CN115329530A - Structure mixed gradual change reliability assessment method based on self-adaptive dotting strategy - Google Patents
Structure mixed gradual change reliability assessment method based on self-adaptive dotting strategy Download PDFInfo
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Abstract
The utility model relates to a structure mixed gradual change reliability assessment method based on self-adaptive dotting strategy, which comprises the steps of constructing a functional function of a target structure by obtaining failure information of a reliability key part of the target structure, converting original multiple uncertain mixed gradual change reliability assessment problems into single uncertain static reliability assessment problems by constructing an extreme value response surface of the functional function on the basis, and then assessing the reliability of the target structure based on the constructed extreme value response surface. In the whole process, only the functional function and the extreme value response surface of the functional function are needed to be constructed, the structure gradual change reliability evaluation process under various uncertain mixed conditions can be simplified, the evaluation complexity is reduced, and the evaluation precision can be ensured.
Description
Technical Field
The application relates to the field of structural reliability, in particular to a structural hybrid gradual change reliability assessment method based on a self-adaptive dotting strategy.
Background
Uncertainties are prevalent in engineered structures, such as uncertainties in material properties, geometry, and loading. With time, the structural performance gradually deteriorates, and the reliability exhibits a remarkably gradual characteristic. In addition, due to the fact that information accumulation is difficult, part of uncertainty parameters can be represented only by adopting an interval model, and structural gradient reliability assessment has the mixed characteristics of random-interval uncertainty.
For gradual reliability evaluation of a structure, a first-pass method is often used at present, for example, a PHI2 method, a Rice formula analysis method, an approximate calculation method, and the like, and a structure reliability evaluation is performed by discretizing a time interval and then based on a Monte Carlo (MC) method. For the structural reliability problem of various uncertain mixtures, a method for converting two uncertain parameters into a single uncertain parameter is adopted for evaluation calculation, and a first-order reliability analysis method for random-interval uncertain mixed unified uncertain analysis is further developed on the basis of a first-order second-order moment method.
The above method is a structural gradient reliability assessment for a single uncertainty, or a static structural mixture reliability assessment, but for the structural mixture gradient reliability problem, an effective assessment method is lacked due to the complexity of the calculation.
Disclosure of Invention
Therefore, in order to solve the above problems, it is necessary to provide a structure mixed gradual change reliability assessment method based on an adaptive dotting strategy, which can efficiently and accurately process the structure gradual change reliability assessment problems under various uncertain mixed conditions, and reduce the assessment complexity.
In a first aspect, the present application provides a method for evaluating reliability of structure mixed gradual change, the method including:
acquiring failure information of a reliability key piece of a target structure; the failure information comprises a plurality of influence factors influencing the reliability of the target structure; the multiple influence factors comprise random uncertainty influence factors, interval uncertainty influence factors, time influence factors and failure criteria;
constructing a functional function of a target structure according to various influence factors;
substituting the sample data of the target influence factors into the functional function to obtain an extreme value response surface of the functional function; the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors;
and carrying out reliability evaluation on the target structure based on the extreme value response surface to obtain a reliability evaluation result of the target structure.
In one embodiment, the substituting sample data of the target influencing factor into the functional function to obtain an extremum response surface of the functional function includes:
determining a target influence factor from a plurality of influence factors;
acquiring sample data of target influence factors;
substituting the sample data of the target influence factors into the functional function to obtain the minimum value response of the structural functional function;
and constructing an extreme value response surface of the functional function according to the sample data of the target influence factors and the corresponding minimum value response.
In one embodiment, the determining the target influence factor from the plurality of influence factors includes:
acquiring distribution information of each influence factor; the distribution information includes: the distribution type and the distribution parameters of the random uncertainty influence factors, the upper and lower bounds of the interval uncertainty influence factors and the range of the time influence factors;
and acquiring a plurality of groups of initial samples according to the distribution information of random and interval uncertainty influence factors and the range of time influence factors.
In one embodiment, the constructing an extremum response surface of the functional function according to the sample data of the target influencing factor and the corresponding minimum response includes:
constructing an initial extreme value response surface of the function according to the initial sample of the target influence factor and the corresponding minimum value response;
updating the initial extreme value response surface to obtain a first updated extreme value response surface;
if the first updated extreme value response surface meets the preset update convergence criterion, determining the first updated extreme value response surface as the extreme value response surface of the function;
and if the first updating extreme value response surface does not meet the preset updating convergence criterion, continuously updating the first updating extreme value response surface until the latest updating extreme value response surface meets the updating convergence criterion.
In one embodiment, the reliability evaluation method for the structure mixed gradual change further includes:
acquiring a first candidate reliability value of the target structure based on the initial extreme value response surface;
acquiring a second candidate reliability value of the target structure based on the first updated extreme value response surface;
and if the difference value between the first candidate reliability value and the second candidate reliability value is smaller than a preset error value, determining that the first updated extreme value response surface meets the update convergence criterion.
In one embodiment, the updating the initial extremum response surface to obtain a first updated extremum response surface includes:
acquiring candidate sample data of random uncertainty influence factors;
and updating the initial extreme value response surface according to the candidate sample data of the random uncertainty influence factors to obtain a first updated extreme value response surface.
In one embodiment, the updating the initial extremum response surface according to the candidate sample data of the random uncertainty influencing factor to obtain a first updated extremum response surface includes:
determining an updating point from candidate sample data of random uncertainty influence factors;
substituting the update point into the functional function to obtain the update minimum response of the functional function;
and updating the initial extreme value response surface by using the updated minimum value response of the functional function to obtain a first updated extreme value response surface.
