Winch safety performance evaluation method

Abstract

The invention relates to the technical field of safety monitoring, in particular to a winch safety performance evaluation method, which comprises the following steps of: measuring motor current and rope tension of a winch as observation parameters, recording data in a differentiated working state, identifying dynamic characteristics of the winch by analyzing the data, and generating a dynamic parameter recording result. According to the method, fine changes of the winch in different working states are allowed to be captured in real time through dynamic parameter records, a basis is provided for accurately recognizing dynamic characteristics of equipment, dynamic records are further utilized for application of performance fluctuation indexes, fluctuation and deviation outside a normal range are recognized by analyzing data changes, and the accuracy of the performance fluctuation indexes is improved. A basis is provided for judging the severity of fluctuation and taking corresponding adjustment measures, the monitoring capability of the operation state of the winch and the fault prevention efficiency are improved, identification and measures can be taken in time before the potential risk develops into a serious fault, the accident occurrence probability is reduced, and the operation safety and efficiency are improved.

Description

Technical Field

[0001] The invention relates to the technical field of safety monitoring, and in particular to a method for evaluating the safety performance of a winch. Background Art

[0002] The field of safety monitoring technology focuses on real-time monitoring of the operating conditions of equipment and systems to promptly discover and resolve safety hazards and prevent accidents. In mechanical engineering, especially when it comes to important lifting equipment such as winches, safety monitoring is not only related to the safe operation of the equipment itself, but also directly affects the safety of operators and the surrounding environment. Safety monitoring technology plays a vital role in the design, use and maintenance of lifting equipment such as winches. The content covered in this technical field includes real-time monitoring of equipment status, performance evaluation, early warning of safety hazards, fault diagnosis and risk assessment.

[0003] Among them, the winch safety performance evaluation method is a key part in the field of safety monitoring technology. Its core lies in comprehensively evaluating the operating status of the winch through the comprehensive application of various technical means, aiming to ensure the safety and reliability of equipment operation. The purpose of the method is to accurately evaluate the performance and potential risks of the winch, provide a scientific basis for the maintenance, repair and operation of the equipment, thereby reducing the risk of accidents and improving operational efficiency and safety levels. In order to achieve this effect, the winch safety performance evaluation 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, such as load, speed, temperature, etc. in real time, and evaluate the health status and performance level of the equipment by analyzing the data.

[0004] Traditional winch safety performance evaluation methods are deficient in real-time data capture and detailed fluctuation analysis. Traditional methods rely on relatively static safety inspections and regular maintenance, and lack the ability to monitor and respond to subtle changes in winch operation in real time. This results in the inability to detect and handle equipment performance degradation or small-scale failures in a timely manner, increasing the risk of accidents. If the winch experiences slight changes in rope tension or abnormal motor current during operation, traditional methods cannot capture signals in real time, thus missing the best time to prevent the development of faults. The lack of the ability to analyze dynamic characteristics makes it difficult to accurately locate problems and formulate targeted adjustment measures even if fault diagnosis is performed, affecting the efficiency and timeliness of maintenance and repairs. Summary of the invention

[0005] This application provides a winch safety performance evaluation method to solve the shortcomings of traditional winch safety performance evaluation methods in real-time data capture and detailed fluctuation analysis. Traditional methods rely on relatively static safety inspections and regular maintenance, and lack the ability to monitor and respond to subtle changes in the operation of the winch in real time. As a result, when the performance of the equipment degrades or a small-scale failure occurs, it is impossible to discover and handle it in time, increasing the risk of accidents. If there is a slight change in rope tension or abnormal motor current during the operation of the winch, the traditional method cannot capture the signal in time, thus missing the best time to prevent the development of the fault. The lack of the ability to analyze dynamic characteristics makes it difficult to accurately locate the problem and formulate targeted adjustment measures even if fault diagnosis is performed, affecting the efficiency and timeliness of maintenance and repair.

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

[0007] The present application provides a method for evaluating the safety performance of a winch, wherein the method comprises the following steps: S1: By measuring the winch motor current and rope tension as observation parameters, recording the data under differentiated working conditions, analyzing the data to identify the dynamic characteristics of the winch, screening key dynamic parameters, calculating the dynamic change range, and generating dynamic parameter recording results; S2: Using the dynamic parameter recording results, monitor the lifting and lowering actions of the lifting control system, compare the changes in data under differentiated working states, screen the abnormal fluctuation range, determine the impact of load changes on the system operation state, and obtain performance fluctuation indicators; S3: Calculate the severity of the fluctuation according to the performance fluctuation index, and determine whether it reaches the preset adjustment standard. If so, indicate that parameter adjustment is required, formulate adjustment measures, and obtain a parameter adjustment decision; S4: executing the measures in the parameter adjustment decision, updating the settings of the winch control unit configuration, recording the adjustment content and execution time, monitoring the parameter changes after adjustment, screening the adjustment data, and obtaining the operation adjustment execution log; S5: Based on the operation adjustment execution log, analyze the fault sequence and impact, screen the fault mode that causes performance degradation, record the fault conditions, establish a fault trend prediction model, calculate the fault sequence correlation, and establish a fault sequence prediction model; S6: According to the fault sequence prediction results, evaluate the randomness of the fault, screen high-risk working conditions, formulate targeted preventive measures and emergency response strategies, record measures and strategy information, adjust the winch safety management control parameters, and obtain comprehensive risk management measures.

