Insulating soft sling performance evaluation method, medium and equipment

Abstract

The invention provides an insulating soft sling performance evaluation method, medium and equipment, and relates to the technical field of electric power engineering.The method comprises the steps that mechanical performance data, electrical performance data and environment data of an insulating soft sling are collected and preprocessed, a mean value and standard deviation are calculated, and data characteristic description is generated; calculating performance scores of the mechanical performance and the electrical performance to obtain a mechanical performance score and an electrical performance score; calculating a comprehensive health score of the insulating soft sling by combining the mechanical performance score and the electrical performance score; a health rating threshold value is set, the comprehensive health score corresponds to a health grade, and a health grade result is generated; according to the health grade result and the historical trend data, analyzing and predicting a performance change trend in a future time period by using a time sequence, and generating a risk prediction result; and analyzing the risk of the sling in the operation environment, and generating a risk assessment result. According to the method, the health state of the insulating soft sling can be comprehensively and accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a method, medium and equipment for evaluating the performance of an insulating soft sling. Background Art

[0002] Power engineering is an engineering field that deals with the generation, transmission, distribution, and use of electrical energy. This field focuses on the design, construction, operation, and maintenance of power systems to ensure the safe, efficient, and economical supply of electrical energy. The performance evaluation method for insulating soft slings is used to test and evaluate the performance of insulating soft slings used in power systems. These slings are typically used to safely hang or support high-voltage wires and other electrical components during installation, maintenance, or repair of power equipment.

[0003] In the process of evaluating insulated soft slings, existing technologies usually rely on scattered single performance indicators, which are difficult to provide a comprehensive health status of the equipment, which may lead to one-sided evaluation results and fail to fully reflect the overall performance and potential risks of the slings in actual environments. For example, a separate mechanical performance evaluation may not accurately reveal electrical safety issues, and relying solely on electrical testing may ignore the impact of physical wear and tear. Summary of the invention

[0004] The purpose of the present invention is to provide a method, medium and device for evaluating the performance of an insulating soft sling, so as to comprehensively and accurately evaluate the health status of the insulating soft sling. The specific technical solution is as follows:

[0005] A method for evaluating the performance of an insulating soft sling, the method comprising the following steps:

[0006] S100, collecting mechanical performance data, electrical performance data and environmental data of the insulating soft sling, preprocessing the mechanical performance data, electrical performance data and environmental data and calculating the mean and standard deviation, and generating a data characteristic description;

[0007] S200, based on the data characteristic description, calculate the performance scores of mechanical properties and electrical properties to obtain the mechanical performance score and the electrical performance score; combine the mechanical performance score and the electrical performance score to calculate the comprehensive health score of the insulating soft sling;

[0008] S300, based on the comprehensive health score, set the health rating threshold, correspond the comprehensive health score to the health level, and generate the health level result; according to the health level result and the historical trend data described by the data characteristics, use time series analysis to predict the performance change trend in the future time period and generate the risk prediction result;

[0009] S400. Analyze the risks faced by the sling in the operating environment based on the risk prediction results and environmental data, and generate risk assessment results.

[0010] Furthermore, step S100 includes:

[0011] S110, collecting mechanical performance data, electrical performance data, and environmental data of the insulating soft sling through sensors to obtain original performance data; the mechanical performance data include tensile strength, torsional strength, and wear resistance, the electrical performance data include power frequency withstand voltage value, leakage current, and arc tolerance, and the environmental data include temperature and humidity;

[0012] S120, based on the original performance data, remove errors and outliers through data cleaning, and process the data using data standardization to obtain cleaned and standardized performance data;

[0013] S130. Based on the cleaned and standardized performance data, the mean and standard deviation of each performance indicator are calculated, and the central trend and degree of variation of the data are extracted through statistical analysis to form a data characteristic description.

[0014] Further, step S200 includes:

[0015] S210. Based on the data characteristic description, extract the mechanical performance data and the electrical performance data to obtain the mechanical performance statistical index and the electrical performance statistical index. Based on the mechanical performance statistical index, calculate the mechanical performance score. The calculation formula is:

[0016]

[0017] Among them, M T is the mean tensile strength, M TW is the mean torsional strength, M WE is the mean value of wear resistance, P mech Score mechanical properties;

[0018] Based on the electrical performance statistical indicators, the electrical performance score is calculated using the following formula:

[0019]

[0020] Among them, E V is the average value of power frequency withstand voltage, E L is the mean leakage current, E A is the mean value of arc tolerance, P elec Score electrical performance;

[0021] S220. Summarize the mechanical performance scores and the electrical performance scores to obtain a performance score set. Based on the performance score set, calculate the comprehensive health score of the insulating soft sling. The calculation formula is:

[0022]

[0023] Among them, P mech For mechanical performance rating, P elec For electrical performance rating, P total For comprehensive health score.

