Anti-radiation obtaining method and system of strong current pulse measurement system

Abstract

The invention provides an anti-radiation obtaining method and system of a strong current pulse measurement system, and belongs to the technical field of pulse power, and the method comprises the steps: analyzing the influence of each device parameter in the strong current pulse measurement system on the output characteristics of the strong current pulse measurement system, and recognizing a key device; inputting the irradiation sensitivity error distribution proportionality function of the key device into a deep convolutional neural network, and performing global collaborative optimization distribution on the irradiation sensitivity error distribution proportionality coefficient of the key device by taking the minimum comprehensive error of a strong electric pulse measurement system as a target function; and performing modular hierarchical redundancy setting on the key device by taking the consistency of the irradiation sensitivity error distribution proportionality coefficients of the actually and optimally distributed key device as a target, and performing irradiation hierarchical shielding according to the hierarchical redundancy setting of the key device. The radiation-proof requirement of the system can be met, and meanwhile, the radiation-proof setting cost and the size of the measurement system are remarkably reduced.

Description

Technical Field

[0001] The present application belongs to the field of pulse power technology, and more specifically, relates to a radiation-resistant acquisition method and system for a strong electric pulse measurement system. Background Art

[0002] Pulse power technology is widely used in nuclear physics, accelerators, lasers, electromagnetic emission and other technologies, and also has great potential in chemical engineering, environmental engineering and medical fields. Reliability and precision are important guarantees for the performance of pulse power equipment, and it must be ensured that it can still operate stably in extreme environments or under high-intensity use.

[0003] With the development of pulse power technology, it is gradually applied to extreme environments such as high altitude and extraterrestrial space environment. The measurement system, as the key to the reliable and stable operation of pulse power equipment, is extremely susceptible to various radiation effects in such extreme environments, which in turn leads to the failure of pulse power equipment. At present, the radiation resistance of integrated circuits in the measurement system is deeply studied, and many high-performance radiation-resistant chips are set up from new materials, new processes, to radiation effect mechanisms and reinforcement. However, the application of new materials and new processes has not only improved the performance, but also produced new radiation effects, bringing new problems and challenges. The existing radiation-resistant reinforcement process needs to analyze the radiation sensitivity of the entire measurement system, determine the radiation capacity of each subsystem and device, and use limiters and filters to reinforce the subsystems and devices. Partial or comprehensive shielding is performed on the entire system from the aspects of shielding structure, filling materials and reliability. However, the existing radiation-resistant settings are mostly aimed at sensitive integrated electronic components in the measurement system, and lack the overall radiation-resistant optimization setting method. The impact of external radiation on the reliability and accuracy of the measurement system is difficult to quantitatively evaluate. The existing system shielding method adopts a full shielding method using shielding materials, which can effectively protect the measurement system. However, the full protection solution significantly increases the pulse power cost and volume, limits the application scope of the measurement system, and lacks methods to guide the radiation protection settings of the measurement system. It is difficult to achieve the optimal radiation protection settings while ensuring system reliability and miniaturization of the equipment. Summary of the invention

[0004] In view of the defects of the prior art, the purpose of the present application is to provide a radiation-resistant acquisition method and system for a high-voltage pulse measurement system, aiming to solve the problem that the existing radiation-resistant acquisition method cannot ensure the reliability and miniaturization of the measurement system.

[0005] To achieve the above objectives, in a first aspect, the present application provides a method for obtaining radiation resistance of a strong electric pulse measurement system, comprising the following steps:

[0006] Step 1: Based on different irradiation conditions, analyze the influence of each device parameter in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system and identify key devices;

[0007] Step 2: Input the radiation sensitivity error allocation ratio function of key components into the deep convolutional neural network, take the minimum comprehensive error of the strong electric pulse measurement system as the objective function, perform global collaborative optimization allocation of the radiation sensitivity error allocation ratio coefficients of key components, and obtain the optimally allocated radiation sensitivity error allocation ratio coefficients of key components;

[0008] Step 3: With the goal of making the actual key component radiation sensitivity error allocation ratio coefficient consistent with the optimally allocated key component radiation sensitivity error allocation ratio coefficient, modular hierarchical redundancy settings are performed on the key components. At the same time, radiation hierarchical shielding is performed according to the hierarchical redundancy settings of the key components to obtain the optimal radiation resistance setting of the high-voltage pulse measurement system.

