Cable fault probability prediction method and device considering joint interface stress, and medium

Abstract

The invention relates to a cable fault probability prediction method and device considering joint interface stress and a medium, and the method comprises the following steps: obtaining and preprocessing historical data of a cable, and recognizing abnormal data and fault data; analyzing the preprocessed historical data of the cable, and determining factors causing a cable fault; establishing a cable fault probability model based on Weibull distribution or Poisson regression analysis for each factor; and determining the overall cable fault probability based on the cable fault probability model of each factor. Compared with the prior art, the method has the advantages of accurate prediction and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of cable fault prediction, and in particular to a method, device and medium for predicting cable fault probability taking into account joint interface stress. Background Art

[0002] With the rapid development of the power system and the acceleration of urbanization, power cables, as an important medium for power transmission, are crucial to ensuring power supply and urban operation due to their stability and reliability. Since cables operate in complex and changeable underground environments, their failure probability is affected by many factors, including but not limited to material aging, mechanical damage, environmental erosion, overload operation, construction damage, etc. The diversity and uncertainty of these factors pose huge challenges to the maintenance and management of cables. Cable joints are the weak link in the cable system, and their failure rate accounts for a large proportion of cable failures. Only when the joint interface stress is distributed between 0.1 and 0.3 MPa can the electrical strength requirements be guaranteed and the cable insulation will not be damaged. However, the analysis of cable failure probability in the prior art does not take this into account.

[0003] Patent No. (CN202410550851.7) invented "A method and system for early warning of power cable operation". The invention samples the online temperature of the power cable and uses the Arrhenius life equation to calculate the remaining life of the power cable. The probability of power cable failure at a certain moment in the future is predicted based on the failure rate distribution function, and the operating risk of the power cable is evaluated based on this probability, and then early warning measures are implemented. This invention only considers the effect of temperature on thermal aging of power cables in establishing a power cable failure probability model, which will reduce the accuracy of the failure probability prediction, thereby affecting the reliability of the power cable operation early warning.

[0004] Patent No. (CN202010118891.6) invented "A cable system fault analysis method". The invention collects data information that affects cable operation and establishes a cable fault probability model based on Weibull distribution. The model takes into account the influencing factors related to cable failure. The cable failure probability model of this invention takes into account the impact of multiple factors on cables, but the model does not clearly state the factors that cause cable failures, and believes that all events that affect cable failures satisfy the Weibull distribution, which is inaccurate. Because some events that cause cable failures are discrete and memoryless, they are not suitable for the Weibull distribution failure probability model.

[0005] Cable failures can cause local or even large-scale power outages, affecting industrial production and daily life, causing economic losses and social inconvenience. In addition, cable failures may also damage the stability of the power system, increase the risk of power grid collapse, and affect power supply in a wider area. In the long run, frequent cable failures will also increase the cost of maintenance and replacement, placing an economic burden on power companies. Therefore, it is very important to accurately predict the probability of cable failures. Summary of the invention

[0006] The purpose of the present invention is to provide a cable fault probability prediction method, device and medium taking into account the joint interface stress, by collecting cable historical data, especially cable fault records, to support in-depth analysis of the cause of the fault, and on the basis of these data, further propose a mathematical model for predicting the cable fault probability, thereby achieving accurate cable fault probability prediction.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A cable failure probability prediction method considering joint interface stress comprises the following steps:

[0009] Obtain cable historical data and pre-process it to identify abnormal data and fault data;

[0010] Analyze the pre-processed cable historical data to determine the factors that lead to cable failure;

[0011] Establish cable failure probability models based on Weibull distribution or Poisson regression analysis for each factor;

[0012] The overall cable failure probability is determined based on the cable failure probability model of each factor.

[0013] The factors causing cable failure include aging failure, overvoltage failure, overcurrent failure, user factor change failure and connector failure.