[0008] Preferably, the dynamic parameter recording results include motor current peak, rope tension and operation time, the performance fluctuation indicators include fluctuation amplitude, frequent deviation times and abnormal fluctuation time period, the parameter adjustment decision includes the adjustment value of lifting speed, the adjustment scheme of braking configuration and the optimization range of control parameters, the operation adjustment execution log includes the description of adjustment measures, the date and time of execution and verification data of adjustment effect, the fault sequence prediction model includes fault type identification algorithm, key parameters affecting fault development and fault occurrence time prediction, and the comprehensive risk management measures include regular maintenance plan, emergency shutdown, fault repair priority and spare parts inventory list.

[0009] Preferably, the specific steps of using the dynamic parameter recording results to monitor the lifting and lowering actions of the winch, comparing the changes in data under differentiated working states, identifying abnormal fluctuations and deviations, and obtaining the performance fluctuation index are as follows: S201: Based on the dynamic parameter recording results, the dynamic parameters of the winch under differentiated working conditions are monitored, including speed, acceleration and load, and real-time data of the parameters in each lifting and lowering action are recorded, and the actions whose parameter values ​​exceed the preset thresholds are marked and recorded to generate abnormal action recording results; S202: Analyze the data in the abnormal action record result, compare the parameter changes under normal and abnormal conditions, identify the parameters and change trends that cause performance deviation, and obtain the deviation parameters and trend analysis results; S203: Based on the deviation parameters and trend analysis results, the occurrence frequency and impact degree of the parameters are refined, the causes of the deviations are distinguished, including operational errors, mechanical failures and external environmental factors, and the impact on the performance is evaluated to obtain a performance fluctuation index.

[0010] Preferably, the decision tree classification method is according to the formula: ; in, is the information gain, For the dataset The entropy of For the characteristics under conditions The conditional entropy of Features In the dataset The proportion of is the adjustment factor, Features variability index.

[0011] Preferably, according to the performance fluctuation index, it is determined whether the severity of the fluctuation reaches a preset adjustment standard. If so, it is indicated that parameter adjustment is required, and adjustment measures are formulated. The specific steps for obtaining a parameter adjustment decision are as follows: S301: collecting the performance fluctuation index, recording the equipment operation data through real-time monitoring, using data comparison analysis, comparing the difference between the data and the predetermined adjustment standard, identifying the fluctuation situation beyond the predetermined range, and classifying and summarizing the fluctuation situation to generate the fluctuation analysis result; S302: Based on the fluctuation analysis results, screen parameters that affect performance, including lifting speed and brake configuration, adjust parameters through simulation experiments, monitor the impact on equipment performance, record performance differences before and after the adjustment, summarize and analyze the differences, and obtain a list of adjustment options; S303: According to the adjustment option list, a modification plan for the lifting speed and the braking configuration is formulated, the adjustment measures are executed, and the effect of the adjustment plan is tested in a controlled environment. By comparing and analyzing the test results, it is determined that the adjustment measures can improve the equipment performance and obtain parameter adjustment decisions.

[0012] Preferably, based on the operation adjustment execution log, analyzing the failure sequence and impact, recording the failure mode and conditions that lead to performance degradation, and establishing the failure sequence prediction model, the specific steps are as follows: S501: Based on the operation adjustment execution log, data is collected, and change records in the log are analyzed one by one, including changes in operation parameters and adjustments to environmental conditions, and the time of each performance degradation and the environmental parameters before and after are recorded, and the initial factors causing the performance degradation are identified, and a fault initial signal set is generated; S502: Based on the initial fault signal set, perform time series analysis through an autoregressive integrated moving average model, sort the fault events, classify the fault events according to the time sequence and the degree of impact, refine the development stage of the fault event, including from the initial signal recognition to the expansion of the fault and the impact on the performance, record the development process of the fault, and obtain the fault development path analysis result; S503: Utilizing the fault development path analysis result, by analyzing the fault mode and development conditions, predicting the fault sequence and its impact on performance in the future time period, analyzing the existing fault data, and obtaining the fault sequence prediction result.

[0013] Preferably, the autoregressive integrated moving average model is according to the formula: ; Calculate time series data of failure events; in, For time point The observed value of and is the autoregressive parameter, and is the moving average parameter, is the error term, is a constant term, is the seasonally adjusted weight, is the seasonal component at the previous time point, Adjust weights for trends, is the trend component at the previous time point, Adjust weights for external influences, is the external influencing factor at the previous time point.

