Method for generating mission reliability test samples for reconfigurable ship electronic information system

Abstract

A method for generating task reliability test samples for a reconfigurable ship electronic information system belongs to the technical field of ship electronic information system reliability testing. The present invention is to solve the problem of doubts about the credibility of the test results of task reliability assessment using existing digital simulation methods. It includes taking the failure time of each device as a test factor and determining the range of value parameters; calculating the coverage strength of different value parameters of the test factor based on the failure probability distribution function; then defining the test sample matrix form based on the number of test samples, the selected test factor dimension and the value parameter range of all test factors, and using the slime mold optimization algorithm to determine all value parameter combinations covering the selected test factors; combining the coverage strength of different value parameters of each test factor, so that all value parameter combinations are arranged in the test sample matrix from large to small according to the coverage strength, and the final test sample matrix is ​​determined to obtain reliability test samples. The present invention is used to generate reliability test samples.

Description

Technical Field

[0001] The invention relates to a method for generating task reliability test samples of a reconfigurable ship electronic information system, and belongs to the technical field of ship electronic information system reliability tests. Background Art

[0002] Ship electronic information systems typically refer to complex electronic information systems carried on surface vessels, capable of performing functions such as detection and perception, command and control, equipment engagement, and combat support. They are a vital component of modern ships and a key factor in determining their mission-performing capabilities. Traditional ship electronic information systems typically employ a functionally divided, two-tiered subsystem design approach: a front-end perception system, a mid-tier command and control system, and a back-end strike system. However, due to the internal hardware and software coupling within these subsystems, these systems are relatively closed, leading to low overall system utilization, insufficient flexibility, and insufficient scalability. Failures can paralyze the entire subsystem, severely hindering improvements in a ship's mission-performing capabilities. With the rapid advancement of electronics, computers, and network technologies in recent years, new reconfigurable ship electronic information systems, based on an open architecture, have been developed. These systems break down the two-tiered subsystems into their component units, enabling the sharing and integration of comprehensive information, providing diverse, high-quality services, and significantly enhancing a ship's mission-performing capabilities. This has become a key development direction for future ship electronic information systems.

[0003] New reconfigurable ship electronic information systems feature shared resources, separate software and hardware, and dynamic task reconfiguration. This makes it difficult to obtain reliable mission reliability indicators using traditional reliability assessment methods. Using digital simulation tests to conduct mission reliability assessments faces credibility issues, while conducting physical experiments is constrained by factors such as time, cost, and safety. Hardware-in-the-loop (HIL) testing allows for mission reliability assessments to be completed in a laboratory environment by designing and executing test samples. This addresses the drawbacks of both digital simulation and physical experiments, achieving a balance between the credibility of reliability assessment results and the balance of time, cost, and safety.

[0004] Currently, there is still a lack of effective test sample generation methods in the field of mission reliability assessment of reconfigurable ship electronic information systems. The test sample generation strategy with full factor coverage faces the problem of sample space explosion and is difficult to apply in actual engineering. Summary of the Invention

[0005] Aiming at the problem that the credibility of the existing mission reliability evaluation test results using digital simulation methods is questionable, the present invention provides a method for generating mission reliability test samples for a reconfigurable ship electronic information system.

[0006] A method for generating task reliability test samples for a reconfigurable ship electronic information system according to the present invention comprises:

[0007] The hardware equipment of the reconfigurable ship electronic information system is divided into three components: perception equipment, basic support equipment, and equipment equipment. The failure time of each device in each component is used as a test factor, and the value parameter range of each test factor is determined based on the system mission duration.

[0008] Calculate the coverage strength of different value parameters of each test factor based on the failure probability distribution function of each device;

[0009] Then, based on the number of test samples, the dimensions of the selected test factors, and the range of the value parameters of all test factors, the test sample matrix form is defined, and the slime mold optimization algorithm is used to determine all the value parameter combinations covering the selected test factors; at the same time, combined with the coverage strength of the different value parameters of each test factor, all the value parameter combinations are arranged in the test sample matrix from large to small according to the coverage strength, and the final test sample matrix is ​​determined, thereby obtaining the reliability test samples.