[0014] For cable aging failure, the failure probability model is constructed as follows:

[0015] Convert the cable's operating time at different temperatures to the equivalent operating time T at standard temperature eq :

[0016]

[0017] Where N is the total number of intervals for running time division; θ Ri is the actual operating temperature of the cable during the operating time interval i, in °C; t i The cable is at temperature θ Ri The running time under the condition is the length of the running time interval i, in units of h; θ0 is the standard temperature, in °C; B t is an empirical constant measured experimentally;

[0018] Estimation of the equivalent life T of the cable based on the Arrhenius equation l for:

[0019]

[0020] In the formula, C l is the standardized coefficient obtained based on the statistical fitting of historical data; θ R The actual operating temperature of the current cable;

[0021] Based on the Weibull distribution, taking into account the influence of operating temperature, the cable aging failure probability model is established based on the equivalent operating time and the equivalent life of the cable:

[0022]

[0023] In the formula, β l is the shape parameter, obtained based on historical data.

[0024] For cable overvoltage fault, the fault probability model is constructed as follows:

[0025] When the maximum induced lightning peak voltage U max Less than the cable withstand voltage U D When , the cable will not have an overvoltage fault, otherwise an overvoltage fault will occur. The cable overvoltage fault probability model is expressed as:

[0026]

[0027] For cable overcurrent fault, the fault probability model is constructed as follows:

[0028] The impact assessment level of moisture and short circuit on the cable caused by overcurrent fault is set to k 1 :

[0029]

[0030] In the formula, Λ j , N j , M j It is the standardized coefficient calculated based on the statistical fitting of historical data, where x is the month, j=1 represents moisture, and j=2 represents short circuit;

[0031] The Poisson regression model is used to construct the cable overcurrent fault probability model:

[0032]

[0033] In the formula, m1 , n 1 It is the standardized coefficient obtained based on the statistical fitting of historical data; is the probability of cable aging failure.

[0034] For user factor change failures, the failure probability model is constructed as follows:

[0035] The impact assessment level of different time on the cable is set to k 2 ;

[0036]

[0037] Where N t is the total number of faults that occurred in the jth time period on a certain day in the past fault data; N total The total number of failures that occurred on the same day in the past;

[0038] The failure probability model of user factor changes based on the Poisson regression model is:

[0039]

[0040] In the formula, m 2 , n 2 , b 2 It is a standardized coefficient obtained based on the statistical fitting of historical data.

[0041] For the joint failure probability, the failure probability model is constructed as follows:

[0042] In view of the stress relaxation phenomenon occurring at the cable joint interface, a joint failure probability model is established based on the Weibull distribution:

[0043]

[0044] Where t is the running time, and β and η are standardized coefficients obtained based on statistical fitting of historical data.

[0045] The cable failure probability model based on various factors determines the overall cable failure probability as follows:

[0046]

[0047] Among them, i=0, 1, 2, 3, 4 correspond to the cable aging failure probability, overvoltage failure probability, overcurrent failure probability, user factor change failure probability and connector failure probability respectively.

[0048] A cable fault probability prediction device taking into account joint interface stress comprises a memory, a processor, and a program stored in the memory, wherein the processor implements the method described above when executing the program.

[0049] A storage medium stores a program, which implements the method described above when executed.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) Comprehensiveness: The present invention proposes a cable failure probability prediction method that takes into account the joint interface stress. The method collects cable historical data and scientifically and rationally classifies the factors that cause cable failures. A failure probability model is established based on Weibull distribution and Poisson regression analysis. The model can comprehensively consider the impact of multiple factors on cable failures, such as temperature, pressure, humidity, etc., and reasonably classifies various factors to predict potential risks in cable operation.

[0052] (2) Accuracy: The present invention comprehensively considers the impact of various factors on cable failures, pays special attention to the weakest link in the cable system - the joint part, and establishes a failure probability model for the cable joint to improve the accuracy of fault prediction. Considering the importance of joints in the cable system, the establishment of this model has great practical significance for evaluating and improving the reliability of the cable system. The model can also become a part of the cable management system of the power company, providing strong data support and decision-making assistance for the realization of intelligent and automated cable operation and maintenance.