In one embodiment, the determining the update point from the candidate sample data of the random uncertainty influence factor includes:
constructing a learning function of random uncertainty influence factors according to a self-adaptive point adding strategy;
and determining the corresponding candidate sample as the update point when the learning function takes the minimum value.
In a second aspect, the present application further provides a structural hybrid gradual change reliability evaluation apparatus. The device includes:
the information acquisition module is used for acquiring failure information of the key part of the reliability of the target structure; the failure information comprises various influence factors influencing the reliability of the target structure; the multiple influence factors comprise random uncertainty influence factors, interval uncertainty influence factors, time influence factors and failure criteria;
the data processing module is used for constructing a function of the target structure according to various influence factors;
the response surface acquisition module is used for substituting the sample data of the target influence factors into the function to acquire an extreme value response surface of the function; the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors;
and the reliability evaluation module is used for evaluating the reliability of the target structure based on the extreme value response surface to obtain an evaluation result of the reliability of the target structure.
In a third aspect, the application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the method steps as described in any of the embodiments of the first aspect when the processor executes the computer program.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the method steps of any of the embodiments of the first aspect.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, performs the method steps of any of the embodiments of the first aspect.
According to the structure mixed gradual change reliability assessment method, the structure mixed gradual change reliability assessment device and the computer equipment based on the self-adaptive dotting strategy, the functional function of the target structure is constructed by obtaining the failure information of the reliability key part of the target structure, on the basis, the original multiple uncertain mixed gradual change reliability assessment problems can be converted into single uncertain static reliability assessment problems by constructing the extreme value response surface of the functional function, and then the reliability of the target structure is assessed based on the constructed extreme value response surface. In the whole process, only the functional function and the extreme value response surface of the functional function are needed to be constructed, the evaluation process of the gradual change reliability of the structure under various uncertain mixed conditions can be simplified, the evaluation complexity is reduced, and the evaluation precision can be ensured.
Drawings
FIG. 1 is a diagram illustrating an exemplary embodiment of a method for evaluating reliability of mixed-gradient structure;
FIG. 2 is a flow diagram illustrating a method for assessing reliability of a structured mixed gradient, according to one embodiment;
FIG. 3 is a schematic diagram of a structural failure mode in one embodiment;
FIG. 4 is a flowchart illustrating step S203 according to an embodiment;
FIG. 5 is a flowchart illustrating step S401 in one embodiment;
FIG. 6 is a flowchart illustrating step S403 according to an embodiment;
FIG. 7 is a flowchart illustrating step S603 according to an embodiment;
FIG. 8 is a flowchart illustrating step S604 according to an embodiment;
FIG. 9 is a flowchart illustrating step S602 according to an embodiment;
FIG. 10 is a flowchart illustrating step S901 in one embodiment;
FIG. 11 is a flowchart illustrating a method for evaluating reliability of a structured mixed gradient in another embodiment;
FIG. 12 is a diagram showing a spatial distribution of update points in one embodiment;
FIG. 13 is a comparison of reliability evaluation results in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It will be understood that the terms "first," "second," and the like as used herein may be used herein to describe various data, but the data is not limited by these terms. These terms are only used to distinguish one datum from another. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It will be further understood that the terms "comprises/comprising," "includes" or "including," or "having," etc., specify the presence of stated features, integers, steps, operations, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, or combinations thereof. Also, as used in this specification, the term "and/or" includes any and all combinations of the associated listed items.
The method for evaluating reliability of mixed gradual change of a structure provided by the embodiment of the present application may be applied to a computer device, where the computer device may be any type of device, for example, a terminal device, or various personal computers, laptops, tablets, wearable devices, servers, and the like, and the embodiment of the present application does not limit the type of the computer device. As shown in FIG. 1, a schematic diagram of an internal structure of a computer device is provided, and the processor of FIG. 1 is used for providing computing and control capabilities. The memory includes a nonvolatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database is used for relevant data of the reliability evaluation process of the structure. The network interface is used for communicating with other external devices through network connection. The computer program is executed by a processor to implement a structural hybrid grading reliability assessment method.
As mentioned in the background, uncertainties are prevalent in engineering structures, such as uncertainties in material properties, geometry, and loading. With time, the structural performance gradually deteriorates, and the reliability exhibits a remarkably gradual characteristic. In addition, due to the fact that information accumulation is difficult, part of uncertainty parameters can be represented only by adopting an interval model, and structural gradient reliability assessment has the mixed characteristics of random-interval uncertainty.
For the gradual change reliability evaluation of the structure, the first-pass method is used, for example, the PHI2 method, the Rice formula analysis method, the approximation calculation method, etc., and the structure reliability evaluation is performed by discretizing the time interval and then based on the Monte Carlo (MC) method. For the structural reliability problem of various uncertain mixtures, a method of converting two uncertain parameters into a single uncertain parameter is adopted for evaluation and calculation, and a first-order reliability analysis method for further developing random-interval uncertain mixed unified uncertain analysis on the basis of a first-order second-order moment method is also researched.
The above method is a structural gradient reliability assessment for a single uncertainty, or a static structural mixture reliability assessment, but for the structural mixture gradient reliability problem, an effective assessment method is lacked due to the complexity of the calculation.
Based on this, the embodiment of the application provides a structural hybrid gradient reliability assessment method, which can convert the original gradient reliability problem of multiple mixed uncertainties into a static reliability assessment problem of a single uncertainty by constructing an extremum response surface of a functional function. The reliability evaluation method has the advantages that the accuracy of the reliability evaluation result is kept, meanwhile, the calculation complexity of the reliability evaluation problem is obviously reduced, and the method has important reference significance for popularization and application of the reliability evaluation in engineering practice. It should be understood that the above is only an example of the effects achieved in the embodiments of the present application, and in practical applications, the effects achieved by the technical means provided in the embodiments of the present application are not limited thereto, and specific reference may be made to the following embodiments for description.
