A Contingency Modeling Approach for Command and Control Based on Organizational Contingency Theory

Abstract

A command and control adaptation modeling method based on organizational contingency theory includes: acquiring the constituent elements within a defense system; setting defense system nodes based on organizational contingency theory; and setting command and control adaptation model constraints by combining OODA operational loop theory and organizational contingency theory; and determining an optimization objective based on the command and control adaptation model constraints, wherein the optimization objective is the command and control node load balancing (RMS) of the command and control adaptation model. All With equipment load balancing E; obtain the load balancing RMS of the command and control node. All By finding the minimum value of [value] and the maximum value of equipment load balancing E, the command and control adaptation model is constructed. An improved sparrow search algorithm is used to solve the model, obtaining the adapted command and control relationship matrix. Organizational contingency theory and multi-objective optimization design are organically integrated into the system architecture design process, improving the adaptability of the defense system. From the perspective of command and control relationship reconstruction, dynamic adaptation of the defense system is achieved, meeting the needs of real-time dynamic resource reorganization faced by the defense system.

Description

Technical Field

[0001] This invention belongs to the field of command and control technology of defense systems, specifically involving a command and control adaptive modeling method based on organizational contingency theory. Background Technology

[0002] In response to the dynamic, system-on-system confrontation warfare in future complex battlefield environments, defense systems need to possess the ability to adaptively adjust to dynamic changes on the battlefield. Essentially, a defense system is a complex, large-scale system organically formed by multiple elements such as early warning and detection, command and control, interception and destruction, and electronic warfare, according to a specific operational process, and how this large-scale system can achieve adaptive adjustment is a current research hotspot.

[0003] Existing design methods have the main drawback of being unable to describe the hierarchical structure, interaction relationships, and dynamic behavior of a system in dynamic adaptive design, and thus cannot meet the needs of defense systems for real-time dynamic resource reorganization.

[0004] For example, patent document CN117650988A discloses a method for modeling and evaluating command and control structures for clustered unmanned combat scenarios. It models command and control network nodes and the information interaction relationships between them, thereby constructing a clustered unmanned combat command and control network model. It abstracts and extracts improved information flow modules from the command and control network model, using these improved modules to evaluate the efficiency, flexibility, and robustness of the command and control network, and based on this, evaluates the network's effectiveness. The improved information flow modules describe the recurring information generation, processing, and usage patterns in unmanned combat command and control networks, making them more suitable for clustered unmanned combat. They fully consider the command and control modes under unmanned combat, and the proposed evaluation method can effectively measure whether the current command and control structure can effectively leverage the collaborative combat capabilities of unmanned clusters. However, the technical solution in this patent document cannot meet the needs of the defense system for real-time dynamic reorganization of complex resources in complex battlefield environments with complex information flows, and it fails to address the technical problem of describing the hierarchical structure and dynamic behavior in the dynamic adaptive design of the system.

[0005] Since dynamic resource reorganization is essentially a problem of reconstructing command and control relationships, and the realization of command and control relationships 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, so as to realize the dynamic adaptation of the system and solve the above-mentioned defects of the existing technology. Summary of the Invention

[0006] To address the above technical problems, this application provides a command and control adaptation modeling method based on organizational contingency theory, comprising the following steps:

[0007] Obtain the constituent elements within the defense system, set up defense system nodes based on organizational contingency theory, and set up command and control adaptation model constraints by combining OODA operational loop theory and organizational contingency theory.

[0008] Based on the constraints of the command and control adaptation model, an optimization objective is determined, which is the command and control node load balancing RMS that reflects the merits of the command and control adaptation model. All With equipment load balancing E;

[0009] Obtain the load balancer RMS of the command and control node. All The minimum value and the maximum value of equipment load balancing E are used to complete the construction of the command and control adaptation model;

[0010] The command and control adaptation model is the load balancing RMS of the command and control node obtained under the constraints of the command and control adaptation model. All A multi-objective optimization model for minimizing the load and maximizing the equipment load balance E.

