Service life evaluation method, system and equipment of closing spring and storage medium

Abstract

The embodiment of the invention discloses a service life evaluation method, system and equipment of a closing spring and a storage medium, and the method comprises the steps: obtaining the closing time of the closing spring under different stress loss rates, and constructing a mapping model between the stress loss rates and the closing time; obtaining a stress loss rate threshold value of the closing spring according to the mapping model and a preset closing time range, and determining a failure state of the closing spring; and obtaining the service life of the closing spring at different temperatures according to the spring loss rate and the stress loss rate threshold of the closing spring at different temperatures, thereby accurately evaluating the service life of the closing spring.

Description

[Technical field]

[0001] The present invention relates to the technical field of power system equipment, and in particular to a life assessment method, system, equipment and storage medium for a closing spring. [Background technology]

[0002] The fault types of high-voltage circuit breakers are generally divided into insulation abnormalities, power-on abnormalities and mechanical abnormalities. Among them, the degradation of mechanical properties is usually caused by deformation or damage of materials due to the action of long-term stress. In engineering, the opening and closing performance of circuit breakers is usually used as an important criterion for evaluating the quality of their mechanical properties. The instability of the opening and closing performance often indicates that the circuit breaker is facing potential failure risks. Under actual working conditions, the closing spring is in a state of energy storage compression for a long time, and it is inevitable that problems such as strength degradation and stress relaxation will occur. These problems will not only affect the closing performance of the circuit breaker, but may also cause damage to the spring, thereby causing power grid accidents. The current technical solutions mainly focus on the status monitoring of the closing spring installed on the circuit breaker, with the aim of judging the working status of the closing spring in real time through data collection and analysis. However, the existing solutions can realize real-time monitoring of the operating status of the spring, but fail to predict the future operating status of the spring, so it is impossible to reasonably evaluate the service life of the spring. [Summary of the invention]

[0003] In view of this, the present invention provides a method, system, device and storage medium for evaluating the life of a closing spring.

[0004] The specific technical scheme of the first embodiment of the present invention is: a method for evaluating the life of a closing spring, the method comprising: obtaining the closing time of the closing spring under different stress loss rates; constructing a mapping model between the stress loss rate and the closing time according to the closing time under the different stress loss rates and the different stress loss rates; obtaining the stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range; obtaining the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold.

[0005] Preferably, the stress loss rate is obtained by the following method: obtaining a first stress required when the closing spring is compressed to a preset length at a first time; obtaining a second stress required when the closing spring is compressed to a preset length at a second time; the second time is later than the first time; and obtaining the stress loss rate based on the first stress and the second stress.

[0006] Preferably, the mapping model between the stress loss rate and the closing time is obtained using the following formula:

[0007] t(s)~G(m(s),k(s,s'))

[0008] Wherein, t(s) is the closing time under the stress loss rate s, g is the Gaussian distribution, m(s) is the mean function of the stress loss rate s, and k(s, s') is the covariance function of the stress loss rate s.

[0009] Preferably, the step of obtaining the stress loss rate threshold of the closing spring according to the mapping model and the preset closing time range includes: using Gaussian process regression and Bayesian inference methods to map the preset closing time range to the stress loss rate of the mapping model, and obtaining the probability that the closing time of the closing spring under different stress losses is within the preset closing time range; solving the probability according to a preset confidence level to obtain the stress loss rate threshold of the closing spring.

[0010] Preferably, the probability is obtained using the following formula:

[0011] P(t)=P(t min ≤t(s)≤t max )

[0012] Where P(t) is the probability, t min is the minimum time value in the preset closing time range, t max is the maximum time in the preset closing time range, and t(s) is the closing time under the stress loss rate s.

[0013] Preferably, obtaining the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold comprises: fitting the spring loss rate of the closing spring at different temperatures and the energy storage time of the closing spring to obtain the stress loss rate of the closing spring at different temperatures; obtaining the service life of the closing spring at different temperatures according to the stress loss rate of the closing spring and a preset life usage model.

[0014] Preferably, the service life of the closing spring at different temperatures is obtained using the following formula:

[0015]

[0016] s max =e (a-b / T) lnt+c

[0017] Where k is the stress loss rate, A is the Arrhenius constant, E a is the activation energy, T is the temperature, a, b and c are constants, R is the Boltzmann constant, s max is the preset spring failure threshold, and t is the service life of the closing spring at temperature T.