In one embodiment, as shown in fig. 2, a structural hybrid grading reliability assessment method is provided, which in this embodiment includes the steps of:
s201, acquiring failure information of the reliability key piece of the target structure.
The failure information includes various influence factors that influence the reliability of the target structure, such as a random uncertainty parameter, an interval uncertainty parameter, a time variable, and the like that influence the reliability of the target structure. In addition, the failure information may also include failure modes of the structure, failure criteria, and the like. In practical application, the Failure information may be obtained through manual Analysis, or obtained from the existing experimental experience, if the structure is complex, the Failure information may be performed by combining technologies such as Failure Mode Analysis and Criticality Analysis (FMECA) and Failure Tree Analysis (FTA), and the specific obtaining manner is not limited in this application.
Taking a rusted reinforcement beam as an example, as shown in fig. 3, in a reinforcement scene, the target structure is the reinforcement beam, and the key reliability component is the reinforcement beam. The reinforcing bar roof beam bears equipartition load and concentrated load simultaneously (act on and stride), because the corrosion effect, the corrosion degree of depth of reinforcing bar roof beam deepens gradually, and reinforcing bar roof beam cross section size changes, and then leads to the ultimate bending moment that the reinforcing bar roof beam can bear to diminish. Namely, the failure mode or failure criterion of the reinforced beam is as follows: and when the ultimate bending moment is smaller than the specified maximum bending moment, the reinforcing steel bar beam fails. The random uncertainty parameter of the reliability of the reinforced beam is a cross section parameter comprising the length and the width of an interface, and the interval uncertainty parameter influencing the reliability of the reinforced bending beam is a concentrated load.
S202, constructing a function of the target structure according to various influence factors.
The multiple influence factors comprise factors such as random uncertainty parameters, interval uncertainty parameters and time variables which influence the reliability of the target structure, and are used as random uncertainty input information, interval uncertainty input information and time variable input information of the function of the target structure.
The function refers to a mathematical equation including a random uncertainty parameter, an interval uncertainty parameter, and a time variable, and may be expressed as: g = G (x, y, t), where x denotes a random uncertainty parameter, y denotes an interval uncertainty parameter, and t denotes a time variable. The function is used for representing the state of the target structure and can be constructed by combining the stress-intensity interference theory in practical application. The core of the stress-intensity interference theory is a stress-intensity interference model from which the cause of engineering structural failure can be clearly revealed. When the strength of the structure is greater than the stress, the structure can work normally; when the structure is less strong than the stress, it fails.
Taking a reinforcing steel bar beam as an example, the failure mode of the reinforcing steel bar beam is related to the ultimate bending moment of the reinforcing steel bar beam, the stress model of the reinforcing steel bar beam is analyzed by combining a stress-strength interference theory, and the ultimate bending moment M (t) borne by the reinforcing steel bar bending beam can be expressed as follows:
where t is a time variable and t ∈ [0,40 ]]B (t) is the cross-sectional length at time t, h (t) is the cross-sectional width at time t, σ e Is yield stress and σ e ＝2.4×10 8 MPa。
According to the stress-intensity interference theory, the function of a steel bar bending beam can be expressed as:
and S203, substituting the sample data of the target influence factors into the function to obtain an extreme value response surface of the function.
The target influence factors are random influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors. The sample data is obtained by sampling according to the distribution information of the target influence factors and is used for representing the distribution condition of the target influence factors. In practical application, a Latin Hypercube Sampling (LHS) technology can be adopted to integrally sample the target influencing factors of the target structure, so that the initial samples of the target influencing factors are uniformly distributed in an uncertainty space. The extreme value response surface is a minimum value response surface of the function and is used for approximately representing the distribution condition of the response of the target structure in the state space.
And S204, evaluating the reliability of the target structure based on the extreme value response surface to obtain a reliability evaluation result of the target structure.
The reliability of the target structure is evaluated based on the extreme value response surface, and the reliability can be evaluated by using an evaluation method for evaluating static single uncertainty, for example, the reliability evaluation method can be evaluated by using a conventional structure reliability evaluation method such as an MC method and a first-order second-order moment method.
The various influence factors influencing the reliability of the target structure, which are related in the embodiment of the application, include random uncertainty influence factors, interval uncertainty influence factors and the like, so that the evaluation method provided by the embodiment of the application can be applied to the evaluation of the structure mixing reliability, and the evaluation method provided by the embodiment of the application can be applied to the evaluation of the structure mixing gradual change reliability due to the fact that time influence factors are also considered.
In the embodiment, the functional function of the target structure is obtained by obtaining the failure information of the reliability key component of the target structure, the sample data of the target influence factor is substituted into the functional function, the extreme value response surface of the functional function is constructed, and then the reliability of the target structure is evaluated based on the extreme value response surface, so that the reliability evaluation result of the target structure is obtained. The method utilizes an extreme value response surface of a functional function to convert the original gradient reliability assessment problem of multiple mixed uncertainties into a static reliability assessment problem of a single uncertainty, and then assesses the reliability of a target structure based on the constructed extreme value response surface. The whole process only needs to construct a functional function and a functional function extreme value response surface, so that the structure gradual change reliability assessment process under various uncertain mixed conditions can be simplified, the assessment complexity is reduced, the assessment precision is ensured, and the method has important reference significance for popularization and application of reliability assessment in engineering practice.
