Orchestration and control adaptive modeling method based on organization right change theory

Abstract

A command and control adaptation modeling method based on an organization right change theory comprises the steps that internal components of a defense system are obtained, defense system nodes are set based on the organization right change theory, and command and control adaptation model constraints are set in combination with an OODA combat ring theory and the organization right change theory; according to the commanding and controlling adaptive model constraint, determining an optimization target, the optimization target being commanding and controlling node load balancing RMSall and equipment load balancing E of the commanding and controlling adaptive model; and obtaining a minimum value of command and control node load balancing RMSall and a maximum value of equipment load balancing E, completing construction of a command and control adaptive model, and performing resolving by using an improved sparrow search algorithm to obtain an adaptive command and control relation matrix. The organization weight change theory and the multi-objective optimization design are organically integrated into the system architecture design process, the adaptation capability of the defense system is improved, the dynamic adaptation of the defense system is realized from the perspective of command and control relation reconstruction, and the requirement of instant and dynamic resource recombination faced by the defense system is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of defense system command and control, and specifically relates to a command and control adaptive modeling method based on organizational contingency theory. Background Art

[0002] In view of the dynamic confrontation and game war forms of the system in the complex battlefield environment in the future, the defense system needs to have the ability to adjust itself internally according to the dynamic changes of the battlefield. In essence, the defense system is a complex giant system oriented to specific missions and tasks, which is organically formed by multiple factors such as early warning detection, command and control, interception and destruction, and electronic countermeasures according to a certain combat process. How to achieve adaptive adjustment of this giant system is a current research hotspot.

[0003] The main defect of existing design methods is that it is difficult to describe the hierarchical structure, interactive relationship, and dynamic behavior of the system during dynamic adaptive design, and they cannot meet the needs of the defense system for instant and dynamic reorganization of resources.

[0004] For example, the patent document with the publication number CN117650988A specifically discloses a command and control structure modeling and evaluation method for clustered unmanned combat scenarios; modeling the command and control network nodes, modeling the information interaction relationship between the command and control network nodes, and constructing a clustered unmanned combat command and control network model based on this; abstracting and refining the improved information flow model existing in the command and control network model, using the improved information flow model to evaluate the efficiency, flexibility, and robustness of the command and control network, and based on this, evaluating the effectiveness of the command and control network based on the improved information flow model. The improved information flow model describes the information action mode of generating, processing, and using information that repeatedly appears in the unmanned combat command and control network, which is more suitable for clustered unmanned combat, and fully considers the command and control mode under unmanned combat. The proposed evaluation method can effectively measure whether the current command and control structure can effectively exert the unmanned cluster collaborative combat capability. However, the technical solution of this patent document cannot meet the needs of the instant dynamic reorganization of complex resources faced by the defense system in the face of complex information flows in complex battlefield environments, and is difficult to describe in order to solve the technical problem of the hierarchical structure and dynamic behavior in the dynamic adaptive design of the system.

[0005] Since the dynamic reorganization of resources is essentially a reconstruction problem of command and control relations, and the realization of command and control relations is based on a specific organizational structure, it is necessary to propose a command and control adaptive modeling method and its related supporting algorithms from the perspective of command and control relationship reconstruction to achieve dynamic adaptability of the system and solve the above-mentioned defects of the prior art. Summary of the invention

[0006] To solve the above technical problems, the present application provides a charge adaptive modeling method based on organizational contingency theory, which includes the following steps:

[0007] Obtain the internal components of the defense system, set the defense system nodes based on the organizational contingency theory, and set the command and control adaptive model constraints by combining the OODA combat loop theory and the organizational contingency theory;

[0008] According to the constraints of the command and control adaptive model, the optimization target is determined, and the optimization target is the command and control node load balancing RMS of the command and control adaptive model. All and equipment load balancing E;

[0009] Get the command and control node load balancing RMS All The minimum value of and the maximum value of equipment load balance E complete the construction of the command and control adaptive model;

[0010] The command and control adaptive model is the load balancing RMS of the command and control node obtained under the constraints of the command and control adaptive model. All Multi-objective optimization model for minimum and maximum value of equipment load balancing E.

[0011] Furthermore, the defense system nodes include command and control nodes C, detection nodes D, impact nodes I and target nodes T, and the impact nodes include firepower nodes p a and electronic countermeasure node p e The control node C, detection node D, influence node I and target node T are combined with the OODA combat ring theory to obtain the defense system organizational structure SN.

