Cooperative task efficiency analysis method and device based on ring network fusion

Abstract

The invention discloses a collaborative task efficiency analysis method and device based on ring network fusion, and relates to the technical field of efficiency analysis. The unmanned aerial vehicle cooperative task system is simplified into four node types including an OODA ring; based on four types of nodes of the OODA ring, determining an actual connection edge, obtaining a task network, extracting a generalized OODA ring from the task network, and covering a cooperative relationship; constructing a generalized OODA ring node attribute quantification framework based on task requirements, including task-driven node attribute extraction, a quantification method, a data acquisition path and data normalization processing; and performing nonlinear aggregation based on a generalized OODA ring node attribute quantification framework, and performing comprehensive analysis on task network efficiency based on a task success probability. The problems that in the prior art, the efficiency change of an unmanned aerial vehicle cooperative task system in the dynamic confrontation process cannot be accurately captured, and the system is limited in practical application are solved.

Description

Technical Field

[0001] This application relates to the field of performance analysis technology, and in particular to a collaborative task performance analysis method and apparatus based on ring network fusion. Background Technology

[0002] In the context of UAV collaborative mission systems, complex network theory provides a powerful tool for analyzing the structural characteristics of these systems. This theory allows for a clear representation of the topological structure of the UAV collaborative mission system.

[0003] However, analytical methods based on complex network theory primarily focus on the static structural characteristics of networks. This results in analysis results that often only reflect the static structural characteristics of the UAV collaborative mission system at a specific moment, failing to capture its dynamic changes. In actual UAV collaborative mission scenarios, the mission system is not static. Existing techniques based on complex network theory struggle to accurately capture the performance changes of the UAV collaborative mission system during this dynamic confrontation process, thus affecting the accuracy of mission execution analysis and leading to low mission efficiency or failure to achieve expected goals.

[0004] Currently, there are methods for analyzing the performance of tasks related to the OODA loop theory based on neural networks. Although this method has a certain adaptive learning capability, it heavily relies on a large number of data samples for training. In actual UAV collaborative tasks, obtaining comprehensive and high-quality data samples often faces many difficulties. Summary of the Invention

[0005] In this embodiment of the application, a collaborative task performance analysis method based on ring network fusion is provided, which solves the problem that the existing technology cannot accurately capture the performance changes of the UAV collaborative task system in the dynamic confrontation process, which affects the accuracy of the analysis of task execution, resulting in low task execution efficiency or failure to achieve the expected goal, and is limited in practical applications.

[0006] In a first aspect, embodiments of this application provide a collaborative task performance analysis method based on ring network fusion. This method includes: simplifying the UAV collaborative task system into four node types containing an OODA ring; wherein the node types include perception nodes, decision nodes, execution nodes, and target nodes; determining actual connection edges based on the four OODA ring node types to obtain the task network; extracting a generalized OODA ring from the task network to cover collaborative relationships; constructing a generalized OODA ring node attribute quantification framework based on task requirements, including task-driven node attribute extraction, quantification methods, data acquisition paths, and data normalization processing; and performing nonlinear aggregation based on the generalized OODA ring node attribute quantification framework, and comprehensively analyzing the task network performance based on the probability of task success.

[0007] In one possible implementation, perception nodes are used to acquire environmental information, collect target information, and monitor the environment; decision nodes are used to process information, analyze the situation, formulate decision-making plans, and issue instructions; execution nodes are used to execute operational tasks; and target nodes are used to identify key locations.

[0008] In one possible implementation, determining the actual connection edge based on the four types of nodes in the OODA ring includes: when each of the four types of nodes interacts with the four types of nodes including itself, considering both the sending and receiving directions of information, the single-direction information interaction between each type of node and the four types of nodes including itself is regarded as a potential connection edge; and determining the actual connection edge based on the potential connection edge.