[0053] (3) Preventiveness: The present invention establishes a cable failure probability model based on historical data, which can predict the failure probability, identify potential failure risks in advance, take preventive measures, and reduce unexpected failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the method of the present invention;

[0055] Figure 2 A flowchart of historical data collection in one embodiment;

[0056] Figure 3 A schematic diagram of factors affecting the probability of cable failure in one embodiment;

[0057] Figure 4 It is a schematic diagram of multivariate linear regression analysis of interface stress under the influence of different factors in an embodiment. DETAILED DESCRIPTION

[0058] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0059] Example 1

[0060] This embodiment provides a cable failure probability prediction method considering the joint interface stress. Figure 1 As shown, the following steps are included:

[0061] S1, obtain the cable historical data and pre-process it to identify abnormal data and fault data.

[0062] The development of the equipment operation status follows the evolution law of "normal → abnormal → fault". In the process of cable status evolution, the historical data collected must include a large amount of normal data, very few abnormal data and a small amount of fault data. Among them, fault data mainly comes from events such as construction and user factors that directly cause cable failures; normal data and abnormal data mainly come from variables collected by sensors. From probability statistics, it can be seen that historical data is in a normal state with a high probability, and in an abnormal state and a fault state with a lower probability. In order to ensure the accuracy and reliability of the data used in the present invention, the long-term historical data of the same type of cable in a certain area is used as sample data, and the data is preprocessed, and then the cable historical data is used for abnormal data identification and fault data identification.

[0063] like Figure 2 As shown in the figure, the data collection method depends on the type of data, and the data collection method depends on the purpose of data use, the speed of change, and the real-time requirements. The data collection used only for statistical acquisition includes the number of cable failures caused by user factors and the number of cable failures caused by construction. The data collection quantities that need to be acquired in real time include temperature, current, voltage, humidity, and pressure.

[0064] The present invention uses a variety of sensors to collect data, including temperature, current, voltage, humidity and pressure sensors, and performs the following preprocessing on the signals collected by the sensors:

[0065] 1. Signal sampling

[0066] Convert the collected analog signal into a discrete digital signal. Set the appropriate sampling frequency and sampling points. The sampling point number N and the sampling time f s The relationship between them is:

[0067] N=T×f s

[0068] Where T is the pulse waveform width output by the sensor.

[0069] For sensors such as temperature and humidity, it is usually not necessary to set a too high sampling frequency because these quantities change relatively slowly. However, pressure signals change rapidly and usually require a higher sampling frequency.

[0070] 2. Signal filtering

[0071] Filtering is a signal processing technology that can remove noise from a signal, thereby improving the quality of the signal. Wavelet denoising is a signal processing technology based on wavelet transform. Its core is to use wavelet transform to decompose the signal into components at different scales, then remove the noise component through threshold processing, and finally reconstruct the signal to obtain the denoising result.

[0072] Hard threshold denoising formula:

[0073]

[0074] Soft threshold denoising formula:

[0075]

[0076] Where w is the wavelet coefficient, λ is the threshold parameter, and sgn is the sign function.

[0077] 3. Signal amplification

[0078] Signals may encounter various losses during transmission and may be drowned by noise. Signal amplification helps improve the transmission quality, reliability and efficiency of signals. Common signal amplification technologies include low noise amplifiers (LNA) to amplify received signals.

[0079] Data after signal processing is the key to building a cable fault probability model. Only in this way can useful information be extracted from the cable operation data. Whether the collected voltage, temperature, pressure and other signals need to be normalized depends on subsequent needs. Normalization is a common data preprocessing step that scales the data so that it falls within a specific small interval, usually [0,1]. The normalization formula is:

[0080]

[0081] In the formula, X is the value in the original data, X norm is the normalized value, X max is the maximum value of the data set, X min is the minimum value of the data set.

[0082] Identify anomalies and faults on preprocessed data:

[0083] 1. Identification of normal data

[0084] Normal data refers to data within the reasonable operating range of the cable, such as the reasonable range of set temperature is [-40℃~90℃], and the reasonable range of interface stress is [0.1MPa~0.3MPa].

