Defense system architecture flexibility analysis and evaluation method, electronic equipment and storage medium

Abstract

The present invention provides a defense system architecture flexibility analysis and assessment method, electronic device, and storage medium. The defense system architecture flexibility analysis and assessment method comprises: a first server obtains first data, second data, and third data from a second server; the first server analyzes the first data, second data, and third data using a resilience assessment model to obtain a first indicator, a second indicator, and a third indicator, respectively; and the first server determines the flexibility indicator of the target defense system architecture based on the first indicator, the second indicator, and the third indicator, and returns the flexibility indicator to a client. The present invention has the beneficial effects of improving the ability of a distributed combat equipment system to passively cope with various uncertain changes, while also being able to predict and proactively take measures to ensure the system completes its assigned combat mission.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a defense system architecture flexibility analysis and evaluation method, electronic equipment, and storage medium. Background Art

[0002] The flexibility of the system architecture is the basic requirement for the architecture to adapt to the network complexity of the information age. Its connotation mainly includes several aspects: (1) Flexibility not only reflects the adaptability of the architecture during the combat process, but also reflects the ability of the architecture to adapt to the development of technology and equipment. The technology here refers to the computer, communication network, basic software, software integration and other technologies involved in the system. The equipment refers to the various types of unmanned equipment integrated into the system, including unmanned ISR equipment, communication and navigation equipment, unmanned countermeasure systems, etc. (2) Structure determines function. The flexibility of the structure determines the flexibility of the unmanned combat system architecture and is the basis for realizing the "dynamic openness, flexible access, and flexible reorganization" capability characteristics of the unmanned combat system architecture.

[0003] The main features of architectural flexibility include the following:

[0004] (1) Configurability: The members of the architecture can be reconfigured according to task requirements and have certain structural reconstruction capabilities, such as configuring new resources and functions for a member; (2) Self-organization characteristics: The members of the architecture can adjust according to changes in the external environment and internal operating conditions and have certain self-organization and self-adaptation characteristics; (3) Openness: The architecture can easily add new members or update or withdraw existing members; (4) Dynamic combination: During operation, the architecture can dynamically combine different members according to task requirements and establish task coordination relationships between these members; (5) Distribution: The smaller the granularity of the members and the more distributed the functions, the more flexible the architecture; (6) Heterogeneity: The members of the architecture have different types, forms, functions and behavioral characteristics, which enables the architecture to adapt to diverse tasks and complex and changing environments; (7) Mobility: The members of the architecture have certain flexibility and mobility in physical space, adapting to the rapid and mobile characteristics of modern warfare, but mobility is not a necessary condition for achieving architectural flexibility; (8) Responsiveness: The members of the architecture and the relationships between them can be quickly adjusted according to the internal and external environment to ensure that the architecture adjustment is completed within the specified time.

[0005] Existing technologies do not conduct a comprehensive assessment of the defense system architecture in a flexible manner, resulting in the inability to effectively assess the degree to which the defense system architecture is affected by the environment, the degree to which the system changes, and the degree to which the system affects the environment, and thus the inability to effectively respond. Summary of the Invention

[0006] The main purpose of the embodiments of the present invention is to propose a defense system architecture flexibility analysis and evaluation method, electronic equipment and storage medium, so as to achieve accurate evaluation of the defense system architecture flexibility.

[0007] One aspect of the present invention provides a defense system architecture flexibility analysis and evaluation method, comprising:

[0008] Acquire first data, second data and third data of the target defense system architecture, wherein the first data includes mission tasks, system capability constraints, system elements and the relationship between system elements, wherein the system elements include capabilities, activities, activity effects, capability measurement indicators, resources and executors, wherein the relationship between system elements includes the mapping relationship between capabilities and activities, the mapping relationship between activities and activity effects, the measurement indicators associated with each effect corresponding to each capability and each activity, the resource relationship between activities and the relationship between activities and the relationship between activities and executors; the second data includes the information acquisition unit identifier, the information processing unit identifier, the decision control unit identifier, the response execution unit identifier and the information relationship between system members; the third data includes the second data, and also includes the geographical location of the task node, the major semi-axis of the response task node movement area and the minor semi-axis of the response task movement area;

[0009] Analyzing the first data, the second data, and the third data using a resilience assessment model, a flexibility assessment model, and an autonomy assessment model to obtain a first indicator, a second indicator, and a third indicator, and determining a flexibility analysis and assessment result of a target defense system architecture based on the first indicator, the second indicator, and the third indicator;

[0010] The evaluation steps of the resilience assessment model include: constructing a multi-tuple based on the first data, the multi-tuple including the mission tasks, environmental constraints, system components and component associations of the target defense system architecture, the multi-tuple being used to characterize the system capability of the target defense system architecture; quantifying the system capability using a capability assessment method to obtain a capability quantification result; performing an intrinsic capability measurement assessment on the target defense system architecture based on the multi-tuple and the capability quantification result to obtain a capability index level; and determining the first indicator based on the capability index level.

[0011] The evaluation steps of the flexibility evaluation model include: constructing a structural model of the target defense system architecture using the OPDAR model based on the second data, determining the node types and node relationships of the target defense system architecture based on the structural model; determining the task type based on the node types and the node relationships, and determining the second indicator based on the system capability, the node types, the node relationships, and the task type;

[0012] The evaluation steps of the autonomy evaluation model include: determining the system operation and maintenance autonomy index based on the third data and the system operation dimension of the target defense system architecture; determining the system architecture autonomy index based on the system unit composition dimension of the target defense system architecture, and determining the third index based on the system operation and maintenance autonomy index and the system architecture autonomy index.

[0013] According to the defense system architecture flexibility analysis and evaluation method, constructing a multi-tuple based on the first data includes:

[0014] Obtain mission tasks, environmental constraints, system components and the relationship between system components from the first data, and determine a multi-tuple based on the mission tasks, environmental constraints, system components and the relationship between system elements, wherein the system components include a capability set, an activity set, a capability measurement indicator set, an activity effect set, a resource set and an executor set, wherein the relationship between system elements includes a mapping relationship between capabilities and activities, an association relationship between activities and effects, a measurement indicator associated with each effect corresponding to each capability and each activity, a resource association relationship between activities and an association relationship between activities and executors; based on the multi-tuple, determine the association relationship between executors and resources, and determine the association relationship between capabilities and measurement indicators.

[0015] According to the defense system architecture flexibility analysis and evaluation method, the system capability is quantified using a capability evaluation method to obtain a capability quantification result, and the intrinsic capability measurement evaluation of the target defense system architecture is performed based on the multi-tuple and the capability quantification result to obtain a capability index level, including:

[0016] quantifying the system capability by using at least one of activity and effect, qualitative and quantitative analysis methods, Bayesian network, and functional dependency network analysis methods to obtain capability quantification results;

[0017] Obtaining a capability activity set, an activity effect set, a capability indicator set, and an activity effect indicator from the capability quantification result, wherein the activity effect indicator has multiple levels;

[0018] Determining the capability requirement level for each indicator in the capability indicator set;

[0019] For each capability indicator in the capability activity set, determine the first scoring of the capability indicator value based on the capability indicator description, target description, indicator value and activity effect grade model;

[0020] Normalize the first scoring of the capability index values ​​according to the capability requirement level to obtain the normalized capability index level;

[0021] Assign importance to the activities corresponding to the capabilities, determine the capabilities and corresponding activities and activity effects, and obtain the capability indicator weights between the capabilities and capability indicators;

[0022] The normalized capability index level is converted into a capability level frequency vector. The components of the capability level frequency vector are calculated based on the weight sum of the level components of the capability level frequency vector in the activity effect index. The capability level frequency distribution vector is determined based on the components of the capability level frequency vector. The capability level frequency distribution vector is used to represent the distribution of capability index levels and the weight ratio of capability index to the total number of indicators.

[0023] The ability level with the highest weight ratio is selected as the measurement value of the ability, and the result of the ability intrinsic measurement evaluation is determined by the measurement value.

[0024] According to the defense system architecture flexibility analysis and evaluation method, determining the first indicator based on the capability indicator level includes:

[0025] Calculate the measurement of battlefield perception capability based on capability indicator weights and capability indicator levels, as well as capability indicators of battlefield perception capability, where battlefield perception capability includes intelligence acquisition capability, environmental perception capability, and networking application capability;

[0026] Based on the measurement of battlefield perception capability, determine the capability reduction resilience index, capability recovery resilience index, capability recovery cycle, and capability recovery ratio of the target defense system architecture. The capability reduction resilience index is used to characterize the ratio of the total capability reduction to the theoretical total capability at that stage. The capability recovery resilience index is used to characterize the ratio of the total capability recovery to the ideal total capability recovery at that stage as an indicator for evaluating capability recovery resilience. The capability recovery cycle is the length of time it takes for the system capability to decline from failure to recover to stability. The minimum capability recovery time is used to characterize the ratio of the capability measure after the system capability declines from failure to recovery to stability to the system capability measure under normal conditions.

[0027] A first indicator is obtained according to the capacity reduction resilience indicator, the capacity recovery resilience indicator, the capacity recovery period and the capacity recovery ratio, and the resilience assessment model is determined according to the first indicator.

[0028] According to the defense system architecture flexibility analysis and evaluation method, constructing a structural model of the target defense system architecture using the OPDAR model based on the second data, and determining the node types and node relationships of the target defense system architecture based on the structural model includes:

[0029] Obtaining system members, information relationship matrices between members, attributes of system members, and information relationship attribute matrices from the structural model according to the second data;

[0030] According to the OPDAR model, the relationship types of the information in the target defense system architecture are divided into intelligence relationships, command and control relationships, and collaborative relationships. Intelligence relationships are used to represent the original information obtained by surveillance devices, as well as the fused or integrated intelligence situation information generated by intelligence processing; the information transmitted is used by command agencies to command or control subordinate forces and counter equipment; and the information generated by system members reporting their own and surrounding environment status or sharing it with friendly neighbors.

