Urban low-altitude control area unmanned aerial vehicle prevention and control performance comprehensive evaluation method and system

Abstract

This invention provides a comprehensive evaluation method and system for the prevention and control performance of unmanned aerial vehicles (UAVs) in urban low-altitude control zones. The method includes: Step 1, constructing a state evolution model of UAV intrusion and prevention and control behavior in the control zone; Step 2, constructing a multi-dimensional performance evaluation index system covering the entire process of perception, early warning, disposal, and post-disaster recovery, and clarifying the definition and quantification method of the indexes; Step 3, constructing a three-level evaluation framework composed of a scenario domain, a performance domain, and an index domain, determining the weights of the scenario domain, performance domain, and indexes respectively, and proposing a method for calculating the comprehensive prevention and control performance index of UAVs in urban low-altitude control zones; Step 4, integrating and constructing a simulation model of the UAV prevention and control process in urban low-altitude control zones, comprehensively evaluating and comparing the performance indices under various prevention and control strategies and deployment schemes of prevention and control facilities under different scenario conditions, and realizing quantitative evaluation and optimization decision support for the UAV prevention and control capabilities of urban low-altitude control zones.

Description

Technical Field

[0001] This invention relates to the field of low-altitude airspace safety management and drone control technology, specifically to a comprehensive evaluation method and system for drone control performance in urban low-altitude control zones. Background Technology

[0002] With the rapid development of drone technology and its widespread application in logistics, inspection and monitoring, and emergency rescue, operational activities in urban low-altitude airspace are becoming increasingly frequent. However, the presence of non-cooperative or illegally intruding drones poses risks to urban public safety, critical infrastructure protection, and the security of major events. Against this backdrop, constructing a drone control system for urban low-altitude control zones has become an important research direction for low-altitude airspace management.

[0003] Existing drone control systems typically consist of multi-source sensing devices, communication links, and intervention measures. They control and counter intruding drones by detecting, identifying, tracking, and intervening in their targets. However, in practical applications, different control strategies and deployment schemes exhibit significant performance differences, with their effectiveness influenced by environmental conditions, operational status, and the uncertainty of target behavior. Therefore, how to conduct scientific, systematic, and quantifiable performance evaluations of different control schemes is a critical issue that urgently needs to be addressed.

[0004] Current research largely focuses on performance analysis of single aspects, such as evaluating the detection performance of sensing devices, analyzing the effects of jamming devices, or assessing response capabilities in specific scenarios. It lacks a systematic characterization of the entire drone control process. Furthermore, existing evaluation methods often employ static indicators or empirical evaluation approaches, failing to reflect the dynamic evolution of the control process under complex environmental conditions and multi-scenario constraints. In terms of comprehensive evaluation, a unified indicator system and hierarchical evaluation framework are lacking, hindering horizontal comparisons and quantitative analysis between different control strategies and facility deployment schemes. On the other hand, with the development of simulation technology, simulation-based performance evaluation methods are increasingly applied to complex system analysis; however, their application in drone control remains limited, particularly in system-level evaluation under multi-scenario and multi-strategy coupling conditions, where systematic modeling methods and unified evaluation methods are lacking.

[0005] Therefore, it is necessary to propose a drone control performance evaluation method that can be applied to urban low-altitude control areas and comprehensively consider various environmental conditions, operational statuses, and differences in control strategies. This method would enable quantitative analysis and comprehensive evaluation of the entire control system across multiple dimensions, providing technical support for the optimization of control strategies and facility deployment decisions. Summary of the Invention

[0006] Purpose of the invention: The technical problem to be solved by the present invention is to provide a comprehensive evaluation method and system for the prevention and control performance of drones in urban low-altitude control areas, which addresses the shortcomings of the existing technology. This method enables a full-process, multi-dimensional quantitative evaluation of the drone prevention and control process, as well as a performance comparison and comprehensive analysis of different prevention and control strategies and facility deployment schemes under multiple scenario conditions, providing a scientific basis for the optimized design and decision-making of drone prevention and control systems in urban low-altitude control areas.

[0007] The method includes the following steps:

[0008] Step 1: Analyze the key links involved in the prevention and control process of urban low-altitude control zones, and construct a state evolution model of drone intrusion and control zone prevention and control behavior by combining different prevention and control strategies and prevention and control facility deployment schemes.

[0009] Step 2: Construct a multi-dimensional performance evaluation index system for prevention and control covering the entire process of perception, early warning, response and follow-up, and clarify the definition and quantification methods of the indicators;

[0010] Step 3: Construct a three-level evaluation framework consisting of scenario domain, performance domain, and indicator domain, determine the weights of scenario domain, performance domain, and indicator domain respectively, and propose a method for calculating the comprehensive prevention and control performance index of UAVs in urban low-altitude control areas.

[0011] Step 1 includes:

[0012] Step 1-1: Analyze the key characteristics of the drone intrusion and prevention process in the urban low-altitude control zone, including target perception, target identification, threat assessment, early warning triggering, response decision-making, and disposal execution, and clarify the logical relationships and information transmission mechanisms between each link.

[0013] Steps 1-2: For different prevention and control strategies and deployment plans for prevention and control facilities, parameterize the elements including the configuration of sensing equipment, detection coverage, target identification capability, prevention and control response mechanism and disposal method, and construct a strategy and facility configuration model that reflects the differences in prevention and control capabilities.

[0014] A parameterized expression for a prevention and control strategy and a prevention and control facility deployment plan is defined as follows:

[0015] ,

[0016] in, This indicates the prevention and control strategy and the deployment plan for prevention and control facilities; Indicates the configuration parameters of the sensing device; Indicates the detection coverage parameters; Target recognition capability parameters; Indicates the parameters for prevention and control response; Indicates the parameters of handling capacity;

[0017] A strategy and facility configuration model reflecting differences in prevention and control capabilities is constructed, represented as follows:

[0018] ,

[0019] in, The output of the strategy and facility configuration model represents the comprehensive prevention and control capability characteristics corresponding to a specific prevention and control strategy and prevention and control facility deployment plan. It represents a mapping relationship function composed of sensing device configuration parameters, detection coverage parameters, target recognition capability parameters, prevention and control response parameters, and handling capability parameters, and is used to describe the comprehensive effect mechanism of the combination of parameters on the formation of prevention and control capabilities;

[0020] The aforementioned prevention and control strategy and prevention and control facility deployment plan refers to the overall prevention and control plan formed by combining and configuring the types, quantities, spatial layout and collaborative mechanisms of relevant prevention and control equipment in the perception, early warning, response and disposal stages within the urban low-altitude control zone in response to the needs of drone intrusion prevention and control.

[0021] Steps 1-3 establish the dynamic interaction between drone intrusion behavior and control zone deployment, construct an evolutionary model based on state transition, and describe the temporal evolution characteristics of the drone intrusion process and the control zone prevention and control process.

[0022] Steps 1-3 include:

[0023] The process of drone intrusion and control zone prevention is abstracted as a discrete-time state evolution process, denoted as the time series. ,in This represents the total number of time steps within the preset evaluation period; let the set of intruding drones be... ,in, Indicates the first One intruding drone, Indicates the number of intruding drones;

[0024] The operational status of the drone within the controlled area is divided into two or more discrete states, and a state set S is constructed, represented as:

[0025] ,

[0026] in, This indicates that the device is not being detected. This indicates that the system has been detected. This indicates that the system has been identified. This indicates that the situation has been determined to be a threat. This indicates that a response status has been triggered. Indicates that the issue has been resolved;

[0027] At any time , No. The state of each intruding drone is represented as follows: Define the system at time 10:00 global state vector for:

[0028] ,

[0029] Define from state Transition to state State transition probability To establish state transition relations, we can represent them as follows:

[0030] ,

[0031] in Indicates at time , No. The intruding drone is currently in a state of... Under the conditions, at the next moment Transition to state The probability of; Indicates the first An intrusion drone at any moment The state; and Both are sets of states The elements in the table represent the state before the transition and the state after the transition, respectively. ;

[0032] The state transition probabilities are determined by the strategy and facility configuration parameters constructed in steps 1-2, specifically including:

[0033] Never detected state To the detected state The transition probability depends on the detection coverage parameters and the sensing device configuration parameters;

[0034] From the detected state To the already identified state The transition probability depends on the target recognition capability parameter;

[0035] From the already identified state To threat assessment status The probability of transition depends on the risk assessment mechanism and threshold setting;

[0036] From threat assessment status To the response trigger state The probability of transition depends on the prevention and control response parameters;

[0037] From the response trigger state Towards completion of processing status The probability of transfer depends on the disposal capacity parameters and available prevention and control resources, wherein the set of prevention and control resources is as follows: , Indicates the first The availability or quantity of resources for prevention and control; This indicates the total number of types of resources available for prevention and control.

[0038] A prevention and control resource scheduling mechanism is introduced to make resource allocation decisions among two or more intrusion targets. This mechanism refers to the ability to allocate resources at any given time. Based on the status, threat level, and spatial location of each intruding drone, the available defense resources of the system are allocated to determine the resource allocation scheme for each target. The decision variable for defense and response resource allocation is defined as follows: ,like , indicating as in The moment will be the first Classification of prevention and control resources for handling intrusion drones , Indicates other situations;

[0039] Decision variables for resource allocation in prevention and control The following constraints must be satisfied:

[0040] ,

[0041] After considering the resource scheduling mechanism for prevention and control in the case of multiple drone intrusions, the state transition probability is expressed as:

[0042] ,

[0043] Introducing a state transition time function to reflect the characteristics of time evolution, defining the transition from state... Transition to state The time delay is A state evolution model of the drone intrusion and control zone prevention process is constructed, and the state transition process is used. Represented as a state transition function:

[0044] ,

[0045] in, This represents the state transition function, used to describe the transition of the system state from time [time value missing], given the current system state, prevention and control strategies and deployment plans for prevention and control facilities, the set of prevention and control resources, and the resource allocation matrix. Towards the moment Evolutionary relationships; Indicates time The resource allocation matrix for prevention and control.

[0046] Step 2 includes:

[0047] Step 2-1: Construct perception and recognition performance indicators for the controlled area. Construct performance indicators including detection coverage, target detection probability, and average detection latency. Quantify the perception and recognition performance indicators by quantitatively modeling the detection range, detection accuracy, and environmental influencing factors of the sensing equipment.

[0048] Step 2-2: Construct risk assessment performance indicators for the control area. Focusing on the risk assessment and situation judgment capabilities of intruding drones, construct performance indicators including risk identification accuracy, risk classification consistency, and early warning lead time. Quantitative characterization of risk assessment performance indicators is achieved by analyzing the behavioral characteristics, operational trajectories, and environmental constraints of intruding drones.

[0049] Steps 2-3: Construct system response performance indicators for the control area. Focusing on the response efficiency and coordination capabilities of the prevention and control system, construct performance indicators including average response latency, instruction transmission latency, and response unit utilization. And by modeling the communication link, scheduling mechanism, and task execution process, realize the quantitative characterization of system response performance indicators.