[0014] Preferably, according to the fault sequence prediction results, the specific steps of evaluating the randomness of the fault, formulating targeted preventive measures and emergency response strategies, recording the measures and strategy information, and obtaining comprehensive risk management measures are as follows: S601: Based on the fault sequence prediction result, by analyzing each predicted fault type, including mechanical wear and electrical fault, examining the environmental factors and operating conditions of the fault, evaluating the occurrence probability of each fault, recording the fault type, occurrence condition and predicted probability, and generating a fault probability table; S602: Based on the failure probability table, formulate preventive measures and emergency response plans, including optimizing maintenance frequency and enhancing employee safety training, specifying execution steps, responsible persons and time arrangements for each measure, and building a preventive and emergency execution plan; S603: Based on the prevention and emergency implementation plan, record the implementation details of the preventive measures and emergency response strategies, including the purpose of the measures, implementation schedule, assigned responsible persons and expected assessments, to form comprehensive risk management measures.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through the application of dynamic parameter recording and performance fluctuation indicators, dynamic parameter recording allows real-time capture of subtle changes in the winch under differentiated working conditions, providing a basis for accurately identifying the dynamic characteristics of the equipment. The application of performance fluctuation indicators further utilizes dynamic records to identify fluctuations and deviations outside the normal range by analyzing data changes, providing a basis for judging the severity of fluctuations and taking corresponding adjustment measures, thereby improving the monitoring capabilities 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, reduce the probability of accidents, and improve the safety and efficiency of operations.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention provides a schematic diagram of the overall process of a winch safety performance evaluation method; Figure 2 The present invention proposes a specific flow chart of S1 of a winch safety performance evaluation method; Figure 3 The present invention proposes a specific flow chart of S2 of a winch safety performance evaluation method; Figure 4 The present invention proposes a specific flow chart of S3 of a winch safety performance evaluation method; Figure 5 A specific flow chart of S4 of a winch safety performance evaluation method proposed by the present invention; Figure 6 The present invention proposes a specific flow chart of S5 of a winch safety performance evaluation method; Figure 7 The present invention proposes a specific flow chart of S6 of a winch safety performance evaluation method. DETAILED DESCRIPTION

[0018] The present application provides a method for evaluating the safety performance of a winch.

[0019] Application Overview The existing technology has deficiencies in the traditional winch safety performance assessment methods in terms of real-time data capture and detailed fluctuation analysis. The traditional methods rely on relatively static safety inspections and regular maintenance, and lack the ability to monitor and respond to subtle changes in the operation of the winch in real time. This results in the inability to detect and handle equipment performance degradation or small-scale failures in a timely manner, increasing the risk of accidents. If the winch has a slight change in rope tension or abnormal motor current during operation, the traditional method cannot capture the signal in real time, thus missing the best time to prevent the development of the fault. The lack of the ability to analyze dynamic characteristics makes it difficult to accurately locate the problem and formulate targeted adjustment measures even if fault diagnosis is performed, affecting the efficiency and timeliness of maintenance and repair.

[0020] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows: like Figure 1 As shown, the present application provides a method for evaluating the safety performance of a winch, wherein the method comprises the following steps: S1: By measuring the winch motor current and rope tension as observation parameters, recording the data under differentiated working conditions, analyzing the data to identify the dynamic characteristics of the winch, screening key dynamic parameters, calculating the dynamic change range, and generating dynamic parameter recording results; S2: Using dynamic parameter recording results, monitor the lifting and lowering actions of the lifting control system, compare the changes in data under differentiated working conditions, screen the abnormal fluctuation range, determine the impact of load changes on the system operating status, and obtain performance fluctuation indicators; S3: Calculate the severity of the fluctuation according to the performance fluctuation index and determine whether it reaches the preset adjustment standard. If so, it indicates that parameter adjustment is required, formulates adjustment measures, and obtains parameter adjustment decisions. S4: Execute the measures in the parameter adjustment decision, update the settings of the winch control unit configuration, record the adjustment content and execution time, monitor the parameter changes after adjustment, filter the adjustment data, and obtain the operation adjustment execution log; S5: Based on the operation adjustment execution log, analyze the fault sequence and impact, screen the fault mode that causes performance degradation, record the fault conditions, establish a fault trend prediction model, calculate the fault sequence correlation, and establish a fault sequence prediction model; S6: Based on the fault sequence prediction results, evaluate the randomness of the faults, screen high-risk working conditions, formulate targeted preventive measures and emergency response strategies, record measures and strategy information, adjust the winch safety management control parameters, and obtain comprehensive risk management measures.