[0031] According to the defense system architecture flexibility analysis and evaluation method, wherein the task type is determined based on the node type and the node relationship, and the second indicator is determined based on the system capability, the node type, the node relationship, and the task type, including:

[0032] Determine the number of tasks, task differences, and costs to be completed by the target defense system architecture based on the structural model and information relationship types;

[0033] A second indicator of the target defense system architecture is determined according to the number of tasks, task differences, cost, and structural task flexibility indicators, wherein the second indicator is used to characterize the architectural flexibility of the target defense system architecture based on tasks.

[0034] According to the defense system architecture flexibility analysis and evaluation method, the method further includes:

[0035] Add task nodes to the OPDAR model, where the task nodes have a mapping relationship with the nodes in the OPDAR model; use a path number-based method to analyze the task space flexibility of the target defense architecture, including:

[0036] Determine the tasks that each responding execution node A needs to complete; check whether the attack range of the execution node A overlaps with the moving range of the task node M; check whether the moving range of the task node M overlaps with the detection range of the intelligence node O. If there is an overlap, construct an OPDAMR structure of the row node A, the task node M and the intelligence node O; based on the OPDAMR structure, calculate the number of loops passing through each task node M; determine the number of relationships in the loop and the total number of relationships of the corresponding class based on the number of loops passing through each task node M, and calculate the fluency of each task node M; based on the number of loops and the fluency of the task node M, determine the flexibility of the target defense system architecture based on the number of paths.

[0037] According to the defense system architecture flexibility analysis and evaluation method, obtaining the third data and determining the system autonomy index according to the system operation dimension of the target defense system architecture includes:

[0038] Obtaining autonomy data of the autonomous behaviors from the third data, determining a capability of each autonomous behavior based on the autonomy data, and determining an autonomy index of each autonomous behavior based on the capability of the autonomous behavior, wherein the autonomous behaviors include reconnaissance and early warning, command and control, joint strike, and protection and assurance;

[0039] Determine the system autonomy index based on the autonomy index of all autonomous behaviors.

[0040] According to the defense system architecture flexibility analysis and evaluation method, determining the system architecture autonomy index based on the system unit composition dimension of the target defense system architecture also includes:

[0041] The system architecture autonomy index is determined by at least one of the following: autonomous collaborative loop ratio, collaborative loop decision creativity, system architecture autonomous evolution effectiveness, system architecture autonomous evolution success rate, and average time consumption rate of system architecture autonomous evolution;

[0042] The proportion of autonomous collaborative loops is A L , taking the ratio of OODA loops in the target defense architecture to all OODA loops as the evaluation indicator;

[0043] The collaborative loop decision creativity is achieved through formula A D

[0044]

[0045] OK, among them in for L p j Decision nodes in the loop, Indicates the creativity level of the node, is the set of loops with creativity level i, where j represents the decision node number, The maximum number of creative levels in the OODA loop of the target defense architecture is used as the collaborative loop decision creativity of the system; wherein, the architecture autonomous evolution effectiveness EE is expressed by the formula

[0046]

[0047] OK, where C i ,i=1,2,…,N C Indicates system capability. The capability standard required by the mission of the target defense system architecture at the current stage is S i ,S i ∈{1,2,...,L}, the ability of the system evaluation after architecture evolution is EC i ,EC i ∈{1,2,…,L};

[0048] Among them, the architecture autonomous evolution success rate ESR is calculated by the formula

[0049]

[0050] Determine, where N is the number of evolution adjustments, and the effectiveness of each autonomous evolution is EE i ;

[0051] Average time consumption for architecture automation evolution Off_T R , through the formula

[0052]

[0053] The running time of the architecture before evolution is t1 and t2, and the corresponding evolution time is ε1, ε2, T R is the time taken for the structural evolution, and

[0054]

[0055] Before the evolution, the architecture running time is t1 and t2, and the evolution time is ε1 and ε2 respectively. Then the time rate of architecture evolution is T R for:

[0056]

[0057] Another aspect of an embodiment of the present invention provides an electronic device, including a processor and a memory;

[0058] The memory is used to store programs;

[0059] The processor executes the program to implement the method described above.

[0060] Embodiments of the present invention further disclose a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the method described above.

[0061] The beneficial effects of the present invention are as follows: by modeling and quantifying the capabilities of the defense system architecture, determining the overall measurement of the capabilities and activity execution effects based on the quantified system capability evaluation, and dividing the capabilities according to the capability evaluation results, including decomposing the capabilities into activities, activity effects, and capability indicators, determining the weights between capabilities and capability indicators, and finally determining the resilience indicators of the defense system architecture through capability level frequency analysis; using the OPDAR and OPDAMR models to perform the flexibility of the task architecture and the flexibility of the task path on the defense system architecture, and determining the flexibility indicators of the defense system architecture; based on the system operation dimension and the system unit composition dimension, using the OODA loop to evaluate the autonomy indicators of the defense system architecture; determining the flexibility evaluation results through the resilience indicators, flexibility indicators and autonomy indicators of the defense system architecture, that is, improving the stability and flexibility of the defense system architecture when affected by the outside world. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0063] Figure 1 2 is a schematic diagram of a defense system architecture flexibility analysis and evaluation method according to an embodiment of the present invention.

[0064] Figure 2 1 is a flow chart of a defense system architecture flexibility analysis and evaluation method according to an embodiment of the present invention.

[0065] Figure 3 4 is a schematic diagram of the toughness index analysis process of an embodiment of the present invention.

[0066] Figure 4 2 is a schematic diagram of a curve showing changes in the comprehensive capability of an embodiment of the present invention.

[0067] Figure 5 1 is a schematic diagram of the flexibility index analysis process of an embodiment of the present invention.

[0068] Figure 6 2 is a schematic diagram of the task flexibility calculation steps according to an embodiment of the present invention.

[0069] Figure 7 It is a schematic diagram of the steps for searching for feasible unit combination solutions for combat missions according to an embodiment of the present invention.

[0070] Figure 8 It is a schematic diagram of the connectivity judgment steps of a feasible unit combination scheme for a combat mission according to an embodiment of the present invention.

[0071] Figure 9 It is a schematic diagram of the cost calculation steps for establishing a feasible unit combination scheme interface for combat missions according to an embodiment of the present invention.

[0072] Figure 10 2. It is a schematic diagram of a task flexibility cost measurement case in an embodiment of the present invention.

[0073] Figure 11 Schematic diagram of a simple OPDAMR model according to an embodiment of the present invention.

[0074] Figure 12 2 is a schematic diagram of the analysis steps of the path number method according to an embodiment of the present invention.

[0075] Figure 13 2 is a schematic diagram of a fully connected situation according to an embodiment of the present invention.

[0076] Figure 14 2 is a flowchart of the autonomous indicator analysis process according to an embodiment of the present invention.

[0077] Figure 15 It is a schematic diagram of the time process of the architecture evolution of an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. In the subsequent description, suffixes such as "module," "component," or "unit" used to represent elements are used solely to facilitate the description of the present invention and have no specific meaning in themselves. Therefore, "module," "component," or "unit" may be used interchangeably. "First," "second," and the like are used solely to distinguish technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In this subsequent description, the consecutive numbering of method steps is for ease of review and understanding. In conjunction with the overall technical solution of the present invention and the logical relationship between the various steps, adjusting the order of implementation of the steps does not affect the technical effects achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and should not be construed as limiting the present invention.

[0079] refer to Figure 1 , Figure 11 is a schematic diagram of a system for analyzing and evaluating the flexibility of a defense architecture according to an embodiment of the present invention, which includes a defense architecture 100, a second server 200, a first server 300, and a client 400, wherein the defense architecture 100, the second server 200, the first server 300, and the client 400 are connected in sequence by remote communication, wherein the defense architecture 100 is a series of sets of countermeasure equipment and communication equipment, which constitute the defense architecture; wherein the second server 200 is used to obtain a series of basic information, status information, combat information, and communication information of equipment such as countermeasure equipment and communication equipment from the defense architecture 100, wherein the first server 300 is used to respond to the client 400 for performing a target defense architecture analysis. A flexibility indicator construction request is made to obtain the first data, the second data and the third data from the second server; the first server 300 uses a resilience assessment model to analyze the first data to obtain the first indicator; the first server 300 uses a flexibility assessment model to analyze the second data to obtain the second indicator; the first server 300 uses an autonomy assessment model to analyze the third data to obtain the third indicator; the first server 300 determines the flexibility indicator of the target defense system architecture according to the first indicator, the second indicator and the third indicator, and returns the flexibility indicator to the client 400, wherein the flexibility indicator is used to characterize the degree to which the target defense system architecture is affected by the environment, the degree to which the system is affected, and the degree to which the system affects the environment.

[0080] Reference Figure 2 , Figure 2 1 is a flow chart of a defense system architecture flexibility analysis and evaluation method according to an embodiment of the present invention.

[0081] S100, obtaining first data, second data and third data of a target defense system architecture.

[0082] In some embodiments, the first data includes mission tasks, system capability constraints, system elements and the relationship between system elements, the system elements include capabilities, activities, activity effects, capability measurement indicators, resources and executors, wherein the relationship between system elements includes the mapping relationship between capabilities and activities, the mapping relationship between activities and activity effects, the measurement indicators associated with each effect corresponding to each capability and each activity, the resource relationship between activities and the relationship between activities and the relationship between activities and executors; the second data includes the information acquisition unit identifier, the information processing unit identifier, the decision control unit identifier, the response execution unit identifier and the information relationship between system members; the third data includes the second data, and also includes the geographical location of the task node, the major semi-axis of the response task node moving area and the minor semi-axis of the response task moving area.