[0024] Further, step S300 includes:

[0025] S310, based on the comprehensive health score, determining the health rating threshold of the score, including good, fair and poor, setting the health rating threshold to distinguish the health level, and obtaining a health rating threshold set;

[0026] S320, based on the health rating threshold set, mapping the comprehensive health score to the corresponding health level, determining the health level by comparing the relationship between the score and the health rating threshold, and obtaining a predetermined health level;

[0027] S330. Collect historical trend data of health level results and data characteristic descriptions to obtain a data set; based on the data set, apply time series analysis to analyze and predict performance change trends to obtain preliminary trend analysis results; based on the preliminary trend analysis results, evaluate future estimated risk levels and generate risk prediction results.

[0028] Furthermore, in the step S310, based on the comprehensive health score, the existing health data is first statistically analyzed to determine the health rating threshold. The threshold is set based on the percentile rule of the sling performance historical data. The good threshold is set at the 75% percentile of the historical score data, the general threshold is set between 25% and 75%, and the poor threshold is set below the 25% percentile; in the step S320, the comprehensive health score is matched with the previously set health rating threshold set to identify the health level of the sling. In this process, it is first checked whether the comprehensive score of the sling exceeds the good level threshold. If so, it is classified as good; if not, it is determined whether it is lower than the general level threshold. If so, it is classified as poor; otherwise, it is classified as general.

[0029] Furthermore, in step S330, the autoregressive moving average model ARMA is applied to analyze the data set, firstly determining the order of the model, and selecting the appropriate number of lag terms through the autocorrelation and partial autocorrelation function graphs of the data; then estimating the model parameters, and using the maximum likelihood estimation method to find the parameter values ​​so that the model best fits the historical data; performing model diagnosis to check whether the residual sequence shows white noise characteristics, ensuring that the model does not contain any unexplained data structure, so as to predict future performance trends and obtain preliminary trend analysis results; extracting risk indicators from the preliminary trend analysis results, including comparing the predicted rate of increase in maintenance requirements or performance degradation with the set risk management standards, such as marking the equipment performance as high risk when it drops 10% below the lower limit of the safe operating range.

[0030] Further, step S400 includes:

[0031] S410. Based on the risk prediction results, conduct risk analysis, identify the impact of temperature and humidity factors in the operating environment on material properties, and derive risk impact analysis results;

[0032] S420. Based on the results of the risk impact analysis, describe each risk factor and its impact, and formulate and generate risk assessment results.

[0033] Furthermore, in step S410, key indicators that indicate possible attenuation of material properties are extracted from the risk prediction results, and these indicators are identified as major risk factors. The interaction between these factors and specific operating environment factors is analyzed, including evaluating the frequency and amplitude of changes in these material properties under high temperature and high humidity conditions, and using statistical methods to determine whether these environmental conditions significantly accelerate material fatigue or affect structural integrity. In addition, the effects of these environmental conditions on different performance parameters are compared to clarify which conditions are the most stringent in the current operating environment, and to determine environmental factors that may pose a greater risk in future operations. Finally, based on these analysis results, the level of risk that may be faced in the future is predicted; in step S420, the level of detail of various risk factors and their potential impacts is analyzed. These analyses are based on specific data points in the risk prediction results and actual conditions observed in the operating environment. Safety thresholds are set for each risk factor. These thresholds are determined based on past accident data and performance degradation records. The degree of impact of different risk factors on performance is compared through quantitative methods to generate risk assessment results.

[0034] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the insulating soft sling performance evaluation method as described above are implemented.

[0035] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned insulating soft sling performance evaluation method when executing the program.