[0010] According to the method for generating test samples for mission reliability of reconfigurable ship electronic information system of the present invention, the test factor is expressed as x i , where i = 1, 2, 3, ..., I; I is the total number of experimental factors;

[0011] Set the system task duration period to T, divide it into N intervals according to the fixed time Δt, and get N intervals, N = T / Δt; take {1, 2, 3, ..., N} intervals as x i The value parameter range of x i =nΔt, n∈{1,2,3,…,N}.

[0012] According to the method for generating task reliability test samples of the reconfigurable ship electronic information system of the present invention, the coverage strength calculation method of different value parameters of each test factor is as follows:

[0013] Failure probability distribution function F of device i i (x i )for:

[0014]

[0015] Where λ i is the failure probability of device i;

[0016] The coverage intensity of different value parameters of each experimental factor is for:

[0017]

[0018] According to the method for generating task reliability test samples of reconfigurable ship electronic information system of the present invention, the test sample matrix is ​​defined as L(m, t, I N ), is a two-dimensional matrix of m×I; m is the number of matrix rows, indicating the number of test samples; t is the dimension of the selected test factor, indicating the dimension of the combination of the value parameters of the test samples; I N Represents I experimental factors, each of which has N value parameters.

[0019] According to the method for generating task reliability test samples of a reconfigurable ship electronic information system of the present invention, a method for determining all parameter combinations covering the selected test factors using a slime mold optimization algorithm is as follows:

[0020] Assume that the t selected experimental factors are where i1,i2,...,i t ∈[1,I], and they are not equal to each other; make L(m,t,I N )cover Corresponding value parameters

[0021] L(m,t,I N ) is represented by all the parameter combinations with different values ​​as Π L :

[0022]

[0023] Combine the calculation results of the coverage intensity of different parameters of each test factor to make L(m,t,I N ) The coverage strength of the value parameters in each row from top to bottom is arranged from large to small.

[0024] According to the method for generating task reliability test samples of reconfigurable ship electronic information system of the present invention, in the slime mold optimization algorithm, the fitness value of each slime mold individual is determined by the combination Π L The number of elements in the test is determined by the coverage strength of the parameters of each experimental factor. If the combination Π L If the number of elements in is greater than 0, the coverage intensity of the experimental factor value parameter is used as the adaptation value; if the combination Π L If the number of elements in is 0, the fitness value is 0;

[0025] For the test sample matrix L(m,t,I N ) selects a set number of slime mold individuals for the test samples in each row and performs iterative optimization based on the fitness value to obtain the final reliability test samples.

[0026] According to the method for generating mission reliability test samples of a reconfigurable ship electronic information system of the present invention, the value of Δt is T / 10 or T / 100.

[0027] According to the method for generating task reliability test samples of a reconfigurable ship electronic information system of the present invention, the sensing devices include radio frequency integrated devices, optical frequency devices, radars and sonars;

[0028] Basic support equipment includes public computing service equipment, public display and control equipment, access integrated network equipment and core integrated network equipment;

[0029] Equipment includes long-range air defense equipment, short-range air defense equipment, electronic jamming equipment and underwater equipment.

[0030] According to the method for generating test samples for mission reliability of reconfigurable ship electronic information systems of the present invention, the test factors include the time when radio frequency integrated equipment fails, the time when optical frequency equipment fails, the time when radar fails, the time when sonar fails, the time when public computing service equipment fails, the time when public display and control equipment fails, the time when access integrated network equipment fails, the time when core integrated network equipment fails, the time when long-range air defense equipment fails, the time when short-range air defense equipment fails, the time when electronic jamming equipment fails, and the time when underwater equipment fails; I=12.

[0031] Beneficial effects of the present invention: The method of the present invention uses the failure occurrence time of various components in the reconfigurable ship electronic information system as the test factor, discretizes the failure occurrence time to obtain the value parameters of the test factor, calculates the coverage strength of each value parameter according to the failure probability distribution function, and finally generates task reliability test samples by combining the coverage ideas to cover the different strengths of different value parameters of each test factor.