[0083] S200, analyze the first data, the second data and the third data using a resilience assessment model, a flexibility assessment model and an autonomy assessment model to obtain a first indicator, a second indicator and a third indicator, and determine a flexibility analysis and assessment result of the target defense system architecture based on the first indicator, the second indicator and the third indicator.

[0084] In some embodiments, reference Figure 3 The schematic diagram of the evaluation process of the toughness evaluation model shown includes but is not limited to steps S110 to S130:

[0085] S110, constructing a multituple based on the first data, the multituple including the mission, environmental constraints, system components, and component relationships of the target defense system architecture, and the multituple is used to represent the system capability of the target defense system architecture;

[0086] S120, quantify the system capability using a capability assessment method to obtain capability quantification results, and perform intrinsic capability measurement and assessment on the target defense system architecture based on the multi-tuple and capability quantification results to obtain capability indicator levels;

[0087] S130: Determine a first indicator according to the capability indicator level.

[0088] The first indicator is the resilience indicator of the target defense system architecture.

[0089] In some embodiments, mission tasks, environmental constraints, system components and the relationship between system components are obtained from the first data, and a multi-tuple is determined based on the mission tasks, environmental constraints, system components and the relationship between system elements, where the system components include a capability set, an activity set, a capability measurement indicator set, an activity effect set, a resource set and an executor set, where the relationship between system elements includes the mapping relationship between capabilities and activities, the association relationship between activities and effects, the measurement indicators associated with each effect corresponding to each capability and each activity, the resource association relationship between activities and the association relationship between activities and executors; based on the multi-tuple, the association relationship between executors and resources is determined, as well as the association relationship between capabilities and measurement indicators.

[0090] For example, the system capability is described as a multi-tuple SoS cap :

[0091] SoS cap =<mission,cons,E,R>

[0092] in:

[0093] (1) Mission represents the mission that the system needs to complete;

[0094] (2)cons represents the environmental conditions and constraints under which system capabilities operate or take effect;

[0095] (3) E= cap , S act , S ind , S effect , S res , S pfm > is a tuple representing the components of the system, where:

[0096] S cap ={cap l |l=1,2,...,N C} is a collection of capabilities;

[0097] S act ={act i |i=1,2,...,N A} is a collection of activities;

[0098] S index ={index k |k=1,2,...,N I} is a collection of capability metrics;

[0099] S effect ={effect j |j=1,2,...,N E} is the collection of activity effects;

[0100] S res ={res r |r=1,2,...,N R} is a resource collection;

[0101] S pfm ={pfm p |p=1,2,...,N P} is the set of executors;

[0102] (4) R= <C CA , C AE , C Ei , C AR , C AP > is a description of the relationship between system elements, where:

[0103] Represents the mapping relationship between capabilities and activities. i Support Capacity l When f li =1, otherwise f li =0;

[0104] ​ Indicates the relationship between activities and effects. j Used to describe an activity i When a ij =1, otherwise a ij =0;

[0105] Each ability cap l (l=1,2,...,N C ) and each activity act i (i=1,2,...,N A ), each corresponding effect is associated with a set of measurement indicators, using the matrix describe, Indicates index k Used to measure and capacity cap l Associated activity act i effect j Index k According to the needs of analysis, It can be a quantitative value or a vector that represents the requirements at multiple time points;

[0106] Indicates the resource association relationship between activities, h ij Indicates activity act i Output to activity act when executed j The number of resources, h ij ≥0 is a N indicating the number of resources R dimensional vector;

[0107] Indicates the relationship between an activity and an executor. i The execution requires the executor pfm p Participate, u ip =1, otherwise u ip =0.

[0108] In some embodiments, system capabilities are quantified using capability assessment methods, such as a Bayesian network-based capability and capability indicator system, using Bayesian network analysis to comprehensively determine the top-level capability level. In some embodiments, the target defense system architecture is evaluated for intrinsic capability based on the multi-tuple and capability quantification results, resulting in a capability indicator level as follows:

[0109] The set of capability activities can be recorded as S act ={act i |i=1,2,...,N A}, the activity effect set can be recorded as S effect ={effect j |j=1,2,...,N E}, the set of effect attribute indicators (abbreviated as capability indicators) can be recorded as S index ={index k |k=1,2,...,N I The activity effect indicator levels are assumed to have L levels, namely level 1, level 2, ..., level L.

[0110] Determination of the required level of capability indicators. For each indicator in the capability indicator set, the required level of each capability indicator is determined according to the evaluation needs, which is recorded as R satisfies r k ∈{1,2,3,…,L}, k=1,2,…,N I .

[0111] Initial evaluation of capability index level: for each capability index of each capability activity, according to the capability index description, target description and index value (preset value) and activity effect level model, determine the initial level score of capability index value, using a vector Indicates that satisfy s k ∈{1,2,…,L},k=1,2,…,N I .

[0112] Normalize the ability index level. According to the level requirements of the assessment for the ability index, convert the initial evaluation level of the ability index to obtain the normalized ability index level. The specific method is

[0113]

[0114] The significance of this step is to evaluate the ability indicators from the perspective of ability gap according to the ability requirements. For example, if ability a requires level 1 and the ability indicator reaches level 1, the normalized ability indicator is level 5; if ability b requires level 5 and the ability indicator reaches level 3, the normalized ability indicator is level 3. That is to say, although the actual ability a is higher than the ability b in absolute terms, from the perspective of ability gap, ability a is worse than ability b. This can avoid inconsistent standards when grading ability indicators.

[0115] Calculation of capability indicator weights. Here we explain the method for determining indicator weights based on the importance method.

[0116] Determine an importance level for each activity corresponding to the capability, and clarify whether each activity is a key activity, a general activity, or an auxiliary activity. For example, 5 represents a key activity (denoted as A), 3 represents a general activity (denoted as B), 1 represents an auxiliary activity (denoted as C), and 0 represents an activity that is not related to the capability. Represents the importance vector of capability activities, z i ∈{0,1,3,5}, i=1,2,…,N A ;

[0117] Describe the relationship between capabilities, activities, and capability indicators. i (i=1,2,...,N A ) corresponds to a set of activity effects, and the relationship between activities and activity effects is expressed in a matrix Indicates the effect of the activity. j Used to measure activity i , a ij When the values ​​are 5, 3, and 1, the importance of the activity effect is extremely important (denoted as A), generally important (denoted as B), and relatively important (denoted as C). ij =0 means the activity effect is not related to the activity, i=1,2,...,N A , j=1,2,...,N E ; Each activity act i (i=1,2,...,N A ), each activity effect corresponds to a set of indicators, using the matrix Description, index k Used to measure activity i Activity effect attribute effect j According to the importance level of capability indicators, namely extremely important (denoted as A), generally important (denoted as B), relatively important (denoted as C), When the values ​​are 5, 3, and 1 respectively, the capability index is not related to the activity effect j=1,2,...,N E , k=1,2,...,N I .

[0118] The weight vector of each capability index is W is calculated as follows:

[0119] 1) According to the capability index k Relative activity effect j and activities i Importance Calculate the normalized weight as

[0120]

[0121] Remember I x is an x-dimensional column vector (x≥1) whose elements are all 1, and diag(·) is a function that generates a corresponding diagonal matrix based on a column vector or a row vector, so it can be simply written as

[0122]

[0123] Where diag(·) is a function that generates a corresponding diagonal matrix based on a column vector or a row vector. Representation matrix The inverse matrix of A .

[0124] 2) According to the effect of the activity j Relative activity act i The importance of a ij , calculate the normalized weight as

[0125]

[0126] Right now

[0127]

[0128] 3) Computing capability index k Relative activity act i The weight of

[0129]

[0130] Remember e i N is the N whose i-th element is 1 and the rest are 0 A dimensional column vector, then

[0131]

[0132] 4)According to the activity i The importance of relative ability z i , calculate the normalized weight as

[0133]

[0134] Right now

[0135]

[0136] 5) Computing capability index k Relative ability weight

[0137]

[0138] Right now

[0139]

[0140] What this implementation wants to obtain is the weight W between the capability and its sub-capabilities. k In the hierarchical analysis method, the weight is achieved by obtaining the expert-filled importance comparison matrix. In the embodiment of the present invention, the weight between the ability and the ability index is obtained by decomposing the ability into activities, activity effects, and ability indicators. It is necessary to determine the importance coefficient. and a ij .

[0141] Frequency analysis of capability level: After normalization, the capability index level obtained may have non-integer levels (caused by level conversion). Therefore, when converting the capability index level vector into a frequency vector, the function is defined as follows:

[0142]

[0143] The frequency vector of memory ability level V=(v1,v2,…,v L ),statistics The sum of the weights of the components whose median values ​​are in the interval [1, 1.5), [1.5, 2.5), ..., [L-0.5, L] is used to obtain the components of V.

[0144]

[0145] Get the ability level frequency vector

[0146]

[0147] remember

[0148]

[0149] but

[0150] V=WΦ(U)

[0151] The capability level frequency distribution vector reflects the overall distribution of capability index levels, from which we can observe the (weight) proportion of capability indexes of different levels to the total number of capability indexes.

[0152] Based on the ability level frequency distribution vector, the level with the highest proportion is selected as the measurement value EV of the ability:

[0153]

[0154] in

[0155]

[0156] The capability measurement value reflects the level of the capability indicator with the highest proportion, and is an approximate expression of the overall level of the capability indicator.

[0157] It can be understood that when calculating the ability level here, only the ability indicators with the highest frequency of occurrence are selected to participate in the weighted sum calculation. For example, when the levels of the ability indicators are 1, 2, 3, and 2 respectively, the ability measurement can only be level 2.

