UAV countermeasure method and system based on multi-dimensional data fusion

Abstract

The present application provides a method and system for countering drones based on multi-dimensional data fusion. By acquiring the drone defense device sequence and the counter-drone data sequence, the set label of the target drone defense device in the set of K drone defense devices is determined, and the target counter-drone sequence matching the set label is determined, so that the management and control of specific drone defense devices are more refined, and operations can be carried out in a targeted manner according to actual needs. The device quantity update set is determined based on the target counter-drone sequence, so that the changes in the drone defense device can be clearly understood. When the change amount of the drone defense device meets the requirements for the change of the defense device, the corresponding defense intensity change event will be executed based on the multi-dimensional fusion drone detection data. This can not only make full use of existing resources, but also flexibly adjust the defense strategy according to actual conditions, greatly improving the defense effect of the drone defense system and the ability to cope with complex situations.

Description

Technical Field

[0001] The present application relates to the field of drone technology, and more specifically, to a drone countermeasure method and system based on multi-dimensional data fusion. Background Art

[0002] With the advancement of science and technology and the improvement of intelligence level, drone technology has been widely used. However, drones can also be used maliciously, such as illegal surveillance and delivery of items, posing a threat to social security. Therefore, effective defense against drones has become an important issue at present.

[0003] At present, the defense systems against drones mainly include radar detection, electromagnetic interference and other methods. These methods can prevent illegal activities of drones to a certain extent, but they still have their limitations. For example, radar detection may be affected by environmental factors, resulting in poor detection results; and electromagnetic interference may affect other normal operating equipment.

[0004] In order to overcome these shortcomings, a more accurate and efficient drone defense system is needed. An ideal drone defense system should be able to update and adjust the number and configuration of defense devices in real time so as to optimize the defense strategy according to the actual situation. At the same time, it is also necessary to be able to execute corresponding defense intensity change events for the target airspace based on multi-dimensional fusion drone detection data.

[0005] However, existing drone defense systems cannot meet the above requirements. They can only adopt fixed defense strategies and cannot be adjusted according to actual conditions. In addition, existing technologies usually do not consider the application of multi-dimensional fusion drone detection data, so they lack accuracy when executing defense intensity change events. Therefore, there is a need to improve the defense effect of drones and increase the flexibility and accuracy of defense systems. Summary of the invention

[0006] In view of this, the purpose of this application is to provide a drone countermeasure method and system based on multi-dimensional data fusion.

[0007] According to a first aspect of the present application, a method for countering a drone based on multi-dimensional data fusion is provided, the method comprising:

[0008] Obtain a drone defense device sequence and a counter-drone data sequence; the drone defense device sequence includes K drone defense device sets, each of which includes multiple drone defense devices, and the counter-drone data sequence includes K counter-drone sequences, each of which includes the number of counter-drone activations corresponding to multiple defense moments, K is a positive integer, and the drone defense device is used to control the counter-drone that generates a defensive force on the target airspace;

[0009] Determine a set label of a target drone defense device in the set of K drone defense devices, and determine a target counter-drone sequence matching the set label from the K counter-drone sequences; the target drone defense device is any drone defense device in the drone defense device sequence;

[0010] Determine a device quantity update set based on the target counter-drone sequence; the device quantity update set includes the change amount of the drone defense device corresponding to the multiple defense moments;

[0011] When the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirements, a defense intensity change event matching the defense device change requirements is executed on the target airspace based on the multi-dimensional fusion drone detection data of the target airspace; the defense intensity change event includes a defense switch instruction for controlling the counter drone based on the target drone defense device.

[0012] In a possible implementation of the first aspect, the updating set of determining the number of devices based on the target counter-drone sequence includes:

[0013] Determine a change amount of the drone defense device corresponding to the defense trigger moment based on the number of activated counter-drones corresponding to the defense trigger moment among the multiple defense moments and the maximum number of activated counter-drones of the drone defense device;

[0014] Determine a change value between the number of activated counter-drones corresponding to a non-defense triggering moment among the multiple defense moments and the number of activated counter-drones corresponding to a defense moment before the non-defense triggering moment;

[0015] Determine the change amount of the drone defense device corresponding to each of the non-defense triggering moments based on the change value and the maximum number of activated counter drones;

[0016] Based on the change amount of the drone defense device corresponding to the defense triggering moment and the change amount of the drone defense device corresponding to each of the non-defense triggering moments, a device quantity update set is determined.

[0017] In a possible implementation manner of the first aspect, the defense device change requirement includes a defense activation requirement and a defense termination requirement; and the method further includes:

[0018] If the current defense moment is the defense trigger moment, a first defense device change interval is determined based on the change amount of the drone defense device corresponding to the defense trigger moment, and when the change amount of the participating node of the target drone defense device in the corresponding drone defense device set during participation is within the first defense device change interval, it is determined that the defense activation requirement is met;

[0019] If the current defense moment is the non-defense trigger moment, then when the change amount of the drone defense device corresponding to the non-defense trigger moment is a positive change amount, a second defense device change interval is determined based on the change amount of the drone defense device corresponding to the non-defense trigger moment, and when the change amount of the participating node of the target drone defense device in the corresponding drone defense device set when participating is within the second defense device change interval, it is determined that the defense activation requirement is met;

[0020] When the change amount of the drone defense device corresponding to the non-defense trigger moment is a negative change amount, the third defense device change interval is determined based on the change amount of the drone defense device corresponding to the non-defense trigger moment, and when the change amount of the participating nodes of the target drone defense device in the corresponding drone defense device set when participating is within the third defense device change interval, it is determined that the defense termination requirements are met.

[0021] In a possible implementation of the first aspect, when the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirement, based on the multi-dimensional fusion drone detection data of the target airspace, executing a defense intensity change event matching the defense device change requirement for the target airspace, includes:

[0022] When the change amount of the drone defense device corresponding to the current defense moment meets the defense activation requirement, the defense activation information is obtained, and based on the defense activation information, the target counter drone is activated according to the maximum number of enabled counter drones for the target airspace, and the target counter drone is controlled to perform the corresponding counter operation based on the multi-dimensional fusion drone detection data of the target airspace;

[0023] When the change amount of the drone defense device corresponding to the current defense moment meets the defense termination requirement, defense termination information is obtained, and based on the defense termination information, the target counter-drone controlled by the target drone defense device is shut down for the target airspace.

