Anti-irradiation acquisition method and system for a strong electric pulse measurement system

Abstract

The present application provides an anti-irradiation acquisition method and system for a high-voltage pulse measurement system, belonging to the field of pulse power technology. The method is as follows: Analyze the influence of the parameters of each device in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system to identify key devices; input the irradiation sensitivity error allocation ratio function of the key devices into a deep convolutional neural network, and take the minimum comprehensive error of the high-voltage pulse measurement system as the objective function to globally and collaboratively optimize the allocation of the irradiation sensitivity error allocation ratio coefficients of the key devices; aiming at the consistency between the actual and the optimal allocation of the irradiation sensitivity error allocation ratio coefficients of the key devices, perform modular hierarchical redundancy setting on the key devices, and at the same time perform irradiation hierarchical shielding according to the hierarchical redundancy setting of the key devices. The present application can significantly reduce the anti-irradiation setting cost and the volume of the measurement system while meeting the anti-irradiation requirements of the system.

Description

Technical Field

[0001] This application belongs to the technical field of pulsed power, and more specifically, relates to an anti-irradiation acquisition method and system for a high-voltage pulse measurement system. Background Art

[0002] Pulsed power technology is widely used in technologies such as nuclear physics, accelerators, lasers, and electromagnetic emissions, and also has great potential in fields such as chemical engineering, environmental engineering, and medicine. Reliability and accuracy are important guarantees for ensuring the performance of pulsed power equipment, and it must be ensured that it can still operate stably under extreme environments or high-intensity use.

[0003] With the development of pulsed power technology, it has gradually been applied to extreme environments such as high altitudes and extraterrestrial space environments. As the key to the reliable and stable operation of pulsed power equipment, the measurement system is extremely vulnerable to various irradiation effects in such extreme environments, which may lead to the failure of pulsed power equipment. At present, in-depth research has been carried out on the anti-irradiation of integrated circuits in the measurement system. From new materials and new processes to irradiation effect mechanisms and reinforcement, many high-performance anti-irradiation chips have been set up. However, the application of new materials and new processes has, while improving performance, also generated new irradiation effects, bringing new problems and challenges. Existing anti-irradiation reinforcement processes require irradiation sensitivity analysis for the entire measurement system to determine the radiation tolerance of each subsystem and device, and measures such as limiters and filters are used to reinforce the subsystem and device. For the entire system, local or comprehensive shielding is carried out in terms of shielding structure, filling materials, and reliability. However, existing anti-irradiation settings mostly target sensitive integrated electronic components in the measurement system, and there is a lack of an overall anti-irradiation optimization setting method, and it is difficult to quantitatively evaluate the impact of external irradiation on the reliability and accuracy of the measurement system. The existing system shielding method uses a full shielding method of shielding materials, which can effectively protect the measurement system, but the full protection scheme significantly increases the cost and volume of pulsed power, limits the application range of the measurement system, lacks a method to guide the anti-irradiation setting of the measurement system, and it is difficult to achieve the optimal anti-irradiation setting that ensures system reliability while realizing equipment miniaturization. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the purpose of this application is to provide an anti-irradiation acquisition method and system for a high-voltage pulse measurement system, aiming to solve the problem that the existing anti-irradiation acquisition method cannot ensure the reliability and miniaturization of the measurement system.

[0005] To achieve the above object, in the first aspect, this application provides an anti-irradiation acquisition method for a high-voltage pulse measurement system, including the following steps:

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

[0007] Step 2: Input the irradiation sensitivity error allocation ratio function of key devices into the deep convolutional neural network. Taking the minimum comprehensive error of the high-voltage pulse measurement system as the objective function, globally and collaboratively optimize the allocation of the irradiation sensitivity error allocation ratio coefficients of key devices to obtain the optimally allocated irradiation sensitivity error allocation ratio coefficients of key devices.