[0158] In some embodiments, the capability metric (coverage of land and sea targets) is weighted 0.33 relative to the activity effect (coverage):

[0159]

[0160] The weight of campaign effectiveness (reach) relative to activity (discovery) is 1:

[0161]

[0162] The weight of activity (discovery and acquisition) relative to capability (intelligence perception) is 0.13:

[0163]

[0164] Therefore, the weight of the capability indicator (coverage of land and sea targets) relative to the capability (intelligence perception) is 0.3×0.13=0.039.

[0165] By analogy, the ability index weight of intelligence perception capability can be obtained according to the above method. Let the ability index weight be wk (k = 1, 2, ... M), and the corresponding ability index level be u k , respectively select the indicators u=1, 2, 3, 4, 5 in the intelligence perception capability index to calculate the capability measure V i (i=1, 2, 3, 4, 5), then the measurement of intelligence perception capability is The corresponding calculation result is V = (0, 0.053, 0.344, 0.569, 0.034)

[0166] Since V4 is the maximum value in vector V, the rating of the intelligence perception capability is level 4. Similarly, the embodiment of the present invention can determine the measurement and rating of the environmental perception capability and the networking application capability respectively.

[0167] Assuming that the weights of intelligence perception capability, environmental perception capability, and networking application capability are all 1, and the capability measurements are 0.569, 0.321, and 0.11 respectively, the capability measurement in the normal state is 1. The changes in capability after being attacked are shown in Table 1.

[0168] Table 1 Schematic diagram of the resilience changes in capability measurement

[0169]

[0170] The change curve of comprehensive ability is as follows Figure 4 As shown. The areas S1, S2, S3, and S4 are calculated as (ignoring the unit) 0.82, 4.72, 1.275, and 0.575. According to the calculation of the toughness index, it can be seen that the ability to reduce the toughness index R AAbi R AAbi =(S1+S2) / (S1+S2+S5+S6)=0.723, capacity recovery resilience index R RAbi R RAbi =(S3+S4) / (S3+S4+S7+S8)=0.799 The capacity recovery period is T CC =30min. Ability recovery ratio R CR =0.92.

[0171] In some embodiments, reference Figure 5 The evaluation process diagram of the activity evaluation model shown includes but is not limited to steps S140 to S150:

[0172] S140, constructing a structural model of the target defense system architecture using the OPDAR model based on the second data, and determining node types and node relationships of the target defense system architecture based on the structural model;

[0173] S150, determining a task type according to the node type and the node relationship, and determining a second indicator according to the system capability, the node type, the node relationship and the task type.

[0174] In some embodiments, the OPDAR architecture modeling method is based on the OPDAR model, which is a theoretical model for architecture measurement analysis. The OPDAR model represents the intelligence acquisition unit, the P processor represents the intelligence processing unit, the D decision control unit, the Actor represents the response execution unit, and the R relationship represents the information relationship between system members. The model is formally defined as follows:

[0175] G=(N,R,Np,Rp)

[0176] N={n1,n2,…n k} are all types of system members, k is the number of system members; R = [r ij ] is the information relationship matrix between system members, and the relationship r ij Represents two system members n i and n j The information relationship between them; Np={N1,N2,…N k} is the attribute set of the system member set N, Is a member of the system n i The current state, Is a member of the system n i The jth attribute value of ij ] is the information relationship attribute matrix, It is the relationship ij The current state, It is the relationship ij The kth attribute value of .

[0177] The OPDAR model considers the scope of responsibility of each node to be a circle centered on the node. Therefore, the attributes of each node are described as follows:

[0178] (1) The intelligence acquisition unit can be modeled as the following four-tuple:

[0179] O:=<Location,Type,Radius,Fidelity>

[0180] Location: The geographical location of the intelligence acquisition unit, which can be identified by three-dimensional coordinates;

[0181] Type: The type of intelligence obtained, such as radar intelligence, reactance intelligence, image intelligence, etc.

[0182] Radius: The detection radius of the intelligence acquisition unit;

[0183] Fidelity: The accuracy of the intelligence acquired by the intelligence acquisition unit, with a value range of (0, 1).

[0184] (2) The intelligence processing unit can be modeled as the following five-tuple:

[0185] P:=<Location,Types,AOR,BN,Delay>

[0186] Location: The geographical location of the intelligence processing unit, which can be identified by three-dimensional coordinates;

[0187] Types: The range of intelligence types that the intelligence processing unit can process;

[0188] AOR (Area of ​​Responsibility): Area of ​​responsibility of the intelligence processing unit;

[0189] BN (Backup Number): The number of backups of the intelligence processing unit;

[0190] Delay: The average delay of intelligence processing by the intelligence processing unit.

[0191] (3) The decision control unit can be represented as the following six-tuple:

[0192] D:=<Location,UL,LL,AOR,Delay,BN>

[0193] Location: The geographical location of the decision control unit, which can be identified by three-dimensional coordinates;

[0194] UL (UpperLevel): The upper level of the decision control unit;

[0195] LL (Lower Level): the lower level of the decision control unit;

[0196] AOR (Area of ​​Responsibility): The area of ​​responsibility of the decision-making control unit;

[0197] Delay: represents the average delay of the decision control unit in forming a plan or instruction;

[0198] BN (Backup Number): The number of backups of the decision control unit;

[0199] (4) The response execution unit can be modeled as the following two-tuple:

[0200] A:= <Location.Radius>

[0201] Location: The geographical location of the corresponding execution unit, which can be identified by three-dimensional coordinates;

[0202] Radius represents the radius of the response execution unit's combat area.

[0203] System members are connected through various relationships, interact with each other, and jointly form a system architecture with certain functions to meet task requirements. The OPDAR model divides information relationship types into the following three categories:

[0204] (1) Intelligence-related relationships

[0205] The information conveyed by intelligence relationships includes raw information acquired through reconnaissance and surveillance equipment, as well as fused or integrated intelligence situational information generated through intelligence processing. Examples of relationships include O-→A, O-→P, O-→D, P-→A, P→D, and P→P.

[0206] (2) Accusation-type relationships

[0207] The information transmitted by command-and-control relationships refers to the information generated by command organizations in the process of commanding or controlling subordinate forces and combat equipment. Relationship examples include D-→A, D-→P, P-→O, D-→D, and P-→P.

[0208] (3) Collaborative relationships

[0209] The information transmitted by collaborative relationships refers to the information generated by various system members reporting their own and their surrounding environment's status or sharing it with their neighbors. Examples of relationships include A-→P, P-→A, A-→D, D-→A, P-→D, O-→P, P-→O, D-→D, P-→P, A-→A, and O-→O.

[0210] For a simple OPDAR model, it consists of 3 intelligence acquisition nodes, 2 intelligence processing nodes, 4 command and control nodes, and 3 response execution nodes, among which O-→P is an intelligence relationship, P-→D is an intelligence relationship, D is a command and control relationship and a collaborative relationship, and D-→A is a command and control relationship.

[0211] Based on the OPDAR model's classification of node and relationship types, the task types present in the architecture include intelligence tasks, command and control tasks, and collaborative tasks. Each task type is a combination of corresponding nodes and relationships. Table 2 lists the node-relationship combinations corresponding to all types of tasks. Furthermore, the flexibility of the architecture can be analyzed based on task types and examples.

[0212] Table 2 Classification of information flow tasks of OPDAR model

[0213]

[0214]

[0215] In some embodiments, the task-based architecture flexibility analysis method calculates flexibility based on this starting point. It is assumed that there are n types of capability types (i.e., node types) in the system domain, represented by subscript i, then i∈I, I={1, 2, ..., n}, capability types include detection capability (detection node O), information processing capability (information processing node P), command and control capability (command and control unit D), and strike capability (response execution unit A). There are m nodes in the system, represented by subscript j, j∈J, J={1, 2, ..·, m}, and a ij =1 indicates that the system or node has capability i, a ij = 0 means that the system or node j does not have capability i, then the matrix A is an n×m matrix. Suppose there are v types of combat missions, represented by subscript k, then k∈V, V={1, 2, ..., v}, b ik =1 means that completing combat mission k requires capability i, b ik = 0 means that capability i is not required to complete combat mission k, so the matrix B is a v×n matrix. In order to collaboratively complete combat missions, information interaction relationships must exist between systems or nodes. In this paper, it is considered that connectivity between nodes that complete combat missions satisfies the mission requirements. In addition, yrs =1 means that there is an interactive interface between system or node r and system or node s, y rs = 0 means that there is no interactive interface between system or node r and system or node s, r, s∈J, so the matrix Y is an m×m matrix, and If there is no interactive interface between system or node r and system or node s, then define h rs is the cost of establishing an interactive relationship between node r and node s, it is easy to know that h rs =h sr , and there are

[0216]

[0217] The assessment of mission flexibility can be done from the perspective of the system architecture's coverage of combat missions, that is, the types of combat missions that the system can complete, and the cost of building interface interaction relationships that the system needs to pay in order to complete the most types of combat missions. k =1 means that the current system can complete the kth combat mission without creating a new interface interaction relationship, S k =0 means the current system cannot complete the kth combat mission, c k represents the average interface construction cost that the system needs to pay to complete the kth combat mission, c k = 0 means that it is not necessary to create a new interface interaction relationship to complete task k, or task k cannot be completed. Therefore, the flexibility of task change space can be defined as

[0218]

[0219] That is, the ratio of the number of tasks that the system can directly complete to the total number of tasks. The maximum task change space flexibility is defined as

[0220]

[0221] That is, the system adjusts the ratio of the maximum number of tasks that can be completed to the total number of tasks. The flexibility of task change cost is defined as

[0222]

[0223] That is, the ratio of the number of tasks that the system needs to create new interactive relationships in order to complete the task as much as possible to the cost of interface construction. k It represents the number of system node combination schemes to complete combat mission k, and defines the mission change flexibility redundancy as

[0224]

[0225] That is, the average number of node combination schemes for the system to complete a combat mission, indicating the number of implementation schemes for the system to complete a combat mission. Let b = [b k1 , b k2 ,...b kn ] is the capability requirement vector of task k, and the task variation flexibility is defined as

[0226] F V =mean[var(B T )]

[0227]

[0228] That is, the variance of the changes between different task capability requirement vectors. var(X) represents the variance of each column of the matrix X.