[0050] Steps 2-4: Construct performance indicators for intervention and response in the controlled area. Focusing on the effectiveness of intervention and response to intrusion targets, construct performance indicators including response success rate, average response time, and response accuracy. Quantify the scope, intensity, and target response characteristics of intervention measures to achieve quantitative characterization of intervention and response performance indicators.

[0051] Steps 2-5: Construct safety control and recovery performance indicators for the controlled area. Focusing on the impact of the prevention and control process on the system's operational safety and post-event recovery capabilities, construct performance indicators including system recovery time and loss of control risk exposure rate. By modeling the evolution of abnormal states and the recovery process, quantitative characterization of safety control and recovery performance indicators can be achieved.

[0052] Steps 2-6: Construct system operation and support performance indicators for the control area. Focusing on the overall operational efficiency and support capabilities of the prevention and control system, construct performance indicators including system availability and resource utilization. Through quantitative analysis of equipment operating status, resource allocation, and maintenance mechanisms, achieve quantitative characterization of system operation and support performance indicators.

[0053] Step 2-1 includes: detection coverage Represented as:

[0054] ,

[0055] in, This represents the area of ​​the effective detection zone covered by at least one type of sensing device, given a certain detection capability. Indicates the total area of ​​the controlled zone;

[0056] The urban low-altitude airspace control zone is represented as a two-dimensional planar area, and its spatial extent is defined as follows:

[0057] ,

[0058] in, This represents the spatial extent of the urban low-altitude control zone on a two-dimensional plane, with the area corresponding to the total area of ​​the control zone. ; Represents the x-coordinate of the coordinate system; Represents the ordinate of the coordinate system;

[0059] Let the first The spatial coordinates of the sensing devices are , No. The baseline detection radius of each sensing device is Then the first Reference detection area of ​​a sensing device Represented as:

[0060] ,

[0061] Under multi-device conditions, the overall detection area of ​​the system is the union of the detection areas of each device, expressed as:

[0062] ,

[0063] in, This represents the total detectable area formed by the combined efforts of all sensing devices within the controlled area; This indicates the total number of sensing devices within the controlled area;

[0064] The effective detection area is expressed as:

[0065] ,

[0066] Where d is the differential symbol;

[0067] Target detection probability The ability of a sensing system to successfully detect a target when the target is actually present is expressed as: ,in, This indicates the number of intrusion drones that were successfully detected and defeated. This represents the total number of intruding drones that actually existed;

[0068] Recognition accuracy This measure is used to evaluate the system's ability to correctly identify detected targets, and is expressed as: ,in, Indicates the number of targets that were correctly identified;

[0069] Average detection delay The time taken to measure the period from when a target enters the controlled area to when it is successfully detected by the system is expressed as: ,in Indicates the first The time it took for an intruding drone to enter the controlled area. This indicates the time when the drone was successfully detected.

[0070] Step 2-2 includes: Risk identification accuracy The ability of a system to determine whether an intruding drone poses a threat is expressed as: ,in This indicates the number of intrusion drones that were correctly identified as a threat. This represents the total number of intruding drones that actually pose a threat.

[0071] Consistency of risk classification This measure, used to quantify the consistency between the system's classification of target risk levels and the actual risk levels, is expressed as: ,in This indicates the number of targets that are correctly classified into risk levels. This represents the total number of targets participating in the risk assessment; both the actual risk level and the system assessment risk level are divided into three levels: low risk, medium risk, and high risk; let the first... An intrusion drone at any moment The minimum distance to key protected targets in the controlled area is Flight speed is The relative relationship between the flight direction and the key protected target is: Assume the distance threshold satisfies ,in, This represents the distance threshold used to determine high-risk targets. This represents the distance threshold used to determine medium-risk and low-risk targets, and the speed threshold is... The true risk level is determined according to the following rules: when ,and When a flight pattern shows a tendency to move toward a critical protected target, it is considered high-risk; when When, it is judged as medium risk; when When the risk level is low, it is determined to be low risk; when the risk level output by the system is consistent with the actual risk level determined according to the rules, it is recorded as a correct risk classification event.

[0072] Warning lead time The average advance warning level used to measure the system's ability to issue warnings for targets that have successfully triggered warnings is expressed as: ,in Indicates the first The time it takes for an intruding drone to trigger an alert. This indicates the time when an intruding drone enters a critical protected area or when its true risk level first reaches a high-risk level. This indicates the number of times an alert has been successfully triggered. When the value is 1, it indicates that the system has provided an early warning for the intruding drone that has successfully triggered the warning. The higher the value, the higher the degree of advance warning the system provides for this type of intrusion target.

[0073] Steps 2-3 include: average response time The timeliness of a system's response from completing risk assessment to initiating response actions is expressed as: ,in, Indicates the first The time it takes for an intruding drone to be identified as a threat. Indicates the system's response to the first... The time it takes to initiate a response operation against an intruding drone. This indicates the number of intrusion drone targets that the system has initiated a response to within the statistical time frame;

[0074] Instruction transmission delay Used to measure the efficiency of information transmission within a system, denoted as: ,in, Indicates that for the first The time it takes for an intruding drone to generate a scheduling instruction. This indicates the time when the execution unit receives the instruction. Indicates the total number of events transmitted by the instruction;

[0075] Response unit utilization The time utilization rate of the control unit during the response process is expressed as: ,in, This indicates the number of prevention and control units participating in the response mission. For indicator functions, when the first Each prevention and control unit at all times The value is 1 when a response task is being executed; otherwise, the value is 0.

[0076] Steps 2-4 include: success rate of treatment. The ability of a system to eliminate a threat after taking action against an intruding drone is denoted as: ,in, This indicates the number of intruding drone targets that were successfully dealt with. This indicates the total number of intruding drone targets that have entered the disposal and execution phase.

[0077] Average processing time The time cost required for a system to complete an effective response is expressed as: ,in, Indicates the first The time required to initiate response to an intruding drone target. Indicates the time required to complete the disposal of this target;

[0078] Treatment accuracy It is used to measure the degree to which the system's processing results meet the preset target requirements after processing is completed, and is expressed as: ,in, This indicates the number of intruding drones whose handling results meet the preset handling target conditions. This indicates the number of intruding drones that were successfully dealt with;

[0079] Steps 2-5 include: system recovery time The time required for a system to return to normal operation after an intrusion incident has been resolved is denoted as: ,in, Indicates the time it took to complete the disposal of the last intruding drone target; Indicates the time it takes for the system to recover to normal operating status;

[0080] Exposure rate of risk of loss of control The proportion of time an intrusion drone is in a high-risk, out-of-control state during the prevention and control process is expressed as: ,in, Indicates the first The cumulative time that an intruding drone is at risk of going out of control during the simulation. Indicates the first Total observation time for each intruding drone;

[0081] Steps 2-6 include: System availability. The proportion of time during which the control system is in a normal and usable state during simulation operation out of the total operation time is expressed as: ,in, This represents the cumulative time the system has been in normal operating condition. Indicates the total simulation runtime;

[0082] resource utilization rate It is used to measure the efficiency of the system's use of prevention and control resources during operation, and is expressed as: ,in, Indicates time No. Is the class resource allocated to the first One goal, This represents the maximum total available time for all resources throughout the entire simulation cycle.

[0083] Step 3 includes:

[0084] Step 3-1: Construct a hierarchical evaluation framework consisting of a scenario domain, a performance domain, and a metric domain, as follows:

[0085] The scenario domain is used to characterize different external environmental conditions and the status of the control area. The external environmental conditions include a baseline environmental scenario, a scenario with deteriorating meteorological conditions, a scenario with a complex electromagnetic environment, and a scenario with a complex geographical environment. The status of the control area includes a normal operation status, a status with critical infrastructure, a status with a major security task, and a status with dense population.

[0086] The performance domain is used to characterize the capability dimension of the control system;

[0087] The indicator domain is used to provide a detailed and quantitative description of the performance domain using specific indicators.

[0088] Step 3-2: For the scenario domain, performance domain, and indicator domain, construct weight determination methods respectively. The scenario domain weight is used to reflect the differences in importance of different application scenarios, the performance domain weight is used to reflect the relative importance of various prevention and control capability dimensions, and the indicator weight is used to reflect the degree of contribution of each specific indicator to the corresponding performance dimension.

[0089] Assume the scene domain includes For each evaluation scenario, the scenario weight vector is... Represented as:

[0090] ,

[0091] in, Indicates the first The weights of each scenario, and satisfying ;

[0092] Let the first The scenarios include a total of The performance domain, then the first Performance domain weight vectors in each scenario Represented as:

[0093] ,

[0094] in Indicates the first In the scenario, the first The weights of each performance domain, and satisfying ;

[0095] Let the first Each performance domain includes The evaluation index is then the first one. The performance domain's index weight vector Represented as:

[0096] ,

[0097] in, Indicates the first In the performance domain, the first The weights of each indicator, and satisfying .

[0098] Step 3-3: Based on the three-level evaluation framework and corresponding weights, the indicator domain data is summarized level by level to obtain the performance domain evaluation results and the scenario domain comprehensive evaluation results. The comprehensive anti-droneous drone control performance index for urban low-altitude control zones is calculated to characterize the overall anti-droneous capability level under different control strategies and facility deployment schemes. Specifically, this includes:

[0099] Calculate the performance domain evaluation results based on index weights: Let the first... In the first scenario, the... The first performance domain The standardized values ​​of each evaluation indicator are: The corresponding weight is Then the first In the scenario, the first Evaluation results of each performance domain Represented as:

[0100] ;

[0101] Calculate the scene domain comprehensive evaluation result based on performance domain weights: Let the first... The first scenario The evaluation results for each performance domain are The corresponding performance domain weights are Then the first Comprehensive evaluation results of each scenario Represented as:

[0102] ;

[0103] Let the first The overall evaluation result for each scenario is as follows: The corresponding scenario weight is The comprehensive prevention and control performance index Represented as:

[0104] .

[0105] The present invention also provides a comprehensive evaluation system for the anti-drone control performance in urban low-altitude control zones, implemented according to the method, comprising the following functional modules:

[0106] The simulation scenario construction module is used to generate simulation scenarios under different combinations of environmental conditions and control zone operating states.

[0107] The intrusion behavior modeling module is used to perform parametric modeling and sample generation of the number of intruding drones, their entry sequence, flight trajectory, and behavior patterns.

[0108] The prevention and control process simulation module is used to simulate the dynamic interactive behavior of the prevention and control system in the process of perception, judgment, response and disposal, based on the UAV intrusion and prevention and control state evolution model.

[0109] The comprehensive performance evaluation module is used to calculate indicators and evaluate performance based on the prevention and control performance evaluation index system and the comprehensive prevention and control performance index calculation method.