[0036] The present invention provides a method, medium and device for evaluating the performance of an insulating soft sling, which have the following beneficial effects:

[0037] The present invention generates a comprehensive health score for the insulating soft sling by comprehensively scoring the mechanical and electrical properties, and by mapping the comprehensive health score to a standardized health level, the evaluation result becomes comprehensive, accurate, more intuitive and easy to operate. The use of time series analysis to predict future performance change trends enhances predictability and foresight, thereby facilitating the implementation of preventive maintenance strategies and effectively reducing unexpected downtime and costs. Based on risk prediction results and environmental data, the impact of the operating environment on the performance of the insulating soft sling is analyzed, providing clear risk management and improving the reliability of the equipment and the safety of operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flow chart of a method for evaluating the performance of an insulating soft sling provided by the present invention;

[0039] Figure 2 is an overall flow chart of an embodiment of the present invention;

[0040] Figure 3 4 is a structural block diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the accompanying drawings provided by the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. According to the following description, the advantages and features of the present invention will be more clear. It should be noted that the accompanying drawings are all in a very simplified form and are not in precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0042] Example 1

[0043] See also Figure 1 , 2 The embodiment of the present invention provides a method for evaluating the performance of an insulating soft sling, comprising the following steps:

[0044] S100, collecting mechanical performance data, electrical performance data and environmental data of the insulating soft sling, preprocessing the mechanical performance data, electrical performance data and environmental data and calculating the mean and standard deviation, and generating a data characteristic description;

[0045] S200, based on the data characteristic description, calculate the performance scores of mechanical properties and electrical properties to obtain the mechanical performance score and the electrical performance score; combine the mechanical performance score and the electrical performance score to calculate the comprehensive health score of the insulating soft sling;

[0046] S300, based on the comprehensive health score, set the health rating threshold, correspond the comprehensive health score to the health level, and generate the health level result; according to the health level result and the historical trend data described by the data characteristics, use time series analysis to predict the performance change trend in the future time period and generate the risk prediction result;

[0047] S400. Analyze the risks faced by the sling in the operating environment based on the risk prediction results and environmental data, and generate risk assessment results.

[0048] The embodiment of the present invention generates a comprehensive health score for the insulating soft sling by comprehensively scoring the mechanical properties and electrical properties, and by mapping the comprehensive health score to a standardized health level, the evaluation result becomes comprehensive, accurate, more intuitive and easy to operate. The use of time series analysis to predict future performance change trends enhances predictability and foresight, thereby facilitating the implementation of preventive maintenance strategies and effectively reducing unexpected downtime and costs. Based on risk prediction results and environmental data, the impact of the operating environment on the performance of the insulating soft sling is analyzed, providing clear risk management and improving the reliability of the equipment and the safety of operation.

[0049] In one embodiment, step S100 includes:

[0050] S110, collecting mechanical performance data, electrical performance data, and environmental data of the insulating soft sling through sensors to obtain original performance data; the mechanical performance data include tensile strength, torsional strength, and wear resistance, the electrical performance data include power frequency withstand voltage value, leakage current, and arc tolerance, and the environmental data include temperature and humidity;

[0051] Specifically, the mechanical and electrical performance data of the insulating soft slings are collected, covering indicators such as tensile strength, torsional strength and wear resistance. At the same time, the power frequency withstand voltage value, leakage current and arc tolerance are measured, and environmental variables such as temperature and humidity are recorded to ensure the accuracy of the test conditions. During the detection process, each indicator is continuously monitored to ensure the real-time and accuracy of the data. All collected data are immediately screened to identify abnormal or expected readings to ensure that the original performance data reflecting the actual performance of the slings is obtained.

[0052] S120, based on the original performance data, remove errors and outliers through data cleaning, and process the data using data standardization to obtain cleaned and standardized performance data;

[0053] Specifically, the raw performance data is cleaned to remove error values ​​and outliers that are obviously inconsistent with physical measurement possibilities, such as records of abnormally high leakage current or temperature readings that exceed the safety range set by the experiment; a unified standardization process is applied, such as calculating the deviation of each data relative to its mean and dividing it by the standard deviation, converting all performance data to the same standard scale. This process improves the comparability and usability of the data in subsequent analysis by precisely controlling the proportion and distribution of the data.

[0054] S130. Based on the cleaned and standardized performance data, the mean and standard deviation of each performance indicator are calculated, and the central trend and degree of variation of the data are extracted through statistical analysis to form a data characteristic description.