[0032] The method of the present invention can provide a test sample generation strategy for task reliability assessment based on semi-physical simulation tests, thereby solving the problems of doubtful credibility of digital simulation test results, long time, high cost and poor safety of physical tests. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the composition structure of the reconfigurable ship electronic information system of the present invention;

[0034] Figure 2 It is a schematic diagram of the hardware structure of the reconfigurable ship electronic information system of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0037] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0038] Specific implementation method 1. Combination Figure 1 and Figure 2 As shown, the present invention provides a method for generating task reliability test samples of a reconfigurable ship electronic information system, comprising:

[0039] The hardware equipment of the reconfigurable ship electronic information system is divided into three components: perception equipment, basic support equipment, and equipment equipment. The failure time of each device in each component is used as a test factor, and the value parameter range of each test factor is determined based on the system mission duration.

[0040] Calculate the coverage strength of different value parameters of each test factor based on the failure probability distribution function of each device;

[0041] Then, based on the number of test samples, the dimensions of the selected test factors, and the range of the value parameters of all test factors, the test sample matrix form is defined, and the slime mold optimization algorithm is used to determine all the value parameter combinations covering the selected test factors; at the same time, combined with the coverage strength of the different value parameters of each test factor, all the value parameter combinations are arranged in the test sample matrix from large to small according to the coverage strength, and the final test sample matrix is ​​determined, thereby obtaining the reliability test samples.

[0042] In this embodiment, the secondary subsystem structure of the reconfigurable ship electronic information system is divided into hardware units and software units. The software units include command and control software, such as Figure 1 shown.

[0043] Furthermore, the experimental factor is represented as x i , where i = 1, 2, 3, ..., I; I is the total number of experimental factors;

[0044] Set the system task duration period to T, divide it into N intervals according to the fixed time Δt, and get N intervals, N = T / Δt; take {1, 2, 3, ..., N} intervals as x i The value parameter range of x i =nΔt, n∈{1,2,3,…,N}.

[0045] In this embodiment, the coverage intensity calculation method for different value parameters of each test factor is:

[0046] Most hardware units in the reconfigurable ship electronic information system are electronic devices. When a failure occurs, the failure probability density function generally obeys an exponential distribution. The failure probability density f of device i is i (x i ) is expressed as:

[0047]

[0048] Failure probability distribution function F of device i i (x i )for:

[0049]

[0050] Where λ i is the failure probability of device i;

[0051] The coverage intensity of different value parameters of each experimental factor is for:

[0052]

[0053] As shown in Table 2, there are 12 test factors for the mission reliability of the reconfigurable ship electronic information system, which are denoted as x1, x2, ..., x 12 , the value parameters of each experimental factor are {1,2,3,...,N}, the experimental factor x i The coverage strength of the parameter n∈{1,2,3,...,N} is p xi (n). Define the experimental sample matrix as L(m,t,I N ), is a two-dimensional matrix of m×I; where m is the number of matrix rows, indicating the number of test samples, I columns correspond to I test factors, and the intersection of each row and column places a value of the test factor; t is the dimension of the selected test factor, indicating the coverage dimension of the value parameter combination of the test sample, that is, any t test factors satisfy the N×N full factor coverage; I N Represents I experimental factors, each of which has N value parameters.

[0054] This embodiment proposes a combination coverage generation method for reliability test samples of reconfigurable ship electronic information systems based on the slime mold optimization algorithm. The slime mold optimization algorithm is inspired by the foraging behavior of slime molds. The algorithm simulates the various behaviors and morphological changes of slime molds in the process of searching for food (searching for targets). During the foraging process, slime molds will approach the food source through the smell in the air (optimization process), forming a venous network of varying thicknesses. The thickness of the venous network is related to the quality of the food source. When the slime mold approaches the food, oscillation and contraction will occur. This behavior is used to simulate the search and update process in the optimization algorithm. The slime mold optimization algorithm has fast convergence speed, strong optimization ability, and global and local search capabilities. In view of this, this embodiment uses the slime mold optimization algorithm to generate test samples L(m, t, 12 N The position of each slime mold represents a solution (a row in the test sample), and the solution with the largest fitness value represents the location of the food. All solutions are found by using the process of slime molds searching for food.

