Method, system and device for evaluating threat level of protection airspace intrusion and medium

Abstract

The invention discloses a protective airspace intrusion threat level evaluation method, system and device and a medium, and the method employs a multi-source sensor to collect the data of an intrusion target in a protective airspace, so as to obtain data sets corresponding to different sensors. And according to the data sets corresponding to the different sensors, target type discrimination and confidence calculation are respectively carried out to obtain discrimination results and confidence scores of the intrusion target by the sensors. And then fusion processing is carried out on the discrimination results of different sensors and the confidence score to obtain a comprehensive target type discrimination result and a comprehensive confidence score. And finally, according to a fusion result, evaluating a threat level in real time by using a preset intrusion threat level evaluation model, and dynamically displaying the threat level through a visual interface. According to the method, multiple factors are comprehensively considered, the threat level of the invasion target in the nuclear power plant protection airspace is comprehensively evaluated, and the low-altitude defense capability of the nuclear power plant is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of nuclear power plant safety protection technology, and in particular to a method, system, equipment and medium for assessing the level of airspace intrusion threats. Background Technology

[0002] In recent years, with the rapid development of drone technology and its widespread application in aerial photography, inspection, logistics, and other fields, the number of drones has increased rapidly, and their performance has continued to improve. Given the characteristics of drones—low flight altitude, high speed, and flexible trajectory—their activity around critical infrastructure has increased significantly. Especially in key protected areas such as nuclear power plants, nuclear fuel facilities, and chemical industrial parks, unauthorized intrusion by drones can pose a serious threat to equipment security, information confidentiality, and public safety.

[0003] However, existing low-altitude defense systems for nuclear power plants rely heavily on empirical formulas or calculations based on a single parameter when assessing the threat level of drones. This approach results in inaccurate assessments that fail to accurately reflect the true threat posed by drones. Furthermore, these systems lack effective data fusion mechanisms when processing multi-source data, leading to significant data redundancy and inconsistencies. This severely impacts the accuracy of target identification and the timeliness of threat assessment, ultimately reducing the system's real-time performance and reliability. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and medium for assessing the threat level of airspace intrusion. This method utilizes multi-source information fusion technology to comprehensively assess the threat level of intrusion targets within the protected airspace, thus solving the technical problems of insufficient accuracy of assessment results and imperfect multi-source data processing mechanisms in existing low-altitude defense systems.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0006] This invention provides a method for assessing the threat level of airspace intrusion, comprising:

[0007] Multi-source sensors are used to collect data on intrusion targets within the protected airspace to obtain sensor datasets corresponding to different types of sensors.

[0008] Based on the sensor datasets corresponding to different types of sensors, the target type of the intrusion target is identified and the confidence score is calculated, so as to obtain the target type identification results of the intrusion target by different types of sensors and the corresponding confidence scores;

[0009] The target type discrimination results of different types of sensors on the intrusion target and the corresponding confidence scores are fused to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score.

[0010] Based on the comprehensive target type identification results and the corresponding comprehensive confidence score, the threat level of the intrusion target is evaluated in real time using a preset intrusion threat level assessment model, and the evaluation results are dynamically displayed through a visual interface.

[0011] In one embodiment of the present invention, the multi-source sensor includes a monitoring radar, a radio detection device, and a photoelectric detection device;

[0012] The method involves using multi-source sensors to collect data on intrusion targets within the protected airspace to obtain sensor datasets corresponding to different types of sensors, including:

[0013] The monitoring radar is used to collect radar reflection data of intruding targets in the protected airspace in real time to obtain a radar dataset;

[0014] The radio detection equipment is used to collect radio signal data of intruding targets within the protected airspace in real time to obtain a radio dataset;

[0015] The photoelectric detection equipment is used to collect video or image data of intruding targets within the protected airspace to obtain a photoelectric dataset.

[0016] In one embodiment of the present invention, the multi-source sensor further includes a weather radar, which is used to monitor the weather conditions in the protected airspace in real time and dynamically adjust the weights of visible light recognition and infrared recognition during the identification process of the photoelectric detection device according to different weather conditions.

