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

Abstract

The invention provides a defense system architecture flexibility analysis and evaluation method, electronic equipment and a storage medium. The defense system architecture flexibility analysis and evaluation method comprises the steps that a first server obtains first data, second data and third data from a second server; analyzing the first data, the second data and the third data through the first server by adopting a toughness evaluation model to respectively obtain a first index, a second index and a third index; and determining a flexibility index of the target defense system architecture according to the first index, the second index and the third index through the first server, and returning the flexibility index to the client. The method has the beneficial effects that the capability of passively dealing with various uncertain changes of the distributed combat equipment system is improved, measures can be predicted and actively taken, and the mission of combat missions carried by the system is kept to be completed.

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 complexity of the network in 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 various types of unmanned equipment integrated into the system, including unmanned ISR equipment, communication and navigation equipment, unmanned confrontation systems, etc. (2) The structure determines the function. The flexibility of the structure determines the flexibility of the unmanned combat system architecture and is the basis for realizing the capability characteristics of the unmanned combat system architecture such as "dynamic openness, flexible access, and flexible reorganization".

[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 certain 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 adaptive characteristics; (3) Openness: The architecture can easily add new members or update or withdraw existing members; (4) Dynamic combination: During the operation of the architecture, different members can be dynamically combined according to task requirements to 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, so that the architecture can adapt to diverse tasks and complex and changing environments; (7) Mobility: The members of the architecture have certain flexibility and mobility in physical space to adapt to the rapid and mobile characteristics of modern confrontation, 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 adjustment of the architecture is completed within the specified time.

[0005] The existing technology does 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 conduct effective response and processing. 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 flexibility of the defense system architecture.

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

[0008] Acquire the 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 the system elements, wherein the system elements include capabilities, activities, activity effects, capability measurement indicators, resources and executors, wherein the relationship between the 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] The first data, the second data and the third data are analyzed 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 a flexibility analysis and assessment result of a target defense system architecture is determined according to the first indicator, the second indicator and the third indicator;

[0010] The evaluation steps of the resilience evaluation model include: constructing a multi-tuple according to the first data, the multi-tuple including the mission task, environmental constraints, system components and element associations of the target defense system architecture, and the multi-tuple is used to characterize the system capability of the target defense system architecture; quantifying the system capability by a capability evaluation method to obtain a capability quantification result, and performing an intrinsic capability measurement evaluation on the target defense system architecture according to the multi-tuple and the capability quantification result to obtain a capability indicator level; and determining the first indicator according to the capability indicator 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 according to the second data, determining the node type and node relationship of the target defense system architecture according to the structural model; determining 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;

[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 according to 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 by a capability evaluation method to obtain a capability quantification result, and the intrinsic capability measurement evaluation of the target defense system architecture is performed according to the multi-tuple and the capability quantification result to obtain a capability indicator level, including:

[0016] At least one of activity and effect, qualitative and quantitative analysis methods, Bayesian network and functional dependency network analysis methods is used to quantify the system capability and 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] Determine the capability requirement level of 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] The first scoring of the capability index value is normalized 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 the 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 index frequency vector. The components of the capability index frequency vector are calculated based on the weights of the capability index frequency vector in the level components of the activity effect index. The capability index frequency distribution vector is determined based on the components of the capability index frequency vector. The capability index frequency distribution vector is used to characterize the distribution of the capability index level and the weight ratio of the 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 assessment is determined by the measurement value.

[0024] According to the defense system architecture flexibility analysis and evaluation method, the first indicator is determined according to the capability indicator level, including:

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

[0026] According to the measurement of battlefield perception capability, the capability reduction resilience index, capability recovery resilience index, capability recovery cycle and capability recovery ratio of the target defense system architecture are determined. The capability reduction resilience index is used to characterize the ratio of the total capability reduction to the theoretical total capability of this stage. The capability recovery resilience index is used to characterize the ratio of the total capability recovery to the ideal total capability recovery of this stage as an indicator for evaluating capability recovery resilience. The capability recovery cycle is the length of time from the decline of system capability from failure to the recovery to stability. The minimum capability recovery time is used to characterize the ratio of the capability measurement after the system capability declines from failure to the recovery to stability to the system capability measurement under normal conditions.

