System performance evaluation method based on OODA loop closing

Abstract

The invention provides a system efficiency evaluation method based on OODA ring closing, and the method comprises the steps: dividing an efficiency index system into three dimensions: a task, an OODA ring flow, and a killing chain, and constructing the overall dimension of the efficiency index system; establishing a multi-layer efficiency index system of task-OODA ring process-killing chain sub-chain-efficiency index by analyzing the OODA ring process and killing chain closing; and acquiring expert comprehensive weights of each node in the task layer, the OODA ring flow layer and the killing chain layer and each efficiency index in the efficiency index layer, and performing layer-by-layer upward recursion by adopting a weighted summation method according to the expert score and the expert comprehensive weight of each node or index in each layer, thereby determining a comprehensive efficiency value of the system, and completing efficiency evaluation of the cross-domain collaborative system. According to the method, the system efficiency index is constructed based on OODA ring closing, the richness and integrity of task requirements are ensured, and meanwhile, the finally obtained efficiency index can more comprehensively and effectively represent the capability of the system on the system level from the perspective of OODA ring closing.

Description

Technical Field

[0001] The present invention belongs to the technical field of system effectiveness evaluation, and in particular relates to a system effectiveness evaluation method based on OODA loop closure. Background Art

[0002] In recent years, various new systems have emerged one after another, and the demand for the pros and cons and effectiveness evaluation of the systems has become increasingly urgent. Rapid closure of the evaluation system through the kill chain (OODA loop) is an effective method. Among them, OODA (Observe, Orient, Decide, Act) is a cycle theory used to describe the decision-making process in a task. In the context of the kill chain, each step of the OODA loop corresponds to or affects a certain link in the kill chain. Rapid closure of the kill chain (i.e., rapid completion of the OODA loop) is crucial to improving the mission effectiveness of the system.

[0003] The construction of the indicator system is the primary premise and core content of the system effectiveness evaluation. According to the requirements and key points of the UAV support mission, Chen Xu et al. constructed a three-layer structure of the effectiveness indicator system with the overall goal layer, sub-goal layer and indicator layer; Liu Shuai et al. formed a capability-based system test effectiveness indicator framework design process according to the four links of capability demand analysis, test system construction, effectiveness indicator framework establishment, and effectiveness indicator system decomposition. As a dynamic analysis framework empowering indicator generation method, the OODA cycle theory can effectively characterize the dynamic task process.

[0004] However, current system effectiveness evaluation usually only targets a specific type of system, such as drone systems or satellite systems, and does not comprehensively evaluate multiple types of systems. The construction of effectiveness indicators cannot cover cross-domain collaborative systems involving multiple systems, and there are problems such as difficulty in comprehensively and effectively characterizing the comprehensive capabilities of the system, resulting in low accuracy in system effectiveness evaluation. Summary of the invention

[0005] In order to overcome the deficiencies in the prior art, the inventors have conducted intensive research and provided a system performance evaluation method based on OODA loop closure to solve the problem that the indicator construction during performance evaluation cannot cover the cross-domain collaborative system including multiple systems, and it is difficult to comprehensively and effectively characterize the comprehensive capabilities of the system, resulting in low accuracy of system performance evaluation.

[0006] The technical solution provided by the present invention is as follows:

[0007] In the first aspect, a system effectiveness evaluation method based on OODA loop closure includes:

[0008] The effectiveness indicator system is divided into three dimensions: tasks, OODA loop processes, and kill chains, to construct the overall dimension of the effectiveness indicator system;

[0009] Through the analysis of OODA loop process and kill chain closure, a multi-layer performance indicator system of task-OODA loop process-kill chain sub-chain-performance indicator is established;

[0010] Obtain the expert comprehensive weights of each node in the task layer, OODA loop process layer, and kill chain layer, and each performance indicator in the performance indicator layer. According to the expert scores and expert comprehensive weights of each node or indicator in each layer, use the weighted summation method to recursively push upward layer by layer to determine the comprehensive performance value of the system and complete the performance evaluation of the cross-domain collaborative system.

[0011] Second, a system effectiveness evaluation system based on OODA loop closure,

[0012] The effectiveness evaluation system includes the task layer, OODA loop process layer, kill chain layer and effectiveness indicator layer;

[0013] Each task in the top-level task layer is mapped to four process links: observation link, judgment link, decision link, and action link, where each process link consists of several activities;

[0014] Each process link is mapped into five sub-chains of the kill chain: capability chain, accusation chain, information chain, time chain, and precision chain. According to the indicator information described by different sub-chains, there are quantifiable underlying performance indicators.

