System capability quantitative evaluation method under complex adaptive system confrontation condition
By decomposing tasks, identifying components and adversarial elements, and correcting sub-task probabilities, the problem of inaccurate capability assessment of complex equipment systems under adversarial conditions is solved, achieving comprehensive and accurate capability assessment. This is applicable to the design and assessment of complex equipment systems such as high-speed trains, space transportation, and CNC centers.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-03-27
AI Technical Summary
Existing technologies fail to adequately consider the effectiveness of adversary systems and the conditions of confrontation when assessing the capabilities of complex equipment systems, resulting in inaccurate assessments and a lack of robustness.
By decomposing the mission tasks, identifying sub-tasks and their dependencies, constructing sets of component elements and sets of adversarial elements, correcting the completion probability of sub-tasks, and comprehensively calculating the overall completion probability of the target system, the system reflects the capability gains or losses of complex equipment systems in adversarial environments.
It enables a comprehensive and accurate assessment of the capabilities of complex equipment systems, improves the accuracy and practical applicability of quantitative assessment, and is robust and scalable, making it suitable for the design optimization, mission planning, and risk analysis of various complex equipment systems.
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Figure CN121745756A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of quantitative assessment technology for the capabilities of complex equipment systems, and specifically to a method for quantitative assessment of the capabilities of complex adaptive systems under combat conditions. Background Technology
[0002] With the advancement of modern science and technology and the increasing demands of engineering applications, complex equipment products integrating multiple physical processes and unit technologies, such as mechanics, electronics, hydraulics, and optics, are gradually becoming the mainstream trend. These complex equipment products are often composed of multiple complex systems articulated together, including mechanical structures, control units, and electrical equipment. Examples include system-of-systems combat platforms, unmanned combat systems, command and control centers, missile defense networks, and electronic countermeasures systems. These equipment products are characterized not only by long development cycles, large product investments, and high quality requirements during the design process, but also by significant features such as ambiguity, hierarchy, empiricism, and comprehensiveness in actual use scenarios.
[0003] Complex system capability refers to the various capabilities and combinations of capabilities required for the execution of a complex system when its components work together under different operating scenarios. For example, emergent performance is usually manifested as the combined gain of system effectiveness under different task scenarios, resulting in an effect where "the whole is greater than the sum of its parts".
[0004] With the deepening application of complex systems theory in engineering, biology, society and other fields, various fields have proposed quantitative evaluation methods for the capabilities of complex systems with their own application characteristics. However, these methods usually only consider the target evaluation system itself, ignoring the fact that complex systems generally operate in an environment full of adversarial forces. Therefore, they do not take into account the relevant effectiveness of the adversary system, or, when the effectiveness of the adversary system is unknown, they do not fully consider the vulnerability of the system under strong adversarial conditions. Summary of the Invention
[0005] The purpose of this invention is to provide a method for quantitatively evaluating the system capabilities of complex adaptive systems under adversarial conditions, so as to solve the above-mentioned problems existing in the prior art.
[0006] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a method for quantitatively evaluating the capabilities of complex adaptive systems under adversarial conditions, comprising the following steps: S1. Determine the target system that needs to be quantitatively evaluated for its system capabilities, and determine the mission tasks that the target system intends to accomplish. Use the probability of completing the mission tasks as the quantitative evaluation target for the target system capabilities. S2. Decompose the mission task to obtain several sub-tasks that the target system needs to execute to complete the mission task, and establish the task dependency relationship between each sub-task so that the completion probability of the mission task can be represented by a combination of the completion probabilities of each sub-task. S3. For each subtask, determine the set of component elements of the target system that participate in completing the subtask. The set of component elements is a single entity or a subsystem composed of multiple entities. Based on the set of component elements, establish a completion probability evaluation model for the corresponding subtask. S4. During the execution of mission tasks by the target system, introduce complex adaptive system adversarial conditions, identify the adversarial system that forms an adversarial relationship with the target system, and for each sub-task, identify the set of adversarial elements in the adversarial system that affect the component elements participating in the sub-task. S5. Based on the impact of the set of adversarial elements on the set of component elements in performing sub-tasks, the completion probability of each sub-task is corrected to obtain the sub-task completion probability under the adversarial conditions of a complex adaptive system. S6. Combine the completion probabilities of each sub-task under complex adaptive system adversarial conditions to obtain the overall completion probability of the target system completing its mission under the existence of the adversarial system, and use the overall completion probability as the quantitative evaluation result of the target system's capabilities.