[0024] In a possible implementation of the first aspect, the step of controlling the target countermeasure drone to perform a corresponding countermeasure operation based on the multi-dimensional fusion drone detection data of the target airspace includes:

[0025] Acquire multi-dimensional fusion drone detection data of the target airspace, and acquire countermeasure matching templates generated according to countermeasure strategies of each drone, wherein the multi-dimensional fusion drone detection data includes: fusion sensor detection data of multiple dimensions extracted from drone detection data of the target airspace;

[0026] The trained target drone countermeasure decision network is used to extract drone feature information of each detection time series data in the multi-dimensional fusion drone detection data, and feature interaction is performed on each drone feature information obtained to generate interactive features, wherein the drone feature information includes at least one of drone position features, drone speed features, drone shape features, and drone heat features, or a combination of multiple features;

[0027] Generate a UAV dynamic vector of the multi-dimensional fusion UAV detection data according to a dynamic change parameter of the UAV feature information between every two detection time series data within the target interval; generate a countermeasure basis feature based on the interaction feature and the UAV dynamic vector;

[0028] Extracting the template features of each counter-matching template respectively;

[0029] Based on the feature distance between each template feature and the countermeasure basis feature, respectively obtain the support degree of the multi-dimensional fusion drone detection data on each drone countermeasure strategy;

[0030] According to each support degree, a corresponding target UAV countermeasure strategy is determined, so as to control the target countermeasure UAV to perform a corresponding countermeasure operation based on the target UAV countermeasure strategy.

[0031] In a possible implementation of the first aspect, the step of training the target UAV countermeasure decision network includes:

[0032] Acquire an initialized neural network model and each sample learning data, wherein the initialized neural network model includes a first neural network and a second neural network with initialized weight parameters, and other neural networks whose parameters are to be updated; each sample learning data includes: a sample drone detection data, each sample countermeasure matching template generated according to each sample countermeasure strategy, and countermeasure strategy annotation data;

[0033] Based on the sample learning data, the parameters of the initialized neural network model are updated until the training termination condition is met, thereby generating a trained target drone countermeasure decision network.

[0034] In a possible implementation of the first aspect, in a process of updating each round of parameters of the initialized neural network model according to the sample learning data:

[0035] For each sample detection time series data in the sample drone detection data, coded data is obtained respectively, and based on the second neural network, corresponding drone feature information is extracted according to each coded data, and interaction features and drone dynamic vectors are generated according to each drone feature information;

[0036] For each counter-matching template, feature association data under the influence of the UAV dynamic vector is obtained respectively, and based on the first neural network, corresponding template features are extracted respectively according to each feature association data;

[0037] According to the feature distance between each template feature and the countermeasure basis feature, the support of the example drone detection data on each example countermeasure strategy is obtained respectively, and the weight parameters of the other neural networks are updated according to the loss function value between the countermeasure strategy prediction data determined by each support and the countermeasure strategy labeled data.

[0038] In a possible implementation of the first aspect, the method further includes:

[0039] Performing encoding processing on the drone defense devices in the drone defense device sequence to generate a drone defense device encoding distribution;

[0040] Determine the coding ID of the target drone defense device from the drone defense device coding distribution, and determine the trigger IDs of multiple counter drones that need to be activated by the target drone defense device based on the coding ID;

[0041] Defense activation information is generated based on the trigger ID and the maximum number of counter-drone activations.

[0042] In a possible implementation of the first aspect, the method further includes:

[0043] Each of the multiple defense moments is taken as the current defense moment, and when the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirement, a defense intensity change event matching the defense device change requirement is executed on the target airspace, and then the device quantity update set is reported to the target defense command system, so that when the target defense command system receives the device quantity update set reported by each drone defense device in the drone defense device sequence, it determines the drone countermeasure performance data of the target airspace based on the device quantity update set reported by each drone defense device and the countermeasure feedback data of the target airspace.

[0044] According to the second aspect of the present application, a drone countermeasure system based on multidimensional data fusion is provided, the drone countermeasure system based on multidimensional data fusion comprising a machine-readable storage medium and a processor, the machine-readable storage medium storing machine-executable instructions, and when the processor executes the machine-executable instructions, the drone countermeasure system based on multidimensional data fusion implements the aforementioned drone countermeasure method based on multidimensional data fusion.

[0045] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed, the aforementioned drone countermeasure method based on multi-dimensional data fusion is implemented.

[0046] According to any one of the above aspects, the technical effect of the present application is:

[0047] By obtaining the sequence of drone defense devices and the sequence of counter-drone data, the number of counter-drones enabled corresponding to each drone defense device at each defense moment can be quickly and accurately understood. This provides strong data support for the management and adjustment of the drone defense system, thereby improving the efficiency of the defense work. Determine the set label of the target drone defense device in the set of K drone defense devices, and determine the target counter-drone sequence that matches the set label, so that the management and control of specific drone defense devices are more refined, and can be operated in a targeted manner according to actual needs. Determine the device quantity update set based on the target counter-drone sequence, so that the changes of the drone defense device can be clearly understood, thereby providing real-time reference information for adjusting the defense strategy. When the change amount of the drone defense device meets the requirements of the defense device change, the corresponding defense intensity change event will be executed based on the multi-dimensional fusion drone detection data, which can not only make full use of existing resources, but also flexibly adjust the defense strategy according to actual conditions, greatly improving the defense effect of the drone defense system and the ability to deal with complex situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A schematic diagram of a flow chart of a drone countermeasure method based on multi-dimensional data fusion provided in an embodiment of the present application;

[0050] Figure 2A schematic diagram of the component structure of a drone countermeasure system based on multidimensional data fusion for implementing the above-mentioned drone countermeasure method based on multidimensional data fusion provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below according to the drawings in the embodiment of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description, and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the embodiment of the present application. It should be understood that the operations of the flowchart can be implemented out of order, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add at least one other operation to the flowchart under the guidance of the content of the present application, or destroy at least one operation from the flowchart.

[0052] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. According to the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work correspond to the scope of protection of the present application.

[0053] Figure 1 The flowchart of the method and system for countering drones based on multidimensional data fusion provided by the embodiment of the present application is shown. It should be understood that in other embodiments, the order of some steps of the method for countering drones based on multidimensional data fusion of this embodiment can be shared with each other according to actual needs, or some steps can be omitted or maintained. The detailed steps of the method for countering drones based on multidimensional data fusion include:

[0054] Step S110, obtaining a drone defense device sequence and a drone countermeasure data sequence.

[0055] In this embodiment, the drone defense device sequence includes K drone defense device sets, each of which includes multiple drone defense devices. The counter-drone data sequence includes K counter-drone sequences, each of which includes a number of counter-drone activations corresponding to multiple defense moments. K is a positive integer, and the drone defense device is used to control the counter-drones that produce a defensive force on the target airspace.

[0056] For example, suppose there is a city facing the threat of drones. In order to protect the city's safety, the city management department deploys a drone defense system, which includes K drone defense devices, each of which is responsible for controlling a certain number of counter-drones. At the same time, K counter-drone sequences are also recorded, and each counter-drone sequence contains the number of counter-drones enabled at multiple defense moments. These data can be used to analyze the effects of different defense strategies in order to develop more effective defense plans.

[0057] For example, a city may have multiple defense areas, each of which has one or more drone defense devices. Each drone defense device can control a certain number of counter drones, which can generate defense strength for the target airspace.