[0015] Determine the expert comprehensive weights of each node in the task layer, OODA loop process layer, and kill chain layer, and each performance indicator in the performance indicator layer. Based on the expert scores and expert comprehensive weights of each node or indicator in each layer, use the weighted summation method to recursively push upward layer by layer to determine the comprehensive performance value of the system and complete the performance evaluation of the cross-domain collaborative system.

[0016] In a third aspect, a system performance evaluation device based on OODA loop closure includes:

[0017] one or more processors;

[0018] a storage device for storing one or more programs,

[0019] When the one or more programs are executed by the one or more processors, the one or more processors run the system effectiveness evaluation system based on OODA loop closure described in the second aspect.

[0020] In a fourth aspect, a readable storage medium stores a computer program, which, when executed by a processor, runs the system effectiveness evaluation system based on OODA loop closure described in the second aspect.

[0021] According to the present invention, a system performance evaluation method based on OODA loop closure has the following features:

[0022] Beneficial effects:

[0023] The present invention provides a system effectiveness evaluation method based on OODA loop closure, which divides the effectiveness index system into three dimensions: task, OODA loop process, and kill chain, and constructs the overall dimension of the effectiveness index system; through the analysis of the OODA loop process and the kill chain closure, a multi-layer effectiveness index system of task-OODA loop process-kill chain subchain-performance index is established; the expert comprehensive weights of each node in the task layer, OODA loop process layer, and kill chain layer and each performance index in the performance index layer are obtained, and according to the expert scores and expert comprehensive weights of each node or index in each layer, a weighted summation method is used to recursively push upward layer by layer to determine the system comprehensive effectiveness value, and complete the effectiveness evaluation of the cross-domain collaborative system. The present invention constructs system performance indicators based on OODA loop closure, which ensures the richness and completeness of task requirements. At the same time, the performance indicators finally obtained can, from the perspective of OODA loop closure, more comprehensively and effectively characterize the capabilities of the system at the system level, and be more in line with the actual tasks of the system. Therefore, compared with the performance indicator construction method based on system capabilities, it has higher integrity and accuracy, and solves the problem of insufficient consideration of the emergence of the system in the construction of system performance indicators, and difficulty in comprehensively and effectively characterizing the comprehensive capabilities of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a system effectiveness evaluation method based on OODA loop closure of the present invention;

[0025] Figure 2 It is a schematic diagram of the overall dimensions of the performance indicator system;

[0026] Figure 3 It is a schematic diagram of the multi-layer performance indicator system;

[0027] Figure 4 Construct a schematic diagram for the effectiveness indicators of the observation phase (O);

[0028] Figure 5 A schematic diagram is constructed for the effectiveness indicators of the judgment link (O);

[0029] Figure 6 Construct a schematic diagram for the effectiveness indicators of the decision-making stage (D);

[0030] Figure 7 Construct a schematic diagram for the effectiveness indicators of the action link (A);

[0031] Figure 8 A schematic diagram for constructing system effectiveness indicators based on OODA loop kill chain closure;

[0032] Fig. 9 This is a schematic diagram of the OODA loop action flow of the star-machine collaborative system;

[0033] Fig.10 Schematic diagram of the performance indicators of the star-machine collaborative system. DETAILED DESCRIPTION

[0034] The following detailed description of the present invention will make the features and advantages of the present invention more clear and explicit.

[0035] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0036] The present invention provides a system performance evaluation method based on OODA loop closure, such as Figure 1 As shown, the following steps are included:

[0037] The first step is to divide the overall performance indicator system into three dimensions: tasks, OODA loop processes, and kill chains, to construct the overall dimensions of the performance indicator system.

[0038] The construction process of the overall dimension of the performance indicator system is as follows:

[0039] (1.1) According to the purpose and needs of the assessment, the indicator system is developed at the top level according to the system task dimension and is divided into different specific tasks according to different scenarios. Taking the mission scenario of the satellite-aircraft collaborative intelligent system destroying the blue side's ground targets as an example, it includes satellite systems and drone systems, and the specific task is: "The satellite-aircraft collaborative system strikes ground targets."