[0055] Specifically, based on the cleaned and standardized data, the tensile strength, torsional strength, wear resistance, and electrical properties such as power frequency withstand voltage and leakage current mean and standard deviation are calculated; statistical methods are used to analyze the distribution characteristics of these data in detail, such as by calculating the skewness and kurtosis of the data to evaluate the stability and reliability of performance indicators; these analysis results are integrated to form a data characteristic description.

[0056] In one embodiment, step S200 includes:

[0057] S210. Based on the data characteristic description, extract the mechanical performance data and the electrical performance data to obtain the mechanical performance statistical index and the electrical performance statistical index. Based on the mechanical performance statistical index, calculate the mechanical performance score. The calculation formula is:

[0058]

[0059] Among them, M T is the mean tensile strength, M TW is the mean torsional strength, M WE is the mean value of wear resistance, P mech Score mechanical properties;

[0060] Based on the electrical performance statistical indicators, the electrical performance score is calculated using the following formula:

[0061]

[0062] Among them, E V is the average value of power frequency withstand voltage, E L is the mean leakage current, E A is the mean value of arc tolerance, P elec Score electrical performance;

[0063] Specifically, based on the data format description file A recorded in the previously obtained data characteristic description and the corresponding original value record file B, the marked fields in file A are read one by one, and the value content of the corresponding field in file B is matched, the index position of the mechanical performance data and the electrical performance data is matched with the data characteristic description one by one, the tensile strength data in the mechanical performance data list are extracted one by one and marked with corresponding timestamps, the torsional strength data are divided into multiple sub-intervals and recorded in an independent value list, the wear resistance data is normalized according to the standardization scheme in the data characteristic description, the power frequency withstand voltage value data item in the electrical performance data list is parsed for a fixed period, and the leakage The current data items are read in segments and screened one by one according to the reference range provided in the data characteristic description file (the average of the 200 measurement results in the previous monitoring is set as the reference upper limit threshold of 0.001A. Data below this threshold do not need to be eliminated, and data above this threshold need to be additionally determined whether a measurement error occurs. The measurement error is manually checked by the corresponding number of the sensor log file C, and then the abnormal mark is recorded and eliminated). The arc tolerance data items are queried for the corresponding item number in file A, and the corresponding record value is read from file B in a fixed index manner. All numerical sequences mapped by the data characteristic description are integrated and output into two types of performance statistical indicator lists to obtain mechanical performance statistical indicators and electrical performance statistical indicators.

[0064] The benefit of the mechanical property scoring formula is that the square term of the mean tensile strength, the square root term of the mean torsional strength and the absolute value term of the mean wear resistance are jointly calculated, so that the mechanical property scoring takes into account both the nonlinear characteristics of the performance parameters and the comprehensive contribution of different parameters to the overall performance.

[0065] For example, M T The acquisition steps are as follows: the original data of tensile strength (fluctuating around 30 MPa) is mapped to the interval of 0-1 using a standardized formula, and the statistical mean is about 0.8;

[0066] M TW The acquisition steps are as follows: the torsional strength data (about 28 N·m) is obtained, and the average value after normalization mapping is 0.7;

[0067] M WE The acquisition steps are as follows: the obtained wear resistance test data is recorded in a specific friction coefficient range (0-1), and the statistical mean is 0.6.

[0068] Calculation process:

[0069] First calculate the parameters:

[0070] M T =0.8,M TW =0.7, MWE =0.6

[0071] Compute square roots:

[0072] |M WE |=0.6

[0073] Substituting the above values ​​into the formula:

[0074] Molecular part:

[0075] 0.3 0.64 + 0.4 0.83666 + 0.3 0.6 = 0.192 + 0.334664 + 0.18 = 0.706664 Denominator:

[0076] 0.8+0.7+0.6=2.1

[0077] Final calculation:

[0078]

[0079] The result shows that the current mechanical property score is about 0.3365. If the result is greater than 0.5, it means that the mechanical property is relatively high, and if it is less than 0.5, it means that it is relatively average. The calculation result is lower than 0.5, which means that the mechanical property has not reached a high level under the current conditions. The corresponding step results show that the mechanical property score is at a medium level, which can be used as a reference indicator for subsequent evaluation or improvement.

[0080] The benefit of the electrical performance scoring formula is that it measures the electrical performance through the square term of the mean value of the power frequency withstand voltage, the square root term of the mean value of the leakage current, and the absolute value term of the mean value of the arc tolerance capability, thereby reflecting the differences and importance of different electrical parameters in a unified score.