[0058] For example, a counter-drone sequence might include the following data:

[0059] Defense moment 1: The number of anti-drone activations is 3

[0060] Defense Moment 2: The number of anti-drone activations is 5

[0061] Defense Moment 3: The number of counter drones activated is 2

[0062] This means that at defense moment 1, 3 counter drones are activated; at defense moment 2, 5 counter drones are activated; and at defense moment 3, 2 counter drones are activated.

[0063] By obtaining this data, we can understand the usage of each drone defense device at different defense moments and the changes in the number of enabled counter-drones. This helps the system analyze the effects of different defense strategies so as to develop more effective defense plans.

[0064] Step S120, determining a set label of the target drone defense device in the set of K drone defense devices, and determining a target counter drone sequence matching the set label from the K counter drone sequences. The target drone defense device is any drone defense device in the drone defense device sequence.

[0065] For example, suppose that a specific drone defense device needs to be analyzed, and the drone defense device is marked as set label A in the set of K drone defense devices. Then, a target anti-drone sequence matching set label A can be found from the K anti-drone sequences. This target anti-drone sequence records the number of anti-drones controlled by the target drone defense device at different defense moments, as well as the corresponding defense effect.

[0066] Exemplarily, the set tag here is an identifier used to distinguish different drone defense device sets. For example, the set tag of one drone defense device set may be "City A Area 1", and the set tag of another drone defense device set may be "City A Area 2".

[0067] If the target drone defense device is located in City A Area 1, then the target counter-drone sequence that matches the collection tag "City A Area 1" is the counter-drone sequence associated with this drone defense device.

[0068] Specifically, assume that the K sets of drone defense devices are:

[0069] Collection 1 (Collection Tag: City A Area 1): Drone Defense Device 1, Drone Defense Device 2, Drone Defense Device 3

[0070] Set 2 (Set Tag: City A Area 2): Drone Defense Device 4, Drone Defense Device 5, Drone Defense Device 6

[0071] Set 3 (Set Tag: City B Area 1): Drone Defense Device 7, Drone Defense Device 8, Drone Defense Device 9

[0072] Assume that the K anti-drone sequences are:

[0073] Sequence 1: Anti-drone activation quantity 1, Anti-drone activation quantity 2, Anti-drone activation quantity 3

[0074] Sequence 2: Anti-drone activation quantity 4, Anti-drone activation quantity 5, Anti-drone activation quantity 6

[0075] Sequence 3: Anti-drone activation quantity 7, Anti-drone activation quantity 8, Anti-drone activation quantity 9

[0076] If the target drone defense device is drone defense device 1, then the target counter-drone sequence that matches the collection label "city A area 1" is sequence 1. This is because drone defense device 1 is in collection 1, and the collection label of collection 1 is "city A area 1". In this way, the system can find the counter-drone sequence related to the target drone defense device, so as to understand the use of the drone defense device at different defense moments and the changes in the number of enabled counter-drones.

[0077] Step S130: determining a device quantity update set based on the target counter-drone sequence, wherein the device quantity update set includes the change amount of the drone defense devices corresponding to the multiple defense moments.

[0078] For example, the change in the drone defense device corresponding to each defense moment can be calculated based on the data in the target counter-drone sequence. These changes will be collected into a device quantity update set. This device quantity update set can be used to analyze the use of drone defense devices at different defense moments and the changes in the number of enabled counter-drones.

[0079] For example, assume that the target counter-drone sequence is as follows:

[0080] Defense moment 1: The number of anti-drone activations is 3

[0081] Defense Moment 2: The number of anti-drone activations is 5

[0082] Defense Moment 3: The number of counter drones activated is 2

[0083] According to this sequence, the change in the drone defense device corresponding to each defense moment can be calculated:

[0084] Defense moment 1: The change is 0 because this is the first defense moment and there is no data from the previous defense moment.

[0085] Defense Moment 2: The change is 2 because the number of counter drones activated has increased from 3 to 5.

[0086] Defense Moment 3: The change is -3 because the number of counter drones activated is reduced from 5 to 2.

[0087] Collecting these changes into a set gives us the device quantity update set. This set can be used to analyze the use of drone defense devices at different defense moments and the changes in the number of enabled counter drones. For example, if the change at a certain defense moment is found to be abnormally large, it may mean that an emergency has occurred at that moment and requires further attention.

[0088] Step S140, when the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirement, based on the multi-dimensional fusion drone detection data of the target airspace, a defense intensity change event matching the defense device change requirement is executed for the target airspace. The defense intensity change event includes a defense switch instruction for controlling the counter drone based on the target drone defense device.

[0089] For example, assuming that the change in the drone defense device corresponding to the current defense moment meets the defense device change requirements, then the corresponding defense intensity change event can be executed for the target airspace based on the multi-dimensional fusion drone detection data of the target airspace. For example, if it is found that drone activity in a certain area increases, the defense intensity of the area may be increased to respond to the threat by controlling more counter drones. At the same time, the number of enabled counter drones will be adjusted according to the changes in the drone defense device to achieve the best defense effect.

[0090] For example, a threshold value may be set, and only when the change in the drone defense device exceeds this threshold value is it considered that the defense device change requirement is met.

[0091] Multi-dimensional fusion drone detection data refers to drone information about the target airspace collected from multiple angles and dimensions. For example, it can include information such as the drone's location, speed, direction, and altitude. Based on these multi-dimensional fusion drone detection data, the threat level of the target airspace can be determined and the corresponding defense strength can be selected.

[0092] Defense intensity change events refer to events in which drone defense devices are adjusted accordingly based on the threat level of the target airspace. For example, this may include increasing or decreasing the number of counter-drones, adjusting the location of counter-drones, changing the defense mode of counter-drones, etc.

[0093] Specifically, assume that the change amount of the drone defense device corresponding to the current defense moment is 2, and the preset change threshold is 3. Since the change amount does not exceed the threshold, the defense device change requirement is not met, and the system will not execute the defense intensity change event for the target airspace.

[0094] If the change is 5, which exceeds the threshold of 3, then it can be considered that the requirements for defense device changes are met, and the corresponding defense intensity change event will be executed for the target airspace based on the multi-dimensional fusion drone detection data of the target airspace. For example, the number of counter-drones may be increased, or the position of the counter-drones may be adjusted to enhance the defense of the target airspace. At the same time, a defense switch command will be sent to the target drone defense device to control the opening or closing of the counter-drones. For example, if the system determines that the threat level of the target airspace is high, it can send a command to turn on all counter-drones; if the threat level is low, it can send a command to turn off some counter-drones to save energy and reduce accidental injuries.