[0007] Furthermore, in step S1, in the complex equipment system formed by the integration of multiple physical processes and multiple unit technologies, an equipment system consisting of at least two types of mechanical structures, control units, and electrical equipment is selected as the target system. Based on the specific engineering or combat mission that the target system needs to complete in the actual operation scenario, the system operation target corresponding to the mission is determined. The success probability of the target system completing the mission in the mission operation scenario is used as a quantitative evaluation index characterizing the system capability of the target system.
[0008] Furthermore, in step S2, the mission task decomposition step includes: hierarchically decomposing the mission task according to the functional composition and operation process of the target system in the mission operation scenario, obtaining multiple sub-tasks with sequential or dependent relationships, and establishing a combination relationship between the mission task completion probability and the completion probability of each sub-task according to the sequential relationship and mutual constraint relationship of each sub-task in the mission execution process, so that the completion of the mission task can be quantitatively represented by the completion status of each sub-task.
[0009] Further, in step S3, for each subtask, the internal functional units and equipment entities of the target system participating in completing the subtask are identified, and the functional units and equipment entities are formed into a corresponding set of component elements. When the set of component elements consists of multiple equipment entities, it is treated as a subsystem that collaboratively executes the subtask. The processing includes: when the set of component elements consists of multiple equipment entities, the multiple equipment entities are constructed into a subsystem that collaboratively executes the subtask according to their functional division, information interaction relationship and execution order in the subtask execution process, and the completion probability of the subsystem's ability to complete the subtask is obtained by comprehensively calculating the functional capabilities corresponding to each equipment entity.
[0010] Furthermore, in step S3, when there is a shared equipment entity among the component element sets corresponding to different sub-tasks, the completion probability of each sub-task is modeled based on the influence relationship of the shared equipment entity on the execution process of multiple sub-tasks.
[0011] Furthermore, in step S4, in the operational scenario where the target system performs its mission, external equipment systems that interfere with, weaken, or damage the target system's mission execution are identified as adversarial systems. For each sub-task, the way in which the adversarial system affects the component elements participating in the sub-task in the sensing, communication, control, or execution stages is analyzed, and the set of adversarial elements corresponding to the sub-task is determined to characterize the impact of the adversarial system on the target system's ability to complete the sub-task. The analysis of the adversarial system includes: identifying the objects and links of interaction between the adversarial elements and the component elements, determining the impact of the interaction links on the execution process of the sub-task, and evaluating the change in the completion probability of the sub-task under adversarial conditions.
[0012] Furthermore, in step S5, under the condition of introducing an adversarial system, the success probability of the component elements participating in the sub-task completing the sub-task under non-adversarial conditions is corrected based on the success probability of the component elements completing the sub-task under adversarial conditions, and the probability and relationship of the adversarial elements exerting influence on the component elements.
[0013] Furthermore, in step S5, when the ability or probability of occurrence of the adversarial system cannot be obtained or determined, the probability of occurrence of the adversarial system is set to a preset value, thereby reducing the probability of completion of the subtask to the probability of completion without considering the adversarial conditions.
[0014] Furthermore, in step S6, the comprehensive evaluation step of system capability includes: comprehensively calculating the overall completion probability of the target system in completing its mission based on the completion probability of each sub-task under the condition of the existence of the adversarial system.
[0015] Furthermore, in step S6, the overall completion probability is used as a quantitative evaluation result characterizing the target system's ability to perform mission tasks in a complex adaptive system adversarial environment, so as to reflect the overall capability gain or capability weakening effect of the target system under the influence of multi-sub-task collaboration and adversarial factors.