[0021] The dynamic parameter recording results include motor current peak, rope tension and operation time. The performance fluctuation indicators include fluctuation amplitude, frequent deviation times and abnormal fluctuation period. The parameter adjustment decisions include the adjustment value of lifting speed, the adjustment plan of braking configuration and the optimization range of control parameters. The operation adjustment execution log includes the description of adjustment measures, the date and time of execution and the verification data of adjustment effect. The fault sequence prediction model includes fault type identification algorithm, key parameters affecting fault development and prediction of fault occurrence time. The comprehensive risk management measures include regular maintenance plan, emergency shutdown, fault repair priority and spare parts inventory list.

[0022] Specifically, if Figure 2 As shown in the figure, by measuring the winch motor current and rope tension as observation parameters, recording the data under differentiated working conditions, identifying the dynamic characteristics of the winch by analyzing the data, and generating the dynamic parameter recording results, the specific steps are as follows: S101: measuring the motor current and rope tension of the winch as observation parameters, collecting the motor current value and rope tension data of the winch under differentiated loads and speeds in real time, transmitting the data to the data collection platform in real time, and generating the original data set. The execution process is as follows; Substep S101 adopts a real-time data acquisition method based on the operation status of the winch, uses sensors to measure motor current and rope tension as key observation parameters, configures sensors to monitor motor current and rope tension in real time, ensures accurate data capture, and then transmits the data to the data collection platform in real time through the data acquisition module. During the data transmission process, the MQTT protocol is used as the data transmission protocol to ensure the reliability and real-time performance of data transmission. The data received by the data collection platform is immediately stored in the original data set, which is subsequently used for further data processing and analysis. The focus is on ensuring real-time data collection and accurate transmission to generate the original data set.

[0023] S102: Arrange the current value and tension data in the original data set in time series, identify the mutation points of the data, separate the data of normal and abnormal working states, and construct the working state database. The execution process is as follows; In sub-step S102, the current value and tension data in the original data set are arranged using a time series analysis method, and the mutation points in the data are identified using an anomaly detection algorithm, thereby separating the data of normal and abnormal working states. The anomaly point detection method based on statistics is used to identify anomalies by analyzing the deviation between the data point and the overall trend of the time series. By setting an anomaly detection threshold, when a data point deviates from the average level to a certain extent, it is marked as abnormal. The purpose is to classify the data into normal working state and abnormal working state, so as to facilitate more targeted analysis and processing in the future and build a working state database.

[0024] S103: Based on the working status database, the relationship between current and tension is analyzed to reveal the dynamic characteristics of the winch operation, and the execution process of obtaining the dynamic parameter recording result is as follows; Substep S103 uses a correlation analysis method based on the working status database to explore the relationship between current and tension, and uses scatter plots and correlation coefficient calculations to analyze the strength of the linear relationship between the two. By comprehensively analyzing the current value 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 the performance of the winch. Including under abnormal working conditions, this relationship analysis can reveal potential failure modes or reasons for performance degradation. The analysis process not only relies on mathematical processing of data, but also interprets the results in combination with actual work experience to obtain dynamic parameter recording results.

[0025] Specifically, if Figure 3 As shown in the figure, the specific steps of using the dynamic parameter recording results to monitor the lifting and lowering actions of the winch, compare the changes in data under differentiated working conditions, identify abnormal fluctuations and deviations, and obtain the performance fluctuation index are as follows: S201: Based on the dynamic parameter recording results, the dynamic parameters of the winch under differentiated working conditions, including speed, acceleration and load, are monitored, the real-time data of the parameters in each lifting and lowering action is recorded, and the actions whose parameter values ​​exceed the preset thresholds are marked and recorded. The execution process of generating abnormal action recording results is as follows; The S201 sub-step is based on the dynamic parameter recording results of the winch under differentiated working conditions. It adopts the time series analysis method and uses Python's Pandas library to process the speed, acceleration and load data. The sliding window technology is used to calculate the moving average of each parameter to smooth short-term fluctuations. The standard deviation is calculated through the stats module in the SciPy library to determine the data fluctuation range. The threshold is set to the moving average plus or minus twice the standard deviation. The real-time data of the parameters in each lifting and lowering action is analyzed, and the actions whose parameter values ​​exceed the preset thresholds are marked and recorded to generate abnormal action record results.

[0026] S202: Analyze the data in the abnormal action record results, compare the parameter changes under normal and abnormal conditions, identify the parameters and change trends that cause performance deviation, and obtain the deviation parameters and trend analysis results. The execution process is as follows; Sub-step S202 uses a machine learning algorithm, specifically a support vector machine (SVM), which is implemented using the Scikit-learn library. It analyzes the data in the abnormal action record results and uses MinMaxScaler to normalize the data to ensure that the values ​​of each parameter are at the same order of magnitude. The parameter data under normal conditions is used as the training set, and the data under abnormal conditions is used as the test set. The SVM model is used to compare the parameter changes under normal and abnormal conditions, identify the parameters and change trends that cause performance deviations, and obtain the deviation parameters and trend analysis results.

