A method for evaluating the anti-gamma radiation performance of a robot perception system

Abstract

The application discloses a kind of robot perception system anti-gamma radiation performance evaluation method, comprising: the perception system is divided into system-module-device three levels;Obtain the performance parameter of device to radiation sensitive, and carry out module irradiation experiment to obtain the performance parameter of each device changes with the data of irradiation cumulative dose;Degradation process of the performance parameter of device affected by irradiation is fitted using Wiener process;The correlation of the performance parameter of each device in module is constructed using Copula function, and the reliability of module changes with the curve of irradiation cumulative dose is calculated;Reliability of each module is used as the basis, and the reliability of perception system is calculated using dynamic Bayesian network;When the reliability of perception system is 50%, the corresponding cumulative dose is used as robot perception system anti-gamma radiation performance index.The application makes full use of the degradation data of performance parameter when device is affected by irradiation, and considers the correlation of the reliability change of each device in module, and evaluates the anti-gamma radiation performance of robot perception system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of circuit system radiation resistance performance, and mainly relates to a robot perception system gamma radiation resistance performance evaluation method. BACKGROUND

[0002] Nuclear industry robots usually work in a radiation environment for a long time, and the internal perception system is susceptible to radiation, which can cause system material damage and performance degradation, affect system efficiency and reliability, and further affect robot life and task reliability. In order to avoid the performance degradation or failure of the perception system caused by radiation, anti-radiation devices or anti-radiation reinforcement measures are needed to make the system have the required radiation resistance. Before the anti-radiation reinforcement, the anti-radiation performance of the perception system needs to be evaluated to select appropriate reinforcement measures. However, due to the constraints of test funds and conditions, the robot perception system can only perform a small amount of anti-radiation capability test, and even it is difficult to perform a system-level radiation test, so it is difficult to obtain reliable anti-radiation performance evaluation results. With the new development of nuclear industry robots, the new changes in robot development mode, design technology, components and materials have put forward new requirements for anti-radiation reinforcement technology of complex circuit systems.

[0003] However, in the prior art, it is difficult to meet the performance requirements of complex circuit systems in a radiation environment. The prior art cannot develop a small-subsample multi-factor complex system anti-radiation performance evaluation method and application research for the small sample size and difficult test of system-level radiation test, and further cannot reasonably verify and evaluate the system anti-radiation performance, and analyze and improve the system anti-radiation reinforcement design. SUMMARY

[0004] The purpose of the embodiment of the present application is to provide a nuclear industry robot perception system anti-radiation performance evaluation method which solves the above problems.

[0005] Specifically, the present application provides a robot perception system gamma radiation resistance performance evaluation method, which comprises the following steps:

[0006] Step 1, analyze the radiation-sensitive devices in the perception system, and divide the perception system into three levels of system-module-device;

[0007] Step 2, obtain the performance parameters of the radiation-sensitive devices and the data of the cumulative metering changes with radiation;

[0008] Step 3, use a Wiener process fitting device to fit the degradation process of the characteristic parameters affected by radiation, and further obtain the edge distribution value F(Y) of the characteristic parameter value;

[0009] Step 4, the correlation of the performance parameters of each device in the module is constructed by using a Copula function, and a curve of the reliability of the module changing with the irradiation cumulative dose is calculated;

[0010] Step 5, the reliability of the sensing system is calculated by using a dynamic Bayesian network based on the reliability of each module.

[0011] Further, in step 1, considering the influence of the reliability change of the devices on the reliability of the sensing system and the mutual influence among the functions of the devices, the devices that influence each other are divided into the same module, forming a three-level system-module-device.

[0012] Further, in step 3, the following steps are included:

[0013] Step 31, a Wiener process is used to fit the degradation process of the characteristic parameters of the devices affected by irradiation, and the value of the unknown parameter in the model is obtained;

[0014] Step 32, the Wiener process model with the best fitting effect is selected according to the BIC value of each model, and the edge distribution value F(Y) of the measured characteristic parameter value is obtained.

[0015] Further, in step 31, three Wiener process models in formulas M1-M3 are used to fit the degradation process of the characteristic parameters of the devices affected by irradiation, respectively:

[0016] M1:

[0017] M2:

[0018] M3:

[0019] In the formula, Y is the irradiation cumulative dose,

[0020] Y is the irradiation cumulative dose, Y is the performance parameter measurement value, a is a drift coefficient, σ is a diffusion coefficient, b is an unknown parameter indicating that the degradation of the characteristic parameter is a nonlinear process, B is a standard Brownian motion, N is a normal distribution symbol, μ a is the mean of the normal distribution, σ a is the standard deviation of the normal distribution.