In an embodiment, in the above step, sample data of the target influencing factor is substituted into the functional function to obtain an extremum response surface of the functional function, as shown in fig. 4, the method includes the following steps:
s401, determining a target influence factor from a plurality of influence factors.
The target influence factors are random influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors.
S402, acquiring sample data of the target influence factors.
The distribution data of the target influence factors comprise the probability distribution type of the random uncertainty parameters, the upper and lower bounds of the interval uncertainty parameters and the range of the time variable. Determining a sampling boundary of a random uncertainty parameter as F i -1 (Φ (+ 5)), i =1,2, \ 8230;, n, where n is the number of elements in the random uncertainty parameter x, F i -1 As random uncertainty parameter x i Phi is a standard normal distribution function; the sampling boundary of the interval uncertainty parameter y is y U 、y D Wherein, the former is an upper boundary, and the latter is a lower boundary; the time variable t ranges from 0, T]And then, according to the information, integrated sampling is carried out by utilizing an LHS technology to obtain sample data of the target influence factors.
And S403, substituting the sample data of the target influence factors into the functional function to obtain the minimum value response of the structural functional function.
The minimum value response of the structure function comprises the minimum value response corresponding to each group of sample data of the target influence factor.
S404, constructing an extreme value response surface of the function according to the sample data of the target influence factors and the corresponding minimum value response.
The sample data of the target influence factors is input data of an extreme value response surface of the functional function, and the minimum value response of the functional function is output data of the extreme value response surface of the functional function.
In the above embodiment, the minimum response of the structural function may be obtained by determining the target influencing factor from the multiple influencing factors, then obtaining sample data of the target influencing factor, and substituting the sample data of the target influencing factor into the functional function. By constructing the minimum value response of the function, the original gradient reliability problem of multiple mixed uncertainties can be converted into the static reliability problem of single uncertainty, the structure gradient reliability evaluation process under the multiple mixed uncertainties can be simplified, and the complexity of reliability evaluation is reduced.
In one embodiment, the step of determining the target influence factor from the plurality of influence factors, as shown in fig. 5, comprises the steps of:
s501, acquiring distribution information of each influence factor; the distribution information includes: the distribution type and the distribution parameters of the random uncertainty influence factors, the upper and lower bounds of the interval uncertainty influence factors and the range of the time influence factors.
In practical application, the distribution information of each influence factor can be determined according to information such as a related data manual, similar product information, engineering experience, test data, outfield data and the like.
Taking a reinforced beam as an example, the distribution information of each influence factor of the reinforced beam and the reinforced bending beam is detailed in table 1, wherein for random variables, parameters 1 and 2 respectively correspond to a mean value and a variation coefficient, and for interval variables, respectively correspond to a lower bound and an upper bound.
TABLE 1
| Uncertain variable | Type of distribution | Parameter 1 | Parameter 2 |
| F/N | Variable of interval | 1400 | 5600 |
| b 0 /m | Normal distribution | 0.2 | 0.05 |
| h 0 /m | Normal distribution | 0.04 | 0.1 |
S502, obtaining a plurality of groups of initial samples according to the distribution type and the distribution parameters of the random uncertainty influence factors, the upper and lower bounds of the interval uncertainty influence factors and the range of the time influence factors.
Wherein, the plurality of groups of initial samples are obtained by sampling according to the distribution information of the target influence factors. For example, q initial samples may be decimated using LHS decimation, and the decimated samples may be expressed as:
wherein n is the number of elements in the random uncertainty parameter x, and m is the number of elements in the interval uncertainty parameter y.
In the above embodiment, multiple groups of initial samples are obtained by obtaining distribution parameters of the target influence factors, each group of initial samples includes all the target influence factors, and the random and interval uncertainty influence factors of the structure and the time variable are integrally sampled, so that the initial samples are uniformly distributed in the uncertainty space. The initial sample data is substituted into the function to obtain the minimum value response of the structure function, so that the original gradient reliability problem of multiple kinds of uncertainty mixing can be converted into the static reliability problem of single uncertainty, the structure gradient reliability evaluation process under the multiple kinds of uncertainty mixing is simplified, and the complexity of reliability evaluation is reduced.
In an embodiment, in the above step, an extremum response surface of the function is constructed according to the sample data of the target influencing factor and the corresponding minimum response, as shown in fig. 6, including the following steps:
s601, constructing an initial extreme value response surface of the function according to initial sample data of the target influence factors and corresponding minimum value responses.
The minimum value response of the function is obtained by substituting a group of random samples in the initial samples into the function to solve the minimum value, and the initial extreme value response surface is obtained by repeating the steps to solve the minimum value response corresponding to each group of random samples in the initial samples. A set of random samples
Introducing a function, the function being transformed into a function with respect to the interval uncertainty parameter y and the time variable t
The influence of random uncertainty parameters is eliminated, and Genetic Algorithm (GA) is used to solve the function
The response of the minimum value of (c) can be expressed as:
after repeated execution for q times, obtaining an initial random sample x 0 Corresponding minimum response g min (x 0 ) Then, constructing a Kriging extreme value response surface by using a DACE tool box of MATLAB
S602, updating the initial extreme value response surface to obtain a first updated extreme value response surface.
Wherein, the initial extreme value response surface is a Kriging extreme value response surface constructed by the DACE toolbox of MATLAB in the above steps
The first updated extremum response surface is an extremum response surface obtained by first updating the initial extremum response surface.
S603, if the first updated extremum response surface meets a preset updating convergence criterion, determining the first updated extremum response surface as an extremum response surface of the function; and if the first updating extreme value response surface does not meet the preset updating convergence criterion, continuously updating the first updating extreme value response surface until the latest updating extreme value response surface meets the updating convergence criterion.
