Method and system for estimating efficiency improvement ratio of warhead

Abstract

The invention discloses a warhead efficiency improvement ratio estimation method and system, and relates to the technical field of warhead efficiency evaluation. The objective of the invention is to solve the problem of low evaluation accuracy and efficiency of the existing warhead efficiency evaluation method. The method comprises the steps of obtaining m evaluation index values of n types of warheads; normalizing the same evaluation index value of each type of warhead in the n types of warheads to obtain m groups of normalized evaluation index values; obtaining the information entropy of the normalized evaluation index value, and obtaining an information entropy vector; obtaining the weight of each evaluation index by using the information entropy vector; obtaining a comprehensive score of each type of warhead by using the weight of each evaluation index; therefore, the warhead efficiency improvement ratio is obtained; and the warhead lifting ratio is compared with a preset lifting threshold value epsilon, and when the warhead lifting ratio is larger than or equal to the preset lifting threshold value, the warhead of the current model is stored. The method is used for obtaining the efficiency improvement ratio of the warhead.

Description

Technical Field

[0001] The present invention relates to the technical field of warhead effectiveness evaluation, and in particular to a method and system for estimating a warhead effectiveness improvement ratio. Background Art

[0002] The warhead is the core component of the weapon system, and its effectiveness directly affects the combat effectiveness of the weapon system. Therefore, the design of the warhead scheme and the evaluation of the upgrade of the current equipment are important contents of the development and improvement of the weapon system. In order to effectively carry out the design of the warhead scheme and the evaluation of the upgrade of the current equipment, it is necessary to conduct a comprehensive evaluation of different warhead schemes or current equipment in multiple indicators to determine their effectiveness. The warhead effectiveness improvement ratio refers to the percentage of comprehensive performance improvement of the new warhead relative to the old warhead in multiple evaluation indicators. It can intuitively reflect the advantages and disadvantages of the warhead scheme or equipment and the room for improvement.

[0003] At present, there are mainly the following methods for multi-index comprehensive evaluation of warhead effectiveness:

[0004] Analytical Hierarchy Process: By constructing a hierarchical model, the evaluation problem is decomposed into the target layer, the criterion layer and the scheme layer, and then the weights between the layers are determined by expert scoring, and finally the priority of the scheme is obtained through consistency test. This method can handle qualitative and quantitative indicators, but it requires a large number of expert scoring when conducting a comprehensive evaluation of multiple indicators, which has great subjectivity and uncertainty, and is prone to low evaluation accuracy, which in turn makes it impossible to accurately determine the advantages and disadvantages of different warhead schemes or equipment.

[0005] Fuzzy comprehensive evaluation method: By establishing a fuzzy evaluation matrix and using fuzzy mathematics theory to perform weighted summation of each indicator, a comprehensive evaluation value of the scheme is obtained. This method can handle uncertainty and fuzzy information, but the fuzzy membership function and weight coefficient need to be manually determined during the evaluation, which is subjective and can easily lead to low evaluation accuracy, which in turn makes it impossible to accurately determine the advantages and disadvantages of different warhead schemes or equipment.

[0006] Grey correlation analysis method: By calculating the grey correlation between each scheme and the reference sequence (optimal or worst scheme), the similarity between the scheme and the reference sequence is reflected, thereby determining the order of merit of the warhead optimization scheme. This method can handle incomplete information and small sample data, but it requires manual determination of the reference sequence and correlation coefficient, which is subjective and can easily lead to low evaluation accuracy, which in turn makes it impossible to accurately determine the merits of different warhead schemes or equipment.

[0007] Finite element analysis method: By establishing a finite element model of the warhead structure and materials, numerical calculation methods are used to simulate the distribution and changes of physical quantities such as stress, strain, and temperature of the warhead under different working conditions. This method can provide relatively accurate and detailed warhead effectiveness data, but the evaluation process requires a lot of computing resources and time, which is not conducive to rapid feedback and decision-making, resulting in low efficiency in warhead effectiveness evaluation, and thus making it impossible to quickly determine the advantages and disadvantages of different warhead plans or equipment.