[0110] Based on the simulation scenario construction module, multiple types of simulation environments for urban low-altitude control zones are generated; wherein, the simulation environment includes two types of elements: external environmental conditions and the operational status of the control zone;

[0111] External Link Condition Set Represented as:

[0112] ,

[0113] in, Indicates the baseline environment scenario. This indicates a deteriorating weather environment. This indicates a complex electromagnetic environment. Indicates a complex geographical environment;

[0114] The operational status of the controlled area includes: normal operation status, operational status with critical infrastructure, status of major security tasks, and status of dense population.

[0115] Controlled Area Operation Status Set Represented as:

[0116] ,

[0117] in, Indicates normal operating status. This indicates the operational status of critical infrastructure within the controlled area. Indicates a critical security mission status. Indicates a densely populated area;

[0118] Form a set of simulation scenarios ;

[0119] Based on the aforementioned intrusion behavior modeling module, the Monte Carlo simulation method is used to randomize the intrusion drone behavior: probability distributions are set for the number of intruding drones, entry time, flight path, flight speed, and behavior pattern, and two or more sets of intrusion behavior samples are generated through random sampling to characterize the uncertainty and randomness of drone intrusion behavior under different simulation scenarios; let the first... The intrusion behavior samples generated in this simulation are represented as follows:

[0120] ,

[0121] in, Indicates the first The set of intrusion behavior samples corresponding to this simulation. Indicates the first In the simulation, the first A set of behavioral parameters for an intruding drone. Indicates the first The number of intruding drones in this simulation;

[0122] No. In the simulation, the first A set of behavioral parameters for an intruding drone Represented as:

[0123] ,

[0124] in, Indicates the entry time. Indicates the initial position parameter. Indicates flight path parameters, Indicates flight speed parameters, Indicates behavioral pattern parameters;

[0125] Based on the aforementioned prevention and control process simulation module and comprehensive performance evaluation module, simulation calculations are performed on different simulation scenarios and different prevention and control strategies and facility deployment schemes to obtain various prevention and control performance indicators. Based on the comprehensive prevention and control performance index calculation method, the prevention and control performance of each scheme under different scenario conditions is quantitatively evaluated and compared, and comprehensive performance evaluation results are output to provide a basis for the optimization of prevention and control strategies and facility deployment decisions.

[0126] Beneficial Effects: This invention addresses the needs of non-cooperative drone intrusion prevention and control in urban low-altitude control zones, enabling quantitative assessment of the entire process, multiple stages, and multiple dimensions of drone prevention and control. Compared to existing methods that evaluate only the performance of a single device or a single prevention and control stage, this invention comprehensively characterizes key capabilities such as target perception, risk assessment, system response, intervention and handling, safety control and recovery, and system operation and maintenance. This improves the comprehensiveness, objectivity, and accuracy of the evaluation results, reducing the limitations and risks of misjudgment caused by single-indicator evaluation. Furthermore, this invention comprehensively considers the impact of external environmental conditions, the operational status of the control zone, and differences in prevention and control deployment methods on prevention and control performance, improving the applicability and generalization ability of the assessment method to complex urban low-altitude control scenarios. By quantitatively comparing the comprehensive performance of different prevention and control schemes in different scenarios, this invention can identify the performance advantages, applicable scope, and weaknesses of each scheme, promoting scientific decision-making in prevention and control equipment deployment, communication node optimization, resource allocation, and prevention and control strategy adjustment. It solves problems such as unreasonable allocation of prevention and control resources and lack of basis for scheme selection, improving the systematicness, operability, and decision support capability of drone prevention and control performance assessment in urban low-altitude control zones. Attached Figure Description

[0127] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0128] Figure 1 This is a flowchart of the method of the present invention.

[0129] Figure 2 This is a schematic diagram illustrating the state evolution of an intruding drone.

[0130] Figure 3 A diagram illustrating the performance evaluation index system for drone control in urban low-altitude control zones.

[0131] Figure 4 This diagram illustrates the comprehensive performance index of drone control in urban low-altitude control zones under different scenarios and deployment schemes. Detailed Implementation

[0132] like Figure 1 , Figure 2 and Figure 3 As shown in the figure, this invention provides a comprehensive evaluation method for the control performance of unmanned aerial vehicles (UAVs) in urban low-altitude control zones, comprising the following steps:

[0133] Step 1: Analyze the key aspects involved in the prevention and control process of urban low-altitude control zones, and construct a state evolution model of drone intrusion and control zone behavior based on different prevention and control strategies and facility deployment schemes; specifically including:

[0134] Step 1-1 analyzes the characteristics of key stages in the process of drone intrusion and prevention within urban low-altitude control zones, including target perception, target identification, threat assessment, early warning triggering, response decision-making, and disposal execution, clarifying the logical relationships and information transmission mechanisms between each stage. The specific implementation method is as follows:

[0135] Delineate the spatial scope of the urban low-altitude control zone and clarify the functional positioning of various sensing devices, communication nodes, and response units in the prevention and control system;

[0136] The target perception stage serves as the initial input stage, continuously monitoring the low-altitude airspace through radar equipment, radio monitoring equipment, and photoelectric detection equipment to obtain initial detection information of the target UAV, including the target's position, speed, and basic motion trajectory.

[0137] The target identification stage performs feature matching and classification processing based on the received target state information, generates target type identification results and corresponding confidence levels, and transmits the identification results to the threat determination stage;

[0138] The threat assessment process calculates the target risk level based on the target identification results and the target's movement status. When the risk level exceeds a preset threshold, an early warning trigger is activated, and the threat level information and target status information are transmitted simultaneously.

[0139] The early warning triggering stage outputs an early warning signal based on the threat level and sends the early warning information to the response decision-making stage as a basis for decision-making;

[0140] Upon receiving the early warning information, the response decision-making stage generates a response plan and control instructions based on the current status of prevention and control resources and preset strategy rules, and sends the instructions to the response execution stage.

[0141] The response and execution phase involves intervening in the target based on the received control instructions and feeding back the response and target status updates to the response decision-making and threat assessment phases for subsequent dynamic adjustments.

[0142] Steps 1-2 involve parameterizing elements such as sensing device configuration, detection coverage, target identification capability, prevention and control response mechanism, and handling methods for different prevention and control strategies and facility deployment schemes, thereby constructing a strategy and facility configuration model that reflects the differences in prevention and control capabilities. The specific implementation method is as follows:

[0143] The prevention and control strategy and deployment plan of prevention and control facilities are represented as a set of parameters consisting of multiple key elements. The set of parameters includes at least sensing device configuration parameters, detection coverage parameters, target identification capability parameters, prevention and control response parameters, and handling capability parameters. Among them, the sensing device configuration parameters are used to describe the type, quantity, and spatial layout of prevention and control equipment; the detection coverage parameters are used to describe the equipment's coverage capability over the controlled area; the target identification capability parameters are used to describe the system's accuracy in identifying intruding drones; the prevention and control response parameters are used to describe the system's response efficiency from early warning triggering to handling execution; and the handling capability parameters are used to describe the effectiveness and accuracy of intervening in the target drone.

[0144] The parameterized expression of a certain prevention and control strategy and prevention and control facility deployment plan is defined as follows:

[0145] ,

[0146] in, This indicates the prevention and control strategy and the deployment plan for prevention and control facilities; Indicates the configuration parameters of the sensing device; Indicates the detection coverage parameters; Target recognition capability parameters; Indicates the parameters for prevention and control response; This indicates the parameters of handling capacity.

[0147] A strategy and facility configuration model reflecting differences in prevention and control capabilities is constructed, represented as follows:

[0148] ,

[0149] The aforementioned prevention and control strategy and prevention and control facility deployment plan refers to the overall prevention and control plan formed by combining and configuring the types, quantities, spatial layout and collaborative mechanisms of relevant prevention and control equipment in the perception, early warning, response and disposal stages within the urban low-altitude control zone to meet the needs of drone intrusion prevention and control.

[0150] In this embodiment, the three typical prevention and control strategies and prevention and control facility deployment schemes correspond to different parameter value characteristics:

[0151] The early warning-priority prevention and control deployment scheme is characterized by increasing the configuration parameters of sensing devices. and detection coverage parameters And improve target recognition parameters To enhance the ability to detect and continuously track intruding drones in advance;

[0152] The characteristic of a response-oriented collaborative prevention and control deployment scheme is to reduce the prevention and control response parameters. Furthermore, by optimizing equipment layout and coordination mechanisms, the overall response efficiency is improved, thereby enhancing the ability to respond quickly and coordinately under multi-objective conditions.

[0153] The key feature of the precise response-oriented prevention and control deployment plan is to improve the response capacity parameters. and target recognition capability parameters This aims to enhance the ability to accurately identify and target intruding drones in key areas, and reduce collateral impacts on the surrounding environment.

[0154] Steps 1-3 establish a dynamic interaction relationship between drone intrusion behavior and control zone deployment, constructing an evolutionary model based on state transitions to describe the temporal evolution characteristics of the drone intrusion process and the control zone prevention and control process. The specific implementation method is as follows:

[0155] The process of drone intrusion and control zone prevention is abstracted as a discrete-time state evolution process, denoted as the time series. Let the set of intrusion drones be... ,in, Indicates the first One intruding drone, This indicates the number of intruding drones.

[0156] The operational status of the drone within the controlled area is divided into multiple discrete states, and a set of states is constructed, represented as follows:

[0157] ,

[0158] in, This indicates that the device is not being detected. This indicates that the system has been detected. This indicates that the system has been identified. This indicates that the situation has been determined to be a threat. This indicates that a response status has been triggered. This indicates that the issue has been resolved.

[0159] At any time , No. The state of an invading drone can be represented as follows: Define the system at time 10:00 The overall state vector is:

[0160] ,

[0161] Define from state Transition to state State transition probability To establish state transition relations, we can represent them as follows:

[0162] ,

[0163] The state transition probabilities are determined by the strategy and facility configuration parameters constructed in steps 1-2, specifically including:

[0164] Never detected state To the detected state The transition probability depends on the detection coverage parameters and the sensing device configuration parameters;

[0165] From the detected state To the already identified state The transition probability depends on the target recognition capability parameter;

[0166] From the already identified state To threat assessment status The probability of transition depends on the risk assessment mechanism and threshold setting;

[0167] From threat assessment status To the response trigger state The probability of transition depends on the prevention and control response parameters;

[0168] From the response trigger state Towards completion of processing status The probability of transfer depends on the disposal capacity parameters and available prevention and control resources, wherein the set of prevention and control resources is as follows: , Indicates the first The availability or quantity of resources for prevention and control.

