Method, device and medium for identifying key performance indicators of a combat system

Abstract

A method for identifying key performance indicators of a combat system includes determining, from a database, a prior distribution of grey correlation degrees between a plurality of indicators and system performance, the system performance being associated with a plurality of stages. For each stage, a processor performs operations including calculating, for each of a plurality of indicators, a sample grey correlation degree between a data sequence sample of the indicator and a data sequence sample of the system performance. Based on the prior distribution and the sample grey correlation degrees, a respective posterior grey correlation degree of the grey correlation degrees between the plurality of indicators and the system performance is determined. In response to determining that a posterior grey correlation degree of the respective posterior grey correlation degrees is greater than a correlation threshold for the stage, an indicator corresponding to the posterior grey correlation degree is determined to be a key performance indicator for the stage.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for identifying key performance indicators of a combat system, an electronic device, a non-transient computer-readable storage medium, and a computer program product. Background Technology

[0002] Combat effectiveness is influenced by a variety of factors, including equipment status, enemy and friendly combat situations, and weapon performance. Identifying the key factors among these numerous elements, as well as those that drive combat effectiveness improvement, is crucial for combat effectiveness assessment. In system-of-systems warfare, the battlefield environment changes rapidly, and mission objectives and specific combat tasks also shift accordingly. Given this changing battlefield situation, proposing a method for quickly and effectively identifying key indicators is of great significance for situation assessment and analysis, and battlefield decision support. Summary of the Invention

[0003] This disclosure provides a method for identifying key performance indicators of a combat system, an electronic device, a computer-readable storage medium, and a computer program product.

[0004] According to one aspect of this disclosure, a method for identifying key performance indicators of a combat system is provided, comprising: determining a prior distribution of grey relational degrees between multiple indicators and system performance from a database, wherein system performance is associated with multiple stages; for each stage, performing the following operations by at least one processor: calculating the sample grey relational degree between the data sequence sample of each of the multiple indicators in the data sequence sample of the stage and the data sequence sample of system performance; determining each posterior grey relational degree of the grey relational degree between the multiple indicators and system performance based on the prior distribution and each sample grey relational degree; and in response to determining that the posterior grey relational degree in each posterior grey relational degree is greater than the correlation threshold of the stage, determining the indicator corresponding to the posterior grey relational degree as the key performance indicator of the stage.

[0005] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that, when executed by the at least one processor, implements the above-described method.

[0006] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing a computer program, wherein the computer program implements the above-described method when executed by a processor.

[0007] According to another aspect of this disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the above-described method when executed by a processor.

[0008] This invention addresses the problems of high dimensionality, strong coupling, limited sample data, and multi-stage nature of combat system effectiveness evaluation indicators. It proposes a Bayesian intelligent identification and reasoning method for key effectiveness indicators of complex combat systems based on dynamic grey relational analysis. The method is then validated by combining it with a missile defense system.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The accompanying drawings exemplify embodiments and form part of the specification, serving to explain exemplary implementations of the embodiments together with the textual description. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements. In the drawings:

[0011] Figure 1 A schematic diagram illustrating the operational phases and effectiveness indicators according to embodiments of this disclosure is shown;

[0012] Figure 2 A flowchart is shown for a method for identifying key performance indicators of a combat system according to an embodiment of the present disclosure;

[0013] Figure 3 A flowchart is shown for a method for identifying key performance indicators of a combat system according to another embodiment of the present disclosure;

[0014] Figure 4 A schematic diagram illustrating the determination of key performance indicators for multiple operational phases according to embodiments of the present disclosure is shown;

[0015] Figure 5 A schematic diagram showing performance indicators associated with the effectiveness of a missile defense system according to an embodiment of the present disclosure is shown;

[0016] Figure 6 A schematic diagram of a key performance indicator system for a missile defense system according to an embodiment of this disclosure is shown; and

[0017] Figure 7 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0018] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0020] The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0021] In order to alleviate, mitigate or eliminate at least one technical problem in the related technologies, a method for identifying key performance indicators of a combat system is proposed according to one or more embodiments of the present disclosure.