[0095] Based on the above steps, this application can quickly and accurately understand the number of counter-drones enabled by each drone defense device at each defense moment by obtaining the drone defense device sequence and the counter-drone data sequence. This provides strong data support for the management and adjustment of the drone defense system, thereby improving the efficiency of the defense work. Determine the set label of the target drone defense device in the set of K drone defense devices, and determine the target counter-drone sequence that matches the set label, so that the management and control of the specific drone defense device is more refined, and can be operated in a targeted manner according to actual needs. Determine the device quantity update set based on the target counter-drone sequence, so that the changes in the drone defense device can be clearly understood, thereby providing real-time reference information for adjusting the defense strategy. When the change amount of the drone defense device meets the requirements of the defense device change, the corresponding defense intensity change event will be executed based on the multi-dimensional fusion drone detection data, so that the existing resources can be fully utilized, and the defense strategy can be flexibly adjusted according to the actual situation, which greatly improves the defense effect of the drone defense system and the ability to deal with complex situations.

[0096] In a possible implementation, step S130 may include:

[0097] Step S131, based on the number of activated counter-drones corresponding to the defense triggering moment among the multiple defense moments and the maximum number of activated counter-drones of the drone defense device, determine the change amount of the drone defense device corresponding to the defense triggering moment.

[0098] For example, suppose the target counter drone sequence is as follows:

[0099] Defense moment 1: The number of anti-drone activations is 3

[0100] Defense Moment 2: The number of anti-drone activations is 5

[0101] Defense Moment 3: The number of counter drones activated is 2

[0102] Defense Moment 4: The number of counter-drones activated is 3

[0103] Defense Moment 5: The number of counter-drones activated is 4

[0104] In this example, it is assumed that the maximum number of anti-drone activations of the drone defense device is 5.

[0105] Defense moment 2 is the defense triggering moment, because the number of counter-drones activated increases from 3 to 5. Therefore, the change in the drone defense device corresponding to defense moment 2 is 2 (i.e. 5-3).

[0106] Step S132, determining a change value between the number of activated counter-drones corresponding to a non-defense triggering moment among the multiple defense moments and the number of activated counter-drones corresponding to a defense moment before the non-defense triggering moment.

[0107] For example, defense moment 3 is a non-defense trigger moment, and the number of anti-drone activations is reduced from 5 to 2. Therefore, the change value is -3 (i.e. 2-5).

[0108] Defense moment 4 is a non-defense trigger moment, and the number of counter drones activated increases from 2 to 3. Therefore, the change value is 1 (i.e. 3-2).

[0109] Defense moment 5 is a non-defense trigger moment, and the number of counter drones activated increases from 3 to 4. Therefore, the change value is 1 (i.e. 4-3).

[0110] Step S133, determining the change amount of the drone defense device corresponding to each non-defense triggering moment based on the change value and the maximum number of activated counter-drones.

[0111] For example, defense moment 3 is a non-defense trigger moment, and its change value is -3 (that is, the number of counter-drone activations is reduced from 5 to 2). A rule can be set that when the change value is negative, the change amount of the drone defense device is the change value, that is, -3.

[0112] Defense moment 4 is a non-defense trigger moment, and its change value is 1 (i.e., the number of enabled counter-drones increases from 2 to 3). Since this change value is a positive number and does not exceed the maximum number of enabled counter-drones of the drone defense device (5), this change value can be directly used as the change amount of the drone defense device. Therefore, the change amount of the drone defense device corresponding to defense moment 4 is 1.

[0113] Defense moment 5 is a non-defense trigger moment, and its change value is also 1 (i.e., the number of enabled counter-drones increases from 3 to 4). Similarly, since this change value is a positive number and does not exceed the maximum number of enabled counter-drones of the drone defense device (5), this change value can be directly used as the change amount of the drone defense device. Therefore, the change amount of the drone defense device corresponding to defense moment 5 is 1.

[0114] Step S134, determining a device quantity update set based on the change amount of the drone defense device corresponding to the defense triggering moment and the change amount of the drone defense device corresponding to each of the non-defense triggering moments.

[0115] For example, the device quantity update set includes the following deltas:

[0116] Defense Moment 2: Change amount is 2

[0117] Defense Time 3: Change is -3

[0118] Defense moment 4: Change amount is 1

[0119] Defense moment 5: Change amount is 1

[0120] In this example, the device quantity update set contains the changes in the drone defense devices corresponding to each defense moment. These changes can be used to analyze the usage of the drone defense devices at different defense moments and the changes in the number of enabled counter-drones.

[0121] In a possible implementation, the defense device change request includes a defense activation request and a defense termination request. The method further includes:

[0122] Step A110, if the current defense moment is the defense trigger moment, then the first defense device change interval is determined based on the change amount of the drone defense device corresponding to the defense trigger moment, and when the change amount of the participating nodes of the target drone defense device in the corresponding drone defense device set when participating is within the first defense device change interval, it is determined that the defense activation requirements are met.

[0123] Among them, a node refers to the identifier of a specific device or equipment in the drone defense device set. For example, if there are multiple drone defense devices forming a set, then each drone defense device can be regarded as a node. The participating node can refer to a specific drone defense device that is currently being considered for activation or termination. In addition, for the defense trigger moment, the first defense device change interval can be defined as [change amount, change amount + X], where X is a predetermined constant used to determine the width of the interval, assuming that X is equal to 2. For example, assume that the change amount of the participating node of the target drone defense device in the drone defense device set is 3 when participating.

[0124] The current defense moment is the defense trigger moment, for example, defense moment 2:

[0125] The change amount of the drone defense device corresponding to defense time 2 is 2. Based on this change amount, the first defense device change interval is determined to be [2, 4]. Because the change amount of the participating node of the target drone defense device at the time of participation is 3, which is within the first defense device change interval, it meets the defense activation requirements.

[0126] Step A120, if the current defense moment is the non-defense trigger moment, then when the change amount of the drone defense device corresponding to the non-defense trigger moment is a positive change amount, the second defense device change interval is determined based on the change amount of the drone defense device corresponding to the non-defense trigger moment, and when the change amount of the participating node of the target drone defense device in the corresponding drone defense device set when participating is within the second defense device change interval, it is determined that the defense activation requirement is met. Among them, for the non-defense trigger moment and the change amount is a positive change amount, the second defense device change interval can be defined as [0, Y+change amount], where Y is also a predetermined constant, for example, 2.

[0127] Step A130, when the change amount of the drone defense device corresponding to the non-defense trigger moment is a negative change amount, determine the third defense device change interval based on the change amount of the drone defense device corresponding to the non-defense trigger moment, and when the change amount of the participating nodes of the target drone defense device in the corresponding drone defense device set when participating is within the third defense device change interval, determine that the defense termination requirements are met, wherein, for the non-defense trigger moment and the change amount is a negative change amount, the third defense device change interval can be defined as [0, Z-change amount], where Z is also a predetermined constant, for example, 0.