[0027] A first indicator is obtained according to a capacity reduction resilience indicator, a capacity recovery resilience indicator, a capacity recovery period and a 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, the structural model of the target defense system architecture is constructed using the OPDAR model according to the second data, and the node type and node relationship of the target defense system architecture are determined according to the structural model, including:

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

[0030] According to the OPDAR model, the relationship types of the information of the target defense system architecture are divided into intelligence relationships, command and control relationships, and collaborative relationships. The intelligence relationship is used to characterize the original information obtained through the monitoring device, as well as the fused or integrated intelligence situation information generated by intelligence processing; the information transmitted is used by the command organization to command or control the subordinate troops and the information generated in the process of confrontation equipment; the system members report the status of themselves and the surrounding environment or share the generated information with friendly neighbors.

[0031] According to the defense system architecture flexibility analysis and evaluation method, the task type is determined according to the node type and the node relationship, and the second indicator is determined according to 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 according to the structural model and the relationship type of the information;

[0033] A second indicator of the target defense system architecture is determined according to the number of tasks, task differences, costs, 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] Adding task nodes in 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 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; according to the OPDAMR structure, calculate the number of loops passing through each task node M; determine the number of relations in the loop and the total number of relations of the corresponding class according to the number of loops passing through each task node M, and calculate the fluency of each task node M; according to the number of loops and 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, the step of 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 behavior from the third data, determining the capability of each autonomous behavior according to the autonomy data, and determining the autonomy index of each autonomous behavior according to the capability of the autonomous behavior, wherein the autonomous behavior includes reconnaissance and early warning, command and control, joint strike, protection and guarantee;

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

[0040] According to the defense system architecture flexibility analysis and evaluation method, the system architecture autonomy index is determined according to the system unit composition dimension of the target defense system architecture, and further includes:

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

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

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

[0044]

[0045] Determine, among which 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 creativity level with the largest number in the OODA loop of the target defense system architecture is used as the collaborative loop decision creativity of the system; wherein the architecture autonomous evolution effectiveness EE is calculated by the formula

[0046]

[0047] OK, where C i ,i=1,2,…,N C Indicates the system capability. The capability standard required by the mission task 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 of architecture automation 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] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes 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 easily understood from the description of the embodiments in conjunction with the following drawings, in which:

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

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

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

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

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

[0068] Figure 6 Schematic diagram of the task flexibility calculation steps of an embodiment of the present invention.

[0069] Figure 7 It is a schematic diagram of the steps for searching 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] Fig. 9 It is a schematic diagram of cost calculation steps for establishing a feasible unit combination scheme interface for combat missions according to an embodiment of the present invention.

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

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

[0074] Fig.12 Schematic diagram of the analysis steps of the path number method according to an embodiment of the present invention.

[0075] Fig.13 4 is a schematic diagram of a full connectivity situation according to an embodiment of the present invention.

[0076] Fig.14 It is a schematic diagram of the autonomous indicator analysis process of an embodiment of the present invention.

[0077] Fig.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 embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. In the subsequent description, the use of suffixes such as "module", "component" or "unit" used to represent elements is only for the purpose of facilitating the description of the present invention, and has no specific meaning in itself. Therefore, "module", "component" or "unit" can be used in a mixed manner. "First", "second" and the like are only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of the indicated technical features. In this subsequent description, the continuous numbering of the method steps is for the convenience of review and understanding. In combination with the overall technical solution of the present invention and the logical relationship between the various steps, adjusting the implementation order between the steps will not affect the technical effect achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0079] refer to Figure 1 , Figure 11 is a schematic diagram of a defense architecture flexibility analysis and evaluation method system of 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 confrontation equipment and communication equipment, which constitute the defense architecture; wherein the second server 200 is used to obtain a series of basic equipment information, status information, combat information and communication information of confrontation equipment, communication equipment and the like from the defense architecture 100, wherein the first server 300 is used to respond to the client 400 to the target defense architecture 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 adopts a resilience assessment model to analyze the first data to obtain a first indicator; the first server 300 adopts a flexibility assessment model to analyze the second data to obtain a second indicator; the first server 300 adopts an autonomy assessment model to analyze the third data to obtain a 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 of reduction of the impact of the target defense system architecture on the system, the degree of system change and the degree of system impact on the environment when the target defense system architecture is affected by the environment.