[0017] In one embodiment of the present invention, the step of performing target type discrimination and confidence score calculation on the intrusion target based on sensor datasets corresponding to different types of sensors, to obtain the target type discrimination results and corresponding confidence scores of the intrusion target for different types of sensors, includes:

[0018] Based on the radar dataset, the target type of the intrusion target is identified and the confidence level is calculated to obtain the target type identification result of the monitoring radar and the corresponding confidence score of the intrusion target;

[0019] Based on the radio dataset, the target type of the intrusion target is identified and confidence is calculated to obtain the target type identification result and corresponding confidence score of the intrusion target identified by the radio detection device;

[0020] Based on the photoelectric dataset, the target type is identified and confidence level is calculated for the intrusion target to obtain the target type identification result and corresponding confidence level score of the photoelectric detection device.

[0021] In one embodiment of the present invention, the step of fusing the target type discrimination results of different types of sensors on the intrusion target and the corresponding confidence scores to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score includes:

[0022] The location and azimuth information of the intrusion target collected by the monitoring radar and the radio detection equipment are obtained, and the position difference and azimuth difference are calculated.

[0023] When the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of different types of sensors on the intrusion target are fused to obtain a comprehensive target type discrimination result.

[0024] The confidence scores corresponding to the target type discrimination results of different types of sensors for the intrusion target are fused to obtain a comprehensive confidence score.

[0025] In one embodiment of the present invention, when the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of different types of sensors on the intrusion target are fused to obtain a comprehensive target type discrimination result, including:

[0026] When the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of the monitoring radar and the radio detection equipment on the intrusion target are fused to obtain a fused target type discrimination result.

[0027] The fused target type determination result is fused with the target type discrimination result of the photoelectric detection device on the intrusion target to obtain a comprehensive target type discrimination result.

[0028] In one embodiment of the present invention, the preset intrusion threat level assessment model is constructed as follows:

[0029] T(i)=α×W_dist(i)+β×W_reg(i)+γ×W_feat(i)+δ×W_ctr(i)+ε×W_hum(i)

[0030] +ζ×W_env(i)+η×W_adsb(i)

[0031] Where T(i) represents the threat level of the i-th target, W_dist(i) is the distance weighting function, W_reg(i) is the area weighting function, W_feat(i) is the target characteristic function, W_ctr(i) is the countermeasure status correction term, W_hum(i) is the manual confirmation correction term, W_env(i) is the environmental factor correction term, W_adsb(i) is the ADS-B correction term, and α, β, γ, δ, ε, ζ, and η are empirical coefficients.

[0032] Based on the same inventive concept, another embodiment of the present invention provides a system for assessing the threat level of airspace intrusion, implemented using the airspace intrusion threat level assessment method as described in any of the above embodiments, including:

[0033] The data acquisition module is used to collect data on intrusion targets within the protected airspace using multi-source sensors, in order to obtain sensor datasets corresponding to different types of sensors.

[0034] The target classification module is used to perform target type discrimination and confidence calculation on the intrusion target according to the sensor datasets corresponding to different types of sensors, so as to obtain the target type discrimination results of different types of sensors on the intrusion target and the corresponding confidence scores;

[0035] The multi-source fusion module is used to fuse the target type discrimination results and corresponding confidence scores of different types of sensors on the intrusion target to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score.

[0036] The threat assessment module is used to assess the threat level of the intrusion target in real time based on the comprehensive target type discrimination result and the corresponding comprehensive confidence score, using a preset intrusion threat level assessment model, and dynamically display the assessment results through a visual interface.

[0037] Based on the same inventive concept, another embodiment of the present invention also provides an electronic device, the electronic device comprising:

[0038] One or more processors;

[0039] A storage device for storing one or more programs, which, when executed by one or more processors, enable the electronic device to implement the airspace intrusion threat level assessment method as described in any of the above embodiments.

[0040] Based on the same inventive concept, another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, causes the computer to perform the airspace intrusion threat level assessment method as described in any of the above embodiments.

