K-shell-based method and system for automatically identifying irradiation-sensitive signals of FPGA, and storage medium

Abstract

The present application relates to a kind of K-Shell-based FPGA irradiation sensitive signal automatic identification method, system and storage medium, overcome the limitation and inflexibility of prior art only a small amount, limited coverage irradiation sensitive signal is carried out circuit function monitoring, this method is with the aid of K-Shell network model, the top module network of FPGA engineering code file is constructed, all nodes of top module network are screened by calculating the node comprehensive degree or Ks value of node, to obtain key submodule, then according to K-Shell network model, establish key submodule network, with key submodule network related parameters as evaluation index, based on evaluation method, select the irradiation sensitive signal to be determined;Finally, based on specific screening method, determine the final irradiation sensitive signal from it.This method compared with the irradiation sensitive signal selected by human, wide coverage and algorithm identification speed is fast, efficiency is high, can be applicable to different FPGA engineering.

Description

Technical Field

[0001] This invention relates to a method for identifying radiation-sensitive signals in FPGAs, specifically to an automatic identification method, system, and storage medium for radiation-sensitive signals in FPGAs based on K-Shell. Background Technology

[0002] FPGA (Field-Programmable Gate Array) is a semiconductor device that can be reprogrammed to implement user-defined logic circuits. It is widely used in high-altitude equipment, including deep space exploration, satellite navigation, and missiles. With continuous advancements in science and technology and ever-improving technical specifications, the use of FPGAs can reduce the size, weight, and power consumption of spaceborne electronic equipment, improve the payload and efficiency ratio of spacecraft, and meet most of the miniaturization requirements of spacecraft. Currently, most aerospace devices use SRAM-based FPGAs, and FPGAs are increasingly being developed towards lower voltage and miniaturization, making them more susceptible to single-event events. Therefore, high-reliability design is essential to harden and protect FPGAs, maximizing the prevention, detection, and mitigation of signals that could affect FPGA devices due to space radiation.

[0003] When an FPGA is exposed to space radiation, it may experience brief malfunctions or even severe internal damage leading to loss of control. To address anomalies caused by single-event effects within FPGAs, researchers have proposed corresponding anomaly detection design methods, such as radiation-sensitive signal anomaly detection. Currently, there are three methods for setting radiation-sensitive signals for FPGAs: one is a status indicator signal during FPGA hardware circuit operation, such as the DONE signal; the second is a self-test signal added during engineering design, such as a data synchronization signal; and the third is a key functional signal of a typical structure, such as the error status signal in triple-modulus redundancy or the status_heartbeat signal in SEM IP. However, the number of these signals is limited, their coverage is not broad, and manually selecting signals is inefficient. Summary of the Invention

[0004] The purpose of this invention is to overcome the limitations and inflexibility of existing technologies that can only provide a small number of radiation-sensitive signals with limited coverage for circuit function monitoring. This invention provides an automatic identification method, system, and storage medium for FPGA radiation-sensitive signals based on K-Shell. This method can quickly, efficiently, and over a wide range search for multiple radiation-sensitive signals in known circuits, providing an automated identification method for finding radiation-sensitive signals.

[0005] The technical solution of this invention is to provide an automatic identification method for radiation-sensitive signals in FPGAs based on K-Shell, which is characterized by including the following steps:

[0006] Step 1: Construct the K-Shell network model;

[0007] Step 2: Locate the key sub-modules in the FPGA project code file;

[0008] Based on the K-Shell network model, a top-level module network is constructed in the FPGA project code file. Key sub-modules are found from the top-level module network and the internal connection relationship of the top-level module is saved.