[0080] Reference Figure 2 , Figure 2 It is a schematic diagram of the process flow of the 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 executor; 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 multi-tuple according to the first data, the multi-tuple including the mission task, environmental constraints, system components and element association relationships of the target defense system architecture, and the multi-tuple is used to characterize the system capability of the target defense system architecture;

[0086] S120, quantify the system capability by using the capability assessment method to obtain the capability quantification result, and perform intrinsic capability measurement assessment on the target defense system architecture according to the multi-tuple and the capability quantification result to obtain the capability indicator level;

[0087] S130, determining 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 relationships 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 relationships 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 a executor set, where the relationships between system elements include a mapping relationship between capabilities and activities, an association relationship between activities and effects, measurement indicators 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, 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 accomplish;

[0094] (2)cons represents the environmental conditions and constraints under which the 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 a 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 Capability l When f li =1, otherwise f li =0;

[0104] ​ Indicates the relationship between an activity and an effect. j Used to describe an activity i When 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 metrics, 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, which indicates the requirements at multiple time points.

[0106] Indicates the resource association relationship between activities, h ij Indicates activity act i Output to the 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, ip =1, otherwise u ip =0.

[0108] In some embodiments, the system capability is quantified by a capability assessment method, such as an assessment capability and capability index system based on a Bayesian network, and a Bayesian network analysis is used to comprehensively obtain the top-level capability level. In some embodiments, the intrinsic capability measurement and evaluation of the target defense system architecture is performed based on the multi-tuple and capability quantification results, and the capability index level is obtained as follows:

[0109] The set of capability activities can be denoted 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 level is assumed to have L levels, namely level 1, level 2, ..., level L.

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

[0111] The initial evaluation of the capability indicator level is to determine the initial level of the capability indicator value for each capability activity based on the capability indicator description, target description, indicator value (preset value) and activity effect level model, using a vector Indicates that satisfies s k ∈{1,2,…,L},k=1,2,…,N I .

[0112] Normalize the ability index level. According to the level requirements of the ability index in the assessment, 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 represented by a matrix Indicates. Activity effect 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 irrelevant 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), and relatively important (denoted as C), The values ​​are 5, 3, and 1 respectively. When 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, then 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 is

[0129]

[0130] Remember 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 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 the sub-capability k In the hierarchical analysis method, the weight is realized 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 has 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 ability level frequency vector 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] gives 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 frequency distribution vector of capability level, the level with the highest proportion is selected as the measurement value EV of the capability:

[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 weight of the capability indicator (land and sea target coverage) relative to the activity effect (coverage) is 0.33:

[0159]

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

[0161]

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

[0163]

[0164] Then, 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 capability index weight of intelligence perception capability can be obtained according to the above method. Let the capability index weight be wk (k = 1, 2, ... M), and the corresponding capability 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 measure 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 the 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 under normal conditions 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 measures

[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 For R AAbi =(S1+S2) / (S1+S2+S5+S6)=0.723, capacity recovery resilience index R RAbi For R RAbi =(S3+S4) / (S3+S4+S7+S8)=0.799The capacity recovery period is T CC =30min. Capacity 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 according to the second data, and determining node types and node relationships of the target defense system architecture according to the structural model;

[0173] S150, determining 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.

[0174] In some embodiments, based on the OPDAR architecture modeling method, the OPDAR model is a theoretical model for architecture metric analysis, where 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. The formal definition of the model is 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 i The current status of Is a member of the system i The jth attribute value of ij ] is the information relationship attribute matrix, It is a relationship ij The current status of It is a relationship ij The k-th attribute value of .

[0177] The OPDAR model considers the scope of responsibility of each node to be a circle centered on the node, so the attributes of each node are introduced 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 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 expressed 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 transmitted by intelligence relations includes the original information obtained by reconnaissance and surveillance detection equipment, as well as the fused or integrated intelligence situation information generated by intelligence processing. Relationship examples include O-→A, O-→P, O-→D, P-→A, P→D, and P→P.