[0012] Furthermore, the defense system organizational structure SN is:

[0013] SN=<[(T,D,C,I),f],I C ,E C ,W C >,

[0014] Among them, f represents the command and control relationship between the nodes of the defense system, including the target detection link relationship, information sharing link relationship, information transmission link relationship, decision allocation link relationship, command and control coordination link relationship, and firepower strike link relationship; I C represents the internal workload, that is, the number of equipment directly commanded by a command and control node, which is a positive integer; E C represents the external workload, that is, the number of equipment coordinated by a command and control node, which is a positive integer; W C Total workload of the command and control node.

[0015] Furthermore, the defense system organizational structure SN combines organizational contingency theory to set up the charge adaptation model constraints.

[0016] Furthermore, the constraints of the command and control adaptive model include control right cost constraints, detection resource and detection capability constraints, command and control resource and command capability constraints, firepower resource capability constraints, electronic countermeasure resource and electronic countermeasure capability constraints, information transmission and decision allocation relationship constraints and target damage threshold constraints.

[0017] Further, the load balancing RMSA11 of the command and control node is:

[0018]

[0019] Where N represents the total number of control nodes; h represents the node, h≤N; W represents the load in the organizational structure.

[0020] Further, the equipment load balancing E is:

[0021] E=max(v j ·p j )

[0022] Among them, v j represents the number of attacks, p j represents survivability or joint lethality, and j represents the target type.

[0023] Furthermore, when the detection capability of the detection node D for the j-type target is p dij , the command and control capability of the command and control node C for the target of type j is p ckj , the attack capability of firepower node Pa against target of type j is p ahj , the survivability of target tj is p tj When , the survival ability p is:

[0024]

[0025] Furthermore, after obtaining the command and control adaptive model, the command and control adaptive model is solved by using an improved sparrow search algorithm to obtain the command and control relationship f between the adapted defense system nodes, and to construct a new command and control adaptive model constraint.

[0026] Further, the improved sparrow search algorithm specifically includes the following steps:

[0027] Introduce adaptive mechanism when calculating population fitness;

[0028] Introduce random t-distribution perturbations in the follower stage;

[0029] In the individual fitness stage, a reverse learning strategy is introduced and combined with the Cauchy-Gaussian hybrid operator to improve the quality of the population.

[0030] The beneficial effects of the present invention are as follows: based on the organizational contingency theory, defense system nodes, defense system organizational structure SN, command and control adaptive model constraints are set, optimization goals are determined, and the optimal value of the optimization goal is obtained to complete the construction of the command and control adaptive model. Moreover, by solving the constructed command and control adaptive model, a new command and control relationship matrix can be obtained, and the organizational contingency theory and multi-objective optimization design are organically integrated into the system architecture design process, thereby improving the adaptability of the defense system, and realizing the dynamic adaptability of the system from the perspective of command and control relationship reconstruction. Moreover, the hierarchical structure, the interactive relationship of each node, and the dynamic behavior in the dynamic adaptation process of the defense system are clearly describable in the constructed adaptation model and relationship matrix, thereby meeting the needs of the defense system for instant dynamic reorganization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The flowchart of the charge adaptive modeling method based on the organizational contingency theory according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solution of the present invention is further described below, but the scope of protection claimed is not limited to the description.

[0033] The embodiment of the present invention provides a charge adaptive modeling method based on organizational contingency theory to solve the problem that the hierarchical structure, interactive relationship and dynamic behavior are difficult to describe during the dynamic adaptive design of the system. The specific implementation of the present invention is described in detail below in conjunction with the drawings of the specification.

[0034] The embodiment of the present invention provides a charge adaptive modeling method based on organizational contingency theory, comprising the following steps:

[0035] Obtain the internal components of the defense system, set the defense system nodes based on the organizational contingency theory, and set the command and control adaptive model constraints by combining the OODA combat loop theory and the organizational contingency theory;

[0036] According to the constraints of the command and control adaptive model, the optimization target is determined, and the optimization target is the command and control node load balancing RMS of the command and control adaptive model. All and equipment load balancing E;

[0037] Get the command and control node load balancing RMS All The minimum value of and the maximum value of equipment load balance E complete the construction of the command and control adaptive model;

[0038] The command and control adaptive model is the load balancing RMS of the command and control node obtained under the constraints of the command and control adaptive model. All Multi-objective optimization model for minimum and maximum value of equipment load balancing E.