[0128] The current defense moment is a non-defense trigger moment, for example, defense moment 3:

[0129] The change amount of the drone defense device corresponding to the non-defense trigger time 3 is -3. Because the change amount is a negative change amount, based on this change amount, the change interval of the third defense device is determined to be [0,3]. Because the change amount of the participating node of the target drone defense device when participating is 3, which is within the change interval of the third defense device, it meets the defense termination requirements.

[0130] The current defense moment is a non-defense trigger moment, for example, defense moment 4:

[0131] The change amount of the drone defense device corresponding to the non-defense triggering time 4 is 1. Because the change amount is a positive change amount, based on this change amount, the second defense device change interval is determined to be [0,3]. Because the change amount of the participating node of the target drone defense device when participating is 3, which is within the second defense device change interval, it meets the defense activation requirements.

[0132] In this example, by using different defense device change intervals, the activation and termination of the drone defense device can be controlled more accurately. For example, at the defense trigger moment, if the participating nodes of the target drone defense device are within the first defense device change interval, it can be determined that the defense activation requirements are met. At the non-defense trigger moment, if the participating nodes of the target drone defense device are within the second defense device change interval, it can be determined that the defense activation requirements are met; if the participating nodes of the target drone defense device are within the third defense device change interval, it can be determined that the defense termination requirements are met.

[0133] In a possible implementation, step S140 may include:

[0134] Step S141, when the change amount of the drone defense device corresponding to the current defense moment meets the defense activation requirement, the defense activation information is obtained, and based on the defense activation information, the target counter-drone is activated for the target airspace according to the maximum number of enabled counter-drones, and based on the multi-dimensional fusion drone detection data of the target airspace, the target counter-drone is controlled to perform the corresponding counter-operation.

[0135] Step S142, when the change amount of the drone defense device corresponding to the current defense moment meets the defense termination requirement, obtain defense termination information, and shut down the target counter-drone controlled by the target drone defense device for the target airspace based on the defense termination information.

[0136] For example, assuming that the current defense moment is defense moment 4, the participating node of the target drone defense device in the drone defense device set is the third node.

[0137] At defense moment 4, the change amount of the target drone defense device is 1, which meets the defense activation requirements. At this time, the defense activation information is obtained, and based on the maximum number of counter-drone activations 5, 5 target counter-drones are activated for the target airspace. Then, based on the multi-dimensional fusion drone detection data of the target airspace, these 5 target counter-drones are controlled to perform corresponding counter-operations. For example, if a drone is detected approaching the target airspace, the target counter-drone can be controlled to transmit signals to interfere with the drone's communication or navigation system, making it unable to work normally.

[0138] Assume that at defense time 5, the change amount of the target drone defense device is -1, which meets the defense termination requirement. At this time, the defense termination information is obtained, and based on the information, the target counter drone controlled by the target drone defense device is turned off for the target airspace. For example, a command can be sent to the target counter drone to stop executing the counter operation and return to the base for maintenance or charging.

[0139] In this example, by adjusting the amount of change in the drone defense device, the activation and termination of the target counter drone can be flexibly controlled to respond to different drone threat situations. At the same time, by using the multi-dimensional fusion drone detection data of the target airspace, the counter operation of the target counter drone can be more accurately controlled to improve the defense effect.

[0140] In a possible implementation, step S141 may include:

[0141] Step S1411, obtain the multi-dimensional fusion drone detection data of the target airspace, and obtain the countermeasure matching templates generated according to each drone countermeasure strategy, wherein the multi-dimensional fusion drone detection data includes: multi-dimensional fusion sensor detection data extracted from the drone detection data of the target airspace.

[0142] Step S1412, using the trained target drone countermeasure decision network, respectively extract the drone feature information of each detection time series data in the multi-dimensional fusion drone detection data, and perform feature interaction on the obtained drone feature information to generate interactive features, wherein the drone feature information includes at least one or a combination of drone position features, drone speed features, drone shape features, and drone heat features.

[0143] Step S1413, generating a drone dynamic vector of the multi-dimensional fusion drone detection data according to the dynamic change parameters of the drone feature information between every two detection time series data within the target interval. Based on the interaction feature and the drone dynamic vector, generating a countermeasure basis feature.

[0144] Step S1414: extracting the template features of each counter-matching template respectively.

[0145] Step S1415, based on the feature distance between each template feature and the countermeasure basis feature, respectively obtain the support degree of the multi-dimensional fusion drone detection data on each drone countermeasure strategy.

[0146] Step S1416: determining a corresponding target UAV countermeasure strategy according to each support degree, so as to control the target UAV countermeasure strategy to perform a corresponding countermeasure operation.

[0147] For example, if there is a drone threat in the current target airspace and the drone is approaching the target airspace, the drone detection device can be used to obtain the multi-dimensional fusion drone detection data of the drone, including the drone's position, speed, shape, heat and other information.

[0148] First, the trained target drone countermeasure decision network is used to extract the drone feature information of each detection time series data in the multi-dimensional fusion drone detection data, including drone position features, drone speed features, drone shape features, drone heat features, etc. Then, the obtained drone feature information is subjected to feature interaction to generate interactive features. For example, the drone position features and speed features can be fused to obtain the drone motion trajectory features.

[0149] Next, based on the dynamic change parameters of the drone feature information between every two detection time series data within the target interval, a drone dynamic vector of the multi-dimensional fusion drone detection data is generated. For example, the change rate of the drone position feature can be calculated to obtain the drone position change vector.

[0150] Based on the interaction feature and the UAV dynamic vector, a countermeasure basis feature is generated. For example, the interaction feature and the UAV dynamic vector can be fused to obtain the countermeasure basis feature.

[0151] Then, the template features of each counter-matching template are extracted respectively, and based on the feature distance between each template feature and the counter-basis feature, the support of the multi-dimensional fusion drone detection data on each drone counter-strategy is obtained respectively. For example, the similarity between the counter-basis feature and each counter-matching template can be calculated to obtain the support of each counter-strategy.

[0152] Finally, according to each support, the corresponding target UAV countermeasure strategy is determined to control the target countermeasure UAV to perform the corresponding countermeasure operation based on the target UAV countermeasure strategy. For example, the countermeasure strategy with the highest support can be selected, and the target countermeasure UAV can be controlled to perform the corresponding countermeasure operation, such as transmitting a signal to interfere with the UAV's communication or navigation system, making it unable to work normally.

[0153] In a possible implementation, the training step of the target UAV countermeasure decision network includes:

[0154] Step S101, obtaining an initialized neural network model and each sample learning data, wherein the initialized neural network model includes a first neural network and a second neural network with initialized weight parameters, and other neural networks to be updated. Each sample learning data includes: a sample drone detection data, each sample countermeasure matching template generated according to each sample countermeasure strategy, and countermeasure strategy annotation data.

[0155] Step S102, based on the sample learning data, the parameters of the initialized neural network model are updated until the training termination condition is met, and a trained target drone countermeasure decision network is generated.

