An intelligent prediction system and algorithm for the landing point of low-altitude moving targets

Abstract

The present invention discloses an intelligent prediction system and algorithm for the landing point of low-altitude moving targets, which relates to the technical field of drone countermeasures and safety monitoring. The system includes an environmental perception module, a calculation module, a data extraction module, an analysis and evaluation module, a result correction module, and a display output module. The environmental perception module obtains the status information of the low-altitude moving target, the environmental information of the falling area, and real-time meteorological data; the data extraction module is used to extract the environmental data of the falling area and real-time meteorological data; the calculation module, the analysis and evaluation module, and the result correction module constitute the algorithm part of the system, including a horizontal projection motion model under the influence of wind speed, and a landing point area calculation method based on the Bayesian algorithm and the Sigmoid function; the display output module displays the precise range of the target's fall based on the final result. The present invention forms a complete set of low-altitude moving target countermeasure methods, which can ensure that the countermeasured target falls into a safe area, thereby preventing secondary disasters to people and buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of drone countermeasures and safety monitoring, and more specifically, to an intelligent prediction system and algorithm for the landing point of low-altitude moving targets. Background Art

[0002] "Low, slow, and small" targets refer to small, slow-flying targets operating at low or ultra-low altitudes, primarily including drones and other small aircraft. With the rapid development of drone technology, UAVs have been widely used in various fields, including military reconnaissance, forest fire prevention, aerial photography, agricultural plant protection, and power inspections, and their number has exploded. While drones have greatly facilitated people's lives, they also pose significant safety risks. Due to the low barrier to entry and the lack of unified regulations in the drone industry, the misuse of drones and illegal flights are common. The safety risks posed by small drones, such as low-altitude, slow-flying targets with small radar cross-sections, continue to increase, and have become a serious aerial threat to both military and civilian use. For example, in September 2021, a pilot near Chengdu Shuangliu International Airport released a miniature civilian drone he had purchased for selfies. The drone subsequently lost control and drifted toward the airport, posing a serious safety threat to flights. The pilot was sentenced to 10 days of administrative detention. Since it is difficult to control all aspects of drones, in addition to addressing the source, we also need to prepare for the future, and disposal and prevention measures are also imminent.

[0003] Currently, countermeasures against low-altitude moving targets are just emerging. Common countermeasures include signal jamming, kinetic weapons, and directed energy weapons. These countermeasures primarily rely on equipment and host computers, with few methods involving system-based plans and decision-making support. Furthermore, existing countermeasure systems only consider target type and frequency, and only consider the countermeasure process before and during the countermeasure, not after the countermeasure. This makes it difficult to calculate the target's impact point, and recover debris after the countermeasure is difficult. Furthermore, my country's current low-altitude security and defense measures are relatively limited, so low-altitude moving targets that are countermeasured often fall to the ground at random, making secondary disasters highly likely. Therefore, accurately predicting and assessing the target's impact point is crucial for countermeasures against low-altitude moving targets. With the increasing maturity of environmental perception technology, various high-precision sensors can accurately capture information about the surrounding conditions of the target after the countermeasure, including wind speed, direction, humidity, temperature, and air pressure. This makes it possible to accurately predict the target's impact point. Summary of the Invention

[0004] In response to the low-altitude safety issues mentioned above, based on drone countermeasure technology and environmental perception technology, the present invention proposes an intelligent prediction system and algorithm for the landing point of low-altitude moving targets. It performs surrounding environment perception, target data extraction, landing point position calculation, and landing area evaluation on the countered target, forming a complete low-altitude moving target countermeasure system and method, and ultimately ensures that the countered target falls into a safe area, thereby preventing secondary disasters to people and buildings.

[0005] The technical means adopted in the present invention are as follows:

[0006] An intelligent prediction system and algorithm for the impact point of low-altitude moving targets, including:

[0007] The environmental perception module integrates multiple sensors to obtain the status information of low-altitude moving targets, the environmental information of the falling area and real-time meteorological data, and transmits the status information of low-altitude moving targets to the calculation module, and the environmental information of the falling area and real-time meteorological data to the data extraction module.

[0008] The calculation module is connected to the environmental perception module, and preliminarily calculates the landing position and falling kinetic energy of the target according to a preset algorithm, and sends the calculation results to the analysis and evaluation module.

