Multi-dimensional collaborative detection and guidance method and system

Abstract

The application provides a multi-dimensional cooperative detection and guidance method and system, relates to the technical field of unmanned aerial vehicle cooperative detection and intelligent guidance, and the method comprises the following steps: obtaining multi-dimensional information of a target cluster in a space, including target quantity, coordinates and movement direction, and scheduling an interception equipment cluster according to a preset multiple of the target quantity to preliminarily allocate resources and realize distributed interception. Then, the multi-dimensional information is associated with the resource allocation result, a space cooperation allocation model is used for space partition and responsibility division, and an allocation scheme containing a partition result, a cooperative boundary and a responsibility instruction is generated. Finally, based on the scheme, a multi-machine target feature sharing mechanism is used to guide the interception equipment cluster to implement tracking and interception on the target according to the priority, and the target that escapes the net is processed in a targeted manner. The application improves the accuracy and overall efficiency of the cooperative interception of low-altitude dense targets.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle cooperative detection and intelligent guidance, in particular to a multi-dimensional cooperative detection and guidance method and system. BACKGROUND

[0002] In the management of low-altitude airspace around important facilities, there is often a need for cooperative monitoring and disposal of multiple batches of high-density moving targets. Such scenarios require the system to have the ability of rapid perception of dynamic targets, multi-device cooperative scheduling and intelligent decision-making to form an efficient regional defense effectiveness.

[0003] An existing technical solution adopts a centralized decision-making architecture, which processes perception data and generates interception instructions through a ground control center. This solution relies on high-performance computing units to predict the trajectories of airspace targets and uses a fixed partition mode to allocate interception resources. In the case of stable communication links, it can achieve basic level of cooperative work.

[0004] However, when dealing with sudden target cluster maneuvers, there is a delay in issuing and responding to instructions, and the flexibility of dynamic task allocation is limited. The processing efficiency for specific targets that deviate from the predicted trajectory is also reduced. The adaptability of the overall system in complex environments still has room for improvement. SUMMARY

[0005] The present application provides a multi-dimensional cooperative detection and guidance method and system to solve the problem of poor accuracy and low overall efficiency in the cooperative interception of low-altitude dense targets in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a multi-dimensional cooperative detection and guidance method, comprising:

[0007] Obtaining multi-dimensional information of a plurality of to-be-intercepted targets in an airspace, the multi-dimensional information including the number of targets, airspace coordinates and motion direction, and the plurality of to-be-intercepted targets forming a target cluster;

[0008] Based on the number of targets, scheduling a cluster of interception devices from an interception device library according to a preset multiple relationship, and performing preliminary resource allocation on the cluster of interception devices according to the airspace division result and the distribution result of the target cluster, to perform distributed interception on the target cluster by the cluster of interception devices;

[0009] After preliminary allocation is completed, associating the multi-dimensional information with the resource allocation result of the cluster of interception devices to obtain an associated data set;

[0010] Based on the associated data set, the airspace is partitioned and the responsibility is divided by using the airspace cooperative allocation model to obtain the airspace cooperative allocation result, which includes the airspace partition result, the cooperative boundary between partitions and the airspace responsibility instruction of the interception device;

[0011] Based on the airspace cooperative allocation result, the data transmitted by each interception device is shared by using the multi-machine target feature sharing mechanism, and based on the transmitted data, the interception device cluster is guided to implement tracking and interception on the target cluster according to the priority, and the missed target in the target cluster that is not tracked or lost is tracked and intercepted, the transmitted data includes the priority of the to-be-intercepted target and the identification of the missed target generated based on the lightweight algorithm.

[0012] Optionally, the airspace is partitioned and the responsibility is divided based on the associated data set by using the airspace cooperative allocation model to obtain the airspace cooperative allocation result, which includes:

[0013] Based on the airspace coordinates and motion directions of all to-be-intercepted targets in the associated data set, combined with the distribution position of the to-be-intercepted target in the target cluster, the geometric center coordinates and diffusion radius of the target cluster are calculated through the clustering analysis module of the airspace cooperative allocation model;

[0014] Through the dynamic partition module of the airspace cooperative allocation model, the three-level airspace partition is dynamically divided according to the distance from the geometric center coordinates, the three-level airspace partition includes the tracking area, the alert area and the backup area, and the cooperative boundary between the partitions;

[0015] Based on the matching relationship between the airspace coordinates of each to-be-intercepted target and the partition range, each to-be-intercepted target is mapped to the corresponding partition through the coordinate mapping module of the airspace cooperative allocation model, and the airspace partition result containing the target number and the corresponding partition is generated;

[0016] Combined with the resource allocation result of the interception device cluster, the resource matching module of the airspace cooperative allocation model is used to allocate the interception device for each partition, wherein different partitions are allocated with interception devices with different configuration information;

[0017] Through the instruction generation module of the airspace cooperative allocation model, the airspace responsibility instruction of each interception device is generated based on the cooperative boundary and combined with the priority corresponding to the partition;

[0018] The airspace partition result, the cooperative boundary between the partitions and the airspace responsibility instruction of the interception device are combined as the airspace cooperative allocation result, so as to encapsulate the airspace cooperative allocation result as a standardized data frame through the synchronization module of the airspace cooperative allocation model, and push it to all interception devices through the inter-machine communication protocol.

[0019] Optionally, the step of dynamically defining the three-level spatial partitions based on the distance from the geometric center coordinates includes:

[0020] Generate partitioning basic parameters based on the distance from the coordinates of the geometric center;

[0021] Using the geometric center coordinates as the origin, a three-dimensional spatial coordinate system with three levels of spatial partition range is constructed based on preset partitioning parameters;

[0022] In a three-dimensional spatial coordinate system, the overall motion vector of the target cluster is generated by combining the motion direction and velocity of each target to be intercepted in the target cluster. The overall motion vector includes a velocity value.

[0023] When the rate value is greater than the preset rate threshold, it is determined that the target cluster is in a high-speed motion state. In the high-speed motion state, the expansion coefficient is calculated according to the product of the rate value and the basic parameter. Based on the expansion coefficient, the adjusted three-level airspace partition range is determined.

[0024] For targets to be intercepted outside the adjusted three-level airspace partition range, a separate temporary sub-partition is designated. The temporary sub-partition is centered on the airspace coordinates of the target to be intercepted, with a diameter of a preset length, and the temporary sub-partition belongs to the original partition.

[0025] Based on the temporary sub-partitions, the target distribution density of each partition is determined, and the range of each partition is optimized according to the target distribution density of each partition to obtain a three-level airspace partition.

[0026] Optionally, the step of generating airspace responsibility instructions for each interception device based on the cooperative boundary and the priority corresponding to the partition includes:

[0027] Based on collaborative boundary data, combined with the real-time coordinates and motion performance parameters of the interception devices, a dedicated activity airspace range is defined for each interception device. The data collection and reporting frequency of each device is set based on the priority of the corresponding partition and the dynamic characteristics of the target.

[0028] Based on the configuration parameters and partition responsibilities of the interception devices, generate corresponding operation instructions for each interception device;

[0029] The activity airspace range, the data collection and reporting frequency, the operation instructions, and the pre-set collaborative response rules for cross-boundary collaborative scenarios are integrated into airspace responsibility instructions. The airspace responsibility instructions are distributed and stored using blockchain technology to ensure that the entire process of instruction generation, transmission, and execution is traceable. Each airspace responsibility instruction includes a unique identifier of the intercepting device, the instruction effective time, the validity period, and a verification code.

[0030] Optionally, the preliminary resource allocation of the interception device cluster based on the airspace partitioning results and the distribution results of the target cluster includes:

[0031] The airspace is divided into N macro-control areas according to the distribution density of the target clusters. Each macro-control area corresponds to a sub-target cluster, where N is a positive integer and matches the distribution number of the target clusters. The range of the macro-control area is larger than the range of the partition.

[0032] Based on the size of each sub-target cluster and the threat prediction coefficient, calculate the base number of interception equipment required for each macro-control area;

[0033] Interception devices in the status of pending deployment are retrieved from the interception device library and allocated to each macro-control area according to the required number of interception devices, ensuring that the number of interception devices in each macro-control area is not less than a preset threshold. The preset threshold is the product of the required number and a preset percentage. The remaining interception devices are reserved for cross-regional support.

[0034] Initial communication channels and network identifiers are assigned to interception devices within each macro-control area. Different interception devices within the same macro-control area share data through multicast communication, while interception devices in different macro-control areas interact across regions through gateway devices, forming a preliminary communication architecture for distributed interception.

[0035] Based on the preliminary communication architecture of distributed interception, the resource allocation results of the interception device cluster are generated. The resource allocation results include the device number, quantity, initial coordinates and communication parameters of the interception devices in each macro-control area.

[0036] Optionally, based on the airspace cooperative allocation results, a multi-machine target feature sharing mechanism is used to share the data transmitted by each interception device. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and targeted tracking and interception are performed on targets that have not been tracked or lost in the target cluster, including:

[0037] Based on the airspace cooperative allocation results, a multi-aircraft target feature sharing mechanism for interception equipment is initiated, allowing each interception device to collect multimodal information of the corresponding target to be intercepted through a multimodal perception module. The multimodal information includes optical images, radar echoes, and infrared features. A lightweight algorithm is used to extract target key points and motion parameters from the multimodal information, and a priority of the target to be intercepted is generated by combining it with a preset threat feature library. The priority, target key points, motion parameters, and interception equipment status are encapsulated into shared data frames through a distributed data sharing protocol to form a distributed database.

[0038] Based on the priorities in the distributed database, collaborative guidance instructions are generated, and interception devices in different partitions are scheduled to execute corresponding interception strategies according to their priorities.

[0039] The target tracking status is monitored by a target slippage identification model. Targets that are not tracked or whose tracking error exceeds a preset error threshold are marked as slippage targets and a corresponding unique identifier is generated.

[0040] Based on the identification of the escaped targets, interception equipment is dispatched to predict the movement trajectory of the escaped targets based on historical trajectory data, and the positioning error is reduced by the collaborative positioning of multiple interception equipment to implement forward interception.

[0041] Optionally, before implementing pre-interception, the method further includes:

[0042] Based on the movement direction of the escaped target and the relative position of the escaped target to the core defense area, the number of devices participating in the interception and the task allocation results are dynamically adjusted. If the movement direction of the escaped target points to the core defense area, the interception priority is increased and additional interception devices are scheduled to join the interception queue.

[0043] The interception strategy is optimized in real time through a reinforcement learning algorithm. The reinforcement learning algorithm uses the interception success rate as a reward function. If the first interception fails, it automatically switches to the backup interception scheme and adjusts the cooperative formation of the interception queue.

[0044] An interception assessment model is established to calculate the interception probability in real time based on the relative speed and distance between the interception device and the target that slipped through the net, as well as historical interception data. Based on the interception probability, it is determined whether to issue a final interception command or terminate the interception mission.

[0045] Secondly, this application provides a multi-dimensional collaborative detection and guidance system, including:

[0046] The acquisition module is used to acquire multi-dimensional information of multiple targets to be intercepted in the airspace. The multi-dimensional information includes the number of targets, airspace coordinates and direction of movement. The multiple targets to be intercepted constitute a target cluster.

[0047] The scheduling module is used to schedule an interception device cluster from the interception device library according to a preset multiple relationship based on the target quantity, and to perform preliminary resource allocation on the interception device cluster according to the airspace partitioning result and the distribution result of the target cluster, so as to perform distributed interception of the target cluster through the interception device cluster;

[0048] The association module is used to associate the multi-dimensional information with the resource allocation results of the interception device cluster after the initial allocation is completed, so as to obtain an association data set.

[0049] The partitioning module is used to perform airspace partitioning and responsibility division based on the associated data set and the airspace cooperation allocation model to obtain the airspace cooperation allocation result. The airspace cooperation allocation result includes: airspace partitioning result, cooperation boundary between partitions and airspace responsibility instructions of interception equipment.