[0156] For example, suppose there is a training dataset containing multiple drone detection data and corresponding countermeasure strategy annotation data. Each drone detection data includes information such as the drone position, speed, shape, heat, etc., and each countermeasure strategy annotation data indicates which countermeasure strategy should be adopted to deal with the drone detection data.

[0157] First, an initialization neural network model needs to be constructed, including the first neural network, the second neural network and other neural networks whose parameters need to be updated. The first neural network is used to extract the features of drone detection data, the second neural network is used to generate countermeasures, and other neural networks are used to assist in data processing.

[0158] Next, the training dataset will be used to train the initialized neural network model. Specifically, the drone detection data is input into the first neural network to extract the drone features, and then the drone features are input into the second neural network to generate a countermeasure strategy. At the same time, other neural networks will be used to assist in data processing, such as dimensionality reduction or normalization of drone features.

[0159] During the training process, the gradient descent algorithm or other optimization algorithms will be used to update the weight parameters of the neural network model to minimize the prediction error. Specifically, the error between the prediction result and the actual countermeasure strategy will be calculated, and the gradient descent algorithm will be used to update the weight parameters of the neural network model to reduce the prediction error.

[0160] When the training is complete, a target drone countermeasure decision network will be generated, which will be able to generate the correct countermeasure strategy based on the drone detection data. Specifically, the trained first neural network will be used to extract the features of the drone detection data, and the trained second neural network will be used to generate the countermeasure strategy.

[0161] In summary, by using a training dataset and a gradient descent algorithm, a target drone countermeasure decision network can be trained that is able to generate the correct countermeasure strategy based on drone detection data.

[0162] In a possible implementation, in the process of updating each round of parameters of the initialized neural network model according to the sample learning data:

[0163] 1. For each sample detection time series data in the sample drone detection data, coded data is obtained respectively, and based on the second neural network, the corresponding drone feature information is extracted according to each coded data, and the interaction feature and drone dynamic vector are generated according to each drone feature information.

[0164] 2. For each counter-matching template, feature association data under the influence of the UAV dynamic vector is obtained respectively, and based on the first neural network, corresponding template features are extracted respectively according to each feature association data.

[0165] 3. According to the feature distance between each template feature and the countermeasure basis feature, the support of the example drone detection data on each example countermeasure strategy is obtained respectively, and the weight parameters of the other neural networks are updated according to the loss function value between the countermeasure strategy prediction data determined by each support and the countermeasure strategy labeled data.

[0166] For example, suppose there is a training data set that includes multiple drone detection data and corresponding countermeasure strategy annotation data. Each drone detection data includes information such as the drone's position, speed, shape, and heat, and each countermeasure strategy annotation data indicates which countermeasure strategy should be adopted to deal with the drone detection data.

[0167] First, the second neural network is used to encode each drone detection data to obtain encoded data. Specifically, the drone detection data is input into the second neural network to extract the drone feature information, and then the encoder is used to encode the drone feature information to obtain encoded data.

[0168] Next, the second neural network will be used to generate interactive features and drone dynamic vectors based on the coded data. Specifically, the coded data is input into the second neural network to extract the drone feature information, and then the interactive layer is used to interact with the drone feature information to obtain the interactive features. At the same time, the dynamic layer will also be used to generate the drone dynamic vector based on the drone feature information.

[0169] Then, the first neural network is used to generate feature association data based on the UAV dynamic vector. Specifically, the UAV dynamic vector is input into the first neural network to extract the feature association data.

[0170] Finally, the loss function will be used to update the weight parameters of other neural networks based on the countermeasure strategy prediction data and the countermeasure strategy annotation data. Specifically, the countermeasure strategy prediction data and the countermeasure strategy annotation data are input into the loss function, the loss value is calculated, and the gradient descent algorithm is used to update the weight parameters of other neural networks based on the loss value.

[0171] In summary, by using encoded data, interaction features, drone dynamic vectors, feature association data, and loss functions, the weight parameters of other neural networks can be updated, thereby improving the performance of the drone countermeasure decision network.

[0172] In a possible implementation, the method further includes:

[0173] Step B110, encoding the drone defense devices in the drone defense device sequence to generate a drone defense device encoding distribution.

[0174] Step B120, determining the coding ID of the target drone defense device from the drone defense device coding distribution, and determining the triggering IDs of multiple counter drones that need to be activated by the target drone defense device based on the coding ID.

[0175] Step B130, generating defense activation information based on the trigger ID and the maximum number of enabled counter-drones.

[0176] For example, consider a drone defense sequence that includes multiple drone defense devices, each with a unique coded ID. The goal is to determine which counter drones need to be activated to counter the threat.

[0177] First, the drone defense devices in the drone defense device sequence are encoded to generate a drone defense device encoding distribution. Specifically, an encoder can be used to map the encoding ID of each drone defense device into a high-dimensional vector space to generate a drone defense device encoding distribution.

[0178] Next, the coding ID of the target drone defense device is determined from the drone defense device coding distribution. Specifically, a classifier can be used, the drone defense device coding distribution is input into the classifier, and the coding ID of the target drone defense device is output.

[0179] Then, the trigger IDs of the multiple counter-drones to be activated are determined based on the coded ID of the target drone defense device. Specifically, a mapping function can be used to map the coded ID of the target drone defense device to the trigger IDs of the multiple counter-drones.

[0180] Finally, a defense activation message is generated based on the trigger ID and the maximum number of enabled counter-drones. Specifically, the trigger ID can be used as part of the activation message, and the number of activation messages is ensured not to exceed the maximum number of enabled counter-drones.

[0181] In this way, it is possible to determine which countermeasure drones need to be activated to counter the threat and generate defense activation information.

[0182] In a possible implementation, the method further includes:

[0183] Each of the multiple defense moments is taken as the current defense moment, and when the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirement, a defense intensity change event matching the defense device change requirement is executed on the target airspace, and then the device quantity update set is reported to the target defense command system, so that when the target defense command system receives the device quantity update set reported by each drone defense device in the drone defense device sequence, it determines the drone countermeasure performance data of the target airspace based on the device quantity update set reported by each drone defense device and the countermeasure feedback data of the target airspace.

[0184] For example, first, each of the multiple defense moments is used as the current defense moment, and when the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirements, a defense intensity change event matching the defense device change requirements is executed for the target airspace. Specifically, a timer can be used to record the current defense moment, and at the current defense moment, check whether the change amount of the drone defense device meets the defense device change requirements. If yes, a defense intensity change event matching the defense device change requirements can be executed for the target airspace.

[0185] Then, the device quantity update set is reported to the target defense command system. Specifically, a communication module can be used to send the device quantity update set to the target defense command system.