[0009] A data extraction module, connected to the environmental perception module, is used to extract environmental data and real-time meteorological data of the falling area and send the information to the calculation module and the result correction module;

[0010] The analysis and evaluation module integrates high-precision map software. It is used to preliminarily judge the falling consequences of the "low and slow target" and whether the ground area allows falling based on the calculation results of the calculation module, and sends the evaluation criteria to the result correction module.

[0011] A result correction module is connected to the analysis and evaluation module and the data extraction module. On the premise of determining that the ground area allows falling, it corrects the preliminary calculation results in combination with real-time meteorological data to obtain the final falling range and falling kinetic energy, and sends them to the display output module;

[0012] The display output module is connected to the analysis and evaluation module and the result correction module, and is used to receive the final result sent by the result correction module, display the precise range of the target fall and the kinetic energy of the fall in combination with the map, and give a prompt of possible damage to the fall area.

[0013] Furthermore, in the above-mentioned intelligent prediction system for the landing point of low-altitude moving targets, the multiple sensors include but are not limited to lidar, temperature sensor, humidity sensor, speed sensor, and pressure sensor.

[0014] Furthermore, the above-mentioned low-altitude moving target disposal and landing point intelligent prediction system includes, but is not limited to, the state information of the low-altitude moving target, including but not limited to the target's mass m, speed v0, position (x0, y0), and height z0; the environmental information of the falling area includes but is not limited to the population density, number of buildings, and falling restricted area of the falling area; the real-time meteorological data includes but is not limited to atmospheric pressure P, air humidity H and temperature T, wind direction and wind speed v w .

[0015] Furthermore, in the above-mentioned intelligent prediction system for the landing point of low-altitude moving targets, the situations judged in the analysis and evaluation module include but are not limited to: whether the landing point preliminarily calculated by the calculation module allows the target to fall, whether the kinetic energy of the target falling is within the acceptable range, and the difficulty of recovering the target after it falls.

[0016] The present invention also provides an intelligent prediction algorithm for the landing point of low-altitude moving targets, including:

[0017] The target landing point is preliminarily calculated. After obtaining the status information of the low-altitude moving target, the landing point of the target is estimated under ideal conditions without considering the external environment, and the influence of air resistance is ignored. Since the moving target has an initial horizontal velocity, the falling process after the impact can be abstracted as a horizontal projection motion, with uniform motion in the horizontal direction and free fall motion in the vertical direction. The target initial horizontal velocity is set to v0, and the position is (x0, y0, z0). x0, y0, z0 are the initial longitude, latitude and altitude of the target respectively. The angle between the initial horizontal velocity and the latitude is θ. The initial horizontal velocity can be decomposed into the angle along the latitude direction v x and the velocity v along the meridian direction y .

[0018] v x =v0·cosθ

[0019] v y =v0·sinθ

[0020] The time of horizontal projection is

[0021]

[0022] g is the acceleration due to gravity, which is 9.8 m / s 2 , combining the above formulas, the target landing point position is

[0023] Preliminary calculation of the target's falling kinetic energy, the kinetic energy calculation formula is E = mv 2 / 2, m is the mass of the target, v is the speed of the target when it lands

[0024]

[0025] vz is the vertical velocity:

[0026]

[0027] The kinetic energy is

[0028]

[0029] Furthermore, the above-mentioned low-altitude moving target disposal landing point intelligent prediction algorithm also includes: considering the real-time wind direction and wind speed as the main factors to correct the results, assuming that the wind speed is constant with vector v w Indicates that the angle between the target initial velocity v0 mentioned above is α, and v w The vector synthesis method is used to synthesize v0, and the angle between the two is α to obtain the corrected horizontal initial velocity

[0030]

[0031] The angle between the corrected horizontal initial velocity and the latitude is θ′, and the corrected horizontal initial velocity is decomposed into v′ along the latitude direction x and the velocity v′ along the meridian direction y

[0032] v′ x =v′0·cosθ′

[0033] v′ y =v′0·sinθ′

[0034] Assuming that the air resistance F is constant, the falling process can still be regarded as a horizontal projection motion, and its vertical acceleration is

[0035] a=(mg-F) / m

[0036] The time of horizontal projection is

[0037]

[0038] The target landing point is

[0039]

[0040] The kinetic energy correction calculation of the target falling is based on the deformation of the kinetic energy theorem. The kinetic energy of the target when it lands is

[0041] Furthermore, the above-mentioned intelligent prediction algorithm for the landing point of low-altitude moving targets also includes: considering atmospheric pressure P, air humidity H and temperature T as secondary factors to correct the results, and using a method combining Bayesian algorithm and Sigmoid function for reasoning.