[0009] Further preferably, step 1 is specifically as follows: based on different irradiation conditions, the device parameters of the high-voltage pulse measurement system are adjusted, the influence of each device parameter on the system output characteristics is analyzed, and a normalized comprehensive evaluation is performed to select devices that have a significant influence on the output characteristics of the high-voltage pulse measurement system as key devices.

[0010] Further preferably, the method for obtaining the radiation sensitivity error distribution ratio function of the key components in step 2 is:

[0011] By comparing the changes in device parameters in the non-irradiated high-electric pulse measurement system and the irradiated high-electric pulse measurement system of the same batch, the influence of irradiation intensity and duration on device parameters is analyzed, the output characteristics of key devices are normalized, and the radiation sensitivity error distribution proportional function of key devices is obtained.

[0012] Further preferably, the method for training the deep convolutional neural network is:

[0013] An error synthesis model was constructed through a semi-centralized link model, and a global quantitative analysis method based on the Fourier amplitude sensitivity test expansion method was adopted to analyze the relationship between the irradiation sensitivity error allocation proportional coefficient and the comprehensive error of the strong electric pulse measurement system. The deep convolutional neural network was trained with the irradiation sensitivity error allocation proportional coefficient as input and the comprehensive error of the strong electric pulse measurement system as output. Among them, the comprehensive error of the strong electric pulse measurement system included the measurement accuracy error, linearity error and response time error of the device.

[0014] Further preferably, modular hierarchical redundancy includes device redundancy, circuit redundancy and module redundancy; the setting of radiation hierarchical shielding includes radiation shielding material, radiation shielding thickness and radiation shielding volume.

[0015] In a second aspect, the present application provides a radiation-resistant acquisition system for a strong electric pulse measurement system, comprising:

[0016] The key component identification module is used to analyze the influence of each component parameter in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system under different irradiation conditions and identify key components;

[0017] The optimal sensitivity error allocation module is used to input the radiation sensitivity error allocation ratio function of key components into the deep convolutional neural network, and take the minimum comprehensive error of the strong electric pulse measurement system as the objective function to perform global collaborative optimization allocation of the radiation sensitivity error allocation ratio coefficients of key components to obtain the optimally allocated radiation sensitivity error allocation ratio coefficients of key components;

[0018] The optimal sensitivity error optimization module is used to set modular hierarchical redundancy for key components with the goal of making the actual key component radiation sensitivity error allocation ratio coefficient consistent with the optimally allocated key component radiation sensitivity error allocation ratio coefficient. At the same time, radiation hierarchical shielding is performed according to the hierarchical redundancy setting of key components to obtain the optimal radiation resistance setting of the high-voltage pulse measurement system.

[0019] Further preferably, the method for the key component identification module to obtain key components is: based on different irradiation conditions, the device parameters of the high-voltage pulse measurement system are adjusted, the influence of each device parameter on the system output characteristics is analyzed, and a normalized comprehensive evaluation is performed to select devices that have a significant influence on the output characteristics of the high-voltage pulse measurement system as key components.

[0020] Further preferably, the optimal sensitivity error allocation module includes a pulse power supply, a first strong electric pulse measurement system, a second strong electric pulse measurement system, a strontium lamp and a data processing unit;

[0021] The pulse power supply is connected to the first strong electric pulse measurement system and the second strong electric pulse measurement system through the Rogowski coil, and is used to input the strong pulse current signal into the first strong electric pulse measurement system and the second strong electric pulse measurement system;

[0022] The first strong electric pulse measurement system is always in a non-irradiated environment and is used to provide a reference signal;

[0023] The second strong electric pulse measurement system is located under the irradiation of the strontium lamp and is used to obtain the actual output signal under different irradiation conditions;

[0024] Strontium lamps are used to provide irradiation environments of varying duration and intensity;

[0025] The data processing unit is used to set different device parameters of the second high-power electric pulse measurement system based on different irradiation conditions, analyze the influence of irradiation intensity and duration on device parameters by comparing the reference signal output by the first high-power electric pulse measurement system with the actual output signal output by the second high-power electric pulse measurement system in the same batch, normalize the output characteristics of the second high-power electric pulse measurement system, and obtain the irradiation sensitivity error distribution ratio function of key components.