[0009] In one possible implementation, the extraction of a generalized OODA ring from the task network, covering cooperative relationships, includes: cooperative relationships comprising cooperative-aware connection edges, cooperative-instruction connection edges, and cooperative-execution connection edges; the expression for the task network is: ;in, For task networks, nodes Including sensing nodes Decision nodes Execution Node and target node Actual connecting edge This includes collaborative sensing connections from sensing nodes to sensing nodes, decision-making relationship connections from sensing nodes to decision-making nodes, first command relationship connections from decision-making nodes to sensing nodes, collaborative command and control connections from decision-making nodes to decision-making nodes, second command relationship connections from decision-making nodes to execution nodes, collaborative execution connections from execution nodes to execution nodes, operational relationship connections from execution nodes to target nodes, and reconnaissance relationship connections from target nodes to sensing nodes; a counter is initialized to 0 to count target nodes. The corresponding number of task cycles; based on the target node Starting from the target node, the system traverses adjacent nodes using a depth-first search. Each time a new node is visited, a recursive call is initiated from that new node, continuing the depth-first search traversal. The system records the current set of nodes and their type order. During the traversal, node type constraints are followed. These constraints include: starting from the target node... The next step after setting off is the sensing node. From the sensing node The next step after setting off is the sensing node. or decision node From the decision-making node The next step after departure is the decision-making node. or execution node From the execution node The next step after starting is the execution node. or target node ; except for the target node in the path In addition, other nodes are not traversed repeatedly; when traversing to the execution node, check if there is a job relationship connection edge from the execution node to the target node. If there is, increment the counter value by 1; after each recursive call, remove the current node from the node set, explore other paths, and reduce invalid search paths through pruning and backtracking mechanisms to obtain a generalized OODA cycle.

[0010] In one possible implementation, the construction of a generalized OODA node attribute quantification framework based on task requirements includes: task-driven node attribute extraction, which includes parsing task intent, analyzing scenario requirements, and identifying constraints; combining expert experience and data-driven approaches to select core attributes for each node type related to the task; quantification methods and data acquisition paths, which include adopting a fifth-order quantification criterion and selecting appropriate quantification methods and data acquisition methods according to the type of core attributes; wherein the fifth-order quantification criterion includes experimental testing, simulation exercises, historical data statistical analysis, adversarial analysis, and comprehensive integration methods; and data normalization processing, which includes converting core attribute data of different dimensions or ranges to a unified dimension or range range.

[0011] In one possible implementation, the step-by-step attribute quantization framework based on generalized OODA performs nonlinear aggregation and conducts a comprehensive analysis of the task network performance based on the probability of task success, including: based on Obtain the probability of each node type completing the task; where, for or or or , To determine the probability that a sensing node will complete its task. The probability that a decision node will complete its task. The probability that the execution node will complete the task. The probability of completing the task for the target node. For the corresponding node's first The weight of each influencing factor For the corresponding node's first The membership degree or probability of each influencing factor in completing the task is used; based on the probability of each node type completing the task, the minimum value method is used to chain and aggregate the node sets to obtain the task completion rate of each node set, expressed as: ;in, For the first Task completion rate of a set of nodes For the first The number of nodes in each generalized OODA ring; based on the task completion rate of each node set, a probabilistic parallel model is adopted, treating the multiple generalized OODA rings of the target node as a parallel structure to obtain the probability of achieving the goal, expressed as: ;in, To achieve the first The probability of achieving a goal For the first Task completion rate of a set of nodes To achieve the first The total number of node sets that can be formed for each objective; based on the probability of achieving the objective, the probability of task success is obtained; and a comprehensive analysis of the task network performance is performed based on the probability of task success, expressed as: ;in, This represents the probability of the task succeeding. The importance of achieving the goal To achieve the first The probability of achieving a goal The total number required to achieve the goal.

[0012] Secondly, embodiments of this application provide a collaborative task performance analysis device based on ring network fusion. This device includes: a simplification module for simplifying the UAV collaborative task system into four node types including an OODA ring; wherein the node types include perception nodes, decision nodes, execution nodes, and target nodes; an extraction module for determining actual connection edges based on the four types of nodes in the OODA ring, obtaining the task network, and extracting a generalized OODA ring from the task network to cover collaborative relationships; a construction module for constructing a generalized OODA ring node attribute quantification framework based on task requirements, including task-driven node attribute extraction, quantification methods, data acquisition paths, and data normalization processing; and an analysis module for performing nonlinear aggregation based on the generalized OODA ring node attribute quantification framework, and comprehensively analyzing the task network performance based on the probability of task success.