[0021] Further, in step 32, the BIC value of each model is calculated:

[0022] BIC = -2log(L) + M log(m)

[0023] In the formula,

[0024] L is the maximum likelihood estimation value of the Wiener process model, M is the number of unknown parameters in the Wiener process model, and m is the sample number.

[0025] Further, in step 4, the Copula function in formula M4-M6 is used to build the correlation of each device performance parameter in the module, respectively.

[0026] M4: C(F(Y1), F(Y2); theta) = exp{-[(-ln F(Y1)) θ +(-ln F(Y2)) θ ] 1 / θ}

[0027] M5: C(F(Y1), F(Y2); theta) = max{(F(Y1) -θ +F(Y2) -θ -1) 1 / θ , 0}

[0028] M6:

[0029] In the formula,

[0030] F(Y1) is the edge distribution function of the first characteristic parameter, F(Y2) is the edge distribution function of the second characteristic parameter, theta is the unknown parameter in the Copula function, and C is the symbol of the Copula function;

[0031] The maximum likelihood estimation method is used to solve the unknown parameter value in the Copula function, and the Copula function best describing the correlation of each device performance parameter is selected according to the principle of minimum BIC value.

[0032] Further, in step 5, according to the connection relationship between the functions of each module in the system, a dynamic fault tree is established, the dynamic fault tree is converted into a dynamic Bayesian network according to the conversion principle of dynamic fault tree into dynamic Bayesian network, and the reliability of the sensing system is calculated.

[0033] Further, in step 5, the cumulative dose corresponding to the reliability of the sensing system of 50% is taken as the performance index of the robot sensing system against gamma radiation.

[0034] The beneficial effects achieved by the present application are:

[0035] The application is not limited to the simulation model of the device, and the anti-radiation performance of the circuit system is evaluated based on experimental data. An anti-radiation performance evaluation method based on a Copula function is proposed to consider the mutual influence of the reliability of the device in a radiation environment. Compared with the existing anti-radiation performance evaluation method based on data, the evaluation result of the application is more accurate. A dynamic Bayesian network is used to evaluate the anti-radiation performance of the circuit system, so that the application is more widely applicable. In addition to conventional static circuits, the dynamic circuit containing the circuit and the sequential correlation circuit can also be applied. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 is a schematic diagram of a robot perception system anti-gamma radiation performance evaluation method provided by an embodiment of the application;

[0037] Figure 2 is a schematic diagram of a perception system hierarchical division;

[0038] Figure 3 is a schematic diagram of a Wiener process fitting device characteristic parameter degradation process affected by irradiation;

[0039] Figure 4 is a schematic diagram of a module reliability calculation process based on a Copula function. DETAILED DESCRIPTION

[0040] The technical solutions of the application will be described in more detail below with reference to the drawings. The application includes but is not limited to the following embodiments.

[0041] In order to more clearly understand the above-mentioned purposes, features and advantages of the application, the application will be further described in detail below with reference to the drawings and specific embodiments.

[0042] As shown in the accompanying Figure 1 , a schematic diagram of a robot perception system anti-gamma radiation performance evaluation method provided by an embodiment of the application is shown. The embodiment provides a robot perception system anti-gamma radiation performance evaluation method based on a dynamic Bayesian network and a Copula function. The method comprises the following steps:

[0043] Step 1, perception system hierarchical division;

[0044] As shown in the accompanying Figure 2 , the devices sensitive to radiation in the perception system are analyzed. For the devices insensitive to irradiation, such as resistors and capacitors, the influence of the reliability change of the devices on the reliability of the perception system is not considered. The mutual influence between the functions of the devices (such as the decrease of the output voltage of the power supply chip which will affect the function of the Flash digital device) is considered. The devices that will influence each other are divided into the same module. Based on this, the perception system is divided into three levels of system-module-device.

[0045] Step 2, analysis and acquisition of performance parameters of radiation-sensitive devices;

[0046] Referring to literature or performing experimental analysis on the performance parameters of devices sensitive to radiation in the sensing system, performing module irradiation test, and obtaining data of changes in performance parameters of each device with irradiation cumulative dose.