[0081] For example, E V The acquisition steps are as follows: normalize the power frequency withstand voltage test data (varies between 9.5kV and 10.5kV) with the maximum value of 10.5kV to obtain a mean value of 0.9;

[0082] E L The acquisition steps are as follows: the leakage current data (0-1mA range) is normalized using 1mA as the reference, and the mean value is 0.5;

[0083] E A The acquisition step is to map the results from 0 to 1 through the obtained arc tolerance test records, and the average value is 0.7.

[0084] Calculation process:

[0085] E V =0.9, E L=0.5, E A =0.7

[0086] Compute square roots:

[0087] |E A |=0.7

[0088] Substitute into the numerator of the formula:

[0089] 0.2 0.81 + 0.5 0.7071 + 0.3 0.7 = 0.162 + 0.35355 + 0.21 = 0.72555 Denominator:

[0090] 0.9+0.5+0.7=2.1 Final calculation:

[0091]

[0092] The result shows that the current electrical performance score is about 0.3455. When the score exceeds 0.4, it means that the electrical performance is relatively good. When it is less than 0.4, it means that the electrical performance is still at a lower-middle level. This result is lower than 0.4, which means that the electrical performance score is not high under the current conditions. For the results of this step, this score can provide a quantitative reference for subsequent electrical performance improvement strategies.

[0093] S220. Summarize the mechanical performance scores and the electrical performance scores to obtain a performance score set. Based on the performance score set, calculate the comprehensive health score of the insulating soft sling. The calculation formula is:

[0094]

[0095] Among them, P mech For mechanical performance rating, P elec For electrical performance rating, P total For comprehensive health score.

[0096] Specifically, according to the corresponding record files of mechanical performance scores and electrical performance scores obtained above, the mechanical performance score data marked in the file are compared with the electrical performance score data, and the marked score sequences in the mechanical performance score data file are read one by one to extract the numerical items of each score value, and are collected according to the number index order in the file, and then the electrical performance score data file is opened, and the electrical performance score data is located in the same number index order, and the score data value consistent with the corresponding number of the mechanical performance score is selected from the file, and the two score data values ​​are matched one by one. In the matching process, the association table recorded in a parameter description file is queried. For the case where some numbers are different, the number correction is performed according to the corresponding relationship listed in the parameter description file to ensure that the two types of score data can find the corresponding mechanical and electrical score values ​​under the same group of numbers, and after the matching is completed, a two-dimensional array structure is created, and the mechanical and electrical score values ​​under the corresponding numbers are written into it in the form of array rows and columns, and then the two-dimensional array is traversed, and the mechanical and electrical score values ​​under the same number are written in parallel into a new performance score set file, each row in this performance score set file corresponds to a group of mechanical and electrical score data pairs, and finally a performance score set is obtained.

[0097] The formula is helpful because it takes the square root of the square average of the mechanical and electrical performance scores to provide a comprehensive measure of both performance scores, highlighting the impact of a low score in either performance category on overall health.

[0098] For example, P mech The steps for obtaining the parameters are as follows: mech =0.3365, which is obtained by normalizing and weighting the previously completed mechanical properties data (tensile strength, torsional strength, wear resistance);

[0099] P elec The steps to obtain the parameters are as follows: elec =0.3455, which is obtained by normalizing and weighting the power frequency withstand voltage, leakage current, and arc withstand capability data obtained previously;

[0100] Calculation process:

[0101] First, bring in the parameters:

[0102] P mech =0.3365, P elec =0.3455

[0103] Compute the squares of the terms:

[0104]

[0105] Compute the average of the sum of squared values:

[0106]

[0107] Finally, the square root:

[0108] P total =(0.1163) 0.5 ≈0.3409

[0109] The results show that the current comprehensive health score is about 0.3409. When the comprehensive health score is greater than 0.4, it means that the overall health status of the insulating soft sling is good. When it is less than 0.4, it means that the health status is average. The result this time is lower than 0.4, indicating that the overall health score is at a lower-middle level under current conditions, providing a reference for subsequent use and maintenance strategies.