[0008] Numerical simulation method: By establishing mathematical models of physical processes such as warhead explosion, killing, and armor penetration, numerical calculation methods are used to simulate the effects of warheads on different targets. This method can provide relatively comprehensive and intuitive warhead effectiveness data, but the evaluation process also requires a lot of computing resources and time, which is not conducive to rapid feedback and decision-making, resulting in low efficiency in warhead effectiveness evaluation, and thus making it impossible to quickly determine the advantages and disadvantages of different warhead plans or equipment. Summary of the invention

[0009] The purpose of the present invention is to solve the problems of low evaluation accuracy and efficiency in existing warhead effectiveness evaluation methods, which in turn leads to the problem of being unable to quickly and accurately determine the advantages and disadvantages of different warhead schemes or equipment, and proposes a warhead effectiveness improvement ratio estimation method and system.

[0010] A method for estimating the warhead effectiveness improvement ratio has the following specific steps:

[0011] Step 1: Obtain n types of warheads, and obtain m evaluation index values ​​for each type of warhead;

[0012] The evaluation index values ​​include: TNT equivalent of the warhead, the maximum penetration distance of the fragment target, the density of fragments in the dispersion area, the fragment dispersion angle, the shock wave overpressure, the penetration depth, the explosion depth, the static armor penetration depth, and the intensive killing radius;

[0013] Step 2, normalizing the evaluation index values ​​corresponding to the same evaluation index of each type of warhead in the n types of warheads obtained in step 1, respectively, to obtain m groups of normalized evaluation index values;

[0014] Step 3: Obtain the information entropy of each group of normalized evaluation index values, and use all the information entropies to form a row vector to obtain an information entropy vector;

[0015] Step 4: Use the information entropy vector obtained in step 3 to obtain the weight of each evaluation index;

[0016] Step 5: Using the weight of each evaluation index and the evaluation index value of the warhead obtained in step 4, obtain the comprehensive score of each type of warhead;

[0017] Step 6: Using the comprehensive score of each type of combat obtained in step 5, obtain the warhead effectiveness improvement ratio;

[0018] Step 7: Compare the warhead enhancement ratio obtained in step 6 with the preset enhancement threshold ε. If the warhead enhancement ratio is less than the preset enhancement threshold ε, adjust the combat parameters and then return to step 1; if the warhead enhancement ratio is greater than or equal to the preset enhancement threshold ε, save the current model warhead.

[0019] Furthermore, in the step 2, the evaluation index values ​​corresponding to the same evaluation index of each type of warhead in the n types of warheads obtained in the step 1 are normalized respectively, as follows:

[0020]

[0021] Among them, norm_data ij is the normalized value of the jth evaluation index value of the i-th type warhead, data ij is the j-th evaluation index value of the i-th type of warhead, i∈[1,n], and n is the total number of combat models.

[0022] Furthermore, the information entropy of each group of normalized evaluation index values ​​obtained in step 3 is as follows:

[0023]

[0024] Among them, ent j is the information entropy of the normalized evaluation index value of the jth group, j∈[1,m], and m is the total number of evaluation indicators.

[0025] Furthermore, the information entropy vector obtained in step 3 is used in step 4 to obtain the weight of each evaluation index, as shown in the following formula:

[0026]

[0027] Among them, weights j is the weight of the jth evaluation indicator.

[0028] Furthermore, the comprehensive score of each type of warhead in step 5 is as follows:

[0029]

[0030] Among them, composite_score i It is the comprehensive score of the i-th type of warhead.

[0031] Furthermore, the warhead effectiveness improvement ratio in step 6 is as follows:

[0032]

[0033] Among them, a, b∈[1,n], a is the label of the old model warhead, b is the label of the new model warhead, efficiency_improvement_ratio ab It is the improvement ratio of type B warhead relative to type A warhead.