[0041] As described above, this invention provides a method for assessing the threat level of intrusion into protected airspace. This method utilizes multi-source sensors to collect data on intrusion targets within the protected airspace, obtaining sensor datasets corresponding to different sensor types. Based on these datasets, target type discrimination and confidence level calculations are performed on the intrusion targets to obtain target type discrimination results and corresponding confidence scores for each sensor type. The target type discrimination results and corresponding confidence scores from different sensor types are then fused to obtain a comprehensive target type discrimination result and a comprehensive confidence score. Based on the comprehensive target type discrimination result and the corresponding comprehensive confidence score, a preset intrusion threat level assessment model is used to assess the threat level of the intrusion targets in real time, and the assessment results are dynamically displayed through a visual interface. This method possesses the capability of multi-sensor data fusion processing and collaborative identification, enabling deep integration and analysis of data collected from multiple sensor sources. Based on this, it comprehensively considers multiple factors to conduct a comprehensive assessment of the threat level of intrusion targets within the protected airspace of nuclear power plants, thereby achieving a real-time, accurate, and highly interpretable assessment of the threat status of intrusion targets, significantly improving the low-altitude defense capabilities of nuclear power plants. Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating a method for assessing the threat level of airspace intrusion, provided as an exemplary embodiment of this application.

[0044] Figure 2 This is a schematic diagram illustrating the determination of the type of intrusion target and data fusion within the protected airspace, provided as an exemplary embodiment of this application.

[0045] Figure 3 This is a schematic diagram illustrating the intrusion threat level assessment of intrusion targets within the protected airspace, provided as an exemplary embodiment of this application.

[0046] Figure 4 This is a schematic diagram of a system for assessing the threat level of airspace intrusion, provided as another exemplary embodiment of this application.

[0047] Figure 5This is a schematic diagram of the structure of an electronic device provided for another exemplary embodiment of this application. Detailed Implementation

[0048] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0049] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0050] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, publicly known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0051] To address the technical problems of insufficient accuracy in assessment results and imperfect multi-source data processing mechanisms in existing low-altitude defense systems, this invention innovatively proposes a method for assessing the threat level of airspace intrusion. Primarily applied to the design of low-altitude defense systems for nuclear power plants, this method constructs a target type determination system based on multi-source information fusion. By deeply integrating various sensor data, it significantly improves the accuracy of target type determination. Furthermore, based on the characteristics of the intruding target and combined with multi-dimensional information such as countermeasure status, regional importance, and environmental factors, this method efficiently calculates the threat level of the intruding target, thereby achieving a comprehensive assessment of the threat of airspace intrusion.

[0052] Please see Figure 1 As shown, the method for assessing the threat level of airspace intrusion includes the following steps:

[0053] S100: Uses multi-source sensors to collect data on intrusion targets within the protected airspace to obtain sensor datasets corresponding to different types of sensors.

[0054] S200: Based on the sensor datasets corresponding to different types of sensors, the target type of the intrusion target is identified and the confidence score is calculated to obtain the target type identification results of the intrusion target by different types of sensors and the corresponding confidence scores;

[0055] S300: The target type discrimination results of different types of sensors on the intrusion target and the corresponding confidence scores are fused to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score;

[0056] S400: Based on the comprehensive target type discrimination result and the corresponding comprehensive confidence score, the threat level of the intrusion target is evaluated in real time using a preset intrusion threat level assessment model, and the evaluation results are dynamically displayed through a visual interface.

[0057] The steps in the above-mentioned method for assessing the threat level of airspace intrusion will be discussed in detail below.

[0058] First, step S100 is executed, which involves using multi-source sensors to collect data on intrusion targets within the protected airspace to obtain sensor datasets corresponding to different types of sensors.

[0059] It should be noted that, in this embodiment, the multi-source sensor includes a monitoring radar, a radio detection device, and a photoelectric detection device.

[0060] In an exemplary embodiment of this application, step S100 further includes the following steps:

[0061] S110: Use the monitoring radar to collect radar reflection data of intruding targets in the protected airspace in real time to obtain a radar dataset;

[0062] S120: Use the radio detection equipment to collect radio signal data of intruding targets in the protected airspace in real time to obtain a radio dataset;

[0063] S130: Use the photoelectric detection device to collect video or image data of intruding targets within the protected airspace to obtain a photoelectric dataset.