[0169] To address the problem of limited defense resources under conditions of simultaneous intrusion by multiple drones, a defense resource scheduling mechanism is introduced to make resource allocation decisions among multiple intrusion targets. This resource scheduling mechanism refers to the ability to allocate resources among multiple intrusion targets at any given time. Based on information such as the status, threat level, and spatial location of each intruding drone, the system allocates available prevention and control resources to determine the resource allocation scheme for each target. In this embodiment, prevention and control resources are allocated preferentially to targets with higher threat levels. The decision variable for prevention and control resource allocation is defined as follows: ,like , indicating as in The moment will be the first Classification of prevention and control resources for handling intrusion drones , This indicates other situations. The decision variables for allocating prevention and control resources are: The system should satisfy the constraint that the total amount of resources allocated to each intruding drone at any given time does not exceed the available resource capacity, expressed as:

[0170] ,

[0171] After considering the resource scheduling mechanism for prevention and control in the case of multiple drone intrusions, the state transition probability is expressed as:

[0172] ,

[0173] Introducing a state transition time function to reflect the characteristics of time evolution, defining the transition from state... Transition to state The time delay is The time delay is determined by parameters such as response time, communication time, and execution time. Based on this, a state evolution model of the drone intrusion and control zone prevention process can be constructed, and its state transition process can be used... Represented as a state transition function:

[0174] ,

[0175] in, Indicates time The resource allocation matrix for prevention and control. This is achieved through the analysis of state sequences. By tracking, the entire evolution path of an intruding drone from its approach to the controlled area to its disposal can be obtained, enabling a temporal description of the prevention and control process under different prevention and control strategies and facility deployment schemes.

[0176] Step 2: Construct a multi-dimensional performance evaluation index system for epidemic prevention and control, covering the entire process of perception, early warning, response, and post-disaster recovery, and clarify the definition and quantification methods of the indicators; specifically including:

[0177] Step 2-1: Construct perception and recognition performance indicators for the controlled area. Starting from the overall perception capability of the system, quantitatively model the detection and recognition performance of UAV targets within the controlled area, and construct performance indicators including detection coverage, target detection probability, recognition accuracy, and average perception latency. This comprehensively characterizes the perception and recognition performance of the controlled area from aspects such as spatial coverage capability, target detection capability, target recognition capability, and real-time detection capability. The specific implementation method is as follows:

[0178] Detection coverage Used to measure the effective coverage capability of a sensing system over a controlled area, it is defined as the ratio of the effective detection area of ​​the sensing device to the total area of ​​the controlled area, expressed as:

[0179] ,

[0180] in, This represents the area of ​​the effective detection zone covered by at least one type of sensing device, given a certain detection capability. This represents the total area of ​​the controlled area. The area of ​​the effective detection area... The location is calculated by superimposing the detection positions of various sensing devices. In a specific embodiment, the urban low-altitude viewing area is represented as a two-dimensional planar area, and its spatial range is defined as follows:

[0181] ,

[0182] Let the first The spatial coordinates of the sensing devices are Its baseline detection radius is Then the reference detection area of ​​the device can be represented as:

[0183] ,

[0184] Under multi-device conditions, the overall detection area of ​​the system is the union of the detection areas of each device, expressed as:

[0185] ,

[0186] in, This represents the total detectable area formed by the combined efforts of all sensing devices within the controlled area; This represents the total number of sensing devices within the controlled area. The effective detection area can be expressed as:

[0187] ,

[0188] Target detection probability Used to measure the ability of a sensing system to successfully detect targets when the targets are actually present, it is defined as the ratio of the number of targets successfully detected to the total number of actual targets within a statistical time range, expressed as: ,in, This indicates the number of intrusion drones that were successfully detected and defeated. This represents the total number of intrusion drones that actually exist.

[0189] Recognition accuracy It is used to measure the system's ability to correctly identify detected targets, defined as the ratio of the number of correctly identified targets to the total number of detected targets, expressed as: ,in, This indicates the number of targets that were correctly identified.

[0190] Average detection delay Used to measure the time elapsed from a target entering the controlled area to being successfully detected by the system, it is defined as the average detection time of all targets, expressed as: ,in Indicates the first The time it took for an intruding drone to enter the controlled area. This indicates the time when the drone was successfully detected.

[0191] Step 2-2: Construct performance indicators for risk assessment in the controlled area. Focusing on the risk assessment and situational awareness capabilities of intruding drones, construct performance indicators including risk identification accuracy, risk classification consistency, and early warning lead time. Quantitative characterization of these risk assessment performance indicators is achieved through analysis of the behavioral characteristics, operational trajectories, and environmental constraints of intruding drones. The specific implementation method is as follows:

[0192] Risk identification accuracy The ability of a system to determine whether an intruding drone poses a threat is measured by the ratio of the number of correctly identified threat targets to the actual number of threat targets, expressed as: ,in This indicates the number of intrusion drones that were correctly identified as a threat. This represents the total number of intrusion drones that actually pose a threat. The number of intrusion drones correctly identified as a threat... The total number of intrusion drones that actually pose a threat All data are obtained statistically from the simulation process described in step 4. Specifically, based on pre-set real risk assessment rules, the flight trajectories of each intrusion drone during the simulation are analyzed. When the real risk level of an intrusion drone reaches or exceeds a preset threat threshold at any given time, it is determined to be an actual threat target, and the total number of actual threat targets is calculated. Meanwhile, based on the output of the system risk assessment module, when an intruding drone is identified as a threat and its actual status is also a threat, it is recorded as a correct identification event. All such events are statistically analyzed to obtain the number of intruding drones correctly identified as threats. .

[0193] Consistency of risk classification This measure assesses the consistency between the system's classification of target risk levels and the actual risk levels. It is defined as the ratio of the number of targets with correctly classified risk levels to the number of targets that have undergone risk assessment, expressed as: ,in This indicates the number of targets that are correctly classified into risk levels. This represents the total number of targets participating in the risk assessment. Both the actual risk level and the system-assessed risk level are divided into three levels: low risk, medium risk, and high risk. The actual risk level is determined based on pre-set risk assessment rules; the total number of targets participating in the risk assessment is defined as the number of intruding drones that enter the threat assessment stage within the statistical time frame. In a specific embodiment, let the first... An intrusion drone at any moment The minimum distance to key protected targets in the controlled area is Flight speed is The relative relationship between the flight direction and the key protected target is: Assume the distance threshold satisfies The speed threshold is The true risk level is determined according to the following rules: when ,and When a flight pattern shows a tendency to move toward a critical protected target, it is considered high-risk; when When, it is judged as medium risk; when When the flight trend toward the critical protection target is defined as a flight direction of the intruding drone that is less than the angle between its current flight direction and the line connecting it to the critical protection target, the risk level is determined to be low. When the risk level output by the system matches the actual risk level determined according to the above rules, it is recorded as a correct risk classification event. All such events are statistically analyzed to obtain... The total number of targets participating in the risk assessment is defined as the number of intruding drones that enter the threat assessment stage within the statistical time frame. Specifically, when a target completes the detection and identification process and enters the threat assessment state, it is recorded as a target participating in the risk assessment. .

[0194] Warning lead time The system is used to measure the average advance warning level for targets that have successfully triggered warnings. It is defined as the average time difference between the system issuing a warning and the time the target enters the critical protection zone or reaches a high-risk threshold, expressed as: ,in Indicates the first The time it takes for an intruding drone to trigger an alert. This indicates the time when the intruding drone entered the critical protection zone or when its true risk level first reached a high-risk level. This indicates the number of times an alert has been successfully triggered. When the value is 0, it indicates that the system has provided an early warning for this type of target; the larger the value, the higher the degree of advance warning for targets that have successfully triggered the warning.

[0195] The data involved in the risk assessment performance indicators, including the trajectory of the intruding drone, the system risk assessment results, the actual risk level, the warning trigger time, and the time when the target enters the critical protection zone, are all recorded by the simulation model described in step 4 during operation and obtained through statistical calculation.

[0196] Steps 2-3 involve constructing system response performance indicators for the control area. Focusing on the timeliness of the control system during the response phase, performance indicators are constructed, including average response time, command transmission latency, and response unit utilization. Furthermore, by modeling the communication link, scheduling mechanism, and task execution process, the system response performance indicators are quantitatively characterized. The specific implementation method is as follows:

[0197] Average response time The timeliness of the system's response from completing risk assessment to initiating response actions is used to measure the average response time of each intruding drone, and is expressed as: ,in, Indicates the first The time it takes for an intruding drone to be identified as a threat. Indicates the system's response to the first... The time it takes to initiate a response operation against an intruding drone. This represents the number of intrusion drone targets for which the system has initiated a response within the statistical timeframe. The average response time is only counted for targets for which the system has successfully initiated a response.

[0198] Instruction transmission delay Used to measure the efficiency of information transmission within a system, it is defined as the average time delay from the generation of a control command to its receipt by the executed unit, and is expressed as: ,in, Indicates that for the first The time it takes for an intruding drone to generate a scheduling instruction. This indicates the time when the execution unit receives the instruction. Indicates the total number of events transmitted by the instruction.

[0199] Response unit utilization The time utilization of control units during the response process is used to measure the proportion of the cumulative time each response unit spends executing tasks to its maximum available working time, expressed as: ,in, This indicates the number of prevention and control units participating in the response mission. For indicator functions, when the first Each prevention and control unit at all times The value is 1 when a response task is being executed; otherwise, the value is 0. This indicates the time step of the simulation.

[0200] Steps 2-4 involve constructing performance indicators for intervention and response in the controlled area. These indicators focus on the effectiveness of intervention and response against intruding targets, including success rate, average response time, and response accuracy. The performance indicators are quantitatively characterized by quantifying the scope, intensity, and target response characteristics of the intervention measures. The specific implementation method is as follows:

[0201] Success rate of handling This measure assesses a system's ability to eliminate threats after taking action against an intruding drone. It is defined as the proportion of successfully eliminated targets out of the total number of targets dealt with, expressed as: ,in, This indicates the number of intruding drone targets that were successfully dealt with. This indicates the total number of intrusion drone targets that have entered the disposal execution phase. Successful disposal means that after the intrusion drone is disposed of, its risk level drops below a preset threshold or it does not enter the critical protection zone.

[0202] Average processing time The time cost required for a system to complete an effective response is used to measure the average time difference between initiating and completing the response to a successful response to an intruding drone, and is expressed as: ,in, Indicates the first The time required to initiate response to an intruding drone target. This indicates the time required to complete the handling of the target. The average handling time is only statistically analyzed for targets that have been successfully handled.

[0203] Treatment accuracy This measure assesses the degree to which the system's handling results meet preset target requirements after completion. It is defined as the proportion of intrusion drones that meet the preset handling target conditions to the total number of successfully handled intrusion drones, expressed as: ,in, This indicates the number of intruding drones whose handling results meet the preset handling target conditions. This indicates the number of intrusion drones successfully handled. Meeting the preset handling target conditions means that the intrusion drones achieve the predetermined control effect after the handling is completed, including being guided to a designated safe area, being driven out of the controlled area, or having their risk level reduced to a preset safe range.