[0050] The guidance module is used to share the data transmitted by each interception device based on the airspace cooperative allocation results and a multi-machine target feature sharing mechanism. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and to specifically track and intercept any targets that have not been tracked or lost in the target cluster. The transmitted data includes: the priority of the target to be intercepted and the identifier of the target that has escaped the net, generated based on a lightweight algorithm.

[0051] Thirdly, this application provides an electronic device, comprising:

[0052] Memory, used to store computer programs;

[0053] A processor for executing the computer program to implement the steps of the multi-dimensional collaborative detection and guidance method as described in the first aspect above.

[0054] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the multi-dimensional collaborative detection and guidance method described in the first aspect above.

[0055] This application provides a multi-dimensional collaborative detection and guidance method, which includes: acquiring multi-dimensional information of multiple targets to be intercepted within an airspace, the multi-dimensional information including the number of targets, airspace coordinates, and direction of movement, wherein the multiple targets to be intercepted constitute a target cluster; scheduling an interception equipment cluster from an interception equipment library according to a preset multiple relationship based on the number of targets; performing preliminary resource allocation on the interception equipment cluster according to the airspace division result and the distribution result of the target cluster, so as to perform distributed interception of the target cluster through the interception equipment cluster; and performing correlation processing on the multi-dimensional information and the resource allocation result of the interception equipment cluster after the preliminary allocation is completed to obtain a correlation data set. Based on the associated data set, an airspace cooperative allocation model is used to perform airspace partitioning and responsibility division processing to obtain airspace cooperative allocation results. The airspace cooperative allocation results include: airspace partitioning results, cooperative boundaries between partitions, and airspace responsibility instructions of the interception equipment. Based on the airspace cooperative allocation results, a multi-machine target feature sharing mechanism is used to share the data transmitted by each interception equipment. Based on the transmitted data, the interception equipment cluster is guided to track and intercept the target cluster according to priority, and to specifically track and intercept targets that have not been tracked or lost in the target cluster. The transmitted data includes: the priority of the target to be intercepted and the identifier of the target that has escaped the net, generated based on a lightweight algorithm.

[0056] The technical solution provided in this application has the following beneficial effects: It achieves comprehensive and accurate perception of group targets within the monitored airspace, providing a reliable data foundation for subsequent resource scheduling and collaborative decision-making. It realizes automated and rational pre-scheduling of interception resources, forming a numerical advantage and laying the material foundation for building a multi-layered defense system, thus improving the overall robustness against group targets. It achieves deep integration and unified representation of target information and interception resources, breaking down data silos and providing structured data support for executing refined airspace collaborative allocation. It divides the vast airspace into dynamically managed zones with clearly defined responsibilities and orderly collaboration, and issues clear instructions to each device, realizing a shift from "group melee" to "zoned governance and collaborative operations," greatly improving the orderliness and efficiency of system management. Through distributed information sharing and collaborative decision-making, it achieves priority processing of major targets and precise supplementary defense for missed targets, improving the intelligence and completeness of the overall interception operation.

[0057] Furthermore, this application employs an airspace collaborative allocation model. First, it calculates the geometric center and diffusion range of the target group, dynamically dividing the airspace into three levels: tracking zone, alert zone, and backup zone, and establishing collaborative boundaries. Then, each target is mapped to its corresponding partition, and interception equipment with different characteristics is matched to each partition. Finally, responsibility instructions for each device are generated, and the complete allocation scheme is encapsulated and pushed to all devices. Moreover, it achieves dynamic and refined airspace management and precise resource matching. The hierarchical partitioning strategy clarifies the monitoring and responsibility scope of each unit, and relying on an efficient instruction synchronization mechanism, it ensures that large-scale device clusters can operate in an orderly and collaborative manner in complex airspace environments, thereby improving the overall task execution efficiency and responsiveness of the system.

[0058] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0059] 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 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.

[0060] Figure 1 A flowchart illustrating a multi-dimensional collaborative detection and guidance method provided in the embodiments of this application;

[0061] Figure 2 A schematic diagram illustrating a specific implementation of a multi-dimensional collaborative detection and guidance method provided in this application;

[0062] Figure 3 This is a schematic diagram of the structure of a multi-dimensional collaborative detection and guidance system provided in an embodiment of this application. Detailed Implementation

[0063] In the field of low-altitude security for critical facilities, existing centralized control solutions inherently suffer from delays in response to sudden, highly mobile groups of targets. This system relies on unified calculations and command distribution from a central node. When target clusters rapidly maneuver or change formation, the time lag between command updates and on-site execution leads to delays in the allocation of control resources and limits the flexibility of dynamic adjustments, thus impacting the efficiency of handling targets that deviate from their expected trajectories.

[0064] To address the aforementioned challenges, this application proposes a multi-dimensional collaborative detection and guidance method. The core of this approach lies in constructing a distributed intelligent decision-making system. Through a pre-emptive resource scheduling and airspace collaborative allocation model, the vast airspace is dynamically divided into clearly defined zones, and clear action instructions are generated for each interception unit. Based on this, a real-time data sharing mechanism between devices guides the entire interception cluster to form an orderly, collaborative, and intelligently prioritized tracking and handling network. This method effectively overcomes the shortcomings of centralized systems, such as response latency and insufficient flexibility. Through decentralized collaborative decision-making and precise resource mapping, it improves the system's overall control efficiency and adaptability for low-altitude, high-speed, and high-density moving targets.

[0065] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] The core of this application is to provide a multi-dimensional collaborative detection and guidance method, the flowchart of one specific implementation of which is shown below. Figure 1 As shown, the method includes:

[0067] Step 101: Obtain multi-dimensional information of multiple targets to be intercepted in the airspace. The multi-dimensional information includes the number of targets, airspace coordinates and direction of movement. The multiple targets to be intercepted constitute a target cluster.

[0068] In step 101, "airspace" refers to a three-dimensional spatial range that needs to be monitored and managed, such as the low-altitude airspace around an important facility. "Target to be intercepted" refers to a moving object identified by the system within this airspace as needing to be tracked and dealt with, such as a non-cooperative drone. "Multi-dimensional information" refers to various data about the target collected from one or more detection devices (such as radar or optical sensors). This data is not singular but describes the target's state from different perspectives. "Target quantity" refers to the total number of targets to be intercepted detected by the system at a specific moment. "Airspace coordinates" refers to the specific location information of each target to be intercepted in three-dimensional space, typically including values ​​in the east, north, and elevation directions. "Direction of motion" refers to the current direction of motion of each target to be intercepted, which can be represented by a velocity vector, including the magnitude and direction of the velocity. "Target cluster" means that these targets to be intercepted are not isolated but exhibit a certain grouping or clustering characteristic in the airspace, and are treated as a whole.

[0069] In this embodiment, a variety of sensor devices, such as ground-based radar systems and optical observation stations, are used to continuously scan and monitor a designated airspace. These sensors fuse the raw data they collect, eliminating false signals and fixed background objects to accurately identify each valid moving target. A data packet containing its unique number, real-time three-dimensional position, and velocity vector is generated for each target. The system then aggregates these data packets and, based on the distribution of targets in the airspace, determines whether they constitute a group with cooperative characteristics, thus forming complete intelligence on the target cluster.

[0070] For example, a circular low-altitude monitoring zone with a radius of 10 kilometers was established around important facility A. Ground-based detection networks (including radar and electro-optical equipment) deployed in this area simultaneously detected 35 fast-moving small targets. Through data processing, the system confirmed that all 35 targets were valid targets and calculated the real-time latitude, longitude, and altitude coordinates and direction of movement (e.g., moving 15 meters per second in the due north direction) for each target. These targets appeared in three relatively concentrated groups in the airspace, therefore the system determined that they constituted a target cluster of 35 targets to be intercepted.

[0071] Step 102: Based on the target quantity, schedule an interception device cluster from the interception device library according to a preset multiple relationship. Based on the airspace partitioning results and the distribution results of the target cluster, perform preliminary resource allocation on the interception device cluster to perform distributed interception of the target cluster through the interception device cluster.

[0072] In step 102, the preset multiplier relationship refers to a rule pre-set by the system, specifying how many interceptor devices need to be deployed for each target to ensure an effective interception. This multiplier is typically greater than 1 to create a numerical advantage and to handle unforeseen circumstances. The interceptor device library refers to a management system that records the current status (e.g., idle, busy, charging), location, and performance parameters of all available interceptor devices (such as interceptor drones). The interceptor device cluster refers to the collection of all interceptor devices selected from the library and ready to execute the current task according to scheduling instructions. Preliminary resource allocation refers to the approximate division of the interceptor device cluster into different macro-regions based on the general distribution of the target cluster before precise tactical deployment; it is a macro-level, coarse-grained force deployment.

[0073] In this embodiment, the system first calculates the total number of interception devices needed by multiplying the target quantity obtained in step 101 by a preset multiple (e.g., 2.5 times). Then, the system sends an instruction to the interception device library to check whether the number of currently available devices meets the requirements. If it does, the system marks the status of these devices as "in mission" and forms an interception device cluster. Next, the system divides the entire airspace into several macro-control areas based on the several aggregation points naturally formed by the target clusters in the airspace. Finally, the system proportionally allocates the interception device cluster to these macro-control areas according to the number of targets in each area and assigns a temporary communication network identifier to the devices in each area, completing the initial deployment.

[0074] For example, continuing from the previous example, the system's preset multiplier is 3. The target quantity is 35 aircraft, therefore, it is calculated that 105 interceptor devices need to be deployed (35 × 3 = 105). The system deploys 105 idle interceptor drones from its database to form the interceptor cluster for this operation. Based on the three groups formed by the target cluster, the system divides the airspace into three macro-control zones. The target quantities in each zone are 10, 15, and 10 respectively. Therefore, the system initially allocates the 105 interceptor aircraft proportionally to these three zones, with approximate quantities of 30, 45, and 30 aircraft respectively.

[0075] Step 103: After the initial allocation is completed, the multi-dimensional information is correlated with the resource allocation results of the interception device cluster to obtain a correlated data set.

[0076] In step 103, the association processing refers to matching and linking information from two different sources (target information and interception resource information) to establish a correspondence between them, thereby forming a unified, global view. The associated data set refers to a structured data set generated after the association processing. This set no longer treats the target and interception device as two independent lists, but binds them with spatial location and allocation relationships, which is the basis for subsequent refined processing.

[0077] In this embodiment, the detailed information (such as location and speed) of all targets to be intercepted obtained in step 101 is fused with the interception equipment resource allocation results (such as which interception equipment is available in each macro-region) generated in step 102. The system creates a unified data structure that associates each target to be intercepted with its macro-control region, and also associates all interception equipment allocated under that macro-region with that region. Through this association, the system generates a global data view that clearly shows "which regions have which targets and which interception equipment is allocated to them", i.e., the associated data set.

[0078] For example, continuing from the previous example, the system performs data association. It generates an associated data set that explicitly records: there are 10 targets to be intercepted in macro-control area one, with 30 interception devices allocated; there are 15 targets in area two, with 45 interception devices allocated; and there are 10 targets in area three, with 30 interception devices allocated. This set also contains a detailed attribute list of these targets and interception devices.

[0079] Step 104: Based on the associated data set, the airspace cooperation allocation model is used to perform airspace partitioning and responsibility division processing to obtain the airspace cooperation allocation result. The airspace cooperation allocation result includes: airspace partitioning result, cooperation boundary between partitions and airspace responsibility instructions of interception equipment.