[0018] The specific technical scheme of the second embodiment of the present invention is: a life assessment system for a closing spring, the system comprising: a closing time acquisition module, a model mapping module, a threshold acquisition module and a service life acquisition module; the closing time acquisition module is used to acquire the closing time of the closing spring under different stress loss rates; the model mapping module is used to construct a mapping model between the stress loss rate and the closing time according to the closing time under the different stress loss rates and the different stress loss rates; the threshold acquisition module is used to acquire the stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range; the service life acquisition module is used to acquire the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold.

[0019] The specific technical solution of the third embodiment of the present invention is: a life assessment device for a closing spring, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in any one of the first embodiments of the present application.

[0020] The specific technical solution of the fourth embodiment of the present invention is: a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method described in any one of the first embodiments of the present application.

[0021] Implementing the embodiments of the present invention will have the following beneficial effects:

[0022] The present invention obtains the closing time of a closing spring under different stress loss rates and constructs a mapping model between the stress loss rate and the closing time; obtains the stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range, and determines the failure state of the closing spring; obtains the service life of the closing spring at different temperatures according to the spring loss rate and the stress loss rate threshold of the closing spring at different temperatures, thereby accurately evaluating the service life of the closing spring.

Brief Description of the Drawings

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flow chart of the steps of a method for evaluating the life of a closing spring;

[0025] Figure 2 This is the installation diagram of the closing spring long-term energy storage test;

[0026] Figure 3a is the spring stress loss curve at different temperatures;

[0027] Figure 3b is the spring stress loss rate at different temperatures;

[0028] Figure 4 It is a schematic diagram of the relationship between spring stress loss and closing time;

[0029] Figure 5 It is a structural schematic diagram of the life assessment system of the closing spring;

[0030] Figure 6 A diagram of the internal structure of a computer device;

[0031] Among them, 201, temperature monitor; 202, environmental test chamber; 203, spring fixed pressure plate support rod; 204, displacement sensor; 205, closing spring; 206, spring fixed pressure plate; 301, closing time acquisition module; 302, model mapping module; 303, threshold acquisition module; 304, service life acquisition module. [Specific implementation method]

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] The terms "first", "second", etc. in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally includes steps or modules that are not listed, or optionally includes other steps or modules that are inherent to these processes, methods, products or devices.

[0034] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0035] The closing spring is a key component used to store elastic potential energy in the circuit breaker. Under normal working conditions, the closing spring is tightly compressed and placed inside the circuit breaker, waiting for the trigger signal. When the circuit breaker needs to be triggered, the closing spring quickly releases its elastic potential energy, thereby quickly closing the circuit breaker to achieve the function of cutting off the circuit.

[0036] See also Figure 1 , is a flowchart of a method for evaluating the life of a closing spring in the first embodiment of the present application, so as to accurately evaluate the service life of the closing spring, the method comprising:

[0037] Step 101, obtaining the closing time of the closing spring under different stress loss rates;

[0038] Step 102: constructing a mapping model between stress loss rate and closing time according to the closing time under different stress loss rates and different stress loss rates;

[0039] Step 103: acquiring a stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range;

[0040] Step 104: Obtain the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold.

[0041] Specifically, for the simulation of the performance degradation of the closing spring during long-term closing operation, the following Figure 2 The test equipment shown includes a temperature monitor 201, an environmental test chamber 202, a spring fixing pressure plate support rod 203, a displacement sensor 204, a closing spring 205, and a spring fixing pressure plate 206; the spring fixing pressure plate is used to compress the closing spring and make it reach the preset stress F2 / height H2 of closing; the environmental simulation test chamber can simulate in the temperature range of -20℃ to 150℃; the displacement sensor monitors the compression height of the closing spring in real time to ensure that it remains at the preset stress / height.

[0042] Compress and fix the spring with a fixed pressure plate so that the closing spring reaches the preset stress F2 / height H2 for closing. Place the compressed and fixed closing spring in an environmental simulation test chamber. Use the support rod and displacement sensor of the fixed pressure plate to monitor the compression height of the closing spring in real time to ensure that it can maintain the preset stress F2 / height H2 to simulate the long-term energy storage state of the closing spring. Set the temperature of the environmental simulation test chamber and conduct the working temperature influence test according to the test requirements. The test can be carried out for 10-36 months according to the closing spring models of different voltage levels. Measure and record the stress value F when the spring is compressed to H2 at regular intervals (such as half a month or one month). testAnd install it in the circuit breaker to close the circuit, and record the closing time t. Through the above test steps, the data of the spring stress change and closing time corresponding to the test conditions are recorded.