[0040] (1.2) For the processes under different tasks, according to the observation link (O), judgment link (O), decision link (D) and action link (A), follow the OODA loop process dimension to clarify the different task completion stages from the system level;

[0041] (1.3) In order to facilitate the construction of quantifiable underlying performance indicators, the different links of the OODA loop process are carried out according to the kill chain dimension, and the construction of underlying performance indicators is completed from the perspective of the five sub-chains of the kill chain, namely, the capability chain, the command chain, the information chain, the time chain and the precision chain. See Figure 2 .

[0042] The second step is to establish a multi-layer performance indicator system of task-OODA loop process-kill chain sub-chain-performance indicator through analysis of the OODA loop process and kill chain closure.

[0043] The process of establishing a multi-layer performance indicator system is as follows:

[0044] (2.1) Starting from the task layer, for different tasks, we analyze the composition of task activities and map each task to process links such as observation link (O), judgment link (O), decision link (D), and action link (A), where each process link consists of several activities.

[0045] Taking the mission scenario of the satellite-aircraft collaborative intelligent system destroying the blue side's ground targets as an example, the observation link (O) includes activities such as "receiving mission commands and giving combat mission areas", "UAV swarm trajectory planning", "UAV take-off", and "satellite continuous reconnaissance"; the judgment link (O) includes activities such as "after entering the mission area, the satellite and the UAV host perform data fusion"; the decision-making link (D) includes activities such as "the host performs mission planning and issues tasks to the slave"; the action link (A) includes activities such as "meeting launch conditions and conducting guided strikes", "host damage assessment", "mission completion, UAV swarm returns", and "return and land".

[0046] (2.2) According to the idea of ​​closed kill chain, each process link is mapped into the five sub-chains of the kill chain, namely capability chain, command chain, information chain, time chain and precision chain. The quantifiable underlying performance indicators can be obtained according to the indicator information mapping described by different sub-chains.

[0047] (2.3) The corresponding performance indicators are merged and adjusted to finally form a set of performance indicators for the corresponding tasks (see Figure 3 ). After the first construction of each kill chain effectiveness index, if the effectiveness index is divided too finely, it will be merged; if the effectiveness index is not constructed reasonably, it will be adjusted accordingly.

[0048] The process of constructing the performance indicators of the system observation link (O), judgment link (O), decision link (D) and action link (A) is as follows:

[0049] (2.2.1) Based on the overall dimensions of the performance indicator system and the multi-layer performance indicator system established above, the form of the performance indicator is represented as follows:

[0050] E=(E|a,b,c k )

[0051] In the formula, E represents the effectiveness index, a represents the task number of the system, a=1,2,…,m, where m is the number of tasks to be completed; b represents the OODA loop process, b=1,2,3,4, corresponding to the observation link (O), judgment link (O), decision link (D) and action link (A) of the OODA loop process respectively; c represents the kill chain subchain, c=1,2,3,4,5, corresponding to the capability chain, command chain, information chain, time chain and precision chain of the kill chain subchain respectively; k represents the effectiveness index number under a kill chain subchain, k=1,2,…,n, where n is the number of effectiveness indicators corresponding to a kill chain subchain.

[0052] (2.2.2) According to the definition and level classification of performance evaluation, performance indicators are divided into three types: inherent attribute type, probability statistics type and time indicator type.

[0053] The inherent attribute performance index is the relevant design attribute of each system that constitutes the system, and its value can fluctuate within a small range under different task environment conditions. Let the state parameter of a system in the system be Y, and its influencing factor be the technical parameter X. The relationship between the two is f: X→Y. It conforms to a certain type of statistical distribution characteristics near the design value Y0. Under given task environment conditions, the possible range of X is [X1, X2], and the task requires that the parameter must not be lower than Y', and Y' is the minimum state parameter to complete the task. Then the inherent attribute performance index can be expressed as:

[0054] (E|a,b,c k )=P{min[f(X1),f(X2)]≥Y'},k=1,2,...,n

[0055] Taking the mission scenario of the satellite-aircraft collaborative intelligent system destroying the blue side's ground targets as an example, "maximum detection distance", "scanning frequency", "communication delay rate", "transmission rate", "communication capacity", "communication coverage", "ground resolution", "number of target identification", "target identification range", "number of collaborative task plans", "number of firepower allocation targets", "maximum range", "maneuvering speed", "damage equivalent" and other inherent attribute performance indicators fluctuate in a small range under different environmental conditions. Their parameters must not be lower than the minimum state parameters for completing the corresponding task. The size of their values ​​affects the comprehensive weight of the indicator in the performance evaluation process.