[0016] The present invention provides a method for quantitatively evaluating the system capability of complex adaptive systems under adversarial conditions, which has the following advantages compared with the prior art: This invention decomposes the mission, clarifies the sub-tasks and their dependencies, identifies the components involved in the sub-tasks, and constructs a subsystem collaborative model. Simultaneously, it introduces an adversarial system and its impact on the component components, corrects the sub-task completion probability, and comprehensively calculates the overall completion probability of the target system. This achieves a comprehensive and accurate assessment of the capabilities of complex equipment systems. This method not only reflects positive and negative emergence effects, improving the accuracy and practical applicability of system capability quantification, but also possesses robustness and scalability under adversarial conditions. It can be widely applied to various complex equipment systems such as high-speed trains, space transportation, CNC centers, dispatch centers, and automobiles, providing reliable technical support for system design optimization, mission planning, risk analysis, and operational assessment. Attached Figure Description
[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but includes other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0020] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1: S1. Determine the target system that needs to be quantitatively evaluated for its system capabilities, and determine the mission tasks that the target system intends to accomplish. Use the probability of completing the mission tasks as the quantitative evaluation target for the target system capabilities. Specifically: First, the target system for which quantitative assessment of system capabilities needs to be conducted is determined. In this embodiment, the target system is a complex equipment system formed by the integration of multiple physical processes and multiple unit technologies, including multiple functional units such as mechanical structures, control units, and electrical equipment. For example, high-speed trains, CNC machining centers, or aerospace launch equipment can be selected as the target system.
[0021] The mission tasks that the target system intends to accomplish are then determined. In this embodiment, the mission tasks are the specific engineering or combat tasks that the equipment system needs to complete in actual operational scenarios, such as a "detection-destruction" mission. The probability of the target system completing these mission tasks in the operational scenario is used as a quantitative evaluation indicator of the system's capabilities. S2. Decompose the mission task to obtain several sub-tasks that the target system needs to execute to complete the mission task, and establish the task dependency relationship between each sub-task so that the completion probability of the mission task can be represented by a combination of the completion probabilities of each sub-task. Specifically: The mission task is broken down hierarchically into several sub-tasks. Taking the "discover-eliminate" task as an example, it can be decomposed into: The discovery task, communication task, and elimination task have sequential and dependent relationships among their subtasks. The combination relationship of their completion probabilities can be expressed as follows: ,in This represents the probability of the entire mission being completed. , PT_elimination and PT_elimination represent the completion probability of each subtask, respectively.
[0022] S3. For each subtask, determine the set of component elements of the target system that participate in completing the subtask. The set of component elements is a single entity or a subsystem composed of multiple entities. Based on the set of component elements, establish a completion probability evaluation model for the corresponding subtask. Specifically: For each subtask, determine the set of component elements involved in completing that subtask. Component elements can be a single entity or a subsystem composed of multiple entities. For example, the discovery task is performed by equipment entity A1, and the elimination task is performed by equipment entity A2. If the set of component elements consists of multiple entities, they are treated as a whole subsystem according to their functional division and collaborative relationships. The processing includes: The functions and corresponding capability indicators of each main equipment in the sub-tasks were determined.
[0023] Based on the collaborative relationship between the main equipment components, a subsystem completion probability is generated to characterize the subsystem's ability to perform the subtask.
[0024] Taking the "detect-eliminate" mission as an example, the probability of completion without considering adversarial conditions can be expressed as: ,in, These represent the probabilities of each entity or subsystem completing its respective sub-task; S4. During the execution of mission tasks by the target system, introduce complex adaptive system adversarial conditions, identify the adversarial system that forms an adversarial relationship with the target system, and for each sub-task, identify the set of adversarial elements in the adversarial system that affect the component elements participating in the sub-task. Specifically, this involves introducing Complex Adaptive System Adversarial Conditions (CASOF) during the target system's mission execution. External equipment systems that form an adversarial relationship with the target system are identified as adversarial systems. For example, adversarial system B might impose interference on communication links.
[0025] S5. Based on the impact of the set of adversarial elements on the set of component elements in performing sub-tasks, the completion probability of each sub-task is corrected to obtain the sub-task completion probability under the adversarial conditions of a complex adaptive system. Under the condition of introducing an adversarial system, the completion probability of each sub-task is modified.