[0013] Thirdly, embodiments of this application provide a collaborative task performance analysis server based on ring network fusion, including a memory and a processor; the memory is used to store computer-executable instructions; the processor is used to execute the computer-executable instructions to implement the method described in the first aspect or any possible implementation of the first aspect.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing executable instructions, which, when executed by a computer, enable the method described in the first aspect or any possible implementation thereof.

[0015] One or more technical solutions provided in this application embodiment have at least the following technical effects: This application embodiment provides a collaborative task performance analysis method based on ring network fusion, simplifying the UAV collaborative task system into four types of nodes including OODA rings. Based on the four types of nodes of the OODA ring, the actual connection edges are determined to obtain the task network. A generalized OODA ring is extracted from the task network to cover collaborative relationships. A generalized OODA ring node attribute quantification framework based on task requirements is constructed, including task-driven node attribute extraction, quantification methods and data acquisition paths, and data normalization processing. Based on the generalized OODA ring node attribute quantification framework, nonlinear aggregation is performed, and the task network performance is comprehensively analyzed based on the probability of task success. It can accurately present the task system structure and collaborative relationships, and achieve a scientific and comprehensive analysis of task network performance through node attribute quantification and nonlinear aggregation. It solves the problem that existing technologies cannot accurately capture the performance changes of the UAV collaborative task system in the dynamic confrontation process, affecting the accuracy of task execution analysis, resulting in low task execution efficiency or failure to achieve expected goals, and is limited in practical applications. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a collaborative task performance analysis method based on ring network fusion, provided for embodiments of this application; Figure 2 A schematic diagram of a collaborative task performance analysis device based on ring network fusion provided in an embodiment of this application; Figure 3 This is a schematic diagram of a collaborative task performance analysis server based on ring network fusion, provided as an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0020] This application provides a method for analyzing the performance of collaborative tasks based on ring network fusion, such as... Figure 1 As shown, the method includes steps S101 to S104. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application, and does not represent the only execution order for a collaborative task performance analysis method based on ring network fusion. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order.

[0021] S101: The UAV collaborative mission system is simplified into four node types, including an OODA loop. These node types include perception nodes, decision-making nodes, execution nodes, and target nodes.

[0022] The OODA loop is a method used to describe the decision-making process of individuals or organizations when dealing with complex, dynamic environments.

[0023] Sensing nodes are used to acquire environmental information, collect target information, and monitor the environment, including weather sensors, environmental monitoring drones, traffic cameras, satellites, and sensor networks.

[0024] Specifically, weather sensors can detect meteorological data in various ways, such as temperature, humidity, and wind speed. Environmental monitoring drones can monitor specific areas over a large area, while traffic cameras can acquire images of traffic flow and road conditions in real time. Satellites provide wide-area, all-weather monitoring capabilities. Sensor networks are distributed in various key locations to perceive environmental changes and target dynamics in real time.

[0025] It should be noted that in the mission effectiveness analysis system involved in this application, the two parties mentioned can represent the red and blue opposing sides in any mission scenario. The mission of this application can be a multi-UAV cooperative detection mission.

[0026] Decision nodes are used to process information, analyze the battlefield situation, formulate decision-making plans, and issue instructions. They include control centers, joint dispatch centers, aircraft with dual monitoring and dispatch functions, and information processing centers.

[0027] Specifically, the control center and joint dispatch center, as the core locations for mission command, gather various types of intelligence information and formulate mission plans and instructions through professional analysis and decision-making processes. Aircraft with dual monitoring and dispatching functions can not only detect abnormal targets in advance but also make real-time command decisions during flight. The information processing center focuses on the rapid processing and analysis of massive amounts of information, providing accurate data support for decision-making.

[0028] Execution nodes are used to perform operational tasks, including automated work equipment (such as drones).

[0029] Target nodes are used to identify key locations, including important facility sites, command centers, large buildings, communication hubs, and critical production facilities.