[0110] In one embodiment, step S300 includes:

[0111] S310, based on the comprehensive health score, determining the health rating threshold of the score, including good, fair and poor, setting the health rating threshold to distinguish the health level, and obtaining a health rating threshold set;

[0112] Specifically, based on the comprehensive health score, we first conduct a statistical analysis of the existing health data to determine the health rating threshold. The threshold is set based on the percentile rule of the sling performance historical data. The good threshold is set at the 75% percentile of the historical scoring data, the general threshold is set between 25% and 75%, and the poor threshold is set below the 25% percentile. This quantile method ensures that the threshold is statistically significant and can be applied to slings with different performances. The specific values ​​of these thresholds are strictly verified to ensure that they can actually reflect the distinction criteria for different health levels. These thresholds are combined into a structured health rating threshold set.

[0113] S320, based on the health rating threshold set, mapping the comprehensive health score to the corresponding health level, determining the health level by comparing the relationship between the score and the health rating threshold, and obtaining a predetermined health level;

[0114] Specifically, the comprehensive health score is matched with the previously set health rating threshold set to identify the health level of the sling. In this process, it is first checked whether the comprehensive score of the sling exceeds the threshold of the good level. If so, it is classified as good; if not, it is then determined whether it is lower than the threshold of the general level. If so, it is classified as poor; otherwise, it is classified as general. In this classification process, direct comparison logic is adopted. There is no need for complex data processing or advanced algorithms. The score is simply and directly compared with the threshold to ensure the accuracy of the classification and the efficiency of the operation. In this way, each sling is accurately assigned to the corresponding health level.

[0115] S330. Collect historical trend data of health level results and data characteristic descriptions to obtain a data set; based on the data set, apply time series analysis to analyze and predict performance change trends to obtain preliminary trend analysis results; based on the preliminary trend analysis results, evaluate future estimated risk levels and generate risk prediction results.

[0116] Specifically, organize health levels and historical trend data, including maintenance logs and system monitoring records, to ensure the integrity and accuracy of the information; identify and delete data that does not meet the standards, such as records that exceed the normal operating parameters of the equipment, fill in missing maintenance time points, and standardize the format of all data to match the requirements of the analysis tool; ensure that each data point is timestamped and correct discontinuous timelines so that the temporal nature of the data set is maintained, providing a reliable basis for subsequent analysis, thereby obtaining a prepared data set.

[0117] The autoregressive moving average model ARMA is used to analyze the data set. First, the order of the model is determined, and the appropriate number of lag terms is selected through the autocorrelation and partial autocorrelation function graphs of the data. Then, the model parameters are estimated, and the maximum likelihood estimation method is used to find the parameter values ​​so that the model best fits the historical data. Model diagnosis is performed to check whether the residual sequence shows white noise characteristics and to ensure that the model does not contain any unexplained data structure, so as to predict future performance trends and obtain preliminary trend analysis results.

[0118] Risk indicators are extracted from the preliminary trend analysis results, such as the predicted rate of increase in maintenance demand or performance degradation, and compared with the set risk management standards. These standards are determined based on the equipment's historical failure data and safe operating range. For example, when equipment performance drops 10% below the lower limit of the safe operating range, it is marked as high risk. Based on these comparison results, the future risk level is determined, and specific maintenance or replacement time points are recommended to the operation and maintenance team to form risk prediction results.

[0119] In one embodiment, step S400 includes:

[0120] S410. Based on the risk prediction results, conduct risk analysis, identify the impact of temperature and humidity factors in the operating environment on material properties, and derive risk impact analysis results;

[0121] Specifically, key indicators are extracted from the risk prediction results, focusing on variables that indicate possible attenuation of material properties, such as marking the continuously observed trend of decreased tensile strength or decreased compressive strength, and identifying these indicators as major risk factors. Next, a detailed analysis is conducted on how these factors interact with specific operating environment factors. For example, the frequency and magnitude of changes in these material properties under high temperature and high humidity conditions are evaluated, and statistical methods are used to determine whether these environmental conditions significantly accelerate material fatigue or affect structural integrity. In addition, the effects of these environmental conditions on different performance parameters are compared to clarify which conditions are the most stringent in the current operating environment, and based on this, determine which environmental factors may pose greater risks in future operations. Finally, through these analysis results, the level of risk that may be faced in the future is predicted to ensure that all results are based on data-driven decisions.

[0122] S420. Based on the results of the risk impact analysis, describe each risk factor and its impact, and formulate and generate risk assessment results.