[0022] In the field of indicator system dimensionality reduction, mathematical statistics methods such as regression analysis, analysis of variance, and principal component analysis are used for system analysis. However, in specific technical applications, these methods have many prerequisites, such as requiring a large amount of data and samples following a typical probability distribution. This is particularly evident in military technology applications. For example, in the effectiveness evaluation of combat systems (such as missile defense systems), data may be scarce, and many data sequences may fluctuate frequently, even exhibiting large swings, making it difficult to find typical distribution patterns. Therefore, using traditional mathematical statistics methods to identify important indicators is often ineffective.

[0023] The grey relational model proposed by Professor Deng Julong has been widely used in the selection of indicator systems. Its basic idea is to judge whether the connection between different sequences is close based on the similarity of the geometric shape of the sequence curve. However, the application of this method to the selection of combat system indicators still has limitations: (1) In actual combat, due to the limitations of decision-making time, observation capability and other conditions, the sample size of effectiveness indicators is small and it is difficult to obtain the correlation rules between indicators; (2) Grey relational selection is mostly a static indicator selection, which cannot realize the identification of important indicators under rapid dynamic changes; (3) Existing research believes that the correlation degree between indicators is a constant value. For some indicators with "evolutionary characteristics" and "time-varying characteristics", the correlation degree and effect change with time. The correlation degree of indicators should be a random variable. Furthermore, research on the dimensionality reduction of indicators in joint operations systems, both domestically and internationally, mainly focuses on specific or single combat missions or objectives, emphasizing the processing of indicator parameter information. This research suffers from the following problems: 1) The continuous evolution of battlefield operational situations makes key effectiveness influencing factors dynamic, and the observation of relevant parameters at different operational stages is also time-sensitive. Static assessments are affected by time-varying factors and lack robustness; 2) The evaluation indicators have high dimensionality, and the coupling between indicators leads to computational redundancy in the evaluation; 3) The sample data for joint operations system evaluation indicators is limited, and the intelligent identification and reasoning of key effectiveness influencing factors under (extremely) small subsamples urgently needs to be addressed.

[0024] The embodiments of this disclosure dynamically identify key performance indicators to address the evolution and time-varying nature of the indicator system. Furthermore, based on dynamic grey relational analysis, Bayesian intelligent identification and reasoning are employed to identify and infer key performance indicators for complex combat systems.

[0025] Figure 1 A schematic diagram illustrating operational phases and effectiveness indicators according to embodiments of this disclosure is shown. Figure 1 As shown, the combat system possesses strike / defense effectiveness E. During combat operations, the combat system typically executes multiple sub-task phases consecutively, including reconnaissance, command, offense, and defense, such as phase 110, phase 120, and phase 130. Equipment provides continuous combat support. As the battlefield situation evolves, the combat mission, tasks, and objectives change, leading to changes in the system's equipment's grouping and usage, attrition rate, and repair rate in each sub-task. Consequently, the indicators affecting the combat system's combat effectiveness also change, exhibiting multi-stage dynamic variation characteristics in key system effectiveness indicators.

[0026] like Figure 1As shown, the operational configurations differ across the three phases (110, 120, and 130), and the key performance indicators (KPIs) for operational effectiveness also vary in each phase. For example, in Phase 1, the KPIs are X1, X2, and X3, further refined into KPIs X11, X12, X21, X22, X32, and X32. In Phase 2, the KPIs are X1 and X2, further refined into KPIs X11, X12, X21, X22, X23, and X23. In Phase 3, the KPIs are X1, X2, and X3, further refined into KPIs X11, X21, X22, X23, X24, X31, and X32. It is evident that the KPIs for system effectiveness exhibit a multi-phase dynamic change characteristic.

[0027] The key performance indicators of a combat system exhibit multi-stage dynamic changes are mainly influenced by three factors:

[0028] a) The system-of-systems operational missions, objectives, and tasks differ at different mission phases. The operational requirements differ at different mission phases. By decomposing the macroscopic and abstract operational missions and objectives, we can obtain the specific and detailed operational tasks of each entity at each phase. The operational activities of different entities at different phases will vary.

[0029] b) The battlefield situation differs at different mission phases. The components of the battlefield situation and their interrelationships change as the operation progresses, and their development and changes depend on the intentions / objectives, combat capabilities, and battlefield environment of both sides.