[0034] Further, a warhead effectiveness improvement ratio estimation system is used to implement a warhead effectiveness improvement ratio estimation method, characterized in that: the system comprises: an evaluation index data table construction module, an evaluation index normalization module, an information entropy vector acquisition module, an evaluation index weight acquisition module, a warhead comprehensive score acquisition module, and an effectiveness improvement ratio acquisition module;

[0035] The evaluation index data table construction module is used to obtain n types of warheads and obtain m evaluation index values ​​for each type of warhead, use the evaluation index values ​​as columns and the warhead models as rows to construct an evaluation index data table, and send the evaluation index data table to the evaluation index normalization module;

[0036] The evaluation index values ​​include: TNT equivalent of the warhead, the maximum penetration distance of the fragment target, the density of fragments in the dispersion area, the fragment dispersion angle, the shock wave overpressure, the penetration depth, the explosion depth, the static armor penetration depth, and the intensive killing radius;

[0037] The evaluation index normalization module is used to normalize each column of evaluation index in the evaluation index data table, obtain the normalized evaluation index value, thereby forming a normalized evaluation index data table, and send the normalized evaluation index data table to the information entropy vector acquisition module;

[0038] The normalized evaluation index value is obtained by the following formula:

[0039]

[0040] Among them, norm_data ij is the normalized value of the jth evaluation index value of the i-th type warhead, data ij is the jth evaluation index value of the i-th type of warhead, i∈[1,n], n is the total number of warhead types;

[0041] The information entropy vector acquisition module is used to obtain the information entropy of each column of evaluation index data in the normalized evaluation index data table, and form all the information entropies into a row vector to obtain an information entropy vector, and then send the information entropy vector to the evaluation index weight acquisition module;

[0042] The information entropy of each column of the evaluation index in the normalized evaluation index data table is obtained by the following formula:

[0043]

[0044] Among them, ent j is the information entropy of the normalized evaluation index value of the jth group, j∈[1,m], m is the total number of evaluation indicators;

[0045] The evaluation index weight acquisition module obtains the weight of each evaluation index by using the information entropy vector, and sends each evaluation index weight to the warhead comprehensive score acquisition module;

[0046] The warhead comprehensive score acquisition module is used to obtain the comprehensive score of each type of warhead using each evaluation index weight, and send the comprehensive score of each type of warhead to the efficiency improvement ratio acquisition module;

[0047] The effectiveness improvement ratio acquisition module is used to obtain the warhead effectiveness improvement ratio by using the comprehensive score of each type of warhead.

[0048] Furthermore, the weight of each evaluation index is obtained by the following formula:

[0049]

[0050] Among them, weights j is the weight of the jth evaluation indicator.

[0051] Furthermore, the comprehensive score of each type of warhead is obtained by the following formula:

[0052]

[0053] Among them, composite_score i It is the comprehensive score of the i-th type of warhead.

[0054] Furthermore, the warhead effectiveness improvement ratio is obtained by the following formula:

[0055]

[0056] Among them, a, b∈[1,n], a is the label of the old model warhead, b is the label of the new model warhead, efficiency_improvement_ratio ab It is the improvement ratio of type B warhead relative to type A warhead.

[0057] The beneficial effects of the present invention are:

[0058] The present invention uses information entropy theory to weight multi-index data, objectively reflects the degree of influence of each index on warhead effectiveness, avoids the uncertainty and deviation of subjective assignment or expert scoring, improves the accuracy of warhead evaluation, and can accurately determine the pros and cons of different warhead schemes or equipment. The present invention realizes rapid estimation of warhead effectiveness by normalizing combat evaluation index value data, calculating information entropy, calculating weights, calculating comprehensive scores and calculating efficiency improvement ratios, saves evaluation time and resources, and can quickly feedback and make decisions on the pros and cons of different warhead schemes or equipment. The present invention intuitively expresses the performance improvement degree of warhead schemes or equipment through efficiency improvement ratios, is more convenient for optimizing and selecting schemes or equipment, and improves the efficiency and quality of warhead scheme design and upgrade evaluation of active equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0060] Specific implementation method 1: Figure 1 As shown, the specific process of a warhead effectiveness improvement ratio estimation method in this embodiment is as follows:

[0061] Step 1: Obtain multiple evaluation index values ​​of n types of warheads, take the evaluation index values ​​as columns and the warhead model i as rows, and construct an evaluation index data table;

[0062] Evaluation indicators include: TNT equivalent of the warhead, the maximum penetration distance of fragments against a 6mm thick Q235 target, fragment density in the flying area, fragment flying angle, shock wave overpressure, penetration depth, explosion depth, static armor penetration depth, and intensive killing radius;

[0063] The evaluation indicators are the warhead performance and power indicators involved in warhead optimization and improvement.

[0064] Step 2: normalize the multiple evaluation index values ​​of the n types of warheads obtained in step 1 to obtain the normalized multiple evaluation index values, and form a normalized evaluation index data table:

[0065] Normalization is performed by dividing each evaluation metric value by the sum of its column. The normalization process can be expressed as:

[0066]

[0067] Among them, norm_data ij is the normalized evaluation index value of the i-th row and j-th column, data ij is the i-th row and j-th column in the evaluation index data table, i∈[1,n], n is the total number of rows in the evaluation index data table;

[0068] Step 3: Obtain the information entropy of each column of data in the normalized evaluation index data table, and form a row vector with all the information entropies to obtain an information entropy vector;

[0069] The information entropy of each column of data in the normalized evaluation index data table is obtained in the following way:

[0070]

[0071] In this step, information entropy is a way to measure the complexity of data distribution. The more uniform the data distribution, the greater the information entropy, and vice versa. Therefore, the smaller the information entropy, the greater the impact on the warhead effectiveness, and should be given a higher weight. The present invention calculates the information entropy complement of each indicator and normalizes it to obtain the weight, thereby objectively reflecting the degree of influence of each indicator on the warhead effectiveness.

[0072] Step 4: Normalize the complement of each information entropy in the information entropy vector obtained in step 3 to obtain the weight of each evaluation index, as shown in the following formula:

[0073]

[0074] Among them, j∈[1,m], m is the total number of columns of row vectors.

[0075] Step 5: Use the weight of each column of data obtained in step 4 to obtain the comprehensive score of each type of warhead:

[0076]

[0077] Among them, composite_score i It is the comprehensive score of the i-th type warhead.

[0078] Step 6. Use the comprehensive score of each type of warhead obtained in step 5 to obtain the warhead effectiveness improvement ratio:

[0079]

[0080] Among them, a, b∈[1,n], a is the label of the old model warhead, b is the label of the new model warhead, efficiency_improvement_ratio ab It is the improvement ratio of type B warhead relative to type A warhead.

[0081] Step 7: Use the efficiency improvement ratio obtained in step 6 ab Compared with the preset improvement threshold ε, if efficiency_improvement_ratio ab<ε, it means dissatisfaction, adjust the warhead parameters or warhead model, and then return to step 1; if efficiency_improvement_ratio ab ≥ε, it means satisfaction, and warhead b is adopted.

[0082] Specific implementation method 2: A warhead effectiveness improvement ratio estimation system, used to execute a warhead effectiveness improvement ratio estimation method described in specific implementation method 1, the system comprising:

[0083] Evaluation index data table construction module, evaluation index normalization module, information entropy vector acquisition module, evaluation index weight acquisition module, warhead comprehensive score acquisition module, and efficiency improvement ratio acquisition module;

[0084] The evaluation index data table construction module is used to obtain n types of warheads and obtain m evaluation index values ​​for each type of warhead, use the evaluation index values ​​as columns and the warhead models as rows to construct an evaluation index data table, and send the evaluation index data table to the evaluation index normalization module;