[0009] Step 3: Identify radiation-sensitive signals from key sub-modules;

[0010] Step 3.1: Based on the K-Shell network model, construct and save the key sub-module network, and calculate the relevant parameters of the key sub-module network;

[0011] Step 3.2: Using the network-related parameters of the key sub-modules obtained in Step 3.1 as evaluation indicators, establish the evaluation matrix X:

[0012]

[0013] Among them, X mn Let represent the nth evaluation index of the mth node, where M ranges from {0≤M≤V}, V is the total number of nodes in the network, N ranges from {0≤N≤x′}, x′ is an integer greater than or equal to 1, and represents the number of evaluation indexes, m ranges from {0≤m≤M}, and n ranges from {0≤n≤N}.

[0014] Step 3.3: Based on the weights obtained by combining the gray relational degree and subjective weighting method with the TOPSIS method, evaluate the evaluation matrix to obtain the relative proximity of each node. Select the nodes with a relative proximity greater than the set threshold as the key nodes of the network, that is, obtain the radiation sensitive signal to be determined.

[0015] Step 3.4: Perform radiation sensitivity tests on each radiation-sensitive signal to be determined, obtain sensitivity test parameters, and calculate the node importance I of each key network node. i ;Analyze sensitivity test parameters and node importance I i The sensitivity test parameters and node importance I will be combined. i Signals exceeding a set threshold are used as radiation-sensitive signals.

[0016] Furthermore, the K-Shell network model constructed in step 1 is a traditional K-Shell network model; the parameters of the traditional K-Shell network model include nodes and the edges connecting the nodes;

[0017] Step 2 specifically involves: based on the K-Shell network model, constructing the top-level module network of the FPGA project code file, calculating the K-shell (Ks) value of all nodes in the top-level module network, taking the instantiated module corresponding to the node with the largest K-shell value (most connected edges) as the key sub-module, and saving the internal connection relationship of the top-level module.

[0018] In step 3.1, based on the traditional K-Shell network model, the key sub-module network is constructed and saved, and the Ks value of the relevant nodes in the key sub-module network is calculated;

[0019] In step 3.2, x′ equals 1 and N equals 1.

[0020] To further improve recognition accuracy, the K-Shell network model constructed in step 1 is a six-tuple K-Shell network model;

[0021] The parameters of the six-tuple K-Shell network model include V, E, KI, KT, C, and S; where V is the total number of network nodes, E is the number of edges in the two-step neighborhood of a node in the network, KI and KT are the in-degree and out-degree of a node in the network, respectively, C is the node comprehensive degree, and S is a strongly connected graph.

[0022] In step 3.1, based on the six-tuple K-Shell network model, the network of key sub-modules is constructed and saved, and the network-related parameters V, E, KI, KT, and C are calculated.

[0023] In step 3.2, x′ equals 5, and the range of N is {0≤N≤5};

[0024] Step 3.3 also includes the process of obtaining the strongly connected graph S of the key sub-module network, and nodes with a relative proximity greater than a set threshold and nodes located on or at a set distance from nodes on the strongly connected graph S are identified as key network nodes.

[0025] Furthermore, steps 2 and 3.1 calculate the node synthesis degree C based on the following formula:

[0026] C = KI + KT + (μI + μT) * D

[0027] Where μI and μT are the influence coefficients of each node, μI = KI / E, μT = KT / E, E is the number of edges in the two-step neighborhood of a node in the network; D is the number of secondary neighbor nodes, D = E - KI - KT.

[0028] Further, in step 2, the call relationships between all modules in the FPGA project code file are read to determine the top-level module.

[0029] Furthermore, in step 2, the internal connection relationships of the top-level module are saved in the following manner:

[0030] mo d {mo d i}: This variable represents the name of each instantiated module in the top-level module, where mo d Let i be the i-th instantiated module, i = 0, 1, 2, 3…m k m k This represents the total number of instantiated modules in the top-level module. When i = 0, it means that the top-level module does not contain any instantiated modules.

[0031] i_input{}: This variable represents the set of input port connections between the top-level module and the i-th instantiated module;

[0032] i_output{}: This variable represents the set of connection relationships between the output ports of the top-level module and the i-th instantiated module.

