Life conversion method of loss type product based on improved wolf pack algorithm

Abstract

The invention relates to an improved wolf pack algorithm-based life conversion method for a loss type product, and belongs to the technical field of life conversion methods. In order to solve the problems that an existing life calculation method of a loss type product in a new environment is easy to interfere and can only estimate simple parameters, and cannot be used for parameter estimation with complex estimation, the life conversion method comprises the following steps: determining that the life of the loss type product obeys Weibull distribution; determining a target function according to Weibull distribution; solving the objective function by adopting an improved wolf pack algorithm to obtain the solved parameters in the new and old environments; defining a conversion coefficient and calculating the conversion coefficient according to the solved parameters; and calculating the service life of the wear-type product in the new environment by using the conversion coefficient. The improved wolf pack algorithm is high in convergence speed and high in calculation precision; the improved wolf pack algorithm is used for life conversion of the wear type product in a new environment, complex parameters can be estimated, and estimation is accurate and stable.

Description

Technical Field

[0001] The invention relates to a life conversion method for a consumable product, in particular to a life conversion method for a consumable product based on an improved wolf pack algorithm, and belongs to the technical field of life conversion methods. Background Art

[0002] The wolf pack algorithm is an optimal decision-making algorithm generated by simulating the hunting behavior of wolves. By observing the hunting behavior of wolves, the wolves in the wolf pack are divided into three types: alpha wolves, scout wolves and fierce wolves. It works according to the following rules: (1) Generation of alpha wolves: Generate n wolves in the initial space, calculate the fitness value of each wolf, and the best one is the alpha wolf. If there are multiple wolves, they are randomly assigned. (2) Wandering behavior: Select the wolf king and the wolves with better fitness as scout wolves, with a number of Take an integer between is the wolf detection ratio factor, that is, the proportion of the alpha wolf in the wolf pack. Walk around. If the fitness after walking is better than that of the leader wolf, it will replace the leader wolf. Otherwise, it will return to the original position and continue to walk until the maximum number of walks is reached. At the end of the walk, the position of the scout wolf is updated according to the optimal fitness during the walk. The calculation formula of the scout wolf position is as follows: , Among them, W' is the new position after walking, W is the position before walking, and randA is a uniformly distributed random number between [-1,1]; The wandering behavior of the scout wolf is to find the local optimal solution other than the position of the alpha wolf. This behavior is related to the ability of the algorithm to jump out of the local optimal solution. The number of explorations and the number of scout wolves are configurable parameters. However, the standard wolf pack algorithm adopts the method that each scout wolf must complete a fixed number of explorations in each iteration, which obviously takes up a lot of time. (3) Running behavior: The fierce wolf is the artificial wolf that is closer to the leader wolf. The fierce wolf is based on the step length. Run towards the leader wolf. If the fitness is better than the leader wolf during the run, it will replace the leader wolf until the distance with the leader wolf meets the requirement. The formula for calculating the position of the fierce wolf is as follows: , Among them, randB is a uniformly distributed random number between [0,1], It represents the fierce wolf moving towards the leader wolf; The purpose of the rush behavior is to change the position of the global individuals. On the one hand, it can jump out of the local optimal solution to a certain extent. On the other hand, it is to gather enough individuals for the siege behavior to improve the accuracy of the solution. However, for the fierce wolf in the position of the inferior solution, it is meaningless to blindly rush towards the alpha wolf in the hope of encountering a better solution during the rush. (5) Siege behavior: The fierce wolves and exploring wolves conduct sieges in step lengths and if the fitness during the siege is better than the original position, the position is updated; otherwise, the position remains unchanged. The position calculation formula after the siege is as follows: , (6) Survival of the fittest: After the siege, the worst X wolves are eliminated, and at the same time, X wolves are randomly generated; In the above behaviors, the three step lengths respectively correspond to the three behaviors of wandering, raiding, and sieging. The relationship between the three step lengths is expressed by the following formula: , where max and min are the boundaries of the optimization solution space.