[0008] Step 3: Taking the consistency between the actual irradiation sensitivity error allocation ratio coefficients of key devices and the optimally allocated irradiation sensitivity error allocation ratio coefficients of key devices as the goal, perform modular hierarchical redundancy setting on key devices, and at the same time perform irradiation hierarchical shielding according to the hierarchical redundancy setting of key devices to obtain the optimal anti-irradiation setting of the high-voltage pulse measurement system.

[0009] Further preferably, Step 1 is specifically: Based on different irradiations, adjust the device parameters of the high-voltage pulse measurement system, analyze the influence of each device parameter on the output characteristics of the system, and conduct a normalized comprehensive evaluation, and select the devices that have a significant impact on the output characteristics of the high-voltage pulse measurement system as key devices.

[0010] Further preferably, the method for obtaining the irradiation sensitivity error allocation ratio function of key devices in Step 2 is:

[0011] By comparing the changes in device parameters in the non-irradiated high-voltage pulse measurement system and the irradiated high-voltage pulse measurement system in the same batch, analyze the influence of irradiation intensity and duration on device parameters, and perform normalization processing on the output characteristics of key devices to obtain the irradiation sensitivity error allocation ratio function of key devices.

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

[0013] Construct an error synthesis model through a semi-centralized link model, adopt a global quantitative analysis method based on the extended Fourier amplitude sensitivity test method, analyze the relationship between the irradiation sensitivity error allocation ratio coefficient and the comprehensive error of the high-voltage pulse measurement system, use the irradiation sensitivity error allocation ratio coefficient as the input, and the comprehensive error of the high-voltage pulse measurement system as the output to train the deep convolutional neural network; among them, the comprehensive error of the high-voltage pulse measurement system includes the measurement accuracy error, linearity error, and response time error of the device.

[0014] Further preferably, the modular hierarchical redundancy includes device redundancy, circuit redundancy, and module redundancy; the settings of the irradiation hierarchical shielding include irradiation shielding materials, irradiation shielding thickness, and irradiation shielding volume.

[0015] In a second aspect, the present application provides an anti-irradiation acquisition system for a high-voltage pulse measurement system, including:

[0016] A key device identification module, which is used to analyze the influence of the parameters of each device in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system under different irradiations, and identify key devices;

[0017] An optimal sensitivity error allocation module, which is used to input the irradiation sensitivity error allocation ratio function of key devices into a deep convolutional neural network, and take the minimum comprehensive error of the high-voltage pulse measurement system as the objective function to globally and collaboratively optimize the allocation of the irradiation sensitivity error allocation ratio coefficients of key devices, so as to obtain the optimal allocated irradiation sensitivity error allocation ratio coefficients of key devices;

[0018] An optimal sensitivity error optimization module, which is used to modularly and hierarchically redundantly set key devices with the goal that the actual irradiation sensitivity error allocation ratio coefficients of key devices are consistent with the optimally allocated irradiation sensitivity error allocation ratio coefficients of key devices, and at the same time perform irradiation hierarchical shielding according to the hierarchical redundant settings of key devices to obtain the optimal anti-irradiation settings of the high-voltage pulse measurement system.

[0019] Further preferably, the method for the key device identification module to obtain key devices is as follows: under different irradiations, adjust the device parameters of the high-voltage pulse measurement system, analyze the influence of each device parameter on the system output characteristics, and perform normalized comprehensive evaluation, and select the devices that have a significant influence on the output characteristics of the high-voltage pulse measurement system as key devices.