[0204] Steps 2-5 involve constructing performance indicators for safety control and recovery in the controlled area. These indicators focus on the system's operational recovery capability after the control process concludes, including system recovery time and the risk of loss of control exposure rate. Furthermore, by modeling the evolution of abnormal states and the recovery process, a quantitative representation of these safety control and recovery performance indicators is achieved. The specific implementation method is as follows:

[0205] System recovery time The time required for a system to return to normal operation after an intrusion incident has been resolved is used to measure this time. It is defined as the time difference between the time when the last intruding drone was dealt with and the time when the system returned to normal operation, and is expressed as: ,in, Indicates the time it took to complete the disposal of the last intruding drone target; This indicates the time it takes for the system to return to normal operation. The system returning to normal operation means that the following conditions are met during the simulation: there are no intruding drones in the controlled area that have not been dealt with, there are no ongoing early warning, response, or handling tasks in the system, and all control units have returned to standby or idle status.

[0206] Exposure rate of risk of loss of control This measure, used to quantify the proportion of time an intruding drone is in a high-risk, out-of-control state during the prevention and control process, reflects the degree of secondary security risks caused during the handling process and is expressed as: ,in, Indicates the first The cumulative time that an intruding drone is at risk of going out of control during the simulation. Indicates the first The total observation time for each intruding drone. The "out-of-control risk state" refers to a state where, during or after the intervention, the intruding drone's flight status becomes abnormal, posing a risk of entering a critical protection zone or other dangerous area. During the simulation, a drone is considered to be in an out-of-control risk state when any of the following conditions are met: the distance between the drone and the critical protection zone or dangerous area is less than a preset safety threshold; or the drone's flight status exhibits abnormal changes, including sudden speed changes, trajectory deviations, or unstable motion. The total observation time refers to the total observation time for the first intruding drone. The start time of the intruding drone entering the controlled area. The time until the disposal of the animal is completed or the animal leaves the controlled area. The length of the time interval up to that point, i.e. .

[0207] Steps 2-6 involve constructing system operation and support performance indicators for the control area. Focusing on the overall operational efficiency and support capabilities of the prevention and control system, performance indicators including system availability and resource utilization are constructed to quantitatively evaluate the system's stability, resource utilization efficiency, and continuous support capabilities under long-term operating conditions. The specific implementation method is as follows:

[0208] System availability The proportion of time during which the control system is in a normal and usable state during simulation operation out of the total operation time is expressed as: ,in, This represents the cumulative time the system has been in normal operating condition. This indicates the total simulation runtime. The system being in normal operating condition means that the system can normally perform its sensing, risk assessment, response, and handling functions, and there are no critical equipment failures or system interruptions.

[0209] resource utilization rate Used to measure the efficiency of system utilization of prevention and control resources during operation, it is defined as the proportion of actual resource usage time to total resource availability time, expressed as: ,in, Indicates time No. Is the class resource allocated to the first One goal, This represents the maximum total available time of all resources throughout the entire simulation period. In this embodiment, the quantity of various prevention and control resources is considered fixed during the simulation period, without considering resource failures or dynamic online / offline situations.

[0210] Step 3: Construct a three-level evaluation framework consisting of a scenario domain, a performance domain, and an indicator domain; determine the weights of the scenario domain, performance domain, and indicator domain respectively; and propose a method for calculating the comprehensive performance index of UAV control in urban low-altitude control zones; specifically including:

[0211] Step 3-1: Construct a hierarchical evaluation framework consisting of a scenario domain, a performance domain, and a metric domain. The specific implementation method is as follows:

[0212] After completing the construction of various prevention and control performance indicators as described in step 2, in order to achieve unified evaluation and horizontal comparison among different prevention and control strategies and prevention and control facility deployment schemes under different scenarios, a three-level evaluation framework consisting of scenario domain, performance domain and indicator domain is further constructed.

[0213] The scenario domain describes the external operating conditions and regional operational status of UAV control missions within urban low-altitude control zones. This scenario domain comprises both external environmental conditions and the control zone status. External environmental conditions reflect the impact of meteorological, electromagnetic, and spatial obstruction factors on the performance of the control system, specifically including baseline environmental scenarios, scenarios with deteriorating meteorological conditions, complex electromagnetic environments, and complex geographical environments. The control zone status reflects the differences in control requirements under different mission backgrounds and protection targets, specifically including normal operation status, status with critical infrastructure, status for major security missions, and status with high population density. By combining external environmental conditions and control zone status, multiple typical application scenarios can be formed for evaluation, reflecting the applicability of the control system under different real-world conditions.

[0214] The performance domains describe the capabilities of the prevention and control system at different functional levels. In this embodiment, the performance domains are consistent with the indicator system constructed in step 2, including the perception and identification performance domain, risk assessment performance domain, system response performance domain, intervention and handling performance domain, safety control and recovery performance domain, and system operation and support performance domain. Each performance domain corresponds to a different functional stage in the entire process of UAV prevention and control, and is used to characterize the system's comprehensive capabilities from target discovery, risk identification, response scheduling, intervention execution to post-event recovery and continuous operation support.

[0215] The indicator domain is used to further quantify and characterize each performance domain. The indicator domain consists of the specific evaluation indicators constructed in step 2. Specifically, the perception and recognition performance domain includes detection coverage, target detection probability, recognition accuracy, and average detection latency; the risk assessment performance domain includes risk recognition accuracy, risk classification consistency, and early warning time; the system response performance domain includes average response time, command transmission latency, and response unit utilization; the intervention and handling performance domain includes handling success rate, average handling duration, and handling accuracy; the safety control and recovery performance domain includes system recovery time and loss-of-control risk exposure rate; and the system operation and support performance domain includes system availability and resource utilization. Through this three-level structure, the original simulation results can be progressively mapped to indicator-level evaluation results, performance-level evaluation results, and scenario-level evaluation results, providing a unified framework for calculating the comprehensive prevention and control performance index.

[0216] Step 3-2: For the scenario domain, performance domain, and indicator domain, weight determination methods are constructed respectively. The scenario domain weight reflects the importance differences of different application scenarios, the performance domain weight reflects the relative importance of various prevention and control capability dimensions, and the indicator weight reflects the contribution of each specific indicator to the corresponding performance dimension. The weights are determined using the analytic hierarchy process (AHP), and are normalized under the condition of meeting consistency requirements to obtain the weight parameters for each level. The specific implementation method is as follows:

[0217] To reflect the relative importance of different scenarios, performance dimensions, and evaluation indicators in the comprehensive evaluation, corresponding weights are set for the scenario domain, performance domain, and indicator domain, forming a three-level weight system.

[0218] The scenario domain weights are used to characterize the importance of different application scenarios in the overall assessment. Since the risk levels and prevention and control requirements caused by drone intrusion vary significantly under different control zone states and external environmental conditions, different weights need to be assigned to each scenario based on its importance. Specifically, factors such as target protection level, task sensitivity, scenario occurrence frequency, and potential risk consequences can be comprehensively considered to construct a pairwise comparison judgment matrix for the scenario domains, and the weights of each scenario can be obtained using the analytic hierarchy process (AHP). Let the scenario domains include... For each evaluation scenario, the scenario weight vector is represented as:

[0219] ,

[0220] in, Indicates the first The weights of each scenario, and satisfying .

[0221] Performance domain weights are used to characterize the importance of different performance dimensions in a specific scenario. Considering the different emphases of system capabilities in different application scenarios—for example, in complex electromagnetic environments, perception and identification capabilities and risk assessment capabilities are more important; in critical security tasks, system response capabilities and intervention capabilities are more prominent—the performance domain weights in this embodiment can be determined separately for different scenarios. Let the performance domain weights be... The scenarios include a total of If there are multiple performance domains, then the performance domain weight vector in this scenario is represented as:

[0222] ,

[0223] in Indicates the first In the scenario, the first The weights of each performance domain, and satisfying .

[0224] Indicator weights are used to characterize the degree of contribution of each evaluation indicator within the same performance domain to the evaluation results of that performance domain. Since the indicators within the same performance domain have different capabilities to reflect system performance, the analytic hierarchy process (AHP) is used to rank the indicators by importance, construct an indicator layer judgment matrix, and calculate the corresponding weights. Let the [missing information - likely a specific index or value] be... Each performance domain includes If there are 10 evaluation indicators, then their indicator weight vector is represented as follows:

[0225] ,

[0226] in, Indicates the first In the performance domain, the first The weights of each indicator, and satisfying .

[0227] In the weight calculation process, judgment matrices are constructed for the scene domain, performance domain, and indicator domain, respectively, and the weights of each layer are solved using the eigenvector method. To ensure the rationality of the weight results, a consistency check is performed on each judgment matrix. If the consistency ratio is less than a preset threshold, the judgment matrix is ​​considered to meet the consistency requirements; otherwise, the judgment matrix is ​​readjusted. After passing the consistency check, the obtained weight results are normalized to obtain the weight parameters of each level that meet the requirements of subsequent evaluation calculations.

[0228] Step 3-3: Based on the three-level evaluation framework and corresponding weights, the indicator domain data is summarized level by level to obtain the performance domain evaluation results and the scenario domain comprehensive evaluation results. Then, the comprehensive anti-drone performance index for urban low-altitude control zones is calculated to characterize the overall anti-drone capability level under different anti-drone strategies and facility deployment schemes. The specific implementation method is as follows:

[0229] After obtaining the corresponding weights for the scenario domain, performance domain, and indicator domain, the performance of the prevention and control system is further summarized and calculated step by step based on the evaluation indicator results obtained in step 2, so as to obtain the comprehensive prevention and control performance index of UAVs in the urban low-altitude control area.

[0230] The indicator domain data undergoes standardization. Since different evaluation indicators differ in their dimensions, value ranges, and performance attributes, the original values ​​of each indicator need to be dimensionless to eliminate dimensional differences and unify the evaluation direction. For benefit-type indicators where larger values ​​indicate better performance, a forward standardization method is used; for cost-type indicators where smaller values ​​indicate better performance, a backward standardization method is used. After standardization, the values ​​of each indicator are uniformly mapped to a preset range to serve as the basic input for subsequent weighted calculations.

[0231] Calculate the performance domain evaluation results based on index weights: Let the first... In the first scenario, the... The first performance domain The standardized values ​​of each evaluation indicator are: The corresponding weight is Then the first In the scenario, the first Evaluation results of each performance domain It can be represented as:

[0232] ,

[0233] in, Indicates the first The number of metrics contained in each performance domain.

[0234] Calculate the scene domain comprehensive evaluation result based on performance domain weights: Let the first... The first scenario The evaluation results for each performance domain are The corresponding performance domain weights are Then the first Comprehensive evaluation results of each scenario It can be represented as:

[0235] ,

[0236] in, Indicates the number of performance domains.