[0064] Specifically, the monitoring radar includes, but is not limited to, phased array radar and single-array radar. It continuously scans low-altitude targets within the protected area using a fixed or rotatable radar array, enabling real-time acquisition of information such as latitude, longitude, speed, altitude, azimuth, heading, and elevation angle of intruding targets. In this embodiment, the monitoring radar collects radar reflection data of various intruding targets within the protected airspace in real time, thereby obtaining a radar dataset R = {r1, r2, ... r...}. n}, where r nThis represents the data information corresponding to the nth intrusion target within the protected airspace. The radio detection equipment scans a specified frequency band to capture the radio signal characteristics of the intrusion target in real time. These radio signal characteristics include, but are not limited to, latitude and longitude, speed, altitude, azimuth, signal strength, and operating frequency band. In this embodiment, the radio detection equipment collects radio signal data of each intrusion target within the protected airspace in real time, thereby obtaining a radio dataset O = {o1, o2, ... o...}. n}, where o n This represents the data information corresponding to the nth intrusion target within the protected airspace. The photoelectric detection device consists of infrared and visible light equipment. It collects video or image data of intrusion targets through optical sensors and uses built-in algorithms to detect, classify, and count the intrusion targets, thereby identifying the type, quantity, and appearance characteristics of intrusion targets within the protected airspace. In this embodiment, the photoelectric detection device collects video or image data of each intrusion target within the protected airspace, thus obtaining the photoelectric dataset S = {s1, s2, ... sn}. n}, where s n This represents the data information corresponding to the nth intrusion target within the protected airspace.

[0065] It should be noted that in this embodiment, the multi-source sensor also includes a weather radar. The weather radar is used to monitor weather conditions within the protected airspace in real time and dynamically adjusts the weights of visible light recognition and infrared recognition during the photoelectric detection device's identification process according to different weather conditions. For example, under good lighting conditions such as sunny days, the weight of visible light recognition is increased, while under adverse weather conditions such as rainy or foggy days, the weight of infrared recognition is increased, thereby ensuring the accuracy of the photoelectric identification results. It is understood that in other embodiments, the multi-source sensor may also include an ADS-B device. The ADS-B device is used to receive ADS-B signals broadcast by civil aircraft, obtain the latitude, longitude, heading, distance, and identity information of legitimate aircraft, and compare this information with intrusion target information to achieve legitimate flight path filtering and counter-restricted identification.

[0066] Next, step S200 is executed, which involves performing target type discrimination and confidence calculation on the intrusion target based on the sensor datasets corresponding to different types of sensors, so as to obtain the target type discrimination results and corresponding confidence scores of the intrusion target by different types of sensors.

[0067] In an exemplary embodiment of this application, step S200 further includes the following steps:

[0068] S210: Based on the radar dataset, the target type of the intrusion target is identified and confidence is calculated to obtain the target type identification result and corresponding confidence score of the intrusion target identified by the monitoring radar;

[0069] S220: Based on the radio dataset, the target type of the intrusion target is determined and the confidence level is calculated to obtain the target type determination result and the corresponding confidence level score of the intrusion target identified by the radio detection device;

[0070] S230: Based on the photoelectric dataset, the target type of the intrusion target is determined and the confidence level is calculated to obtain the target type determination result and the corresponding confidence level score of the photoelectric detection device in identifying the intrusion target.

[0071] Specifically, in this embodiment, please refer to Figure 2 As shown, the monitoring radar, photoelectric detection equipment, and radio detection equipment each independently identify and initially classify intrusion targets, and simultaneously calculate the single-source confidence score for each intrusion target. Specifically, the monitoring radar identifies the type of intrusion target by emitting radio waves and receiving the reflected signals from the intrusion target, and calculates the radar detection probability P_d based on information such as the intensity, speed, and location of the reflected signals. r (i) and data uncertainty σ r (i), and thus obtain the confidence level C of the monitoring radar for the intruding target i. r (i)=f1(P_d r (i),σ r (i) The photoelectric detection device uses a visible light or infrared camera to collect video or image data of the intrusion target, identifies the type of the intrusion target through a built-in algorithm, and calculates the photoelectric detection probability P_d of the intrusion target. o (i) and data uncertainty σ o (i), and then obtain the confidence level Co(i) of the photoelectric detection device for the intrusion target i = f2(P_d o (i),σ o (i) The radio detection equipment identifies the type of intrusion target by analyzing the characteristics of the radio signals emitted by the intrusion target (such as signal strength, operating frequency band, etc.), and simultaneously calculates the radio detection probability P_d of the intrusion target. s (i) and data uncertainty σ s (i), and then obtain the confidence level Cs(i) of the radio detection equipment for the intrusion target i = f3(P_d s (i),σ s (i)).