[0080] In step 104, the airspace cooperative allocation model is a core computational model. It receives associated datasets as input and, through the collaborative computation of multiple internal modules, outputs a refined airspace management and responsibility allocation scheme. The airspace cooperative allocation result is the final scheme calculated by this model; it is a set of various instructions used to guide the specific actions of each interceptor device. The airspace partitioning result refers to the model further dividing each macro-region into smaller, more functionally specific sub-regions. The cooperative boundary refers to the virtual boundary between different partitions, defining the operational scope and information sharing rules of the interceptor devices in each partition, ensuring no conflicts or omissions occur during cooperation. The airspace responsibility instructions are specific commands issued to each interceptor device, specifying its patrol airspace, monitored targets, communication frequencies, etc.

[0081] In this embodiment, the airspace cooperative allocation model first calculates a geometric center based on the distribution of targets in the associated dataset and estimates the dispersion of targets. Then, using this center as the origin, it dynamically divides the airspace into three concentric spherical layers: a core area responsible for close tracking, a standby area responsible for interception, and a backup area responsible for support, from the inside out. Next, the model maps each target to be intercepted to its specific layer. Then, based on the threat level and density of targets in each layer, the model allocates an appropriate number and type of interception equipment to each layer. Finally, the model generates personalized duty instructions for each interception device, clearly defining its activity range, monitored targets, and cooperation rules, and packages all these instructions into a package, which is then synchronously sent to all interception devices via a data link.

[0082] For example, continuing the previous example, let's take Region 2 (with 15 targets) as an example. The model calculates the center point C of this group of targets and delineates three layers of airspace: the innermost layer is the tracking zone, the middle layer is the alert zone, and the outermost layer is the backup zone. The 15 targets are mapped to these three layers respectively, with 5 in the tracking zone, 7 in the alert zone, and 3 in the backup zone. The model then allocates 45 interceptor devices to these three layers: 15 high-performance devices are deployed to the tracking zone, 20 to the alert zone, and 10 to the backup zone. Specific instructions are generated for each device, such as "Interceptor device number 2005, please patrol the airspace below 150 meters altitude in the southeast quadrant of the alert zone, focusing on monitoring targets T25 and T26, and maintaining data sharing with neighboring devices." All instructions are issued.

[0083] Step 105: Based on the airspace cooperative allocation results, a multi-machine target feature sharing mechanism is adopted to share the data transmitted by each interception device. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and to specifically track and intercept the targets that have not been tracked or lost in the target cluster. The transmitted data includes: the priority of the target to be intercepted and the identifier of the target that has escaped the network, generated based on the lightweight algorithm.

[0084] In step 105, the multi-machine target feature sharing mechanism is a distributed communication and data management rule that allows all interception devices to share the target information they detect with their partners in real time, thereby forming a shared and continuously updated target situation map across the entire cluster. The lightweight algorithm is a computationally inefficient and fast artificial intelligence algorithm deployed on each interception device to quickly analyze sensor data, identify targets, and determine their priorities. A target that slips through the network refers to a target that, during the collaborative interception process, temporarily escapes system monitoring or is not successfully handled due to obstruction, interference, or other reasons. The identifier refers to the unique identity code assigned to each target that slips through the network, used for continuous tracking and location.

[0085] In this embodiment, all interception devices fly to the designated airspace and activate their sensors according to the received duty instructions. Each device uses its lightweight algorithm to identify and track the target assigned to it and calculate the target's priority (e.g., larger and faster targets have higher priority). The devices share this target information (identity, location, priority) with all other devices in the cluster in real time via a high-speed data link. Thus, the entire cluster jointly maintains a constantly updated, unified, dynamic target database. The system continuously monitors this database, and once a target is found to be lost (i.e., becomes a target that slips through the net), it immediately marks it and assigns the nearest or most suitable interception device to conduct targeted tracking and interception, ensuring that no target is missed.

[0086] For example, continuing from the previous example, the interceptor group in Area 2 begins operation. Interceptor device number 2005 uses its onboard visual sensor to lock onto target T25 and, through a lightweight algorithm, determines that T25 is a large aircraft, setting its priority to "high." It immediately shares the information "T25, high priority, coordinates (X,Y,Z)." Simultaneously, another device detects that target T10, which was originally in the tracking area, suddenly descends in altitude, using the terrain to evade detection and disappearing from the cluster's shared database. The system immediately marks T10 as a slippery target, generates a unique identifier, and instructs the interceptor device waiting in the backup area, closest to T10's last known location, to search for and take over the tracking and handling of that target.

[0087] By employing a complete technological chain from macro to micro levels, and from centralized planning to distributed execution, collaborative control of densely moving targets at low altitudes is achieved. This method first comprehensively perceives the situation of the target group, then intelligently pre-schedules and allocates interception resources, followed by orderly deployment through dynamic airspace division and precise responsibility assignment. Finally, relying on real-time information sharing and collaborative decision-making within the cluster, it ensures that key targets are handled efficiently, while also enabling rapid responses to emergencies and overlooked targets. This improves the overall efficiency, adaptability, and reliability of multi-target collaborative handling in complex low-altitude environments.

[0088] To achieve refined collaborative management and control of the target cluster and further improve the utilization efficiency of interception resources and system response speed, in some embodiments, step 104: based on the associated data set, a spatial domain collaborative allocation model is used to perform spatial domain partitioning and responsibility division processing to obtain the spatial domain collaborative allocation result, such as... Figure 2 As shown, it includes:

[0089] Step 201: Based on the spatial coordinates and movement direction of all targets to be intercepted in the associated dataset, and combined with the distribution of the targets to be intercepted in the target cluster, calculate the geometric center coordinates and diffusion radius of the target cluster through the clustering analysis module of the spatial collaborative allocation model.

[0090] In step 201, the airspace coordinates and motion directions of all targets to be intercepted refer to the real-time position data (usually coordinates in the east, north, and altitude directions) of each target in three-dimensional space acquired by radar, electro-optical, and other detection equipment, as well as the velocity vector information (including velocity magnitude and direction angle) describing its instantaneous motion state. These data together constitute the basic description of the instantaneous spatial state of the targets. The distribution position of the targets to be intercepted in the target cluster refers to the spatial relationship and arrangement of each target relative to the cluster as a whole and other individuals. This information is naturally derived by performing an overall spatial relationship analysis (such as calculating relative distance, azimuth, and density) on the airspace coordinates of all targets acquired in step 101. It reflects whether the targets are densely clustered or dispersed, and the specific formation or pattern they form in the airspace. The geometric center coordinates refer to the average center point of the airspace coordinates of all targets to be intercepted in the target cluster, reflecting the core position of the cluster in space. The diffusion radius is a statistic used to describe the degree of diffusion of the target cluster from the geometric center. It indicates the looseness or tightness of the cluster distribution and is usually determined by calculating the maximum distance or standard deviation of all targets from the geometric center.

[0091] In this embodiment, the clustering analysis module of the airspace cooperative allocation model first receives the airspace coordinates and motion direction data of all targets to be intercepted from the associated dataset. This module employs a spatial point set center calculation method to calculate the arithmetic mean of the airspace coordinates of all targets, thus determining the geometric center coordinates of the target cluster. Next, the module calculates the distance between each target's airspace coordinate and this geometric center coordinate, selecting the largest distance value as the diffusion radius of the target cluster. This process provides a spatial reference for subsequent airspace partitioning.

[0092] Step 202: Using the dynamic partitioning module of the airspace cooperative allocation model, three levels of airspace partitions are dynamically defined according to the distance between the geometric center coordinates. The three levels of airspace partitions include a tracking zone, a guard zone, and a backup zone, and the partitions have cooperative boundaries.

[0093] In step 202, the three-level airspace partition refers to three concentric spherical airspace layers dynamically defined from the inside out around the geometric center coordinates. The tracking zone is the innermost layer, primarily responsible for close monitoring and tracking of the target. The alert zone is the middle layer, the main area for interception and response. The backup zone is the outermost layer, responsible for providing support, filling gaps, and responding to emergencies. The cooperation boundary is a virtual interface between adjacent partitions, defining the action rules and information exchange protocols for interception equipment within different partitions to ensure no conflicts occur during cooperation.

[0094] In this embodiment, the dynamic partitioning module constructs a three-dimensional spatial model using the geometric center coordinates and diffusion radius calculated in step 201, with this center as the origin. Based on preset partitioning rules and referencing multiples of the diffusion radius, the module dynamically defines the specific ranges of the tracking zone, the alert zone, and the backup zone. For example, the radius of the tracking zone might be set to one time the diffusion radius, the radius of the alert zone to twice the diffusion radius, and the radius of the backup zone to three times the diffusion radius. Simultaneously, the module clearly defines the cooperative boundaries between each partition layer, forming a three-layered, ordered defensive airspace.

[0095] Step 203: Based on the matching relationship between the airspace coordinates and partition range of each target to be intercepted, the coordinate mapping module of the airspace collaborative allocation model maps each target to be intercepted to the corresponding partition, generating an airspace partition result containing the target number and the partition to which it belongs.

[0096] In step 203, coordinate mapping refers to a judgment process that compares the airspace coordinates of each target to be intercepted with the range of the three-level airspace partitions defined in step 202. The airspace partitioning result is a data list that clearly records the correspondence between the number of each target to be intercepted and the specific partition (tracking zone, alert zone, or backup zone) to which it is assigned.

[0097] In this embodiment, the coordinate mapping module obtains the airspace coordinates of all targets to be intercepted and the three-level airspace partition range defined by the dynamic partitioning module. The module iterates through each target to be intercepted, calculates the distance between its airspace coordinates and geometric center coordinates, and determines which partition's radius the distance falls within, thereby mapping the target to the corresponding partition. Finally, the module generates a structured list, i.e., the airspace partitioning result, where entries display, for example, "Target T01, Partition: Tracking Zone".

[0098] Step 204: Based on the resource allocation results of the interception device cluster, the interception device is allocated to each partition through the resource matching module of the airspace collaborative allocation model. Different partitions are allocated interception devices with different configuration information.

[0099] In step 204, resource matching refers to assigning appropriate interception equipment to each target within each partition based on its characteristics (such as quantity and type). Interception equipment with different configuration information refers to equipment with different performance parameters. For example, some equipment may be equipped with more advanced sensors and are suitable for working in the tracking zone, while other equipment may be more mobile and suitable for performing tasks in the guard zone.

[0100] In this embodiment, the resource matching module first reads the airspace partitioning results to understand the number and type of targets in each partition. Simultaneously, the module also knows the resource allocation results of the interceptor equipment cluster, understanding the quantity and performance of various types of interceptor equipment available for this mission. Based on a predetermined strategy (e.g., equipping the tracking zone with high-performance sensing equipment, the alert zone with highly mobile equipment, and reserving general-purpose equipment in the backup zone), the module allocates appropriate types and quantities of interceptor equipment to each partition, ensuring that resources match mission requirements.

[0101] Step 205: The instruction generation module of the airspace cooperation allocation model generates airspace responsibility instructions for each interception device based on the cooperation boundary and the priority corresponding to the partition.

[0102] In step 205, the instruction generation module is the unit responsible for producing specific action commands. The airspace responsibility instruction is a detailed operational command issued to each interceptor device. Its content integrates the definition of the cooperation boundary, the global priority of the partition to which the device belongs, and the device's own performance parameters. The instruction content includes, but is not limited to, the active airspace range, data reporting frequency, and cooperation rules.

[0103] In this embodiment, the instruction generation module generates personalized airspace responsibility instructions based on the definition of the cooperation boundary and the preset priority of each partition (e.g., the tracking zone has the highest priority), combined with the partition affiliation assigned to each interceptor in step 204. The instructions clearly specify the device's activity area, the targets that need to be focused on, and the methods of cooperation with other devices, ensuring that each device clearly understands its own tasks and action guidelines.

[0104] Step 206: Combine the airspace partitioning results, the collaborative boundaries between the partitions, and the airspace responsibility instructions of the intercepting devices into an airspace collaborative allocation result. Then, through the synchronization module of the airspace collaborative allocation model, encapsulate the airspace collaborative allocation result into a standardized data frame and push it to all intercepting devices through the inter-device communication protocol.