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

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

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

[0023] The second high-voltage pulse measurement system is located under the irradiation of the strontium lamp and is used to obtain the actual output signals under different irradiations;

[0024] The strontium lamp is used to provide irradiation environments with different durations and intensities;

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

[0026] Further preferably, the optimal sensitivity error allocation module further includes a network training unit, which embeds a deep convolutional neural network. The training method of the deep convolutional neural network is as follows: construct an error synthesis model through a semi-centralized link model, and adopt a global quantitative analysis method based on the extended method of Fourier amplitude sensitivity test to analyze the relationship between the irradiation sensitivity error allocation ratio coefficient and the comprehensive error of the high-voltage pulse measurement system. Take the irradiation sensitivity error allocation ratio coefficient as the input and the comprehensive error of the high-voltage pulse measurement system as the output to train the deep convolutional neural network; among them, the comprehensive error of the high-voltage 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 settings of irradiation hierarchical shielding include irradiation shielding materials, irradiation shielding thickness, and irradiation shielding volume.

[0028] Generally speaking, compared with the prior art through the above technical solutions conceived in this application, the following beneficial effects are obtained:

[0029] This application provides a method for obtaining anti-irradiation of a high-voltage pulse measurement system. Among them, based on different irradiations, analyze the influence of the parameters of each device in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system, determine the key devices, and only consider the anti-irradiation sensitivity of the key devices, which can significantly reduce the anti-irradiation test cost of the devices in the high-voltage pulse measurement system.

[0030] This application provides a method for obtaining anti-irradiation of a high-voltage pulse measurement system. By constructing an error synthesis model through a semi-centralized link model, it can quantitatively analyze the influence of the irradiation sensitivity of key devices on the system irradiation sensitivity, and through the proportional allocation model, realize the global collaborative allocation of the system anti-irradiation sensitivity based on a deep learning network, obtain the optimal proportional allocation coefficient, and consider the combined influence of the device's own parameters and irradiation sensitivity, which can significantly optimize the influence of the high-voltage pulse measurement system affected by irradiation.

[0031] The present application provides an anti - irradiation acquisition method for a strong - electric - pulse measurement system. Through irradiation - level shielding and device - level redundancy iterative optimization, it can meet the anti - irradiation requirements of the system while significantly reducing the anti - irradiation setup cost and the volume of the measurement system, achieving a balanced setting of anti - irradiation performance, setup cost, and system volume, and realizing the optimal anti - irradiation setup. Description of the Drawings

[0032] Figure 1 is a flowchart of the anti - irradiation acquisition method for a strong - electric - pulse measurement system based on global - optimization hierarchical redundancy provided by an embodiment of the present application;

[0033] Figure 2 is a typical signal - processing circuit of the anti - irradiation acquisition method for a strong - electric - pulse measurement system based on global - optimization hierarchical redundancy provided by an embodiment of the present application;

[0034] Figure 3 is a device - irradiation sensitivity test system of the anti - irradiation acquisition method for a strong - electric - pulse measurement system based on global - optimization hierarchical redundancy provided by an embodiment of the present application;

[0035] Figure 4 is the performance change of key devices under irradiation in the anti - irradiation acquisition method for a strong - electric - pulse measurement system based on global - optimization hierarchical redundancy provided by an embodiment of the present application;

[0036] Figure 5 is a semi - centralized association link of the anti - irradiation acquisition method for a strong - electric - pulse measurement system based on global - optimization hierarchical redundancy provided by an embodiment of the present application;

[0037] Figure 6 is a sensitivity ratio allocation model of the anti - irradiation acquisition method for a strong - electric - pulse measurement system based on global - optimization hierarchical redundancy provided by an embodiment of the present application;

[0038] Figure 7 is a comparison of the anti - irradiation performance of different protection methods in the anti - irradiation acquisition method for a strong - electric - pulse measurement system based on global - optimization hierarchical redundancy provided by an embodiment of the present application. Detailed Embodiment

[0039] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to 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" and "second" in the description and claims of this article are used to distinguish different objects, rather than to describe a specific order of objects.

[0041] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0042] In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" refers to two or more.