[0072] Next, step S300 is executed, which involves fusing the target type discrimination results and corresponding confidence scores of different types of sensors on the intrusion target to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score.

[0073] In an exemplary embodiment of this application, step S300 further includes the following steps:

[0074] S310: Obtain the position information and azimuth information of the intrusion target collected by the monitoring radar and the radio detection equipment, and calculate the position difference and azimuth difference;

[0075] S320: When the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of different types of sensors on the intrusion target are fused to obtain a comprehensive target type discrimination result.

[0076] S330: The confidence scores corresponding to the target type discrimination results of different types of sensors on the intrusion target are fused to obtain a comprehensive confidence score.

[0077] In an exemplary embodiment of this application, step S320 further includes the following steps:

[0078] S321: When the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of the monitoring radar and the radio detection equipment on the intrusion target are fused to obtain a fused target type discrimination result.

[0079] S322: The fusion target type determination result is fused with the target type discrimination result of the photoelectric detection device on the intrusion target to obtain a comprehensive target type discrimination result.

[0080] Specifically, please refer to Figure 2 As shown, for the same intrusion target detected by both monitoring radar and radio detection equipment, the difference in the detected position of the intrusion target, ||pos, is calculated. r -pos s ||and angular difference|θ r -θ s |, where pos r and pos s θ represents the location of the intrusion target detected by the monitoring radar and radio detection equipment, respectively. r and θ s These represent the azimuth angles of the intrusion target detected by the monitoring radar and the radio detection equipment, respectively. When the position difference satisfies ||posr -pos s ||≤ε_pos and the direction angle difference satisfies|θ r -θ s When |≤ε_ang, the intrusion target detected by the monitoring radar and the radio detection equipment can be determined to be the same target, where ε_pos and ε_ang represent the preset position error threshold and angle error threshold, respectively. At this point, the target type information identified by the monitoring radar and the radio detection equipment is fused to update the target type. Simultaneously, unique features detected by the radio detection equipment are added to the target information to improve its completeness. Subsequently, based on high-resolution video or image data collected by the photoelectric detection equipment, a preset AI image recognition algorithm is used to more accurately identify the target type after the initial fusion, thereby further improving the accuracy of target type identification. It should be noted that when the automatic identification results show uncertainty or when higher precision is required for target type identification, a manual review process will be initiated to confirm the target type, especially for targets suspected to be drones. It is understood that in this embodiment, the fusion process also includes generating the trajectory characteristics of the intrusion target based on its historical location data, analyzing the movement trend of the intrusion target through these trajectory characteristics, and then determining the intrusion target's movement intention. The movement intention includes, but is not limited to, approaching, moving away, or loitering, and the intrusion target's movement intention will be an important factor in subsequent intrusion threat level assessment. Then, according to preset weights, the single-source confidence scores of the monitoring radar, radio detection equipment, and photoelectric detection equipment for the intrusion target are weighted and fused to obtain a multi-source fusion confidence score F(i). The formula for calculating the multi-source fusion confidence score F(i) is: F(i) = w r ×C r (i)+w o ×C o (i)+w s ×C s (i), where w r w o w s Let w be the weights of each information source, and satisfy w r +w o +w s =1. The final comprehensive confidence score C_final(i) update formula is: C_final(i)=F(i)×(1+λ×V_weather+μ×V_manual), where V_weather is the meteorological correction factor, V_manual represents the manual review weighting factor, and λ and μ are empirical coefficients.

[0081] Finally, step S400 is executed, which involves using a preset intrusion threat level assessment model to assess the threat level of the intrusion target in real time based on the comprehensive target type discrimination result and the corresponding comprehensive confidence score, and dynamically displaying the assessment results through a visual interface.