[0030] c) The system composition and combat modes differ at different mission phases. First, the participating equipment differs at different mission phases. During operations, different types of equipment are required to execute missions at different phases, with some equipment joining or leaving the mission according to the established combat plan. Second, the equipment utilization mode changes. As combat missions transform, the compositional relationships between equipment change, and the logical relationships between equipment corresponding to the success of a mission phase continuously evolve.

[0031] Figure 2 A flowchart of a method 200 for identifying key performance indicators of a combat system according to an embodiment of the present disclosure is shown.

[0032] In actual combat, limitations such as changing battlefield environment, decision-making time, and observation capabilities make it difficult to obtain effective indicator samples and correlation rules between indicators, resulting in small sample size characteristics in experimental data. Because it is difficult to provide a definite prior distribution from small sample experimental data, classical Bayesian statistical methods are no longer applicable. For example, methods can be based on... Figure 2The multilevel Bayesian method shown enables dynamic and rapid updates of the correlation degree of performance indicators at each stage. For the difficult-to-provide prior distribution, an additional prior distribution, called the hyper-prior, is provided, significantly reducing the risk of inaccurate small-sample evaluation results. Using multilevel Bayesian techniques, a non-informative prior is typically used as the hyperparameter (the parameter in the first-level prior), i.e., the second-level prior distribution. Simultaneously, the first-level prior distribution is provided based on historical experience information. The first-level and second-level prior distributions are merged to obtain the multilevel prior distribution of the unknown parameter. This is then combined with the overall distribution of the grey correlation degree of performance indicators, and the Bayesian formula is used to give the multilevel Bayesian estimate of the grey correlation degree of performance indicators. The resulting multilevel Bayesian model is as follows:

[0033] Overall distribution: x ~ f(x|γ)

[0034] First-level prior: γ|λ~π1(ε|λ), parameter ε∈Θ

[0035] Second-level prior: λ~π2(λ), parameter λ∈Λ

[0036] Where π2(λ) is a known density function containing no unknown parameters. The system efficiency x is a random variable following the probability density function f(x|γ), and the efficiency index grey relational degree γ is an unknown quantity in the probability density function f(x|γ). Simultaneously, γ is a random variable following the probability density function π1(γ|λ), λ is an unknown quantity in the probability density function π1(γ|λ), and λ is a random variable following the probability density function π2(λ).

[0037] like Figure 2 As shown, the Bayesian intelligent identification and reasoning process of key performance indicators of the system based on dynamic grey relational degree includes steps 210, 220, 230 and 240.

[0038] In step 210, a prior distribution of the grey relational degree between multiple indicators and system effectiveness is determined from the database, wherein system effectiveness is associated with multiple stages. Exemplarily, the prior distribution of the grey relational degree between multiple indicators and system effectiveness can be determined based on historical experience information, historical data, expert experience, or expert systems. For example, the prior distribution can follow a normal distribution with known mean and variance, and the mean and variance can be determined, for example, through historical experience information or expert experience.

[0039] In step 220, for each stage, at least one processor performs the following operation: calculates the sample grey correlation degree between the data sequence sample of each of the multiple indicators in that stage and the data sequence sample of the system performance. For example, the data sequence samples of the indicators and the data sequence samples of the system performance can be obtained from historical data or through simulation.

[0040] In step 230, based on the prior distribution and the grey relational degree of each sample, the posterior grey relational degree of the grey relational degree between multiple indicators and system effectiveness is determined.

[0041] In step 240, in response to determining that the posterior grey relational degree in each posterior grey relational degree is greater than the relational degree threshold of this stage, the index corresponding to the posterior grey relational degree is determined as the key performance index of this stage.

[0042] Method 200 takes the multi-stage application of the combat system as its background. Based on the continuous evolution of the battlefield combat situation, the key effectiveness influencing factors are dynamic and the observation of relevant parameters in different combat stages is also timely. It analyzes the influencing factors of the multi-stage nature of the key indicators of system effectiveness and then establishes a multi-stage key effectiveness indicator system.

[0043] To address the issue of limited sample data for combat system effectiveness evaluation indicators, Method 200 uses dynamic grey relational analysis to characterize the correlation between indicator sequences and employs a multi-level Bayesian model to fuse historical data or simulation information, thereby achieving dynamic and intelligent identification of key effectiveness indicators at each stage. The method proposed in this disclosure overcomes the challenges posed by limited indicator sample data and the difficulty in obtaining correlation rules between indicators.