[0186] Finally, when the target defense command system receives the device quantity update set reported by each drone defense device in the drone defense device sequence, it determines the drone countermeasure performance data of the target airspace based on the device quantity update set reported by each drone defense device and the countermeasure feedback data of the target airspace. Specifically, the target defense command system can use a data processing module to input the device quantity update set reported by each drone defense device and the countermeasure feedback data of the target airspace into the data processing module, and output the drone countermeasure performance data of the target airspace.

[0187] In this way, the drone countermeasure performance data in the target airspace can be determined and the device quantity update set can be reported.

[0188] Figure 2 The diagram schematically shows a drone countermeasure system 100 based on multi-dimensional data fusion that can be used to implement various embodiments described in the present application.

[0189] For one embodiment, Figure 2A drone countermeasure system 100 based on multi-dimensional data fusion is shown. The drone countermeasure system 100 based on multi-dimensional data fusion has at least one processor 102, a control module (chip set) 104 coupled to at least one of the (at least one) processors 102, a memory 106 coupled to the control module 104, a non-volatile memory (NVM) / storage device 108 coupled to the control module 104, at least one input / output device 110 coupled to the control module 104, and a network interface 112 coupled to the control module 104.

[0190] The processor 102 may include at least one single-core or multi-core processor, and the processor 102 may include any combination of general-purpose processors or dedicated processors (such as graphics processors, application processors, baseband processors, etc.). In an alternative implementation, the drone countermeasure system 100 based on multi-dimensional data fusion can be used as a server device such as a gateway described in the embodiments of the present application.

[0191] Figure 2 The diagram schematically shows a drone countermeasure system 100 based on multi-dimensional data fusion that can be used to implement various embodiments described in the present application.

[0192] For one embodiment, Figure 2 A drone countermeasure system 100 based on multi-dimensional data fusion is shown. The drone countermeasure system 100 based on multi-dimensional data fusion has at least one processor 102, a control module (chip set) 104 coupled to at least one of the (at least one) processors 102, a memory 106 coupled to the control module 104, a non-volatile memory (NVM) / storage device 108 coupled to the control module 104, at least one input / output device 110 coupled to the control module 104, and a network interface 112 coupled to the control module 104.

[0193] The processor 102 may include at least one single-core or multi-core processor, and the processor 102 may include any combination of general-purpose processors or dedicated processors (such as graphics processors, application processors, baseband processors, etc.). In an alternative implementation, the drone countermeasure system 100 based on multi-dimensional data fusion can be used as a server device such as a gateway described in the embodiments of the present application.

[0194] In an alternative embodiment, the multi-dimensional data fusion-based drone countermeasure system 100 may include at least one computer-readable medium (e.g., a memory 106 or an NVM / storage device 108) having instructions 114 and at least one processor 102 that is integrated with the at least one computer-readable medium and configured to execute the instructions 114 to implement a module to perform the actions described in the present disclosure.

[0195] For one embodiment, the control module 104 may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) 102 and / or any suitable device or component in communication with the control module 104 .

[0196] The control module 104 may include a memory controller module to provide an interface to the memory 106. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0197] The memory 106 may be used, for example, to load and store data and / or instructions 114 for the multi-dimensional data fusion based drone countermeasure system 100. For one embodiment, the memory 106 may include any suitable volatile memory, for example, a suitable DRAM. In an alternative embodiment, the memory 106 may include a double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

[0198] For one embodiment, the control module 104 may include at least one input / output controller to provide an interface to the NVM / storage device 108 and the (at least one) input / output device 110 .

[0199] For example, NVM / storage device 108 may be used to store data and / or instructions 114. NVM / storage device 108 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (at least one) non-volatile storage device (e.g., at least one hard disk drive (HDD), at least one compact disk (CD) drive, and / or at least one digital versatile disk (DVD) drive).

[0200] The NVM / storage device 108 may include storage resources that are physically part of the device on which the multi-dimensional data fusion-based drone countermeasure system 100 is installed, or it may be accessible to the device without being part of the device. For example, the NVM / storage device 108 may be accessed via (at least one) input / output device 110 based on a network.

[0201] The (at least one) input / output device 110 may provide an interface for the multi-dimensional data fusion-based UAV countermeasure system 100 to communicate with any other appropriate device, and the input / output device 110 may include a communication component, a pinyin component, an online monitoring component, etc. The network interface 112 may provide an interface for the multi-dimensional data fusion-based UAV countermeasure system 100 to communicate based on at least one network, and the multi-dimensional data fusion-based UAV countermeasure system 100 may wirelessly communicate with at least one component of a wireless network based on any standard and / or protocol of at least one wireless network standard and / or protocol, for example, access a wireless network based on a communication standard, or a combination thereof for wireless communication.

[0202] For one embodiment, at least one of the (at least one) processors 102 may be loaded together with the logic of at least one controller (e.g., a memory controller module) of the control module 104. For one embodiment, at least one of the (at least one) processors 102 may be loaded together with the logic of at least one controller of the control module 104 to form a system-level load. For one embodiment, at least one of the (at least one) processors 102 may be integrated on the same die with the logic of at least one controller of the control module 104. For one embodiment, at least one of the (at least one) processors 102 may be integrated on the same die with the logic of at least one controller of the control module 104 to form a system on chip (SoC).

[0203] In various embodiments, the drone countermeasure system 100 based on multi-dimensional data fusion may be, but is not limited to, a terminal device such as a server, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the drone countermeasure system 100 based on multi-dimensional data fusion may have more or fewer components and / or different architectures. For example, in an alternative embodiment, the drone countermeasure system 100 based on multi-dimensional data fusion includes at least one camera, a keyboard, a liquid crystal display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application-specific integrated circuit (ASIC), and a speaker.

[0204] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A drone countermeasure method based on multi-dimensional data fusion, characterized in that: The method comprises: Obtain a drone defense device sequence and a counter-drone data sequence; the drone defense device sequence includes K drone defense device sets, each of which includes multiple drone defense devices, and the counter-drone data sequence includes K counter-drone sequences, each of which includes the number of counter-drone activations corresponding to multiple defense moments, K is a positive integer, and the drone defense device is used to control the counter-drone that generates a defensive force on the target airspace; Determine a set label of a target drone defense device in the set of K drone defense devices, and determine a target counter-drone sequence matching the set label from the K counter-drone sequences; the target drone defense device is any drone defense device in the drone defense device sequence; Determine a device quantity update set based on the target counter-drone sequence; the device quantity update set includes the change amount of the drone defense device corresponding to the multiple defense moments; When the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirements, a defense intensity change event matching the defense device change requirements is executed on the target airspace based on the multi-dimensional fusion drone detection data of the target airspace; the defense intensity change event includes a defense switch instruction for controlling the counter drone based on the target drone defense device.