[0033] Furthermore, in step 3.1, the key submodule network is saved in the following form:

[0034] i_j input{input a,input b}: This variable represents the set of connections between the input ports of the i-th layer of the key submodule and the ports or variables of the j-th instantiated module in this layer;

[0035] i_j_output{output a,output b}: This variable represents the set of connections between the output port of the i-th layer of the key submodule and the port or variable of the j-th instantiated module in this layer;

[0036] i_j_wire{wire a,wire b}: This variable represents the set of connections between the wire net type variable of the i-th layer of the critical submodule and the port or variable of the j-th instantiated module in this layer.

[0037] Furthermore, step 3.4 specifically includes:

[0038] Step 3.41: Perform radiation sensitivity tests on each radiation-sensitive signal to be determined, and obtain sensitivity test parameters;

[0039] Step 3.42: Calculate the node importance I of each key network node. i :

[0040]

[0041] Where, k i Let V be the sum of the out-degree and in-degree of the i-th critical node in the network, where V is the total number of nodes and k is the number of nodes. i ≠0;

[0042] Step 3.43: Assign node importance Ii Normalize the sensitivity test parameter SN respectively;

[0043] Step 3.44: Plot a three-dimensional scatter plot with the network key nodes, normalized node importance, and sensitivity test parameters on the X, Y, and Z axes, respectively. Signals with sensitivity test parameters and node importance greater than the set threshold are used as irradiation sensitive signals.

[0044] Furthermore, the specific steps for constructing the top-level module network and the key sub-module network based on the K-Shell network model in steps 2 and 3 are as follows:

[0045] a. Retrieve the input ports, output ports, and input / output ports in the top-level module and key sub-modules, and their corresponding port names;

[0046] b. Using port name as a keyword, retrieve the location of the keyword in the corresponding module, analyze the logical function of the statement containing the port name, and construct the logical relationship between different port names and between port names and variables declared within the module.

[0047] c. Using the different port names or variables declared within the modules in step b as nodes in the network, and the logical relationships between the different port names or variables declared within the modules as directed edges between the corresponding nodes, the network is constructed.

[0048] The present invention also provides an automatic identification system for FPGA radiation-sensitive signals based on K-Shell, including a memory and a processor. The memory stores a computer program, which is characterized in that: when the computer program is run in the processor, it executes the steps of the above-mentioned automatic identification method for FPGA radiation-sensitive signals based on K-Shell.

[0049] The present invention also provides a computer storage medium storing a computer program, wherein the computer program, when run in a processor, executes the steps of the above-described method for automatic identification of FPGA radiation-sensitive signals based on K-Shell.

[0050] The beneficial effects of this invention are:

[0051] This method utilizes the K-Shell network model to construct the top-level module network of the FPGA project code file. Key sub-modules are identified by calculating the node synthesis degree or K-shell (Ks) value of all nodes in the top-level module network. Then, a key sub-module network is established based on the K-Shell network model. Using relevant parameters of the key sub-module network as evaluation indicators, and based on the evaluation method, radiation-sensitive signals are selected. Finally, based on a specific screening method, the final radiation-sensitive signals are determined. Compared with manually selected radiation-sensitive signals, this method has a wider coverage, faster algorithm identification speed, and higher efficiency, making it applicable to various FPGA projects. Attached Figure Description

[0052] Figure 1 Here is a flowchart of an FPGA-based automatic identification method for radiation-sensitive signals based on K-Shell, as an example.

[0053] Figure 2 This is a schematic diagram of the network construction of the adder IP in the embodiment;

[0054] Figure 3 This is a schematic diagram illustrating the calculation process of node integration degree in the embodiment;

[0055] Figure 4 This is a three-dimensional scatter plot obtained in the embodiment. Detailed Implementation

[0056] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0057] like Figure 1 As shown, this embodiment mainly includes the following steps:

[0058] Step 1: Construct the K-Shell network model;

[0059] The K-Shell network model here can be a traditional K-Shell network model, including two types of parameters: nodes and the edges connecting them. To improve recognition accuracy, this invention can also improve the traditional K-Shell network model. The improved K-Shell network model includes six types of parameters, so it is defined as a six-tuple K-Shell network model. The parameters include V, E, KI, KT, C, and S. Wherein, V is the total number of network nodes, E is the number of edges in the two-step neighborhood of a node in the network, KI and KT are the in-degree and out-degree of a node in the network, respectively, C is the node integration degree, and S is a strongly connected graph.