The updating convergence criterion is a stop condition for updating the extreme value response surface, and the latest updated extreme value response surface is the extreme value response surface obtained when the preset updating convergence criterion is met.
In the above embodiment, an initial extremum response surface of the function is constructed according to the initial sample and the corresponding function minimum response, and the initial extremum response surface is updated to obtain a first updated extremum response surface. If the first updated extreme value response surface meets the preset update convergence criterion, determining the first updated extreme value response surface as the extreme value response surface of the function; and if the first updating extreme value response surface does not meet the preset updating convergence criterion, continuously updating the first updating extreme value response surface until the latest updating extreme value response surface meets the updating convergence criterion. By constructing an extreme value response surface of a functional function, the original gradient reliability problem of multiple mixed uncertainties is converted into a static reliability problem of single uncertainty, the structure gradient reliability evaluation process under the multiple mixed uncertainties can be simplified, and the complexity of reliability evaluation is reduced.
In an embodiment, the method for evaluating reliability of hybrid gradual change of the above structure, as shown in fig. 7, further includes the following steps:
s701, acquiring a first candidate reliability value of the target structure based on the initial extreme value response surface.
And the first candidate reliability value is a reliability value obtained by reliability evaluation based on the initial extreme value response surface.
S702, a second candidate reliability value of the target structure is obtained based on the first updated extreme value response surface.
The first updated extreme value response surface is an extreme value response surface obtained after the initial extreme value response surface is updated for the first time, and the second candidate reliability value is a reliability value obtained by reliability evaluation based on the first updated extreme value response surface.
S703, if the difference between the first candidate reliability value and the second candidate reliability value is smaller than the preset error value, determining that the first updated extremum response surface satisfies the update convergence criterion.
If the predetermined error value can be a sufficiently small constant, the update convergence criterion can be expressed as:
wherein R is i Representing the i-th candidate reliability value, R i+1 Represents the i +1 th candidate reliability value, δ is sufficiently smallIs constant.
In the above embodiment, a first candidate reliability value of the target structure is obtained based on the initial extremum response surface, a second candidate reliability value of the target structure is obtained based on the first updated extremum response surface, and if a difference between the first candidate reliability value and the second candidate reliability value is smaller than a preset error value, it is determined that the first updated extremum response surface satisfies the update convergence criterion. By judging that the extreme value response surface meets the updating convergence criterion, the reliability evaluation result accuracy is kept, meanwhile, the structure gradual change reliability evaluation process under various uncertain mixed conditions can be simplified, and the reliability evaluation complexity is reduced.
In an embodiment, the updating the initial extremum response surface in the foregoing step to obtain a first updated extremum response surface, as shown in fig. 8, includes the following steps:
s801, acquiring candidate sample data of random uncertainty influence factors.
The candidate sample data of the random uncertainty influence factors are obtained by sampling according to the distribution type and the distribution parameters. For example, the MC method can be used to extract N groups (N should be as large as possible) of random samples x N As candidate samples.
S802, updating the initial extreme value response surface according to the candidate sample data of the random uncertainty influence factors to obtain a first updated extreme value response surface.
In the embodiment, the first updated extreme value response surface is obtained by acquiring the candidate sample data of the random uncertainty influence factors and updating the initial extreme value response surface according to the candidate sample data, so that the reliability evaluation result accuracy is maintained, the structure gradual change reliability evaluation process under various uncertainty mixed conditions can be simplified, and the complexity of reliability evaluation is reduced.
In an embodiment, in the above step, the initial extremum response surface is updated according to the candidate sample data of the random uncertainty influencing factor, so as to obtain a first updated extremum response surface, as shown in fig. 9, the method includes the following steps:
s901, determining an updating point from candidate sample data of random uncertainty influence factors.
Wherein, the update point should satisfy the following characteristics:
1) The update point should be located near the extremum response surface. Expressed as follows by the mathematical equation:
where μ represents the expectation of Kriging extremal response surface prediction.
2) And no aggregation and no pile-up are generated between the update point and the existing random samples. A minimum distance function may be introduced to regulate the aggregation level of random samples, and the minimum distance function may be expressed as:
wherein x is 0+i ＝[x 0 ,x i ](1. Ltoreq. I.ltoreq.N) represents the currently existing sample point. To prevent the aggregation phenomenon, the point that maximizes the minimum distance function is selected and formulated as: max (dis) min (x i ))1≤i≤N。
3) The update point falls in a key area that has a large influence on the reliability of the structure. Joint probability density function f of available random uncertainty parameter x of key area x (x) The product with the predicted standard deviation σ (x) of the update point is characterized: max (σ (x) i )f x (x i ))1≤i≤N。
And S902, substituting the update point into the function to obtain the update minimum response of the function.
And the updating minimum value response is each updating minimum value response obtained by substituting each updating point band into the function correspondingly.
And S903, updating the initial extreme value response surface by using the updated minimum value response of the function to obtain a first updated extreme value response surface.
In the embodiment, the update point is determined from the candidate sample data of random uncertainty influence factors, the update point is substituted into the function to obtain the update minimum response of the function, the initial extreme value response surface is updated according to the update minimum response of the function to obtain the first update extreme value response surface, a set of complete self-adaptive point adding strategies is formed by constructing the learning function, the function extreme value response surface and the convergence criterion, the automatic update of the function extreme value response surface is realized, and therefore, the structure gradual change reliability evaluation process under various uncertainty mixed conditions can be simplified and the complexity of reliability evaluation is reduced while the accuracy of the reliability evaluation result is maintained.
In one embodiment, the determining the update point from the candidate sample data of the random uncertainty influencing factor in the above steps, as shown in fig. 10, includes the following steps:
s1001, according to the self-adaptive point adding strategy, a learning function of random uncertainty influence factors is constructed.