[0039] In this embodiment, the defense system nodes include command and control nodes C, detection nodes D, impact nodes I and target nodes T. The impact nodes include firepower nodes p a and electronic countermeasure node p e The control node C, detection node D, influence node I and target node T are combined with the OODA combat ring theory to obtain the defense system organizational structure SN.

[0040] The command and control node C, in the defense system, the decision-making entity in the organizational contingency theory is the command and control node C in the defense system and is recorded in the form of a set as C = {C n |n=1,2,3,…,N}, N is the total number of command and control nodes. For any command and control node C i , whose attributes can be determined by the coordinated command rate XT i , command and control rate KZL i , RL i and maximum load ZKFZ i Describe, that is, C i = <XT i ,KZL i ,RL i ,ZKFZ i >.

[0041] The detection node D, in the defense system, the detection entity in the organizational contingency theory is the detection node D in the defense system and is recorded in the form of a set as D = {D m |m=1,2,3,…,M}, M is the total number of detection nodes. s Its properties can be detected by TCP s , detectable capacity TCRL s , target resolution FBL s And the cooperative detection rate XTCL s Describe, that is, d s = <TCP s ,TCRL s ,FBL s ,XTCL s >.

[0042] The firepower node p a , for the firepower node p a , whose properties can be obtained through the interceptable range matching LJP a 、Interceptable means type LJLX a 、Interceptable target capacity LJRL a and interception reaction time LJS a Describe, that is, p a= <LJP a ,LJLX a ,LJRL a ,LJS a >.

[0043] The electronic countermeasure node p e , for the electronic countermeasure node p e , whose properties can be determined by the interference range matching GRP e 、Interference means type GRLX e Describe, that is, p e = <GRP e ,GRLX e >.

[0044] The target node T is the combination of all targets, that is, T = {t i |i=1,2,…,n}.

[0045] In this embodiment, the information interaction relationship between two nodes is combined with the OODA operational loop theory, that is, the complex network adjacency matrix, and the number 1 is used to indicate that there is a relationship between the two nodes, otherwise it is represented as 0, thereby obtaining a relationship matrix corresponding to the relationship between the two nodes.

[0046] Therefore, the defense system organizational structure SN based on the distributed control mode is described by a set of four-element equations.

[0047] The defense system organizational structure SN is:

[0048] SN=<[(T,D,C,I),f],I C ,E C ,W C >,

[0049] Among them, f represents the command and control relationship between the nodes of the defense system, including the target detection link relationship, information sharing link relationship, information transmission link relationship, decision allocation link relationship, command and control coordination link relationship, and firepower strike link relationship; I C represents the internal workload, that is, the number of equipment directly commanded by a command and control node, which is a positive integer; E C represents the external workload, that is, the number of equipment coordinated by a command and control node, which is a positive integer; W C Total workload of the command and control node.

[0050] According to the characteristics of cluster defense operations, the equipment layer in the organizational structure can be divided into firepower nodes according to their functions. a And detection node D. For firepower node p aFor cluster targets, the first thing to do is to have the ability to strike the target, including the probability of killing, combat range, amount of ammunition, etc. In addition, due to the firepower node p a It is controlled by the command and control node C, so it needs to meet the command constraints of the command and control node C. Similarly, for the detection node D, it must first meet the detection capability of the detection node D for cluster targets, including detection accuracy, detection range, channel capacity and other constraints, and at the same time meet the collaborative combat constraints of the command and control node C.

[0051] Assume that in a certain operation, the set of incoming clusters is represented by T = {t1, t2, …, t n}, n is the total number of targets, and the set of detection nodes D in the system is represented by D = {d1, d2, ..., d m}, m is the number, and the command and control node set is represented by C = {c1, c2, ..., c p}, the number is p, the firepower node is p a The set is represented by A = {a1, a2, ..., a q The number of nodes is q; the coordination matrix between the detection node D and the command and control node C is represented by DC. When the element dc in DC mp =1 indicates that there is a command and control relationship between the mth detection node D and the pth command and control node C, otherwise it is 0; the command and control node C and the firepower node p a The synergy matrix between them is represented by CA. When the element ca in CA pq =1 indicates that the pth command and control node and the qth firepower node p a There is a command-control relationship between them, otherwise it is 0, that is:

[0052]

[0053]

[0054] Define the system for specific goals j The relationship between the equipment involved in attacking the target and the target is represented by the participation matrix, and the combination of participation matrices of all links represents the decision matrix of the entire combat process.