[0085] 2. Identification of abnormal data

[0086] Abnormal data is data beyond the normal operating range, such as current I r,i If the following relationship is satisfied, then I r,i Out of normal value range.

[0087] I r,i >I h |I r,i <I l

[0088] In the formula, I h , I l They are the upper and lower limits of the normal current range respectively.

[0089] 3. Identification of fault data

[0090] Fault data refers to the data that directly causes the cable to fail. For example, when an overvoltage fault occurs in the cable, when the maximum induced lightning peak voltage U max >Cable withstand voltage U D When the cable is damaged by lightning, it will fail.

[0091] S2, analyze the pre-processed cable historical data to determine the factors causing the cable failure.

[0092] In this embodiment, the factors that lead to cable failure include aging failure, overvoltage failure, overcurrent failure, user factor change failure and connector failure. Figure 3 shown.

[0093] 1) Factors that lead to cable aging failure include ambient temperature, laying conditions, years of operation, and load rate.

[0094] 2) Factors that lead to cable overcurrent failure include short circuit, moisture, and changes in user factors.

[0095] 3) Factors that cause cable overvoltage failure include induced overvoltage.

[0096] 4) Factors that cause cable connector failure include ambient temperature, current, and bending radius.

[0097] S3, establish a cable failure probability model based on Weibull distribution or Poisson regression analysis for each factor.

[0098] S31, construction of cable aging failure probability model.

[0099] Convert the cable's operating time at different temperatures to the equivalent operating time T at standard temperature eq :

[0100]

[0101] Where N is the total number of intervals for running time division; θRi is the actual operating temperature of the cable during the operating time interval i, in °C; t i The cable is at temperature θ Ri The running time under the condition is the length of the running time interval i, in units of h; θ 0 is the standard temperature, in °C; B t is an empirical constant measured experimentally.

[0102] Estimation of the equivalent life T of the cable based on the Arrhenius equation l for:

[0103]

[0104] In the formula, C l is the standardized coefficient obtained based on the statistical fitting of historical data; θ R It is the actual operating temperature of the current cable.

[0105] Based on the Weibull distribution, taking into account the influence of operating temperature, the cable aging failure probability model is established based on the equivalent operating time and the equivalent life of the cable:

[0106]

[0107] In the formula, β l is the shape parameter, obtained based on historical data.

[0108] S32, cable overvoltage fault probability model construction.

[0109] Buried cables are generally not affected by direct lightning strikes, but in thunderstorms, cables are often affected by induced lightning. Induced lightning refers to the overvoltage phenomenon induced in cables by the strong electromagnetic field changes generated by lightning discharge. This overvoltage is usually caused by lightning discharge near the cable, rather than directly hitting the cable.

[0110] When the maximum induced lightning peak voltage U max Less than the cable withstand voltage U D When , the cable will not have an overvoltage fault, otherwise an overvoltage fault will occur. The cable overvoltage fault probability model is expressed as:

[0111]

[0112] S33, cable overcurrent fault probability model construction.

[0113] In addition to aging, the causes of cable failure also include overcurrent problems caused by short circuits or moisture. The main causes of short circuits are pests and construction. Certain pests (such as termites) may bite the outer sheath and insulation layer of the cable, exposing the conductor and causing a short circuit; during construction, the cable may be physically compressed or impacted, causing the insulation layer to rupture, and a short circuit will occur after long-term operation.

[0114] Moisture and short circuit will damage the insulation of the cable, and both factors are affected by monthly changes. The impact assessment level of moisture and short circuit on the cable is set to k 1 :

[0115]

[0116] In the formula, Λ j , N j , M j It is a standardized coefficient calculated based on the statistical fitting of historical data, where x is the month, j=1 represents moisture, and j=2 represents short circuit.