[0043] By simulating experiments in different temperature environments and working conditions (such as long-term energy storage and reverse compound braking operations), the stress relaxation characteristics of the closing spring can be fully analyzed. The test process does not require complicated equipment and operating steps, and is easy to operate and highly efficient.

[0044] According to the above closing spring performance degradation test, the spring closing time data under different stress loss rates can be obtained, and the stress loss rate s and closing time t can be obtained. Therefore, the collected data set can be expressed as {(s i ,t i )|i=1,2,...,n}, and construct a mapping model between stress loss rate and closing time. Obtain the stress loss rate threshold of the closing spring according to the mapping model and the preset closing time range; wherein, the preset closing time range can be set according to actual conditions, and this application does not limit the value of the preset closing time range. Obtain the service life of the closing spring at different temperatures according to the spring loss rate and stress loss rate threshold of the closing spring at different temperatures.

[0045] It should be noted that, due to the different sizes of closing springs of circuit breakers of different voltage levels, the sizes of corresponding test equipment will also be different. Figure 2 The drawings are not to scale and some details have been exaggerated and omitted for clarity. Figure 2 The shapes of various regions and layers shown in the figure and their relative sizes and positional relationships are only examples. In the actual manufacturing process, there may be deviations caused by manufacturing tolerances or technical limitations. Those skilled in the art can design regions or layers with different shapes, sizes, and relative positions according to actual needs. For ease of explanation, the graphical expressions in the structure diagram are simplified.

[0046] The method in this embodiment obtains the closing time of the closing spring at different stress loss rates and constructs a mapping model between the stress loss rate and the closing time; obtains the stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range, and determines the failure state of the closing spring; obtains the service life of the closing spring at different temperatures according to the spring loss rate and the stress loss rate threshold of the closing spring at different temperatures, thereby accurately evaluating the service life of the closing spring.

[0047] In a specific embodiment, the stress loss rate is obtained by the following method: obtaining a first stress required when the closing spring is compressed to a preset length at a first time; obtaining a second stress required when the closing spring is compressed to a preset length at a second time; the second time is later than the first time; and obtaining the stress loss rate according to the first stress and the second stress.

[0048] Specifically, the first stress F2 required when the closing spring is compressed to a preset length when not in use is obtained, and the first stress F2 required when the closing spring is compressed to a preset length after being used for a period of time is obtained. test , according to the formula Get the stress loss rate s.

[0049] In a specific embodiment, the mapping model between the stress loss rate and the closing time is obtained by using the following formula:

[0050]

[0051] Where, t(s) is the closing time under stress loss rate s, is a Gaussian distribution, m(s) is the mean function of the stress loss rate s, and k(s,s') is the covariance function of the stress loss rate s.

[0052] Specifically, Gaussian distribution is a distribution that describes the form of continuous data. Its probability density function is a bell-shaped curve, which can well fit the data distribution in many natural and social phenomena. Therefore, when the relationship between stress loss rate and closing time conforms to Gaussian distribution, the mapping model established using Gaussian distribution can more accurately describe the relationship between the two, thereby improving the prediction accuracy of the model.

[0053] In a specific embodiment, the method of obtaining the stress loss rate threshold of the closing spring according to the mapping model and the preset closing time range includes: using Gaussian process regression and Bayesian inference methods to map the preset closing time range to the stress loss rate of the mapping model, and obtaining the probability that the closing time of the closing spring under different stress losses is within the preset closing time range; solving the probability according to a preset confidence level to obtain the stress loss rate threshold of the closing spring.

[0054] Specifically, if the model parameter is θ, the observed data is D = {(s i ,t i )}, then the posterior distribution is:

[0055]

[0056] Among them, p(θ|D) is the likelihood function, which represents the probability of observing data D under parameter θ; p(θ) is the prior distribution, which represents the prior belief about parameter θ; p(D) is the evidence, which represents the total probability of observing data D.

[0057] According to the circuit breaker technical manual, the closing time of the closing spring is generally between [t min ,t max ] range, so the Gaussian process regression and Bayesian inference method are used to convert the closing time operation range [t min ,t max ] is mapped to the stress loss rate to obtain the probability that the closing time of the closing spring under different stress losses is within the preset closing time range.