[0206] (2) Accusatory relationships

[0207] The information transmitted by the command-and-control relationship refers to the information generated by the command organization in the process of commanding or controlling subordinate troops 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 surrounding environment status or sharing it with their neighbors. Relationship examples 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] According to the OPDAR model's classification of node and relationship types, the task types in the system architecture include intelligence tasks, command and control tasks, and collaborative tasks. Each task is a combination of corresponding nodes and relationships. The node-relationship combinations corresponding to all types of tasks are enumerated, as shown in Table 2. The flexibility of the system architecture can then 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}, and the 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, and the matrix B is a v×n-order matrix. In order to collaboratively complete combat missions, there needs to be an information interaction relationship between systems or nodes. In this paper, it is considered that the connectivity between nodes that complete combat missions meets the mission requirements. In addition, yrs =1 indicates 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 is

[0216]

[0217] The assessment of mission flexibility can be done from the perspective of the coverage of the system architecture for 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 as many types of combat missions as possible. k =1 means that the current system can complete the kth combat mission without creating a new interface interaction relationship, S k =0 means that 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 there is no need 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 represents the number of system node combination schemes to complete combat mission k, and defines the task change flexibility redundancy as

[0224]

[0225] That is, the average number of node combination schemes for the system to complete a combat mission, indicating that there are several implementation schemes for the system to complete a combat mission. k1 , b k2 , ...b kn ] is the capability requirement vector of task k, and the task variation difference 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) means finding the variance of each column of the matrix X.

[0229] In some implementations, in order to determine the task flexibility index, it is necessary to analyze the number of types of tasks that the system nodes can complete under the constraints of connectivity, which 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 unique.

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

[0232] (3) If the constraints are not met, then 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 The first step is further broken down. Each combat mission has a corresponding feasible solution search strategy. The capabilities required for each mission are judged one by one. 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 can be used to search for node combinations that meet the capacity requirements. but For a feasible solution.

[0234] Figure 7Schematic diagram of the search steps for feasible unit combination schemes for combat missions in an embodiment of the present invention. The second step is further disassembled, and the Warshall algorithm is used to determine the connectivity of each scheme. 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 Schematic diagram of connectivity judgment steps of feasible unit combination schemes for combat missions in an embodiment of the present invention. The third step is further disassembled to judge whether the schemes that cannot directly complete 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 system conditions, 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] Fig. 9 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 analysis and measurement method for architectural flexibility, such as Fig.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, namely O1, O2, P1, P2, D1, D2, A1, and L1. Two tasks need to be completed, and the node requirements of the combat tasks are shown in matrix B. The two combat tasks require the participation of O, P, D, A and O, P, D, L nodes 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 according to 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 for 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 cost h67 of establishing the interface, the schemes {O1, P1, D2, A1}, {O1, P2, D2, A1}, {O2, P1, D2, A1}, and {O2, P2, D2, A1} can meet the connectivity requirements and complete 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 task flexibility index of the system 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, but the combat mission difference is 0.2, which is low, indicating that the difference between tasks is 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 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 moving 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 moving area in response to the task;

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

[0256] According to the attributes Location, aRadius and bRadius of M, the main activity range of the task node can be obtained. According to the known attack range of node A and the detection range of node O, the connection lines A->M and M->O can be obtained. Adding them to the OPDAR model will result in the OPDAMR model.

[0257] refer to Fig.11 The simple OPDAMR model shown in the figure, in which 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 moving 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 Fig.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 variation space. The architecture flexibility analysis based on the number of paths is given by Fig.12 The first three steps are the process of building a complete OPDAMR structure, which is responsible for adding some foreseeable changes to the static structure of the system; the last three steps are the process of calculating the flexibility of the system architecture. The meaning of each step is explained in detail as follows.

[0259] Step 1: Determine the tasks that each response execution node A needs to complete. According to the combat 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 attributes Location and Radius of node A, which is a circle with node A as the center. Similarly, determine the movement range of task node M based on the attributes Location, aRadius and bRadius of task node M, which is an ellipse with M as the center. Pay attention to whether the two areas overlap, and connect the overlapping nodes A and M, and add the impact of the movement of task node M 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 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 task node M on node O is added to the static structure. The new structure formed by combining the structure of AMO with the previous OPDAR structure is called OPDAMR structure;

[0262] Step 4: Calculate the number of loops passing through each task node M. According to 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 more loops there are, the greater the possibility of completing the task and the better the flexibility;

