures and emergency response strategies. It clarifies the purpose and expected outcomes of each measure, uses Primavera's time management capabilities to create a detailed implementation schedule, including the start and end dates and key milestones for each measure, assigns responsible personnel through the resource management module, monitors resource allocation and usage, sets evaluation indicators, and regularly generates implementation status and effectiveness evaluation reports using Primavera's reporting functions, thus forming a comprehensive risk management measures implementation process.

[0113] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of the claims and their equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for evaluating the safety performance of a winch, characterized in that, The method includes the following steps: By measuring the winch motor current and rope tension as observation parameters, data under different working conditions are recorded, the data is analyzed to identify the dynamic characteristics of the winch, key dynamic parameters are screened, the dynamic change range is calculated, and dynamic parameter recording results are generated. By using the recorded results of the dynamic parameters, the hoist's lifting and lowering actions are monitored, the changes in data under different working conditions are compared, the range of abnormal fluctuations is filtered, the impact of load changes on the hoist's operating status is determined, and the performance fluctuation index is obtained. Based on the performance fluctuation index, the severity of the fluctuation is calculated, and it is determined whether the preset adjustment standard has been reached. If it has been reached, it is indicated that parameter adjustment is required, adjustment measures are formulated, and a parameter adjustment decision is obtained. The measures in the parameter adjustment decision are executed, the settings of the winch control unit are updated, the adjustment content and execution time are recorded, the parameter changes after adjustment are monitored, the adjustment data is filtered, and the operation adjustment execution log is obtained. Based on the operation adjustment execution log, analyze the failure sequence and impact, filter failure modes that lead to performance degradation, record failure conditions, establish a failure trend prediction model, calculate the correlation of failure sequences, and establish a failure sequence prediction model. The specific steps of the fault sequence prediction model are as follows: Based on the operation adjustment execution log, data is collected, and the change records in the log are analyzed one by one, including changes in operation parameters and adjustments to environmental conditions. The time of each performance degradation and the environmental parameters before and after are recorded, the initial factors that cause performance degradation are identified, and a set of initial fault signals is generated. Based on the initial fault signal set, time series analysis is performed using an autoregressive integral moving average model to sort fault events, classify fault events according to time sequence and degree of impact, refine the development stages of fault events, including from initial signal identification to fault expansion and impact on performance, record the fault development process, and obtain fault development path analysis results. Using the fault development path analysis results, by analyzing fault modes and development conditions, the fault sequence and its impact on performance in the future time period are predicted. Existing fault data is analyzed to obtain fault sequence prediction results. The autoregressive integral moving average model is based on the formula: ； Calculate time-series data of fault events; in, For time points The observed values, and For autoregressive parameters, and For moving average parameters, For error terms, For constant terms, To adjust the weights seasonally, The seasonal component from the previous point in time. Adjusting weights for trends The trend component at the previous time point. Adjusting weights for external influences These are external influencing factors at the previous point in time. Based on the failure sequence prediction results, the randomness of the failure is assessed, high-risk operating conditions are screened, targeted preventive measures and emergency response strategies are formulated, the measures and strategy information is recorded, and the safety management and control parameters of the winch are adjusted to obtain comprehensive risk management measures.

2. The method for evaluating the safety performance of a winch according to claim 1, characterized in that, The dynamic parameter recording results include peak motor current, rope tension, and operation time. The performance fluctuation indicators include fluctuation amplitude, number of frequent deviations, and abnormal fluctuation periods. The parameter adjustment decisions include the adjustment value of the lifting speed, the adjustment scheme of the braking configuration, and the optimization range of the control parameters. The operation adjustment execution log includes a description of the adjustment measures, the date and time of execution, and verification data of the adjustment effect. The fault sequence prediction model includes a fault type identification algorithm, key parameters affecting fault development, and fault occurrence time prediction. The comprehensive risk management measures include a regular maintenance plan, emergency shutdown, fault repair priority, and a spare parts inventory list.

3. The method for evaluating the safety performance of a winch according to claim 1, characterized in that, The specific steps for monitoring the hoist's lifting and lowering actions using the recorded dynamic parameters, comparing data changes under different operating conditions, filtering abnormal fluctuation ranges, determining the impact of load changes on the hoist's operating status, and obtaining performance fluctuation indicators are as follows: Based on the dynamic parameter recording results, monitor the dynamic parameters of the winch under different working conditions, including speed, acceleration and load, record the real-time data of parameters in each lifting and lowering action, and mark and record actions where the parameter values ​​exceed the preset threshold, generating abnormal action recording results; Analyze the data in the abnormal action record results, compare the parameter changes under normal and abnormal states, identify the parameters and trends that cause performance deviation, and obtain the deviation parameters and trend analysis results; Based on the deviation parameters and trend analysis results, the decision tree classification method is used, employing the DecisionTreeClassifier from the Scikit-learn library, to refine the frequency and degree of influence of the parameters, distinguish the causes of deviation, including operational errors, mechanical failures, and external environmental factors, and assess the impact on performance to obtain performance fluctuation indicators.

4. The method for evaluating the safety performance of a winch according to claim 3, characterized in that, The decision tree classification method follows the formula: ； in, For information gain, For dataset entropy, In the feature under conditions conditional entropy, Features In the dataset The proportion in To adjust the coefficient, Features The variability index.

5. The method for evaluating the safety performance of a winch according to claim 1, characterized in that, Based on the performance fluctuation index, the severity of the fluctuation is calculated to determine whether the preset adjustment standard has been met. If it has, parameter adjustment is indicated, and adjustment measures are formulated. The specific steps for obtaining the parameter adjustment decision are as follows: The performance fluctuation indicators are collected, and the equipment operation data is recorded in real time. Data comparison and analysis are used to compare the differences between the data and the predetermined adjustment standards, identify fluctuations that exceed the predetermined range, classify and summarize the fluctuations, and generate fluctuation analysis results. Based on the fluctuation analysis results, parameters affecting performance are screened, including lifting speed and braking configuration. Through simulation experiments, the parameters are adjusted, the impact on equipment performance is monitored, the performance differences before and after adjustment are recorded, and the differences are summarized and analyzed to obtain a list of adjustment options. Based on the aforementioned list of adjustment options, a modification plan for the lifting speed and braking configuration is formulated, adjustment measures are implemented, and the effect of the adjustment plan is tested in the control environment. Through comparative analysis of the test results, it is determined that the adjustment measures can improve equipment performance, and parameter adjustment decisions are obtained.

6. The method for evaluating the safety performance of a winch according to claim 1, characterized in that, Based on the failure sequence prediction results, the randomness of the failure is assessed, targeted preventive measures and emergency response strategies are formulated, and the information on the measures and strategies is recorded. The specific steps for obtaining comprehensive risk management measures are as follows: Based on the fault sequence prediction results, by analyzing each predicted fault type, including mechanical wear and electrical faults, examining the environmental factors and operating conditions of the fault occurrence, evaluating the probability of occurrence of each fault, recording the fault type, occurrence conditions and predicted probability, and generating a fault probability table. Based on the aforementioned failure probability table, develop preventive measures and emergency response plans, including optimizing maintenance frequency and enhancing employee safety training. Specify the implementation steps, responsible persons, and timelines for each measure, and construct a prevention and emergency implementation plan. Based on the aforementioned prevention and emergency response plan, record the implementation details of the prevention measures and emergency response strategies, including the purpose of the measures, the implementation timeline, the assigned responsible persons and expected assessments, to form a comprehensive risk management approach.

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