[0042] The Bayesian algorithm is a statistical classification method that includes the following concepts: Prior probability: the probability of an event A or B occurring

[0043] Likelihood probability: the probability of event B occurring under the premise that event A occurs. Posterior probability: the probability of event A occurring under the condition that another event B has already occurred. Posterior probability = (prior probability × likelihood probability) / constant (Bayesian decision making makes decisions based on posterior probability)

[0044]

[0045] Sigmoid function, that is, y = 1 / (1+e -x ). It is a nonlinear action function of neurons. It is widely used in neural networks. Neural networks learn based on a set of samples, consisting of inputs and outputs. There are as many input and output neurons as there are input and output components. After determining the offset probability under different meteorological conditions, we can substitute it into the sigmoid function to infer the corresponding offset range. This function has the following characteristics: as x approaches negative infinity, y approaches 0; as x approaches positive infinity, y approaches 1; and when x = 0, y = 1 / 2. Slight modifications to this function can effectively satisfy the correspondence between offset probability and offset range.

[0046] The Bayesian algorithm and Sigmoid function are combined to calculate the influence of the secondary factors humidity, temperature and air pressure on the landing point deviation. Substitute into the Bayesian formula

[0047]

[0048] P(A) is the prior probability of the target point shift when it is not affected by atmospheric pressure, air humidity, and temperature. P(P=P0,H=H0,T=T0) is the prior probability of a specific meteorological condition. P(P=P0,H=H0,T=T0|A) is the probability of the meteorological condition occurring when the target point shifts, that is, the likelihood probability. P(A|P=P0,H=H0,T=T0) is the probability of the target point shifting under the meteorological condition, that is, the posterior probability. According to the value of the posterior probability y=P(A|P=P0,H=H0,T=T0), the Sigmoid function y=[2 / (1+e -x )] - 1, we can get the value of x, which is the radius of the falling area. We can determine the circular falling area with the falling point as the center and x as the radius. The possible range of y is [0, 1], corresponding to the range of x [0, +∞].

[0049] Furthermore, the present invention also provides a storage medium, including a stored program, wherein when the program is run, the steps of the intelligent prediction algorithm for the landing point of low-altitude moving targets are executed.

[0050] Furthermore, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the above-mentioned intelligent prediction algorithm for the landing point of low-altitude moving targets through the computer program.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] 1. The intelligent prediction system and method for the landing point of low-altitude moving targets provided by the present invention are fully automatic processing processes when used in practice, without the need for human intervention, which greatly saves manpower and material resources, and also improves the efficiency of countermeasures against low-altitude moving targets.

[0053] 2. The environmental perception module of the intelligent prediction system for the disposal and landing point of low-altitude moving targets provided by the present invention adopts multi-sensor data fusion technology, which can collect various types of data in real time. At the same time, it is not affected by bad weather and complex geographical environment. It can accurately obtain information under various working conditions and has the characteristics of accuracy and stability.

[0054] 3. The intelligent prediction method for the landing point of low-altitude moving targets provided by the present invention takes into account the influence of various meteorological conditions on the final result. It is based on the physical model of horizontal projection motion under the influence of wind speed, and uses the Sigmoid function and Bayesian algorithm to infer the landing point range. The preliminary calculation results are corrected, which can effectively reduce errors and improve the alignment accuracy.

[0055] 4. The intelligent prediction system and method for the landing point of low-altitude moving targets provided by the present invention can ultimately output the specific geographical area and falling kinetic energy of the low-altitude moving target, can accurately assess the impact of the falling target on the ground area, can be well adapted to actual applications, is suitable for various low-altitude defense systems and host computers, and can be used in the entire low-altitude defense industry.

[0056] 5. The intelligent prediction system and method for the landing point of low-altitude moving targets provided by the present invention fully considers the subjective operating intentions of the staff on the basis of intelligent decision-making, and ultimately displays and outputs scientific prediction results and evaluations, giving the staff the initiative to make the final decision.

[0057] Based on the above reasons, the present invention can be widely promoted in the fields of drone countermeasures and security monitoring technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0059] Figure 1 This is a structural block diagram of the intelligent prediction system provided by the present invention.