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

[0044] Embodiment 1

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

[0046] Key device identification: According to the strong electric pulse measurement system, identify key devices by experimentally analyzing the influence of devices on the output characteristics of the strong electric pulse measurement system;

[0047] Global collaborative allocation of device sensitivity: Determine the radiation sensitivity function of key devices through data manuals or experiments, obtain the comprehensive error of the strong electric pulse measurement system through an error synthesis model, and perform global collaborative optimization allocation of the radiation sensitivity of key devices based on a deep convolutional neural network to optimize the allocation of the radiation sensitivity of key devices;

[0048] Device anti-radiation hierarchical redundancy setting: According to the radiation sensitivity of the optimally allocated key devices, perform modular hierarchical redundancy setting on the key devices, and at the same time perform radiation hierarchical shielding according to the hierarchical redundancy setting of the devices. The two 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 strong electric pulse measurement system, analyze the influence of each device parameter on the system output characteristics, and perform normalized comprehensive evaluation, and select the devices that have a significant influence on the output characteristics of the strong electric pulse measurement system as key devices.

[0050] More specifically, a typical signal processing circuit of the strong electric pulse measurement system is as Figure 2 shown, including an integration circuit and an inverting proportional circuit; the system output characteristics are affected by the integration circuit resistance, the integration circuit capacitance, and the inverting proportional circuit resistance. In this embodiment, the integration resistance is 10 kΩ, the integration capacitance is 200 nF, and the proportional resistance R 1 is 10 kΩ, R 2is 20 kΩ; the key devices in this circuit are the operational amplifiers, the capacitors in the integration circuit, and the resistors in the integration circuit in the two circuits.

[0051] More specifically, by comparing the parameter changes of non-irradiated devices and irradiated devices in the same batch, analyzing the influence of irradiation intensity and duration on the key parameters of the devices, and normalizing the results, the sensitivity of the key devices to irradiation is obtained.

[0052] More specifically, the device irradiation test system is as Figure 3 shown. A strontium lamp is used as an adjustable irradiation source, with an adjustable irradiation range of 0 - 1000 kGy. By comparing the characteristics of standard devices and irradiated devices under pulse conditions, the irradiation sensitivity of the key devices is obtained; among them, 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 is the frequency-domain characteristic of the operational amplifier under 100 kGy irradiation. Before irradiation, the loop gain of the operational amplifier is 82 dB, and under 500 kGy irradiation, the loop gain of the operational amplifier is 62.5 dB, and the loop gain has decreased by 20 dB.

[0053] Furthermore, comprehensively considering key parameters such as the measurement accuracy, linearity, and response time of the device, an error synthesis model is constructed through a semi-centralized link model, and a global quantitative analysis method based on the extended method of Fourier amplitude sensitivity test is used to analyze the irradiation sensitivity of the comprehensive error of the measurement system; considering the change in the error allocation proportionality coefficient of the irradiation sensitivity of the key devices in the system, its influence on the comprehensive error of the measurement system is statistically analyzed, the deep convolutional neural network is trained, and based on the trained deep convolutional neural network, the irradiation error allocation proportionality coefficients of each key device in the proportional allocation method are determined, so that the irradiation sensitivity of the comprehensive error of the measurement system reaches the lowest.

[0054] More specifically, the semi-centralized link model is as Figure 5 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] where 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 function affecting the circuit characteristics; E is the irradiation 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, an 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 Figure 6 shown. Given the initial setting method (and determining the reasonable range of parameters), randomly initialize the parameter space. Through sensitivity analysis, with the aid of the proportional allocation principle, allocate the sensitivity / correlation degree to each error source. Combine the parameter range to obtain a new parameter space and calculate the comprehensive error; further, conduct error tracing and sensitivity analysis until the total sensitivity and error change reach the minimum.

[0063] More specifically, use the optimal irradiation sensitivity error allocation proportional coefficient of the key devices to perform hierarchical redundancy setting and irradiation hierarchical shielding setting on the devices of the measurement system. Among them, the key devices and their circuits are set modularly. Through multi-level redundancy settings such as device redundancy, circuit redundancy, and module redundancy, and cooperate and iteratively optimize with the irradiation shielding material, thickness, and volume, so that the sensitivity allocation ratio of each device in the system meets the optimal allocation result, and at the same time, the system volume and cost reach the lowest.