[0044] In some exemplary embodiments, method 200 may further include: establishing a key performance indicator system for system performance based on key performance indicators for each stage.

[0045] In some exemplary embodiments, the combat system includes a missile defense system, with multiple phases including a reconnaissance phase, a decision-making phase, and a strike phase. Key performance indicators for the reconnaissance phase include IFF accuracy, endurance, early warning range, radar detection range, communication network failure rate, and missile accuracy. Key performance indicators for the decision-making phase include IFF accuracy, missile accuracy, endurance, maximum network latency, communication network failure rate, and maximum missile range. Key performance indicators for the strike phase include endurance, missile accuracy, IFF accuracy, communication network failure rate, and fire allocation time.

[0046] Figure 3 A flowchart of a method 300 for identifying key performance indicators of a combat system according to another embodiment of this disclosure is shown. Figure 3 As shown, the combat system consists of multiple stages, and key performance indicators change dynamically in each stage, exhibiting multi-stage characteristics.

[0047] In step 310, the grey relational degree between the system performance sequence and the performance index sequence is calculated based on the sample data.

[0048] For example, the data sequence sample of system performance is as follows:

[0049] X0 = x0(1), x0(2), ..., x0(k), ..., x0(n), where k = 1, 2, ..., n, and n is a positive integer.

[0050] For stage j, the data sequence samples of multiple indicators in stage j are as follows:

[0051] X ij =x ij (1),x ij (2),…,x ij (k),…,x ij (n), where X ij The data sequence sample represents multiple indicators, i = 1, 2, ..., m, j = 1, 2, ..., s, where m is a positive integer and s is a positive integer.

[0052] Furthermore, the grey relational degree of the sample data is:

[0053]

[0054] in, γ j (X0,X i ) indicates that the index γ is at stage j. j Data sequence sample X i The sample grey correlation degree between the system performance sequence X0 and the system performance sequence X0.

[0055] In step 320, the prior distribution of the grey relational degree of the performance index can be constructed based on the prior information of the grey relational degree of the performance index (such as historical data, expert experience, etc.).

[0056] For example, the prior distribution can be determined to be a normal distribution:

[0057] γ~N(μ,τ 2 ), where γ represents the grey relational degree, μ is the mathematical expectation, and τ 2 Let Variance be the variance.

[0058] Furthermore, the probability density of the prior distribution is:

[0059]

[0060] in, U(0,1) and U(0,c) represent uniform distributions.

[0061] In other exemplary embodiments, the probability density of the prior distribution may be known, for example, the expected value μ and variance τ of the probability density of the prior distribution have been determined based on historical data, expert experience, etc. 2 :

[0062]

[0063] In step 330, based on the prior distribution of grey relational degree and the sample grey relational degree, the posterior distribution of grey relational degree between multiple indicators and system effectiveness is determined.

[0064] In some exemplary embodiments, for each performance index: based on the prior distribution and the grey relational degree of each sample, the posterior distribution of the performance index is determined; the mean of the posterior distribution of the index is calculated as the posterior grey relational degree of the performance index.

[0065] For example, determining the posterior distribution of performance indicators includes:

[0066] According to Bayes' theorem, the probability density function of the posterior distribution is calculated as follows:

[0067]

[0068] Where L(X|θ) is the likelihood function.

[0069] Furthermore, the mean of the posterior distribution of the performance index is:

[0070]

[0071] In summary, by establishing a posterior distribution for each performance indicator at each stage, the multilevel Bayesian estimate or posterior grey relational degree of that performance indicator can be determined.

[0072] In step 340, a gray relational degree threshold is set. For example, the gray relational degree threshold r is in the range r∈[0,1]. A value such as r>0.5 can be set as the relational degree threshold.

[0073] In step 350, the posterior grey relational degree of each performance indicator is compared with a grey relational degree threshold r. For example, this can be based on... The rules identify key performance indicators that affect operational effectiveness at each stage.