[0229] In some implementations, to determine the task flexibility index, it is necessary to analyze the number of tasks that the system nodes can complete under the constraints of connectivity. This can be divided into the following three steps:

[0230] (1) According to the mission capability requirements, determine the system node combination scheme required to complete the combat mission k. The scheme may not be the only one.

[0231] (2) Analyze whether the combination scheme of system nodes meets the connectivity constraints. If the constraints are met, the combat mission can be completed;

[0232] (3) If the constraints are not met, analyze whether the connectivity constraints can be met by establishing an interface interaction relationship between nodes. If so, calculate the required cost. Otherwise, it is considered that the system cannot complete the combat mission.

[0233] Figure 6 This is a schematic diagram of the task flexibility calculation steps of the embodiment of the present invention. For the first step, we further decompose it. Each combat task has a corresponding feasible solution search strategy. The capabilities required for each task are judged one by one, and T=B is calculated. T A, then T is a v×m-order matrix, t kj >0 means that node j can be used as one of the nodes to complete task k. Then for each task k, a node set J can be determined k ={j|t kj >0, k=1, 2, ..., v}. In this set, an exhaustive search method can be used to search for node combination solutions that meet the capability requirements. If but For a feasible solution.

[0234] Figure 7This is a schematic diagram of the steps for searching for feasible unit combination schemes for combat missions according to an embodiment of the present invention. The second step is further disassembled, and the connectivity of each scheme is judged using the Warshall algorithm. If the scheme meets the connectivity requirements, then S k =1, and the feasible solution counter of task k is count(k)+1, otherwise the cost of establishing connectivity is further analyzed. When all node solutions of all tasks are analyzed, this step ends.

[0235] Figure 8 This is a schematic diagram of the connectivity determination steps for a feasible unit combination scheme for a combat mission according to an embodiment of the present invention. Further analysis of the third step is performed to determine whether the scheme that cannot directly complete the combat mission k can meet the connectivity requirements by establishing an interactive relationship between interfaces, such as Figure 8 As shown, for each node combination scheme, a fully connected graph G between nodes is constructed, that is, the vertices of the graph are composed of system nodes J k The edges of the graph are the connections between all system nodes, and the weight of the edge is the cost h of establishing a connection between system nodes. rs , due to the limitations of the real conditions of the system, there may be h rs →∞. According to Prim's algorithm, establish the minimum spanning tree of G. If the minimum spanning tree does not exist h rs →∞, and compare it with the original solution. The newly added edges and weights on the basis of the original solution are the cost c of the solution k , and there is S k =1, and the feasible solution counter of task k is count(k)+1, otherwise it is considered that the solution cannot complete combat task k.

[0236] Figure 9 This is a schematic diagram of the cost calculation steps for establishing a feasible unit combination scheme interface for combat missions according to an embodiment of the present invention. This embodiment demonstrates a task-based architecture flexibility analysis and measurement method, such as Figure 10 Figure 2 shows a case diagram of mission flexibility cost measurement. Assume that the system has five node types: O (detection), P (processing), D (decision-making), A (hard kill action), and L (soft kill). There are a total of 18 nodes: O1, O2, P1, P2, D1, D2, A1, and L1. Two tasks need to be completed. The node requirements of the combat tasks are shown in matrix B. The two combat tasks require the participation of nodes O, P, D, A and O, P, D, and L respectively.

[0237]

[0238] The nodes, node capabilities (types), and connection relationships between nodes are as follows.

[0239]

[0240]

[0241] First, we need to determine the possible node composition schemes based on the task capability requirements. For the first task, there are 8 possible schemes, including {O1, P1, D1, A1}, {O1, P1, D2, A1}, {O1, P2, D1, A1}, {O1, P2, D2, A1}, {O2, P1, D1, A1}, {O2, P1, D2, A1}, {O2, P2, D1, A1}, and {O2, P2, D2, A1}. Similarly, there are 8 possible schemes for the second task. Then, analyze the connectivity problem of each scheme. The node connectivity cost matrix H is

[0242]

[0243] According to H, for task 1, except for the solutions {O1, P1, D2, A1}, {O1, P2, D2, A1}, {O2, P1, D2, A1}, and {O2, P2, D2, A1}, the remaining solutions all meet the connectivity requirements. The cost of establishing interface interaction connectivity for the above solutions is h 67 ,After paying the interface establishment cost h67, the schemes {O1, P1, D2, A1}, {O1, P2, D2, A1}, {O2, P1, D2, A1}, {O2, P2, D2, A1} can meet the connectivity requirements and complete the ,combat mission one.

[0244] According to H, for task 1, except for the solutions {O1, P1, D1, L1}, {O1, P2, D1, L1}, {O2, P1, D1, L1}, and {O2, P2, D1, L1}, the remaining solutions all meet the connectivity requirements. The cost of establishing interface interaction connectivity for the above solutions is h 58 , due to h 58 →∞, so the above solution cannot meet the connectivity requirements and cannot complete combat mission 2.

[0245] Based on the above analysis, S1=1, S2=1, count(1)=8, count(2)=4, and the calculation result of the system's task flexibility index is

[0246] F T =F ALL =100%

[0247] F C =0

[0248] F R =3

[0249] F V =0.2

[0250] That is, the system can complete each combat mission and can complete the combat mission without creating a new interface interaction relationship. The average redundancy of the flexibility of the combat mission is 3, and the flexibility of the combat mission difference is 0.2. The redundancy reaches 3 times the number of combat missions, indicating that the flexibility is good. However, the combat mission difference is low at 0.2, indicating that the differences between tasks are small. This means that although the redundancy is high, the tasks are similar to each other. It is easy to use repeated nodes when completing tasks, which may affect the concurrency of tasks.

[0251] In some embodiments, an improved OPDAR model, the OPDAMR model, is obtained through the task node M and its related relationships. The attributes of O, P, D, A, and R in the OPDAMR model are consistent with those of the general OPDAR model. It is assumed that the movement range of the node M is an ellipse centered on M, and the node M can be modeled as the following triple: O (Observer) represents the intelligence acquisition unit, P (Processor) represents the intelligence processing unit, D (Decision) represents the decision control unit, A (Actor) represents the response execution unit, and R (Relationship) represents the information relationship between system members.

[0252] M::=<Location,aRadius,bRadius>

[0253] Location: The geographical location of the task node, which can be identified by three-dimensional coordinates;

[0254] aRadius represents the long semi-axis of the movement area in response to the task;

[0255] bRadius represents the minor semi-axis of the moving area in response to the task.

[0256] Based on the attributes Location, aRadius, and bRadius of M, we can obtain the main activity range of the task node. Based on the known attack range of node A and the detection range of node O, we can obtain the connections A->M and M->O. Adding them to the OPDAR model results in the OPDAMR model.

[0257] refer to Figure 11 In the simple OPDAMR model shown, each response execution unit A completes its own task, such as A1 can complete M1, A2 can complete M2, and so on. Since the attack range of A2 overlaps with the movement area of ​​M1, it is considered that A2 can also complete M1. Similarly, each task can be detected by the corresponding detection node, such as M1 is detected by O1 and M2 is detected by O2.

[0258] In some embodiments, reference Figure 12The task space flexibility analysis method based on the number of paths shown in the figure can be used to calculate the flexibility of the task change space. The architecture flexibility analysis based on the number of paths is given by Figure 12 The system consists of six steps. The first three steps build the complete OPDAMR architecture, incorporating foreseeable changes into the static system structure. The last three steps calculate the flexibility of the architecture. The meaning of each step is explained in detail below.

[0259] Step 1: Determine the tasks that each response execution node A needs to complete. Based on the operational mission, determine the tasks that each response execution node is responsible for completing, and add each task as an M node to the OPDAR model.

[0260] Step 2: Check whether the attack range of node A overlaps with the movement range of node M. Determine the attack range of node A based on the Location and Radius attributes of node A; it is a circle centered on node A. Similarly, determine the movement range of task node M based on its Location, aRadius, and bRadius attributes; it is an ellipse centered on M. Check whether the two areas overlap, and connect nodes A and M where they overlap. Add the impact of task node M's movement on node A to the static structure.

[0261] Step 3: Check whether the moving range of node M overlaps with the detection range of node O. Since the task node M may move, different detection nodes O are required to detect the task target before it can be passed to the execution unit A for execution. At this time, the role of O is crucial. According to the attributes Location, aRadius and bRadius of the task node M, the moving range of M is determined. According to the attributes Location and Radius of the node O, the detection range of O is determined. If the moving range of M overlaps with the detection range of O, a line is drawn from M to O to form a many-to-many relationship between M and O. In this way, the impact of the movement of the task node M on the node O is added to the static structure. The new structure formed by combining the structure of the AMO that appears with the previous OPDAR structure is called the OPDAMR structure;

[0262] Step 4: Calculate the number of loops passing through each task node M. Based on the OPDAMR model formed in the first three stages, use the OAMO loops stored in the model to determine the number of loops passing through each task node M. The greater the number of loops, the greater the possibility of completing the task and the better the flexibility.