The learning function is used for selecting the update points with the above 3 characteristics, and is essentially a multi-target optimization process, and the learning function model can be expressed as:
wherein, σ represents the standard deviation of the current prediction, and r represents a small normal number, so as to prevent the condition that the denominator of the learning function is zero.
And S1002, determining the corresponding candidate sample as an update point when the learning function takes the minimum value.
In the embodiment, according to the adaptive point adding strategy, the learning function about random uncertainty influence factors is constructed, and the corresponding candidate sample when the learning function takes the minimum value is determined as the update point, so that the selected update point can be ensured to be positioned in the important area of the extreme value response surface, the occurrence of sample point aggregation can be avoided, and the action effect of the update point is ensured, so that the reliability evaluation result accuracy is maintained, the structure gradual change reliability evaluation process under various uncertainty mixed conditions can be simplified, and the reliability evaluation complexity is reduced.
According to the method and the device, the structure gradual change reliability evaluation process under various uncertain mixed conditions can be simplified, the complexity of reliability evaluation is reduced, in one embodiment, the embodiment of the method and the device further provide a structure reliability evaluation method, specific limitations can be referred to the limitations in the above, and details are not repeated. As shown in fig. 11, this embodiment includes:
s1, acquiring failure information of a reliability key piece of a target structure; the failure information comprises a plurality of influence factors influencing the reliability of the target structure; the plurality of influencing factors include random uncertainty influencing factors, interval uncertainty influencing factors, time influencing factors and failure criteria.
And S2, constructing a function of the target structure according to various influence factors.
And S3, acquiring the distribution type and the distribution parameters of the random uncertainty influence factors, the upper and lower bounds of the interval uncertainty influence factors and the range of the time influence factors.
And S4, acquiring a plurality of groups of initial samples according to the distribution type and the distribution parameters of the random uncertainty influence factors, the upper and lower bounds of the interval uncertainty influence factors and the range of the time influence factors.
And S5, substituting the sample data of the target influence factors into the function to obtain the minimum response of the structural function.
And S6, constructing an initial extreme value response surface of the function according to the initial sample data of the target influence factors and the corresponding minimum value response.
And S7, acquiring candidate sample data of the random uncertainty influence factors.
And S8, constructing a learning function of random uncertainty influence factors according to a self-adaptive point adding strategy.
And S9, determining the corresponding candidate sample as the updating point when the learning function takes the minimum value.
And S10, substituting the updating point into the function to obtain the updating minimum response of the function.
And S11, updating the initial extreme value response surface by using the updated minimum value response of the function to obtain a first updated extreme value response surface.
And S12, acquiring a first candidate reliability value of the target structure based on the initial extreme value response surface.
And S13, acquiring a second candidate reliability value of the target structure based on the first updated extreme value response surface.
And S14, if the difference value between the first candidate reliability value and the second candidate reliability value is smaller than a preset error value, determining that the first updated extreme value response surface meets the updating convergence criterion.
S15, if the first updating extreme value response surface meets a preset updating convergence criterion, determining the first updating extreme value response surface as an extreme value response surface of the function; and if the first updating extreme value response surface does not meet the preset updating convergence criterion, continuously updating the first updating extreme value response surface until the latest updating extreme value response surface meets the updating convergence criterion.
And S16, evaluating the reliability of the target structure based on the latest updated extreme value response surface to obtain a reliability evaluation result of the target structure.
Taking a reinforced beam as an example, 12 initial samples are extracted by using LHS technology. And substituting the initial sample into a function, and solving by using a GA (genetic algorithm) to obtain 12 groups of extreme value responses. And then, combining the 12 groups of input and output data and constructing a Kriging (Kriging) extreme value response surface of the steel bar bending beam by using a tool box of mathematical simulation software, and converting the original problem into random static reliability evaluation. For random variables in table 1, N =10000 groups of samples were drawn as candidate samples using the MC method. And constructing a learning function L (x), gradually selecting an update point from N groups of candidate samples to update the Kriging extreme value response surface, and stopping updating when a convergence criterion is met. The spatial distribution of the update points is shown in fig. 12. At this time, the total number of added update points N L ＝31。
On the basis that the updating of the Kriging extreme value response surface is completed, the gradual change reliability R =0.8572 of the steel bar bending beam is evaluated by using the MC method, and in the period, the reliability evaluation method calls GA for 43 times in total; on the basis of the function G (t), the reliability of the steel bar bending beam is evaluated by directly using the MC method, and the obtained result isR * =0.8520, during which GA is called a total of 10000 times. The evaluation error of the adaptive point adding strategy is 0.62%, but the evaluation efficiency is greatly improved. The structural reliability evaluation result and the evaluation result obtained by directly using the MC method on the basis of the function G (t) in the present application are shown in fig. 13, and the detailed comparison between the reliability evaluation results of the two methods is shown in table 2.
TABLE 2
| Method | Degree of reliability | Number of GA calls | Error of the measurement |
| Adaptive dotting strategy | 0.8572 | 43 | 0.62％ |
| MC method | 0.8520 | 10000 | / |
In the embodiment, the Kriging extreme value response surface of the steel bar bending beam function is constructed, the structural mixed gradient reliability assessment is equivalently converted into the single uncertainty static reliability assessment problem, the updating point is selected by constructing the learning function until the convergence condition is met, and the automatic updating of the Kriging extreme value response surface is completed, so that the structural gradient reliability assessment process under various uncertainty mixed conditions can be simplified while the accuracy of the reliability assessment result is maintained, and the complexity of the reliability assessment is reduced.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present application further provides a structure reliability assessment apparatus for implementing the structure reliability assessment method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more embodiments of the structure reliability assessment device provided below may refer to the limitations on the structure reliability assessment method in the above description, and details are not repeated here.