[0030] Specifically, critical infrastructure sites are vital nodes for acquiring key information, and adjustments to them can impact overall information acquisition capabilities. Command centers are the core of mission command; disruptions to command centers can throw the mission system into chaos. Large buildings, communication hubs, and critical production facilities are also crucial supports for mission capabilities; effective adjustments or protection of these facilities are essential for achieving mission objectives.

[0031] S102: Based on the four types of nodes of the OODA ring, determine the actual connection edges, obtain the task network, extract the generalized OODA ring from the task network, and cover the collaborative relationships.

[0032] Based on the four types of nodes in the OODA ring, the actual connection edges are determined, including the following:

[0033] When each of the four types of nodes interacts with the other four types of nodes, we consider both the sending and receiving directions of information. The one-way information interaction between each type of node and the other four types of nodes is regarded as a potential connection edge.

[0034] The actual connecting edges are determined based on the potential connecting edges.

[0035] Specifically, this application determines the actual connection edges based on task requirements and logic.

[0036] Extract the generalized OODA ring from the task network, covering cooperative relationships, including the following.

[0037] Collaborative relationships include collaborative perception connections, collaborative command and control connections, and collaborative execution connections.

[0038] The expression for the task network is: .in, For task networks, nodes Including sensing nodes Decision nodes Execution Node and target node Actual connecting edge This includes collaborative sensing connections from sensing nodes to sensing nodes, decision-making connections from sensing nodes to decision-making nodes, first command connections from decision-making nodes to sensing nodes, collaborative command and control connections from decision-making nodes to decision-making nodes, second command connections from decision-making nodes to execution nodes, collaborative execution connections from execution nodes to execution nodes, operational connections from execution nodes to target nodes, and reconnaissance connections from target nodes to sensing nodes. Table 1 shows the determined actual connections.

[0039] Table 1. Determined actual connection edges

[0040] Initialize the counter to 0 to count the target nodes. The corresponding number of task cycles.

[0041] With target node Starting from the first node, the system visits adjacent nodes using a depth-first search. Each time a new node is visited, the system starts a new recursive call from that new node and continues the depth-first search traversal.

[0042] Record the set of nodes and the order of node types for the current path.

[0043] During the traversal, node type constraints are followed.

[0044] Node type constraints include: from the target node The next step after setting off is the sensing node. From the sensing node The next step after setting off is the sensing node. or decision node From the decision-making node The next step after departure is the decision-making node. or execution node From the execution node The next step after starting is the execution node. or target node The path excluding the target node Apart from that, other nodes are not traversed repeatedly.

[0045] When traversing to the execution node, check if there is a job relationship connection edge from the execution node to the target node. If it exists, increment the counter value by 1.

[0046] After each recursive call, the current node is removed from the node set, and other paths are explored. Through pruning and backtracking mechanisms, invalid search paths are reduced to obtain a generalized OODA cycle.

[0047] The generalized OODA ring relaxes the restrictions on path structure, allowing more intermediate nodes and branches to be included.

[0048] Specifically, the node set includes statistics on the target node. The number of generalized OODA rings starting from a given point, supporting complex paths containing multiple sensing nodes or decision nodes.

[0049] In the worst case, the above process requires traversing all nodes and actual connecting edges, resulting in a time complexity of O(n log n). .in, This represents the total number of nodes in the task network. This represents the total number of actual connecting edges in the task network. Due to node type constraints and pruning, the search space is significantly reduced, making it more efficient than a full graph depth-first search traversal. The space complexity is O(log n). It is used to store access status and path.

[0050] S103: Construct a generalized OODA node attribute quantization framework based on task requirements, including task-driven node attribute extraction, quantization methods and data acquisition paths, and data normalization processing.

[0051] Construct a generalized OODA node attribute quantization framework based on task requirements, including task-driven node attribute extraction, quantization methods and data acquisition paths, and data normalization processing, including the following content.

[0052] Task-driven node attribute extraction includes parsing task intent, analyzing scenario requirements, and identifying constraints. Combining expert experience with data-driven approaches, core attributes for each node type related to the task are selected. Table 2 shows the core attributes of each node type under different task scenarios.