[0237] After obtaining the comprehensive evaluation results for each scenario, the evaluation results under different scenarios are further weighted and summarized by combining the scenario domain weights to obtain the comprehensive performance index of UAV prevention and control in urban low-altitude control areas. Let the first... The overall evaluation result for each scenario is as follows: The corresponding scenario weight is The comprehensive prevention and control performance index Represented as:

[0238] ,

[0239] in, This indicates the total number of evaluation scenarios.

[0240] The comprehensive prevention and control performance index is used to characterize the overall prevention and control capability level of a specific prevention and control strategy and facility deployment scheme under multiple scenario conditions. The higher the comprehensive prevention and control performance index, the stronger the prevention and control capability and the better the comprehensive adaptability of the scheme under different environmental conditions and operating states. By comparing the comprehensive prevention and control performance indices of different prevention and control strategies and facility deployment schemes, a quantitative assessment of the overall performance of each scheme can be achieved, and a unified evaluation basis can be provided for the simulation evaluation, scheme comparison and optimization decision-making in the subsequent step 4.

[0241] Step 4 involves integrating and constructing a simulation model of the drone control process in urban low-altitude control zones, and comprehensively evaluating and comparing the performance indices of various control strategies and facility deployment schemes under different scenario conditions. This specifically includes:

[0242] Step 4-1: Construct a modular system architecture for a simulation model of the drone control process in urban low-altitude control zones. The simulation model includes the following functional modules:

[0243] The simulation scenario construction module is used to generate simulation scenarios under different combinations of environmental conditions and control zone operation status, and to provide environmental input parameters for subsequent intrusion behavior generation and prevention and control process simulation. It is mainly used to describe the spatial range, environmental conditions, key target distribution and operation status characteristics of the urban low-altitude control zone, thereby forming a simulation basic environment corresponding to actual prevention and control needs.

[0244] The intrusion behavior modeling module is used to perform parametric modeling and sample generation of the number of intruding drones, entry timing, flight trajectory and behavior patterns, reflecting the randomness, uncertainty and diversity of drone intrusion behavior under different scenario conditions, and providing input samples for evaluating the performance of the prevention and control system under different threat conditions.

[0245] The prevention and control process simulation module, based on the established UAV intrusion and control zone prevention and control behavior state evolution model, performs time-series simulation of the dynamic interaction process of the prevention and control system in the stages of perception, risk judgment, system response and intervention. By updating the UAV status, system resource status and the evolution of the handling process at each moment, it realizes the dynamic simulation of the entire UAV intrusion process and the control zone prevention and control process.

[0246] The comprehensive performance evaluation module, based on the constructed prevention and control performance evaluation index system, statistically analyzes, organizes, and calculates the data output by the prevention and control process simulation module. It also conducts a comprehensive performance evaluation of the simulation results of different prevention and control strategies and prevention and control facility deployment schemes under different scenario conditions based on the constructed comprehensive prevention and control performance index calculation method, thereby outputting performance evaluation results that can be used for scheme comparison.

[0247] Step 4-2: Construct a multi-type simulation environment for the urban low-altitude control zone. The simulation environment includes two elements: external environmental conditions and the operational status of the control zone. The specific implementation method is as follows:

[0248] The external environmental conditions include: a baseline environmental scenario, a meteorologically deteriorating environment, a complex electromagnetic environment, and a complex geographical environment. The baseline environmental scenario represents an ideal state with favorable meteorological conditions, low background electromagnetic noise, and open, flat terrain without significant obstructions. The meteorologically deteriorating environment represents the impact of adverse meteorological factors such as rainfall, strong winds, or low visibility on sensing and communication performance. The complex electromagnetic environment represents the impact of electromagnetic interference on detection and communication links. The complex geographical environment represents the impact of building obstructions or terrain undulations on detection coverage and target tracking capabilities. (External condition set) Represented as:

[0249] ,

[0250] in, Indicates the baseline environment scenario. This indicates a deteriorating weather environment. This indicates a complex electromagnetic environment. It indicates a complex geographical environment.

[0251] The operational status of the controlled area includes: normal operation, operation with critical infrastructure, major security task, and densely populated state. By combining and configuring the environmental conditions and the operational status of the controlled area, various simulation scenarios are formed to reflect the prevention and control needs under different practical application situations. The controlled area operational status set... Represented as:

[0252] ,

[0253] in, Indicates normal operating status. This indicates the operational status of critical infrastructure within the controlled area. Indicates a critical security mission status. This indicates a densely populated area. Different operational states correspond to different prevention and control requirements and performance priorities.

[0254] In this embodiment, a set of simulation scenarios is formed by combining and configuring external environmental conditions and the operating status of the controlled area, as follows: It can generate multiple simulation scenarios under different combinations of environmental conditions and operating states, and use them as scenario inputs for subsequent intrusion behavior modeling and prevention process simulation.

[0255] Step 4-3: Based on the intrusion behavior modeling module, the Monte Carlo simulation method is used to randomize the behavior of the intruding drone; the specific implementation method is as follows:

[0256] Key parameters such as the number of intruding drones, entry time, flight path, flight speed, and behavior patterns are set with probability distributions. Specifically, the number of intrusions reflects the number of drone targets entering the urban low-altitude control zone in a single simulation, and can be set to a discrete probability distribution based on the scenario risk level or historical experience. Entry time describes the temporal order and time interval of each intruding drone entering the control zone, and can be set to a uniform distribution, Poisson distribution, or other probability distribution suitable for describing random arrival processes. Flight path describes the trajectory characteristics of the intruding drones in two-dimensional or three-dimensional space, and can be set to various trajectory modes such as straight-line penetration, circling, directional approach, or path avoidance depending on the intrusion method. Flight speed describes the speed variation characteristics of the drones during flight, and can be set to a fixed value or follow a continuous distribution within a certain range based on the drone type and mission intent. Behavior pattern characterizes the tactical intent and operation mode of the intruding drones, including reconnaissance, approach, traversal, circling, or multi-target cooperative behavior patterns.

[0257] Using the Monte Carlo simulation method, based on the probability distribution settings of the aforementioned key parameters, multiple sets of intrusion behavior samples are generated. Let the... The intrusion behavior samples generated in this simulation are represented as follows:

[0258] ,

[0259] in, Indicates the first The set of intrusion behavior samples corresponding to this simulation. Indicates the first In the simulation, the first A set of behavioral parameters for an intruding drone. This indicates the number of intruding drones in this simulation. The set of behavioral parameters includes at least the entry time, initial position, flight path parameters, flight speed parameters, and behavioral pattern parameters. In the simulation, the first A set of behavioral parameters for an intruding drone Represented as:

[0260] ,

[0261] in, Indicates the entry time. Indicates the initial position parameter. Indicates flight path parameters, Indicates flight speed parameters, Indicates the behavior pattern parameter.

[0262] By conducting multiple random samplings, various sets of different intrusion behavior samples can be obtained under the same simulation scenario conditions, thus reflecting the random variation characteristics of drone intrusion behavior in the same scenario. Simultaneously, corresponding intrusion behavior sample sets can be constructed under different scenario conditions to reflect the impact of scenario differences on the distribution characteristics of intrusion behavior. Based on these sample sets, the response capability, handling capability, and comprehensive prevention and control capability of the defense system under diverse intrusion threats can be statistically evaluated.

[0263] Step 4-4: Based on the aforementioned prevention and control process simulation module and comprehensive performance evaluation module, simulation calculations are performed on different simulation scenarios and different prevention and control strategies and facility deployment schemes to obtain various prevention and control performance indicators. Furthermore, based on the aforementioned comprehensive prevention and control performance index calculation method, the prevention and control performance of each scheme under different scenario conditions is quantitatively evaluated and compared, and comprehensive performance evaluation results are output to provide a basis for optimizing prevention and control strategies and making facility deployment decisions. The specific implementation method is as follows:

[0264] For any given simulation scenario, firstly, a prevention and control strategy and a prevention and control facility deployment scheme are selected, and the corresponding sensing device configuration parameters, detection coverage parameters, target recognition capability parameters, prevention and control response parameters, and handling capability parameters are input into the state evolution model described in step 1. Then, the intrusion behavior samples generated in step 4-3 are used as target inputs. Under discrete time progression conditions, the state evolution process of each intruding drone in the control area is updated hourly, and the system sensing state, risk judgment state, response state, handling state, and prevention and control resource occupancy state are updated synchronously, thereby simulating the dynamic interactive behavior of the entire process of drone prevention and control in the urban low-altitude control area.

[0265] After each simulation run, the comprehensive performance evaluation module statistically processes the simulation results and calculates the various prevention and control performance indicators constructed in step 2, including perception and identification performance indicators, risk judgment performance indicators, system response performance indicators, intervention and disposal performance indicators, safety control and recovery performance indicators, and system operation and support performance indicators. Furthermore, based on the three-level evaluation framework and comprehensive prevention and control performance index calculation method constructed in step 3, each indicator is standardized, weighted, and calculated level by level to obtain the performance domain evaluation results, scenario comprehensive evaluation results, and comprehensive prevention and control performance index of the corresponding scheme under a specific simulation scenario.

[0266] Let the first Various prevention and control strategies and deployment plans for prevention and control facilities in low-risk areas In the simulation scenario, the first The comprehensive prevention and control performance index in the random simulation is: Then, the average comprehensive prevention and control performance index of this scheme in this scenario is expressed as:

[0267] ,

[0268] in, This represents the total number of Monte Carlo stochastic simulations performed in this scenario. Indicates the first The first scheme The average comprehensive prevention and control performance index under each scenario.

[0269] To compare the overall performance of different solutions under multiple scenario conditions, the average comprehensive prevention and control performance index of each solution under different scenario conditions can be weighted and summarized by combining the scenario domain weights determined in step 3. Let the first step be... The scene weights for each scene are: Then the first Overall performance evaluation results of the scheme Represented as:

[0270] ,

[0271] in, This indicates the total number of simulation scenarios. Indicates the first The overall comprehensive performance evaluation results of various prevention and control strategies and prevention and control facility deployment plans under all evaluation scenarios.

[0272] Through the above calculations, we can obtain the various indicator values, performance domain evaluation results, scenario comprehensive evaluation results, and overall comprehensive performance evaluation results for different prevention and control strategies and deployment schemes under different scenario conditions. Furthermore, by comparing and analyzing these results, we can identify the advantages and disadvantages of different schemes in terms of perception and identification capabilities, risk assessment capabilities, system response capabilities, intervention and handling capabilities, safety control and recovery capabilities, and system operation and support capabilities. This clarifies their applicable scenarios and performance boundaries, and provides quantitative basis for optimizing UAV prevention and control strategies, adjusting the deployment of prevention and control facilities, and making resource allocation decisions in urban low-altitude control zones.