In one embodiment, there is provided a structural reliability evaluation device including: the device comprises an information acquisition module, a data processing module, a response surface acquisition module and a reliability evaluation module, wherein:
the information acquisition module is used for acquiring failure information of the key part of the reliability of the target structure, wherein the failure information comprises various influence factors influencing the reliability of the target structure; the plurality of influencing factors include a random uncertainty influencing factor, an interval uncertainty influencing factor, a time influencing factor, and a failure criterion.
The data processing module is used for constructing a function of the target structure according to various influence factors;
the response surface acquisition module is used for substituting sample data of the target influence factors into the function to acquire an extreme value response surface of the function, wherein the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in various influence factors;
and the reliability evaluation module is used for evaluating the reliability of the target structure based on the extreme value response surface to obtain the reliability evaluation result of the target structure.
In one embodiment, there is provided a structural reliability evaluation device, where the response surface acquisition module includes: the device comprises a factor determining unit, a sample data acquiring unit, an extreme value determining unit and a response surface acquiring unit, wherein:
a factor determining unit for determining a target influence factor from the plurality of influence factors.
And the sample data acquisition unit is used for acquiring the sample data of the target influence factors.
And the extreme value determining unit is used for substituting the sample data of the target influence factors into the functional function to obtain the minimum value response of the structural functional function.
And the response surface acquisition unit is used for constructing an extreme value response surface of the function according to the sample data of the target influence factors and the corresponding minimum value response.
In one embodiment, there is provided a structural reliability evaluation device, the factor determination unit including: a distribution information determining subunit and an initial sample determining subunit, wherein:
the distribution information determining subunit is used for acquiring the distribution information of each influence factor; the distribution information includes: distribution information such as the distribution type and the distribution parameters of the random uncertainty influence factors, the upper and lower bounds of the interval uncertainty influence factors, the range of the time influence factors and the like.
The initial sample determining subunit is used for acquiring a plurality of groups of initial samples according to the distribution information of each influence factor, wherein each group of initial samples comprises all target influence factors;
in one embodiment, there is provided a structural reliability evaluation device, wherein the extreme value determination unit includes: a response surface constructing subunit, an updating subunit and a processing subunit, wherein:
and the response surface construction subunit is used for constructing an initial extreme value response surface of the function according to the initial sample of the target influence factor and the corresponding minimum value response.
And the updating subunit is used for updating the initial extremum response surface to obtain a first updated extremum response surface.
The processing subunit is configured to determine, if the first updated extremum response surface meets a preset update convergence criterion, that the first updated extremum response surface is an extremum response surface of the function; and if the first updated extremum response surface does not meet the preset update convergence criterion, continuously updating the first updated extremum response surface until the latest updated extremum response surface meets the update convergence criterion.
In one embodiment, there is provided a reliability evaluation device of a structure, including: the processing subunit further comprises a response surface updating subunit, wherein the response surface updating subunit is used for acquiring candidate sample data of the random uncertainty influence factors; and updating the initial extreme value response surface according to the candidate sample data of the random uncertainty influence factors to obtain a first updated extreme value response surface.
In one embodiment, a structure reliability evaluation apparatus is provided, where the processing subunit includes an update convergence criterion judgment subunit, and the convergence criterion judgment subunit is configured to obtain a first candidate reliability value of a target structure based on an initial extremum response surface; acquiring a second candidate reliability value of the target structure based on the first updated extreme value response surface; and if the difference value between the first candidate reliability value and the second candidate reliability value is smaller than a preset error value, determining that the first updated extreme value response surface meets the updated convergence criterion.
In one embodiment, there is provided a structural reliability evaluation device, wherein the response surface updating subunit includes: the system comprises an update point determining subunit, a minimum value response subunit and an update extremum response surface subunit, wherein:
the updating point determining subunit is used for determining an updating point from the candidate sample data of the random uncertainty influence factors;
the minimum response subunit is used for substituting the update point into the functional function to obtain the update minimum response of the functional function;
and the updating extreme value response surface subunit is used for updating the initial extreme value response surface by using the updating minimum value response of the functional function to obtain a first updating extreme value response surface.
In an embodiment, a structural reliability assessment apparatus is provided, where the update point determination subunit is further configured to construct a learning function of random uncertainty influence factors; and determining the corresponding candidate sample as an updating point when the learning function takes the minimum value.
The respective modules in the reliability evaluation apparatus of the above-described structure may be entirely or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device, which may be a terminal, is provided that includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a structural hybrid gradual reliability assessment method based on an adaptive dotting strategy. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring failure information of a reliability key piece of a target structure; the failure information comprises a plurality of influence factors influencing the reliability of the target structure; the multiple influence factors comprise random uncertainty influence factors, interval uncertainty influence factors, time influence factors and failure criteria;
constructing a function of a target structure according to various influence factors;
substituting the sample data of the target influence factors into the functional function to obtain an extreme value response surface of the functional function; the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors;
and carrying out reliability evaluation on the target structure based on the extreme value response surface to obtain a reliability evaluation result of the target structure.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring failure information of a reliability key piece of a target structure; the failure information comprises a plurality of influence factors influencing the reliability of the target structure; the multiple influence factors comprise random uncertainty influence factors, interval uncertainty influence factors, time influence factors and failure criteria;
constructing a function of a target structure according to various influence factors;
substituting the sample data of the target influence factors into the functional function to obtain an extreme value response surface of the functional function; the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors;
and carrying out reliability evaluation on the target structure based on the extreme value response surface to obtain a reliability evaluation result of the target structure.