[0117] The cable's overcurrent impact resistance is inversely proportional to its operating life, so the cable's own aging probability is a factor that must be taken into account. The cable failure caused by overcurrent statistically conforms to the exponential distribution characteristics, so the Poisson regression model is used to construct the cable failure probability model:

[0118]

[0119] In the formula, m 1 , n 1 It is the standardized coefficient obtained based on the statistical fitting of historical data; is the probability of cable aging failure.

[0120] S34, construction of failure probability model for user factor changes.

[0121] In addition to the above reasons, the fault caused by overcurrent is also related to user factors (seasonal load changes). In this embodiment, the impact assessment level of the cable at different times is set to k 2 ;

[0122]

[0123] Where N t is the total number of faults that occurred in the jth time period on a certain day in the past fault data; N total It is the total number of failures that occurred on the same day in the past.

[0124] Since the historical cable failures caused by user factor changes can be obtained through statistics, the user factor change failure probability model based on the Poisson regression model is:

[0125] P L3 =m 2 exp(n 2 k 2 )+b 2 ;

[0126] In the formula, m 2 , n 2 , b 2 It is a standardized coefficient obtained based on the statistical fitting of historical data.

[0127] S35, joint failure probability model construction.

[0128] The magnitude of the cable joint interface stress is affected by temperature, current and bending radius. In the interface stress analysis, the multivariate linear regression model can quantify the influence of different factors on the interface stress.

[0129] By collecting the interface stress of the cable joint at different temperatures, currents, and bending radii, the multivariate linear regression equation after the standardized coefficient can be established as follows:

[0130] σ=0.598×T+0.341×I-0.716×R

[0131] Where T is temperature, I is current, and R is bending radius.

[0132] The standardized coefficient of temperature is 0.598, which means that after all independent variables are standardized, the interface stress is expected to increase by 0.598 standard deviations for every standard deviation increase in temperature. The standardized coefficient of current is 0.341, which means that the interface stress is expected to increase by 0.341 standard deviations for every standard deviation increase in current. The standardized coefficient of bending radius is -0.716, which means that the interface stress is expected to decrease by 0.716 standard deviations for every standard deviation increase in bending radius. The larger the absolute value of the standardized coefficient, the more significant the impact of the independent variable on the dependent variable. Multivariate linear regression analysis of interface stress under the influence of different factors is shown in Figure 2. Figure 4 shown.

[0133] 1) The larger the absolute value of the coefficient in the multivariate linear regression equation after standardization, the more significant the impact of the independent variable on the dependent variable.

[0134] 2) In this model, the bending radius has the most significant effect, followed by temperature, and the effect of current is relatively small.

[0135] The normal operating range of the cable joint interface pressure is [0.1MPa~0.3MPa]. With the increase of the operating years, the cable will experience stress relaxation, which will reduce the insulation performance of the cable until the cable is broken down and fails. The probability model of cable failure caused by stress relaxation satisfies the Weibull distribution. The joint failure probability model is established as:

[0136]

[0137] Where t is the running time, and β and η are standardized coefficients obtained based on statistical fitting of historical data.

[0138] S4, considering the independence of cable failure factors, the overall cable failure probability is determined based on the cable failure probability model of each factor:

[0139]

[0140] Among them, i=0, 1, 2, 3, 4 correspond to the cable aging failure probability, overvoltage failure probability, overcurrent failure probability, user factor change failure probability and connector failure probability respectively.

[0141] Example 2

[0142] This embodiment provides a cable fault probability prediction device based on considering the joint interface stress, including a memory, a processor, and a program stored in the memory, and when the processor executes the program, the method described in the above embodiment 1 is implemented.

[0143] In a preferred embodiment, the device comprises:

[0144] Data acquisition and preprocessing module: acquires cable historical data and performs preprocessing to identify abnormal data and fault data;

[0145] Influencing factor determination module: Analyze the pre-processed cable historical data to determine the factors that cause cable failure;

[0146] Cable fault probability model building module: establish cable fault probability models based on Weibull distribution or Poisson regression analysis for each factor;

[0147] Cable fault probability determination module: Determines the overall cable fault probability based on the cable fault probability model of various factors.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0149] Example 3

[0150] This embodiment provides a storage medium on which a program is stored. When the program is executed, the method described in the above embodiment 1 is implemented.