[0055] The detection node D participates in the matrix DT:

[0056]

[0057] Among them, when dt dij =1, indicating detection node d i Participate in attacking the target j Otherwise, dt dij =0.

[0058] The command and control node C participates in the matrix CT:

[0059]

[0060] Among them, when ct ckj =1, indicating the command and control node c k Participate in attacking the target j Otherwise, ct ckj =0.

[0061] The firepower node p a or the electronic countermeasure node p e Participation Matrix AT:

[0062]

[0063] Among them, the firepower node p a or the electronic countermeasure node p e The participation situation is indicated by a h Indicates that when at ahj =1, indicating that the firepower node or electronic countermeasure node a h Participate in attacking the target j Otherwise, at ahj =0.

[0064] Therefore, only when dt dij =ct ckj =at ahj =1 and dc ik =ca kh =1, a closed command and control link is formed. At this time, the detection node d i , the command and control node c k , the firepower node or electronic countermeasure node a h Jointly participate in attacking targets j . It can be formally expressed as:

[0065] k j ={d i ,dt dij ,dc ik ,c k ,ct ckj ,ca kh ,a h ,at ahj ,t j};

[0066] For all cluster targets, then:

[0067] K={k i |i=1,2,…,n};

[0068] Furthermore, combined with the organizational structure network description and constraint rules of the drone cluster terminal defense system, the drone cluster terminal defense system architecture model SoS can be described as:

[0069] SoS=<SN,K> ;

[0070] In this embodiment, the defense system organizational structure SN is combined with the organizational contingency theory to set the charge adaptation model constraints.

[0071] In this embodiment, the command and control adaptive model constraints include control right cost constraints, detection resource and detection capability constraints, command and control resource and command capability constraints, firepower resource capability constraints, electronic countermeasure resource and electronic countermeasure capability constraints, information transmission and decision allocation relationship constraints and target damage threshold constraints.

[0072] The control cost constraint mentioned above takes into account that the essence of organizational structure adjustment is the transfer of equipment control rights, that is, a certain equipment P k Before adjustment, the command and control node C i Control, the relationship matrix can be expressed as , after adjustment, C j At this time, the equipment-command and control node relationship CP is expressed as:

[0073]

[0074] During the system adaptation process, the upper limit of the transfer of equipment control rights is the maximum cost δ that the organization can bear, and the number of equipment whose control rights are transferred during the adaptation adjustment is MP trans ,but:

[0075]

[0076] MP trans ≤δ.

[0077] The detection resource and detection capability constraints

[0078] In terms of detection resources, for one detection node D, it can detect multiple enemy targets at the same time and has a multi-target detection capability of tc, and one target only requires one detection node D, then:

[0079]

[0080] In terms of detection capability, the detection node d is adaptively adjusted. s The detectable power of a target tj tcp sj , target resolution fbl sj And the cooperative detection rate xtcl sj Need to meet sThe inherent ability attributes of , then:

[0081] TCP sj ≥TCP s ;

[0082] fbl sj ≥FBL s ;

[0083] xtcl sj ≥XTCL s .

[0084] The described accusation resources and accusation capability constraints

[0085] In terms of command and control resources, for a command and control node D, it can simultaneously command and control multiple enemy targets and has a multi-target capability of zk, while one target only requires one command and control node D, then:

[0086]

[0087] In terms of command and control capability, the adaptively adjusted command and control node C i When participating in combat against type j targets, its coordination and command rate is xt ij , command and control rate kzl ij , accusation information capacity rl ij Need to meet C i The inherent ability attributes are:

[0088] xt ij ≥XT i ;

[0089] kzl ij ≥KZL i ;

[0090] rl ij ≤RL i .

[0091] The firepower resource capacity constraints

[0092] For a firepower node p a , the number of enemy targets that can be attacked at the same time is at most 1, and one target can be attacked by multiple fire nodes p at the same time. a Joint strike:

[0093]

[0094] In terms of interception capability, the firepower node p after adaptive adjustment a For j-type targets, the interceptable range ljp must be met aj 、Interceptable means type ljp aj、Interceptable target capacity ljrl aj And interception reaction time ljs aj Its interception capability attributes are:

[0095] 1jp aj ≥LJP a ;

[0096] LJL a ≥ljl aj ≥1;

[0097] oeLh aj ≤LJRL a ;

[0098] js aj ≤LJS a .