[0026] Further preferably, the optimal sensitivity error allocation module also includes a network training unit, which has an embedded deep convolutional neural network. The training method of the deep convolutional neural network is: constructing an error synthesis model through a semi-centralized link model, and adopting a global quantitative analysis method based on the Fourier amplitude sensitivity test extension method to analyze the relationship between the irradiation sensitivity error allocation proportional coefficient and the comprehensive error of the high-electric pulse measurement system. The deep convolutional neural network is trained with the irradiation sensitivity error allocation proportional coefficient as input and the comprehensive error of the high-electric pulse measurement system as output; wherein the comprehensive error of the high-electric pulse measurement system includes the measurement accuracy error, linearity error and response time error of the device.

[0027] Further preferably, the modular hierarchical redundancy in the optimal sensitivity error optimization module includes device redundancy, circuit redundancy and module redundancy; the setting of radiation hierarchical shielding includes radiation shielding material, radiation shielding thickness and radiation shielding volume.

[0028] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0029] The present application provides a method for obtaining radiation resistance of a high-power pulse measurement system, wherein the influence of various device parameters in the high-power pulse measurement system on the output characteristics of the high-power pulse measurement system is analyzed under different radiation conditions, and key devices are determined. Only the radiation resistance sensitivity of key devices is considered, which can significantly reduce the radiation resistance test cost of devices in the high-power pulse measurement system.

[0030] The present application provides a method for acquiring radiation resistance of a high-power pulse measurement system. By constructing an error synthesis model through a semi-centralized link model, it is possible to quantitatively analyze the impact of the radiation sensitivity of key components on the radiation sensitivity of the system. Through a proportional allocation model, a global collaborative allocation of the system's radiation resistance sensitivity is achieved based on a deep learning network, and the optimal proportional allocation coefficient is obtained. Considering the joint influence of the device's own parameters and radiation sensitivity, the impact of radiation on the high-power pulse measurement system can be significantly optimized.

[0031] The present application provides a method for obtaining radiation resistance of a high-electric pulse measurement system. Through iterative optimization of radiation graded shielding and device graded redundancy, it can significantly reduce the radiation resistance setting cost and the volume of the measurement system while meeting the system radiation resistance requirements, complete the balanced setting of radiation resistance performance, setting cost and system volume, and achieve the optimal radiation resistance setting. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of a method for obtaining radiation resistance of a strong electric pulse measurement system based on global optimization hierarchical redundancy provided in an embodiment of the present application;

[0033] Figure 2 It is a typical signal processing circuit of the radiation resistance acquisition method of the strong electric pulse measurement system based on global optimization hierarchical redundancy provided in the embodiment of the present application;

[0034] Figure 3 It is a device radiation sensitivity testing system based on a strong electric pulse measurement system radiation resistance acquisition method based on global optimization hierarchical redundancy provided in an embodiment of the present application;

[0035] Figure 4 The performance change of the key components of the radiation-resistant acquisition method of the strong electric pulse measurement system based on global optimization hierarchical redundancy provided in the embodiment of the present application under radiation;

[0036] Figure 5 It is a semi-centralized association link of the radiation resistance acquisition method of the strong electric pulse measurement system based on global optimization hierarchical redundancy provided in the embodiment of the present application;

[0037] Figure 6 It is a sensitivity ratio allocation model of a method for obtaining radiation resistance of a strong electric pulse measurement system based on global optimization hierarchical redundancy provided in an embodiment of the present application;

[0038] Figure 7 This is a comparison of the radiation resistance performance of different protection methods of the radiation resistance acquisition method of the high-voltage pulse measurement system based on global optimization hierarchical redundancy provided in the embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0040] The terms “first”, “second” and the like in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects.

[0041] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0042] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two.

[0043] Next, the technical solutions provided in the embodiments of the present application are introduced.

[0044] Example 1

[0045] like Figure 1 As shown, the present application provides a method for obtaining radiation resistance of a strong electric pulse measurement system based on global optimization hierarchical redundancy, comprising the following steps:

[0046] Identification of key components: Based on the strong electric pulse measurement system, the impact of the components on the output characteristics of the strong electric pulse measurement system is analyzed experimentally to identify the key components;

[0047] Global collaborative allocation of device sensitivity: Determine the radiation sensitivity function of key devices through data sheets or experiments, and obtain the comprehensive error of the strong electric pulse measurement system through the error synthesis model. Perform global collaborative optimization allocation of radiation sensitivity of key devices based on deep convolutional neural networks to achieve the optimal allocation of radiation sensitivity of key devices.