[0060] Step 2: Locate the key sub-modules in the FPGA project code file;

[0061] Read the call relationships of all modules in the FPGA project code file to determine the top-level module. Construct the top-level module network based on the K-Shell network model;

[0062] When using the traditional K-Shell network model, the specific steps are as follows: Based on the K-Shell network model, construct the top-level module network of the FPGA project code file, calculate the K-shell value of all nodes in the top-level module network; take the instantiated module corresponding to the node with the largest K-shell value (most connected edges) as the key sub-module, and save the internal connection relationship of the top-level module;

[0063] When using the six-tuple K-Shell network model, the specific steps are as follows: Based on the K-Shell network model, construct the top-level module network of the FPGA project code file, find the key sub-modules from the top-level module network, calculate the node synthesis degree of all nodes in the top-level module network, find the node with the highest node synthesis degree as the key sub-module, and save the internal connection relationship of the top-level module.

[0064] The process of constructing the top-level module network based on the K-Shell network model is as follows:

[0065] Declaring a module is considered as creating a network, and this embodiment also starts with modules.

[0066] a. Taking a module as an example, retrieve the input ports, output ports, and input / output ports in the top-level module, as well as the corresponding port names;

[0067] b. Using the retrieved port name as a keyword, search for the location of the keyword in the corresponding module, analyze the logical function of the statement containing the port name, and construct directed connection relationships between different port names and between port names and variables declared within the module based on statements and operators such as always, if...else, case, for, assign, <=, and =.

[0068] c. Using the different port names or variables declared within the module in step b as nodes in the network, and using the logical relationships between different port names or variables declared within the module as directed edges between nodes, the network is constructed.

[0069] In addition, attention should be paid to the network construction of encrypted source code modules such as IP and .dcp files. Taking the call of the adder IP as an example, the user only knows the port name of the adder IP and the port declaration given in the comments. In this embodiment, the instantiation name is add_ip, and the connection signal name of the adder IP is consistent with the corresponding port name. The adder IP has input port (CLK), input port (A), input port (B), and output port (S). The network of the adder is named G1, which can be abstracted as G1 = (V1, {E1}), where V1 = {CLK, A, B, add_ip, S}, E1 = {<CLK,add_ip> ,<A,add_ip> ,<B,add_ip> ,<add_ip,S>}, network G1 such Figure 2 As shown.

[0070] Combination Figure 3 It can be seen that the node synthesis degree is calculated through the following process:

[0071] Step b1: Calculate the Ks value of each node in the network, which is the number of edges between the node and its neighboring nodes;

[0072] Step b2: Calculate the degree KI(i), KT(i) of all nodes in the network and the number of edges E(i) in the two-step neighborhood of each node;

[0073] Step b3: Based on KI(i), KT(i) and E(i) obtained in step b2, obtain the influence coefficients μI(i) and μT(i) of each node, μI(i) = KI(i) / E(i) and μT(i) = KT(i) / E(i);

[0074] Step b4: Based on KI(i), KT(i) and E(i) obtained in step b2, obtain the number of secondary neighbor nodes D(i), that is, D(i) = E(i) - KI(i) - KT(i);

[0075] Step b5: Based on KI(i) and KT(i) obtained in step b2 and μI(i), μT(i) and D(i) obtained in steps b3 and b4, obtain the node synthesis degree C(i), that is, C(i) = KI(i) + KT(i) + (μI(i) + μT(i)) * D(i).