[0047] Step 3, fitting the degradation process of the performance parameters of the device affected by irradiation using a Wiener process, and further obtaining the edge distribution value F(Y) of the characteristic parameter value;

[0048] Step 31, fitting the degradation process of the characteristic parameters of the device affected by irradiation using a Wiener process, and obtaining unknown parameter values in the model;

[0049] As shown in the accompanying Figure 3 Figs. M1-M3, three Wiener process models are used to fit the degradation process of the characteristic parameters of the device affected by irradiation, and the maximum likelihood estimation method is used to solve the unknown parameter values in the model.

[0050] M1:

[0051] M2:

[0052] M3:

[0053] In the formula:

[0054] is the irradiation cumulative dose, is the performance parameter measurement value, a is the drift coefficient, σ is the diffusion coefficient, b is an unknown parameter indicating the nonlinear process of the degradation of the characteristic parameter, is a standard Brownian motion, N is a normal distribution symbol, μ a is the mean of the normal distribution, σ a is the standard deviation of the normal distribution.

[0055] Step 32, selecting the Wiener process model with the best fitting effect according to the BIC values of each model, and measuring the edge distribution value F(Y) of the characteristic parameter value;

[0056] The BIC values of each model are calculated according to the following formula, and the Wiener process model with the best fitting effect is selected according to the principle of minimum BIC value. Based on the fitting results of the Wiener process model, the distribution function of the characteristic parameter under any cumulative dose can be obtained, and further the edge distribution value F(Y) of the characteristic parameter value measured by experiment can be obtained.

[0057] BIC = -2log(L) + M log(m)

[0058] In the formula:

[0059] L is the maximum likelihood estimate of the Wiener process model, M is the number of unknown parameters in the Wiener process model, and m is the number of samples.

[0060] Step 4: Use the Copula function to construct the correlation of the performance parameters of each device in the module, and calculate the reliability curve of the module as a function of cumulative irradiation.

[0061] As attached Figure 4 As shown, the correlation of the performance parameters of each device in the module is constructed using three commonly used Copula functions shown in Equations M4 to M6. The unknown parameter values ​​in the Copula function are solved by the maximum likelihood estimation method. The Copula function that best describes the correlation of the performance parameters of each device is selected based on the principle of minimizing the BIC value.

[0062] M4:C(F(Y1),F(Y2);θ)=exp{-[(-lnF(Y1)) θ +(-ln F(Y2)) θ ] 1 / θ}

[0063] M5:C(F(Y1),F(Y2);θ)=max{(F(Y1) -θ +F(Y2) -θ -1) 1 / θ ,0}

[0064] M6:

[0065] In the formula:

[0066] F(Y1) is the marginal distribution function of the first type of feature parameter, F(Y2) is the marginal distribution function of the second type of feature parameter, θ is the unknown parameter in the Copula function, and C is the symbol of the Copula function.

[0067] After determining the Copula function that best describes the correlation of the performance parameters of each device, a function is generated when the cumulative dose is... Random variables F(Y1), F(Y2); θ are given by a joint distribution C(F(Y1), F(Y2); θ). Sample points for each performance parameter are generated using F(Y1), F(Y2) and an edge degradation model. The probability that each performance parameter fails to reach its failure threshold is calculated, and the module is used to calculate the cumulative dose based on the connection relationships. Reliability at any time, gradually increasing the cumulative dose The reliability of the module as a function of cumulative dose can then be obtained. The curve showing the change.

[0068] Step 5, based on the reliability of each module, the reliability of the perception system is calculated by using dynamic Bayesian network.

[0069] According to the connection relationship between the functions of each module in the system, a system fault tree is established, the dynamic fault tree is converted into a dynamic Bayesian network according to the conversion principle of dynamic fault tree conversion into a dynamic Bayesian network, and the reliability of the perception system is calculated; the cumulative dose corresponding to the reliability of the perception system of 50% is taken as the performance index of the robot perception system against gamma radiation.

[0070] The application is not limited to the above specific embodiments, and those skilled in the art can implement the application by using other various specific embodiments according to the content disclosed in the embodiments and the drawings, therefore, any design using the design structure and idea of the application and making some simple changes or modifications falls within the protection scope of the application.