[0003] Although the above wolf pack algorithm has the advantages of being intuitive and easy to understand, easy to implement, cooperation and competition, and dynamic update, and has been widely used in many fields, there are still some disadvantages. For complex large-scale optimization problems, aspects such as algorithm accuracy and the ability to jump out of local optimal solutions can be improved by adjusting parameters such as the number of wolf packs and wandering step lengths, but the biggest drawback is that the algorithm takes a long time. The life distribution of products and the life conversion relationship under multiple stresses cannot be directly measured through specific equipment. The common practice is to conduct life statistics on products. In engineering, the environmental parameters sensitive to products include: temperature, humidity, electrical parameters, vibration, load, etc. The values of these conditions are called stress levels.

[0004] Generally, high-reliability products will conduct corresponding accelerated stress life tests during production. For products whose life follows the Weibull distribution, its distribution parameters can be estimated by statistically analyzing their life. The Weibull distribution is a probability distribution function proposed by the Swedish scientist Weibull in 1951. Due to its flexible performance in fitting random data, it has been widely used at home and abroad. Currently, the main Weibull parameter estimation methods include the maximum likelihood estimation method, the gray method, the approximation estimation method, etc. These methods are vulnerable to interference and can only estimate simple parameters, and cannot be used for estimating complex parameters. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem that the existing life calculation method for lossy products in a new environment is vulnerable to interference and can only estimate simple parameters, and cannot be used for estimating complex parameters, and to provide a life conversion method for lossy products based on an improved wolf pack algorithm. This improved wolf pack algorithm has a fast convergence speed and high calculation accuracy; applying the improved wolf pack algorithm to the life conversion of lossy products in a new environment can estimate complex parameters.

[0006] To solve the above problems, the present application is implemented through the following technical solutions: A life conversion method for lossy products based on an improved wolf pack algorithm, which is characterized in that it includes the following steps: Step 1: Determine that the life of the wear-out product follows a Weibull distribution; Step 2: Determine the objective function according to the Weibull distribution in Step 1; Step 3: Use the improved wolf pack algorithm to solve the objective function in Step 2 to obtain the parameters to be found in the old and new environments; Step 4: Define the conversion coefficient and calculate the conversion coefficient according to the parameters to be found obtained in Step 3; Step 5: Calculate the life of the wear-out product in the new environment using the conversion coefficient in Step 4.