In one embodiment, a computer program product is provided, comprising a computer program which when executed by a processor performs the steps of:
acquiring failure information of a reliability key piece of a target structure; the failure information comprises a plurality of influence factors influencing the reliability of the target structure; the multiple influence factors comprise random uncertainty influence factors, interval uncertainty influence factors, time influence factors and failure criteria;
constructing a function of a target structure according to various influence factors;
substituting the sample data of the target influence factors into the functional function to obtain an extreme value response surface of the functional function; the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors;
and carrying out reliability evaluation on the target structure based on the extreme value response surface to obtain a reliability evaluation result of the target structure.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A structural hybrid gradual reliability assessment method, the method comprising:
acquiring failure information of a target structure reliability key piece; the failure information comprises a plurality of influence factors influencing the reliability of the target structure; the multiple influence factors comprise random uncertainty influence factors, interval uncertainty influence factors, time influence factors and failure criteria;
constructing a function of the target structure according to the multiple influence factors;
substituting sample data of the target influence factors into the functional function to obtain an extreme value response surface of the functional function; the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors;
and evaluating the reliability of the target structure based on the extreme value response surface to obtain an evaluation result of the reliability of the target structure.
2. The method of claim 1, wherein the substituting sample data of the target influencing factor into the functional function to obtain an extremum response surface of the functional function comprises:
determining the target influence factor from the plurality of influence factors;
acquiring sample data of the target influence factors;
substituting the sample data of the target influence factors into the functional function to obtain the minimum value response of the structural functional function;
and constructing an extreme value response surface of the functional function according to the sample data of the target influence factors and the corresponding minimum value response.
3. The method of claim 2, wherein said determining said target influence factor from a plurality of influence factors comprises:
acquiring distribution information of each influence factor; the distribution information includes: the distribution type and the distribution parameters of the random uncertainty influence factors, the upper and lower bounds of the interval uncertainty influence factors and the range of the time influence factors;
and acquiring a plurality of groups of initial samples according to the distribution information of the random and interval uncertainty influence factors and the range of the time influence factors.
4. The method according to claim 2 or 3, wherein the constructing an extremum response surface of the functional function according to the sample data of the target influencing factor and the corresponding minimum response comprises:
constructing an initial extreme value response surface of the functional function according to the initial sample data of the target influence factors and the corresponding minimum value response;
updating the initial extreme value response surface to obtain a first updated extreme value response surface;
if the first updated extreme value response surface meets a preset update convergence criterion, determining that the first updated extreme value response surface is an extreme value response surface of the functional function;
and if the first updated extremum response surface does not meet a preset updating convergence criterion, continuously updating the first updated extremum response surface until the latest updated extremum response surface meets the updating convergence criterion.
5. The method of claim 4, further comprising:
acquiring a first candidate reliability value of the target structure based on the initial extreme value response surface;
obtaining a second candidate reliability value of the target structure based on the first updated extremum response surface;
and if the difference value between the first candidate reliability value and the second candidate reliability value is smaller than a preset error value, determining that the first updated extreme value response surface meets the updated convergence criterion.
6. The method of claim 4, wherein said updating the initial extremum response surface to obtain a first updated extremum response surface comprises:
acquiring candidate sample data of the random uncertainty influence factors;
and updating the initial extreme value response surface according to the candidate sample data of the random uncertainty influence factors to obtain the first updated extreme value response surface.
7. The method of claim 6, wherein said updating the initial extremal response surface according to the candidate sample data of the random uncertainty influencing factor to obtain the first updated extremal response surface comprises:
determining an updating point from the candidate sample data of the random uncertainty influence factor;
substituting the updating point into the functional function to obtain the updating minimum value response of the functional function;
and updating the initial extreme value response surface according to the updated minimum value response of the functional function to obtain the first updated extreme value response surface.
8. The method of claim 7, wherein said determining an update point from candidate sample data for random uncertainty contributors comprises:
constructing a learning function of the random uncertainty influence factors according to a self-adaptive point adding strategy;
and determining the corresponding candidate sample as the updating point when the learning function takes the minimum value.
9. A structural hybrid gradual change reliability assessment apparatus, characterized in that the apparatus comprises:
the information acquisition module is used for acquiring failure information of a reliability key piece of the target structure; the failure information comprises a plurality of influence factors influencing the reliability of the target structure; the multiple influence factors comprise random uncertainty influence factors, interval uncertainty influence factors, time influence factors and failure criteria;
the data processing module is used for constructing a function of the target structure according to the various influence factors;
the response surface acquisition module is used for substituting the sample data of the target influence factors into the functional function to acquire an extreme value response surface of the functional function; the target influence factors are random uncertainty influence factors, interval uncertainty influence factors and time influence factors in the multiple influence factors;
and the reliability evaluation module is used for evaluating the reliability of the target structure based on the extreme value response surface to obtain an evaluation result of the reliability of the target structure.
10. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 8 when executing the computer program.
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| CN115860575A (en) * | 2023-02-13 | 2023-03-28 | 湖南尚医康医疗科技有限公司 | Ward management method, system, equipment and storage medium based on Internet of things |
| CN115935761A (en) * | 2023-01-10 | 2023-04-07 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method and device for simulating reliability of equipment, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115935761A (en) * | 2023-01-10 | 2023-04-07 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method and device for simulating reliability of equipment, computer equipment and storage medium |
| CN115860575A (en) * | 2023-02-13 | 2023-03-28 | 湖南尚医康医疗科技有限公司 | Ward management method, system, equipment and storage medium based on Internet of things |
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