[0056] Probabilistic statistical performance indicators are described by statistical probability, and the target number m in the performance indicators of completing tasks at a certain stage under the same conditions is studied. 1i and the total number m 0i The average of the ratio of in N statistics, i = 1, 2, ..., N, then the probability statistics performance index can be expressed as:

[0057]

[0058] Taking the mission scenario of the satellite-aircraft collaborative intelligent system destroying the blue side's ground targets as an example, "target detection probability", "command issuance accuracy", "command transmission accuracy", "target detection accuracy", "target identification success rate", "collaborative task conflict rate", "firepower allocation accuracy", "track accuracy", "miss range", "target strike success rate" and other probabilistic statistical performance indicators, and their values ​​affect the comprehensive weight of the indicator in the performance evaluation process.

[0059] Time performance indicators need to obtain the time value of completing a specific task through simulation or actual installation test under the same dimension, which can be expressed as:

[0060] (E|a,b,c k )=T k ,k=1,2,...,n

[0061] Where, T k Indicates the time attribute value of the performance indicator numbered k.

[0062] Taking the mission scenario of the satellite-aircraft collaborative intelligent system destroying the blue side's ground targets as an example, "effective coverage time", "target detection time", "information fusion time", "mission planning time", "UAV flight time", "target strike time" and other time-attribute performance indicators are time attribute performance indicators, and the size of their values ​​affects the comprehensive weight of the indicator in the performance evaluation process.

[0063] (2.2.3) Construction of effectiveness indicators for the observation link (O). For the system, the observation link (O) in the OODA loop process is mainly related to the reconnaissance system in the system. The corresponding indicators of the reconnaissance system under the capability chain, command chain, information chain, time chain and accuracy chain under the observation link (O) are mapped into effectiveness indicators (see Figure 4 ).

[0064] (2.2.4) Construction of performance indicators for the judgment link (O). For the system, the judgment link (O) in the OODA loop process is mainly related to the reconnaissance and command systems in the system. The corresponding indicators of the reconnaissance and command systems under the judgment link (O) in the capability chain, command chain, information chain, time chain and accuracy chain are mapped into performance indicators (see Figure 5 ).

[0065] (2.2.5) Construction of performance indicators for the decision link (D). For the system, the decision link (D) in the OODA loop process is mainly related to the command and destruction systems in the system. The corresponding indicators of the command and destruction systems under the decision link (D) in the capability chain, command chain, information chain, time chain and precision chain are mapped into performance indicators (see Figure 6 ).

[0066] (2.2.6) Construction of effectiveness indicators for the action link (A). For the system, the action link (A) in the OODA loop process is mainly related to the damage system in the system. The corresponding indicators of the damage system in the action link (A) under the capability chain, command chain, information chain, time chain and precision chain are mapped into effectiveness indicators (see Figure 7 ).

[0067] (2.2.7) After completing the construction of the effectiveness indicators of the observation link (O), judgment link (O), decision link (D) and action link (A), the system effectiveness indicators based on the closure of the OODA loop kill chain are obtained (see Figure 8 ).

[0068] In the third step, the multi-indicator group decision-making method is used to determine the expert comprehensive weights of each node in the task layer, OODA loop process layer, and kill chain layer, and each performance indicator in the performance indicator layer. According to the expert scores and expert comprehensive weights of each node or indicator in each layer, the weighted summation method is used to recursively push upward layer by layer to determine the comprehensive performance value of the system and complete the performance evaluation of the cross-domain collaborative system.