[0007] Furthermore, the cumulative failure probability density of the wear-out product in Step 1 conforms to the Weibull distribution: ; Furthermore, the objective function in Step 2 includes the objective function in a single environment, i.e., the objective function when the environmental stress does not change, the objective function when the environmental stress changes once, and the objective function when the environmental stress changes in multiple stages; Furthermore, the objective function in a single environment in Step 2 is: , that is , where: X is the observed value of the cumulative failure probability, t is the observed time point, F is the estimated value of the cumulative failure probability, m, are the shape parameter and scale parameter of the Weibull distribution to be estimated; Furthermore, the objective function when the environmental stress changes once in Step 2 is: , that is , where: , is the cumulative failure probability in the new environment, is the time point in the distribution curve where the failure probability is the same as is the moment when the product usage environment changes, is any time point after the product usage environment changes, is the shape parameter in the new environment, is the scale parameter in the new environment, is - the cumulative failure probability of the product at each time point t i in the Furthermore, the objective function when the environmental stress changes in multiple stages in Step 2 is: , wherein: : time point conversion time at the k-th stage, is calculated by the following formula: , the initial condition is , reflecting the time relationship before the first stress change; is the scale parameter at the k-th stage, is the shape parameter at the k-th stage; Further, step 3 specifically includes the following steps: Step 3.1. Initialize each parameter: Set the value range of the parameter, that is, the solution space range: , , is a vector, and each number of the vector is the upper limit of the corresponding parameter to be estimated, is a vector, and each number of the vector is the lower limit of the corresponding parameter to be estimated; Set the maximum number of iterations to Tmax times; Set the step size adjustment rate ; Set the population size, initialize the wolf pack coordinates, and generate a wolf pack in the solution space according to a uniform distribution; Step 3.2. Wolf pack recombination: Calculate the objective function value of each individual in the wolf pack , select the best one as the wolf king, if is a fierce wolf, otherwise it is a scout wolf; wherein: is the exploration position of the wolf king, is the position before exploration, is the walking step size; Step 3.3. Scout wolf walking: The scout wolf performs a walking behavior and calculates its objective function value , if the scout wolf is better than the objective function value of the wolf king, that is , eliminate the leading wolf and fierce wolves, generate new wolves at the new position, and go to step 3.2 until the maximum number of iterations is reached; The scout wolf walks according to , wherein: is the new position after exploration, is the position before exploration, is a random number uniformly distributed in [-1, 1]; Step 3.4. Fierce wolf siege: The fierce wolves perform a siege behavior and calculate their objective function values , if the fierce wolves are better than the objective function value of the wolf king, that is , go to step 3.2 until the maximum number of iterations is reached, The fierce wolves perform siege behavior; Step 3.5, reach the maximum number of iterations, and output the final coordinates of the alpha wolf; Furthermore, in step 3.2, the wandering step size : , where: and are the solution space boundaries, is the step size adjustment rate; Furthermore, the conversion coefficient in step 4 is the ratio of the time used when the reliability is equal in the old and new environments: , where: R is the equal reliability in the two environments. Inside the parentheses, i represents the old environment when the environmental stress undergoes 1 mutation or the i-th stage when the environmental stress undergoes K mutations, j represents the new environment when the environmental stress undergoes 1 mutation or the j-th stage when the environmental stress undergoes K mutations, and k≥2; The conversion coefficient is calculated based on the shape parameter m and the scale parameter μ obtained in step 3 ; Furthermore, the specific method of step 5 is: X （j） =Z * X （i） , where: X （j） is the life of the wear-out product in the new environment, and X （i） is the life of the wear-out product in the old environment.

[0008] The improved wolf pack calculation method of this application improves the wandering behavior of the exploring wolves as follows: changing the behavior of the exploring wolves to search only once in a single iteration cycle, keeping the rules of each round of iteration results unchanged, and the formula for calculating the position of the exploring wolves unchanged; merging the raiding behavior and the siege behavior, and those within the radius close to the alpha wolf are fierce wolves, and the rest are exploring wolves. The fierce wolves search for better solutions within the spherical surface with a radius according to the formula for calculating the position of the fierce wolves. Such improvement can improve the speed and accuracy of the algorithm; The improved wolf pack algorithm can meet the calculation of the life of wear-out products when the environment changes: proposing an accumulated failure model for multiple environmental stress change scenarios; at this time, the objective function is simplified to the superposition of multi-stage Weibull models, maintaining compatibility with the single stress change model; this modeling method realizes the continuous prediction of the failure probability under complex environmental stress histories, providing theoretical support for the design of multi-stage accelerated life tests; the improved wolf pack algorithm has a better convergence speed and higher accuracy. Description of the Drawings

[0009] Figure 1 is a flow chart of the present invention; Figure 2 is a flow chart of step 3; Figure 3 is a comparison chart of the speed between the conventional wolf pack algorithm and the present application; Figure 4 is the cumulative failure probability data of the consumable products in the old and new environments in Example 3; Figure 5 is a prediction chart of the cumulative failure probability of the consumable products in the new environment in Example 3. Detailed Embodiment

[0010] The following refers to the drawings to give the detailed embodiment of the present invention to further illustrate the composition of the present invention.

[0011] Example 1. A method for calculating the life of consumable products based on an improved wolf pack algorithm, the specific process is as Figure 1 shown, including the following steps: Step 1. Determine that the life of the consumable product follows a Weibull distribution; Step 2. Determine the objective function according to the Weibull distribution in Step 1; Step 3. Use the improved wolf pack algorithm to solve the objective function in Step 2 to obtain the required parameters in the old and new environments; Step 4. Define the conversion coefficient and calculate the conversion coefficient according to the required parameters obtained in Step 3; Step 5. Calculate the life of the consumable product in the new environment by using the conversion coefficient in Step 4.