[0123] Specifically, based on the above detailed risk impact analysis results, various risk factors and their potential impacts are further described in detail, such as a specific analysis of how high temperature may lead to reduced material elasticity and how high humidity may accelerate the corrosion process of materials. These analyses are based on specific data points in the risk prediction results and the actual conditions observed in the operating environment. Safety thresholds are set for each risk factor. These thresholds are determined based on past accident data and performance degradation records. The impact of different risk factors on performance is compared through quantitative methods to generate risk assessment results.

[0124] Example 2

[0125] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the insulating soft sling performance evaluation method described above are implemented.

[0126] The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memory.

[0127] Example 3

[0128] This embodiment provides a computer device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the insulating soft sling performance evaluation method described above when executing the program.

[0129] like Figure 3 As shown, the computer device may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, a memory 74, and at least one communication bus 72. The communication bus 72 is used to realize the connection and communication between these components. The communication interface 73 may include a display screen (Display) and a keyboard (Keyboard), and the optional communication interface 73 may also include a standard wired interface and a wireless interface. The memory 74 may be a high-speed RAM memory (Random Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 74 may optionally be at least one storage device located away from the aforementioned processor 71. The memory 74 stores application programs, and the processor 71 calls the program code stored in the memory 74 to execute any of the above method steps.

[0130] The communication bus 72 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The communication bus 72 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0131] Among them, the memory 74 may include a volatile memory (English: volatile memory), such as a random access memory (English: random-access memory, abbreviated: RAM); the memory may also include a non-volatile memory (English: non-volatile memory), such as a flash memory (English: flash memory), a hard disk (English: hard disk drive, abbreviated: HDD) or a solid-state drive (English: solid-state drive, abbreviated: SSD); the memory 74 may also include a combination of the above types of memory.

[0132] The processor 71 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and a NP.

[0133] The processor 71 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0134] Optionally, the memory 74 is also used to store program instructions. The processor 71 can call the program instructions to implement the insulating soft sling performance evaluation method of the present invention.

[0135] Those skilled in the art should understand that the present invention can be implemented in many other specific forms without departing from the spirit and scope of the present invention. Based on the embodiments of the present invention, any changes and modifications made by ordinary technicians in the field of the present invention according to the above disclosure are within the scope of protection of the claims.

Claims

1. A method for evaluating the performance of an insulating soft sling, characterized in that: The method comprises the following steps: S100, collecting mechanical performance data, electrical performance data and environmental data of the insulating soft sling, preprocessing the mechanical performance data, electrical performance data and environmental data and calculating the mean and standard deviation, and generating a data characteristic description; S200, based on the data characteristic description, calculate the performance scores of mechanical properties and electrical properties to obtain the mechanical performance score and the electrical performance score; combine the mechanical performance score and the electrical performance score to calculate the comprehensive health score of the insulating soft sling; S300, based on the comprehensive health score, set the health rating threshold, correspond the comprehensive health score to the health level, and generate the health level result; according to the health level result and the historical trend data described by the data characteristics, use time series analysis to predict the performance change trend in the future time period and generate the risk prediction result; S400. Analyze the risks faced by the sling in the operating environment based on the risk prediction results and environmental data, and generate risk assessment results.

2. The insulating soft sling performance evaluation method according to claim 1, characterized in that: Step S100 includes: S110, collecting mechanical performance data, electrical performance data, and environmental data of the insulating soft sling through sensors to obtain original performance data; the mechanical performance data include tensile strength, torsional strength, and wear resistance, the electrical performance data include power frequency withstand voltage value, leakage current, and arc tolerance, and the environmental data include temperature and humidity; S120, based on the original performance data, remove errors and outliers through data cleaning, and process the data using data standardization to obtain cleaned and standardized performance data; S130. Based on the cleaned and standardized performance data, the mean and standard deviation of each performance indicator are calculated, and the central trend and degree of variation of the data are extracted through statistical analysis to form a data characteristic description.