[0074] In summary, methods 200 and 300 address the limitations of existing technologies by combining dynamic grey relational analysis and multi-layer Bayesian inference techniques to intelligently identify and infer key performance indicators (KPIs) of complex combat systems. Since combat systems involve multi-stage operational applications and transformations, key performance indicators exhibit multi-stage influencing factors. Methods 200 and 300, by integrating historical information, simulation sample information, and expert experience, achieve the identification of multi-stage key performance indicators and the dimensionality reduction of the indicator system.

[0075] Figure 4 A schematic diagram illustrating the determination of key performance indicators for multiple operational phases according to embodiments of this disclosure is shown. For example...Figure 4 As shown, the combat system executes multiple combat phases 410, 420, and 430.

[0076] During operational phase 420, multiple effectiveness indicators 422, 424, 426, and 428 exert an effectiveness impact on the system indicators of the operational system. During operational phase 420, through grey relational analysis 440 (e.g., step 220 or step 310), combined with prior distribution 450, the data is input into a Bayesian model for Bayesian inference 460 (e.g., step 230 or step 330), yielding the posterior grey relational degrees of each effectiveness indicator 422, 424, 426, and 428. Further, based on the relational degree threshold, it is determined whether each effectiveness indicator belongs to the key effectiveness indicators.

[0077] In operational phases 410 and 430, the steps are similar to those in operational phase 420 to determine the key effectiveness indicators for the corresponding phases. After determining the key effectiveness indicators for all phases, a key effectiveness indicator system for the overall effectiveness of the operational system can be established.

[0078] The following example, using a missile defense system, illustrates the method for identifying key performance indicators proposed in this disclosure. Figure 5 A schematic diagram is shown showing performance indicators associated with the effectiveness 500 of a missile defense system according to an embodiment of this disclosure.

[0079] like Figure 5 As shown, the eleven performance indicators associated with the effectiveness of a certain missile defense system (X1) include early warning range (X2), radar detection range (X3), IFF accuracy (X4), fire allocation time (X5), fire control calculation time (X6), missile accuracy (X7), maximum missile range (X8), communication network failure rate (X9), endurance (X1), and maximum cruise speed (X2). 10 ), network maximum latency (X) 11 ).

[0080] Figure 6 A schematic diagram of the key performance indicators of a missile defense system 600 according to an embodiment of the present disclosure is shown.

[0081] In some exemplary embodiments, a missile defense system comprises four reconnaissance and surveillance units: a space-based infrared system, an improved early warning radar, and an X-band radar; one command and control unit; and three strike units: Patriot missiles, GBI interceptors, and Standard Missile-3 (SM-3). In addition, there are 17 reconnaissance satellites and 18 communication satellites. This system currently faces threats from two enemy targets and will execute three sub-tasks in this defensive operation: reconnaissance, decision-making, and strike. The operational phases are divided according to these sub-tasks: reconnaissance phase 610, decision-making phase 620, and strike phase 630. Figure 5The missile system's operational effectiveness index system, and the process of identifying key effectiveness indicators for each operational phase, are as follows:

[0082] 1) Identification of key performance indicators in the investigation phase 610 (phase 1).

[0083] First, calculate as follows: Figure 5 The grey relational degree between the data sequence samples of the eleven performance indicators and the data sequence samples of system performance is shown. The data sequence samples of system performance after five simulations are as follows:

[0084] X0=(0.9,0.8,0.7,0.85,0.87).

[0085] The data sequence samples for each indicator are as follows:

[0086] X 11 =(8,9,7,8,8), X 21 =(5,4,5,4,7), X 31 =(0.5,0.5,0.9,0.8,0.4), X 41 =(7,4,3,6,5), X 51 =(20,15,10,30,15), X 61 =(0.5,0.8,0.9,0.75,0.8), X 71 =(10,9,5,15,8), X 81 =(0.3,0.4,0.6,0.8,0.5), X 91 = (0.6, 0.4, 0.5, 0.9, 0.55), X 101 =(1,1.5,2,1.2,1.8), X 111 = (11,3,5,9,8).