[0011] Furthermore, the defense system nodes include a command and control node C, a detection node D, an influence node I, and a target node T, wherein the influence node includes a fire support node p. a and electronic countermeasures node p e The command and control node C, detection node D, influence node I, and target node T are combined with the OODA operational loop theory to obtain the defense system organizational structure SN.

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

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

[0014] Where f represents the command and control relationships between nodes in the defense system, including target detection link relationships, information sharing link relationships, information transmission link relationships, decision allocation link relationships, command and control coordination link relationships, and fire strike link relationships; I C E represents the internal workload, specifically the number of pieces of equipment directly commanded by a command and control node, and is a positive integer. C W represents the external workload, specifically the number of equipment that a command and control node coordinates and commands; it is a positive integer. C Total workload of command and control nodes.

[0015] Furthermore, the defense system organizational structure SN incorporates organizational contingency theory to set constraints for the command and control adaptation model.

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

[0017] Furthermore, the load balancing RMSAll of the command and control node is as follows:

[0018]

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

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

[0021] E = max(v) j ·p j )

[0022] Among them, v j p represents the number of incoming attacks. j This indicates survivability or combined damage capability, and j indicates the target type.

[0023] Furthermore, when the detection capability of detection node D for target type j is p dij Command and control node C has the capability to control and command targets of type j as p. ckj The firepower node Pa's ability to strike targets of type j is p. ahj The survivability of target tj is p. tj At that time, the survivability p is:

[0024]

[0025] Furthermore, after obtaining the command and control adaptation model, the command and control adaptation model is solved by improving the sparrow search algorithm to obtain the command and control relationship f between the nodes of the adapted defense system, and a new command and control adaptation model constraint is constructed.

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

[0027] An adaptive mechanism is introduced when calculating population fitness.

[0028] Introduce a random t-distribution perturbation during the follower phase;

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

[0030] The beneficial effects of this invention are as follows: Based on organizational contingency theory, defense system nodes, defense system organizational structure SN, command and control adaptation model constraints, optimization objectives, and optimal values ​​of optimization objectives are set to complete the construction of the command and control adaptation model. Furthermore, by solving the constructed command and control adaptation model, a new command and control relationship matrix can be obtained. Organizational contingency theory and multi-objective optimization design are organically integrated into the system architecture design process, improving the adaptability of the defense system. Dynamic adaptation of the system is achieved from the perspective of command and control relationship reconstruction. Moreover, the hierarchical structure, interaction relationships of each node, and dynamic behavior in the dynamic adaptation process of the defense system are clearly described in the constructed adaptation model and relationship matrix, meeting the needs of the defense system for real-time dynamic resource reorganization. Attached Figure Description

[0031] Figure 1 This is a flowchart of a command and control adaptive modeling method based on organizational contingency theory according to an embodiment of the present invention. Detailed Implementation

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

[0033] This invention provides a command and control adaptive modeling method based on organizational contingency theory to address the difficulty in describing the hierarchical structure, interaction relationships, and dynamic behavior in dynamic adaptive design of systems. The specific implementation of this invention is described in detail below with reference to the accompanying drawings.

[0034] This invention provides a command and control adaptation modeling method based on organizational contingency theory, comprising the following steps:

[0035] Obtain the constituent elements within the defense system, set up defense system nodes based on organizational contingency theory, and set up command and control adaptation model constraints by combining OODA operational loop theory and organizational contingency theory.

[0036] Based on the constraints of the command and control adaptation model, an optimization objective is determined, which is the command and control node load balancing RMS that reflects the merits of the command and control adaptation model. All With equipment load balancing E;

[0037] Obtain the load balancer RMS of the command and control node. All The minimum value and the maximum value of equipment load balancing E are used to complete the construction of the command and control adaptation model;

[0038] The command and control adaptation model is the load balancing RMS of the command and control node obtained under the constraints of the command and control adaptation model. All A multi-objective optimization model for minimizing the load and maximizing the equipment load balance E.