[0055] Furthermore, the method of using the slime mold optimization algorithm to determine all parameter combinations covering the selected test factors is as follows:

[0056] Assume that the t selected experimental factors are where i1,i2,...,i t ∈[1,I], and are not equal to each other; Any set of value parameters It must be possible to N ) is found in a row, namely L(m,t,12 N ) Override value parameters Therefore, L(m,t,12 N ) must satisfy the condition that all possible parameter combinations of any t experimental factors are covered, L(m,t,I N ) is represented by all the parameter combinations with different values ​​as Π L :

[0057]

[0058] Combine the calculation results of the coverage intensity of different parameters of each test factor to make L(m,t,I N ) The coverage strength of the value parameters in each row from top to bottom is arranged from large to small.

[0059] In this embodiment, L(m,t,12 N ) covers all the parameter combinations, generating L(m,t,12 N ) is to find Π L The process of all elements in L(m,t,12 N ) In addition to covering Π LIn addition to all the elements in the , it is also necessary to generate the coverage strength of the value parameters of each test factor from large to small to ensure that L(m,t,12 N ) The coverage strength of the value parameters in the upper row is the greatest.

[0060] In the slime mold optimization algorithm, for each individual, coverage Π L The number of elements in and the coverage strength of each experimental factor parameter will determine the size of the fitness value, that is, the fitness value of each slime mold individual is determined by the combination Π L The number of elements in the matrix is ​​determined by the coverage strength of each test factor value parameter. In order to ensure the priority coverage of the test factors, if the combination Π L If the number of elements in is greater than 0, the coverage intensity of the experimental factor value parameter is used as the adaptation value; if the combination Π L If the number of elements in is 0, the fitness value is 0;

[0061] For the test sample matrix L(m,t,I N ) selects a set number of slime mold individuals for the test samples in each row and performs iterative optimization based on the fitness value to obtain the final reliability test samples.

[0062] As an example, Δt takes a value of T / 10 or T / 100.

[0063] The perception equipment includes radio frequency integrated equipment, optical frequency equipment, radar and sonar, etc.; different from the front-end perception system in the secondary subsystem of the traditional ship electronic information system, the reconfigurable ship electronic information system separates most of the information processing functions of the front-end perception system, such as point generation and target recognition, into the command and control software, which relies on the public resource hardware in the basic support equipment to complete information processing.

[0064] Basic support equipment includes public computing service equipment, public display and control equipment, access integrated network equipment and core integrated network equipment; among them, integrated network equipment, public computing service equipment and public display and control equipment are public resource hardware of reconfigurable ship electronic information systems, which mainly provide network, computing, display and control and other resource support for the operation of command and control software. Command and control software is dynamically deployed on public resource hardware to realize software functions.

[0065] Command and control software includes point trace generation software, target recognition software, track generation software, situation display software, etc. The completion of ship electronic information system tasks depends on the normal operation of command and control software functions, and the normal operation of command and control software functions relies on the working performance and equipment integrity of the common resource hardware in the basic support equipment.

[0066] Equipment includes multi-channel equipment such as long-range air defense equipment, short-range air defense equipment, electronic jamming equipment, and underwater equipment. Unlike the back-end equipment system within the secondary subsystem of traditional ship electronic information systems, the reconfigurable ship electronic information system separates most of the information processing functions of the back-end equipment system, such as equipment allocation and target command and control, into command and control software, relying on the shared resource hardware of the basic support equipment to complete information processing.

[0067] Integrated network equipment, public computing service equipment, and public display and control equipment are the public resource hardware of the ship's electronic information system. They primarily provide network, computing, display, and control resources to support the operation of the ship's electronic information system command and control software. All public resource hardware of the ship's electronic information system has redundant backups.

[0068] The common resource hardware and its numbering of the reconfigurable ship electronic information system are shown in Table 1.