[0273] In one specific embodiment, an experimental scenario for verifying the performance of UAV prevention and control in urban low-altitude control zones is constructed to validate the applicability of the proposed comprehensive evaluation method under multiple scenarios and schemes. The experiment selects a typical urban low-altitude control zone as the object, dividing the control zone into a general control zone, a key protection zone, a personnel activity zone, and a building obstruction zone. UAV intrusion boundaries, key protection targets, and candidate deployment locations for prevention and control facilities are set to form the basic environment for simulating the UAV intrusion and prevention and control process.

[0274] Three types of prevention and control deployment methods are set up: early warning priority deployment scheme, response coordination deployment scheme, and precision response deployment scheme. Among them, the early warning priority scheme focuses on increasing the density of sensing equipment deployment and detection coverage, the response coordination scheme focuses on optimizing the spatial layout of communication nodes and response units, and the precision response scheme focuses on deploying directional intervention equipment around key protection areas.

[0275] Six typical scenarios were selected, including baseline environment - normal operation, deteriorating weather - normal operation, electromagnetic complexity - critical infrastructure, geographical complexity - densely populated area, baseline environment - major security mission, and electromagnetic complexity - major security mission. These six scenarios were combined with three types of prevention and control deployment methods to form 18 sets of verification experiments. Each set of experiments used the Monte Carlo method to repeat the simulation sampling 300 times. The comprehensive prevention and control capability index obtained from each simulation was statistically analyzed, and the prevention and control performance under different scenarios and deployment schemes was compared and analyzed using box plots.

[0276] During the evaluation process, based on the aforementioned weight determination method, the weights of the performance evaluation index system for UAV control in urban low-altitude control zones were calculated, resulting in index-level weights and performance domain-level weights. Table 1 shows the weight calculation results for each index item within each performance domain. The index-level weights characterize the relative contribution of each evaluation index within the same performance domain to the evaluation result of that performance domain. Using these results, specific indicators such as detection coverage, target detection probability, and average detection latency can be weighted and summarized to obtain the evaluation result for the corresponding performance domain. Table 2 shows the weight calculation results for each performance domain under different scenarios. The performance domain weights characterize the relative impact of perception and identification performance, risk assessment performance, system response performance, intervention and handling performance, safety control and recovery performance, and system operation and support performance on the overall control performance under different experimental scenarios.

[0277] Monte Carlo simulations were conducted for six typical experimental scenarios and three prevention and control deployment methods. Each experiment was repeated 300 times to obtain the distribution results of the comprehensive prevention and control capability index. Figure 4 As can be seen, under the baseline environment-normal operation scenario, the indices of the three schemes are generally high, with indices of 0.72 for the early warning priority scheme, 0.70 for the response coordination scheme, and 0.73 for the precision handling scheme, indicating that each scheme has good prevention and control capabilities under normal conditions. Under the weather deterioration-normal operation scenario, the indices of the three schemes decrease to 0.66, 0.62, and 0.64, respectively, with the early warning priority scheme being the highest, indicating that improving detection coverage and early warning capabilities can mitigate the impact of adverse weather on the sensing link. Under the electromagnetic complexity-critical infrastructure scenario, the response coordination scheme has an index of 0.65, higher than the early warning priority and precision handling schemes, indicating that communication coordination and response scheduling capabilities are more critical in complex electromagnetic environments. Under the geographical complexity-dense population scenario, the indices of the three schemes are 0.62, 0.59, and 0.61, respectively, lower than the baseline environment, reflecting that building obstruction and dense population conditions increase the uncertainty of the prevention and control process. In the baseline environment—major security mission scenario—the precision-response solution index is 0.76, higher than the early warning-priority and response-coordination solutions, indicating that the success rate and accuracy of response significantly enhance comprehensive prevention and control capabilities in major security missions. In the electromagnetic complexity—major security mission scenario, the indices for the three solutions are 0.55, 0.61, and 0.64, respectively, with the precision-response solution maintaining its relative advantage. These results demonstrate that the proposed evaluation method can effectively distinguish the differences in prevention and control performance under different scenarios and deployment schemes.

[0278] Table 1. Calculation Results of Weights for Each Indicator in the Performance Evaluation of UAV Prevention and Control in Urban Low-Altitude Control Zones

[0279]

[0280] Table 2. Weights of Prevention and Control Performance Domains in Different Scenarios

[0281]

[0282] This invention provides a method and system for comprehensive evaluation of the performance of unmanned aerial vehicle (UAV) control in urban low-altitude control zones. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A comprehensive evaluation method for the control performance of unmanned aerial vehicles (UAVs) in urban low-altitude control zones, characterized in that, Includes the following steps: Step 1: Analyze the key links involved in the prevention and control process of urban low-altitude control zones, and construct a state evolution model of drone intrusion and control zone prevention and control behavior by combining different prevention and control strategies and prevention and control facility deployment schemes. Step 2: Construct a multi-dimensional performance evaluation index system for prevention and control covering the entire process of perception, early warning, response and follow-up, and clarify the definition and quantification methods of the indicators; Step 3: Construct a three-level evaluation framework consisting of scenario domain, performance domain, and indicator domain, determine the weights of scenario domain, performance domain, and indicator domain respectively, and propose a method for calculating the comprehensive prevention and control performance index of UAVs in urban low-altitude control areas.

2. The method according to claim 1, characterized in that, Step 1 includes: Step 1-1: Analyze the key characteristics of the drone intrusion and prevention process in the urban low-altitude control zone, including target perception, target identification, threat assessment, early warning triggering, response decision-making, and disposal execution, and clarify the logical relationships and information transmission mechanisms between each link. Steps 1-2: For different prevention and control strategies and deployment plans for prevention and control facilities, parameterize the elements including the configuration of sensing equipment, detection coverage, target identification capability, prevention and control response mechanism and disposal method, and construct a strategy and facility configuration model that reflects the differences in prevention and control capabilities. A parameterized expression for a prevention and control strategy and a prevention and control facility deployment plan is defined as follows: ， in, This indicates the prevention and control strategy and the deployment plan for prevention and control facilities; Indicates the configuration parameters of the sensing device; Indicates the detection coverage parameters; Target recognition capability parameters; Indicates the parameters for prevention and control response; Indicates the parameters of handling capacity; A strategy and facility configuration model reflecting differences in prevention and control capabilities is constructed, represented as follows: ， in, This represents the output of the strategy and facility configuration model; This represents a mapping function composed of sensing device configuration parameters, detection coverage parameters, target recognition capability parameters, prevention and control response parameters, and handling capability parameters. The aforementioned prevention and control strategy and prevention and control facility deployment plan refers to the overall prevention and control plan formed by combining and configuring the types, quantities, spatial layout and collaborative mechanisms of relevant prevention and control equipment in the perception, early warning, response and disposal stages within the urban low-altitude control zone in response to the needs of drone intrusion prevention and control. Steps 1-3 establish the dynamic interaction between drone intrusion behavior and control zone deployment, construct an evolutionary model based on state transition, and describe the temporal evolution characteristics of the drone intrusion process and the control zone prevention and control process.

3. The method according to claim 2, characterized in that, Steps 1-3 include: The process of drone intrusion and control zone prevention is abstracted as a discrete-time state evolution process, denoted as the time series. ,in This represents the total number of time steps within the preset evaluation period; let the set of intruding drones be... ,in, Indicates the first One intruding drone, Indicates the number of intruding drones; The operational status of the drone within the controlled area is divided into two or more discrete states, and a state set S is constructed, represented as: ， in, This indicates that the device is not being detected. This indicates that the system has been detected. This indicates that the system has been identified. This indicates that the situation has been determined to be a threat. This indicates that a response status has been triggered. Indicates that the issue has been resolved; At any time , No. The state of each intruding drone is represented as follows: Define the system at time 10:00 global state vector for: ， Define from state Transition to state State transition probability To establish state transition relations, we can represent them as follows: ， in Indicates at time , No. The intruding drone is currently in a state of... Under the conditions, at the next moment Transition to state The probability of; Indicates the first An intruding drone at any moment The state; and Both are sets of states The elements in the table represent the state before the transition and the state after the transition, respectively. ; The state transition probabilities are determined by the strategy and facility configuration parameters constructed in steps 1-2, specifically including: Never detected state To the detected state The transition probability depends on the detection coverage parameters and the sensing device configuration parameters; From the detected state To the already identified state The transition probability depends on the target recognition capability parameter; From the already identified state To threat assessment status The probability of transition depends on the risk assessment mechanism and threshold setting; From threat assessment status To the response trigger state The probability of transfer depends on the prevention and control response parameters; From the response trigger state Towards completion of disposal status The probability of transfer depends on the disposal capacity parameters and available prevention and control resources, wherein the set of prevention and control resources is as follows: , Indicates the first The availability or quantity of resources for prevention and control; This indicates the total number of types of resources available for prevention and control. A prevention and control resource scheduling mechanism is introduced to make resource allocation decisions among two or more intrusion targets. This mechanism refers to the ability to allocate resources at any given time. Based on the status, threat level, and spatial location of each intruding drone, the available defense resources of the system are allocated to determine the resource allocation scheme for each target. The decision variable for defense and response resource allocation is defined as follows: ,like , indicating as in The moment will be the first Classification of prevention and control resources for handling intrusion drones , Indicates other situations; Decision variables for resource allocation in prevention and control The following constraints must be satisfied: ， After considering the resource scheduling mechanism for prevention and control in the case of multiple drone intrusions, the state transition probability is expressed as: ， Introducing a state transition time function to reflect the characteristics of time evolution, defining the transition from state... Transition to state The time delay is A state evolution model of the drone intrusion and control zone prevention process is constructed, and the state transition process is described using... Represented as a state transition function: ， in, Represents the state transition function; Indicates time The resource allocation matrix for prevention and control.

4. The method according to claim 3, characterized in that, Step 2 includes: Step 2-1: Construct perception and recognition performance indicators for the controlled area. Construct performance indicators including detection coverage, target detection probability, and average detection latency. Quantify the perception and recognition performance indicators by quantitatively modeling the detection range, detection accuracy, and environmental influencing factors of the sensing equipment. Step 2-2: Construct risk assessment performance indicators for the control area. Focusing on the risk assessment and situation judgment capabilities of intruding drones, construct performance indicators including risk identification accuracy, risk classification consistency, and early warning lead time. Quantitative characterization of risk assessment performance indicators is achieved by analyzing the behavioral characteristics, operational trajectories, and environmental constraints of intruding drones. Steps 2-3: Construct system response performance indicators for the control area. Focusing on the response efficiency and coordination capabilities of the prevention and control system, construct performance indicators including average response latency, instruction transmission latency, and response unit utilization. And by modeling the communication link, scheduling mechanism, and task execution process, realize the quantitative characterization of system response performance indicators. Steps 2-4: Construct performance indicators for intervention and response in the controlled area. Focusing on the effectiveness of intervention and response to intrusion targets, construct performance indicators including response success rate, average response time, and response accuracy. Quantify the scope, intensity, and target response characteristics of intervention measures to achieve quantitative characterization of intervention and response performance indicators. Steps 2-5: Construct safety control and recovery performance indicators for the controlled area. Focusing on the impact of the prevention and control process on the system's operational safety and post-event recovery capabilities, construct performance indicators including system recovery time and loss of control risk exposure rate. By modeling the evolution of abnormal states and the recovery process, quantitative characterization of safety control and recovery performance indicators can be achieved. Steps 2-6: Construct system operation and support performance indicators for the control area. Focusing on the overall operational efficiency and support capabilities of the prevention and control system, construct performance indicators including system availability and resource utilization. Through quantitative analysis of equipment operating status, resource allocation, and maintenance mechanisms, achieve quantitative characterization of system operation and support performance indicators.