[0053] Table 2 Core attributes of various nodes under different task scenarios

[0054] The quantification methods and data acquisition paths include: adopting the fifth-order quantification criterion and selecting the appropriate quantification method and data acquisition method according to the type of core attribute. Among them, the fifth-order quantification criterion includes experimental testing, simulation exercise, historical data statistical analysis, adversarial analysis, and comprehensive integration.

[0055] Experimental testing methods are suitable for physical performance indicators, such as detection range, which are obtained through data acquisition in laboratories or testing sites. Simulation and exercise methods are suitable for performance indicators, such as operational accuracy, which are verified through simulated operations or system-on-system confrontation exercises. Historical data statistical analysis is suitable for statistical regularity indicators, such as the accuracy of situation prediction, which is modeled by mining historical data or expert experience. Adversarial analysis methods are suitable for adversarial indicators, such as state stability, which are tested through simulated confrontation or environmental reproduction tests. Comprehensive integration methods are suitable for complex system-level indicators, such as cross-regional information fusion efficiency, which is analyzed by combining multiple methods and data sources. Table 3 shows the quantification methods and data sources for the four types of node core attributes.

[0056] Table 3. Quantification methods and data sources for the four types of node core attributes

[0057] Data normalization processing includes converting core attribute data of different dimensions or ranges into a unified dimension or range interval.

[0058] Specifically, to ensure comparability of the analysis, some attributes are normalized. Core attributes requiring normalization include physical quantities (e.g., detection range, scaled to the [0,1] range), time-related indicators (e.g., end-to-end delay, standardized units), and probability indicators (e.g., information accuracy, converted to a percentage or decimal with standardized units). Methods for normalization include Min-Max (range standardization) or Z-Score (range standardization). Core attributes that do not require normalization are naturally normalizable indicators (e.g., information accuracy, where percentages or scores are already in the [0,1] range).

[0059] S104: Based on the generalized OODA node attribute quantization framework, nonlinear aggregation is performed, and the network performance of the task is comprehensively analyzed based on the probability of task success.

[0060] Based on the generalized OODA node attribute quantization framework, nonlinear aggregation is performed, and the network performance of the task is comprehensively analyzed based on the probability of task success, including the following:

[0061] based on Obtain the probability of each node type completing the task. Among them, for or or or , To determine the probability that a sensing node will complete its task. The probability that a decision node will complete its task. The probability that the execution node will complete the task. The probability of completing the task for the target node. For the corresponding node's first The weight of each influencing factor For the corresponding node's first The degree of membership or probability of completing a task by each influencing factor.

[0062] Specifically, assuming a certain node exists One of the factors affecting its completion of the task. For the first one... Each influencing factor is set to have a membership degree or probability of 1 when it fully meets the task capability requirements, and 0 when it does not meet them at all. If meeting the requirements of this specific task affects the... The attribute value requirements for each influencing factor are: The attribute value of this node is Then the node's first The degree of membership or probability of each influencing factor in completing the task. for , This step is to... It is converted into a value between 0 and 1 to represent the degree of satisfaction with the completion of the task.

[0063] Based on the probability of completing the task for each node type, the node set is concatenated and aggregated using the minimum value method to obtain the task completion rate of each node set, expressed as: .in, For the first Task completion rate of a set of nodes For the first The number of nodes in a generalized OODA ring.

[0064] Specifically, an OODA loop can have four nodes: a perception node, a decision node, an execution node, and a target node. A generalized OODA loop may have more than four nodes.

[0065] Based on the task completion rate of each node set, a probabilistic parallel model is adopted, treating the multiple generalized OODA loops of the target node as a parallel structure to obtain the probability of achieving the goal. The expression is as follows: .in, To achieve the first The probability of achieving a goal For the first Task completion rate of a set of nodes To achieve the first The total number of node sets that can be formed by a given objective.

[0066] Based on the probability of achieving the goal, the probability of completing the task, and the probability of task success, a comprehensive analysis of the task network performance is performed, expressed as: ;in, This represents the probability of the task succeeding. The importance of achieving the goal To achieve the first The probability of achieving a goal The total number required to achieve the goal.