[0069] The process of determining the comprehensive weight of each performance indicator expert and calculating the comprehensive performance is as follows:

[0070] (3.1) For the nodes in the task layer, OODA loop process layer, kill chain layer, and the performance indicators in the performance indicator layer, let the set of nodes or indicators in each layer be C = (c1, c2, …, c n ), where n is the number of nodes or indicators in the corresponding layer;

[0071] (3.2) Experts assign weights to the above indicator set. Let the expert set be U = (u1, u2, …, u i ,…,u m ), where m is the number of invited experts; indicator weight vector W = (w1, w2, …, w j ,…,w n ) is called expert opinion, w j Indicates expert u i The comprehensive weight of experts for indicator j, 1≤i≤m, 1≤j≤n, w j ≥0, The expert's own weight is recorded as P = (p1, p2, ..., p m ),in,

[0072] (3.3) The expert opinion given for indicator j is denoted as F j =(f 1j ,f 2j ,…,f ij ,…,f mj ), where f ij You are an expert i Give indicator c j The weight given, The expert comprehensive weight of index j is obtained by weighted summation method:

[0073] (3.4) Based on the expert scores and comprehensive weights of each node or indicator in each layer, the weighted summation method is used to recursively calculate the comprehensive performance value of the system.

[0074] Example

[0075] The first step is to divide the overall performance indicator system into three dimensions: tasks, OODA loop processes, and kill chains, to construct the overall dimensions of the performance indicator system.

[0076] The second step is to establish a multi-layer performance indicator system of task-OODA loop process-kill chain sub-chain-performance indicator through analysis of the OODA loop process and kill chain closure.

[0077] The mission scenario is to use the satellite-machine collaborative intelligent system to destroy the blue side's ground targets. The specific scenario is as follows:

[0078] (1) The Red Army uses early warning satellites to continuously monitor a certain area of ​​the Blue Army;

[0079] (2) The satellite detects the blue side's deployment of a high-value ground target and sends general target information to the ground command and control center;

[0080] (3) The ground command and control center then dispatches a drone formation to fly to the target area;

[0081] (4) After arriving at the mission area, the UAV host receives accurate targeting information after merging satellite data with its own data, makes onboard decisions, performs mission planning on the client side, provides an action plan, and sends it to other slaves.

[0082] (5) Based on the mission planning results, the drone fleet launches laser-guided bombs and ultimately destroys the blue side’s high-value ground targets.

[0083] According to the system effectiveness evaluation method based on OODA loop closure, the OODA loop action process of the satellite-machine collaboration system is given (see Fig. 9 ).

[0084] According to the idea of ​​killing chain closure, each process link is mapped into five sub-chains of the killing chain, namely capability chain, accusation chain, information chain, time chain and precision chain. Then, the quantifiable underlying performance indicators can be obtained according to the indicator information mapping described by different sub-chains. The corresponding performance indicators are merged and adjusted to finally form a set of performance indicators for the corresponding tasks.

[0085] According to the efficiency indicator construction process of the system observation link (O), judgment link (O), decision link (D) and action link (A), the indicators are constructed as follows:

[0086] (1) Observation phase (O)

[0087] The observation link is mainly related to the early warning satellite in the system. Therefore, according to the capabilities of the early warning satellite in the capability chain, command chain, information chain, time chain and precision chain in the observation link, the maximum detection distance, scanning frequency and target detection probability can be used to represent the performance indicators under the capability chain; the command release accuracy rate, command transmission accuracy rate, communication delay rate and transmission rate can be used to represent the performance indicators under the command chain; the communication capacity and communication coverage range can be used to represent the performance indicators under the information chain; the effective coverage time and target detection time can be used to represent the performance indicators under the time chain; the ground resolution and target detection accuracy rate can be used to represent the performance indicators under the precision chain. Among them, the maximum detection distance, scanning frequency, communication delay rate, transmission rate, communication capacity, communication coverage range and ground resolution are inherent attribute performance indicators, the target detection probability, command release accuracy rate, command transmission accuracy rate and target detection accuracy rate are probability statistics performance indicators, and the effective coverage time and target detection time are time performance indicators.

[0088] (2) Judgment stage (O)

[0089] The judgment link is mainly related to the UAV host in the system. Therefore, according to the capabilities of the UAV host in the capability chain, command chain, information chain, time chain and precision chain in the judgment link, the target recognition quantity and target recognition range can be used to represent the performance indicators under the capability chain; the command release accuracy rate, command transmission accuracy rate, communication delay rate and transmission rate can be used to represent the performance indicators under the command chain; the communication capacity and communication coverage range can be used to represent the performance indicators under the information chain; the information fusion time can be used to represent the performance indicators under the time chain; and the target recognition success rate can be used to represent the performance indicators under the precision chain. Among them, the target recognition quantity, target recognition range, communication delay rate, transmission rate, communication capacity and communication coverage range are inherent attribute performance indicators, the command release accuracy rate, command transmission accuracy rate and target recognition success rate are probability statistics performance indicators, and the information fusion time is a time performance indicator.