[0064] More specifically, in this embodiment, the key devices of the measurement system are respectively not protected, fully protected, and multi-level redundancy and multi-level shielding protection based on this embodiment. Among them, for the operational amplifier in the operational amplifier circuit, 2 copies of redundancy are performed for multi-level redundancy, and 3 copies of redundancy are performed for the operational amplifier circuit. The thickness of the shielding layer is reduced by 80% compared with full protection, and the shielding material is all isotactic polypropylene. The anti-irradiation performance under different protection conditions is compared as Figure 7 shown. The peak uncertainty of the measurement system with the optimal anti-irradiation setting is lower than 0.5% at 500 kGy, meeting the system setting requirements. At the same time, the system protection volume and cost are respectively reduced by 31% and 56% compared with full protection.

[0065] Embodiment 2

[0066] This application provides an anti-irradiation acquisition system for a high-voltage pulse measurement system, including:

[0067] A key device identification module, which is used to analyze the influence of the parameters of each device in the high-voltage pulse measurement system on the output characteristics of the high-voltage pulse measurement system under different irradiations, and identify key devices;

[0068] An optimal sensitivity error allocation module, which is used to input the irradiation sensitivity error allocation ratio function of key devices into a deep convolutional neural network, and take the minimum comprehensive error of the high-voltage pulse measurement system as the objective function to globally and cooperatively optimize the allocation of the irradiation sensitivity error allocation ratio coefficients of key devices, so as to obtain the optimal allocated irradiation sensitivity error allocation ratio coefficients of key devices;

[0069] An optimal sensitivity error optimization module, which is used to modularly and hierarchically redundantly set key devices with the goal that the actual irradiation sensitivity error allocation ratio coefficients of key devices are consistent with the optimally allocated irradiation sensitivity error allocation ratio coefficients of key devices, and at the same time perform irradiation hierarchical shielding according to the hierarchical redundant settings of key devices to obtain the optimal anti-irradiation settings of the high-voltage pulse measurement system.

[0070] Further preferably, the method for the key device identification module to obtain key devices is as follows: under different irradiations, adjust the device parameters of the high-voltage pulse measurement system, analyze the influence of each device parameter on the system output characteristics, and perform normalized comprehensive evaluation, and select the devices that have a significant influence on the output characteristics of the high-voltage pulse measurement system as key devices.

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

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

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

[0074] The second high-voltage pulse measurement system is under the irradiation of a strontium lamp and is used to obtain the actual output signals under different irradiations;

[0075] The strontium lamp is used to provide irradiation environments with different durations and intensities;

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

[0077] Further preferably, the optimal sensitivity error allocation module further includes a network training unit, which embeds a deep convolutional neural network. The training method of the deep convolutional neural network is: construct an error synthesis model through a semi-centralized link model, adopt a global quantitative analysis method based on the extended method of Fourier amplitude sensitivity test, analyze the relationship between the irradiation sensitivity error allocation ratio coefficient and the comprehensive error of the high-voltage pulse measurement system, use the irradiation sensitivity error allocation ratio coefficient as the input, and the comprehensive error of the high-voltage pulse measurement system as the output to train the deep convolutional neural network; among them, the comprehensive error of the high-voltage 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 settings of irradiation hierarchical shielding include irradiation shielding material, irradiation shielding thickness and irradiation shielding volume.

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

[0080] By determining key devices and only considering the radiation resistance sensitivity of key devices, the present application can significantly reduce the system setting and device radiation resistance test costs.

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

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

[0083] Those skilled in the art can easily understand 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 replacements and improvements made within the spirit and principle of the present application shall be included in the protection scope 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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