[0012] Among them, in Step 1, when the failure mechanism of the consumable product remains unchanged, it is determined that the life X of the consumable product follows a Weibull distribution with a shape parameter m and a scale parameter μ; Cumulative probability density of the consumable product: ; Among them, the objective function in Step 2 includes the objective function in a single environment, that is, the objective function when the environmental stress does not change, the objective function when the environmental stress changes once, and the objective function when the environmental stress changes in multiple stages; Among them, the objective function in the single environment is: , that is , In the formula: X is the observed value of the cumulative failure probability, t is the observed time point, F is the estimated value of the cumulative failure probability, m, are the shape parameter and scale parameter of the Weibull distribution to be estimated; The objective function when the environmental stress changes once is as follows: , that is , In the formula: , is the cumulative failure probability in the new environment, is the time point with the same failure probability in the failure probability distribution curve as , is the moment when the product usage environment changes, is any time point after the product usage environment changes, is the shape parameter in the new environment, is the scale parameter in the new environment, is - the cumulative failure probability of the product at each time point t i in the time period; The objective function when the environmental stress changes in multiple stages is as follows: , Assume that the environmental stress undergoes K mutations, forming K + 1 stages, and the parameters of each stage are and . The objective function is defined as the sum of the absolute deviations between the observed failure probability and the model predicted value; In the formula: : the conversion time of the time point in the k-th stage, is calculated by the following formula: , The initial condition is , reflecting the time relationship before the first stress change; is the scale parameter of the k-th stage, is the shape parameter of the k-th stage; Among them, as Figure 2 shown, step 3 specifically includes the following steps: Step 3.1. Initialize each parameter: Set the value range of the parameter, that is, the solution space range: , , is a vector, and each number of the vector is the upper limit of the corresponding parameter to be estimated, is a vector, and each number of the vector is the lower limit of the corresponding parameter to be estimated; Set the maximum number of iterations to Tmax; set the step size adjustment rate ; set the population size, initialize the coordinates of the wolf pack, and generate the wolf pack in the solution space according to a uniform distribution; Step 3.2, Wolf Pack Recombination: Calculate the objective function value of each individual in the wolf pack , select the best one as the wolf king, if is a fierce wolf, otherwise it is a scout wolf; In the formula: is the exploration position of the wolf king, is the position before exploration, is the wandering step size; Step 3.3, Scout Wolf Wandering: The scout wolf performs the wandering behavior and calculates its objective function value , if the scout wolf is better than the objective function value of the wolf king, that is , eliminate the leading wolf and the fierce wolves, generate new wolves at the new position, and go to Step 3.2 until the maximum number of iterations is reached; The scout wolf wanders according to ; In the formula: is the new position after exploration, is the position before exploration, is a random number uniformly distributed in [-1, 1]; The wandering behavior of the scout wolf is to find local optimal solutions other than the position of the leading wolf. This behavior is related to the ability of the algorithm to jump out of local optimal solutions. The number of exploration times and the number of scout wolves are parameters that can be set; Step 3.4, Fierce Wolf Siege: The fierce wolves perform the siege behavior and calculate their objective function values , if the fierce wolf is better than the objective function value of the wolf king, that is , go to Step 3.2 until the maximum number of iterations is reached, The fierce wolf performs the siege behavior according to ; The raiding behavior is to change the positions of all individuals in the global scope. On the one hand, it can jump out of the local optimal solution to a certain extent. On the other hand, it is to gather enough individuals for the siege behavior to improve the accuracy of the solution. To improve the algorithm speed and retain the ability of the algorithm to improve the solution accuracy, the improvement is as follows: Combine the raiding behavior and the siege behavior. Those within a radius near the leading wolf are fierce wolves, and the rest are scout wolves; Step 3.5, Reach the maximum number of iterations and output the coordinates of the final leading wolf; Among them, in the above Step 3.2, the wandering step size : , In the formula: and is the solution space boundary, is the step factor; among them, the conversion coefficient in step 4 is the ratio of the time used when the reliability is equal in the old and new environments: , In the formula: R is the equal reliability in the two environments, i in the brackets represents the old environment when the environmental stress undergoes 1 mutation or the i-th stage when the environmental stress undergoes K mutations, j represents the new environment when the environmental stress undergoes 1 mutation or the j-th stage when the environmental stress undergoes K mutations, k≥2; the conversion coefficient is calculated according to the shape parameter m and the scale parameter μ obtained in step 3 ; Among them, the specific method of step 5 is: X （j） =Z* X （i） , In the formula: X （j） is the life of the wear-out product in the new environment, X （i） is the life of the wear-out product in the old environment.