[0060] Figure 2 This is a workflow diagram of the intelligent prediction system and algorithm provided by the present invention. DETAILED DESCRIPTION

[0061] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0062] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0063] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0064] Unless otherwise specifically stated, the relative arrangement of the parts and steps, numerical expressions and numerical values described in these embodiments do not limit the scope of the present invention. At the same time, it should be clear that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The techniques, methods and equipment known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0065] In the description of the present invention, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "horizontal, vertical, vertical, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of the present invention: the directional words "inside and outside" refer to the inside and outside relative to the outline of each component itself.

[0066] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used herein to describe the spatial positional relationship of a device or feature to other devices or features as shown in the figures. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figures. For example, if the device in the drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below their position devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0067] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore cannot be understood as limiting the scope of protection of the present invention.

[0068] Figure 1This is a structural block diagram of the intelligent prediction system for low-altitude moving target disposal and landing point according to the present invention. Figure 1 As shown, the intelligent prediction system includes: an environment perception module 10, a calculation module 20, a data extraction module 30, an analysis and evaluation module 40, a result correction module 50, and a display output module 60.

[0069] The environmental perception module 10 integrates multiple sensors to obtain status information of low-altitude moving targets, environmental information about the crash zone, and real-time meteorological data. The low-altitude moving target status information is transmitted to the calculation module, and the crash zone environmental information and real-time meteorological data are transmitted to the data extraction module.

[0070] The calculation module 20 is connected to the environmental perception module, and preliminarily calculates the landing area of the target according to a preset algorithm, and sends the calculation result to the analysis and evaluation module.

[0071] Data extraction module 30, data extraction module, connected to the environment perception module, is used to extract the environmental data and real-time meteorological data of the falling area, and send the information to the calculation module and the result correction module;

[0072] The analysis and evaluation module 40 is used to preliminarily judge the falling consequences of the "low and slow target" and whether the ground area allows the falling according to the calculation results of the calculation module, and sends the evaluation results to the result correction module.

[0073] The result correction module 50 is connected with the analysis and evaluation module and the data extraction module. On the premise that falling is allowed in the ground area, the preliminary calculation results are corrected in combination with real-time meteorological data to obtain the final result and send it to the display output module.

[0074] The display output module 60 is connected to the analysis and evaluation module and the result correction module, and is used to receive the final result sent by the result correction module, display the precise range of the target fall and the kinetic energy of the fall in combination with the map, and give a warning of the possible damage to the fall area.

[0075] By perceiving the surrounding environment, extracting target data, calculating the landing point, and evaluating the landing area of the target after countermeasure, a complete low-altitude moving target countermeasure plan is formed, which can accurately predict the target landing point range and falling kinetic energy, and provide guidance for actual low-altitude moving target countermeasure work.

[0076] Furthermore, as a preferred embodiment of the present invention, Figure 2 This is a workflow diagram of the intelligent prediction system and algorithm provided by the present invention. Figure 2 As shown, its working process includes the following steps:

[0077] Step 101: The environment perception module 10 obtains target data, surrounding environment data, and meteorological data. If there is any data that has not been obtained, the connection status of each sensor needs to be checked. After all data are successfully obtained, the process proceeds to step 2.

[0078] In step 102 , after receiving the data from the environment perception module 10 , the data extraction module 30 extracts the low-altitude moving target data and transmits it to the calculation module 20 , and extracts the surrounding environment data and transmits it to the analysis and evaluation module 40 . Then, the process proceeds to step 103 .

[0079] Step 103: The calculation module preliminarily calculates the target ground landing point and falling kinetic energy based on the low-altitude moving target data. The target's initial horizontal velocity is v0, its mass is m, and its position is (x0, y0, z0). x0, y0, z0 are the target's initial longitude, latitude, and altitude, respectively. The angle between the initial horizontal velocity and the latitude is θ, and g is the acceleration of gravity, which is 9.8 m / s. 2 .

[0080] The target landing point is The kinetic energy of the falling target is The preliminary calculation results are sent to the analysis and evaluation module, and the process goes to step 104 .

[0081] In step 104, the analysis and assessment module receives the initially calculated landing point and determines whether the area in question permits landing of moving targets. If the area prohibits low-altitude landing, such as at important transportation hubs like airports and train stations, the timing and location of the countermeasure target are adjusted to cause it to land elsewhere. If the area permits low-altitude landing, the process proceeds to step 105.