[0082] Specifically, please refer to Figure 3 As shown, when comprehensively calculating the threat level of an intrusion target, multiple key factors need to be considered, including distance weighting, regional weighting, target characteristics, countermeasure status, manual judgment, weather factors, and ADS-B correction. Among these, distance weighting reflects the distance between the intrusion target and the nuclear power plant protection zone and whether there is any obstruction; regional weighting reflects the type of area where the intrusion target is located (including different types such as early warning, alert, response, operation, and whitelist) and the priority order of protected targets within that area; target characteristics include multi-dimensional information such as target type confidence, speed, altitude, flight path intent, size, model, and signal strength; countermeasure status reflects the activation status of anti-drone jamming measures; manual judgment reflects whether the type of the intrusion target has been manually confirmed; weather factors include the correction of threat assessment by environmental conditions such as wind speed, visibility, and precipitation; and ADS-B correction reflects the impact of civil aviation data on the limitations of countermeasures. In this embodiment, the construction of the preset intrusion threat level assessment model is as follows:

[0083] T(i)=α×W_dist(i)+β×W_reg(i)+γ×W_feat(i)+δ×W_ctr(i)+ε×W_hum(i)

[0084] +ζ×W_env(i)+η×W_adsb(i)

[0085] Where T(i) represents the threat level of the i-th target, W_dist(i) is the distance weighting function, W_reg(i) is the area weighting function, W_feat(i) is the target characteristic function, W_ctr(i) is the countermeasure status correction term, W_hum(i) is the manual confirmation correction term, W_env(i) is the environmental factor correction term, W_adsb(i) is the ADS-B correction term, and α, β, γ, δ, ε, ζ, and η are empirical coefficients used to adjust the weight of each factor in the threat level assessment.

[0086] The specific calculation methods for each function are as follows:

[0087] Distance weighting function: W_dist(i)=exp(-D(i) / D)×(1-O(i)), where D(i) is the target distance, D is the pre-set safety baseline distance, and O(i) is the occlusion factor, which ranges from 0 to 1.

[0088] Regional weighting function: W_reg(i)=Σ_j(R_j×P_j), where R_j is the regional risk level and P_j is the probability of the target existing in region j.

[0089] Target feature function: W_feat(i)=ω0×C_final(i)+ω1×V(i)+ω2×H(i)+ω3×I(i), where C_final(i) is the target type confidence, V(i) is the velocity normalized value, H(i) is the height normalized value, I(i) is the intent score, and ω0, ω1, ω2, and ω3 are weight coefficients.

[0090] Countermeasure status correction term: W_ctr(i)=(1-S_ctr(i))×κ, where S_ctr(i) represents the countermeasure system status, κ is the correction coefficient, and S_ctr(i)=1 indicates that the countermeasure has been activated.

[0091] Manual confirmation correction item: W_hum(i)=ρ×M(i), where M(i) is the manual confirmation mark, ρ is the correction coefficient, and M(i)=1 indicates that the target type has been manually confirmed.

[0092] ADS-B Correction Items: Where overlap(i) represents the degree of overlap between the target and the civil aviation track. This is a correction factor.

[0093] In summary, this invention provides a method for assessing the threat level of intrusion into protected airspace. This method utilizes multi-source sensors to collect data on intrusion targets within the protected airspace, obtaining sensor datasets corresponding to different sensor types. Based on these datasets, target type discrimination and confidence level calculations are performed on the intrusion targets to obtain target type discrimination results and corresponding confidence scores for each sensor type. The target type discrimination results and corresponding confidence scores from different sensor types are then fused to obtain a comprehensive target type discrimination result and a comprehensive confidence score. Based on the comprehensive target type discrimination result and the comprehensive confidence score, a preset intrusion threat level assessment model is used to assess the threat level of the intrusion targets in real time, and the assessment results are dynamically displayed through a visual interface. This method possesses the capability of multi-sensor data fusion processing and collaborative identification, enabling deep integration and analysis of data collected from multiple sensor sources. Based on this, it comprehensively considers multiple factors to conduct a comprehensive assessment of the threat level of intrusion targets within the protected airspace of nuclear power plants, thereby achieving a real-time, accurate, and highly interpretable assessment of the threat status of intrusion targets, significantly improving the low-altitude defense capabilities of nuclear power plants.