[0026] Taking a detection-elimination mission as an example, if the adversarial system acts on the communication link, reducing the probability of successful communication, it can be expressed as: The formula for calculating the subtask completion probability under adversarial conditions is: in, This indicates the ability of component elements to complete sub-tasks under adversarial conditions. This represents the probability of the antagonistic system's effect on this component element. When the effectiveness of the antagonistic system is unknown, it can be represented as... Setting it to 1 causes the modified probability to degenerate into the completion probability without considering adversarial conditions, i.e.: Degenerate into: ; S6. Combine the completion probabilities of each sub-task under the complex adaptive system adversarial conditions to obtain the overall completion probability of the target system completing its mission under the existence of the adversarial system, and use the overall completion probability as the quantitative evaluation result of the target system's capabilities. Specifically: By combining the completion probabilities of each sub-task under adversarial conditions, the overall completion probability of the target system fulfilling its mission is obtained: Furthermore: To further improve the accuracy of quantification, experimental level variables or environmental parameters, such as the average distance L between the equipment and the combat system, can be introduced into the calculation of the sub-task completion probability. The corrected formula can then be expressed as: It can be extended to any complex equipment system and multi-mission combination scenario, and is applicable to the system capability assessment of complex systems in multiple fields such as aerospace, automobile, and industrial equipment; Example 2: Step 1: Determine the target system A to be evaluated and the specific mission T to be achieved, then estimate the probability of mission achievement. The quantitative assessment objective for the capabilities of System A.
[0027] Step 2: Decompose the specific tasks required for A to achieve T and their dependencies. For ease of quantitative evaluation, this can usually be simplified into a series of sequential tasks, denoted as Ti, i.e.: Step 3: Determine A's participation in each task The set of component elements, denoted as In a relatively simple system capability assessment, It may be just a single entity; conversely, Typically, it is a subsystem composed of multiple entities, along with subsystems that perform other tasks. There may be a shared entity. In this case, the above equation can be simplified to: like For a single entity, then... The estimation degenerates from a quantitative assessment of system capabilities to a quantitative assessment of the effectiveness of a single piece of equipment; otherwise, for The estimate can be referenced. The method can be further broken down as needed.
[0028] Step 4: Based on the above consideration of only system A and task T, further consider the adversarial conditions of complex adaptive systems. Let B be the main adversarial system of A in the process of achieving T, and let the influence of system B be... If the complete set of component elements of Ti is Bi, then the quantitative evaluation method for the capability of system A considering the influence of B is as follows: Compare the estimation results of Ti under the condition of considering and not considering the adversarial nature of the complex adaptive system: It can be seen that the prerequisite for evaluating the system capability under complex adaptive system adversarial conditions lies in the evaluation of Bi and its counterparts in this step. Complete the estimation of the impact of Ti. When the effectiveness of the impact on system B is unknown at the time of evaluation, The possible value is 1. The probability of mission T being completed under adversarial conditions in a non-complex adaptive system is... ; Scenario Example: Assuming the system A to be quantified consists of only two entities, A1 is responsible for detection and A2 is responsible for destruction, then, without considering adversarial conditions, A's ability to achieve the "detection-destruction" task T can be evaluated by the following formula: Suppose that an adversarial system B emerges that can affect A's ability to achieve T, and its adversarial methods only target the communication link between A1 and A2, making... Therefore, the quantitative assessment of the capability of system A at this point deviates greatly depending on whether the impact of B is considered. The assessment method that considers the adversarial conditions of complex adaptive systems is a more realistic measure of the capability of system A.
[0029] To further illustrate the scalability of this technical solution, in the previous embodiment, based on some evaluation practices, an experimental level variable L is added to better estimate the probability of completing a specific task. Here, L represents the average distance between B and A1 and A2, then: The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for quantitatively evaluating system capacity of complex adaptive systems under antagonistic conditions, characterized in that, The method comprises the following steps: S1, determining a target system which needs to be quantitatively evaluated, and determining a mission task to be completed by the target system, and taking the completion probability of the mission task as a quantitative evaluation target of the target system capability; S2, decomposing the mission task to obtain a plurality of sub-tasks required to be executed by the target system to complete the mission task, and establishing a task dependency relationship between the sub-tasks, so that the completion probability of the mission task can be represented by the completion probabilities of the sub-tasks; S3, for each sub-task, determining a component element set of the target system participating in completing the sub-task, the component element set being a single subject or a sub-system composed of a plurality of subjects, and establishing a completion probability evaluation model of the corresponding sub-task based on the component element set; S4, in the process of the target system executing the mission task, introducing a complex adaptive system confrontation condition, determining an opposing system in confrontation with the target system, and for each sub-task, determining a set of opposing elements in the opposing system which have an impact on the component elements participating in the sub-task; S5, according to the impact of the set of opposing elements on the set of component elements executing the sub-task, correcting the completion probability of each sub-task to obtain the completion probability of the sub-task under the complex adaptive system confrontation condition; S6, combining the completion probabilities of the sub-tasks under the complex adaptive system confrontation condition to obtain the overall completion probability of the target system completing the mission task in the presence of the opposing system, and taking the overall completion probability as the quantitative evaluation result of the target system capability.