[0105] In step 206, the synchronization module is responsible for packaging and distributing the planning results generated from all the preceding steps. A standardized data frame is a predefined data packet formatted to ensure that all interception devices can correctly parse the information within it. The inter-device communication protocol is a set of communication rules that interception devices adhere to in order to achieve fast and reliable data exchange.

[0106] In this embodiment, the synchronization module combines the airspace partitioning results, cooperative boundary information, and the generated airspace duty instructions for all interceptor devices into a complete airspace cooperative allocation result. Subsequently, the module encapsulates this result into one or more standardized data frames according to a predetermined format. Finally, using a high-speed data link formed by an inter-device communication protocol, this complete operational plan is instantly pushed to every device in the interceptor device cluster, ensuring that all units receive unified action instructions synchronously.

[0107] Here is a specific example:

[0108] Continuing the example, for the 15 targets to be intercepted within the second macro-control area, the airspace cooperative allocation model begins to work. First, the cluster analysis module uses the airspace coordinates of all 15 targets in the associated dataset. These coordinates are three-dimensional values ​​in the east, north, and altitude directions with a certain point on the ground as the origin. The geometric center coordinates are determined by calculating the arithmetic mean of all target points in each dimension. The specific formula is: the geometric center coordinates equal to the sum of the airspace coordinates of all targets divided by the number of targets. ,in The value is 15. Substituting the coordinate data, we obtain the geometric center coordinates C, for example, [5000 meters east, 3000 meters north, 150 meters altitude]. Then, we calculate the diffusion radius, which is the maximum Euclidean distance from all targets to the geometric center C. The formula is: Diffusion Radius. ,in Indicates the diffusion radius of the target group. Representing the The east, north, and altitude coordinates of the target. The coordinates representing the geometric center C are used to calculate the diffusion radius. The target's radius is 600 meters. The dynamic zoning module then dynamically delineates three levels of airspace zones based on the geometric center C and preset rules. The tracking zone is a spherical airspace with a radius of 700 meters centered at C, its range set slightly larger than the diffusion radius to cover the core target. The guard zone is a spherical shell airspace extending from the tracking zone to a radius of 1300 meters. The backup zone is a spherical shell airspace extending from the guard zone to a radius of 2000 meters. Virtual cooperative boundaries are set between each zone. The coordinate mapping module compares the airspace coordinates of each target with the zone range and calculates its distance from C. If the distance is less than or equal to 700 meters, it is mapped to the tracking zone; if it is greater than 700 meters but less than or equal to 1300 meters, it is mapped to the guard zone; and if it is greater than 1300 meters, it is mapped to the backup zone. Calculations show that 5 targets fall into the tracking zone, 7 targets fall into the guard zone, and 3 targets fall into the backup zone, generating the airspace zoning results. The resource matching module, combining the 45 interceptor devices allocated to the area, distributes them according to the characteristics of the zones: 15 interceptor devices equipped with high-performance sensors are allocated to the tracking zone requiring close tracking; 20 highly maneuverable interceptor devices are allocated to the standby zone primarily performing tasks; and 10 standard interceptor devices are allocated to the backup zone responsible for support. The instruction generation module generates airspace duty instructions for each device based on the cooperation boundary and zone priority: the tracking zone has the highest priority, the standby zone the next highest, and the backup zone the lowest. For example, it instructs interceptor device number 2005 to patrol the airspace below 150 meters in the southeast quadrant of the standby zone, focusing on monitoring targets T25 and T26. Finally, the synchronization module combines the airspace partitioning results, cooperation boundaries, and all airspace duty instructions into a standardized data frame, which is then pushed to all 45 interceptor devices in Zone 2 via an inter-device communication protocol.

[0109] The aforementioned complete procedure, through a series of coherent operations including calculation, partitioning, mapping, matching, generation, and synchronization, breaks down the originally massive and complex airspace control problem into clearly defined and granular tasks. This enables a large number of interception devices to work collaboratively based on a unified global plan, avoiding resource allocation chaos and waste while improving the system's overall control efficiency and responsiveness towards target clusters.

[0110] To further improve the adaptability of airspace partitioning to dynamic target clusters and ensure that the partition range can effectively cover high-speed maneuvering targets, in some embodiments, step 202: dynamically delineating the three-level airspace partitions according to the distance from the geometric center coordinates includes:

[0111] Step 301: Generate partitioning basic parameters according to the distance between the coordinates of the geometric center and the coordinates of the partition.

[0112] In step 301, the basic partitioning parameters are a set of pre-defined scaling factors or absolute distance values ​​used to define the initial size of each level of airspace partition. These parameters form the basis for constructing the three-level airspace partition range and are usually calculated based on historical experience or theory. They are designed to ensure that the partition range can effectively cover the vast majority of targets when the target cluster is stationary or moving at low speed.

[0113] In this embodiment, the system retrieves a set of values ​​matching the current target cluster size and characteristics from a preset strategy library as the basic parameters for partitioning. These parameters directly determine the initial radius or boundary distance of the tracking zone, guard zone, and backup zone with the geometric center as the origin, providing a dimensional basis for the subsequent construction of a three-dimensional spatial coordinate system.

[0114] Step 302: Using the geometric center coordinates as the origin, construct a three-dimensional spatial coordinate system with three levels of spatial partition range based on the preset partitioning parameters.

[0115] In step 302, the three-level airspace partition range is defined in this coordinate system as three concentric spherical regions centered on the origin and determined by the partitioning basic parameters. From the inside out, these are the tracking zone, the alert zone, and the backup zone, each with a clearly defined radius boundary. The three-dimensional spatial coordinate system is a three-dimensional reference frame with the geometric center coordinates as the origin (zero point) and the east-north-height directions as coordinate axes.

[0116] In this embodiment, the dynamic partitioning module first uses the geometric center coordinates calculated in step 201 as the origin of the three-dimensional spatial coordinate system. Then, the module substitutes the partitioning basic parameters generated in step 301 into this coordinate system to define the specific ranges of the three spherical spatial layers. For example, the tracking zone is a sphere from the origin to radius R1, the guard zone is a spherical shell between radii R1 and R2, and the backup zone is a spherical shell between radii R2 and R3. This establishes an initial, static three-level spatial partitioning model.

[0117] Step 303: In a three-dimensional spatial coordinate system, by combining the motion direction and velocity of each target to be intercepted in the target cluster, an overall motion vector of the target cluster is generated, wherein the overall motion vector includes a velocity value.

[0118] In step 303, the overall motion vector is a vector used to describe the overall movement trend of the target cluster. This vector is obtained by comprehensively calculating the individual movement directions and velocities of all targets to be intercepted in the cluster. Its direction represents the overall movement direction of the cluster, and its velocity value (scalar) represents the speed of the overall movement of the cluster.

[0119] In this embodiment, the module acquires the motion direction and speed data of all targets to be intercepted from the associated data set. Using a vector synthesis method, the movement speed vectors of all targets are added together and then divided by the total number of targets to obtain an average vector, which is the overall motion vector of the target cluster. The velocity value can be extracted from this vector to determine the overall speed of the cluster.

[0120] Step 304: When the rate value is greater than the preset rate threshold, it is determined that the target cluster is in a high-speed motion state. In the high-speed motion state, the expansion coefficient is calculated according to the product of the rate value and the basic parameter. Based on the expansion coefficient, the adjusted three-level airspace partition range is determined.

[0121] In step 304, the preset rate threshold is a critical speed value used to determine whether the target cluster is in a rapid movement state requiring special handling. The expansion coefficient is a multiplier greater than 1, calculated based on the rate value when the cluster is in a high-speed movement state, and is used to amplify the initial partitioning base parameters. The adjusted three-level airspace partition range refers to the larger tracking zone, alert zone, and backup zone redefined in the three-dimensional spatial coordinate system using the partitioning base parameters amplified by the expansion coefficient.

[0122] In this embodiment, the module compares the rate value calculated in step 303 with a preset rate threshold. If the rate value is greater than the preset rate threshold, the target cluster is determined to be in a high-speed movement state. At this time, the module calculates an expansion coefficient according to a certain rule based on the degree to which the rate value exceeds the threshold. Subsequently, the partition basic parameters generated in step 301 are multiplied by this expansion coefficient to obtain a set of amplified new parameters. Finally, based on this set of new parameters, the module redefines the range of the three-level spatial partitions in the three-dimensional spatial coordinate system established in step 302, so that each partition expands outward to reserve more space to cope with the rapid movement of the target.

[0123] Step 305: For targets to be intercepted outside the adjusted three-level airspace partition range, a separate temporary sub-partition is defined. The temporary sub-partition is centered on the airspace coordinates of the target to be intercepted, with a diameter of a preset length, and the temporary sub-partition belongs to the original partition.

[0124] In step 305, the temporary sub-partition is a small control area specifically designated for scattered targets that, due to high-speed movement of the cluster, still fall outside the adjusted third-level airspace partition after range adjustment. This sub-partition forms a small spherical airspace with the target's airspace coordinates as the center and a predetermined fixed length as the diameter, and in terms of management, this sub-partition still belongs to its original superior major partition (tracking zone, alert zone, or backup zone).

[0125] In this embodiment, after adjusting the partition range in step 304, the module compares the coordinates of all targets to be intercepted with the new partition range. For any target falling outside the new range, the system will define a temporary, small spherical sub-partition centered on its current coordinates, with a preset fixed value (e.g., a diameter of 500 meters) as the range. Simultaneously, the system will record the partition (e.g., the guard zone) to which the target should have been mapped based on its attributes, and mark this temporary sub-partition as belonging to the original partition, ensuring continuity of management.

[0126] Step 306: Combine the temporary sub-partitions to determine the target distribution density of each partition, and optimize the range of each partition according to the target distribution density of each partition to obtain a three-level airspace partition.

[0127] In step 306, target distribution density refers to the number of targets to be intercepted within a unit airspace volume, used to quantify the density of targets within each partition. Optimizing the range of each partition means dynamically and slightly adjusting the boundaries of each partition based on the actual target distribution density within each partition (including temporary sub-partitions), so that the partition range better matches the actual target distribution and avoids wasted space or insufficient coverage.

[0128] In this embodiment, the module first counts the number of targets contained in the adjusted three-level airspace partitions and all temporary sub-partitions defined in step 305, and calculates the target distribution density within each partition. Subsequently, the module optimizes and fine-tunes the partition range based on the density assessment results. For example, for partitions with target distribution densities higher than the average level, their range is appropriately expanded to reduce density and avoid excessive control pressure; for partitions with lower densities, their range is appropriately reduced, allocating the saved space resources to areas where they are more needed. Finally, the three-level airspace partitions formed after this optimization and adjustment are the final output of this process.