[0094] Based on the same inventive concept, please refer to Figure 4 As shown, another embodiment of the present invention also provides a protective airspace intrusion threat level assessment system 100, which is implemented using the protective airspace intrusion threat level assessment method as described in any of the above embodiments, including:

[0095] The data acquisition module 110 is used to collect data on intrusion targets in the protected airspace using multi-source sensors, so as to obtain sensor datasets corresponding to different types of sensors.

[0096] The target classification module 120 is used to perform target type discrimination and confidence calculation on the intrusion target according to the sensor datasets corresponding to different types of sensors, so as to obtain the target type discrimination results of different types of sensors on the intrusion target and the corresponding confidence scores;

[0097] The multi-source fusion module 130 is used to fuse the target type discrimination results and corresponding confidence scores of different types of sensors on the intrusion target to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score.

[0098] The threat assessment module 140 is used to assess the threat level of the intrusion target in real time based on the comprehensive target type discrimination result and the corresponding comprehensive confidence score, using a preset intrusion threat level assessment model, and dynamically display the assessment results through a visual interface.

[0099] It should be noted that the airspace intrusion threat level assessment system 100 includes the airspace intrusion threat level assessment method described in any of the above embodiments. Since the airspace intrusion threat level assessment system 100 provided in this embodiment belongs to the same inventive concept as the airspace intrusion threat level assessment method provided in any of the above embodiments, it has at least the same beneficial effects, and will not be elaborated further here.

[0100] Based on the same inventive concept, please refer to Figure 5 As shown, another embodiment of the present invention also provides an electronic device 11, which may include a memory 111, a processor 112 and a bus, and may also include a computer program stored in the memory 111 and executable on the processor 112, such as a protection airspace intrusion threat level assessment program.

[0101] The memory 111 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 111 can be an internal storage unit of the electronic device 11, such as a portable hard drive of the electronic device 11. In other embodiments, the memory 111 can be an external storage device of the electronic device 11, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 11. Furthermore, the memory 111 can include both internal and external storage units of the electronic device 11. The memory 111 can be used not only to store application software and various types of data installed on the electronic device 11, such as code for assessing the level of airspace intrusion threats, but also to temporarily store data that has been output or will be output.

[0102] In some embodiments, the processor 112 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 112 is the control unit of the electronic device 11, connecting various components of the electronic device 11 via various interfaces and lines. It executes programs or modules stored in the memory 111 (e.g., airspace intrusion threat level assessment programs) and calls data stored in the memory 111 to perform various functions and process data for the electronic device 11.

[0103] The processor 112 executes the operating system of the electronic device 11 and various installed applications. The processor 112 executes the applications to implement the steps in the above-described method for assessing the level of airspace intrusion threats.

[0104] For example, the computer program may be divided into one or more modules, which are stored in the memory 111 and executed by the processor 112 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 11. For example, the computer program may be divided into a data acquisition module 110, a target classification module 120, a multi-source fusion module 130, and a threat assessment module 140.

[0105] The integrated unit implemented as a software functional module can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module, stored in the storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some functions of the airspace intrusion threat level assessment method described in the various embodiments of this application.

[0106] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for assessing the threat level of airspace intrusion, characterized in that, include: Multi-source sensors are used to collect data on intrusion targets within the protected airspace to obtain sensor datasets corresponding to different types of sensors. Based on the sensor datasets corresponding to different types of sensors, the target type of the intrusion target is identified and the confidence score is calculated, so as to obtain the target type identification results of the intrusion target by different types of sensors and the corresponding confidence scores; The target type discrimination results of different types of sensors on the intrusion target and the corresponding confidence scores are fused to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score. Based on the comprehensive target type identification results and the corresponding comprehensive confidence score, the threat level of the intrusion target is evaluated in real time using a preset intrusion threat level assessment model, and the evaluation results are dynamically displayed through a visual interface.

2. The method for assessing the threat level of airspace intrusion according to claim 1, characterized in that, The multi-source sensor includes monitoring radar, radio detection equipment, and photoelectric detection equipment; The method involves using multi-source sensors to collect data on intrusion targets within the protected airspace to obtain sensor datasets corresponding to different types of sensors, including: The monitoring radar is used to collect radar reflection data of intruding targets in the protected airspace in real time to obtain a radar dataset; The radio detection equipment is used to collect radio signal data of intruding targets within the protected airspace in real time to obtain a radio dataset; The photoelectric detection equipment is used to collect video or image data of intruding targets within the protected airspace to obtain a photoelectric dataset.