[0085] The evaluation index values ​​include: TNT equivalent of the warhead, the maximum penetration distance of the fragment target, the density of fragments in the dispersion area, the fragment dispersion angle, the shock wave overpressure, the penetration depth, the explosion depth, the static armor penetration depth, and the intensive killing radius;

[0086] The evaluation index normalization module is used to normalize each column of evaluation index in the evaluation index data table, obtain the normalized evaluation index value, thereby forming a normalized evaluation index data table, and send the normalized evaluation index data table to the information entropy vector acquisition module;

[0087] The normalized evaluation index value is obtained by the following formula:

[0088]

[0089] Among them, norm_data ij is the normalized value of the jth evaluation index value of the i-th type warhead, data ij is the jth evaluation index value of the i-th type of warhead, i∈[1,n], n is the total number of warhead types;

[0090] The information entropy vector acquisition module is used to obtain the information entropy of each column of the evaluation index in the normalized evaluation index data table, and form all the information entropies into a row vector to obtain the information entropy vector, and then send the information entropy vector to the evaluation index weight acquisition module;

[0091] The information entropy of each column of the evaluation index in the normalized evaluation index data table is obtained by the following formula:

[0092]

[0093] Among them, ent j is the information entropy of the normalized evaluation index value of the jth group, j∈[1,m], m is the total number of evaluation indicators;

[0094] The evaluation index weight acquisition module obtains the weight of each evaluation index by using the information entropy vector, and sends each evaluation index weight to the warhead comprehensive score acquisition module;

[0095] The weight of each evaluation index is obtained by the following formula:

[0096]

[0097] Among them, weights j is the weight of the jth evaluation indicator;

[0098] The warhead comprehensive score acquisition module is used to obtain the comprehensive score of each type of warhead using each evaluation index weight, and send the comprehensive score of each type of warhead to the efficiency improvement ratio acquisition module;

[0099] The comprehensive score of each type of warhead is obtained by the following formula:

[0100]

[0101] Among them, composite_score i is the comprehensive score of the i-th type of warhead;

[0102] The efficiency improvement ratio acquisition module is used to obtain the warhead efficiency improvement ratio by using the comprehensive score of each type of warhead;

[0103] The warhead effectiveness improvement ratio is obtained by the following formula:

[0104]

[0105] Among them, a, b∈[1,n], a is the label of the old model warhead, b is the label of the new model warhead, efficiency_improvement_ratio ab It is the improvement ratio of type B warhead relative to type A warhead.

[0106] Embodiment: The present invention carried out the following test:

[0107] This embodiment can be implemented in Python, Matlab, Mathmatica and other environments. There are two types of warheads, A and B. A is the old model, and B is an upgraded version of A. Four indicators are used to evaluate the performance improvement, namely: the TNT equivalent of the warhead, the maximum penetration distance of the fragments to the 6mm thick Q235 target, the density of the fragments in the scattering area, and the scattering angle of the fragments. The data are summarized in Table 1. Figure 1 The process shown performs the following calculations:

[0108] Table 1 Data input statistics

[0109]

[0110] 1. First, normalize the evaluation index data:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] 2. Calculate the information entropy of each column:

[0120] ent1=-(0.4915·log2(0.4915)+0.5085·log2(0.5085))=0.9997

[0121] ent2=-(0.4343·log2(0.4343)+0.5657·log2(0.5657))=0.9919

[0122] ent3=-(0.3191·log2(0.3191)+0.6809·log2(0.6809))=0.9256

[0123] ent4=-(0.3333·log2(0.3333)+0.6667·log2(0.6667))=0.9183

[0124] 3. First, calculate the complement of the information entropy and normalize the calculation weights:

[0125] complement1=1-0.9997=0.0003

[0126] complement2=1-0.9919=0.0081

[0127] complement3=1-0.9256=0.0744

[0128] complement4=1-0.9183=0.0817

[0129]

[0130]

[0131]

[0132]

[0133] 4. Use weights to calculate the comprehensive score:

[0134] composite_score a =100 0.0011+115 0.0660+15 0.5053+15 0.4276=21.6993

[0135] composite_score b =103.5 0.0011 + 150 0.0660 + 32 0.5053 + 30 0.4276 = 39.0179

[0136] 5. Computing performance improvement ratio:

[0137]

[0138] Therefore, the performance improvement ratio of product b relative to product a is 79.8114%.