[0027] S203: Based on the deviation parameters and trend analysis results, the occurrence frequency and impact degree of the parameters are refined, the causes of the deviation are distinguished, including operation errors, mechanical failures and external environmental factors, and the impact on the performance is evaluated to obtain the execution process of the performance fluctuation index as follows; In sub-step S203, the decision tree classification method is used, and the DecisionTreeClassifier in the Scikit-learn library is adopted to refine the deviation parameters and trend analysis results, and the deviation causes are defined as three categories: operational errors, mechanical failures, and external environmental factors. The frequency of occurrence and the degree of influence of the parameters are used as feature vectors, and the information gain is used as the splitting criterion to train the model. The impact of each deviation cause on the performance is evaluated according to the decision tree model to obtain the performance fluctuation index.

[0028] The decision tree classification method follows the formula: ; in, is the information gain, For the dataset The entropy of For the characteristics under conditions The conditional entropy of Features In the dataset The proportion of is the adjustment factor, Features variability index.

[0029] The execution process is as follows; Computational Dataset Entropy , represents the uncertainty of the entire data set, for each feature , calculate its conditional entropy , that is, given the feature After the uncertainty of the dataset, the features are calculated In the dataset The proportion of , used to weight the contribution of each conditional entropy and introduce a variability index , measure the characteristics The degree of variation in the data set increases the sensitivity of the model to uneven feature distribution, through the parameter Adjusting the variability index The degree of influence on the final information gain is confirmed The specific value of can be determined by cross-validation and other methods to achieve the best model performance. Combining the newly introduced parameters and the original information gain calculation method, the improved formula not only considers the uncertainty reduction of the feature, but also considers the distribution variability of the feature, thereby improving the classification accuracy and the robustness of the model.

[0030] Specifically, if Figure 4 As shown, according to the performance fluctuation index, it is judged whether the severity of the fluctuation reaches the preset adjustment standard. If it reaches the preset adjustment standard, it is marked that parameter adjustment is required, and adjustment measures are formulated. The specific steps for obtaining parameter adjustment decisions are as follows: S301: Collect performance fluctuation indicators, record equipment operation data through real-time monitoring, use data comparison analysis to compare the difference between the data and the predetermined adjustment standard, identify fluctuations beyond the predetermined range, and classify and summarize the fluctuations. The execution process of generating fluctuation analysis results is as follows; In sub-step S301, performance fluctuation indicators are collected, and data is processed using the time series analysis method and the Python language combined with the Pandas library. The equipment operation data is monitored in real time, and the NumPy library is used to perform numerical comparison and analysis on the data. The comparison operation is based on the set threshold. The matplotlib library is used to generate fluctuation charts, and fluctuation data that exceeds the predetermined range is marked. The SciPy library is used to perform cluster analysis on the marked data, and the fluctuation situation is classified through the clustering algorithm. The fluctuation data of each category is summarized, and JupyterNotebook is used as the development environment to record the analysis process and generate fluctuation analysis results.

[0031] S302: Based on the fluctuation analysis results, screen the parameters that affect the performance, including the lifting speed and the brake configuration, adjust the parameters through simulation experiments, monitor the impact on the equipment performance, record the performance difference before and after the adjustment, and summarize and analyze the difference. The execution process of obtaining the adjustment option list is as follows; In sub-step S302, based on the results of fluctuation analysis, the parameters that affect the performance are screened, and a genetic algorithm is used to optimize the lifting speed and brake configuration parameters using Python language and DEAP library. The population size of the genetic algorithm is set to 100, the crossover rate is 0.7, and the mutation rate is 0.2. A simulation experiment is performed. During the parameter adjustment process, the fitness value of each iteration is recorded, and the performance change trend chart is generated using the matplotlib library. The optimal and average performance values ​​of all iterations are summarized and analyzed, and a list of adjustment options is generated by comparing the iterative results.

[0032] S303: According to the list of adjustment options, a modification plan for the lifting speed and brake configuration is formulated, the adjustment measures are implemented, and the effect of the adjustment plan is tested in a controlled environment. By comparing and analyzing the test results, it is determined that the adjustment measures can improve the equipment performance. The execution process of obtaining the parameter adjustment decision is as follows; In sub-step S303, based on the list of adjustment options, a modification plan for the lifting speed and brake configuration is formulated, and adjustment measures are implemented. A simulated annealing algorithm is adopted, and the global optimization of parameters is realized using the Python language and the SciPy library. The initial annealing temperature is set to 1000, the cooling rate is set to 0.95, and the stop temperature is set to 1. The global optimal solution is found through the simulated annealing algorithm, and the results of each annealing process are recorded. The effect test diagram of the adjustment plan is generated using the matplotlib library. Through the comparative analysis of the parameter optimization trend in the effect test diagram, the parameter selection of the adjustment measures is determined, and the parameter adjustment decision is generated.