[0048] Device anti-radiation hierarchical redundant setting: According to the optimally allocated radiation sensitivity of key components, modular hierarchical redundant setting is performed for key components. At the same time, radiation hierarchical shielding is performed according to the hierarchical redundant setting of the components. Both are iteratively optimized according to the optimally allocated radiation sensitivity to achieve the optimal anti-radiation setting of the measurement system.

[0049] More specifically, by adjusting the device parameters of the high-voltage pulse measurement system, analyzing the impact of each device parameter on the system output characteristics, and performing a normalized comprehensive evaluation, devices that have a significant impact on the output characteristics of the high-voltage pulse measurement system are selected as key devices.

[0050] More specifically, the typical signal processing circuit of the strong electric pulse measurement system is as follows Figure 2 As shown, it includes an integration circuit and an inverting proportional circuit; the system output characteristics are affected by the integration circuit resistance, integration circuit capacitance and inverting proportional circuit resistance. In this embodiment, the integration resistance is 10kΩ, the integration capacitance is 200nF, the proportional resistance R1 is 10kΩ, and R2 is 20kΩ; the key components in the circuit are the operational amplifier, integration circuit capacitance and integration circuit resistance in the two circuits.

[0051] More specifically, by comparing the parameter changes of unirradiated devices and irradiated devices in the same batch, the impact of irradiation intensity and duration on the key parameters of the devices is analyzed, and the results are normalized to obtain the sensitivity of key devices to irradiation.

[0052] More specifically, the device irradiation test system is as follows Figure 3 As shown, a strontium lamp is used as an adjustable irradiation source, and the irradiation range is adjustable from 0 to 1000 kGy. The characteristics of the standard device and the irradiated device under pulse conditions are compared to obtain the irradiation sensitivity of the key device; wherein, the standard device is a device that has not been irradiated, and the irradiated device and the strontium lamp are placed in the same space, and the irradiated device is irradiated with different intensities; Figure 4 This is the frequency domain characteristic of the operational amplifier under 100kGy irradiation. The loop gain of the operational amplifier before irradiation is 82dB, and the loop gain of the operational amplifier under 500kGy irradiation is 62.5dB, and the loop gain decreases by 20dB.

[0053] Furthermore, the error synthesis model is constructed through a semi-centralized link model by comprehensively considering the key parameters of the device, such as measurement accuracy, linearity and response time, and the global quantitative analysis method based on the Fourier amplitude sensitivity test extension method is used to analyze the radiation sensitivity of the comprehensive error of the measurement system. The change of the radiation sensitivity error allocation proportional coefficient of the key components of the system is considered, and its impact on the comprehensive error of the measurement system is statistically analyzed. The deep convolutional neural network is trained, and the radiation error allocation proportional coefficient of each key component in the proportional allocation method is determined based on the trained deep convolutional neural network, so that the radiation sensitivity of the comprehensive error of the measurement system is minimized.

[0054] More specifically, the semi-centralized link model is Figure 5 As shown, the error between the measured value and the true value is:

[0055] p=m(u,x)-x=v(E,T,r,l,c,d,x)

[0056] Among them, p is the error between the measured value and the true value; m is the measured value function; u is the output voltage; x is the true value; v is the circuit characteristic influence function; E is the radiation intensity; T is the temperature; r is the resistance per unit length; l is the inductance per unit length; c is the capacitance per unit length; d is the structural size;

[0057] Considering that r, l, c, d, E and T all have certain fluctuations, the actual accuracy is:

[0058] p+Δp=v(E+ΔE,T+ΔT,r+Δr,l+Δl,c+Δc,d+Δd,x)

[0059] Among them, Δp is the error fluctuation; ΔE is the irradiation intensity fluctuation; ΔT is the temperature fluctuation; Δr is the resistance fluctuation per unit length; Δl is the inductance fluctuation per unit length; Δc is the capacitance fluctuation per unit length; Δd is the structural size fluctuation;

[0060] Based on this, the error synthesis model can be constructed:

[0061] Δp=v(E+ΔE,T+ΔT,r+Δr,l+Δl,c+Δc,d+Δd,x)-v(E,T,r,l,c,d,x)

[0062] More specifically, the error allocation model of the measurement system is as follows: Figure 6 As shown in the figure, given the initial setting method (and determining the reasonable range of parameters), the parameter space is randomly initialized, and through sensitivity analysis, the sensitivity / correlation is distributed to each error source with the help of the proportional distribution principle. Combined with the parameter range, a new parameter space is obtained, and the comprehensive error is calculated; further, error tracing and sensitivity analysis are carried out until the total sensitivity and error change are minimized.

[0063] More specifically, the optimal radiation sensitivity error allocation ratio coefficient of key components is used to set up hierarchical redundancy and radiation hierarchical shielding for the measurement system components. Among them, the key components and their circuits are set up in a modular manner. Through multi-level redundancy settings such as device redundancy, circuit redundancy and module redundancy, the radiation shielding material, thickness and volume are coordinated and iteratively optimized, so that the sensitivity allocation ratio of each component in the system meets the optimal allocation result, and at the same time the system volume and cost are minimized.

[0064] More specifically, in this embodiment, no protection, full protection, and multi-level redundancy and multi-level shielding protection based on this embodiment are respectively adopted for the key components of the measurement system, wherein the multi-level redundancy respectively performs two redundancies on the operational amplifier in the operational amplifier circuit and three redundancies on the operational amplifier circuit, and the thickness of the shielding layer is reduced by 80% relative to the full protection, and the shielding material is isotactic polypropylene. The comparison of radiation resistance under different protection conditions is as follows: Figure 7 As shown, the peak uncertainty of the measurement system using the optimal radiation protection setting at 500kGy is less than 0.5%, which meets the system setting requirements. At the same time, the system protection volume and cost are reduced by 31% and 56% respectively compared with full protection.

[0065] Example 2

[0066] The present application provides a radiation-resistant acquisition system for a strong electric pulse measurement system, comprising:

[0067] The key component identification module is used to analyze the influence of each component parameter in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system under different irradiation conditions and identify key components;

[0068] The optimal sensitivity error allocation module is used to input the radiation sensitivity error allocation ratio function of key components into the deep convolutional neural network, and take the minimum comprehensive error of the strong electric pulse measurement system as the objective function to perform global collaborative optimization allocation of the radiation sensitivity error allocation ratio coefficients of key components to obtain the optimally allocated radiation sensitivity error allocation ratio coefficients of key components;

[0069] The optimal sensitivity error optimization module is used to set modular hierarchical redundancy for key components with the goal of making the actual key component radiation sensitivity error allocation ratio coefficient consistent with the optimally allocated key component radiation sensitivity error allocation ratio coefficient. At the same time, radiation hierarchical shielding is performed according to the hierarchical redundancy setting of key components to obtain the optimal radiation resistance setting of the high-voltage pulse measurement system.

[0070] Further preferably, the method for the key component identification module to obtain key components is: based on different irradiation conditions, the device parameters of the high-voltage pulse measurement system are adjusted, the influence of each device parameter on the system output characteristics is analyzed, and a normalized comprehensive evaluation is performed to select devices that have a significant influence on the output characteristics of the high-voltage pulse measurement system as key components.

[0071] Further preferably, the optimal sensitivity error allocation module includes a pulse power supply, a first strong electric pulse measurement system, a second strong electric pulse measurement system, a strontium lamp and a data processing unit;

[0072] The pulse power supply is connected to the first strong electric pulse measurement system and the second strong electric pulse measurement system through the Rogowski coil, and is used to input the strong pulse current signal into the first strong electric pulse measurement system and the second strong electric pulse measurement system;

[0073] The first strong electric pulse measurement system is always in a non-irradiated environment and is used to provide a reference signal;

[0074] The second strong electric pulse measurement system is located under the irradiation of the strontium lamp and is used to obtain the actual output signal under different irradiation conditions;

[0075] Strontium lamps are used to provide irradiation environments of varying duration and intensity;

[0076] The data processing unit is used to set different device parameters of the second high-power electric pulse measurement system based on different irradiation conditions, analyze the influence of irradiation intensity and duration on device parameters by comparing the reference signal output by the first high-power electric pulse measurement system with the actual output signal output by the second high-power electric pulse measurement system in the same batch, normalize the output characteristics of the second high-power electric pulse measurement system, and obtain the irradiation sensitivity error distribution ratio function of key components.