[0087] Based on steps 220 and 310, the sample grey relational degree between each indicator of the reconnaissance phase 610 and the combat effectiveness X0 is calculated, and the results are shown in Table 1:

[0088] Table 1. Sample grey relational degree between each indicator and combat effectiveness X0 (reconnaissance phase 610)

[0089] <![CDATA[γ1(X0,X i1 )]]> 0.789 0.788 0.876 0.546 0.554 0.770 0.673 0.788 0.804 0.666 0.524

[0090] Assume that the correlation between each indicator and combat effectiveness X0 follows a normal distribution N(γ, 0.06) with known variance, and the conjugate prior distribution of the mean γ follows a normal distribution N(μ, τ). 2 ), where μ = 0.65, τ 2 =0.05. Therefore, the prior probability density function is:

[0091]

[0092] Using the grey relational data in Table 1 as a sample, the Bayesian estimate of the grey relational degree of the index was calculated using Bayes' theorem. The posterior grey relational degree between each indicator and combat effectiveness X0 is shown in Table 2.

[0093] Table 2. Posterior grey relational degree between each indicator and combat effectiveness X0 (reconnaissance phase 610)

[0094] 01 i1 ​​​ 0.713 0.713 0.753 0.603 0.606 0.705 0.660 0.713 0.720 0.657 0.593

[0095] The results show that, with combat effectiveness as the objective, the indicators are ranked according to importance based on the posterior grey relational analysis results: {X3,X9,X1,X2,X8,X6,X7,X...} 10 ,X5,X4,X 11}. Setting the correlation threshold r = 0.7, the key performance indicators of the 610 in the reconnaissance phase are: IFF accuracy (X3), endurance (X9), early warning range (X1), radar detection range (X2), communication network failure rate (X8), and missile accuracy (X6).

[0096] 2) Identification of key performance indicators in the decision-making stage 620 (stage 2).

[0097] In the decision-making phase 620, calculations are performed as follows: Figure 5 The sample grey relational degree between the data sequence samples of the eleven effectiveness indicators and the data sequence samples of system effectiveness is shown. The system combat effectiveness data sequence is as follows:

[0098] X0=(0.9,0.8,0.7,0.85,0.87).

[0099] The data sequences for each indicator are as follows:

[0100] X 12 =(7,6,4,3,2), X 22 =(6,7,5,4,5), X 32 =(0.7,0.8,0.65,0.85,0.9),X 42 =(8,5,7,5,6), X 52 =(30,25,15,20,25), X 62 = (0.9, 0.85, 0.7, 0.8, 0.7), X 72 =(12,18,10,8,15), X 82 = (0.5, 0.7, 0.9, 0.85, 0.7), X 92 =(0.9,0.85,0.7,0.65,0.8),X102 =(2,1.5,0.8,1.2,1), X 112 =(8,6,5,10,12).

[0101] Based on steps 220 and 310, the sample grey relational degree between each indicator of decision-making stage 620 and combat effectiveness X0 is calculated, and the results are shown in Table 3.

[0102] Table 3. Sample grey absolute correlation between each indicator and combat effectiveness X0 (decision stage 620)

[0103] i2 )]]> ​ 0.539 0.644 0.962 0.551 0.513 0.955 0.716 0.779 0.912 0.624 0.788

[0104] Assume that the correlation between each indicator and combat effectiveness X0 follows a normal distribution N(γ, 0.04) with known variance, and the conjugate prior distribution of the mean γ follows a normal distribution N(μ, τ). 2 ), where μ=0.72, τ 2 =0.03. Therefore, the prior probability density function is:

[0105]

[0106] Using the grey relational data in Table 3 as a sample, the Bayesian estimate of the grey relational degree of the index was calculated using Bayes' theorem. The posterior grey relational degree between each indicator and combat effectiveness X0 is shown in Table 4.

[0107] Table 4. Posterior grey relational degree between each indicator and combat effectiveness X0 (decision stage 620)

[0108] i2 )]]> ​ 0.642 0.687 0.824 0.648 0.631 0.821 0.718 0.745 0.802 0.679 0.749

[0109] The results show that, with combat effectiveness as the objective, the indicators are ranked according to importance based on the posterior grey relational analysis results: {X3, X6, X9, X...} 11 ,X8,X7,X2,X 10 ,X4,X1,X5}. Setting the correlation threshold r = 0.7, the key performance indicators for the decision-making stage 620 are: friend-or-foe identification accuracy (X3), missile accuracy (X6), endurance (X9), and minimum network latency (X1). 11 ), communication network failure rate (X8), and missile maximum range (X7).