3. The insulating soft sling performance evaluation method according to claim 1, characterized in that: Step S200 includes: S210. Based on the data characteristic description, extract the mechanical performance data and the electrical performance data to obtain the mechanical performance statistical index and the electrical performance statistical index. Based on the mechanical performance statistical index, calculate the mechanical performance score. The calculation formula is: Among them, M T is the mean tensile strength, M TW is the mean torsional strength, M WE is the mean value of wear resistance, P mech Score mechanical properties; Based on the electrical performance statistical indicators, the electrical performance score is calculated using the following formula: Among them, E V is the average value of power frequency withstand voltage, E L is the mean leakage current, E A is the mean value of arc tolerance, P elec Score electrical performance; S220. Summarize the mechanical performance scores and the electrical performance scores to obtain a performance score set. Based on the performance score set, calculate the comprehensive health score of the insulating soft sling. The calculation formula is: Among them, P mech For mechanical performance rating, P elec For electrical performance rating, P total For comprehensive health score.

4. The insulating soft sling performance evaluation method according to claim 1, characterized in that: Step S300 includes: S310, based on the comprehensive health score, determining the health rating threshold of the score, including good, fair and poor, setting the health rating threshold to distinguish the health level, and obtaining a health rating threshold set; S320, based on the health rating threshold set, mapping the comprehensive health score to the corresponding health level, determining the health level by comparing the relationship between the score and the health rating threshold, and obtaining a predetermined health level; S330. Collect historical trend data of health level results and data characteristic descriptions to obtain a data set; based on the data set, apply time series analysis to analyze and predict performance change trends to obtain preliminary trend analysis results; based on the preliminary trend analysis results, evaluate future estimated risk levels and generate risk prediction results.

5. The insulating soft sling performance evaluation method according to claim 4, characterized in that: In step S310, based on the comprehensive health score, the existing health data is first statistically analyzed to determine the health rating threshold. The threshold is set based on the percentile rule of the sling performance history data. The good threshold is set at the 75% percentile of the historical score data, the general threshold is set between 25% and 75%, and the poor threshold is set below the 25% percentile; in step S320, the comprehensive health score is matched with the previously set health rating threshold set to identify the health level of the sling. In this process, it is first checked whether the comprehensive score of the sling exceeds the good level threshold. If so, it is classified as good; if not, it is determined whether it is lower than the general level threshold. If so, it is classified as poor; otherwise, it is classified as general.

6. The insulating soft sling performance evaluation method according to claim 4, characterized in that: In step S330, the autoregressive moving average model ARMA is applied to analyze the data set. First, the order of the model is determined, and the appropriate number of lag terms is selected through the autocorrelation and partial autocorrelation function graphs of the data; then the model parameters are estimated, and the parameter values ​​are found using the maximum likelihood estimation method so that the model best fits the historical data; model diagnosis is performed to check whether the residual sequence shows white noise characteristics and ensure that the model does not contain any unexplained data structure, so as to predict future performance trends and obtain preliminary trend analysis results; Risk indicators are extracted from preliminary trend analysis results, including comparing the predicted rate of increase in maintenance requirements or performance degradation with set risk management standards, such as marking equipment performance as high risk when it drops 10% below the lower limit of the safe operating range.

7. The insulating soft sling performance evaluation method according to claim 1, characterized in that: Step S400 includes: S410. Based on the risk prediction results, conduct risk analysis, identify the impact of temperature and humidity factors in the operating environment on material properties, and derive risk impact analysis results; S420. Based on the results of the risk impact analysis, describe each risk factor and its impact, and formulate and generate risk assessment results.

8. The insulating soft sling performance evaluation method according to claim 7, characterized in that: In step S410, key indicators that indicate possible attenuation of material properties are extracted from the risk prediction results, and these indicators are identified as major risk factors. The interaction between these factors and specific operating environment factors is analyzed, including evaluating the frequency and amplitude of changes in these material properties under high temperature and high humidity conditions, and using statistical methods to determine whether these environmental conditions significantly accelerate material fatigue or affect structural integrity. In addition, the effects of these environmental conditions on different performance parameters are compared to clarify which conditions are the most stringent in the current operating environment, and based on this, determine the environmental factors that may pose a greater risk in future operations. Finally, through these analysis results, predict the level of risk that may be faced in the future; in step S420, the level of detail of various risk factors and their potential impacts is analyzed. These analyses are based on specific data points in the risk prediction results and actual conditions observed in the operating environment. Safety thresholds are set for each risk factor. These thresholds are determined based on past accident data and performance degradation records. The degree of impact of different risk factors on performance is compared through quantitative methods to generate risk assessment results.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the insulating soft sling performance evaluation method according to any one of claims 1 to 8 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the insulating soft sling performance evaluation method according to any one of claims 1 to 8 are implemented.

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