[0033] Specifically, if Figure 5As shown in the figure, the specific steps of executing the measures in the parameter adjustment decision, updating the settings of the winch control configuration, recording the adjustment content and execution time, monitoring the adjustment effect, and obtaining the operation adjustment execution log are as follows: S401: Based on the parameter adjustment decision, by reviewing the current operating performance indicators of the winch, determine the parameters that need to be adjusted, including adjusting the lifting speed to the current load value, entering the new parameter value 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; The S401 sub-step is based on the current operating performance indicators of the winch and adopts a decision analysis method. It is implemented through Python programming and uses the NumPy library to perform a numerical analysis on the relationship between the current load and the lifting speed. The new parameter value for adjusting the lifting speed to the current load value is determined. The new parameter value, parameter name, and adjustment reason are written into the control configuration file using Python's csv module. The data format is ensured to be CSV for subsequent processing. The adjusted parameter name, new value, and adjustment reason are recorded to generate a parameter adjustment detail.

[0034] S402: Enter the new parameter value shown in the parameter adjustment details in the winch control configuration, record the time of each parameter update, track the time point of the adjustment operation, and form the execution process of the adjustment operation and time recording results as follows; In sub-step S402, the new parameter value shown in the parameter adjustment details is input into the winch control configuration. The file operation method is used to open the control configuration file in write mode through Python's open function. The datetime module is used to obtain the current timestamp, and the time of each parameter update is recorded. The new parameter value and the corresponding update time are written into the file using the write method. This can track the timing of the adjustment operation and form the adjustment operation and time record results.

[0035] S403: Based on the adjustment operation and time recording results, the operation monitoring tool is used to record the performance of the winch after the parameters are updated, and attention is paid to the changes in the lifting speed, the descent stability and the response time, and the performance changes are recorded to form the execution process of the operation adjustment execution log as follows; The S403 sub-step is based on the adjustment operation and time recording results. The logging tool, specifically Python's logging module, is used to set the log level to INFO, record the performance of the winch after the parameters are updated, monitor the winch operation programmatically, pay attention to the changes in lifting speed, descent smoothness and response time, and record the performance change data through the logging.info method and store it in the form of a text file. This recording method facilitates subsequent performance analysis and auditing, and forms an operation adjustment execution log.

[0036] Specifically, if Figure 6 As shown in the figure, based on the operation adjustment execution log, analyzing the failure sequence and impact, recording the failure mode and conditions that lead to performance degradation, and establishing the fault sequence prediction model, the specific steps are as follows: S501: Based on the operation adjustment execution log, data is collected, and the change records in the log are analyzed one by one, including the changes in the operation parameters and the adjustments of the environmental conditions, and the time of each performance degradation and the environmental parameters before and after are recorded, and the initial factors causing the performance degradation are identified. The execution process of generating the initial fault signal set is as follows; Sub-step S501 adjusts the execution log based on the operation, adopts log analysis technology, uses Python language combined with Pandas library and regular expression library re to collect data, analyzes the change records in the log one by one, uses regular expressions to identify key information in the log, including changes in operating parameters and adjustments to environmental conditions, records the moment of each performance degradation and the environmental parameters before and after, uses a logistic regression model in combination with the Scikit-learn library to analyze the cause of performance degradation, identifies the initial factors that cause performance degradation, and trains the model for the initial factors of performance degradation by setting the logistic regression model parameters, such as max_iter is 1000 and solver is liblinear. Based on the training results, a set of initial fault signals is generated.

[0037] S502: Based on the initial fault signal set, the autoregressive integral moving average model is used to perform time series analysis, sort the fault events, classify the fault events according to the time sequence and the degree of impact, refine the development stage of the fault event, including from the initial signal recognition to the expansion of the fault and the impact on the performance, record the development process of the fault, and obtain the execution flow of the fault development path analysis result as follows; In sub-step S502, based on the initial fault signal set, the time series analysis method is used, and the Python language and Statsmodels library are used to sort the fault events, classify the fault events according to the time sequence and the degree of impact, and refine the development stage of the fault event, including the expansion of the fault from the initial signal recognition to the impact on the performance. The development process of the fault is recorded, and the fault event is analyzed using the autoregressive integrated moving average model. The parameters of the ARIMA model are set, such as p is 2, d is 1, and q is 2. The model of the fault development process is trained, and the fault development path analysis results are obtained through the training results.

[0038] The autoregressive integrated moving average model follows the formula: ; Calculate time series data of failure events; in, For time point The observed value of and is the autoregressive parameter, and is the moving average parameter, is the error term, is a constant term, is the seasonally adjusted weight, is the seasonal component at the previous time point, Adjust weights for trends, is the trend component at the previous time point, Adjust weights for external influences, is the external influencing factor at the previous time point.