[0129] Here is a specific example:

[0130] Continuing the previous example, based on the calculated geometric center coordinates C of the target cluster within the second macro-control area as [5000, 3000, 150] meters and a diffusion radius of 600 meters, the process of dynamically delineating the three-level airspace partitions is as follows: First, generate the basic parameters for the partitions according to the distances from the geometric center coordinates. Here, the initial radius parameters for the tracking zone, alert zone, and backup zone are preset to 700 meters, 1300 meters, and 2000 meters, respectively. These parameters are set based on a diffusion radius of 600 meters with a certain margin. The tracking zone radius of 700 meters is approximately equal to 1.17 times the diffusion radius of 600 meters. Next, a three-dimensional spatial coordinate system is constructed with the geometric center coordinate C as the origin, and the initial three-level airspace partition range is defined based on the partitioning basic parameters: the tracking zone is a spherical airspace with a radius of 700 meters from the origin, the alert zone is a spherical shell airspace with a radius of 700 meters to 1300 meters, and the backup zone is a spherical shell airspace with a radius of 1300 meters to 2000 meters. Subsequently, in the three-dimensional spatial coordinate system, the overall motion vector is calculated by combining the motion direction and velocity of each target cluster to be intercepted. Its velocity value is calculated by the vector synthesis method. The specific formula is: the velocity value V of the overall motion vector is equal to the modulus of the sum of the velocity vectors of all targets divided by the number of targets. ,in The value is equal to 15. After substituting this value into the velocity vectors of each target, the speed value is obtained. The speed is 25 meters per second; the system's preset speed threshold is 20 meters per second. Since 25 is greater than 20, the target cluster is determined to be in a high-speed motion state. Under this state, the expansion coefficient is calculated by multiplying the speed value V by the basic parameter. The calculation formula is: ,in Indicates the expansion coefficient. This represents a preset rate threshold, for example. Substituting into the calculation, we get Based on the expansion coefficient This equals 1.25, adjusting the range of the third-level airspace partition. The original base parameters [700, 1300, 2000] are multiplied by [the factor]. The adjusted range is [875, 1625, 2500] meters. After adjustment, a target originally belonging to the restricted area was found with coordinates [6200, 3800, 200]. The distance between this target and point C was calculated to be... This value is less than 1625 meters but greater than the original guard zone boundary by 1300 meters, so it is still within the zone. However, another target with coordinates [6500, 2000, 100] is 1625 meters away from point C. This value is less than the adjusted backup zone boundary by 2500 meters but greater than the original backup zone boundary by 2000 meters. The system separately allocates a temporary sub-zone with a diameter of 500 meters centered at the target's airspace coordinates [6500, 2000, 100], and marks this temporary sub-zone as belonging to the backup zone. Finally, combining all zones and the temporary sub-zone, the target distribution density of each zone is calculated. There are 5 targets with a volume of [missing information - likely a specific value]. Calculation of target density in the tracking area The restricted area has 7 targets with a volume of , The backup zone originally had 2 targets, plus 1 target in the temporary sub-zone, for a total of 3 targets with a volume of [missing information]. , Based on the density distribution, the zoning range was optimized. Because the density of the alert zone is relatively high, its radius was slightly expanded from 1625 meters to 1700 meters, while the density of the backup zone is relatively low, its radius was reduced from 2500 meters to 2400 meters. The final optimized three-level airspace zoning range is as follows: tracking zone radius 875 meters, alert zone radius 1700 meters, and backup zone radius 2400 meters.

[0131] The above complete process effectively overcomes the coverage lag problem that may occur when facing high-speed maneuvering targets in a fixed partitioning mode by introducing a mechanism that dynamically senses the overall motion state of the target cluster and adjusts the partitioning range accordingly. By calculating the expansion coefficient and delineating temporary sub-partitions, the system ensures that it can effectively track and control targets even when they move rapidly. Finally, through range optimization based on distribution density, fine-grained allocation of airspace resources is achieved, improving the system's adaptability to dynamic target clusters and overall control efficiency.

[0132] To further improve the precision and reliability of command generation and ensure that each interceptor device has a clear responsibility and traceable actions in collaborative operations, in some embodiments, step 205: generating airspace responsibility commands for each interceptor device based on the collaborative boundary and the priority corresponding to its respective partition includes:

[0133] Step 401: Based on collaborative boundary data, combined with the real-time coordinates and motion performance parameters of the interception devices, delineate a dedicated activity airspace range for each interception device, and set the data collection and reporting frequency for each device based on the priority of the corresponding partition and the dynamic characteristics of the target.

[0134] In step 401, the activity airspace range refers to the three-dimensional spatial area allocated to each interceptor device, allowing it to perform its tasks. This range must ensure that the device can effectively cover the designated area within its mobility capabilities without conflicting with the activity ranges of other devices. The priority of the assigned zone refers to the pre-defined hierarchical order based on the functional positioning and importance of the three-level airspace zones. The tracking zone, responsible for close monitoring of core targets, has the highest priority; the alert zone, responsible for primary handling tasks, has the next highest priority; and the backup zone, responsible for support and gap filling, has the lowest priority. This priority determines the urgency of resource allocation and data reporting within the zone. Target dynamic characteristics refer to the changes in the motion state exhibited by the monitored target, including parameters describing the intensity and unpredictability of its motion, such as instantaneous velocity, acceleration, and rate of change of direction. Targets with higher dynamic characteristics require higher monitoring frequencies and resource investment. The data acquisition and reporting frequency refers to the rate at which the device sends target information acquired by its sensors back to the system. This frequency setting must comprehensively consider the global importance (priority) of the zone where the device is located, the intensity of the tracked target's motion (dynamic characteristics), and the device's communication capabilities.

[0135] In this embodiment, the instruction generation module first obtains the defined cooperative boundary data and the priority of the partition to which each interceptor device belongs. The module combines the real-time reported coordinates of each interceptor device with its motion performance parameters such as maximum speed and turning radius to allocate a dedicated activity airspace range matching its maneuverability, typically a smaller sector or grid within its partition. Simultaneously, based on the priority of the partition to which the device belongs (e.g., the tracking zone has the highest priority) and the motion state of the target it monitors (e.g., the faster the target's speed and the more drastic the changes), the module sets a differentiated data acquisition and reporting frequency for each device. Devices in high-priority partitions or tracking high-speed dynamic targets need to report data at a higher frequency.

[0136] Step 402: Generate corresponding operation instructions for each interception device based on the configuration parameters and partition responsibilities of the interception device.

[0137] In step 402, the configuration parameters of the interceptor equipment refer to a series of technical indicators of the hardware capabilities and functional characteristics of each interceptor equipment, including but not limited to the type of sensors it carries (such as optical and infrared), maximum flight speed, endurance, and communication module performance. These parameters determine the level of mission the equipment can perform. Zone responsibility refers to the overall task and objective assigned to each of the three-level airspace zones (tracking zone, alert zone, and backup zone). For example, the tracking zone's responsibility is to continuously and stably monitor and track targets; the alert zone's responsibility is to prepare for and execute interception actions; and the backup zone's responsibility is to provide area support and respond to emergencies. Different responsibilities place different requirements on the equipment. Operation instructions are specific action commands issued to the interceptor equipment. Their content is generated based on the equipment's own functional configuration (configuration parameters) and the overall task (zone responsibility) undertaken by the zone it is assigned to. For example, instructions may include activating specific sensors, flying to a certain location, or continuously tracking a target.

[0138] In this embodiment, the module retrieves the configuration parameter list of each interception device to understand its functions (such as whether it is equipped with optical sensors, radar, etc.), and combines this with the activity airspace range defined for the device in step 401 and the responsibilities of its respective zone (such as the tracking zone's responsibility being close monitoring, and the alert zone's responsibility being preparing for action), to generate specific, executable operation instructions. For example, it instructs a device in the alert zone, equipped with a high-performance camera, to fly to a key location within its activity airspace and scan that area.

[0139] Step 403: Integrate the activity airspace range, the data collection and reporting frequency, the operation instructions, and the pre-set collaborative response rules for cross-boundary collaborative scenarios into airspace responsibility instructions, and use blockchain technology to distribute and store the airspace responsibility instructions to ensure that the entire process of instruction generation, transmission, and execution is traceable. Each airspace responsibility instruction includes a unique identifier of the intercepting device, the instruction effective time, the validity period, and a verification code.

[0140] In step 403, the coordinated response rules are a pre-defined set of rules that specify how relevant equipment should respond and cooperate when the target or interceptor moves across the coordination boundary, ensuring seamless control. Distributed evidence storage refers to using blockchain technology to generate a unique, immutable digital record for each airspace responsibility instruction, and synchronously storing this record on all participating nodes (interception equipment or ground stations), thereby achieving traceability throughout the instruction's lifecycle. The unique identifier of the interceptor equipment, the instruction's effective time, validity period, and checksum are the metadata for each instruction, used to ensure that the instruction can be accurately identified, effectively executed within the correct time window, and its integrity verified.

[0141] In this embodiment, the module integrates the activity airspace defined in step 401, the set data collection and reporting frequency, the operation instructions generated in step 402, and the preset cross-boundary collaborative response rules into a complete, structured data packet, namely, the airspace responsibility instruction. Subsequently, the system calls the blockchain service to generate a transaction from this instruction and its contained unique identifier, effective time, validity period, and verification code, and broadcasts it to the blockchain network. After consensus, the transaction is recorded on the distributed ledger of all nodes, completing the notarization. Thereafter, any flow and execution status change of the instruction can be recorded and traced.

[0142] Here is a specific example:

[0143] Following the previous example, based on the allocation of 20 highly mobile interceptor devices to the guard zone within the macro-control area two and the generation of preliminary instructions, the instruction generation module begins to generate refined airspace responsibility instructions for each device based on the collaborative boundary data. Taking interceptor device number 2005 as an example, its real-time coordinates are [5050, 3050, 160] meters, and its motion performance parameters include a maximum speed of 50 meters per second and a minimum turning radius of 80 meters. The module first delineates its exclusive activity airspace range, which is a cuboid area centered on its current position, with an eastward range of 5050±200 meters, a northward range of 3050±200 meters, and an altitude range of 160±50 meters. This range ensures that the device can effectively cover within its maneuverability and does not cross the boundary. Considering that the priority of the guard zone is level two and the dynamic characteristics of the target T25 it is responsible for monitoring (the target T25's current speed is 30 meters per second and it is accelerating), the data acquisition and reporting frequency of this device is set to twice per second. Then, based on the configuration parameters of this interceptor device, including the inclusion of a high-definition optical sensor and an infrared thermal imager... The designated guard zone is responsible for tracking and monitoring the target, generating corresponding operational instructions: fly to point [5060, 3060, 170] and maintain a hovering position, continuously monitor target T25 using optical sensors, and simultaneously activate the infrared thermal imager for assisted tracking; subsequently, the operational airspace range is 4850 to 5250 meters eastward, 2850 to 3250 meters northward, and at an altitude of 110 to 210 meters, with data acquisition and reporting frequency twice per second. The operational instructions are to fly to [5060, 3060, 170] and hover to monitor T25. 25, and pre-defined collaborative response rules for cross-boundary collaborative scenarios, such as if target T25 enters the tracking zone, it must immediately notify the tracking zone equipment and receive new instructions, integrating them into a complete airspace responsibility instruction data packet; finally, the instruction is distributedly stored using blockchain technology, generating a hash record containing the interceptor number 2005, the instruction effective time of October 26, 2023, 14:30:00, the validity period of 300 seconds, and the check code 8f1c6e5a, and broadcast to all nodes to ensure that the entire instruction process is traceable.

[0144] The aforementioned complete process ensures the orderliness and efficiency of cluster collaborative operations by generating tailored, detailed instructions for each interception device, including spatial range, data reporting requirements, specific actions, and coordination rules. Simultaneously, the introduction of blockchain technology for distributed storage of these instructions effectively guarantees their authenticity, immutability, and traceability throughout their entire lifecycle, significantly enhancing the transparency and reliability of the entire system and providing a solid foundation of trust for complex low-altitude collaborative control tasks.

[0145] To further improve the rationality and efficiency of interception resource allocation and ensure the rapid formation of effective defenses when dealing with large-scale target clusters, in some embodiments, step 102: the preliminary allocation of resources to the interception equipment cluster according to the airspace partitioning results and the distribution results of the target clusters includes:

[0146] Step 501: Divide the airspace into N macro-control areas according to the distribution density of the target clusters. Each macro-control area corresponds to a sub-target cluster, where N is a positive integer and matches the distribution number of the target clusters. The range of the macro-control area is larger than the range of the partition.

[0147] In step 501, the macro-control area refers to several large management areas into which the entire airspace is divided based on the natural aggregation of target clusters in the airspace. Each macro-control area corresponds to a relatively concentrated sub-target cluster, and its geographical scope is much larger than the three-level airspace partitions defined in subsequent steps. It is a coarse-grained spatial division used to achieve the initial allocation and localized management of resources.