[0263] Step 5: Calculate the fluency of each task node M. Based on the loops passing through each task node M, and considering indicators such as the ratio of the number of each type of relationship in the loop to the total number of relationships of that type, a fluency method is proposed to calculate the fluency of each task node. The greater the fluency, the greater the possibility of completing the task and the better the flexibility.

[0264] Step 6: Multiply the number of loops and the fluency of each task node M and sum them. The product of the number of loops and the fluency of each task node is considered the flexibility of completing each task. The flexibility of all task nodes is summed and averaged based on the number of task nodes to obtain the flexibility of the architecture based on the number of paths.

[0265] Therefore, the flexibility of the architecture based on the number of paths should not only consider the number of loops passing through the task node M, but also the quality of the loops passing through M. The following three indicators are proposed:

[0266] (1) The number of loops passing through the task node. The more loops passing through the task node, the greater the flexibility, represented by cir. This is the same as the calculation result of step 4 in the path number method analysis, so we will not elaborate on it here. This indicator shows the impact of the width of the "stream" on flexibility;

[0267] (2) The ratio of the number of a certain type of relations (such as the relationship between O->P) passed through by all loops to complete a task to the total number of such relations in the architecture.

[0268] The larger the ratio, the greater the flexibility, represented by rat. Because the loop to complete a task traverses many different relationships, for example, the loop to complete task M involves four relationships: O->D->A->M->O. Therefore, there are multiple ratios, each summed up. This metric indicates the extent to which each section of the "stream" can be widened relative to its original width.

[0269] (3) The total number of relationships of a certain type in the architecture

[0270] The total number of relationships of a certain type (such as O->P relationships) in the architecture should be high. If there is only one, then the number of flows through that type of relationship is limited to one, resulting in poor flexibility. It is believed that the greater the number of relationships of a certain type in the architecture, the better the performance. At the same time, the difference between the number of relationships is not significant. For example, the impact of five or six O->P relationships on the flexibility of the architecture is not significant. Therefore, this metric is expressed as the logarithm of the number of relationships of that type, expressed as lg(num). This metric represents the maximum value that a "stream" can widen. Together with the second metric, it represents the widening capacity of the "stream." Therefore, this metric and the second metric are multiplied in a piecewise manner.

[0271] Based on the three indicators proposed above, the first indicator is the same as the result obtained in the fourth step of the analysis step. The calculation formula of the architecture flexibility based on the number of paths is established, as shown in Formula 4.1, which already includes the result of the fourth step of the analysis step. A Refers to the total number of task nodes M, and n refers to the number of relationship types contained in all loops that complete a task. First, consider a single task node M i , get the number of paths that complete the task node cir i And the types of relationships they contain, find the total number num of each type of relationship in the architecture j And the ratio of the number of such relationships in the loop to the total number of architectures rat ij , calculate the product of the relationship types in sections, then sum all the relationship types, and multiply the result by the number of loops of the task, which is M i The fluency of all task nodes is summed and averaged, which is the flexibility of the task change space of the architecture based on the number of paths.

[0272]

[0273] refer to Figure 12 As shown in the figure, the OPDAR model contains 3 intelligence acquisition nodes, 2 intelligence processing nodes, 4 command and control nodes and 3 response execution nodes. It is assumed that A1, A2, and A3 complete tasks M1, M2, and M3 respectively, and there is no overlap between the responsibility ranges of A and M. At the same time, it is assumed that the movement range of M1 overlaps with the detection range of O1, the movement range of M2 overlaps with the detection range of O2, and the movement range of M3 overlaps with the detection ranges of O2 and O3.

[0274] refer to Figure 11 As shown in the figure, there are 6 rings to complete task M1, 6 rings to complete task M2, and 32 rings to complete task M3, i.e., cir(M1) = 6, cir(M2) = 6, cir(M3) = 24.

[0275] The six loops passing through M1 are shown below.

[0276] O1->P1->D2->A1->M1->O1

[0277] O1->P1->D3->D1->D2->A1->M1->O1

[0278] O1->P1->D4->D1->D2->A1->M1->O1

[0279] O1->P2->D2->A1->M1->O1

[0280] O1->P2->D3->D1->D2->A1->M1->O1

[0281] O 1->P2->D4->D 1->D2->A1->M 1->O 1

[0282] Of these six loops, two have O->P relationships: O1->P1 and O1->P2. There are six O->P relationships in the architecture, accounting for 1 / 3 of the total. Similarly, there are six P->D relationships, accounting for 1 / 3 of the total. There is one D->D relationship, accounting for 3 of the total, accounting for 1 / 3 of the total. There is one D->A relationship, accounting for 4 of the total, accounting for 1 / 4 of the total. There is one A->M relationship, accounting for 3 of the total, accounting for 1 / 3 of the total. There is one M->O relationship, accounting for 4 of the total, accounting for 1 / 4 of the total. Therefore, based on this data, substituting it into Equation 4.1, we obtain the flexibility of M1 as follows.

[0283] flexibility(M1)=6×(1g(6)×1 / 3+lg(6)×1+lg(3)×1 / 3

[0284] +lg(4)×1 / 4+lg(3)×1 / 3+lg(4)×1 / 4)

[0285] =9.75

[0286] Similarly, analyzing the relevant data of M2 and M3 and substituting them into Formula 4.1, we can obtain the flexibility of M2 and M3 as shown below.

[0287] flexibility(M2)=6×(1g(6)×1 / 3+lg(6)×1+lg(3)×1 / 3

[0288] +lg(4)×1 / 4+lg(3)×1 / 3+lg(4)×1 / 4)

[0289] =9.75

[0290] flexibility(M3)=24×(1g(6)×2 / 3+lg(6)×1+lg(3)×1

[0291] +lg(4)×1 / 2+lg(3)×1 / 3+lg(4)×1 / 2)

[0292] =35.40

[0293] The flexibility of the architecture is the sum of the flexibility of each task and the average value is 18.3, so the flexibility of the architecture based on the number of paths is 18.3.

[0294] The results obtained by this method represent a measure of the flexibility of the architecture, which can be used to compare with the results of other structures. The data can also be normalized to between 0 and 1 to represent the degree of flexibility of the architecture. In this case, the flexibility degree can be considered to be 0 when the architecture has no loops, and the flexibility degree can be 1 when the architecture is a fully connected network. The flexibility measure values ​​of the loop-free and fully connected structures can be calculated, and the flexibility measure values ​​can be normalized to between 0 and 1.

[0295] refer to Figure 13 Schematic diagram of the fully connected situation. Assume that at this time, the rings for completing task M1 have cir(M1), the rings for completing task M2 have cir(M2), and the rings for completing task M3 have cir(M3). The flexibility of M1 is flexibility(M1), the flexibility of M1 is flexibility(M2), and the flexibility of M3 is flexibility(M3). The flexibility of the architecture when fully connected is flexibility(all), so the flexibility of the original structure is 18.3 / flexibility(all).

[0296] In some embodiments, reference Figure 14 The third indicator analysis process diagram shown includes but is not limited to steps S160 to S170:

[0297] S160: Determine a system operation and maintenance autonomy indicator based on the third data and the system operation dimension of the target defense system architecture.

[0298] In some embodiments, the autonomy indicators of the architecture can be considered from two dimensions. One dimension is the system operation dimension. From the perspective of the system's behavior, autonomy is reflected in the execution of tasks such as environmental information acquisition, situation assessment, task and behavior planning, and system control and coordination. The autonomy indicators can be analyzed from the following six aspects, and measured by reconnaissance and early warning autonomy, command and control autonomy, joint strike autonomy, comprehensive protection autonomy, and comprehensive support autonomy indicators; the other dimension is the system unit composition dimension. Considering the possibility of innovative behavior in the system, it can be measured from the proportion of autonomous collaborative loops in the task collaborative loop in the system architecture, and the intelligence and autonomy indicators of the decision-making units in the decision-making autonomous collaborative loop.

[0299] (1) Reconnaissance and Early Warning Autonomy: This refers to the system's ability to acquire environmental information through sensors configured on the unmanned system, process this information, build an environmental model, autonomously form a surrounding situation, and identify potential threats. Indicators such as environmental perception and situation prediction are used to characterize the level of environmental perception capability.

[0300] The system's reconnaissance and early warning capabilities are divided into five levels according to the level of autonomy:

[0301] Table 3 List of autonomous reconnaissance and early warning levels

[0302] level Autonomous reconnaissance and early warning level 1 Unmanned systems do not participate in any reconnaissance and early warning assistance or execution tasks; all tasks are completed entirely by humans. 2 Human operators perform reconnaissance and early warning tasks, and unmanned systems are fully controlled by humans as tools. 3 Human-machine collaboration completes reconnaissance and early warning tasks, with humans or unmanned systems taking the lead depending on different situations. 4 Unmanned systems collaborate to perform most reconnaissance and early warning tasks and are monitored by humans. 5 Unmanned systems collaborate to perform all reconnaissance and early warning tasks without the need for human monitoring

[0303] (2) Command and control autonomy: This refers to the system's ability to accept tasks from superiors based on various external input information, tasks, and internal information, understand the tasks, make autonomous decisions, and make action arrangements. Indicators such as task understanding, command decision-making, human-computer interaction, and action control are used here to characterize the level of command and control capabilities.