[0039] In this embodiment, the defense system nodes include a command and control node C, a detection node D, an influence node I, and a target node T, wherein the influence node includes a fire support node p. a and electronic countermeasures node p e The command and control node C, detection node D, influence node I, and target node T are combined with the OODA operational loop theory to obtain the defense system organizational structure SN.

[0040] In the defense system, the command and control node C, according to the organizational contingency theory, is the decision-making entity and is denoted as C = {C}. n |n=1,2,3,…,N}, where N is the total number of command and control nodes. For any command and control node C i Its attributes can be obtained through the Coordinated Command Rate XT i Command and control rate KZL i , Charge Information Capacity RL i and maximum load ZKFZ i To describe, i.e., C i = <XT i KZL i ,RL i ZKFZ i >

[0041] In the defense system, the detection entity in the organizational contingency theory is the detection node D in the defense system, and it is denoted as a set D = {D}. m |m=1,2,3,…,M}, where M is the total number of probe nodes. For probe node d s Its attributes can be detected through TCP's power. s Detectable capacity TCRL s Target resolution FBL s and cooperative detection rate XTCL s Describe, i.e., d s = <TCP s ,TCRL s FBL s XTCL s >

[0042] The fire node p a For the fire node p a Its attributes can be intercepted through range matching LJP a Interceptable methods type LJLX a Target interception capacity LJRL a And interception reaction time LJS a Describe, i.e., p a= <LJP a LJLX a ,LJRL a LJS a >

[0043] The electronic countermeasures node p e For electronic countermeasures node p e Its properties can be obtained through interference range matching GRP e Types of jamming methods GRLX e Describe, i.e., p e = <GRP e GRLX e >

[0044] The target node T is the combination of all targets, i.e., 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, i.e., the adjacency matrix of complex networks. The number 1 indicates that there is a relationship between two nodes, and otherwise it is 0, thus obtaining the relationship matrix corresponding to the relationship between two nodes.

[0046] Therefore, the organizational structure SN of the defense system based on the distributed command and control mode is described by a system of quaternions.

[0047] The organizational structure (SN) of the defense system is as follows:

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

[0049] Where f represents the command and control relationships between nodes in the defense system, including target detection link relationships, information sharing link relationships, information transmission link relationships, decision allocation link relationships, command and control coordination link relationships, and fire strike link relationships; I C E represents the internal workload, specifically the number of pieces of equipment directly commanded by a command and control node, and is a positive integer. C W represents the external workload, specifically the number of equipment that a command and control node coordinates and commands; it is a positive integer. C Total workload of command and control nodes.

[0050] Based on the characteristics of cluster defense operations, the equipment layer in the organizational structure can be functionally divided into fire nodes (p). a And the detection node D. For the fire node p aIn terms of targeting clustered targets, the first requirement is the ability to engage the targets, including kill probability, operational range, and ammunition quantity. Furthermore, due to the nature of fire nodes p... a As it is controlled by command and control node C, it must satisfy the command constraints of command and control node C. Similarly, for detection node D, it must first satisfy the constraints of detection capabilities of detection node D on cluster targets, including detection accuracy, detection range, and channel capacity, while also satisfying the cooperative combat constraints of command and control node C.

[0051] Suppose that in a certain battle, the set of attacking groups is represented as T = {t1, t2, ..., t}. n}, the total number of targets n, and the set of detection nodes D in the system is represented as D={d1,d2,…,d m}, where m is the quantity, and the set of command and control nodes is represented as C = {c1, c2, ..., c p}, the quantity is p, and the number of fire nodes is p. a The set is represented as A = {a1, a2, ..., a...} q The quantity 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 m-th detection node D and the p-th command and control node C; otherwise, it is 0; the command and control node C and the fire node p a The coordination matrix between them is represented by CA. When the element ca in CA is... pq =1 indicates that the p-th command and control node and the q-th fire node p a There must be a command and control relationship between them; otherwise, the value is 0.