[0039] The execution process is as follows; The model adjusts weights by seasonality and seasonal ingredients To consider the seasonal changes in the time series, adjust the weights by trend and trend components To consider the impact of long-term trends, adjust the weights based on external influences and external factors To consider the impact of external events on time series, the model combines the autoregressive parameter and And the moving average parameters and To analyze the dynamic relationship within the time series, ensure that the error term The randomness of To reflect the baseline level of the model. Determine the weight coefficient , and The specific method steps include cross-validation using historical data and optimizing the value of the parameter by minimizing the prediction error.

[0040] S503: Using the fault development path analysis results, by analyzing the fault mode and development conditions, predict the fault sequence and its impact on performance in the future time period, analyze the existing fault data, and obtain the fault sequence prediction results. The execution process is as follows; In sub-step S503, the fault development path analysis results are used to analyze the fault mode and development conditions, adopt machine learning methods, use Python language and Scikit-learn library to predict the fault sequence, analyze the existing fault data, and use the random forest algorithm to predict the fault sequence and performance impact in the future time period. The random forest parameters are set, such as n_estimators is 100, max_depth is 10, and the model is trained on the fault data. The fault sequence prediction results are obtained through the training results.

[0041] Specifically, if Figure 7 As shown in the figure, according to the fault sequence prediction results, the specific steps of evaluating the randomness of faults, formulating targeted preventive measures and emergency response strategies, recording measures and strategy information, and obtaining comprehensive risk management measures are as follows: S601: Based on the fault sequence prediction results, by analyzing each predicted fault type, including mechanical wear and electrical fault, examining the environmental factors and operating conditions of the fault, evaluating the occurrence probability of each fault, recording the fault type, occurrence conditions and predicted probability, and generating the fault probability table, the execution process is as follows; Based on the fault sequence prediction results, sub-step S601 uses statistical probability analysis methods to perform probability analysis on each predicted fault type through the stats module in Python's SciPy library, and uses a logistic regression model to analyze the relationship between mechanical wear and electrical faults and environmental factors and operating conditions. The probability of each fault under given environmental factors and operating conditions is calculated, and the Pandas library is used to create a DataFrame to record the fault type, occurrence conditions, and predicted probability. The matplotlib library is used to generate a probability distribution diagram to intuitively display the probability of each fault type, and a fault probability table is generated.

[0042] S602: Based on the failure probability table, formulate preventive measures and emergency response plans, including optimizing maintenance frequency and enhancing employee safety training, specifying execution steps, responsible persons and time arrangements for each measure, and constructing the execution process of the prevention and emergency execution plan as follows; In sub-step S602, according to the failure probability table, project management tools, specifically Microsoft Project, are used to develop preventive measures and emergency response plans. Based on the failure type and predicted probability, a Gantt chart is used to represent the schedule for maintenance frequency, operating procedure optimization, and employee safety training. For each measure, the execution steps, responsible persons, and specific start and end dates are specified. Through the resource allocation function of Microsoft Project, the task allocation of each responsible person is ensured to be clear, resource utilization is optimized, and a preventive and emergency execution plan is constructed.

[0043] S603: Based on the prevention and emergency implementation plan, record the implementation details of the preventive measures and emergency response strategies, including the purpose of the measures, implementation schedule, assigned responsible persons and expected evaluation, and form the implementation process of the comprehensive risk management measures as follows; Sub-step S603 is based on the prevention and emergency implementation plan, using performance tracking and management tools, specifically Oracle Primavera, to record the implementation details of the preventive measures and emergency response strategies, clarify the purpose and expected results of each measure, and use Primavera's time management function to create a detailed implementation schedule, including the start and end dates and key milestones of each measure. Through the resource management module, responsible persons are assigned, and the allocation and use of resources are monitored, evaluation indicators are set, and Primavera's reporting function is used to regularly generate measures implementation status and effectiveness evaluation reports to form an implementation process for comprehensive risk management measures.