[0099] The electronic countermeasure resources and electronic countermeasure capability constraints

[0100] For an electronic countermeasure node p e , which can interfere with multiple enemy targets at the same time, but at most its multi-target capability gr, and one target can be simultaneously interfered with by multiple electronic countermeasure nodes p e Combined interference, then:

[0101]

[0102] In addition, in order to ensure effective interference to enemy targets, the electronic countermeasure node p is adaptively adjusted. e For class j targets, the interference range grp must be met ej 、Interference means type grlx ej Need to meet the electronic countermeasure node p in the system e The ability attributes are:

[0103] grp ej ≥GRP e ;

[0104] grlx ej ≤GRLX e .

[0105] The information transmission and decision allocation relationship constraints

[0106] For a complete command and control link, when targeting the same target, j When the participating detection node D, command and control node C and firepower node p a or electronic countermeasure node p e There must be a synergistic relationship between them, namely:

[0107]

[0108] in,

[0109] The first term represents the detection-accusation information transmission constraint when dc ik =1, at this time dt dij ·ct ckj ≤1 means detection node d i With command and control node c k There is a collaborative relationship between them and they participate in the target j Form a link; when dc ik = 0, at this time dt dij ·ct ckj ≤0, then dt dij and ct ckj At least one is 0, then the detection node d i With command and control node c k There is no information transmission relationship between them, so the target t j forming a link;

[0110] The second term represents the command-fire decision allocation constraint, a kh Indicates that there is a firepower node p a or electronic countermeasure node p e Participation matrix, when ca kh =1, the command and control node c k With firepower node p a or electronic countermeasure node p e There is a collaborative relationship between them and they participate in the target j Form a link; when ca kh =0, indicating the command and control node c k With firepower node p a or electronic countermeasure node p e There is no information transmission relationship between them, so the target t j Form a link.

[0111] The target damage threshold constraint

[0112] The target damage threshold refers to the maximum tolerance point at which the enemy target's combat capability is suppressed. When the enemy target's damage threshold is not greater than its survivability, it indicates that the target is damaged. Therefore, its survivability is characterized by the joint damage capability. For a class j target, then:

[0113]

[0114] Among them, p j Indicates survivability or joint lethality, Indicates the target damage threshold.

[0115] Under the condition of satisfying the above constraints, it is necessary to determine the optimization target of the command and control adaptive model, that is, the command and control node load balancing RMS of the command and control adaptive model. All And equipment load balancing E.

[0116] In this embodiment, the command and control node load balancing RMS All Characterizes the quantitative relationship between the number of equipment directly commanded by the command and control node C and the number of equipment coordinated with other command and control nodes. In a good combat system, the relationship between the internal equipment under command should be evenly distributed to the command and control nodes C in the system to ensure the robustness of the system during combat. Therefore, for each command and control node C, its load should be balanced. Based on the above, the root mean square (RMS) of the workload of each command and control node can be used to measure the load balance of the command and control node, and then:

[0117]

[0118] The load balancing RMS of the command and control node is derived using the load root mean square of all command and control nodes C. All for:

[0119]

[0120] Where N represents the total number of control nodes; h represents the node, h≤N; W represents the load in the organizational structure.

[0121] The smaller the RMS, the better the load balancing of the command and control node.

[0122] In this embodiment, the equipment load balance E represents the distribution relationship of the equipment participating in the operation to the target under its capability constraint when the system achieves the expected combat mission, so as to avoid the extreme situation that some equipment needs to be disposed of in large quantities while some equipment does not participate in the combat. In the complete link, due to the limitation of equipment performance, there are equipment capability limitation problems in the detection link, the command link, and the attack link. Taking class j as an example, assuming that the number of its attacks is v j , then the system equipment load balance for this target, that is, the equipment load balance E is:

[0123] E=max(v j ·p j );

[0124] Among them, v j represents the number of attacks, p j represents survivability or joint lethality, and j represents the target type.

[0125] In this embodiment, when the detection capability of the detection node D for the j-type target is pdij , the command and control capability of the command and control node C for the target of type j is p ckj , the attack capability of firepower node Pa against target of type j is p ahj , target t j The survival ability is p tj When , the survival ability p is:

[0126]

[0127] Based on the above, the goal of the defense system when making adaptive adjustments, that is, the charge adaptation model, is:

[0128]

[0129] To sum up the above, the command and control adaptive model refers to a multi-objective optimization model for obtaining the minimum load balance of the command and control node and the maximum load balance of the equipment under many constraints such as control right cost constraints, detection resources and detection capability constraints, command and control resources and command capability constraints, firepower resource capability constraints, electronic countermeasure resources and electronic countermeasure capability constraints, and target damage threshold constraints.