[0263] Step 5: Calculate the fluency of each task node M. According to the loop passing through each task node M, consider the ratio of the number of each type of relationship in the loop to the total number of such relationships and other indicators, propose a fluency method, and 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 to be the flexibility of completing each task. The flexibility of all task nodes is summed and averaged according to 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, which is represented by cir. This is the same as the calculation result of step 4 in the path number method analysis step, so I will not go into details here. This indicator represents the impact of the width of the "small river" 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] It is believed that the larger the ratio, the better the flexibility, which is represented by rat. Since there are many types of relationships in the loop to complete the task, for example, the loop to complete the M task O->D->A->M->O passes through 4 relationships, there are multiple ratios, and the ratios are in a summation relationship. This indicator indicates the degree to which each section of the "stream" can be widened compared to its original width;

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

[0270] The total number of a certain type of relationship (such as the relationship between O->P) in the architecture should not be too small. If there is only one, then the number of flows through this type of relationship can only be 1 at most, and the flexibility is of course very poor. It is believed that the more a certain type of relationship there is in the architecture, the better the effect. At the same time, when the number is relatively large, the difference between them is not very large. For example, if there are 5 or 6 O->P relationships in the architecture, the impact on the flexibility of the architecture is not very different. Therefore, this indicator is expressed as the logarithm of the number of a certain type of relationship, expressed as lg(num). This indicator represents the maximum value that the "small river" can be widened, and together with the second indicator, it represents the widening capacity of the "small river". Therefore, this indicator and the second indicator are in a segmented multiplication relationship.

[0271] According to the three indicators proposed above, the first indicator is the same as the result obtained in step 4 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 step 4 of the analysis step, where N 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 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 , we multiply the relationship types by segments, and then sum all the relationship types. The result is multiplied 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 Fig.12 As shown, the OPDAR model includes 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 in the responsibility scope of other A and M. At the same time, it is assumed that the moving range of M1 overlaps with the detection range of O1, the moving range of M2 overlaps with the detection range of O2, and the moving range of M3 overlaps with the detection range of O2 and O3.

[0274] refer to Fig.11 As shown in the example diagram of the path number method, it can be found that there are 6 rings to complete task M1, 6 rings to complete task M2, and 32 rings to complete task M3, that is, 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] Among these six loops, there are two O->P relationships, namely O1->P1 and O1->P2, and there are six O->P relationships in the system structure, accounting for 1 / 3 of the total number; similarly, there are six P->D relationships, accounting for 1 / 3 of the total number; there is one D->D relationship, accounting for 3 of the total number, accounting for 1 / 3; there is one D->A relationship, accounting for 4 of the total number, accounting for 1 / 4; there is one A->M relationship, accounting for 3 of the total number, accounting for 1 / 3; there is one M->O relationship, accounting for 4 of the total number, accounting for 1 / 4. Therefore, according to these data, the flexibility of M1 can be obtained by substituting them into formula 4.1 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, the flexibility of M2 and M3 is obtained 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, the result is 18.3, so the flexibility of the architecture based on the number of paths is 18.3.

[0294] The result obtained by this method represents a measure of the flexibility of the architecture, which can be used to compare with the results of other structures, or the data can be normalized between 0 and 1 to represent the degree of flexibility of the architecture. In this case, the flexibility level can be considered to be 0 when the architecture has no loop, and the flexibility level 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 between 0 and 1.

[0295] refer to Fig.13 Schematic diagram of the full connectivity 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 Fig.14 The third indicator analysis process diagram shown in FIG. 1 includes but is not limited to steps S160 to S170:

[0297] S160, determining a system operation and maintenance autonomy indicator according to the third data and the system operation dimension of the target defense system architecture.

[0298] In some embodiments, the autonomy index 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 index 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. One dimension is the system unit composition dimension. Considering the possibility of innovative behavior in the system, it can be measured by the proportion of autonomous collaborative loops in the task collaboration loop in the system architecture, and the intelligence and autonomy indicators of the decision-making unit in the decision-making autonomous collaborative loop.