[0044] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for evaluating the safety performance of a winch, characterized in that: The method comprises the following steps: By measuring the winch motor current and rope tension as observation parameters, recording data under differentiated working conditions, analyzing the data to identify the dynamic characteristics of the winch, screening key dynamic parameters, calculating the dynamic change range, and generating dynamic parameter recording results; Using the dynamic parameter recording results, the lifting and lowering actions of the lifting control system are monitored, the changes in data under differentiated working states are compared, the abnormal fluctuation range is screened, the impact of load changes on the system operating state is judged, and the performance fluctuation index is obtained; According to the performance fluctuation index, the severity of the fluctuation is calculated to determine whether the preset adjustment standard is reached. If reached, it is marked that parameter adjustment is required, and adjustment measures are formulated to obtain a parameter adjustment decision; Execute the measures in the parameter adjustment decision, update the settings configured by 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; Based on the operation adjustment execution log, analyze the fault sequence and impact, screen the fault mode that causes performance degradation, record the fault conditions, establish a fault trend prediction model, calculate the fault sequence correlation, and establish a fault sequence prediction model; The specific steps of the fault sequence prediction model are as follows: Based on the operation adjustment execution log, data collection is performed, and change records in the log are analyzed one by one, including changes in operating parameters and adjustments to environmental conditions, and the time of each performance degradation and the environmental parameters before and after are recorded to identify the initial factors that cause the performance degradation and generate a fault initial signal set; Based on the initial fault signal set, a time series analysis is performed through an autoregressive integral sliding average model to sort the fault events, classify the fault events according to the time sequence and the degree of impact, refine the development stage of the fault event, including from the initial signal recognition to the expansion of the fault and the impact on the performance, record the development process of the fault, and obtain the fault development path analysis results; By using the fault development path analysis results, by analyzing the fault mode and development conditions, the fault sequence and its impact on performance in the future time period are predicted, and the existing fault data is analyzed to obtain the fault sequence prediction results; The autoregressive integrated moving average model is according to the formula: ； Calculate time series data of failure events; in, For time point The observed value of and is the autoregressive parameter, and is the moving average parameter, is the error term, is a constant term, is the seasonally adjusted weight, is the seasonal component at the previous time point, Adjust weights for trends, is the trend component at the previous time point, Adjust weights for external influences, is the external influencing factor at the previous time point; According to the fault sequence prediction results, the randomness of the fault is evaluated, high-risk working conditions are screened, targeted preventive measures and emergency response strategies are formulated, the measures and strategy information are recorded, the winch safety management control parameters are adjusted, and comprehensive risk management measures are obtained.

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

3. The winch safety performance evaluation method according to claim 1, characterized in that: The specific steps of using the dynamic parameter recording results to monitor the lifting and lowering actions of the winch, comparing the changes in data under differentiated working conditions, identifying abnormal fluctuations and deviations, and obtaining performance fluctuation indicators are as follows: Based on the dynamic parameter recording results, the dynamic parameters of the winch under differentiated working conditions, including speed, acceleration and load, are monitored, the real-time data of the parameters in each lifting and lowering action is recorded, and the actions whose parameter values ​​exceed the preset thresholds are marked and recorded to generate abnormal action recording results; Analyze the data in the abnormal action record results, compare parameter changes under normal and abnormal conditions, identify the parameters and change trends that cause performance deviation, and obtain deviation parameters and trend analysis results; Based on the deviation parameters and trend analysis results, the occurrence frequency and impact degree of the parameters are refined, the causes of the deviation are distinguished, including operational errors, mechanical failures and external environmental factors, and the impact on the performance is evaluated to obtain the performance fluctuation index.

4. The winch safety performance evaluation method according to claim 3, characterized in that: The decision tree classification method is based on the formula: ； in, is the information gain, For the dataset The entropy of For the characteristics under conditions The conditional entropy of Features In the dataset The proportion of is the adjustment factor, Features variability index.

5. The winch safety performance evaluation method according to claim 1, characterized in that: According to the performance fluctuation index, it is determined whether the severity of the fluctuation reaches the preset adjustment standard. If so, it is indicated that parameter adjustment is required, and adjustment measures are formulated. The specific steps for obtaining the parameter adjustment decision are as follows: Collect the performance fluctuation index, record the equipment operation data through real-time monitoring, use data comparison analysis to compare the difference between the data and the predetermined adjustment standard, identify the fluctuation situation beyond the predetermined range, classify and summarize the fluctuation situation, and generate the fluctuation analysis result; Based on the fluctuation analysis results, screen the parameters that affect the performance, including lifting speed and brake configuration, adjust the parameters through simulation experiments, monitor the impact on equipment performance, record the performance differences before and after the adjustments, summarize and analyze the differences, and obtain a list of adjustment options; Based on the adjustment option list, a modification plan for the lifting speed and brake configuration is formulated, the adjustment measures are implemented, and the effects of the adjustment plan are tested in a controlled environment. Through comparative analysis of the test results, it is determined that the adjustment measures can improve equipment performance and obtain parameter adjustment decisions.

6. The winch safety performance evaluation method according to claim 1, characterized in that: According to the fault sequence prediction results, the specific steps of evaluating the randomness of faults, formulating targeted preventive measures and emergency response strategies, recording measures and strategy information, and 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 under which the fault occurs, evaluating the probability of each fault, recording the fault type, occurrence conditions and predicted probability, and generating a fault probability table; According to the failure probability table, formulate preventive measures and emergency response plans, including optimizing maintenance frequency and enhancing employee safety training, specifying execution steps, responsible persons and time arrangements for each measure, and building a preventive and emergency execution plan; Based on the prevention and emergency implementation plan, record the implementation details of the preventive measures and emergency response strategies, including the purpose of the measures, implementation schedule, assigned responsible persons and expected evaluation, to form a comprehensive risk management measure.

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