[0013] Example 2. To verify the effectiveness of the improved wolf pack algorithm in Example 1, a set of cumulative probability densities that conform to the Weibull distribution are simulated and generated: , in the formula and are the parameters to be found, and are respectively set as =1.9, =30, take 100 data points of the uniform distribution to sample and record the cumulative failure probability function value to obtain the simulation measured value , Use the conventional wolf pack algorithm and the improved wolf pack algorithm to solve its and respectively, and the parameter settings are as follows: A. Set the objective function value , where represents the cumulative probability density of the Weibull distribution of the parameter to be solved according to , is the parameter to be found; B. Set the parameter value range, that is, the solution space range 0 ; C. Set the maximum number of iterations T max =50; D. Set the step factor =20; E. Set the population size to 100.

[0014] The final output result of the conventional wolf pack algorithm =1.89201, =30.1373, the final output result of the improved wolf pack algorithm =1.90307, =29.8877, both of which are very close to the true value.

[0015] Such as Figure 3 shown, comparison of the speeds of the conventional wolf pack algorithm and the improved wolf pack algorithm: To compare the algorithm time consumption length, for the conventional wolf pack algorithm, Figure 3 the horizontal axis in represents the number of iterations and the wandering times of the exploring wolves. It can be seen that compared with the conventional wolf pack algorithm, the improved wolf pack algorithm has a better convergence speed and higher accuracy.

[0016] Example 3. Simulation experiment verification for the case where the environmental stress in the method of Example 1 undergoes 1 mutation.

[0017] Using a set of 59 simulation data points as Figure 4 shown for verification, the product usage environment changes at moment.

[0018] The specific steps are as follows: Step 2. Set the objective function value , and the other parameters are the same as those in Example 3; calculate using the improved wolf pack calculation method to obtain =128.9330.

[0019] Set the objective function value , and the other parameters are the same as those in Example 2 ( (1) =1.9, (1) =30); calculate using the improved wolf pack algorithm to obtain =63.7480.

[0020] Step 4. Calculate the conversion coefficient of the product in the new environment .

[0021] Step 5. Since , the service life of the product under the new platform should be halved, and the subsequent usage situation of the product can be inferred based on the calculation results as Figure 5 shown.

[0022] Example 4. Simulation experiment verification for the case where the environmental stress in the method of Example 1 undergoes k (k≥2) mutations. In this example, k takes 4.

[0023] I. List of phased failure observation data Implementation scenario: A batch of products (with a total sample size of N = 220) undergoes environmental stress changes in 4 stages (terrestrial storage → sea storage → sea cyclic load → sea storage), with a total observation period of 21 months, and the cumulative number of failures is recorded once a month.

[0024] The observed data is shown in Table 1: Table 1 Observation data table of failure by stage 。

[0025] II. Parameter setting of wolf pack algorithm The core parameters of the algorithm are defined as follows: The size of the wolf pack is 100, the maximum number of iterations is 100, the search range of the scale parameter μ is [20, 150], and the search range of the shape parameter m is [1, 3].