[0304] The command and control capabilities of the system are divided into five levels according to the level of autonomy:

[0305] Table 4 List of autonomous command and control levels

[0306] level Autonomous command and control level 1 Unmanned systems do not participate in any command and control activities; all activities are completed entirely by humans. 2 Humans complete command and control activities, and unmanned systems are fully controlled by humans as tools 3 Human-machine collaboration completes command and control, with humans or unmanned systems acting as decision makers depending on the situation. 4 Unmanned systems complete most command and control activities, accept human monitoring, and provide reasons for decisions 5 Unmanned systems collaboratively perform all command and control activities without the need for human monitoring or explanation of reasons

[0307] (3) Joint Strike Autonomy: This refers to the system's ability to eliminate incoming targets through coordinated efforts across multiple dimensions, including land, sea, air, space, and power grids, by implementing comprehensive strike capabilities. Indicators such as electronic warfare, cyber attack and defense, and firepower strikes are used to characterize the level of joint strike capability.

[0308] The system's joint strike capability is divided into five levels according to the level of autonomy:

[0309] Table 5 List of autonomous joint strike levels

[0310]

[0311]

[0312] (4) Comprehensive protection capability: This refers to the system's ability to resist enemy attacks or adapt to environmental changes during mission execution, and to achieve its own survival through self-protection. Here, network protection, electromagnetic protection, firepower protection and other indicators are used to characterize the comprehensive protection capability.

[0313] Comprehensive protection capabilities are divided into five levels according to the level of autonomy:

[0314] Table 6 List of autonomous comprehensive protection levels

[0315] level Autonomous comprehensive protection level 1 Unmanned systems do not participate in any integrated protection activities, and all tasks are completed entirely by humans. 2 Human operators perform comprehensive protection activities, and unmanned systems are fully controlled by humans as tools 3 Human-machine collaboration completes comprehensive protection, with humans or unmanned systems taking the lead depending on different situations 4 Unmanned systems collaborate to perform most integrated protection tasks and are monitored by humans 5 Unmanned systems collaborate to perform all integrated protection tasks without the need for human monitoring

[0316] (5) Comprehensive support autonomy: This refers to the auxiliary support capabilities provided by various elements within the system to ensure the successful completion of the mission, including communication support, navigation support, energy support, etc. Here, indicators such as autonomous communication, autonomous navigation, and energy support are used to characterize the level of comprehensive support capabilities.

[0317] Comprehensive support capabilities are divided into five levels according to the level of autonomy:

[0318] Table 7 List of autonomous comprehensive support levels

[0319] level Independent comprehensive security level 1 Unmanned systems do not participate in any integrated support activities, and all tasks are completed entirely by humans. 2 Human operators perform comprehensive support activities, and unmanned systems are fully controlled by humans as tools 3 Human-machine collaboration completes comprehensive support, with human or unmanned systems taking the lead depending on different situations 4 Unmanned systems collaborate to perform most integrated support tasks and are monitored by humans. 5 Unmanned systems collaborate to perform all integrated support tasks without the need for human monitoring

[0320] Finally, the autonomy level of the system is divided according to the above six aspects, as shown in Table 8.

[0321] Table 8 List of system autonomy levels

[0322] level Level of autonomy 1 The unmanned system does not participate in any assistance or task execution, and all activities of the system are completed entirely by humans. 2 Human operators perform all tasks, and unmanned systems only serve as tools or assistance and are fully controlled by humans. 3 Unmanned systems and humans perform tasks together 4 Unmanned systems collaborate to perform most operational and tactical-level tasks, under human supervision 5 Unmanned systems collaborate to perform all campaign and tactical-level tasks and some strategic-level tasks, and are monitored by humans. 6 Unmanned systems accept top-level mission tasks and complete their goals autonomously without the need for human monitoring 7 The system has full autonomous capabilities that reach or exceed the level of human combat systems.

[0323] S170, determining an architecture autonomy index based on the system unit composition dimension of the target defense architecture, and determining a third index based on the system operation and maintenance autonomy index and the architecture autonomy index.

[0324] In some embodiments, the third indicator is an autonomy indicator.

[0325] In some embodiments, system operational autonomy is assessed through the capabilities demonstrated during system operations. However, due to the unique nature of the system, there are relatively few opportunities for it to truly fulfill operational missions. Before the system is operational, the autonomy of the system architecture can indirectly reflect its level of autonomy. This article proposes that system autonomy indicators include at least the proportion of autonomous collaborative loops and the autonomy of collaborative loop decisions.

[0326] The details are as follows:

[0327] (1) Autonomous collaborative loop ratio A L The evaluation metric is the proportion of OODA loops with high autonomous collaborative capabilities among all OODA loops within the system. The higher the autonomy of the OODA loops within the system, and the more distributed they are, the higher the autonomy of the system. A loop is considered autonomous only when each perception, planning, decision-making, and action node meets a certain level of autonomy. Furthermore, if the node is manned, the autonomy requirement is met.

[0328]

[0329] LP={Lp i |i=1, 2, ...}

[0330]

[0331] Among them, LP is the set of OODA loops in the system, and the first i The loop is Lp i , It is a set of OODA loops with autonomous collaboration capabilities within the system. i The loop is ||·|| represents the number of elements in the set, and all nodes in the system are represented by n1, n2, ... n N .

[0332]

[0333] Then Lp i It can be expressed as i N A ring consisting of nodes, and the first node is connected to the second node, It means that the second node is connected to the third node, and so on. Expressed as

[0334]

[0335] That is, when Lp i When the autonomy LOA of each node in is greater than k, is not empty, and Equal to Lp i , by dividing the autonomy level of nodes into Sheridan's automation level.

[0336] (2) Creativity of collaborative loop decision-making: The autonomy in architectural flexibility is mainly manifested in the ability to adopt new behaviors to influence and control the external environment. It can not only respond quickly to changes in the external environment, but more importantly, it can predict such changes and actively deal with changes.

[0337] set up for L p j Decision nodes in the loop, Indicates the creativity level of the node, is a set of loops with a creativity level of i.

[0338]

[0339] The collaborative loop decision creativity A D for

[0340]

[0341] That is, the creativity level with the largest number in the system's OODA loop is taken as the system's collaborative loop decision-making creativity.

[0342] (3) Architecture Autonomous Evolution Effectiveness EE

[0343] The effectiveness of architecture autonomous evolution is used to compare the degree of match between the system's evolved capabilities and mission requirements, to measure whether the architecture autonomous evolution can effectively complete the mission requirements of the distributed defense combat system.

[0344] Assume that the system capability is C i ,i=1,2,...,N C The mission requirements of the distributed defense combat system at the current stage are S i ,S i ∈{1,2,...,L}, the ability of the system evaluation after architecture evolution is EC i ,EC i ∈{1,2,...,L}, then the effectiveness of the architecture's autonomous evolution is:

[0345]

[0346] If S i and EC i It can be calculated quantitatively, and the effectiveness of the autonomous evolution of the architecture is:

[0347]

[0348] (4) Architecture autonomous evolution success rate (ESR)

[0349] The success rate of architecture autonomous evolution reflects the comprehensive performance of the system's autonomous evolution adaptation over a period of time. Suppose that within time T, the system has undergone N evolutionary adjustments, and the effectiveness of each autonomous evolution is EE i , then the success rate of architecture autonomous evolution is

[0350]

[0351] (5) Average time consumption of architecture autonomous evolution Off_T R :

[0352] The time consumption rate of architecture autonomous evolution reflects the agility of architecture evolution and adjustment, and can be used to measure the performance of architecture autonomous evolution in the time dimension. Figure 15 , the architecture undergoes autonomous evolution at time points a and b. The architecture running time before evolution is t1 and t2, and the evolution time is ε1 and ε2 respectively. The time rate of architecture evolution is T R for:

[0353]

[0354] That is, the time consumption rate is the ratio of the architecture adjustment time to the running time before the architecture adjustment. The average time consumption rate of architecture evolution is T R for:

[0355]

[0356] refer to Figure 15 The time process diagram of architecture evolution shown in the figure shows that the average time consumption rate of architecture evolution reflects the ratio of architecture evolution time to architecture running time. Off_T R =1, the system will need to spend half of its time adjusting the architecture. Generally speaking, when the average time consumption of architecture evolution exceeds a certain threshold, it can be considered that the architecture evolution is taking a long time and the time cost of autonomous architecture evolution is high.

[0357] An embodiment of the present invention further provides an electronic device, the electronic device including a processor and a memory;

[0358] The memory stores a program;

[0359] The processor executes the program to perform the aforementioned defense architecture flexibility analysis and evaluation method; the electronic device has the function of carrying and running the defense architecture flexibility analysis and evaluation software system provided by the embodiment of the present invention, such as a personal computer, a minicomputer, a main frame, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or communicates with a charged particle tool or other imaging device, etc.

[0360] An embodiment of the present invention further provides a computer-readable storage medium storing a program, wherein the program is executed by a processor to implement the defense architecture flexibility analysis and evaluation method as described above.

[0361] In some optional embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, the two boxes shown in succession may actually be executed substantially simultaneously or the boxes may sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present invention are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logic flows presented in the embodiments of the present invention. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0362] Embodiments of the present invention also disclose a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned defense architecture flexibility analysis and assessment method.