3. The method for assessing the threat level of airspace intrusion according to claim 2, characterized in that, The multi-source sensor also includes a weather radar, which is used to monitor the weather conditions in the protected airspace in real time and dynamically adjust the weights of visible light recognition and infrared recognition during the identification process of the photoelectric detection equipment according to different weather conditions.

4. The method for assessing the threat level of airspace intrusion according to claim 2, characterized in that, The step involves determining the target type and calculating the confidence level of the intrusion target based on sensor datasets corresponding to different types of sensors, to obtain the target type determination results and corresponding confidence scores for the intrusion target from different sensor types, including: Based on the radar dataset, the target type of the intrusion target is identified and the confidence level is calculated to obtain the target type identification result of the monitoring radar and the corresponding confidence score of the intrusion target; Based on the radio dataset, the target type of the intrusion target is identified and confidence is calculated to obtain the target type identification result and corresponding confidence score of the intrusion target identified by the radio detection device; Based on the photoelectric dataset, the target type is identified and confidence level is calculated for the intrusion target to obtain the target type identification result and corresponding confidence level score of the photoelectric detection device.

5. The method for assessing the threat level of airspace intrusion according to claim 2, characterized in that, The process of fusing the target type discrimination results and corresponding confidence scores of different types of sensors for the intrusion target to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score includes: The location and azimuth information of the intrusion target collected by the monitoring radar and the radio detection equipment are obtained, and the position difference and azimuth difference are calculated. When the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of different types of sensors on the intrusion target are fused to obtain a comprehensive target type discrimination result. The confidence scores corresponding to the target type discrimination results of different types of sensors for the intrusion target are fused to obtain a comprehensive confidence score.

6. The method for assessing the threat level of airspace intrusion according to claim 5, characterized in that, When the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of different types of sensors on the intrusion target are fused to obtain a comprehensive target type discrimination result, including: When the position difference is less than or equal to a preset position error threshold and the direction difference is less than or equal to a preset angle error threshold, the target type discrimination results of the monitoring radar and the radio detection equipment on the intrusion target are fused to obtain a fused target type discrimination result. The fused target type determination result is fused with the target type discrimination result of the photoelectric detection device on the intrusion target to obtain a comprehensive target type discrimination result.

7. The method for assessing the threat level of airspace intrusion according to claim 1, characterized in that, The preset intrusion threat level assessment model is constructed as follows: T(i)=α×W_dist(i)+β×W_reg(i)+γ×W_feat(i)+δ×W_ctr(i)+ε×W_hum(i)+ζ×W_env(i)+η×W_adsb(i) Where T(i) represents the threat level of the i-th target, W_dist(i) is the distance weighting function, W_reg(i) is the area weighting function, W_feat(i) is the target characteristic function, W_ctr(i) is the countermeasure status correction term, W_hum(i) is the manual confirmation correction term, W_env(i) is the environmental factor correction term, W_adsb(i) is the ADS-B correction term, and α, β, γ, δ, ε, ζ, and η are empirical coefficients.

8. A system for assessing the threat level of airspace intrusion, characterized in that, The method for assessing the threat level of protected airspace intrusion as described in any one of claims 1 to 7 is adopted, including: The data acquisition module is used to collect data on intrusion targets within the protected airspace using multi-source sensors, in order to obtain sensor datasets corresponding to different types of sensors. The target classification module is used to perform target type discrimination and confidence calculation on the intrusion target according to the sensor datasets corresponding to different types of sensors, so as to obtain the target type discrimination results of different types of sensors on the intrusion target and the corresponding confidence scores; The multi-source fusion module is used to fuse the target type discrimination results and corresponding confidence scores of different types of sensors for the intrusion target to obtain a comprehensive target type discrimination result and a corresponding comprehensive confidence score. The threat assessment module is used to assess the threat level of the intrusion target in real time based on the comprehensive target type discrimination result and the corresponding comprehensive confidence score, using a preset intrusion threat level assessment model, and dynamically display the assessment results through a visual interface.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the airspace intrusion threat level assessment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the airspace intrusion threat level assessment method as described in any one of claims 1 to 7.

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