[0026] III. Fitness function setting of wolf pack algorithm The single-point deviation is the absolute difference between the two: 。

[0027] Global fitness: The sum of the deviations at all observation time points is used as the fitness value of the wolf pack individuals: 。

[0028] IV. Equivalent time calculation For each wolf (candidate solution): 1. Input: The parameters carried by this wolf (μ1, m1, μ2, m2,...); 2. Calculate the equivalent time term: Calculate ΔT̃ k (using the μ and m parameters of the current wolf); Calculate for each stage ; 3. Calculate the predicted failure probability ; 4. Calculate the deviation: The absolute difference from the observed failure probability ; 5. Fitness value: The sum of the deviations at all time points.

[0029] For example, assume the parameter estimation value of a certain wolf , and now calculate the equivalent time term at the 16th month (stage 4). Then the stage division is shown in Table 2: Table 2 Stage division table 。

[0030] (1) Stage 1 (k = 1, months 1 - 6) , Set ,then: , , , (2) Phase 2 (k = 2, 7 - 12 months) , , , (3) Phase 3 (k = 3, 13 - 15 months) , , , (4) Phase 4 (k = 4, 16 - 21 months) , , , , , Single - point deviation , and this deviation will be included in the total fitness value. The wolf pack algorithm minimizes the total deviation at all time points by continuously adjusting the parameters. Table 3 shows the list of deviations at each point of this wolf.

[0031] Table 3 The deviation table at each point of the wolf with parameter estimation value of Month Stage Predicted failure number Actual observed number Absolute deviation 1 1 ≈0.03 0 ≈0.03 2 1 ≈0.14 0 ≈0.14 3 1 ≈0.31 0 ≈0.31 4 1 ≈0.55 0 ≈0.55 5 1 ≈0.86 1 ≈0.14 6 1 ≈1.23 1 ≈0.23 7 2 ≈1.27 1 ≈0.27 8 2 ≈1.37 1 ≈0.37 9 2 ≈1.54 1 ≈0.54 10 2 ≈1.78 2 ≈0.22 11 2 ≈2.09 2 ≈0.09 12 2 ≈2.46 2 ≈0.46 13 3 ≈2.57 2 ≈0.57 14 3 ≈3.26 3 ≈0.26 15 3 ≈3.73 3 ≈0.73 16 4 ≈3.82 4 ≈0.18 17 4 ≈4.28 5 ≈0.72 18 4 ≈5.35 7 ≈1.65 19 4 ≈7.24 7 ≈0.24 20 4 ≈9.20 7 ≈2.20 21 4 ≈10.93 7 ≈3.93 ; The current wolf fitness value is the sum of the total absolute deviations ; V. Output the fitting parameters and life conversion for each stage, as shown in Table 4 Table 4 The table of fitting parameters and life conversion for each stage .

[0032] If the land - based storage life of this product is 60 months, then, while ensuring the product reliability remains unchanged, the remaining life is .

[0033] The specific embodiments described in this article are only illustrative of the spirit of the present invention. Those skilled in the art to which the present invention pertains can make various modifications or supplements to the described specific embodiments or use similar means for substitution, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A method for converting the life of a consumable product based on an improved wolf pack algorithm, characterized in that: It includes the following steps: Step 1: Determine that the life of the wear-out product follows a Weibull distribution; Step 2: Determine the objective function according to the Weibull distribution in Step 1; Step 3: Use the improved wolf pack algorithm to solve the objective function in Step 2 to obtain the parameters required in the old and new environments; Step 4: Define the conversion coefficient and calculate the conversion coefficient according to the parameters obtained in Step 3; Step 5: Use the conversion coefficient in Step 4 to calculate the life of the wear-out product in the new environment.

2. The life conversion method of a consumable product based on an improved wolf pack algorithm according to claim 1, wherein: In Step 1, the cumulative failure probability density of the wear-out product conforms to the Weibull distribution: 。 3. A method for calculating the life of a consumable product based on an improved wolf pack algorithm according to claim 1 or 2, characterized in that: The objective function in Step 2 includes the objective function in a single environment, i.e., the objective function when the environmental stress does not change, the objective function when the environmental stress changes once, and the objective function when the environmental stress changes in multiple stages.