[0058] In a specific embodiment, the probability is obtained using the following formula:

[0059] P(t)=P(t min ≤t(s)≤t max )

[0060] Where P(t) is the probability, t min is the minimum time value in the preset closing time range, t max is the maximum time in the preset closing time range, and t(s) is the closing time under the stress loss rate s.

[0061] Specifically, the probability is solved according to the preset confidence level and the following formula is used to obtain:

[0062] P(t min ≤t(s)≤t max )≥α

[0063] Where α is the confidence level, which is generally 0.95. Solving this inequality can obtain the stress loss rate threshold of the closing spring [s min ,s max ]. Since the stress level of the closing spring gradually decays under normal actual working conditions, s max As the stress loss rate threshold when the closing spring fails. Solving the probability using the confidence level can enhance the reliability of the result. By setting a higher confidence level, the estimation result can be ensured to have a higher accuracy, thereby reducing the risk of misjudgment.

[0064] In a specific embodiment, obtaining the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold includes: fitting the spring loss rate of the closing spring at different temperatures and the energy storage time of the closing spring to obtain the stress loss rate of the closing spring at different temperatures; obtaining the service life of the closing spring at different temperatures according to the stress loss rate of the closing spring and a preset life usage model.

[0065] Specifically, the Arrhenius acceleration model can be used to describe the degradation process of the closing spring performance. This model is suitable for the influence of temperature on material aging and life prediction. Its formula is: Where k is the degradation rate (spring stress loss rate); A is the Arrhenius constant; E a is the activation energy; T is the absolute temperature; R is the Boltzmann constant. The Arrhenius model can be transformed into a linear form by taking the logarithm of the Arrhenius equation: a, b are constants. The life of the closing spring under long-term energy storage state is predicted based on the experiment: the relationship between the loss rate of the closing spring and the energy storage time is generally in the form of ln, so it can be expressed as s=k·lnt+c. k is the stress loss rate; t is the energy storage time, in months; c is a constant. When t=1 month, s=c, so the constant c can be obtained by the spring stress loss rate obtained when the test is performed at different temperatures for 1 month. By fitting the data of the 1-month test duration at different working temperatures, c=h(T) can be obtained, where T is the absolute temperature during the test. The relationship between the spring loss rate and the energy storage time at different temperatures T is fitted by ln, and the stress loss rate k at different temperatures can be obtained respectively. Substituting k and T into The values ​​of a and b can be obtained.

[0066] Therefore, it can be seen from the above that the combined s max = k·lnt max +c=k·lnt max +h(T) and The two equations can be used to obtain the service life of the closing spring at different temperatures.

[0067] In a specific embodiment, the service life of the closing spring at different temperatures is obtained using the following formula:

[0068]

[0069] s max =e (a-b / T) lnt+c

[0070] Where k is the stress loss rate, A is the Arrhenius constant, Ea is the activation energy, T is the temperature, a, b and c are constants, R is the Boltzmann constant, s max is the preset spring failure threshold, and t is the service life of the closing spring at temperature T. Specifically, when the temperature rises, the closing spring absorbs heat, resulting in intensified molecular activity and enhanced thermal vibration intensity, which will cause the grain boundaries of the closing spring to bend, displace, slip and split, thereby reducing the elastic modulus and yield strength. In a low temperature environment, the cooling speed of the closing spring is slow and the molecular activity is weak. This will lead to an increase in the closing elastic modulus, higher hardness and elastic strength, so temperature has a greater influence on the performance change of the closing spring.

[0071] In a specific embodiment, the parameters of the 110kV closing spring are shown in Table 1:

[0072] parameter Numeric Material 60Si2CrVA Free height 380mm Wire diameter 22mm Effective number of laps 7.5 Total laps 9.5 Maximum compressed length 135mm Stiffness 125±8 Standard working height <![CDATA[H1=347mm,H2=245mm]]> Closing time 50~80ms

[0073] Table 1: Parameters of 110kV closing spring

[0074] Figure 3a and Figure 3b The stress loss rate of the 110kV closing spring of the circuit breaker in the range of -10℃ to 110℃ corresponds to the stress loss value. It can be found that in the temperature range of -15℃ to 50℃, the stress loss rate of the spring after 11 months is in the range of 2.33%-2.64%. When the temperature rises to 110℃ under extreme conditions, the stress loss rate of the spring reaches 5.21%. The relationship between spring stress loss and closing time is shown in the figure below: Figure 4 shown.