2. The method for countering unmanned aerial vehicles based on multi-dimensional data fusion according to claim 1, characterized in that: The method of updating the set of device quantity determination based on the target counter-drone sequence includes: Determine a change amount of the drone defense device corresponding to the defense trigger moment based on the number of activated counter-drones corresponding to the defense trigger moment among the multiple defense moments and the maximum number of activated counter-drones of the drone defense device; Determine a change value between the number of activated counter-drones corresponding to a non-defense triggering moment among the multiple defense moments and the number of activated counter-drones corresponding to a defense moment before the non-defense triggering moment; Determine the change amount of the drone defense device corresponding to each of the non-defense triggering moments based on the change value and the maximum number of enabled counter-drones; Based on the change amount of the drone defense device corresponding to the defense triggering moment and the change amount of the drone defense device corresponding to each of the non-defense triggering moments, a device quantity update set is determined.

3. The method for countering unmanned aerial vehicles based on multi-dimensional data fusion according to claim 2 is characterized in that: The defense device change requirements include defense activation requirements and defense termination requirements; The method further comprises: If the current defense moment is the defense trigger moment, a first defense device change interval is determined based on the change amount of the drone defense device corresponding to the defense trigger moment, and when the change amount of the participating node of the target drone defense device in the corresponding drone defense device set during participation is within the first defense device change interval, it is determined that the defense activation requirement is met; If the current defense moment is the non-defense trigger moment, then when the change amount of the drone defense device corresponding to the non-defense trigger moment is a positive change amount, a second defense device change interval is determined based on the change amount of the drone defense device corresponding to the non-defense trigger moment, and when the change amount of the participating node of the target drone defense device in the corresponding drone defense device set when participating is within the second defense device change interval, it is determined that the defense activation requirement is met; When the change amount of the drone defense device corresponding to the non-defense trigger moment is a negative change amount, the third defense device change interval is determined based on the change amount of the drone defense device corresponding to the non-defense trigger moment, and when the change amount of the participating nodes of the target drone defense device in the corresponding drone defense device set when participating is within the third defense device change interval, it is determined that the defense termination requirements are met.

4. The method for countering unmanned aerial vehicles based on multi-dimensional data fusion according to claim 3 is characterized in that: When the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirement, based on the multi-dimensional fusion drone detection data of the target airspace, a defense intensity change event matching the defense device change requirement is executed on the target airspace, including: When the change amount of the drone defense device corresponding to the current defense moment meets the defense activation requirement, the defense activation information is obtained, and based on the defense activation information, the target counter drone is activated according to the maximum number of enabled counter drones for the target airspace, and the target counter drone is controlled to perform the corresponding counter operation based on the multi-dimensional fusion drone detection data of the target airspace; When the change amount of the drone defense device corresponding to the current defense moment meets the defense termination requirement, defense termination information is obtained, and based on the defense termination information, the target counter-drone controlled by the target drone defense device is shut down for the target airspace.

5. The method for countering unmanned aerial vehicles based on multi-dimensional data fusion according to claim 4 is characterized in that: The step of controlling the target countermeasure UAV to perform a corresponding countermeasure operation based on the multi-dimensional fusion UAV detection data of the target airspace includes: Acquire multi-dimensional fusion drone detection data of the target airspace, and acquire countermeasure matching templates generated according to countermeasure strategies of each drone, wherein the multi-dimensional fusion drone detection data includes: fusion sensor detection data of multiple dimensions extracted from drone detection data of the target airspace; The trained target drone countermeasure decision network is used to extract drone feature information of each detection time series data in the multi-dimensional fusion drone detection data, and feature interaction is performed on each drone feature information obtained to generate interactive features, wherein the drone feature information includes at least one of drone position features, drone speed features, drone shape features, and drone heat features, or a combination of multiple features; Generate a UAV dynamic vector of the multi-dimensional fusion UAV detection data according to a dynamic change parameter of the UAV feature information between every two detection time series data within the target interval; generate a countermeasure basis feature based on the interaction feature and the UAV dynamic vector; Extracting the template features of each counter-matching template respectively; Based on the feature distance between each template feature and the countermeasure basis feature, respectively obtain the support degree of the multi-dimensional fusion drone detection data on each drone countermeasure strategy; According to each support degree, a corresponding target UAV countermeasure strategy is determined, so as to control the target countermeasure UAV to perform a corresponding countermeasure operation based on the target UAV countermeasure strategy.

6. The method for countering unmanned aerial vehicles based on multi-dimensional data fusion according to claim 5 is characterized in that: The training steps of the target UAV countermeasure decision network include: Acquire an initialized neural network model and each sample learning data, wherein the initialized neural network model includes a first neural network and a second neural network with initialized weight parameters, and other neural networks whose parameters are to be updated; each sample learning data includes: a sample drone detection data, each sample countermeasure matching template generated according to each sample countermeasure strategy, and countermeasure strategy annotation data; Based on the sample learning data, the parameters of the initialized neural network model are updated until the training termination condition is met, thereby generating a trained target drone countermeasure decision network.

7. The method for countering unmanned aerial vehicles based on multi-dimensional data fusion according to claim 6 is characterized in that: In the process of updating each round of parameters of the initialized neural network model according to the sample learning data: For each sample detection time series data in the sample drone detection data, coded data is obtained respectively, and based on the second neural network, corresponding drone feature information is extracted according to each coded data, and interaction features and drone dynamic vectors are generated according to each drone feature information; For each counter-matching template, feature association data under the influence of the UAV dynamic vector is obtained respectively, and based on the first neural network, corresponding template features are extracted respectively according to each feature association data; According to the feature distance between each template feature and the countermeasure basis feature, the support of the example drone detection data on each example countermeasure strategy is obtained respectively, and the weight parameters of the other neural networks are updated according to the loss function value between the countermeasure strategy prediction data determined by each support and the countermeasure strategy labeled data.

8. The method for countering unmanned aerial vehicles based on multi-dimensional data fusion according to claim 4, characterized in that: The method further comprises: Performing encoding processing on the drone defense devices in the drone defense device sequence to generate a drone defense device encoding distribution; Determine the coding ID of the target drone defense device from the drone defense device coding distribution, and determine the trigger IDs of multiple counter drones that need to be activated by the target drone defense device based on the coding ID; Defense activation information is generated based on the trigger ID and the maximum number of counter-drone activations.

9. The UAV countermeasure method based on multi-dimensional data fusion according to any one of claims 1 to 5, characterized in that: The method further comprises: Each of the multiple defense moments is taken as the current defense moment, and when the change amount of the drone defense device corresponding to the current defense moment meets the defense device change requirement, a defense intensity change event matching the defense device change requirement is executed on the target airspace, and then the device quantity update set is reported to the target defense command system, so that when the target defense command system receives the device quantity update set reported by each drone defense device in the drone defense device sequence, it determines the drone countermeasure performance data of the target airspace based on the device quantity update set reported by each drone defense device and the countermeasure feedback data of the target airspace.

10. A drone countermeasure system based on multi-dimensional data fusion, characterized in that: It includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by the processor, the drone countermeasure method based on multidimensional data fusion described in any one of claims 1 to 9 is implemented.

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