4. A method for calculating the life of a consumable product based on an improved wolf pack algorithm according to claim 3, characterized in that: The objective function in the single environment in Step 2 is: , namely , Where: X is the observed value of the cumulative failure probability, t is the observed time point, F is the estimated value of the cumulative failure probability, m, are the shape parameter and scale parameter of the Weibull distribution to be estimated.

5. A method for calculating the life of a consumable product based on an improved wolf pack algorithm according to claim 3, characterized in that: The objective function when the environmental stress changes once in Step 2 is: ， That is: , Wherein: , is the cumulative failure probability in the new environment, is the time point with the same failure probability in the distribution curve and the same time point, is the moment when the product usage environment changes, is any time point after the product usage environment changes, is the shape parameter in the new environment, is the scale parameter in the new environment, is - the cumulative failure probability of the product at each time point t i during this period.

6. A method for calculating the life of a consumable product based on an improved wolf pack algorithm according to claim 3, characterized in that: The objective function when the environmental stress changes in multiple stages is: ， wherein: : time point conversion time at the k-th stage, is calculated by the following formula: ， The initial conditions are , reflecting the time relationship before the first stress change; is the scale parameter for the k-th stage, is the shape parameter for the k-th stage.

7. A method for calculating the life of a consumable product based on an improved wolf pack algorithm according to claim 3, characterized in that: Step 3 specifically includes the following steps: Step 3.1: Initialize each parameter: Set the value range of the parameter, i.e., the solution space range: ， ， is a vector, and each number in the vector is the upper limit of the corresponding parameter to be estimated, is a vector, and each number in the vector is the lower limit of the corresponding parameter to be estimated; Set the maximum number of iterations to Tmax; set the step size adjustment rate ; set the population size, initialize the coordinates of the wolf pack, and generate the wolf pack in the solution space according to a uniform distribution Step 3.2, Wolf Pack Reorganization: Calculate the objective function value of each individual in the wolf pack , select the best one as the alpha wolf. If is a fierce wolf, otherwise it is a scout wolf; Where: is the detection position of the wolf king, is the position before detection, is the wandering step length; Step 3.3, Scout Wolf Wandering: The scout wolf performs the wandering behavior and calculates its objective function value , if the scout wolf is better than the alpha wolf in terms of the objective function value, that is , eliminate the alpha wolf and the beta wolf, generate new wolves at the new positions, and go to Step 3.2 until the maximum number of iterations is reached; The wolf detection button performs a patrol. In the formula: is the new position after exploration, is the position before exploration, is a random number uniformly distributed in [-1, 1]; Step 3.4, Wolf Pack Siege: The wolves execute the siege behavior and calculate their objective function values , if the wolves are better than the objective function value of the wolf king, that is , go to Step 3.2 until the maximum number of iterations is reached The fierce wolf performs a siege behavior; Step 3.5: Reach the maximum number of iterations and output the final coordinates of the leading wolf.

8. The method for converting the life of a wear-out product based on an improved wolf pack algorithm according to claim 7, wherein: In step 3.2, the walking step length : , Wherein: and are the solution space boundaries, is the step size adjustment rate.

9. A method for calculating the life of a consumable product based on an improved wolf pack algorithm according to claim 6, characterized in that: The conversion factor described in step 4 is the ratio of the time used when the reliability is equal in the old and new environments: , In the formula: R is the equal reliability in two environments. Inside the parentheses, i represents the old environment when the environmental stress changes once or the i-th stage when the environmental stress changes K times, j represents the new environment when the environmental stress changes once or the j-th stage when the environmental stress changes K times, and k≥2; The conversion coefficient is calculated based on the shape parameter m and the scale parameter μ obtained in step 3 .

10. A method for calculating the life of a consumable product based on an improved wolf pack algorithm according to claim 6, characterized in that: The specific method of the said step 5 is: X （j） =Z * X （i） , Where: X （j） is the lifespan of the consumable product in the new environment, and X （i） is the lifespan of the consumable product in the old environment.

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