[0067] This application also provides a collaborative task performance analysis device 200 based on ring network fusion, such as... Figure 2 As shown, the device includes: a simplification module 201, an extraction module 202, a construction module 203, and an analysis module 204.

[0068] The simplification module 201 is used to simplify the UAV collaborative mission system into four types of nodes, including an OODA loop. These node types include perception nodes, decision nodes, execution nodes, and target nodes.

[0069] The extraction module 202 is used to determine the actual connection edges based on the four types of nodes of the OODA ring, obtain the task network, extract the generalized OODA ring from the task network, and cover the collaborative relationship.

[0070] Module 203 is used to build a generalized OODA node attribute quantization framework based on task requirements, including task-driven node attribute extraction, quantization methods and data acquisition paths, and data normalization processing.

[0071] Analysis module 204 is used to perform nonlinear aggregation based on the generalized OODA link point attribute quantization framework, and to conduct a comprehensive analysis of the task network performance based on the probability of task success.

[0072] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0073] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0074] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, for example, as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0075] like Figure 3 As shown in the figure, this application embodiment also provides a collaborative task performance analysis server based on ring network fusion, including a memory 301 and a processor 302; the memory 301 is used to store computer-executable instructions; the processor 302 is used to execute computer-executable instructions to implement the collaborative task performance analysis method based on ring network fusion described above in this application embodiment.

[0076] This application also provides a computer-readable storage medium storing executable instructions. When a computer executes the executable instructions, it can implement the collaborative task performance analysis method based on ring network fusion described above in this application embodiment.

[0077] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the embodiments of this application.

[0078] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations.

[0079] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A collaborative task performance analysis method based on ring network fusion, characterized in that, include: The UAV collaborative mission system is simplified into four types of nodes, including an OODA loop; these node types include perception nodes, decision nodes, execution nodes, and target nodes. Based on the four types of nodes of the OODA ring, the actual connection edges are determined to obtain the task network. The generalized OODA ring is then extracted from the task network to cover the collaborative relationships. Construct a generalized OODA node attribute quantization framework based on task requirements, including task-driven node attribute extraction, quantization methods and data acquisition paths, and data normalization processing; Based on the generalized OODA node attribute quantization framework, nonlinear aggregation is performed, and the network performance of the task is comprehensively analyzed based on the probability of task success.

2. The collaborative task performance analysis method based on ring network fusion according to claim 1, characterized in that, Sensing nodes are used to acquire environmental information, collect target information, and monitor the environment; Decision nodes are used to process information, analyze the situation, formulate decision-making plans, and issue instructions. Execution nodes are used to perform operational tasks; Target nodes are used to identify key locations.

3. The collaborative task performance analysis method based on ring network fusion according to claim 2, characterized in that, The determination of actual connection edges based on the four types of nodes in the OODA ring includes: When each of the four types of nodes interacts with the other four types of nodes, including itself, we consider both the sending and receiving directions of information. The one-way information interaction between each type of node and the other four types of nodes, including itself, is regarded as a potential connection edge. The actual connecting edges are determined based on the potential connecting edges.

4. The method for analyzing the performance of collaborative tasks based on ring network fusion according to claim 3, characterized in that, The extraction of the generalized OODA ring from the task network, covering cooperative relationships, includes: Collaborative relationships include collaborative perception connections, collaborative command and control connections, and collaborative execution connections. The expression for the task network is: ;in, For task networks, nodes Including sensing nodes Decision nodes Execution Node and target node Actual connecting edge This includes collaborative sensing connection edges from sensing nodes to sensing nodes, decision relationship connection edges from sensing nodes to decision nodes, first command relationship connection edges from decision nodes to sensing nodes, collaborative command and control connection edges from decision nodes to decision nodes, second command relationship connection edges from decision nodes to execution nodes, collaborative execution connection edges from execution nodes to execution nodes, operational relationship connection edges from execution nodes to target nodes, and reconnaissance relationship connection edges from target nodes to sensing nodes. Initialize the counter to 0 to count the target nodes. The corresponding number of task cycles; With target node Starting from the node, the system visits adjacent nodes by depth-first search. Each time a new node is visited, the next recursive call is made starting from that new node, and the depth-first search traversal continues. Record the set of nodes and the order of node types in the current path; During the traversal, node type constraints are followed; Node type constraints include: from the target node The next step after setting off is the sensing node. From the sensing node The next step after setting off is the sensing node. or decision node From the decision-making node The next step after departure is the decision-making node. or execution node From the execution node The next step after starting is the execution node. or target node ; except for the target node in the path Apart from that, other nodes are not traversed repeatedly; When traversing to the execution node, check if there is a job relationship connection edge from the execution node to the target node. If it exists, increment the counter value by 1. After each recursive call, the current node is removed from the node set, and other paths are explored. Through pruning and backtracking mechanisms, invalid search paths are reduced to obtain a generalized OODA cycle.