2. The method of claim 1, wherein, In step S1, in a complex equipment system formed by multi-physical processes and multi-unit technology integration, an equipment system composed of at least two of mechanical structure, control unit and electrical equipment is selected as a target system, and based on a specific engineering or combat mission to be completed by the target system in an actual operation scenario, a system operation target corresponding to the mission is determined, wherein the success probability of the target system completing the mission in the mission operation scenario is taken as a quantitative evaluation index of the system capability of the target system.
3. The method of claim 2, wherein, In step S2, the mission task decomposition step comprises: according to the functional composition and operation process of the target system in the mission operation scenario, hierarchically splitting the mission task to obtain a plurality of sub-tasks with a sequence or dependency relationship, and according to the sequence relationship and mutual constraint relationship of the sub-tasks in the mission execution process, establishing a combination relationship between the completion probability of the mission task and the completion probabilities of the sub-tasks, so that the completion of the mission task can be quantitatively represented by the completion of the sub-tasks.
4. The method of claim 3, wherein, In step S3, for each subtask, identify the target system internal functional units and equipment subjects participating in completing the subtask, and form a corresponding component element set. When the component element set is composed of multiple equipment subjects, it is treated as a sub-system that cooperatively performs the subtask; the processing includes: when the component element set is composed of multiple equipment subjects, the multiple equipment subjects are constructed into a sub-system that cooperatively performs the subtask according to their functional division, information interaction relationship and execution sequence in the subtask execution process, and the completion probability of the sub-system completing the subtask is obtained by comprehensively calculating the corresponding functional capabilities of each equipment subject.
5. The method of claim 4, wherein the method is characterized by, In step S3, when there are common equipment subjects between different subtask corresponding component element sets, the completion probabilities of each subtask are associated modeled based on the influence relationship of the common equipment subjects on the execution process of multiple subtasks.
6. The method of claim 5, wherein, In step S4, in the running scene of the target system executing the mission task, identify the external equipment system that has interference, weakening or destructive effect on the target system mission execution as the counter system, and for each subtask, analyze the way in which the counter system influences the component elements participating in the subtask in the perception, communication, control or execution link, determine the counter element set corresponding to the subtask, which is used to represent the influence of the counter system on the target system's ability to complete the subtask; Analyzing the counter system includes: identifying the action object and action link between the counter element and the component element, determining the influence result of the action link on the subtask execution process, and evaluating the change of the completion probability of the subtask under the counter condition accordingly.
7. The method of claim 6, wherein the method is characterized by, In step S5, under the condition of introducing the counter system, based on the success probability of the component elements participating in the subtask in the non-counter condition, and the occurrence probability and action relationship of the counter elements exerting influence on the component elements, the success probability of the component elements completing the subtask under the counter condition is corrected.
8. The method of claim 7, wherein the method is characterized by, In step S5, when the action capability or occurrence probability of the counter system cannot be obtained or determined, the occurrence probability of the counter system is set to a preset value, so that the subtask completion probability is degraded to the completion probability without considering the counter condition.
9. The method of claim 8, wherein the method is characterized by, In step S6, the system capability comprehensive evaluation step includes: based on the completion probability of each subtask under the condition of the existence of the counter system, the overall completion probability of the target system completing the mission task is comprehensively calculated.
10. The method of claim 8, wherein the method is characterized by, In step S6, the overall completion probability is taken as a quantitative evaluation result representing the target system's ability to execute the mission task in the complex adaptive system counter environment, to reflect the overall ability gain or ability weakening effect of the target system under the cooperative and counter influence conditions of multiple subtasks.