[0090] (3) Decision-making stage (D)

[0091] The decision-making link is mainly related to the UAV host in the system. Therefore, according to the capabilities of the early warning satellite in the capability chain, command chain, information chain, time chain and precision chain in the decision-making link, the number of coordinated mission planning and the number of firepower allocation targets can be used to represent the performance indicators under the capability chain; the coordinated mission conflict rate, the correct rate of command release, the correct rate of command transmission, the communication delay rate, and the transmission rate can be used to represent the performance indicators under the command chain; the communication capacity and communication coverage can be used to represent the performance indicators under the information chain; the mission planning time can be used to represent the performance indicators under the time chain; the firepower allocation accuracy and track accuracy can be used to represent the performance indicators under the precision chain. Among them, the number of coordinated mission planning, the number of firepower allocation targets, the communication delay rate, the transmission rate, the communication capacity, and the communication coverage are inherent attribute performance indicators, the coordinated mission conflict rate, the correct rate of command release, the correct rate of command transmission, the firepower allocation accuracy, and the track accuracy are probability statistics performance indicators, and the mission planning time is a time performance indicator.

[0092] (4) Action phase (A)

[0093] The action link is mainly related to the drone swarm in the system. Therefore, according to the capabilities of the drone swarm in the capability chain, command chain, information chain, time chain and precision chain in the action link, the maximum range, maneuvering speed and damage equivalent can be used to represent the performance indicators under the capability chain; the command release accuracy rate, command transmission accuracy rate, communication delay rate and transmission rate can be used to represent the performance indicators under the command chain; the communication capacity and communication coverage can be used to represent the performance indicators under the information chain; the drone flight time and target strike time can be used to represent the performance indicators under the time chain; the miss amount and target strike success rate can be used to represent the performance indicators under the precision chain. Among them, the maximum range, maneuvering speed, damage equivalent, communication delay rate, transmission rate, communication capacity and communication coverage are inherent attribute performance indicators, the command release accuracy rate, command transmission accuracy rate, miss amount and target strike success rate are probability statistics performance indicators, and the drone flight time and target strike time are time performance indicators.

[0094] This allows us to construct the performance index of the satellite-machine collaborative system (see Fig.10 ).

[0095] In the third step, the multi-index group decision-making method is used to determine the expert comprehensive weight of each performance index and give the calculation result of the system comprehensive performance.

[0096] (1) Based on the expert evaluation opinions, the expert scores of each performance indicator in the performance indicator layer are given as follows: the score of "maximum detection distance" in the capability chain under the observation link (O) is 0.8, the score of "scanning frequency" is 0.7, and the score of "target detection probability" is 0.9. The scores of "command release accuracy", "command transmission accuracy", "communication delay rate" and "transmission rate" in the command chain are all 0.8. The scores of "communication capacity" and "communication coverage range" in the information chain are 0.8 and 0.9 respectively. The scores of "effective coverage time" and "target detection time" in the time chain are The scores of "ground resolution" and "target detection accuracy" in the accuracy chain are all 0.8, 0.75 and 0.9, respectively; the scores of "target identification number" and "target identification range" in the capability chain under the judgment link (O) are all 0.8, the scores of "command release accuracy", "command transmission accuracy", "communication delay rate" and "transmission rate" in the command chain are all 0.9, the scores of "communication capacity" and "communication coverage" in the information chain are 0.6 and 0.85 respectively, the score of "information fusion time" in the time chain is 0.9, and the score of "target identification success rate" in the accuracy chain is 0.8. The score of "the number of coordinated task planning" and "the number of firepower allocation targets" in the capability chain under the decision link (D) are 0.8 and 0.9 respectively, the score of "cooperative task conflict rate", "instruction release accuracy", "instruction transmission accuracy", "communication delay rate" and "transmission rate" in the command chain are all 0.8, the score of "communication capacity" and "communication coverage" in the information chain are 0.6 and 0.7 respectively, the score of "task planning time" in the time chain is 0.8, and the score of "firepower allocation accuracy" and "track accuracy" in the accuracy chain are both 0.9; In the capability chain under section (A), the score value of "maximum range" is 0.8, the score value of "maneuvering speed" is 0.9, and the score value of "damage equivalent" is 0.8. In the command chain, the score values ​​of "instruction release accuracy", "instruction transmission accuracy", "communication delay rate" and "transmission rate" are all 0.7. In the information chain, the score values ​​of "communication capacity" and "communication coverage" are 0.8 and 0.9 respectively. In the time chain, the score values ​​of "UAV flight time" and "target strike time" are both 0.8. In the accuracy chain, the score values ​​of "miss distance" and "target strike success rate" are 0.9 and 0.7 respectively.