[0110] 3) Identification of key performance indicators in the strike phase 630 (phase 3).

[0111] During the strike phase 630, calculations were performed as follows: Figure 5 The sample grey relational degree between the data sequence samples of the eleven effectiveness indicators and the data sequence samples of system effectiveness is shown. The system combat effectiveness data sequence after five simulations is as follows:

[0112] X0=(0.9,0.8,0.7,0.85,0.87).

[0113] The data sequences for each indicator are as follows:

[0114] X 13 =(2,7,3,8,6), X 23 =(9,5,7,3,6), X 33 =(0.6,0.8,0.95,0.7,0.75),X 43 =(5,7,2,8,6), X 53 =(35,15,20,10,18), X 63 =(0.85,0.65,0.7,0.9,0.8),X 73 =(15,8,10,10,9), X 83 =(0.85,0.8,0.9,0.88,0.9),X 93 =(0.7,0.75,0.8,0.85,0.8),X 103 =(2,2.5,3.5,4,3), X 113 =(3,8,10,15,20).

[0115] Based on steps 220 and 310, the sample grey relational degree between each indicator of decision-making stage 630 and combat effectiveness X0 is calculated, and the results are shown in Table 5.

[0116] Table 5. Sample grey correlation between each indicator and combat effectiveness X0 (strike phase 630)

[0117] <![CDATA[γ3(X0,X i3 )]]> 0.530 0.530 0.853 0.644 0.506 0.977 0.521 0.821 0.991 0.587 0.513

[0118] Assume the correlation between the indicators follows a normal distribution N(γ, 0.08) with known variance, and the conjugate prior distribution of the mean γ follows a normal distribution N(μ, τ). 2 ), where μ = 0.75, τ 2 =0.05. Therefore, the prior probability density function is:

[0119]

[0120] Using the grey relational data in Table 5 as a sample, the Bayesian estimate of the grey relational degree of the index was calculated using Bayes' theorem. The posterior grey relational degree between each indicator and combat effectiveness X0 is shown in Table 6.

[0121] Table 6. Posterior grey relational degree between each indicator and combat effectiveness X0 (Decision stage 630)

[0122] γ2(X 01 ,X i1 )]]> 0.665 0.665 0.790 0.709 0.656 0.837 0.662 0.777 0.843 0.687 0.659

[0123] The results show that, with combat effectiveness as the objective, the indicators are ranked according to importance based on the posterior grey relational analysis results: {X9,X6,X3,X8,X4,X...} 10 ,X2,X1,X7,X 11 ,X5}. Setting the correlation threshold r = 0.7, the key performance indicators of the 630 during the strike phase are endurance (X9), missile accuracy (X6), friend-or-foe identification accuracy (X3), communication network failure rate (X8), and fire allocation time (X4).

[0124] The threshold values ​​for correlation at each stage need to be set in accordance with the actual situation. If the threshold is too high, too few key indicators will be identified, and a small number of indicators will not be representative. If the threshold is too low, all indicators may be key indicators, leading to an unreasonable multi-stage indicator system.

[0125] After determining the key performance indicators for each stage, a multi-level key indicator system for system-wide operational effectiveness can be obtained, such as... Figure 4 As shown.

[0126] In summary, due to changes in mission objectives, operational goals, and battlefield environment, the grouping and usage methods, damage rates, and repair rates of system equipment in various sub-missions will change, thereby altering the indicators affecting the system's operational effectiveness. This results in the effectiveness indicator system exhibiting multi-stage dynamic changes. Analysis of the aforementioned missile defense system demonstrates that the key effectiveness indicator identification method based on the embodiments of this disclosure can extract multi-stage key indicators for the actual scenario of a missile defense system, exhibiting good data extraction capabilities even with small sample sizes. Furthermore, it can establish a multi-stage key effectiveness indicator system for a specific missile defense system.

[0127] According to another aspect of this disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that, when executed by the at least one processor, implements the method described above.

[0128] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing a computer program is also provided, wherein the computer program implements the method described above when executed by a processor.

[0129] According to another aspect of this disclosure, a computer program product is also provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the method described above.

[0130] See Figure 7The following description serves as a structural block diagram of the electronic device 700 disclosed herein, which is an example of a hardware device applicable to various aspects of this disclosure. The electronic device can be different types of computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the disclosure described and / or claimed herein.