[0077] Further preferably, the optimal sensitivity error allocation module also includes a network training unit, which has an embedded deep convolutional neural network. The training method of the deep convolutional neural network is: constructing an error synthesis model through a semi-centralized link model, and adopting a global quantitative analysis method based on the Fourier amplitude sensitivity test extension method to analyze the relationship between the irradiation sensitivity error allocation proportional coefficient and the comprehensive error of the high-electric pulse measurement system. The deep convolutional neural network is trained with the irradiation sensitivity error allocation proportional coefficient as input and the comprehensive error of the high-electric pulse measurement system as output; wherein the comprehensive error of the high-electric pulse measurement system includes the measurement accuracy error, linearity error and response time error of the device.

[0078] Further preferably, the modular hierarchical redundancy in the optimal sensitivity error optimization module includes device redundancy, circuit redundancy and module redundancy; the setting of radiation hierarchical shielding includes radiation shielding material, radiation shielding thickness and radiation shielding volume.

[0079] Compared with the prior art, this application has the following advantages:

[0080] This application is determined through key components and only considers the radiation sensitivity of key components, which can significantly reduce the cost of system settings and device radiation testing.

[0081] Through the error synthesis model, this application can quantitatively analyze the impact of the radiation sensitivity of key components on the radiation sensitivity of the system, and through the proportional allocation model, based on the deep learning network, realize the global collaborative allocation of the system's anti-radiation sensitivity, obtain the optimal proportional allocation coefficient, and consider the joint influence of the device's own parameters and radiation sensitivity, which can significantly optimize the impact of radiation on the measurement system.

[0082] Through iterative optimization of radiation graded shielding and device graded redundancy, the present application can significantly reduce the radiation resistance setting cost and the volume of the measurement system while meeting the system radiation resistance requirements, complete the balanced setting of radiation resistance performance, setting cost and system volume, and realize the optimal radiation resistance setting.

[0083] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A radiation-resistant acquisition method for a strong electric pulse measurement system, characterized in that: The following steps are involved: Step 1: Analyze the influence of each device in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system under different irradiation conditions, and identify key devices; Step 2: Input the radiation sensitivity error allocation ratio function of key components into the deep convolutional neural network, take the minimum comprehensive error of the strong electric pulse measurement system as the objective function, perform global collaborative optimization allocation of the radiation sensitivity error allocation ratio coefficients of key components, and obtain the optimally allocated radiation sensitivity error allocation ratio coefficients of key components; Step 3: With the goal of making the actual key component radiation sensitivity error allocation ratio coefficient consistent with the optimally allocated key component radiation sensitivity error allocation ratio coefficient, modular hierarchical redundancy settings are performed on the key components. At the same time, radiation hierarchical shielding is performed according to the hierarchical redundancy settings of the key components to obtain the optimal radiation resistance setting of the high-voltage pulse measurement system.

2. The radiation-resistant acquisition method according to claim 1, characterized in that: Step 1 is specifically as follows: based on different irradiation conditions, by adjusting the device parameters of the high-voltage pulse measurement system, analyzing the impact of each device parameter on the system output characteristics, and performing a normalized comprehensive evaluation, the devices that have a significant impact on the output characteristics of the high-voltage pulse measurement system are selected as key devices.

3. The radiation-resistant acquisition method according to claim 1 or 2, characterized in that: The method for obtaining the radiation sensitivity error allocation ratio function of the key components in step 2 is: By comparing the changes in device parameters in the non-irradiated high-electric pulse measurement system and the irradiated high-electric pulse measurement system of the same batch, the influence of irradiation intensity and duration on device parameters is analyzed, the output characteristics of key devices are normalized, and the radiation sensitivity error distribution proportional function of key devices is obtained.