[0052]

[0053]

[0054] In the definition system, for a specific target t j The relationship between the equipment involved in striking the target and the target itself is represented by the participation matrix. The combination of participation matrices from all stages represents the decision matrix of the entire combat process.

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

[0056]

[0057] Where, when dt dij When = 1, it indicates that the probe node d i Participating in the strike target t j Otherwise, dt dij =0.

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

[0059]

[0060] Among them, when ct ckj When = 1, it indicates that the command and control node c k Participating in the strike target t j Otherwise, ct ckj =0.

[0061] The fire node p a Or the electronic countermeasures node p e Participation Matrix AT:

[0062]

[0063] Among them, the fire node p a Or the electronic countermeasures node p e The participation situation is represented by a h It means that when at ahj When = 1, it indicates that the firepower node or electronic countermeasures node a h Participating in the strike target t j Otherwise, at ahj =0.

[0064] Therefore, only when dt dij =ct ckj =at ahj =1 and dc ik =ca kh When = 1, a closed command and control link is formed, at which point the detection node d i The command and control node c k The fire support node or electronic countermeasures node a h Jointly participate in the strike target t j This 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, combining the network description and constraint rules of the drone swarm end-defense system organizational structure, the drone swarm end-defense system architecture model SoS can be described as follows:

[0069] SoS =<SN,K> ;

[0070] In this embodiment, the defense system organizational structure SN combines organizational contingency theory to set command and control adaptation model constraints.

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

[0072] The aforementioned control cost constraint takes into account that the essence of organizational restructuring is the transfer of equipment control, i.e., the cost of control over a certain piece of equipment P. k Before the adjustment, the command and control node C was... i The control relationship matrix can be represented as follows: After adjustment, affected by C j In this case, the equipment-command control node relationship (CP) is represented as follows:

[0073]

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

[0075]

[0076] MP trans ≤δ.

[0077] The constraints of detection resources and detection capabilities

[0078] Regarding detection resources, for a single detection node D, it can simultaneously detect multiple enemy targets and has a multi-target detection capability of tc. Since only one detection node D is needed for each target, then:

[0079]

[0080] Regarding detection capabilities, the adaptively adjusted detection node d s The detectable power of TCP for a target TJ sj Target resolution fbl sj and cooperative detection rate xtcl sj d needs to be satisfied sGiven the inherent ability attributes, then:

[0081] TCP sj ≥TCP s ;

[0082] fbl sj ≥FBL s ;

[0083] xtcl sj ≥XTCL s .

[0084] The constraints of indictment resources and indictment capabilities

[0085] Regarding command and control resources, for a command and control node D, it can simultaneously command and control the attack on multiple enemy targets and has a multi-target capability of zk. Since only one command and control node D is needed for each target, then:

[0086]

[0087] Regarding command and control capabilities, the command and control node C has been adapted and adjusted accordingly. i When participating in operations against type j targets, its coordinated command rate xt ij Command and control rate kzl ij , accusation information capacity rl ij Need to satisfy C i Its inherent ability attribute, namely:

[0088] xt ij ≥XT i ;

[0089] kzl ij ≥KZL i ;

[0090] rl ij ≤RL i .

[0091] The constraints on firepower resources

[0092] For a given fire node p a It can simultaneously engage a maximum of one enemy target, and a single target can be simultaneously attacked by multiple fire nodes p. a Joint strikes, then:

[0093]

[0094] In terms of interception capabilities, the firepower node p after adaptive adjustments a For target type j, the interceptability range (ljp) must be satisfied. aj Interceptable methods type ljp ajTarget capacity that can be intercepted (ljrl) aj and interception reaction time ljs aj Its interception capability attribute is:

[0095] ljp aj ≥LJP a ;

[0096] LJL a ≥ljl aj ≥1;

[0097] ljrl aj ≤LJRL a ;

[0098] ljs aj ≤LJS a .