[0097] (2) According to the determination process of the expert comprehensive weight of each performance indicator, the expert comprehensive weight values ​​of the nodes or performance indicators of each layer of the satellite-machine collaborative system are as follows: the comprehensive weight values ​​of the observation link (O), judgment link (O), decision link (D), and action link (A) in the OODA loop process layer are 0.25 respectively; the comprehensive weight values ​​of each kill chain under each link of the OODA loop in the kill chain layer are 0.2 respectively; the comprehensive weight value of "maximum detection distance" in the capability chain under the observation link (O) in the performance indicator layer is 0.2, the comprehensive weight value of "scanning frequency" is 0.3, the comprehensive weight value of "target detection probability" is 0.5, and the comprehensive weight value of "instruction release accuracy", "instruction transmission accuracy" and "command release accuracy" in the command chain are 0. The comprehensive weight values ​​of "communication accuracy", "communication delay rate" and "transmission rate" in the information chain are all 0.25, the comprehensive weight values ​​of "communication capacity" and "communication coverage range" in the information chain are all 0.5, the comprehensive weight values ​​of "effective coverage time" and "target detection time" in the time chain are all 0.5, the comprehensive weight value of "ground resolution" in the accuracy chain is 0.3, and the comprehensive weight value of "target detection accuracy" is 0.7; the comprehensive weight values ​​of "target identification number" and "target identification range" in the capability chain under the judgment link (O) are all 0.5, the comprehensive weight values ​​of "command release accuracy", "command transmission accuracy", "communication delay rate" and "transmission rate" in the command chain are all 0.25, and the comprehensive weight values ​​of "instruction release accuracy", "command transmission accuracy", "communication delay rate" and "transmission rate" in the information chain are all 0. The comprehensive weight values ​​of "communication capacity" and "communication coverage" are both 0.5, the comprehensive weight value of "information fusion time" in the time chain is 1, and the comprehensive weight value of "target identification success rate" in the precision chain is 1; the comprehensive weight values ​​of "number of collaborative task planning" and "number of firepower allocation targets" in the capability chain under the decision link (D) are both 0.5, the comprehensive weight values ​​of "collaborative task conflict rate", "instruction release accuracy", "instruction transmission accuracy", "communication delay rate" and "transmission rate" in the command and control chain are all 0.2, the comprehensive weight values ​​of "communication capacity" and "communication coverage" in the information chain are both 0.5, the comprehensive weight value of "task planning time" in the time chain is 1, and the comprehensive weight value of "firepower allocation target" in the precision chain is 1. The comprehensive weights of "distribution accuracy" and "track accuracy" are both 0.5; the comprehensive weight of "maximum range" in the capability chain under the action link (A) is 0.3, the comprehensive weight of "maneuvering speed" is 0.3, and the comprehensive weight of "damage equivalent" is 0.4. The comprehensive weights of "instruction release accuracy", "instruction transmission accuracy", "communication delay rate" and "transmission rate" in the command chain are all 0.25. The comprehensive weights of "communication capacity" and "communication coverage" in the information chain are all 0.5. The comprehensive weights of "UAV flight time" and "target strike time" in the time chain are all 0.5. The comprehensive weights of "miss distance" and "target strike success rate" in the accuracy chain are all 0.5;

[0098] (3) According to the weighted summation method, the comprehensive efficiency value of the satellite-machine collaborative system is calculated recursively layer by layer to be 0.812.

[0099] The present invention has been described in detail above in conjunction with specific implementations and exemplary examples, but these descriptions cannot be understood as limiting the present invention. Those skilled in the art understand that, without departing from the spirit and scope of the present invention, a variety of equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its implementation methods, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be subject to the attached claims.