[0082] In step 105, after the area permits low-altitude targets to land, the analysis and assessment module continues to determine whether the target's kinetic energy is within the acceptable range based on the preliminary calculation of the target's kinetic energy. If the target's kinetic energy exceeds the ground area's tolerance, protective measures are implemented in the landing area or the timing and position of countermeasures are further adjusted. If the target's kinetic energy is within the ground area's tolerance, the process proceeds to step 106.

[0083] In step 106, after the analysis and evaluation module determines that the landing point location and kinetic energy meet the requirements for the landing area, meteorological conditions affect the target landing point location and kinetic energy, so an offset needs to be calculated to correct the results and ultimately determine the target landing point range and kinetic energy. Therefore, the data extraction module extracts real-time meteorological data such as wind direction, wind speed, temperature, air pressure, and humidity, and sends it to the result correction module, which then proceeds to step 107.

[0084] Step 107, the result correction module is divided into two parts to perform correction according to the different degrees of influence of various meteorological data. First, the target landing point position and falling kinetic energy are recalculated with wind direction and wind speed as the main factors. The target landing point position is The kinetic energy of the target when it lands is v0 is the target's initial horizontal velocity, m is its mass, (x0, y0, z0) is its position, x0, y0, z0 are the target's initial longitude, latitude and altitude respectively, v w is the wind speed, α is the horizontal initial velocity v0 and the wind speed v w Angle, θ′ is the angle between the horizontal initial velocity and wind speed and the latitude, F is the air resistance, and g is the acceleration of gravity with a value of 9.8m / s 2 , proceed to step 108.

[0085] Step 108, after recalculating the target landing point position and falling kinetic energy with wind direction and wind speed as the main factors, the Bayesian algorithm and Sigmoid function are combined to calculate the influence of the secondary factors humidity, temperature, and air pressure on the landing point deviation.

[0086]

[0087] P(A) is the prior probability of the target point shift when it is not affected by atmospheric pressure, air humidity, and temperature. P(P=P0,H=H0,T=T0) is the prior probability of a specific meteorological condition. P(P=P0,H=H0,T=T0|A) is the probability of the meteorological condition occurring when the target point shifts, that is, the likelihood probability. P(A|P=P0,H=H0,T=T0) is the probability of the target point shifting under the meteorological condition, that is, the posterior probability. According to the value of the posterior probability y=P(A|P=P0,H=H0,T=T0), substitute it into the transformed Sigmoid function y=[2 / (1+e -x )] - 1, we can obtain the value of x, i.e., the radius of the landing area, and determine the circular landing area with the landing point as the center and x as the radius. The possible value range of y is [0, 1], corresponding to the range of x in [0, +∞], and then proceed to step 109.