5. The method according to claim 4, characterized in that, Step 2-1 includes: detection coverage Represented as: ， in, This represents the area of ​​the effective detection zone covered by at least one type of sensing device, given a certain detection capability. Indicates the total area of ​​the controlled zone; The urban low-altitude airspace control zone is represented as a two-dimensional planar area, and its spatial extent is defined as follows: ， in, This represents the spatial extent of the urban low-altitude control zone on a two-dimensional plane, with the area corresponding to the total area of ​​the control zone. ; Represents the x-coordinate of the coordinate system; Represents the ordinate of the coordinate system; Let the first The spatial coordinates of the sensing devices are , No. The baseline detection radius of each sensing device is Then the first Reference detection area of ​​a sensing device Represented as: ， Under multi-device conditions, the overall detection area of ​​the system is the union of the detection areas of each device, expressed as: ， in, This represents the total detectable area formed by the combined efforts of all sensing devices within the controlled area; This indicates the total number of sensing devices within the controlled area; The effective detection area is expressed as: ， Where d is the differential symbol; Target detection probability Represented as: ,in, This indicates the number of intrusion drones that were successfully detected and defeated. This represents the total number of intruding drones that actually existed; Recognition accuracy Represented as: ,in, Indicates the number of targets that were correctly identified; Average detection delay Represented as: ,in Indicates the first The time it took for an intruding drone to enter the controlled area. This indicates the time when the drone was successfully detected.

6. The method according to claim 5, characterized in that, Step 2-2 includes: Risk identification accuracy Represented as: ,in This indicates the number of intrusion drones that were correctly identified as a threat. This represents the total number of intruding drones that actually pose a threat. Consistency of risk classification Represented as: ,in This indicates the number of targets that are correctly classified into risk levels. This represents the total number of targets participating in the risk assessment; both the actual risk level and the system assessment risk level are divided into three levels: low risk, medium risk, and high risk; let the first... An intruding drone at any moment The minimum distance to key protected targets in the controlled area is Flight speed is The relative relationship between the flight direction and the key protected target is: Assume the distance threshold satisfies ,in, This represents the distance threshold used to determine high-risk targets. This represents the distance threshold used to determine medium-risk and low-risk targets, and the speed threshold is... The true risk level is determined according to the following rules: when ,and When a flight pattern shows a tendency to move toward a critical protected target, it is considered high-risk; when When, it is judged as medium risk; when When the risk level is low, it is determined to be low risk; when the risk level output by the system is consistent with the actual risk level determined according to the rules, it is recorded as a correct risk classification event. Warning lead time Represented as: ,in Indicates the first The time it takes for an intruding drone to trigger an alert. This indicates the time when an intruding drone enters a critical protected area or when its true risk level first reaches a high-risk level. This indicates the number of times an alert has been successfully triggered. When this occurs, it indicates that the system has provided an early warning for the intruding drone that has successfully triggered the warning.

7. The method according to claim 6, characterized in that, Steps 2-3 include: average response time Represented as: ,in, Indicates the first The time it takes for an intruding drone to be identified as a threat. Indicates the system's response to the first... The time it takes to initiate a response operation against an intruding drone. This indicates the number of intrusion drone targets that the system has initiated a response to within the statistical time frame; Instruction transmission delay Represented as: ,in, Indicates that for the first The time it takes for an intruding drone to generate a scheduling instruction. This indicates the time when the execution unit receives the instruction. Indicates the total number of events transmitted by the instruction; Response unit utilization Represented as: ,in, Indicates the number of prevention and control units participating in the response mission. For indicator functions, when the first Each prevention and control unit at any time The value is 1 when a response task is being executed; otherwise, the value is 0. Steps 2-4 include: success rate of treatment Represented as: ,in, This indicates the number of intruding drone targets that were successfully dealt with. This indicates the total number of intruding drone targets that have entered the disposal and execution phase. Average processing time Represented as: ,in, Indicates the first The time required to initiate response to an intruding drone target. Indicates the time required to complete the disposal of this objective; Treatment accuracy Represented as: ,in, This indicates the number of intruding drones whose handling results meet the preset handling target conditions. This indicates the number of intruding drones that were successfully dealt with; Steps 2-5 include: system recovery time Represented as: ,in, Indicates the time it took to complete the disposal of the last intruding drone target; Indicates the time it takes for the system to recover to normal operating status; Exposure rate of risk of loss of control Represented as: ,in, Indicates the first The cumulative time that an intruding drone is at risk of going out of control during the simulation. Indicates the first Total observation time for each intruding drone; Steps 2-6 include: System availability. Represented as: ,in, This represents the cumulative time the system has been in normal operating condition. Indicates the total simulation runtime; resource utilization rate Represented as: ,in, Indicates time No. Is the class resource allocated to the first One goal, This represents the maximum total available time for all resources throughout the entire simulation cycle.

8. The method according to claim 7, characterized in that, Step 3 includes: Step 3-1: Construct a hierarchical evaluation framework consisting of a scenario domain, a performance domain, and a metric domain, as follows: The scenario domain is used to characterize different external environmental conditions and the status of the control area. The external environmental conditions include a baseline environmental scenario, a scenario with deteriorating meteorological conditions, a scenario with a complex electromagnetic environment, and a scenario with a complex geographical environment. The status of the control area includes a normal operation status, a status with critical infrastructure, a status with a major security task, and a status with dense population. The performance domain is used to characterize the capability dimension of the control system; The indicator domain is used to provide a detailed and quantitative description of the performance domain using specific indicators. Step 3-2: For the scenario domain, performance domain, and indicator domain, construct weight determination methods respectively. The scenario domain weight is used to reflect the differences in importance of different application scenarios, the performance domain weight is used to reflect the relative importance of various prevention and control capability dimensions, and the indicator weight is used to reflect the degree of contribution of each specific indicator to the corresponding performance dimension. Assume the scene domain includes For each evaluation scenario, the scenario weight vector is... Represented as: ， in, Indicates the first The weights of each scenario, and satisfying ; Let the first The scenarios include a total of The performance domain, then the first Performance domain weight vectors in each scenario Represented as: ， in Indicates the first In the scenario, the first The weights of each performance domain, and satisfying ; Let the first Each performance domain includes The evaluation index is then the first one. The performance domain's index weight vector Represented as: ， in, Indicates the first In the performance domain, the first The weights of each indicator, and satisfying ; Step 3-3: Based on the three-level evaluation framework and corresponding weights, the indicator domain data is summarized level by level to obtain the performance domain evaluation results and the scenario domain comprehensive evaluation results. The comprehensive anti-droneous drone control performance index for urban low-altitude control zones is calculated to characterize the overall anti-droneous capability level under different control strategies and facility deployment schemes. Specifically, this includes: Calculate the performance domain evaluation results based on index weights: Let the first... In the first scenario, the... The first performance domain The standardized values ​​of each evaluation indicator are: The corresponding weight is Then the first In the scenario, the first Evaluation results of each performance domain Represented as: ； Calculate the scene domain comprehensive evaluation result based on performance domain weights: Let the first... The first scenario The evaluation results for each performance domain are The corresponding performance domain weights are Then the first Comprehensive evaluation results of each scenario Represented as: ； Let the first The overall evaluation result for each scenario is as follows: The corresponding scenario weight is The comprehensive prevention and control performance index Represented as: 。 9. A comprehensive evaluation system for the performance of unmanned aerial vehicle (UAV) control in urban low-altitude control zones, implemented according to the method described in any one of claims 1 to 8, characterized in that, Includes the following functional modules: The simulation scenario construction module is used to generate simulation scenarios under different combinations of environmental conditions and control zone operating states. The intrusion behavior modeling module is used to perform parametric modeling and sample generation of the number of intruding drones, their entry sequence, flight trajectory, and behavior patterns. The prevention and control process simulation module is used to simulate the dynamic interactive behavior of the prevention and control system in the process of perception, judgment, response and disposal, based on the UAV intrusion and prevention and control state evolution model. The comprehensive performance evaluation module is used to calculate indicators and evaluate performance based on the prevention and control performance evaluation index system and the comprehensive prevention and control performance index calculation method.

10. The system according to claim 9, characterized in that, Based on the simulation scenario construction module, multiple types of simulation environments for urban low-altitude control zones are generated; wherein, the simulation environment includes two types of elements: external environmental conditions and the operational status of the control zone; External Link Condition Set Represented as: ， in, Indicates the baseline environment scenario. This indicates a deteriorating weather environment. This indicates a complex electromagnetic environment. Indicates a complex geographical environment; The operational status of the controlled area includes: normal operation status, operational status with critical infrastructure, status of major security tasks, and status of dense population. Controlled Area Operation Status Set Represented as: ， in, Indicates normal operating status. This indicates the operational status of critical infrastructure within the controlled area. Indicates a critical security mission status. Indicates a densely populated area; Form a set of simulation scenarios ; Based on the aforementioned intrusion behavior modeling module, the Monte Carlo simulation method is used to randomize the intrusion drone behavior: probability distributions are set for the number of intruding drones, entry time, flight path, flight speed, and behavior pattern, and two or more sets of intrusion behavior samples are generated through random sampling to characterize the uncertainty and randomness of drone intrusion behavior under different simulation scenarios; let the first... The intrusion behavior samples generated in this simulation are represented as follows: ， in, Indicates the first The set of intrusion behavior samples corresponding to this simulation. Indicates the first In the simulation, the first A set of behavioral parameters for an intruding drone. Indicates the first The number of intruding drones in this simulation; No. In the simulation, the first A set of behavioral parameters for an intruding drone Represented as: ， in, Indicates the entry time. Indicates the initial position parameters. Indicates flight path parameters, Indicates flight speed parameter, Indicates behavioral pattern parameters; Based on the aforementioned prevention and control process simulation module and comprehensive performance evaluation module, simulation calculations are performed on different simulation scenarios and different prevention and control strategies and facility deployment schemes to obtain various prevention and control performance indicators. Based on the comprehensive prevention and control performance index calculation method, the prevention and control performance of each scheme under different scenario conditions is quantitatively evaluated and compared, and comprehensive performance evaluation results are output to provide a basis for the optimization of prevention and control strategies and facility deployment decisions.

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