[0148] In this embodiment, the system first analyzes the distribution of the target cluster and identifies several clustering centers naturally formed by the targets in the airspace using a spatial density clustering algorithm. Then, using these clustering centers as the core, the entire airspace is divided into N consecutive regions, i.e., macro-control areas. Each macro-control area is responsible for controlling the sub-target cluster within its scope, laying the foundation for subsequent zonal management.

[0149] Step 502: Based on the size of each sub-target cluster and the threat prediction coefficient, calculate the base number of interception equipment required for each macro-control area.

[0150] In step 502, the threat prediction coefficient is a comprehensive quantitative value used to assess the level of challenge that a sub-target cluster may pose. Its calculation takes into account factors such as the number, type, speed, and movement patterns of targets in the sub-target cluster. The interception equipment requirement base refers to the minimum theoretically estimated number of interception devices required to effectively deal with a sub-target cluster within a certain macro-control area. This value is the core basis for subsequent actual resource allocation.

[0151] In this embodiment of the application, for each macro-control area and its corresponding sub-target cluster identified in step 501, the system assesses the size (number of targets) of the sub-target cluster and calculates its threat prediction coefficient. Subsequently, based on a preset resource calculation model, the size of the sub-target cluster is multiplied by its threat prediction coefficient, and then multiplied by a basic unit requirement to obtain the base number of interception devices required for the macro-control area.

[0152] Step 503: Retrieve interception devices in the interception device library that are in the state of pending deployment, and allocate them to each macro-control area according to the required number of interception devices, ensuring that the number of interception devices in each macro-control area is not less than a preset threshold. The preset threshold is the product of the required number and a preset percentage. The remaining interception devices are reserved for cross-regional support.

[0153] In step 503, the preset threshold is the minimum guaranteed number of interception devices actually allocated to each macro-control area. This value is usually calculated by adding a certain redundancy ratio (preset percentage) to the demand base to ensure the ability to cope with unexpected situations, such as 90%. The remaining interception devices are 10% of the demand base. Cross-regional support reserves refer to the remaining interception devices that are not directly allocated to specific macro-control areas. These devices serve as a reserve force, ready to be dispatched at any time to support areas under greater pressure.

[0154] In this embodiment, the system retrieves all available interception devices from the interception device library. Then, based on the interception device demand base calculated in step 502 for each macro-control area, a corresponding number of devices are allocated to each area. To ensure reliability, the system checks whether the allocated number reaches a preset threshold; if insufficient, it supplements the remaining devices. After allocation, the remaining interception devices are not permanently assigned to any area but are used as a cross-regional support reserve, managed uniformly by the system.

[0155] Step 504: Assign initial communication channels and network identifiers to the interception devices in each macro-control area. Different interception devices in the same macro-control area share data through multicast communication, and interception devices in different macro-control areas interact across regions through gateway devices, forming a preliminary communication architecture for distributed interception.

[0156] In step 504, the initial communication channel refers to the shared radio frequency or network address allocated to all interception devices within each macro-control area for communication within the area. The network identifier is a unique identification code assigned to each device within the area, used for identification and addressing within the network. Multicast communication is a one-to-many communication method that allows one device to send information, which can be received by all devices within the area, making it crucial for efficient data sharing. Gateway devices are responsible for connecting different communication networks, used here to enable information exchange between different macro-control areas. The preliminary communication architecture of distributed interception refers to the basic communication network framework pre-built to enable collaborative operation of the interception device cluster. This architecture, by allocating independent communication channels and network identifiers to each macro-control area, allows devices within the area to efficiently share data via multicast, while simultaneously enabling limited data exchange between different areas through gateway devices, thus forming a hierarchical communication foundation that ensures real-time collaboration within the area and supports cross-regional linkage.

[0157] In this embodiment, the system allocates a dedicated initial communication channel to each macro-control area and configures a network identifier containing an area number and a device number for each interception device within that area. This configuration enables devices within the same area to share data via multicast communication, forming an independent communication subnet. Communication between different areas is forwarded and routed through gateway devices deployed at area boundaries, thus constructing a preliminary communication architecture that allows for efficient collaboration within areas and supports inter-area linkage.

[0158] Step 505: Based on the preliminary communication architecture of distributed interception, generate the resource allocation results of the interception device cluster. The resource allocation results include the device number, quantity, initial coordinates and communication parameters of the interception devices in each macro-control area.

[0159] In this embodiment, the system summarizes all allocation and configuration information from steps 501 to 504 to generate a complete resource allocation result. This result, in list form, details the number and quantity of interception devices allocated to each macro-control area, their initial deployment coordinates, and the communication parameters configured for them (such as communication channels and network identifiers). This result will be used to guide the initial deployment of the interception device cluster and subsequent collaborative operations.

[0160] Here is a specific example:

[0161] Continuing the example above, after the system detects 35 targets forming 3 clusters and dispatches 105 interceptor devices, initial resource allocation begins. First, the airspace is divided into 3 macro-control zones based on the target cluster density, each corresponding to one sub-target cluster. The radius of each zone is approximately 5 kilometers, larger than the subsequently defined partition areas. Based on the sizes of the sub-target clusters of 10, 15, and 10 targets respectively, and combined with threat prediction coefficients, the model calculates three coefficients of 1.0, 1.2, and 1.0, respectively. The threat prediction coefficient... ,in This represents the threat prediction coefficient. The base threat value is set to 1.0. The speed factor is the ratio of the target average speed to the reference speed. The average speeds of the three sub-clusters are 15 meters per second, 18 meters per second, and 15 meters per second, respectively. The reference speed is 15 meters per second, so the speed factors are 1.0, 1.2, and 1.0, respectively. Assuming the target type factor is uniformly set to 0.1, the threat prediction coefficients for the three regions are calculated as follows: 1.0 × 1.0 + 0.1 = 1.1, 1.0 × 1.2 + 0.1 = 1.3, and 1.0 × 1.0 + 0.1 = 1.1. The required base number for interception equipment is then calculated. ,in This indicates the base number of interception devices required. Indicates the size of the sub-target cluster. The unit demand coefficient is used to calculate the base demand for the three regions as follows: 10 × 1.1 × 2 = 22 units, 15 × 1.3 × 2 = 39 units, and 10 × 1.1 × 2 = 22 units. The 105 interceptor devices in the interceptor equipment library that are currently awaiting deployment are allocated according to the base demand, resulting in allocations of 22, 39, and 22 units respectively, totaling 83 units. A preset threshold is used. The calculated preset thresholds for the three regions are 22 × 0.9 = 19.8 aircraft (rounded up to 20), 39 × 0.9 = 35.1 aircraft (rounded up to 36), and 22 × 0.9 = 19.8 aircraft (rounded up to 20). The allocated quantities all meet the requirement of not being lower than the preset thresholds. The remaining interceptor equipment, as a cross-regional support reserve, is 105 minus 83 = 22 aircraft, approximately 26.5% of the total required base of 83 aircraft, meeting the requirement of being greater than 10%. Initial communication channels are allocated to the interceptor equipment in each macro-control area: channel CH1 for region 1, channel CH2 for region 2, and channel CH3 for region 3. Network identifiers, such as area identifiers, are also assigned to the equipment. The devices in Domain 1 are identified as ZONE1-001 to ZONE1-022. Devices within the same area share data through multicast communication, while devices in different areas interact across regions through deployed gateway devices, forming a preliminary communication architecture for distributed interception. Based on this architecture, the resource allocation results of the interception device cluster are generated, including the device number of the interception devices in each region, such as ZONE1-001, with quantities of 22, 39, and 22 respectively. The initial coordinates are concentrated near the geometric center of each region, such as the initial coordinates of devices in Region 2 being distributed around [5100, 2900, 120]. The communication parameters include channel CH2 and network identifier ZONE2-XXX.

[0162] The aforementioned complete process, through a macro-level classification followed by precise demand calculation, achieves a reasonable match between interception resources and target threats. By setting preset thresholds and support reserves, it ensures both the basic defense capabilities of each region and retains flexibility to respond to emergencies. Simultaneously, the pre-built communication architecture lays a crucial foundation for information exchange in subsequent distributed collaborative interception, enabling the entire system to quickly and orderly complete initial deployment and transition to collaborative combat mode, thus improving the startup efficiency and overall reliability of large-scale cluster management.

[0163] To further improve the coordination and intelligence of interception operations, ensure priority processing of high-priority targets and effectively deal with targets that slip through the net, in some embodiments, step 105: based on the airspace cooperative allocation results, a multi-machine target feature sharing mechanism is used to share the data transmitted by each interception device. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and targeted tracking and interception are performed on targets that have not been tracked or lost in the target cluster, including:

[0164] Step 601: Based on the airspace cooperative allocation results, activate the multi-machine target feature sharing mechanism of the interception equipment, so that each interception equipment can collect multi-modal information of the corresponding target to be intercepted through the multi-modal perception module. The multi-modal information includes: optical image, radar echo and infrared features. Use a lightweight algorithm to extract target key points and motion parameters from the multi-modal information, and generate the priority of the target to be intercepted by combining it with a preset threat feature library. Use a distributed data sharing protocol to encapsulate the priority, target key points, motion parameters and interception equipment status into shared data frames to form a distributed database.

[0165] In step 601, the multi-machine target feature sharing mechanism is a communication and data management rule that allows all interception devices to share the target information they detect with other members of the cluster in real time. A multimodal perception module refers to a combination of sensors integrated into the interception device that can collect target information from different physical dimensions (such as visible light, radio waves, and heat). Multimodal information refers to the complementary data set collected by these different sensors. Target key points refer to features extracted from target images or signals that represent their identity or state. Motion parameters refer to information describing the target's movement state. The preset threat feature database is a data warehouse that pre-stores the correspondence between various target features and their threat levels. The distributed data sharing protocol is a set of rules specifying how data is packaged, sent, and received. A shared data frame is a data packet containing various information, encapsulated according to the above protocol. The distributed database here does not refer to a centrally stored database, but rather to a unified, real-time updated view of target information formed throughout the entire interception device cluster through the sharing mechanism.

[0166] In this embodiment, once the airspace cooperation allocation result is issued, the system immediately initiates a multi-aircraft target feature sharing mechanism. Each interceptor device, according to its assigned instructions, operates its multimodal perception module to scan the designated target, collecting multimodal information such as optical images, radar echoes, and infrared features. Subsequently, the device's built-in lightweight algorithm quickly processes this raw data, extracting key shape features, current speed, heading, and other motion parameters of the target, and comparing these features with a preset threat feature database to calculate the target's real-time priority. Finally, the device packages the calculated priority, target key points, motion parameters, and its own status information (such as battery level and location) into a shared data frame according to a distributed data sharing protocol and broadcasts it. All devices in the cluster send and receive such data frames, thereby jointly maintaining and updating a distributed, shared target information database in real time.

[0167] Step 602: Generate collaborative boot instructions based on the priorities in the distributed database, and schedule the interception devices in different partitions to execute the corresponding interception strategies according to their priorities.

[0168] In step 602, the coordinated guidance instruction is a command generated by the system for the interception device based on the global target priority situation, guiding it to perform coordinated actions. The interception strategy is a differentiated handling plan formulated for targets of different priorities, such as adopting a strategy of close tracking and priority handling for high-priority targets.

[0169] In this embodiment, the system (or the command node in the cluster) continuously monitors information in the distributed database, especially the priority changes of each target. Based on a global perspective, the system generates coordinated guidance instructions. These instructions schedule interception devices in different partitions to execute corresponding interception strategies according to the target's priority. For example, devices in the instruction tracking zone prioritize maintaining a continuous lock on the highest priority target, while devices in the instruction alert zone move closer to high-priority targets to prepare for action, thereby achieving focused attention and resource allocation for important targets.