[0299] (1) Reconnaissance and early warning autonomy: refers to the ability of the system to obtain environmental information through sensors configured on the unmanned system, process this information, establish an environmental model, autonomously form the surrounding situation and judge possible 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 The unmanned system does 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: refers to the system's ability to accept superior tasks, make autonomous decisions, and make action arrangements based on various external input information, tasks, and internal information. Here, indicators such as task understanding, command decision-making, human-computer interaction, and action control are used 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, are monitored by humans, and provide reasons for their decisions 5 Unmanned systems collaborate to perform all command and control activities, without the need for human monitoring or explanation of reasons

[0307] (3) Joint strike autonomy: refers to the ability of the system's multiple strike function platforms to coordinate and cooperate with each other in multiple dimensions, such as land, sea, air, space, and power grids, to carry out full-domain strikes against targets and achieve the goal of destroying incoming targets. Indicators such as electronic countermeasures, network attack and defense, and firepower strikes are used here 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: refers to the ability of the system to resist enemy attacks or adapt to environmental changes during mission execution and 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] The comprehensive protection capability is 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 human or unmanned systems taking the lead depending on different situations 4 Unmanned systems collaborate to perform most of the 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: refers to the auxiliary support capabilities provided by various elements within the system to ensure the successful completion of the task, 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] The comprehensive support capability is 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 of the 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 above six aspects are combined to divide the system's autonomy level, 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 tasks, under human supervision 5 Unmanned systems collaborate to perform all campaign and tactical tasks and some strategic tasks, and are monitored by humans 6 Unmanned systems accept top-level missions 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 a system architecture autonomy index according to the system unit composition dimension of the target defense system architecture, and determining a third index according to the system operation and maintenance autonomy index and the system architecture autonomy index.

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

[0325] In some embodiments, the autonomy of system operation is to evaluate the autonomy of the system from the capabilities shown in the system action process. However, due to the particularity of the system, the system has few opportunities to truly perform combat tasks. Before the system is operational, the autonomy level of the system can be indirectly reflected through the autonomy indicators of the system architecture. This paper believes that the autonomy indicators of the system include at least two aspects: 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 proportion of OODA loops with high autonomous collaborative capabilities in the system is used as an evaluation indicator. The higher the autonomy of the OODA loops in the system and the more they are distributed in the system, the higher the autonomy of the system. The autonomy level of each perception, planning, decision-making, and action node must meet a certain level before the loop can be considered to have autonomous collaborative capabilities. In addition, when the node is a manned node, 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 , 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. Let all nodes in the system be represented by n1, n2, ... n N .

[0332]

[0333] Then Lp i It can be expressed as i N The ring consists 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 according to 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 and proactively deal with changes.

[0337] set up for L p j Decision nodes in the loop, Indicates the creativity level of the node. is a collection 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 autonomy evolution is used to compare the degree of match between the system's evolved capabilities and mission requirements, to measure whether the architecture autonomy 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 capability standard required by the current mission of the distributed defense combat system 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}, 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 architecture autonomous evolution 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 automation 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. Fig.15 , the architecture undergoes autonomous evolution at time points a and b. The architecture runs for t1 and t2 before evolution, 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 Fig.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 needs to spend half of its time adjusting the system architecture. Generally speaking, when the average time consumption rate of architecture evolution exceeds a certain threshold, it can be considered that the architecture evolution takes 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 comprising 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, for example, 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, wherein the storage medium stores a program, and the program is executed by a processor to implement the defense architecture flexibility analysis and evaluation method as described above.

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

[0362] The embodiment of the present invention also discloses a computer program product or a 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 the processor executes the computer instructions, so that the computer device executes the aforementioned defense architecture flexibility analysis and evaluation method.

[0363] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, 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 is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed in the embodiment of the present invention, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also 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, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

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

[0366] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, if necessary, processing in another suitable manner, and then stored in a computer memory.