[0069] Table 1 Common resource hardware and serial numbers of ship electronic information systems

[0070] Public resource hardware Main device number Backup device number Public computing service equipment 1、2、3、4 5、6、7、8 Public display and control equipment 1 2 Access integrated network equipment 1、2 3、4 Core integrated network equipment 1 2

[0071] Figure 2 The figure shows the hardware structure of the reconfigurable ship electronic information system. The redundant backup mechanism of the public resource hardware is the key to enable the reconfigurable ship electronic information system to complete its mission after a fatal failure occurs. Figure 2 In the figure, the solid-line box represents the primary device, and the dashed-line box represents the backup device. Under normal circumstances, when a ship's electronic information system is performing a mission, only the primary device on the common resource hardware is operational, while the backup device is inoperative. The command and control software deployed on the primary device of the common resource hardware performs its functions. However, if the primary device fails, the hardware failure will affect the normal operation of the command and control software deployed on it, thereby disrupting the entire system's mission execution capability. At this time, the reconfigurable ship's electronic information system will perform redundant reconstruction. Through redundant switching, the backup device of the common resource hardware will replace the original device to complete the work, allowing the command and control software to resume normal operation and ensure the normal execution of the mission.

[0072] Because shipboard electronic information systems typically undergo rigorous software testing before delivery, the command and control software itself is generally considered to be free of defects. However, since the public resource hardware serves as the resource support for the software and the software is deployed on it, when the public resource hardware fails, the failure will propagate to the command and control software deployed on it. As a result, the command and control software will change from a normal working state to a faulty state due to this propagation of the failure, affecting the processing and transmission of information in the command and control software, and thus the normal execution of the shipboard electronic information system mission.

[0073] The above analysis shows that the factors affecting the mission reliability of reconfigurable ship electronic information systems include hardware failures and software failures. Hardware failures are spontaneous failures whose probability is related to the operating time and are considered independent variables. Software failures, on the other hand, are caused by failures in the deployed hardware and are considered dependent variables. Therefore, the hardware failure occurrence time is selected as the test factor for the mission reliability of reconfigurable ship electronic information systems.

[0074] The test factors include the failure time of radio frequency integrated equipment, optical frequency equipment, radar, sonar, public computing service equipment, public display and control equipment, access integrated network equipment, core integrated network equipment, long-range air defense equipment, short-range air defense equipment, electronic jamming equipment, and underwater equipment; I = 12. The details are shown in Table 2:

[0075] Table 2 Mission reliability test factors and their value parameters for reconfigurable ship electronic information system

[0076] <![CDATA[Test factor x i > Value parameter n RF integrated device failure occurrence time 1,2,3,…,N Optical frequency equipment failure time 1,2,3,…,N Radar failure time 1,2,3,…,N Sonar failure time 1,2,3,…,N Time when the failure of public computing service equipment 1-8 occurred 1,2,3,…,N Time when the fault of public display and control equipment 1-2 occurred 1,2,3,…,N Time when the fault of access integrated network device 1-4 occurred 1,2,3,…,N Time when the core integrated network device 1-2 failure occurred 1,2,3,…,N Time when the air defense long-range equipment failure occurs 1,2,3,…,N Time when short-range air defense equipment failure occurs 1,2,3,…,N Time when electronic jamming equipment failure occurs 1,2,3,…,N Time when underwater equipment failure occurs 1,2,3,…,N

[0077] Going further, the method to determine the final test sample matrix is:

[0078] Step 1: Initialize the combination π L , set the test sample matrix L(m,t,I N ) The initial number of slime mold individuals corresponding to each row of test samples is M=50; the positions of 50 slime mold individuals are initialized Y d ,d=1,2,...,M, the position of the slime mold individual Y d is a vector of length 12, Y d The value of each element in is randomly selected from Select; initialize the number of iterations K = 500 and the current number of iterations k = 1;

[0079] Step 2: If k≤K, go to step 3; otherwise go to step 7;

[0080] Step 3: Check Y d Whether each element in exceeds the value parameter range of the experimental factor; if it exceeds, randomly select a value in the value parameter range {1,2,3,…,N} to replace the corresponding element, so that Y d All elements in are within the parameter range of the experimental factors;

[0081] Step 4: Calculate Y d The fitness value of , and sort them from large to small according to the fitness value, and get the sorted Y d ;

[0082] Step 5: Generate a random number Rand in [0,1], and sort the Y according to the size of the random number Rand. d To update:

[0083] Y d (k+1)=Randγ(N-1)+1, Rand<z,

[0084]

[0085] Where Y b (k) represents the position of the slime mold individual with the best current fitness value, Y A (k) and Y B (k) represents the positions of two random slime mold individuals, A, B = 1, 2, ..., M; v b It oscillates within [-a, a] and eventually tends to 0, W represents the weight coefficient of the slime mold individual, r represents a random number between [0, 1], p represents the control parameter, v c is a random number, v c ∈[-1,1], z=0.06;

[0086]

[0087] p=tanh|S(d)-DF|,

[0088] Where S(d) represents the fitness value of the d-th slime mold individual, and DF represents the optimal fitness value obtained in the completed k iterations;

[0089]

[0090] Where SmellIndex(d) represents the sorted Y d , r1 represents a random number between [0,1], represents the slime mold individuals in the top half of S(d), bF represents the best fitness value obtained in the current k-th iteration, and ωF represents the worst fitness value obtained in the current k-th iteration;

[0091] Step 6: Set k=k+1 and return to step 2;

[0092] Step 7: Output the position of the slime mold individual with the largest fitness value to L(m,t,12 N ), the value parameter combination of the t experimental factors covered is changed from Π L Remove;

[0093] Step 8: If π L=Φ, the iteration ends; then, combined with the coverage strength of different value parameters of each test factor, all value parameter combinations are arranged in descending order according to the coverage strength in the test sample matrix, and the current L(m, t, 12 N ) as the final test sample matrix; otherwise, set k = 1 and return to step 2 to continue iterating until the end.

[0094] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in conjunction with other described embodiments.

Claims

1. A method for generating task reliability test samples for a reconfigurable ship electronic information system, characterized in that: include: The hardware equipment of the reconfigurable ship electronic information system is divided into three components, including perception equipment, basic support equipment and equipment equipment; The failure occurrence time of each device in each component unit is used as the test factor, and the value parameter range of each test factor is determined based on the system task duration cycle; Calculate the coverage strength of different value parameters of each test factor based on the failure probability distribution function of each device; Then, based on the number of test samples, the dimensions of the selected test factors, and the range of the value parameters of all test factors, the test sample matrix is ​​defined. The slime mold optimization algorithm is used to determine all the value parameter combinations that cover the selected test factors. At the same time, combined with the coverage strength of the different value parameters of each test factor, all the value parameter combinations are arranged in descending order of coverage strength in the test sample matrix to determine the final test sample matrix, thereby obtaining the reliability test samples. Denote the experimental factor as x i , where i = 1, 2, 3, ..., I; I is the total number of experimental factors; Set the system task duration period to T, divide it into N intervals according to the fixed time Δt, and get N intervals, N = T / Δt; take {1, 2, 3, ..., N} intervals as x i The value parameter range of x i =nΔt, n∈{1,2,3,…,N}; The calculation method of coverage intensity of different value parameters of each test factor is: Failure probability distribution function F of device i i (x i )for: Where λ i is the failure probability of device i; The coverage intensity of different value parameters of each experimental factor is for:

2. The method for generating mission reliability test samples of a reconfigurable ship electronic information system according to claim 1, characterized in that: Define the experimental sample matrix as L(m,t,I N ), is a two-dimensional matrix of m×I; m is the number of matrix rows, indicating the number of test samples; t is the dimension of the selected test factor, indicating the dimension of the combination of the value parameters of the test samples; I N Represents I experimental factors, each of which has N value parameters.

3. The method for generating mission reliability test samples for a reconfigurable ship electronic information system according to claim 2, characterized in that: The method for determining all parameter combinations covering the selected test factors using the slime mold optimization algorithm is as follows: Assume that the t selected experimental factors are where i1,i2,...,i t ∈[1,I], and they are not equal to each other; make L(m,t,I N )cover Corresponding value parameters L(m,t,I N ) is represented by all the parameter combinations with different values ​​as Π L : Combine the calculation results of the coverage intensity of different parameters of each test factor to make L(m,t,I N ) The coverage strength of the value parameters in each row from top to bottom is arranged from large to small.