[0139] The method of use and working principle of the present invention are as follows:

[0140] When it is necessary to conduct a multi-index comprehensive evaluation of different warhead schemes or active equipment, first collect relevant data, such as warhead TNT equivalent, fragment dispersion characteristics, penetration performance, shock wave performance, etc., and then perform data normalization processing, information entropy calculation, weight calculation, comprehensive score calculation and performance improvement ratio calculation according to the steps of the present invention, and finally obtain the performance improvement ratio of different schemes or equipment, so as to optimize and select the schemes or equipment.

[0141] The present invention uses information entropy theory to assign weights to multi-index data. Information entropy is a way to measure the complexity of data distribution. The more uniform the data distribution, the greater the information entropy, and vice versa. Therefore, the smaller the information entropy, the greater the impact on the warhead effectiveness, and should be given a higher weight. The present invention calculates the information entropy complement of each indicator and normalizes it to obtain the weight. Then, the present invention calculates the comprehensive score by summing up each column of the data with the corresponding weight. The comprehensive score reflects the comprehensive performance level of the solution or equipment. Finally, the present invention calculates the performance improvement ratio by calculating the ratio of the difference between the comprehensive scores to the benchmark value. The performance improvement ratio represents the percentage of performance improvement of the solution or equipment relative to the benchmark value.

Claims

1. A method for estimating the warhead effectiveness improvement ratio, characterized in that The method comprises the following steps: Step 1: Obtain n types of warheads, and obtain m evaluation index values ​​for each type of warhead; The evaluation index values ​​include: TNT equivalent of the warhead, the maximum penetration distance of the fragment target, the density of fragments in the dispersion area, the fragment dispersion angle, the shock wave overpressure, the penetration depth, the explosion depth, the static armor penetration depth, and the intensive killing radius; Step 2, normalizing the evaluation index values ​​corresponding to the same evaluation index of each type of warhead in the n types of warheads obtained in step 1, respectively, to obtain m groups of normalized evaluation index values; Step 3: Obtain the information entropy of each group of normalized evaluation index values, and use all the information entropies to form a row vector to obtain an information entropy vector; Step 4: Use the information entropy vector obtained in step 3 to obtain the weight of each evaluation index; Step 5: Using the weight of each evaluation index and the evaluation index value of the warhead obtained in step 4, obtain the comprehensive score of each type of warhead; Step 6: Using the comprehensive score of each type of combat obtained in step 5, obtain the warhead effectiveness improvement ratio; Step 7: Compare the warhead enhancement ratio obtained in step 6 with the preset enhancement threshold ε. If the warhead enhancement ratio is less than the preset enhancement threshold ε, adjust the combat parameters and then return to step 1; if the warhead enhancement ratio is greater than or equal to the preset enhancement threshold ε, save the current model warhead.

2. The method for estimating the warhead effectiveness improvement ratio according to claim 1, characterized in that: In the step 2, the evaluation index values ​​corresponding to the same evaluation index of each type of warhead in the n types of warheads obtained in the step 1 are normalized respectively, as follows: Among them, norm_data ij is the normalized value of the jth evaluation index value of the i-th type warhead, data ij is the j-th evaluation index value of the i-th type of warhead, i∈[1,n], and n is the total number of combat models.

3. The method for estimating the warhead effectiveness improvement ratio according to claim 2, characterized in that: The information entropy of each group of normalized evaluation index values ​​obtained in step 3 is as follows: Among them, ent j is the information entropy of the normalized evaluation index value of the jth group, j∈[1,m], and m is the total number of evaluation indicators.