[0151] Storage media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in the present invention, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0152] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A cable failure probability prediction method considering joint interface stress, characterized in that: The following steps are involved: Obtain cable historical data and pre-process it to identify abnormal data and fault data; Analyze the pre-processed cable historical data to determine the factors that lead to cable failure; Establish cable failure probability models based on Weibull distribution or Poisson regression analysis for each factor; The overall cable failure probability is determined based on the cable failure probability model of each factor.

2. A cable failure probability prediction method considering joint interface stress according to claim 1, characterized in that: The factors causing cable failure include aging failure, overvoltage failure, overcurrent failure, user factor change failure and connector failure.

3. A cable failure probability prediction method considering joint interface stress according to claim 2, characterized in that: For cable aging failure, the failure probability model is constructed as follows: Convert the cable's operating time at different temperatures to the equivalent operating time T at standard temperature eq : Where N is the total number of intervals for running time division; θ Ri is the actual operating temperature of the cable during the operating time interval i, in °C; t i The cable is at temperature θ Ri The running time under the condition is the length of the running time interval i, in h; θ0 is the standard temperature, in °C; B t is an empirical constant measured experimentally; Estimation of the equivalent life T of the cable based on the Arrhenius equation l for: In the formula, C l is the standardized coefficient obtained based on the statistical fitting of historical data; θ R The actual operating temperature of the current cable; Based on the Weibull distribution, taking into account the influence of operating temperature, the cable aging failure probability model is established based on the equivalent operating time and the equivalent life of the cable: In the formula, β l is the shape parameter, obtained based on historical data.

4. A cable failure probability prediction method considering joint interface stress according to claim 2, characterized in that: For cable overvoltage fault, the fault probability model is constructed as follows: When the maximum induced lightning peak voltage U max Less than the cable withstand voltage U D When , the cable will not have an overvoltage fault, otherwise an overvoltage fault will occur. The cable overvoltage fault probability model is expressed as:

5. A cable failure probability prediction method considering joint interface stress according to claim 2, characterized in that: For cable overcurrent fault, the fault probability model is constructed as follows: The impact assessment level of moisture and short circuit on cables, which are factors causing overcurrent faults, is set to k1: In the formula, Λ j , N j , M j It is the standardized coefficient calculated based on the statistical fitting of historical data, where x is the month, j=1 represents moisture, and j=2 represents short circuit; The Poisson regression model is used to construct the cable overcurrent fault probability model: In the formula, m1 and n1 are standardized coefficients obtained based on statistical fitting of historical data; is the probability of cable aging failure.

6. A cable failure probability prediction method considering joint interface stress according to claim 2, characterized in that: For user factor change failures, the failure probability model is constructed as follows: The impact assessment level on the cable at different times is set to k2; Where N t is the total number of faults that occurred in the jth time period on a certain day in the past fault data; N total The total number of failures that occurred on the same day in the past; The failure probability model of user factor changes based on the Poisson regression model is: Where m2, n2, and b2 are standardized coefficients obtained based on statistical fitting of historical data.

7. A cable failure probability prediction method considering joint interface stress according to claim 2, characterized in that: For the joint failure probability, the failure probability model is constructed as follows: In view of the stress relaxation phenomenon occurring at the cable joint interface, a joint failure probability model is established based on the Weibull distribution: Where t is the running time, and β and η are standardized coefficients obtained based on statistical fitting of historical data.

8. A cable failure probability prediction method considering joint interface stress according to claim 2, characterized in that: The cable failure probability model based on various factors determines the overall cable failure probability as follows: Among them, i=0, 1, 2, 3, 4 correspond to the cable aging failure probability, overvoltage failure probability, overcurrent failure probability, user factor change failure probability and connector failure probability respectively.

9. A cable fault probability prediction device considering joint interface stress, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 8 is implemented.

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