4. The method for generating mission reliability test samples for a reconfigurable ship electronic information system according to claim 3, characterized in that: In the slime mold optimization algorithm, the fitness value of each slime mold individual is determined by the combination Π L The number of elements in the test is determined by the coverage strength of the parameters of each experimental factor. If the combination Π L If the number of elements in is greater than 0, the coverage intensity of the experimental factor value parameter is used as the adaptation value; if the combination Π L If the number of elements in is 0, the fitness value is 0; For the test sample matrix L(m,t,I N ) selects a set number of slime mold individuals for the test samples in each row and performs iterative optimization based on the fitness value to obtain the final reliability test samples.

5. The method for generating mission reliability test samples for a reconfigurable ship electronic information system according to claim 4, characterized in that: The value of Δt is T / 10 or T / 100.

6. The method for generating mission reliability test samples for a reconfigurable ship electronic information system according to claim 5, characterized in that: The sensing devices include radio frequency integrated devices, optical frequency devices, radars and sonars; Basic support equipment includes public computing service equipment, public display and control equipment, access integrated network equipment and core integrated network equipment; Equipment includes long-range air defense equipment, short-range air defense equipment, electronic jamming equipment and underwater equipment.

7. The method for generating mission reliability test samples for a reconfigurable ship electronic information system according to claim 6, characterized in that: The test factors include the failure time of radio frequency integrated equipment, optical frequency equipment, radar, sonar, public computing service equipment, public display and control equipment, access integrated network equipment, core integrated network equipment, long-range air defense equipment, short-range air defense equipment, electronic jamming equipment, and underwater equipment. I＝12。 8. The method for generating mission reliability test samples for a reconfigurable ship electronic information system according to claim 7, characterized in that: The method to determine the final test sample matrix is: Step 1: Initialize the combination π L , set the test sample matrix L(m,t,I N ) The initial number of slime mold individuals corresponding to each row of test samples is M=50; the positions of 50 slime mold individuals are initialized Y d ,d=1,2,...,M, the position of the slime mold individual Y d is a vector of length 12, Y d The value of each element in is randomly selected from Select; initialize the number of iterations K = 500 and the current number of iterations k = 1; Step 2: If k≤K, go to step 3; Otherwise, go to step 7; Step 3: Check Y d Whether each element in exceeds the value parameter range of the experimental factor; if it exceeds, randomly select a value in the value parameter range {1,2,3,…,N} to replace the corresponding element, so that Y d All elements in are within the parameter range of the experimental factors; Step 4: Calculate Y d The fitness value of , and sort them from large to small according to the fitness value, and get the sorted Y d ; Step 5: Generate a random number Rand in [0,1], and sort the Y according to the size of the random number Rand. d To update: Y d (k+1)=Randγ(N-1)+1,Rand<z, Where Y b (k) represents the position of the slime mold individual with the best current fitness value, Y A (k) and Y B (k) represents the positions of two random slime mold individuals, A, B = 1, 2, ..., M; v b It oscillates within [-a, a] and eventually tends to 0, W represents the weight coefficient of the slime mold individual, r represents a random number between [0, 1], p represents the control parameter, v c is a random number, v c ∈[-1,1], z=0.06; p=tanh|S(d)-DF|, Where S(d) represents the fitness value of the d-th slime mold individual, and DF represents the optimal fitness value obtained in the completed k iterations; Where SmellIndex(d) represents the sorted Y d , r1 represents a random number between [0,1], bF represents the best fitness value obtained in the current k-th iteration, ωF represents the worst fitness value obtained in the current k-th iteration; Step 6: Set k=k+1 and return to step 2; Step 7: Output the position of the slime mold individual with the largest fitness value to L(m,t,12 N ), the value parameter combination of the t experimental factors covered is changed from Π L Remove; Step 8: If π L =Φ, the iteration ends; then, combined with the coverage strength of different value parameters of each test factor, all value parameter combinations are arranged in descending order according to the coverage strength in the test sample matrix, and the current L(m, t, 12 N ) as the final test sample matrix; otherwise, set k = 1 and return to step 2 to continue iterating until the end.

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