Claims

1. A method for evaluating the gamma radiation resistance performance of a robot sensing system, characterized in that, The method for evaluating the gamma radiation resistance performance of the robot sensing system includes the following steps: Step 1: Analyze the radiation-sensitive devices in the sensing system and divide the sensing system into three levels: system-module-device. Step 2: Obtain the performance parameters of the irradiation-sensitive device and the data on changes in cumulative irradiation. Step 3: Use the Wiener process to fit the degradation process of the device characteristic parameters affected by irradiation, and further obtain the marginal distribution value F(Y) of the characteristic parameter values; Step 4: Use the Copula function to construct the correlation of the performance parameters of each device in the module, and calculate the reliability curve of the module as a function of cumulative irradiation. Step 5: Based on the reliability of each module, use a dynamic Bayesian network to calculate the reliability of the sensing system; Step 3 includes the following steps: Step 31: Use the Wiener process to fit the degradation process of device characteristic parameters affected by irradiation and obtain the values ​​of unknown parameters in the model; Step 32: Based on the BIC values ​​of each model, select the Wiener process model with the best fit and measure the marginal distribution value F(Y) of the feature parameter values. In step 4, after determining the Copula function that best describes the correlation of the performance parameters of each device, a function is generated when the cumulative dose is... Random variables F(Y1), F(Y2); θ are given by a joint distribution C(F(Y1), F(Y2); θ). Sample points for each performance parameter are generated using F(Y1), F(Y2) and an edge degradation model. The probability that each performance parameter fails to reach its failure threshold is calculated, and the module is used to calculate the cumulative dose based on the connection relationships. Reliability at any time, gradually increasing the cumulative dose The reliability of the module as a function of cumulative dose can then be obtained. The curve showing the change.

2. The method for evaluating the gamma radiation resistance performance of a robot perception system according to claim 1, characterized in that, In step 1, considering the impact of device reliability changes on the reliability of the sensing system and the mutual influence between the functions of each device, the devices that influence each other are divided into the same module, forming a three-level system-module-device structure.

3. The method for evaluating the gamma radiation resistance of a robot perception system according to claim 1, characterized in that, In step 31, the degradation process of device characteristic parameters affected by irradiation is fitted using three Wiener process models from equations M1 to M3: M1： M2： M3： In the formula: For cumulative irradiation measurement, Here, σ represents the measured values ​​of the performance parameters, a is the drift coefficient, σ is the diffusion coefficient, and b is an unknown parameter representing the degradation of the characteristic parameter into a nonlinear process. This represents standard Brownian motion, where N is the sign of the normal distribution, and μ... a Let σ be the mean of a normal distribution. a is the standard deviation of the normal distribution.

4. The method for evaluating the gamma radiation resistance of a robot sensing system according to claim 1, characterized in that, In step 32, the BIC value of each model is calculated: BIC = -2log(L) + Mlog(m) In the formula: L is the maximum likelihood estimate of the Wiener process model, M is the number of unknown parameters in the Wiener process model, and m is the number of samples.

5. The method for evaluating the gamma radiation resistance of a robot perception system according to claim 1, characterized in that, In step 4, the correlation of performance parameters of each device within the module is constructed using Copula functions in equations M4 to M6 respectively: M4:C(F(Y1),F(Y2);θ)=exp{-[(-lnF(Y1)) θ +(-lnF(Y2)) θ ] 1 / θ } M5:C(F(Y1),F(Y2);θ)=max{(F(Y1) -θ +F(Y2) -θ -1) 1 / θ ,0} M6: In the formula: F(Y1) is the marginal distribution function of the first feature parameter, F(Y2) is the marginal distribution function of the second feature parameter, θ is the unknown parameter in the Copula function, and C is the symbol of the Copula function; The maximum likelihood estimation method is used to solve for the unknown parameter values ​​in the Copula function. The Copula function that best describes the correlation of the performance parameters of each device is selected based on the principle of minimizing the BIC value.

6. The method for evaluating the gamma radiation resistance of a robot sensing system according to claim 1, characterized in that, In step 5, a system fault tree is established based on the connection relationship between the functions of each module in the system. According to the transformation principle of dynamic fault tree to dynamic Bayesian network, the dynamic fault tree is transformed into dynamic Bayesian network, and the reliability of the sensing system is calculated.

7. The method for evaluating the gamma radiation resistance of a robot perception system according to claim 1, characterized in that, In step 5, the cumulative dose corresponding to a reliability of 50% for the sensing system is used as the gamma radiation resistance performance index of the robot sensing system.

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