[0075] Reading the circuit breaker technical manual of the closing spring, we know that the closing time of the closing spring is in the range of 50-80ms. Therefore, using Gaussian process regression and Bayesian inference methods, the closing time operating range [t min =50,t max =80] is mapped to the stress loss rate, that is: P(t min ≤t(s)≤t max )≥0.95, we can solve for s max =4.5, that is, the failure threshold of the closing spring is a stress loss rate of 4.5%. Table 2 shows the linear regression equation between the load loss rate and the logarithm of time at different temperatures s=k·lnt+c.

[0076] Temperature(℃) Regression equation Stress loss rate k <![CDATA[Coefficient of determination R 2 > -10 s=0.563·ln(t)+0.981 0.563 0.983 10 s=0.569·ln(t)+1.025 0.569 0.990 30 s=0.571·ln(t)+1.176 0.571 0.987 50 s=0.592·ln(t)+1.226 0.592 0.986 110 s=0.957·ln(t)+1.907 0.957 0.921

[0077] Table 2: Linear regression equation between load loss rate and time logarithm at different temperatures. Linear fitting is performed between ln(k) and 1 / T at different temperatures. The fitting results are shown in the following formula:

[0078]

[0079] When t = 1 month, s = c, so the constant c can be obtained by the spring stress loss rate obtained by testing at different temperatures for 1 month. By fitting the data of the 1-month test duration at different working temperatures, we can get c = h (T) = e (1.236-356.092) 1 / T, T is the absolute temperature during the test.

[0080] Therefore, when the spring failure threshold is s max =4.5, when the working temperature is room temperature 25 degrees Celsius (298.15K), the spring service life prediction equation can be obtained:

[0081] Calculation shows that at 25°C, the service life of the spring when the stress relaxation threshold, that is, the maximum stress loss rate of the spring is 4.5% is t = 373.34 months, or 31.11 years.

[0082] This embodiment uses Gaussian process regression and Bayesian inference methods to establish a mapping relationship between stress loss rate and closing time. Gaussian process regression can effectively capture complex nonlinear relationships in data, not just linear or simple polynomial relationships. Uncertainty estimation: Bayesian inference methods can provide parameter uncertainty estimation, making the prediction of spring closing failure threshold more accurate.

[0083] This embodiment establishes a more accurate life prediction method based on the improved Arrhenius acceleration model. The method can accurately predict the service life of the closing spring by combining the actual operating temperature and stress loss rate, thereby providing a scientific basis for the maintenance and replacement of high-voltage circuit breakers.

[0084] By using the method in this embodiment, the service life of the closing spring of the high-voltage circuit breaker can be predicted, and preventive maintenance and replacement can be performed in advance, thereby avoiding high maintenance costs and power outage losses caused by sudden failures, and providing a basis for equipment maintenance.

[0085] In the specific embodiments, see Figure 5, is a structural schematic diagram of a life assessment system for a closing spring in the second embodiment of the present application, the system comprising: a closing time acquisition module 301, a model mapping module 302, a threshold acquisition module 303 and a service life acquisition module 304; the closing time acquisition module 201 is used to acquire the closing time of the closing spring under different stress loss rates; the model mapping module 202 is used to construct a mapping model between the stress loss rate and the closing time according to the closing time under the different stress loss rates and the different stress loss rates; the threshold acquisition module 203 is used to acquire the stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range; the service life acquisition module 204 is used to acquire the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold.

[0086] The system in this embodiment obtains the closing time of the closing spring at different stress loss rates and constructs a mapping model between the stress loss rate and the closing time; obtains the stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range, and determines the failure state of the closing spring; obtains the service life of the closing spring at different temperatures according to the spring loss rate and the stress loss rate threshold of the closing spring at different temperatures, thereby accurately evaluating the service life of the closing spring.

[0087] In a specific embodiment, the third embodiment of the present application provides a life assessment device for a closing spring, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described in any one of the first embodiments of the present application. The device in this embodiment obtains the closing time of the closing spring under different stress loss rates and constructs a mapping model between the stress loss rate and the closing time; obtains the stress loss rate threshold of the closing spring according to the mapping model and the preset closing time range, and determines the failure state of the closing spring; obtains the service life of the closing spring at different temperatures according to the spring loss rate and the stress loss rate threshold of the closing spring at different temperatures, thereby accurately assessing the service life of the closing spring.