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

[0368] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0369] Although the 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 present invention, and that the scope of the present 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 described embodiments. Those skilled in the art may 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 the 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 the system elements, wherein the system elements include capabilities, activities, activity effects, capability measurement indicators, resources and executors, wherein the relationship between the 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; The first data, the second data and the third data are analyzed 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 a flexibility analysis and assessment result of a target defense system architecture is determined according to the first indicator, the second indicator and the third indicator; The evaluation steps of the resilience evaluation model include: constructing a multi-tuple according to the first data, the multi-tuple including the mission task, environmental constraints, system components and element associations of the target defense system architecture, and the multi-tuple is used to characterize the system capability of the target defense system architecture; quantifying the system capability by a capability evaluation method to obtain a capability quantification result, and performing an intrinsic capability measurement evaluation on the target defense system architecture according to the multi-tuple and the capability quantification result to obtain a capability indicator level; and determining the first indicator according to the capability indicator level; The evaluation steps of the flexibility evaluation model include: constructing a structural model of the target defense system architecture using the OPDAR model according to the second data, determining the node type and node relationship of the target defense system architecture according to the structural model; determining 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; 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 is characterized in that: The step of constructing a multi-tuple according to the first data comprises: 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 is characterized in that: The system capability is quantified by a capability evaluation method to obtain a capability quantification result, and the intrinsic capability measurement evaluation of the target defense system architecture is performed according to the multi-tuple and the capability quantification result to obtain a capability indicator level, including: At least one of activity and effect, qualitative and quantitative analysis methods, Bayesian network and functional dependency network analysis methods is used to quantify the system capability and obtain a capability quantification result; 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; Determine the capability requirement level of 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; The first scoring of the capability index value is normalized 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 the 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 index frequency vector. The components of the capability index frequency vector are calculated based on the weights of the capability index frequency vector in the level components of the activity effect index. The capability index frequency distribution vector is determined based on the components of the capability index frequency vector. The capability index frequency distribution vector is used to characterize the distribution of the capability index level and the weight ratio of the 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 assessment is determined by the measurement value.

4. The defense system architecture flexibility analysis and evaluation method according to claim 3 is characterized in that: The 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, and based on capability indicators of battlefield perception capability, where battlefield perception capability includes intelligence acquisition capability, environmental perception capability, and networking application capability; According to the measurement of battlefield perception capability, the capability reduction resilience index, capability recovery resilience index, capability recovery cycle and capability recovery ratio of the target defense system architecture are determined. The capability reduction resilience index is used to characterize the ratio of the total capability reduction to the theoretical total capability of this stage. The capability recovery resilience index is used to characterize the ratio of the total capability recovery to the ideal total capability recovery of this stage as an indicator for evaluating capability recovery resilience. The capability recovery cycle is the length of time from the decline of system capability from failure to the recovery to stability. The minimum capability recovery time is used to characterize the ratio of the capability measurement after the system capability declines from failure to the recovery to stability to the system capability measurement under normal conditions. A first indicator is obtained according to a capacity reduction resilience indicator, a capacity recovery resilience indicator, a capacity recovery period and a 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 the node types and node relationships of the target defense system architecture according to the structural model includes: Acquire system members, information relationship matrix between members, attributes of system members, and information relationship attribute matrix from the structural model according to the second data; According to the OPDAR model, the relationship types of the information of the target defense system architecture are divided into intelligence relationships, command and control relationships, and collaborative relationships. The intelligence relationship is used to characterize the original information obtained through the monitoring device, as well as the fused or integrated intelligence situation information generated by intelligence processing; the information transmitted is used by the command organization to command or control the subordinate troops and the information generated in the process of confrontation equipment; the system members report the status of themselves and the surrounding environment or share the generated information 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 according to the structural model and the relationship type of the information; A second indicator of the target defense system architecture is determined according to the number of tasks, task differences, costs, 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 6, characterized in that: The method further comprises: Adding task nodes in 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 architecture, including: 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; according to the OPDAMR structure, calculate the number of loops passing through each task node M; determine the number of relations in the loop and the total number of relations of the corresponding class according to the number of loops passing through each task node M, and calculate the fluency of each task node M; according to the number of loops and fluency of the task node M, determine the flexibility of the target defense system architecture based on the number of paths.

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

9. 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 also includes: The system architecture autonomy index is determined by at least one of the following: autonomous collaborative loop ratio, collaborative loop decision creativity, architecture autonomous transformation effectiveness, architecture autonomous transformation success rate, and architecture autonomous transformation average time consumption rate; The proportion of autonomous collaborative loops is A L , taking the ratio of the OODA loop in the target defense system architecture to all OODA loops as an evaluation indicator; The collaborative loop decision creativity is achieved through formula A D Determine, among which 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, It is used to characterize the most creative level in the OODA loop of the target defense system architecture as the collaborative loop decision creativity of the system; wherein the architecture autonomous evolution effectiveness EE is calculated by the formula OK, where C i ,i=1,2,...,N C Indicates the system capability. The capability standard required by the mission task 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}; 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 of architecture automation Off_T R , through the formula 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 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:

10. 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 architecture flexibility analysis and evaluation method according to any one of claims 1 to 9.

11. 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 9.

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