4. The radiation-resistant acquisition method according to claim 3, characterized in that: The method for training a deep convolutional neural network is: An error synthesis model was constructed through a semi-centralized link model, and a global quantitative analysis method based on the Fourier amplitude sensitivity test expansion method was adopted to analyze the relationship between the irradiation sensitivity error allocation proportional coefficient and the comprehensive error of the strong electric pulse measurement system. The deep convolutional neural network was trained with the irradiation sensitivity error allocation proportional coefficient as input and the comprehensive error of the strong electric pulse measurement system as output. Among them, the comprehensive error of the strong electric pulse measurement system included the measurement accuracy error, linearity error and response time error of the device.

5. The radiation-resistant acquisition method according to claim 1, characterized in that: Modular hierarchical redundancy includes device redundancy, circuit redundancy and module redundancy; the setting of radiation hierarchical shielding includes radiation shielding material, radiation shielding thickness and radiation shielding volume.

6. A radiation-resistant acquisition system for a strong electric pulse measurement system, characterized in that: include: The key component identification module is used to analyze the influence of each component parameter in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system under different irradiation conditions and identify key components; The optimal sensitivity error allocation module is used to input the radiation sensitivity error allocation ratio function of key components into the deep convolutional neural network, and take the minimum comprehensive error of the strong electric pulse measurement system as the objective function to perform global collaborative optimization allocation of the radiation sensitivity error allocation ratio coefficients of key components to obtain the optimally allocated radiation sensitivity error allocation ratio coefficients of key components; The optimal sensitivity error optimization module is used to set modular hierarchical redundancy for key components with the goal of making the actual key component radiation sensitivity error allocation ratio coefficient consistent with the optimally allocated key component radiation sensitivity error allocation ratio coefficient. At the same time, radiation hierarchical shielding is performed according to the hierarchical redundancy setting of key components to obtain the optimal radiation resistance setting of the high-voltage pulse measurement system.

7. The radiation-resistant acquisition system according to claim 6, characterized in that: The method for the key component identification module to obtain key components is: Based on different irradiation conditions, the device parameters of the high-voltage pulse measurement system are adjusted, the influence of each device parameter on the system output characteristics is analyzed, and a normalized comprehensive evaluation is performed. The devices that have a significant impact on the output characteristics of the high-voltage pulse measurement system are selected as key devices.

8. The radiation-resistant acquisition system according to claim 6 or 7, characterized in that: The optimal sensitivity error distribution module includes a pulse power supply, a first strong electric pulse measurement system, a second strong electric pulse measurement system, a strontium lamp and a data processing unit; The pulse power supply is connected to the first strong electric pulse measurement system and the second strong electric pulse measurement system through the Rogowski coil, and is used to input the strong pulse current signal into the first strong electric pulse measurement system and the second strong electric pulse measurement system; The first strong electric pulse measurement system is always in a non-irradiated environment and is used to provide a reference signal; The second strong electric pulse measurement system is located under the irradiation of the strontium lamp and is used to obtain the actual output signal under different irradiation conditions; Strontium lamps are used to provide irradiation environments of varying duration and intensity; The data processing unit is used to set different device parameters of the second high-power electric pulse measurement system based on different irradiation conditions, analyze the influence of irradiation intensity and duration on device parameters by comparing the reference signal output by the first high-power electric pulse measurement system with the actual output signal output by the second high-power electric pulse measurement system in the same batch, normalize the output characteristics of the second high-power electric pulse measurement system, and obtain the irradiation sensitivity error distribution ratio function of key components.

9. The radiation-resistant acquisition system according to claim 8, characterized in that: The optimal sensitivity error allocation module also includes a network training unit, which has an embedded deep convolutional neural network. The training method of the deep convolutional neural network is as follows: an error synthesis model is constructed through a semi-centralized link model, and a global quantitative analysis method based on the Fourier amplitude sensitivity test expansion method is adopted to analyze the relationship between the irradiation sensitivity error allocation proportional coefficient and the comprehensive error of the high-voltage pulse measurement system. The deep convolutional neural network is trained with the irradiation sensitivity error allocation proportional coefficient as input and the comprehensive error of the high-voltage pulse measurement system as output; wherein, the comprehensive error of the high-voltage pulse measurement system includes the measurement accuracy error, linearity error and response time error of the device.

10. The radiation-resistant acquisition system according to claim 6, characterized in that: The modular hierarchical redundancy in the optimal sensitivity error optimization module includes device redundancy, circuit redundancy and module redundancy; the setting of radiation hierarchical shielding includes radiation shielding material, radiation shielding thickness and radiation shielding volume.

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