[0130] In this embodiment, after obtaining the command and control adaptive model, the command and control adaptive model is solved by using an improved sparrow search algorithm to obtain the command and control relationship f between the adapted defense system nodes, and a new command and control adaptive model constraint is constructed to guide the re-construction of simulation modeling relationships on other platforms under the command and control relationship.

[0131] In this embodiment, the improved sparrow search algorithm specifically includes the following steps:

[0132] Introduce adaptive mechanism when calculating population fitness;

[0133] Introduce random t-distribution perturbations in the follower stage;

[0134] In the individual fitness stage, a reverse learning strategy is introduced and combined with the Cauchy-Gaussian hybrid operator to improve the quality of the population.

[0135] The above disclosure is only a specific embodiment of the present invention, but the present invention is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A charge adaptive modeling method based on organizational contingency theory, characterized by: The following steps are involved: Obtain the internal components of the defense system, set the defense system nodes based on the organizational contingency theory, and set the command and control adaptive model constraints by combining the OODA combat loop theory and the organizational contingency theory; According to the constraints of the command and control adaptive model, the optimization target is determined, and the optimization target is the command and control node load balancing RMS of the command and control adaptive model. All and equipment load balancing E; Get the command and control node load balancing RMS All The minimum value of and the maximum value of equipment load balance E complete the construction of the command and control adaptive model; The command and control adaptive model is the load balancing RMS of the command and control node obtained under the constraints of the command and control adaptive model. All Multi-objective optimization model for minimum and maximum value of equipment load balancing E.

2. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 1 is characterized in that: The defense system nodes include command and control nodes C, detection nodes D, impact nodes I and target nodes T. The impact nodes include firepower nodes P a and electronic countermeasure node P e The control node C, detection node D, influence node I and target node T are combined with the OODA combat ring theory to obtain the defense system organizational structure SN.

3. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 2 is characterized in that: The defense system organizational structure SN is: SN=<[(T,D,C,I),f],I C ,E C ,W C >, Among them, f represents the command and control relationship between the nodes of the defense system, including the target detection link relationship, information sharing link relationship, information transmission link relationship, decision allocation link relationship, command and control coordination link relationship, and firepower strike link relationship; I C represents the internal workload, that is, the number of equipment directly commanded by a command and control node, which is a positive integer; E C represents the external workload, that is, the number of equipment coordinated by a command and control node, which is a positive integer; W C Total workload of the command and control node.

4. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 3 is characterized in that: The defense system organizational structure SN combines organizational contingency theory to set up the charge adaptation model constraints.

5. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 4 is characterized in that: The constraints of the command and control adaptive model include control right cost constraints, detection resource and detection capability constraints, command and control resource and command capability constraints, firepower resource capability constraints, electronic countermeasure resource and electronic countermeasure capability constraints, information transmission and decision allocation relationship constraints and target damage threshold constraints.

6. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 1 is characterized in that: The command and control node load balancing RMS All for: Where N represents the total number of control nodes; h represents the node, h≤N; W represents the load in the organizational structure.

7. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 1 is characterized in that: The equipment load balance E is: E=max(v j ·p j ), Among them, v j represents the number of attacks, p j represents survivability or joint lethality, and j represents the target type.

8. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 7 is characterized in that: When the detection capability of the detection node D for the j-type target is p dij , the command and control capability of the command and control node C for the target of type j is p ckj , firepower node P a The attack capability against target of type j is p ahj , target t j The survival ability is p tj When , the survival ability p is:

9. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 3 is characterized in that: After obtaining the command and control adaptive model, the command and control adaptive model is solved by improving the sparrow search algorithm to obtain the command and control relationship f between the adapted defense system nodes, and to construct a new command and control adaptive model constraint.

10. The charge adaptive modeling method based on organizational contingency theory as claimed in claim 9, characterized in that: The improved sparrow search algorithm specifically includes the following steps: Introduce adaptive mechanism when calculating population fitness; Introducing random t-distribution perturbations in the follower stage; In the individual fitness stage, a reverse learning strategy is introduced and combined with the Cauchy-Gaussian hybrid operator to improve the quality of the population.

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