[0170] Step 603: Monitor the target tracking status through the missed target identification model, mark targets that are not tracked or whose tracking error exceeds the preset error threshold as missed targets, and generate corresponding unique identifiers.

[0171] In step 603, the slip-through target identification model is a continuously running monitoring program that analyzes target tracking status data in a distributed database to identify which targets may have escaped monitoring. Tracking error refers to the deviation between the estimated target position and the actual detected position. A preset error threshold is a maximum allowable deviation value; exceeding this value indicates that tracking may have failed. A unique identifier is a unique code assigned to each identified slip-through target for continuous tracking throughout the system.

[0172] In this embodiment, the target slippage identification model analyzes the update status and tracking data of each target in the distributed database in real time. For targets that have not been updated for a long time, or whose latest reported position deviates from the predicted trajectory (tracking error) by more than a preset error threshold, the model will mark them as slippage targets. Once a target is marked, the system immediately generates a unique identifier for it and broadcasts this identifier and its last known information to the distributed database, notifying all interception devices that there is a target that needs to be searched and tracked again.

[0173] Step 604: Based on the identifier of the escaped target, dispatch interception equipment to predict the movement trajectory of the escaped target based on historical trajectory data, and reduce the positioning error through collaborative positioning of multiple interception equipment to implement forward interception.

[0174] In step 604, historical trajectory data refers to the historical record of the movement path of the escaped target before it was marked. Predicting the movement trajectory of the escaped target means estimating the target's possible future location based on historical data using a prediction algorithm. Multi-interception device collaborative positioning refers to scheduling multiple devices to search and detect the area where the target might exist from different directions, and accurately determining its location through triangulation or data fusion. Proactive interception refers to deploying or handling the target not at its current point, but at a point along its predicted future path to improve the success rate.

[0175] In this embodiment, upon detecting a target that has slipped through the network, the system first retrieves the target's historical trajectory data and uses a trajectory prediction algorithm to estimate its possible direction of movement and future location. Subsequently, the system dispatches multiple interception devices closest to the predicted area. These devices collaboratively search the airspace where the target might exist from different directions, rapidly narrowing the search range and accurately locating the target through data convergence and fusion. Once the target is rediscovered, the devices do not directly rush to its current location but, based on the predicted trajectory, implement forward interception, i.e., flying ahead of the target to wait or handle it, thereby efficiently completing the task of supplementing defense against slipping targets.

[0176] Here is a specific example:

[0177] Following the previous example, after the interceptor cluster in Area 2 has been deployed and begun operation according to the airspace cooperation allocation results, the system initiates a multi-aircraft target feature sharing mechanism based on these allocation results. Interceptor device number 2005 collects optical images, radar echoes, and infrared features of target T25 through its multimodal perception module. Using a lightweight algorithm, it extracts key target features from the multimodal information, including features such as a fuselage length of 8 meters and a wingspan of 6 meters, as well as motion parameters such as a speed of 30 meters per second and a northward orientation. Combining this with the rule in the preset threat feature database that large aircraft correspond to high threat levels, the system assigns a high priority to target T25. Subsequently, through a distributed data sharing protocol, this high priority, key target features, motion parameters, and the device's own status (80% battery level) are encapsulated into a shared data frame and broadcast. All devices in the cluster receive and update their local data, collectively forming a distributed database. The system then uses the priority of T25 in the distributed database... Upon receiving the information indicating a high level of alert, a coordinated guidance command is generated, dispatching two additional interceptor devices within the alert zone to converge on the location of T25, assisting interceptor device 2005 in executing the key monitoring interception strategy. Simultaneously, the target smuggling identification model continuously monitors the target tracking status, discovering that target T10 has not reported data for three consecutive seconds and that its last reported position deviates from the predicted position by more than a preset error threshold of 50 meters. Therefore, it is marked as a smuggled target, generating a unique identifier L001 and writing it to the distributed database. Based on the identifier L001, the system retrieves the coordinate sequence of the last 5 seconds of its historical trajectory data: [5050,3050,150], [5060,3060,145], [5070,3070,140], [5080,3080,135], [5090,3090,130]. ​​The system calculates the trajectory using a linear prediction algorithm, with the prediction formula being the position at the next moment. ,in This indicates the predicted position of the target at the next moment. The last position is [5090, 3090, 130]. The average velocity vector is [10, 10, -5] meters per second. With a predicted time interval of 2 seconds, the predicted location was calculated to be [5110, 3110, 120]. Three nearby interception devices were then dispatched to the predicted area for collaborative positioning. The devices detected from different directions and reduced the positioning error through triangulation. Finally, the target T10 was rediscovered at coordinates [5105, 3108, 118] and forward interception was carried out based on its movement trend. The dispatched devices arrived in advance at the position [5120, 3120, 115] to deploy defenses.

[0178] The aforementioned complete steps, through distributed sensing and information sharing, construct a unified situational awareness across the cluster. This enables the system to intelligently guide resource allocation based on global target priorities, ensuring efficient handling of core targets. Simultaneously, through proactive monitoring and collaborative defense mechanisms, the system enhances the ability to rediscover and intercept targets that have slipped through the net, effectively strengthening the completeness and robustness of the overall interception operation and ensuring the ultimate effectiveness of collaborative control tasks in complex environments.

[0179] To further improve the success rate of intercepting targets that slip through the net and the level of intelligent decision-making, and to ensure optimal response in complex and dynamic environments, in some embodiments, step 604: before implementing pre-interception, the method further includes:

[0180] Step 701: Based on the movement direction of the escaped target and the relative position of the escaped target to the core defense area, dynamically adjust the number of devices participating in the interception and the task allocation results. If the movement direction of the escaped target points to the core defense area, increase the interception priority and schedule additional interception devices to join the interception queue.

[0181] In step 701, the defense core area refers to the central area of ​​highest importance within the entire airspace that requires priority protection. Relative position refers to the azimuth and distance relationship between the current location of the escaped target and the center point of the defense core area. Interception queue refers to the formation or group formed by multiple interception devices assigned to participate in this interception mission.

[0182] In this embodiment, after determining that a target that has slipped through the defenses needs to be intercepted in advance, the system first analyzes the target's movement direction and calculates its relative position to the core defense area. If it is determined that the target's movement direction is pointing towards the core defense area, it means that its potential impact has increased, and the system will automatically increase the interception priority of the target. At the same time, the system will dynamically adjust the originally planned interception scheme, increase the number of devices participating in this interception mission, dispatch additional interception devices from support reserves or other areas to join the interception queue, and reallocate their tasks to ensure that there are sufficient resources to deal with heightened threats.

[0183] Step 702: Optimize the interception strategy in real time using a reinforcement learning algorithm. The reinforcement learning algorithm uses the interception success rate as a reward function. If the first interception fails, it automatically switches to the backup interception scheme and adjusts the cooperative formation of the interception queue.

[0184] In step 702, reinforcement learning is an artificial intelligence method where the system learns to take optimal action in different situations by continuously trying different interception strategies and experiencing success or failure (reward or penalty). The reward function is a mathematical function used to evaluate the quality of an action; here, successful interception is the core reward metric. Alternative interception plans are pre-planned alternative routes or methods that differ from the primary plan. Cooperative formation refers to the specific spatial arrangement maintained by multiple interception devices cooperating with each other during mission execution.

[0185] In this embodiment, the system employs a reinforcement learning algorithm to optimize the interception strategy in real time. This algorithm uses historical successful interception data as experience, aiming to maximize the interception success rate (reward function), and continuously adjusts the strategy. If the initial interception attempt fails, the system does not simply repeat the process but automatically triggers and switches to a pre-prepared backup interception plan. Simultaneously, the algorithm adjusts the relative positions and coordination methods of the devices in the interception queue in real time based on the current environment and the new plan, i.e., changing the collaborative formation to better adapt to changes and improve the success rate of subsequent attempts.

[0186] Step 703: Establish an interception evaluation model, calculate the interception probability in real time based on the relative speed and distance between the interception device and the target that slipped through the net, as well as historical interception data, and determine whether to issue a final interception command or terminate the interception task based on the interception probability.

[0187] In step 703, the interception assessment model is a mathematical calculation model that integrates multiple real-time parameters to quantify the probability of a successful interception operation. Relative velocity refers to the speed difference between the interceptor and the escaped target as they approach or move away. Historical interception data refers to statistical data on successful or failed interceptions in similar past situations. The interception probability is a value between zero and one calculated by the interception assessment model, representing the likelihood of a successful interception under current conditions. The final interception command is the decision that instructs the interceptor to execute the final disposal action.

[0188] In this embodiment, an interception evaluation model is activated just before the final execution of the interception operation. This model calculates the current interception probability in real time using a weighted formula, based on real-time data on the relative speed between the interception device and the escaping target, the real-time distance between them, and successful experiences from similar scenarios in the historical interception database. The system then makes a final decision based on this calculated probability: if the probability is higher than a set action threshold, a final interception command is issued; if the probability is too low, the current interception task may be terminated to avoid resource waste and allow for strategy replanning, thus ensuring the scientific and rational nature of the command issuance.

[0189] Here is a specific example:

[0190] Following the previous example, before the system rediscovers the slippery target L001 at coordinates [5105, 3108, 118] and plans to conduct a pre-emptive interception, the following steps are performed: First, based on the calculated direction of movement of the slippery target L001 as approximately 120 degrees southeast, and its relative position to the core defense area with a radius of 1000 meters centered on facility A, the distance is calculated to be 8200 meters and it is approaching. Therefore, it is determined that its direction of movement is pointing towards the core defense area, and the interception priority is raised from medium to high. The number of devices participating in the interception is dynamically increased from the original 3 to 5. Two interception devices are dispatched from the cross-regional support reserve to join the interception queue, and the task is reassigned to two devices responsible for a frontal interception. Two aircraft were assigned to flank the target, and one to conduct high-altitude surveillance. The interception strategy was then optimized in real-time using a reinforcement learning algorithm. This algorithm uses the interception success rate as a reward function. If the initial interception attempt failed due to the target suddenly decelerating and changing direction, the algorithm automatically switched to a backup interception plan employing an encirclement strategy, adjusting the cooperative formation of the interception queue from a fan shape to a ring encirclement. Finally, an interception evaluation model was established. Based on the relative speed between the interception equipment and the escaped target, the average relative speed was calculated to be 25 meters per second, with a real-time distance of 300 meters. Historical interception data showed 8 successful interceptions and 2 failures in similar scenarios, resulting in a base success rate of 80%. The interception probability was calculated by combining relative speed and distance factors. ,in Indicates the probability of interception. Equals 80%. It equals 1 minus the absolute value of the difference between the relative velocity and the reference velocity, divided by the reference velocity, which is taken as 30 meters per second. , It equals 1 minus the absolute value of the difference between the real-time distance and the optimal distance, divided by the optimal distance, where the optimal distance is taken as 200 meters. Substitute into the formula Since the value is below the preset action threshold of 60%, the system decides to terminate the current interception task based on the interception probability, instructs the interception queue to continue monitoring and replan the strategy.

[0191] The aforementioned complete process, through the introduction of dynamic threat assessment and resource adjustment mechanisms, ensures sufficient resources when dealing with high-threat targets that have slipped through the net. Reinforcement learning is used to achieve online intelligent optimization of the interception strategy, improving the ability to respond to emergencies. Finally, interception probability assessment based on multi-source information fusion provides a scientific basis for decision-making, thereby improving the overall success rate and intelligent level of decision-making in intercepting targets that have slipped through the net.

[0192] Figure 3 This application provides a schematic diagram of the structure of a multi-dimensional collaborative detection and guidance system, and the specific implementation details are as follows:

[0193] The acquisition module 31 is used to acquire multi-dimensional information of multiple targets to be intercepted in the airspace. The multi-dimensional information includes the number of targets, airspace coordinates and direction of movement. The multiple targets to be intercepted constitute a target cluster.