[0131] Figure 7 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. (As follows) Figure 7 As shown, the electronic device 700 may include at least one processor 701, working memory 702, input unit 704, display unit 705, speaker 706, storage unit 707, communication unit 708 and other output units 709 that are capable of communicating with each other via system bus 703.

[0132] Processor 701 may be a single processing unit or multiple processing units, and all processing units may include single or multiple computing units or multiple cores. Processor 701 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Processor 701 may be configured to acquire and execute computer-readable instructions stored in working memory 702, storage unit 707, or other computer-readable media, such as program code of operating system 702a, program code of application program 702b, etc.

[0133] Working memory 702 and storage unit 707 are examples of computer-readable storage media for storing instructions that are executed by processor 701 to perform the various functions described above. Working memory 702 may include both volatile and non-volatile memory (e.g., RAM, ROM, etc.). Furthermore, storage unit 707 may include hard disk drives, solid-state drives, removable media including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network-attached storage, storage area networks, etc. Working memory 702 and storage unit 707 may be collectively referred to herein as memory or computer-readable storage media, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by processor 701 as a specific machine configured to perform the operations and functions described in the examples herein.

[0134] Input unit 706 can be any type of device capable of inputting information to electronic device 700. Input unit 706 can receive input digital or character information and generate key signal input related to user settings and / or function control of electronic device, and can include, but is not limited to, a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone and / or remote control. Output unit can be any type of device capable of presenting information, and can include, but is not limited to, display unit 705, speaker 706 and other output units 709. Other output units 709 can include, but are not limited to, video / audio output terminals, vibrators and / or printers. Communication unit 708 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and can include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and / or chipsets, such as Bluetooth™ devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices and / or the like.

[0135] The application program 702b in working register 702 can be loaded to execute the various methods and processes described above. For example, in some embodiments, the image processing methods can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 707. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 700 via storage unit 707 and / or communication unit 708. When the computer program is loaded and executed by processor 701, one or more steps of the image processing methods described above can be performed. Alternatively, in other embodiments, processor 701 can be configured to execute the image processing methods by any other suitable means (e.g., by means of firmware).

[0136] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer 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.

[0139] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0140] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0141] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0142] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0143] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A method for identifying key performance indicators of a combat system, comprising: The prior distribution of grey relational degree between multiple indicators and system effectiveness is determined from the database. The system effectiveness is associated with multiple stages. The prior distribution is a multi-level prior distribution obtained based on a first-level and a second-level prior distribution of the grey relational degree. The second-level prior distribution is the prior distribution of the hyperparameters of the first-level prior distribution. The prior distribution is a normal distribution. ,in, This represents the gray relational degree. For mathematical expectation, Let be the variance, and wherein the probability density of the prior distribution is: ,in, , and Indicates a uniform distribution; For each stage, the following operations are performed by at least one processor: Calculate the sample grey correlation degree between the data sequence sample of each of the multiple indicators in the data sequence sample of the stage and the data sequence sample of the system performance; For each metric: Based on the prior distribution and the grey relational degree of each sample, the probability density function of the posterior distribution is calculated according to Bayes' theorem. Calculate the mean of the posterior distribution of the indicator to serve as the posterior grey relational degree of the indicator; and In response to determining that the posterior grey relational degree in each of the various posterior grey relational degrees is greater than the relational degree threshold of the stage, the index corresponding to the posterior grey relational degree is determined as the key performance index of the stage. The combat system includes a missile defense system, and the multiple stages include a reconnaissance stage, a decision-making stage, and a strike stage. The key performance indicators of the reconnaissance stage include IFF accuracy, endurance, early warning range, radar detection range, communication network failure rate, and missile accuracy. The key performance indicators of the decision-making stage include IFF accuracy, missile accuracy, endurance, maximum network latency, communication network failure rate, and maximum missile range. Furthermore, the key performance indicators of the strike stage include endurance, missile accuracy, IFF accuracy, communication network failure rate, and fire allocation time.

2. The method of claim 1, further comprising: Based on the key performance indicators of each stage, a key performance indicator system for the system's performance is established.

3. An electronic device, comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-2.

4. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-2.

5. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method of any one of claims 1-2.

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