[0100] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A system performance evaluation method based on OODA loop closure, characterized in that: include: The effectiveness indicator system is divided into three dimensions: tasks, OODA loop processes, and kill chains, to construct the overall dimension of the effectiveness indicator system; Through the analysis of OODA loop process and kill chain closure, a multi-layer performance indicator system of task-OODA loop process-kill chain sub-chain-performance indicator is established; Obtain the expert comprehensive weights of each node in the task layer, OODA loop process layer, and kill chain layer, and each performance indicator in the performance indicator layer. According to the expert scores and expert comprehensive weights of each node or indicator in each layer, use the weighted summation method to recursively push upward layer by layer to determine the comprehensive performance value of the system and complete the performance evaluation of the cross-domain collaborative system.

2. The system performance evaluation method based on OODA loop closure according to claim 1 is characterized in that: The steps of dividing the overall effectiveness indicator system into three dimensions: tasks, OODA loop processes, and kill chains, and constructing the overall dimensions of the effectiveness indicator system include: The indicator system is developed at the top level according to the system task dimension; For the processes under different tasks, according to the observation link, judgment link, decision link and action link, they are developed according to the OODA loop process dimension; In different links of the OODA loop process, the five kill chains of capability chain, command chain, information chain, time chain and precision chain are deployed.

3. The system performance evaluation method based on OODA loop closure according to claim 1 is characterized in that: The steps of establishing a multi-layer performance indicator system of task-OODA loop process-kill chain sub-chain-performance indicator through closed analysis of the OODA loop process and the kill chain include: Starting from the task layer, for different tasks, we analyze the composition of task activities and map each task to four process links: observation link, judgment link, decision link, and action link. Each process link consists of several activities. Map each process link into the five sub-chains of the kill chain: capability chain, charge chain, information chain, time chain, and precision chain. Map the indicator information described by different sub-chains to obtain quantifiable underlying performance indicators.

4. The system performance evaluation method based on OODA loop closure according to claim 3 is characterized in that: The step of mapping the indicator information described by different sub-chains to obtain quantifiable underlying performance indicators includes: Map the corresponding indicators of the reconnaissance system in the capability chain, command chain, information chain, time chain and accuracy chain under the observation link into effectiveness indicators; Map the corresponding indicators of the reconnaissance and command and control systems in the capability chain, command and control chain, information chain, time chain and accuracy chain in the judgment link into performance indicators; Map the corresponding indicators of the command and destruction systems in the capability chain, command chain, information chain, time chain and precision chain in the decision-making link into effectiveness indicators; The corresponding indicators of the destructive systems in the capability chain, command chain, information chain, time chain and precision chain in the action link are mapped into effectiveness indicators.

5. The system performance evaluation method based on OODA loop closure according to claim 3 is characterized in that: The performance indicators in the performance indicator layer are divided into three types: inherent attribute performance indicators, probability statistics performance indicators and time indicator performance indicators; Intrinsic attribute performance indicators are related design attributes of each system that constitutes the system. Their values ​​fluctuate within a small range under different task environment conditions and are greater than the minimum state parameters for completing the task. Probabilistic and statistical performance indicators are performance indicators described by statistical probability; Time index performance indicators are the time values ​​for completing specific tasks obtained through simulation or actual installation test methods; The values ​​of inherent attribute performance indicators, probability statistics performance indicators and time index performance indicators affect their weights assigned in the performance evaluation process.

6. A system effectiveness evaluation system based on OODA loop closure, characterized in that: The effectiveness evaluation system includes the task layer, OODA loop process layer, kill chain layer and effectiveness indicator layer; Each task in the top-level task layer is mapped to four process links: observation link, judgment link, decision link, and action link, where each process link consists of several activities; Each process link is mapped into five sub-chains of the kill chain: capability chain, accusation chain, information chain, time chain, and precision chain. According to the indicator information described by different sub-chains, there are quantifiable underlying performance indicators. Determine the expert comprehensive weights of each node in the task layer, OODA loop process layer, and kill chain layer, and each performance indicator in the performance indicator layer. Based on the expert scores and expert comprehensive weights of each node or indicator in each layer, use the weighted summation method to recursively push upward layer by layer to determine the comprehensive performance value of the system and complete the performance evaluation of the cross-domain collaborative system.

7. A system performance evaluation device based on OODA loop closure, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors run the system effectiveness evaluation system based on OODA loop closure as described in claim 6.

8. A readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the system effectiveness evaluation system based on OODA loop closure as claimed in claim 6 is run.

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