[0363] Furthermore, while the present invention has been described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed in the embodiments of the present invention, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art using ordinary skill will be able to implement the present invention set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0364] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0365] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0366] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0367] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0368] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0369] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0370] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A defense system architecture flexibility analysis and evaluation method, characterized in that: include: Acquire first data, second data and third data of the target defense system architecture, wherein the first data includes mission tasks, system capability constraints, system elements and the relationship between system elements, wherein the system elements include capabilities, activities, activity effects, capability measurement indicators, resources and executors, wherein the relationship between system elements includes the mapping relationship between capabilities and activities, the mapping relationship between activities and activity effects, the measurement indicators associated with each effect corresponding to each capability and each activity, the resource relationship between activities and the relationship between activities and the relationship between activities and executors; the second data includes the information acquisition unit identifier, the information processing unit identifier, the decision control unit identifier, the response execution unit identifier and the information relationship between system members; the third data includes the second data, and also includes the geographical location of the task node, the major semi-axis of the response task node movement area and the minor semi-axis of the response task movement area; Analyzing the first data, the second data, and the third data using a resilience assessment model, a flexibility assessment model, and an autonomy assessment model to obtain a first indicator, a second indicator, and a third indicator, and determining a flexibility analysis and assessment result of a target defense system architecture based on the first indicator, the second indicator, and the third indicator; The evaluation steps of the resilience assessment model include: constructing a multi-tuple based on the first data, the multi-tuple including the mission tasks, environmental constraints, system components and component associations of the target defense system architecture, the multi-tuple being used to characterize the system capability of the target defense system architecture; quantifying the system capability using a capability assessment method to obtain a capability quantification result; performing an intrinsic capability measurement assessment on the target defense system architecture based on the multi-tuple and the capability quantification result to obtain a capability index level; and determining the first indicator based on the capability index level. The evaluation steps of the flexibility evaluation model include: constructing a structural model of the target defense system architecture using the OPDAR model based on the second data; determining the node types and node relationships of the target defense system architecture based on the structural model; the OPDAR model is a theoretical model for system architecture measurement analysis, wherein O represents an intelligence acquisition unit, P represents an intelligence processing unit, D represents a decision control unit, A represents a response execution unit, and R represents an information relationship between system members; determining a task type based on the node type and the node relationship; and determining the second indicator based on the system capability, the node type, the node relationship, and the task type. Adding task nodes to the OPDAR model, wherein the task nodes have a mapping relationship with the nodes in the OPDAR model; using a path number-based method to analyze the task space flexibility of the target defense system architecture, including: determining the tasks to be completed by each response execution node A; checking whether the attack range of the execution node A overlaps with the movement range of the task node M; checking whether the movement range of the task node M overlaps with the detection range of the intelligence node O, and if there is overlap, constructing an OPDAMR structure of the row node A, the task node M, and the intelligence node O; calculating the number of loops passing through each task node M according to the OPDAMR structure; determining the number of relationships in the loop and the total number of relationships of the corresponding class according to the number of loops passing through each task node M, and calculating the fluency of each task node M; determining the flexibility of the target defense system architecture based on the path number according to the number of loops and the fluency of the task node M; The evaluation steps of the autonomy evaluation model include: determining the system operation and maintenance autonomy index based on the third data and the system operation dimension of the target defense system architecture; determining the system architecture autonomy index based on the system unit composition dimension of the target defense system architecture, and determining the third index based on the system operation and maintenance autonomy index and the system architecture autonomy index.

2. The defense system architecture flexibility analysis and evaluation method according to claim 1, characterized in that: The constructing a multi-tuple according to the first data includes: Obtain mission tasks, environmental constraints, system components and the relationship between system components from the first data, and determine a multi-tuple based on the mission tasks, environmental constraints, system components and the relationship between system elements, wherein the system components include a capability set, an activity set, a capability measurement indicator set, an activity effect set, a resource set and an executor set, wherein the relationship between system elements includes a mapping relationship between capabilities and activities, an association relationship between activities and effects, a measurement indicator associated with each effect corresponding to each capability and each activity, a resource association relationship between activities and an association relationship between activities and executors; based on the multi-tuple, determine the association relationship between executors and resources, and determine the association relationship between capabilities and measurement indicators.

3. The defense system architecture flexibility analysis and evaluation method according to claim 2, characterized in that: The system capability is quantified using a capability assessment method to obtain a capability quantification result, and the intrinsic capability measurement assessment of the target defense system architecture is performed based on the multi-tuple and the capability quantification result to obtain a capability indicator level, including: quantifying the system capability by using at least one of activity and effect, qualitative and quantitative analysis methods, Bayesian network, and functional dependency network analysis methods to obtain capability quantification results; Obtaining a capability activity set, an activity effect set, a capability indicator set, and an activity effect indicator from the capability quantification result, wherein the activity effect indicator has multiple levels; Determining the capability requirement level for each indicator in the capability indicator set; For each capability indicator in the capability activity set, determine the first scoring of the capability indicator value based on the capability indicator description, target description, indicator value and activity effect grade model; Normalize the first scoring of the capability index values ​​according to the capability requirement level to obtain the normalized capability index level; Assign importance to the activities corresponding to the capabilities, determine the capabilities and corresponding activities and activity effects, and obtain the capability indicator weights between the capabilities and capability indicators; The normalized capability index level is converted into a capability level frequency vector. The components of the capability level frequency vector are calculated based on the weight sum of the level components of the capability level frequency vector in the activity effect index. The capability level frequency distribution vector is determined based on the components of the capability level frequency vector. The capability level frequency distribution vector is used to represent the distribution of capability index levels and the weight ratio of capability index to the total number of indicators. The ability level with the highest weight ratio is selected as the measurement value of the ability, and the result of the ability intrinsic measurement evaluation is determined by the measurement value.

4. The defense system architecture flexibility analysis and evaluation method according to claim 3, characterized in that: Determining the first indicator according to the capability indicator level includes: Calculate the measurement of battlefield perception capability based on capability indicator weights and capability indicator levels, as well as capability indicators of battlefield perception capability, where battlefield perception capability includes intelligence acquisition capability, environmental perception capability, and networking application capability; Based on the measurement of battlefield perception capability, determine the capability reduction resilience index, capability recovery resilience index, capability recovery cycle, and capability recovery ratio of the target defense system architecture. The capability reduction resilience index is used to characterize the ratio of the total capability reduction to the theoretical total capability at that stage. The capability recovery resilience index is used to characterize the ratio of the total capability recovery to the ideal total capability recovery at that stage as an indicator for evaluating capability recovery resilience. The capability recovery cycle is the length of time it takes for the system capability to decline from failure to recover to stability. The minimum capability recovery time is used to characterize the ratio of the capability measure after the system capability declines from failure to recovery to stability to the system capability measure under normal conditions. A first indicator is obtained according to the capacity reduction resilience indicator, the capacity recovery resilience indicator, the capacity recovery period and the capacity recovery ratio, and the resilience assessment model is determined according to the first indicator.

5. The defense system architecture flexibility analysis and evaluation method according to claim 1, characterized in that: The step of constructing a structural model of the target defense system architecture using the OPDAR model according to the second data, and determining node types and node relationships of the target defense system architecture according to the structural model includes: Obtaining system members, information relationship matrices between members, attributes of system members, and information relationship attribute matrices from the structural model according to the second data; According to the OPDAR model, the relationship types of the information in the target defense system architecture are divided into intelligence relationships, command and control relationships, and collaborative relationships. Intelligence relationships are used to represent the original information obtained by surveillance devices, as well as the fused or integrated intelligence situation information generated by intelligence processing; the information transmitted is used by command agencies to command or control subordinate forces and counter equipment; and the information generated by system members reporting their own and surrounding environment status or sharing it with friendly neighbors.

6. The defense system architecture flexibility analysis and evaluation method according to claim 5, characterized in that: The determining of the task type according to the node type and the node relationship, and determining the second indicator according to the system capability, the node type, the node relationship, and the task type, includes: Determine the number of tasks, task differences, and costs to be completed by the target defense system architecture based on the structural model and information relationship types; A second indicator of the target defense system architecture is determined according to the number of tasks, task differences, cost, and structural task flexibility indicators, wherein the second indicator is used to characterize the architectural flexibility of the target defense system architecture based on tasks.

7. The defense system architecture flexibility analysis and evaluation method according to claim 1, characterized in that: The obtaining of the third data and determining a system autonomy indicator according to a system operation dimension of the target defense system architecture includes: Obtaining autonomy data of the autonomous behaviors from the third data, determining a capability of each autonomous behavior based on the autonomy data, and determining an autonomy index of each autonomous behavior based on the capability of the autonomous behavior, wherein the autonomous behaviors include reconnaissance and early warning, command and control, joint strike, and protection and assurance; Determine the system autonomy index based on the autonomy index of all autonomous behaviors.

8. The defense system architecture flexibility analysis and evaluation method according to claim 1, characterized in that: The determining of the system architecture autonomy index according to the system unit composition dimension of the target defense system architecture further includes: The system architecture autonomy index is determined by at least one of the following: autonomous collaborative loop ratio, collaborative loop decision creativity, system architecture autonomous evolution effectiveness, system architecture autonomous evolution success rate, and average time consumption rate of system architecture autonomous evolution; The proportion of autonomous collaborative loops , taking the ratio of OODA loops in the target defense architecture to all OODA loops as the evaluation indicator; The collaborative loop decision-making creativity is achieved through the formula OK, among them ,in for Decision nodes in the loop, Indicates the creativity level of the node, is the set of loops with creativity level i, where j represents the decision node number, The maximum number of creative levels in the OODA loop of the target defense architecture is used as the collaborative loop decision creativity of the system; wherein, the architecture autonomous evolution effectiveness EE is expressed by the formula OK, among them Indicates system capability. The capability standard required by the mission task of the target defense system architecture at the current stage is , the ability of system evaluation after architecture evolution is ; Among them, the architecture autonomous evolution success rate ESR is calculated by the formula Determine, where N is the number of evolution adjustments, and the effectiveness of each autonomous evolution is EE i ; Average time consumption rate of architecture automation evolution Av_T R , through the formula The running time of the architecture before evolution is t1 and t2, and the corresponding evolution time is , The time it takes to evolve the architecture, and The architecture running time before evolution is t1 and t2, and the evolution time is , then the time consumption rate of architecture evolution is for: 。 9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the defense system architecture flexibility analysis and evaluation method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The storage medium stores a program, and the program is executed by a processor to implement the defense architecture flexibility analysis and evaluation method according to any one of claims 1 to 8.

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