[0076] The internal connection relationships of the top-level module can be saved using the following method:

[0077] mod{mod i}: This variable represents the name of each instantiated module in the top-level module, where mod i is the i-th instantiated module, i = 0, 1, 2, 3…m k m k This represents the total number of instantiated modules in the top-level module. When i = 0, it means that the top-level module does not include any instantiated modules.

[0078] i_input{}: This variable represents the set of input port connections between the top-level module and the i-th instantiated module;

[0079] i_output{}: This variable represents the set of connection relationships between the output ports of the top-level module and the i-th instantiated module.

[0080] Step 3: Identify radiation-sensitive signals from key sub-modules;

[0081] Step 3.1: Based on the K-Shell network model, construct and save the key submodule network, and calculate the relevant parameters of the key submodule network; the specific construction process is the same as in Step 2. Save the key submodule network in the following format:

[0082] i_j input{input a,input b}: This variable represents the set of connections between the input ports of the i-th layer of the key submodule and the ports or variables of the j-th instantiated module in this layer; i_j_output{output a,output b}: This variable represents the set of connections between the output ports of the i-th layer of the key submodule and the ports or variables of the j-th instantiated module in this layer;

[0083] i_j_wire{wire a,wire b}: This variable represents the set of connections between the wire (wire type variable) of the i-th layer of the module and the j-th instantiated module port or variable in this layer.

[0084] Step 3.2: Using the network-related parameters of the key sub-modules obtained in Step 3.1 as evaluation indicators, establish the evaluation matrix X:

[0085]

[0086] Among them, X mn Let N represent the nth evaluation index of the m-th node in the network, where M ranges from {0≤M≤V}, V is the total number of nodes in the network, N ranges from {0≤N≤x′}, x′ is an integer greater than or equal to 2, and is the number of evaluation indices, m ranges from {0≤m≤M}, and n ranges from {0≤n≤N}. When using the six-tuple K-Shell network model, N ranges from {0≤N≤5}, and the evaluation indices used are V, E, KI, KT, and C. When using the traditional K-Shell network model, N ranges from {0≤N≤1}, and the evaluation indices used are the Ks values ​​of the relevant nodes in the network.

[0087] Step 3.3: Based on the TOPSIS method, evaluate the evaluation matrix to obtain the relative proximity. Select nodes with a relative proximity greater than a set threshold as key network nodes. Furthermore, when using the six-tuple K-Shell network model, it can be determined whether the key network node is located on the strongly connected graph S or has a set distance from the nodes on the strongly connected graph S, such as a distance of 2 steps. If so, such key network nodes are used as radiation-sensitive signals to be determined.

[0088] Step 3.4: Perform radiation sensitivity testing on the identified radiation-sensitive signal, obtain sensitivity test parameters, and analyze the sensitivity test parameters and node importance I. i The sensitivity test parameters and node importance I will be combined. i Signals exceeding a set threshold are used as radiation-sensitive signals.

[0089] Step 3.41: Perform radiation sensitivity testing on the radiation-sensitive signal to be determined, and obtain the sensitivity test parameters;

[0090] Step 3.42: Calculate the importance I of each node in the network. i :

[0091]

[0092] Where, k i Let V be the sum of the out-degree and in-degree of the i-th node in the network, and let V be the total number of nodes. For k... i Nodes with a value of 0 are not considered;

[0093] Step 3.43: Assign node importance I i The sensitivity test parameter SN of the radiation-sensitive signal to be determined is normalized respectively;

[0094] Step 3.44: Plot a three-dimensional scatter plot with the network key nodes, the normalized node importance IN, and the sensitivity test parameters on the X, Y, and Z axes, respectively. Figure 4 Signals whose sensitivity test parameters and node importance are greater than a set threshold are used as irradiation sensitive signals.

[0095] This embodiment also discloses an automatic identification system for FPGA radiation-sensitive signals based on K-Shell, including a memory and a processor. The memory stores a computer program, and when the computer program is run in the processor, it executes the steps of the above-described automatic identification method for FPGA radiation-sensitive signals based on K-Shell.