[0099] The constraints of electronic warfare resources and electronic warfare capabilities

[0100] For a given electronic countermeasure node p e It can interfere with multiple enemy targets simultaneously, but its maximum capability is only its multi-target capability gr, while a single target can be simultaneously interfered with by multiple electronic countermeasures nodes p. e Combined interference, then:

[0101]

[0102] In addition, to ensure effective interference with enemy targets, the electronic countermeasures node p is adapted and adjusted. e For target type j, the permissible interference range gRP needs to be satisfied. ej Types of interference methods grlx ej The electronic countermeasures node p within the system needs to be satisfied. e The ability attributes, namely:

[0103] grp ej ≥GRP e ;

[0104] grlx ej ≤GRLX e .

[0105] The constraints of the information transmission and decision allocation relationship

[0106] For a complete command and control link, when targeting the same objective t j At that time, the participating detection node D, command and control node C, and fire node p a or electronic countermeasures node p e There must be a collaborative relationship between them, that is:

[0107]

[0108] in,

[0109] The first term represents the constraint on the transmission of detection-command information, when DC ik When dt = 1, dt dij ·ct ckj ≤1 indicates that the probe node d i With command and control node c k There is a collaborative relationship between them and they participate in the target t j Forming a link; when DC ik When dt = 0, dt dij ·ct ckj If ≤0, then dt dij and ct ckj At least one of them is 0, at which point the probe node d i With command and control node c k There is no information transmission relationship between them, so it is impossible to target t. j Forming a link;

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

[0111] The target damage threshold constraint

[0112] The target damage threshold refers to the maximum point at which the combat capability of an enemy target is suppressed. When the damage threshold of an enemy target is not greater than its survivability, it indicates that the target is damaged. Therefore, its survivability is characterized by combined damage capability. For target type j, then:

[0113]

[0114] Where, p j Indicates survivability or combined destructive capability. Indicates the target damage threshold.

[0115] Under the condition of satisfying the above constraints, it is necessary to determine the optimization objective of the command and control adaptive model, that is, the load balancing RMS of the command and control nodes to determine the quality of the command and control adaptive model. All E. Equipment load balancing.

[0116] In this embodiment, the command and control node load balancer RMS All The command and control node C directly commands a certain number of equipment and coordinates with other command and control nodes. In a well-functioning combat system, the command relationships of equipment should be evenly distributed among the command and control nodes C within the system to ensure robustness during combat operations. Therefore, the load on each command and control node C should be balanced. Based on the above, the load balance of the command and control nodes can be measured using the root mean square (RMS) of their workloads. Thus:

[0117]

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

[0119]

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

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

[0122] In this embodiment, the equipment load balancing E represents the allocation relationship between participating equipment and targets under their capability constraints when the system achieves the expected combat mission, avoiding the extreme situation where some equipment needs to handle a large amount of workload while others do not participate in combat. In the complete link, due to equipment performance limitations, there are equipment capability limitations in the detection, command and control, and strike stages. Taking type j targets as an example, assuming the number of incoming targets is v... j Then, the load balancing of the system equipment for this target, i.e., the equipment load balancing E, is:

[0123] E = max(v) j ·p j );

[0124] Among them, v j p represents the number of incoming attacks. j This indicates survivability or combined damage capability, and j indicates the target type.

[0125] In this embodiment, when the detection capability of detection node D for target type j is pdij Command and control node C has the capability to control and command targets of type j as p. ckj The firepower node Pa's ability to strike targets of type j is p. ahj Target t j Its survivability is p tj At that time, the survivability p is:

[0126]

[0127] Based on the above, the objective of the defense system when making adaptive adjustments, namely the command and control adaptation model, is:

[0128]

[0129] In summary, the command and control adaptive model refers to a multi-objective optimization model that obtains the minimum load balance of the command and control node and the maximum load balance of the equipment under various constraints such as control cost constraints, detection resource and detection capability constraints, command and control resource and command and control capability constraints, firepower resource and capability constraints, electronic countermeasures resource and electronic countermeasures capability constraints, and target damage threshold constraints.