[0194] The scheduling module 32 is used to schedule an interception device cluster from the interception device library according to a preset multiple relationship based on the target quantity, and to perform preliminary resource allocation on the interception device cluster according to the airspace partitioning result and the distribution result of the target cluster, so as to perform distributed interception of the target cluster through the interception device cluster.

[0195] The association module 33 is used to associate the multi-dimensional information with the resource allocation results of the interception device cluster after the initial allocation is completed, so as to obtain an associated data set.

[0196] The partitioning module 34 is used to perform airspace partitioning and responsibility division processing based on the associated data set and the airspace cooperation allocation model to obtain the airspace cooperation allocation result. The airspace cooperation allocation result includes: airspace partitioning result, cooperation boundary between partitions and airspace responsibility instructions of interception equipment.

[0197] The guidance module 35 is used to share the data transmitted by each interception device based on the airspace cooperative allocation result and the multi-machine target feature sharing mechanism. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and to perform targeted tracking and interception of targets that have not been tracked or lost in the target cluster. The transmitted data includes: the priority of the target to be intercepted and the identifier of the target that has escaped the net, generated based on the lightweight algorithm.

[0198] The multi-dimensional collaborative detection and guidance system of this application is used to implement the aforementioned multi-dimensional collaborative detection and guidance method. Therefore, the specific implementation of the multi-dimensional collaborative detection and guidance system can be found in the embodiment section of the multi-dimensional collaborative detection and guidance method above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0199] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described multi-dimensional collaborative detection and guidance methods.

[0200] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described multi-dimensional collaborative detection and guidance methods.

[0201] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0202] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the multi-dimensional collaborative detection and guidance method.

[0203] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0204] The foregoing has provided a detailed description of a multi-dimensional collaborative detection and guidance method and system provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A multi-dimensional collaborative detection and guidance method, characterized in that, include: Acquire multi-dimensional information about multiple targets to be intercepted within the airspace. The multi-dimensional information includes the number of targets, airspace coordinates, and direction of movement. The multiple targets to be intercepted constitute a target cluster. Based on the target quantity, an interception device cluster is scheduled from the interception device library according to a preset multiple relationship. According to the airspace partitioning results and the distribution results of the target cluster, the interception device cluster is initially allocated resources to perform distributed interception of the target cluster through the interception device cluster. After the initial allocation is completed, the multi-dimensional information is correlated with the resource allocation results of the interception device cluster to obtain a correlated data set; Based on the associated data set, an airspace cooperation allocation model is used to perform airspace partitioning and responsibility division to obtain airspace cooperation allocation results. The airspace cooperation allocation results include: airspace partitioning results, cooperation boundaries between partitions, and airspace responsibility instructions of interception devices. Based on the results of airspace cooperative allocation, a multi-machine target feature sharing mechanism is adopted to share the data transmitted by each interception device. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and to specifically track and intercept the targets that have not been tracked or lost in the target cluster. The transmitted data includes: the priority of the target to be intercepted and the identifier of the target that has escaped the network, generated based on the lightweight algorithm. The process of using a spatial domain cooperative allocation model based on associated data sets to perform spatial domain partitioning and responsibility division yields spatial domain cooperative allocation results, including: Based on the spatial coordinates and motion direction of all targets to be intercepted in the associated dataset, and combined with the distribution of the targets to be intercepted in the target cluster, the geometric center coordinates and diffusion radius of the target cluster are calculated through the clustering analysis module of the spatial collaborative allocation model. The dynamic partitioning module of the airspace cooperative allocation model dynamically defines three-level airspace partitions according to the distance between the geometric center coordinates. The three-level airspace partitions include a tracking zone, a guard zone, and a backup zone, and the partitions have cooperative boundaries. Based on the matching relationship between the airspace coordinates of each target to be intercepted and the partition range, the coordinate mapping module of the airspace collaborative allocation model maps each target to be intercepted to the corresponding partition, generating an airspace partition result containing the target number and the partition to which it belongs. Based on the resource allocation results of the interception device cluster, the resource matching module of the airspace collaborative allocation model allocates interception devices to each partition, with different partitions being allocated interception devices with different configuration information; The instruction generation module of the airspace cooperation allocation model generates airspace responsibility instructions for each interception device based on the cooperation boundary and the priority of the corresponding partition. The airspace partitioning results, the collaborative boundaries between the partitions, and the airspace responsibility instructions of the intercepting devices are combined into airspace collaborative allocation results. The airspace collaborative allocation results are then encapsulated into standardized data frames by the synchronization module of the airspace collaborative allocation model and pushed to all intercepting devices through the inter-device communication protocol.

2. The method according to claim 1, characterized in that, The dynamic delineation of three-level airspace partitions based on the distance from the geometric center coordinates includes: Generate partitioning basic parameters based on the distance from the coordinates of the geometric center; Using the geometric center coordinates as the origin, a three-dimensional spatial coordinate system with three levels of spatial partition range is constructed based on preset partitioning parameters; In a three-dimensional spatial coordinate system, the overall motion vector of the target cluster is generated by combining the motion direction and velocity of each target to be intercepted in the target cluster. The overall motion vector includes a velocity value. When the rate value is greater than the preset rate threshold, it is determined that the target cluster is in a high-speed motion state. In the high-speed motion state, the expansion coefficient is calculated according to the product of the rate value and the basic parameter. Based on the expansion coefficient, the adjusted three-level airspace partition range is determined. For targets to be intercepted outside the adjusted three-level airspace partition range, a separate temporary sub-partition is designated. The temporary sub-partition is centered on the airspace coordinates of the target to be intercepted, with a diameter of a preset length, and the temporary sub-partition belongs to the original partition. Based on the temporary sub-partitions, the target distribution density of each partition is determined, and the range of each partition is optimized according to the target distribution density of each partition to obtain a three-level airspace partition.

3. The method according to claim 1, characterized in that, Based on the cooperative boundary and the priority corresponding to the partition, the airspace responsibility instructions for each interception device are generated, including: Based on collaborative boundary data, combined with the real-time coordinates and motion performance parameters of the interception devices, a dedicated activity airspace range is defined for each interception device. The data collection and reporting frequency of each device is set based on the priority of the corresponding partition and the dynamic characteristics of the target. Based on the configuration parameters and partition responsibilities of the interception devices, generate corresponding operation instructions for each interception device; The activity airspace range, the data collection and reporting frequency, the operation instructions, and the pre-set collaborative response rules for cross-boundary collaborative scenarios are integrated into airspace responsibility instructions. The airspace responsibility instructions are distributed and stored using blockchain technology to ensure that the entire process of instruction generation, transmission, and execution is traceable. Each airspace responsibility instruction includes a unique identifier of the intercepting device, the instruction effective time, the validity period, and a verification code.

4. The method according to claim 1, characterized in that, The preliminary resource allocation for the interception device cluster based on the airspace partitioning results and the distribution results of the target cluster includes: The airspace is divided into N macro-control areas according to the distribution density of the target clusters. Each macro-control area corresponds to a sub-target cluster, where N is a positive integer and matches the distribution number of the target clusters. The range of the macro-control area is larger than the range of the partition. Based on the size of each sub-target cluster and the threat prediction coefficient, calculate the base number of interception equipment required for each macro-control area; Interception devices in the status of pending deployment are retrieved from the interception device library and allocated to each macro-control area according to the required number of interception devices, ensuring that the number of interception devices in each macro-control area is not less than a preset threshold. The preset threshold is the product of the required number and a preset percentage. The remaining interception devices are reserved for cross-regional support. Initial communication channels and network identifiers are assigned to interception devices within each macro-control area. Different interception devices within the same macro-control area share data through multicast communication, while interception devices in different macro-control areas interact across regions through gateway devices, forming a preliminary communication architecture for distributed interception. Based on the preliminary communication architecture of distributed interception, the resource allocation results of the interception device cluster are generated. The resource allocation results include the device number, quantity, initial coordinates and communication parameters of the interception devices in each macro-control area.

5. The method according to claim 1, characterized in that, Based on the airspace cooperative allocation results, a multi-machine target feature sharing mechanism is adopted to share the data transmitted by each interception device. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and to perform targeted tracking and interception of targets that have not been tracked or lost in the target cluster, including: Based on the airspace cooperative allocation results, a multi-aircraft target feature sharing mechanism for interception equipment is initiated, allowing each interception device to collect multimodal information of the corresponding target to be intercepted through a multimodal perception module. The multimodal information includes optical images, radar echoes, and infrared features. A lightweight algorithm is used to extract target key points and motion parameters from the multimodal information, and a priority of the target to be intercepted is generated by combining it with a preset threat feature library. The priority, target key points, motion parameters, and interception equipment status are encapsulated into shared data frames through a distributed data sharing protocol to form a distributed database. Based on the priorities in the distributed database, collaborative guidance instructions are generated, and interception devices in different partitions are scheduled to execute corresponding interception strategies according to their priorities. The target tracking status is monitored by a target slippage identification model. Targets that are not tracked or whose tracking error exceeds a preset error threshold are marked as slippage targets and a corresponding unique identifier is generated. Based on the identification of the escaped targets, interception equipment is dispatched to predict the movement trajectory of the escaped targets based on historical trajectory data, and the positioning error is reduced by the collaborative positioning of multiple interception equipment to implement forward interception.

6. The method according to claim 5, characterized in that, Before implementing pre-interception, the method further includes: Based on the movement direction of the escaped target and the relative position of the escaped target to the core defense area, the number of devices participating in the interception and the task allocation results are dynamically adjusted. If the movement direction of the escaped target points to the core defense area, the interception priority is increased and additional interception devices are scheduled to join the interception queue. The interception strategy is optimized in real time through a reinforcement learning algorithm. The reinforcement learning algorithm uses the interception success rate as a reward function. If the first interception fails, it automatically switches to the backup interception scheme and adjusts the cooperative formation of the interception queue. An interception assessment model is established to calculate the interception probability in real time based on the relative speed and distance between the interception device and the target that slipped through the net, as well as historical interception data. Based on the interception probability, it is determined whether to issue a final interception command or terminate the interception mission.

7. A multi-dimensional collaborative detection and guidance system, characterized in that, For executing the multi-dimensional collaborative detection and guidance method as described in claim 1, comprising: The acquisition module is used to acquire multi-dimensional information of multiple targets to be intercepted in the airspace. The multi-dimensional information includes the number of targets, airspace coordinates and direction of movement. The multiple targets to be intercepted constitute a target cluster. The scheduling module is used to schedule an interception device cluster from the interception device library according to a preset multiple relationship based on the target quantity, and to perform preliminary resource allocation on the interception device cluster according to the airspace partitioning result and the distribution result of the target cluster, so as to perform distributed interception of the target cluster through the interception device cluster; The association module is used to associate the multi-dimensional information with the resource allocation results of the interception device cluster after the initial allocation is completed, so as to obtain an association data set. The partitioning module is used to perform airspace partitioning and responsibility division based on the associated data set and the airspace cooperation allocation model to obtain the airspace cooperation allocation result. The airspace cooperation allocation result includes: airspace partitioning result, cooperation boundary between partitions and airspace responsibility instructions of interception equipment. The guidance module is used to share the data transmitted by each interception device based on the airspace cooperative allocation results and a multi-machine target feature sharing mechanism. Based on the transmitted data, the interception device cluster is guided to track and intercept the target cluster according to priority, and to specifically track and intercept any targets that have not been tracked or lost in the target cluster. The transmitted data includes: the priority of the target to be intercepted and the identifier of the target that has escaped the net, generated based on a lightweight algorithm.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the multi-dimensional collaborative detection and guidance method as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the multi-dimensional collaborative detection and guidance method as described in any one of claims 1 to 6.

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