[0096] This embodiment also discloses a computer storage medium storing a computer program. When the computer program is run in a processor, it executes the steps of the above-described K-Shell-based FPGA radiation-sensitive signal automatic identification method. In some possible implementations, the present invention can also be implemented as a program product comprising program code. When the program product is run on a terminal device, the program code causes the terminal device to execute the steps described in the above-described method section of this specification according to various exemplary embodiments of the present invention. The program product for implementing the above method may be a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. The program product may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

Claims

1. An automatic identification method for radiation-sensitive signals in FPGAs based on K-Shell, characterized in that, Includes the following steps: Step 1: Construct the K-Shell network model; Step 2: Locate the key sub-modules in the FPGA project code file; Based on the K-Shell network model, a top-level module network of FPGA project code files is constructed, key sub-modules are found from the top-level module network, and the internal connection relationship of the top-level module is saved. Step 3: Identify radiation-sensitive signals from key sub-modules; Step 3.1: Based on the K-Shell network model, construct and save the key sub-module network, and calculate the relevant parameters of the key sub-module network; Step 3.2: Using the network-related parameters of the key sub-modules obtained in Step 3.1 as evaluation indicators, establish the evaluation matrix X: Among them, X mn Let represent the nth evaluation index of the mth node in the network, where M takes values ​​in the range {0≤M≤V}, V is the total number of nodes in the network, N takes values ​​in the range {0≤N≤x′}, x′ is an integer greater than or equal to 1, and represents the number of evaluation indexes, m takes values ​​in the range {0≤m≤M}, and n takes values ​​in the range {0≤n≤N}. Step 3.3: Based on the weights obtained by combining the gray relational degree and subjective weighting method with the TOPSIS method, evaluate the evaluation matrix to obtain the relative proximity of each node. Select the nodes with a relative proximity greater than the set threshold as key nodes of the network to obtain the radiation-sensitive signal to be determined. Step 3.4: Perform irradiation sensitivity tests on each irradiation-sensitive signal to be determined, and obtain sensitivity test parameters; calculate the node importance I of each key network node. i ;Analyze sensitivity test parameters and node importance I i The sensitivity test parameters and node importance I will be combined. i Signals exceeding a set threshold are used as radiation-sensitive signals.

2. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to claim 1, characterized in that: The K-Shell network model constructed in step 1 is a traditional K-Shell network model; the parameters of the traditional K-Shell network model include nodes and the edges connecting the nodes; Step 2 is as follows: Based on the traditional K-Shell network model, a top-level module network of the FPGA project code file is constructed, and the K-shell value of all nodes in the top-level module network is calculated. The instantiated module corresponding to the node with the largest K-shell value is taken as the key sub-module, and the internal connection relationship of the top-level module is saved. In step 3.1, based on the traditional K-Shell network model, the key sub-module network is constructed and saved, and the K-shell value of the relevant nodes in the key sub-module network is calculated; In step 3.2, x′ equals 1 and N equals 1.

3. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to claim 1, characterized in that: The K-Shell network model constructed in step 1 is a six-tuple K-Shell network model; the parameters of the six-tuple K-Shell network model include V, E, KI, KT, C, and S; where V is the total number of network nodes, E is the number of edges in the two-step neighborhood of a node in the network, KI and KT are the in-degree and out-degree of a node in the network, respectively, C is the node comprehensive degree, and S is a strongly connected graph. Step 2 specifically involves: constructing the top-level module network of the FPGA project code file based on the six-tuple K-Shell network model; finding key sub-modules in the top-level module network; calculating the node synthesis degree of all nodes in the top-level module network; taking the instantiated module corresponding to the node with the highest node synthesis degree as the key sub-module; and saving the internal connection relationship of the top-level module. In step 3.1, based on the six-tuple K-Shell network model, the key sub-module network is constructed and saved, and the relevant parameters V, E, KI, KT, and C of the key sub-module network are calculated. In step 3.2, x′ equals 5, and the range of N is {0≤N≤5}; Step 3.3 also includes the process of obtaining the strongly connected graph S of the key sub-module network, and nodes with a relative proximity greater than a set threshold and nodes located on or at a set distance from nodes on the strongly connected graph S are identified as key network nodes.

4. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to claim 3, characterized in that, Steps 2 and 3.1 are based on the following formula to calculate the node synthesis degree C: C = KI + KT + (μI + μT) * D Where μI and μT are the influence coefficients of each node, μI = KI / E, μT = KT / E, E is the number of edges in the two-step neighborhood of a node in the network; D is the number of secondary neighbor nodes, D = E - KI - KT.

5. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to any one of claims 1-4, characterized in that: In step 2, the top-level module is determined by reading the call relationship of all modules in the FPGA project code file.

6. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to claim 5, characterized in that, In step 2, the internal connection relationships of the top-level module are saved using the following method: mod{mod i}: This variable represents the name of each instantiated module in the top-level module, where mod i is the i-th instantiated module, i = 0, 1, 2, 3…m k m k This represents the total number of instantiated modules in the top-level module. When i = 0, it means that the top-level module does not include any instantiated modules. i_input{}: This variable represents the set of connection relationships between the input ports of the top-level module and the input ports of the i-th instantiated module; i_output{}: This variable represents the set of connection relationships between the output ports of the top-level module and the output ports of the i-th instantiated module.

7. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to claim 6, characterized in that, In step 3.1, the key submodule network is saved in the following form: i_j input{input a,input b}: This variable represents the set of connections between the input ports of the i-th layer of the key submodule and the ports or variables of the j-th instantiated module in this layer; i_j_output{output a,output b}: This variable represents the set of connections between the output port of the i-th layer of the key submodule and the port or variable of the j-th instantiated module in this layer; i_j_wire{wire a,wire b}: This variable represents the set of connections between the wire net type variable of the i-th layer of the critical submodule and the port or variable of the j-th instantiated module in this layer.

8. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to claim 7, characterized in that, Step 3.4 specifically involves: Step 3.41: Perform radiation sensitivity tests on each radiation-sensitive signal to be determined, and obtain the sensitivity test parameter SN; Step 3.42: Calculate the node importance I of each key network node. i : Where, k i Let k be the sum of the out-degree and in-degree of the i-th critical node in the network. i ≠0, V is the total number of nodes; It is the sum of the out-degree and in-degree of all critical nodes in the network; Step 3.43: Assign node importance I i Normalize the sensitivity test parameter SN separately; Step 3.44: Plot a three-dimensional scatter plot with the network key nodes, normalized node importance, and node sensitivity test parameters as the X, Y, and Z axes, respectively. Signals with sensitivity test parameters and node importance greater than the set threshold are used as irradiation sensitive signals.

9. The method for automatic identification of FPGA radiation-sensitive signals based on K-Shell according to claim 8, characterized in that, The specific steps for constructing the top-level module network and the key sub-module network based on the K-Shell network model in steps 2 and 3 are as follows: a. Retrieve the input ports, output ports, and input / output ports in the top-level module and key sub-modules, and their corresponding port names; b. Using port name as a keyword, retrieve the location of the keyword in the corresponding module, analyze the logical function of the statement containing the port name, and construct the logical relationship between different port names and between port names and variables declared within the module. c. Using the different port names or variables declared within the modules in step b as nodes in the network, and using the logical relationships between the different port names or variables declared within the modules as directed edges between the corresponding nodes, construct the network.

10. An automatic identification system for FPGA radiation-sensitive signals based on K-Shell, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the computer program is run in the processor, it performs the steps of the FPGA radiation-sensitive signal automatic identification method based on K-Shell as described in any one of claims 1-9.

11. A computer storage medium storing a computer program, characterized in that: When the computer program is run in the processor, it performs the steps of the FPGA radiation-sensitive signal automatic identification method based on K-Shell as described in any one of claims 1-9.

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