[0130] In this embodiment, after obtaining the command and control adaptation model, the improved sparrow search algorithm is used to solve the command and control adaptation model to obtain the command and control relationship f between the nodes of the adapted defense system. New command and control adaptation model constraints are constructed to guide the reconstruction 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] An adaptive mechanism is introduced when calculating population fitness.

[0133] Introduce a random t-distribution perturbation during the follower phase;

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

[0135] The above-disclosed embodiments are merely specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A command and control adaptation modeling method based on organizational contingency theory, characterized in that, Includes the following steps: Obtain the constituent elements within the defense system, set up defense system nodes based on organizational contingency theory, and set up command and control adaptation model constraints by combining OODA operational loop theory and organizational contingency theory. The defense system nodes include command and control node C, detection node D, influence node I, and target node T. The influence node includes fire support node P. a and electronic countermeasures node P e The command and control node C, detection node D, influence node I, and target node T are combined with the OODA operational loop theory to obtain the defense system organizational structure SN; Based on the constraints of the command and control adaptation model, an optimization objective is determined, which is the command and control node load balancing RMS that reflects the merits of the command and control adaptation model. All With equipment load balancing E; The load balancer RMS of the command and control node All for: ； Where N represents the total number of command and control nodes; h represents a node, h≤N; W represents the load in the organizational structure; The equipment load balancing E is: ； Among them, v j p represents the number of incoming attacks. j Indicates survivability or combined destruction capability, where j represents the target type; When the detection capability of detection node D for target type j is The command and control node C has the capability to control and command targets of type j. Firepower Node P a The capability to strike targets of type j is ,Target Survival ability is At that time, the survivability p j for: ； Obtain the load balancer RMS of the command and control node. All The minimum value and the maximum value of equipment load balancing E are used to complete the construction of the command and control adaptation model; The command and control adaptation model is the load balancing RMS of the command and control node obtained under the constraints of the command and control adaptation model. All A multi-objective optimization model for minimizing the load and maximizing the equipment load balance E.

2. The command and control adaptation modeling method based on organizational contingency theory as described in claim 1, characterized in that, The organizational structure SN of the defense system is as follows: ； Where f represents the command and control relationship between nodes in the defense system, including target detection link relationship, information sharing link relationship, information transmission link relationship, decision allocation link relationship, command and control coordination link relationship, and fire strike link relationship; This represents the internal workload, specifically the number of pieces of equipment directly commanded by a command and control node, and is a positive integer. This represents the external workload, specifically the number of equipment that a command and control node coordinates and commands; it is a positive integer. Total workload of command and control nodes.

3. The command and control adaptation modeling method based on organizational contingency theory as described in claim 2, characterized in that, The defense system's organizational structure (SN) incorporates organizational contingency theory to set constraints for the command and control adaptation model.

4. The command and control adaptation modeling method based on organizational contingency theory as described in claim 3, characterized in that, The constraints of the command and control adaptation model include control cost constraints, detection resource and detection capability constraints, command and control resource and command and control capability constraints, firepower resource and capability constraints, electronic countermeasures resource and electronic countermeasures capability constraints, information transmission and decision allocation relationship constraints, and target damage threshold constraints.

5. The command and control adaptation modeling method based on organizational contingency theory as described in claim 1, characterized in that, After obtaining the command and control adaptation model, the improved sparrow search algorithm is used to solve the command and control adaptation model to obtain the command and control relationship f between the nodes of the adapted defense system, and to construct new constraints for the command and control adaptation model.

6. The command and control adaptation modeling method based on organizational contingency theory as described in claim 5, characterized in that, The improved sparrow search algorithm specifically includes the following steps: An adaptive mechanism is introduced when calculating population fitness. Introduce a random t-distribution perturbation during the follower phase; In the individual fitness stage, a reverse learning strategy is introduced in combination with the Cauchy-Gaussian hybrid operator to improve the quality of the population.

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