[0088] In a specific embodiment, the fourth embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor executes the steps of the method described in any one of the first embodiments of the present application. The storage medium in this embodiment obtains the closing time of the closing spring under different stress loss rates and constructs a mapping model between the stress loss rate and the closing time; obtains the stress loss rate threshold of the closing spring according to the mapping model and the preset closing time range, and determines the failure state of the closing spring; obtains the service life of the closing spring at different temperatures according to the spring loss rate and the stress loss rate threshold of the closing spring at different temperatures, thereby accurately evaluating the service life of the closing spring.

[0089] Figure 6 The internal structure of a computer device in one embodiment is shown. The computer device can be a terminal or a server. Figure 6 The computer device includes a processor, a memory, etc. connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the method in this embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the method in this embodiment. Those skilled in the art can understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0090] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0091] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating the life of a closing spring, characterized in that: The method comprises: Obtain the closing time of the closing spring under different stress loss rates; According to the closing time and different stress loss rates under the different stress loss rates, a mapping model between the stress loss rate and the closing time is constructed; Obtaining a stress loss rate threshold of the closing spring according to the mapping model and a preset closing time range; The service life of the closing spring at different temperatures is obtained according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold.

2. The life evaluation method of the closing spring according to claim 1, characterized in that: The stress loss rate is obtained by the following method: Obtaining a first stress required when the closing spring is compressed to a preset length at a first time; Obtaining a second stress required when the closing spring is compressed to a preset length at a second time; The second time is later than the first time; The stress loss rate is obtained according to the first stress and the second stress.

3. The life evaluation method of the closing spring according to claim 1, characterized in that: The mapping model between the stress loss rate and the closing time is obtained by the following formula: t(s)~G(m(s),k(s,s')) Wherein, t(s) is the closing time under the stress loss rate s, g is the Gaussian distribution, m(s) is the mean function of the stress loss rate s, and k(s, s') is the covariance function of the stress loss rate s.

4. The life evaluation method of the closing spring according to claim 1, characterized in that: The obtaining of the stress loss rate threshold of the closing spring according to the mapping model and the preset closing time range includes: Mapping a preset closing time range to the stress loss rate of the mapping model using Gaussian process regression and Bayesian inference methods, and obtaining the probability that the closing time of the closing spring under different stress losses is within the preset closing time range; The probability is solved according to a preset confidence level to obtain a stress loss rate threshold of the closing spring.

5. The method for evaluating the life of a closing spring according to claim 4, characterized in that: The probability is obtained using the following formula: P(t)=P(t min ≤t(s)≤t max ) Where P(t) is the probability, t min is the minimum time value in the preset closing time range, t max is the maximum time in the preset closing time range, and t(s) is the closing time under the stress loss rate s.

6. The method for evaluating the life of a closing spring according to claim 1, characterized in that: The obtaining the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold comprises: Fitting the spring loss rate of the closing spring at different temperatures and the energy storage time of the closing spring to obtain the stress loss rate of the closing spring at different temperatures; The service life of the closing spring at different temperatures is obtained according to the stress loss rate of the closing spring and a preset service life model.

7. The method for evaluating the life of a closing spring according to claim 6, characterized in that: The service life of the closing spring at different temperatures is obtained using the following formula: s=e (a-b / T) ·lnt+c max Wherein, k is the stress loss rate, A is the Arrhenius constant, E a is the activation energy, T is the temperature, a, b and c are constants, R is the Boltzmann constant, s max is the preset spring failure threshold, and t is the service life of the closing spring at temperature T.

8. A closing spring life assessment system, characterized in that: The system comprises: a closing time acquisition module, a model mapping module, a threshold value acquisition module and a service life acquisition module; The closing time acquisition module is used to acquire the closing time of the closing spring under different stress loss rates; The model mapping module is used to construct a mapping model between the stress loss rate and the closing time according to the closing time under the different stress loss rates and the different stress loss rates; The threshold acquisition module is used to acquire the stress loss rate threshold of the closing spring according to the mapping model and the preset closing time range; The service life acquisition module is used to acquire the service life of the closing spring at different temperatures according to the spring loss rate of the closing spring at different temperatures and the stress loss rate threshold.

9. A life evaluation device for a closing spring, comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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