5. The collaborative task performance analysis method based on ring network fusion according to claim 4, characterized in that, The construction of a generalized OODA step-point attribute quantization framework based on task requirements includes: Task-driven node attribute extraction includes: parsing task intent, analyzing scenario requirements, and identifying constraints; combining expert experience and data-driven approaches to select the core attributes of each node type related to the task. The quantification methods and data acquisition paths include: adopting the fifth-order quantification criterion and selecting the appropriate quantification method and data acquisition method according to the type of core attribute; among which, the fifth-order quantification criterion includes experimental testing method, simulation exercise method, historical data statistical analysis method, adversarial analysis method and comprehensive integration method; Data normalization processing includes converting core attribute data of different dimensions or ranges into a unified dimension or range interval.

6. The method for analyzing the performance of collaborative tasks based on ring network fusion according to claim 5, characterized in that, The generalized OODA-based node attribute quantization framework performs nonlinear aggregation and conducts a comprehensive analysis of the task network performance based on the probability of task success, including: based on Obtain the probability of each node type completing the task; where, for or or or , To determine the probability that a sensing node will complete its task. The probability that a decision node will complete its task. The probability that the execution node will complete the task. The probability of completing the task for the target node. For the corresponding node's first The weight of each influencing factor For the corresponding node's first The degree of membership or probability of each influencing factor in completing the task; Based on the probability of completing the task for each node type, the node set is concatenated and aggregated using the minimum value method to obtain the task completion rate of each node set, expressed as: ;in, For the first Task completion rate of a set of nodes For the first The number of nodes in a generalized OODA ring; Based on the task completion rate of each node set, a probabilistic parallel model is adopted, treating the multiple generalized OODA loops of the target node as a parallel structure to obtain the probability of achieving the goal. The expression is as follows: ;in, To achieve the first The probability of achieving a goal For the first Task completion rate of a set of nodes To achieve the first The total number of node sets that can be formed by a given objective; Based on the probability of achieving the goal, the probability of completing the task, and the probability of task success, a comprehensive analysis of the task network performance is performed, expressed as: ;in, This represents the probability of the task succeeding. The importance of achieving the goal, To achieve the first The probability of achieving a goal The total number required to achieve the goal.

7. A collaborative task performance analysis device based on ring network fusion, characterized in that, include: The simplification module is used to simplify the UAV collaborative mission system into four types of nodes, including an OODA loop; among which, the node types include perception nodes, decision nodes, execution nodes, and target nodes; The extraction module is used to determine the actual connection edges based on the four types of nodes of the OODA ring, obtain the task network, extract the generalized OODA ring from the task network, and cover the collaborative relationships. The building module is used to construct a generalized OODA node attribute quantization framework based on task requirements, including task-driven node attribute extraction, quantization methods and data acquisition paths, and data normalization processing. The analysis module is used to perform nonlinear aggregation based on the generalized OODA node attribute quantization framework, and to comprehensively analyze the network performance of the task based on the probability of task success.

8. A collaborative task performance analysis server based on ring network fusion, characterized in that, Including memory and processor; The memory is used to store computer-executable instructions; The processor is configured to execute the computer-executable instructions to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores executable instructions, which, when executed by a computer, enable the implementation of the method as described in any one of claims 1-6.

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