4. The method for estimating the warhead effectiveness improvement ratio according to claim 3, characterized in that: The information entropy vector obtained in step 3 is used in step 4 to obtain the weight of each evaluation index, as shown in the following formula: Among them, weights j is the weight of the jth evaluation indicator.

5. The method for estimating the warhead effectiveness improvement ratio according to claim 4, characterized in that: The comprehensive score of each type of warhead in step 5 is as follows: Among them, composite_score i It is the comprehensive score of the i-th type of warhead.

6. A method for estimating warhead effectiveness improvement ratio according to claim 5, characterized in that: The warhead effectiveness improvement ratio in step 6 is as follows: Among them, a, b∈[1,n], a is the label of the old model warhead, b is the label of the new model warhead, efficiency_improvement_ratio ab It is the improvement ratio of type B warhead relative to type A warhead.

7. A warhead effectiveness improvement ratio estimation system, used to implement a warhead effectiveness improvement ratio estimation method as claimed in any one of claims 1 to 6, characterized in that: The system comprises: an evaluation index data table construction module, an evaluation index normalization module, an information entropy vector acquisition module, an evaluation index weight acquisition module, a warhead comprehensive score acquisition module, and an efficiency improvement ratio acquisition module; The evaluation index data table construction module is used to obtain n types of warheads and obtain m evaluation index values ​​for each type of warhead, use the evaluation index values ​​as columns and the warhead models as rows to construct an evaluation index data table, and send the evaluation index data table to the evaluation index normalization module; The evaluation index values ​​include: TNT equivalent of the warhead, the maximum penetration distance of the fragment target, the density of fragments in the dispersion area, the fragment dispersion angle, the shock wave overpressure, the penetration depth, the explosion depth, the static armor penetration depth, and the intensive killing radius; The evaluation index normalization module is used to normalize each column of evaluation index in the evaluation index data table, obtain the normalized evaluation index value, thereby forming a normalized evaluation index data table, and send the normalized evaluation index data table to the information entropy vector acquisition module; The normalized evaluation index value is obtained by the following formula: Among them, norm_data ij is the normalized value of the jth evaluation index value of the i-th type warhead, data ij is the jth evaluation index value of the i-th type of warhead, i∈[1,n], n is the total number of warhead types; The information entropy vector acquisition module is used to obtain the information entropy of each column of evaluation index data in the normalized evaluation index data table, and form all the information entropies into a row vector to obtain an information entropy vector, and then send the information entropy vector to the evaluation index weight acquisition module; The information entropy of each column of the evaluation index in the normalized evaluation index data table is obtained by the following formula: Among them, ent j is the information entropy of the normalized evaluation index value of the jth group, j∈[1,m], m is the total number of evaluation indicators; The evaluation index weight acquisition module obtains the weight of each evaluation index by using the information entropy vector, and sends each evaluation index weight to the warhead comprehensive score acquisition module; The warhead comprehensive score acquisition module is used to obtain the comprehensive score of each type of warhead using each evaluation index weight, and send the comprehensive score of each type of warhead to the efficiency improvement ratio acquisition module; The effectiveness improvement ratio acquisition module is used to obtain the warhead effectiveness improvement ratio by using the comprehensive score of each type of warhead.

8. The warhead effectiveness improvement ratio estimation system according to claim 7, characterized in that: The weight of each evaluation index is obtained by the following formula: Among them, weights j is the weight of the jth evaluation indicator.

9. The warhead effectiveness improvement ratio estimation system according to claim 8, characterized in that: The comprehensive score of each type of warhead is obtained by the following formula: Among them, composite_score i It is the comprehensive score of the i-th type of warhead.

10. The warhead effectiveness improvement ratio estimation system according to claim 9, characterized in that: The warhead effectiveness improvement ratio is obtained by the following formula: Among them, a, b∈[1,n], a is the label of the old model warhead, b is the label of the new model warhead, efficiency_improvement_ratio ab It is the improvement ratio of type B warhead relative to type A warhead.

Images