[0088] Step 109 : The display output module displays the precise range and kinetic energy of the target's fall based on the calculation results, and indicates the damage that the target's fall may cause to the ground area, thereby providing reference and suggestions for staff decision-making.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0090] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent prediction system for the impact point of low-altitude moving targets, characterized by: include: The environmental perception module is used to obtain the status information of low-altitude moving targets, the environmental information of the falling area, and real-time meteorological data, and transmit the status information of low-altitude moving targets to the calculation module, and transmit the environmental information of the falling area and real-time meteorological data to the data extraction module; A calculation module, connected to the environmental perception module, preliminarily calculates the target's landing area according to a preset algorithm and sends the calculation results to the analysis and evaluation module; A data extraction module, connected to the environmental perception module, is used to extract environmental data and real-time meteorological data of the falling area and send the information to the calculation module and the result correction module; The analysis and evaluation module uses the calculation results of the calculation module to preliminarily determine the consequences of the "low and slow target" falling and whether the ground area allows the falling, and sends the evaluation results to the result correction module; A result correction module is connected to the analysis and evaluation module and the data extraction module. Under the premise that falling is allowed in the ground area, the preliminary calculation results are corrected in combination with real-time meteorological data to obtain the final result and send it to the display output module; a display output module connected to the analysis and evaluation module and the result correction module, configured to receive the final result sent by the result correction module, display the precise range of the target fall and the magnitude of the kinetic energy of the fall in conjunction with a map, and provide a warning of possible damage to the fall area; It also includes an intelligent prediction algorithm for the landing point of low-altitude moving targets, including: The target landing point is preliminarily calculated. After obtaining the status information of the low-altitude moving target, the target landing point is estimated under ideal conditions without considering the external environment, and the influence of air resistance is ignored. Since the moving target has an initial horizontal velocity, the falling process after the impact can be abstracted as a horizontal projection motion, with uniform motion in the horizontal direction and free fall motion in the vertical direction. The target initial horizontal velocity is set to v0, the mass is m, and the position is (x0, y0, z0). x0, y0, z0 are the initial longitude, latitude, and altitude of the target, respectively. The angle between the initial horizontal velocity and the latitude is θ, and g is the acceleration of gravity with a value of 9.8m / s. 2 ; The target landing point is The preliminary calculation of the kinetic energy of the target falling is After obtaining the preliminary calculated landing point, determine whether the area allows moving targets to fall. If the area allows low-altitude targets to land, preliminarily calculate the kinetic energy of the target's fall and continue to determine whether the kinetic energy of the target's fall is within the tolerance range. If the kinetic energy of the target's fall is within the tolerance range of the ground area, the preliminary calculation results are revised considering meteorological conditions. First, consider the real-time wind direction and wind speed as the main factors to correct the results, assuming that the wind speed is constant and the vector v w Indicates that the angle between the initial target velocity v0 is α, the angle between the corrected horizontal initial velocity and the latitude is θ′, the air resistance F is a constant, and v w The vector synthesis method combined with v0 can still be regarded as a horizontal projection motion, and the target landing point is: The kinetic energy of the target when it lands is Secondly, atmospheric pressure P, air humidity H, and temperature T are considered as secondary factors to correct the results. The Bayesian algorithm and the Sigmoid function are combined to calculate the influence of the secondary factors humidity, temperature, and air pressure on the landing point offset. The Bayesian algorithm is used to infer the target offset probability, and then the Sigmoid function is used to convert the offset probability into an offset range, and finally the landing point area range is obtained. The intelligent prediction algorithm for the landing point of low-altitude moving targets also includes: if the area does not allow low-altitude targets to land, adjusting the timing and position of the counter-target to make it fall elsewhere; If the target's kinetic energy exceeds the ground's tolerance, protective measures will be taken in the impact area or the timing and location of the countermeasures will be adjusted. Among them, the result correction module is divided into two parts according to the different degrees of influence of various meteorological data. First, the target landing point position and falling kinetic energy are recalculated with wind direction and wind speed as the main factors. The target landing point position is The kinetic energy of the target when it lands is v0 is the target's initial horizontal velocity, m is its mass, (x0, y0, z0) is its position, x0, y0, z0 are the target's initial longitude, latitude and altitude respectively, v w is the wind speed, α is the horizontal initial velocity v0 and the wind speed v w Angle, θ′ is the angle between the horizontal initial velocity and wind speed and the latitude, F is the air resistance, and g is the acceleration of gravity with a value of 9.8m / s 2 ; After recalculating the target landing point and falling kinetic energy with wind direction and wind speed as the main factors, the Bayesian algorithm and Sigmoid function are combined to calculate the influence of the secondary factors humidity, temperature, and air pressure on the landing point deviation; according to the Bayesian formula P(A) is the prior probability of the landing point shift when it is not affected by atmospheric pressure, air humidity, and temperature. P(P=P0,H=H0,T=T0) is the prior probability of a certain meteorological condition. P(P=P0,H=H0,T=T0|A) is the probability of the meteorological condition occurring when the target landing point shifts, that is, the likelihood probability. P(A|P=P0,H=H0,T=T0) is the probability of the target landing point shifting under the meteorological condition, that is, the posterior probability. According to the value of the posterior probability y=P(A|P=P0,H=H0,T=T0), substitute it into the transformed Sigmoid function y=[2 / (1+e -x )]-1, we can get the value of x, that is, the radius of the landing area, and determine the circular falling area with the falling point as the center and x as the radius. The value range of y is [0,1], and the corresponding range of x is [0,+∞].

2. The intelligent prediction system for low-altitude moving target disposal landing point according to claim 1 is characterized in that: The environmental perception module includes multiple sensors, including but not limited to laser radar, temperature sensor, humidity sensor, speed sensor, and pressure sensor.

3. The intelligent prediction system for low-altitude moving target disposal landing point according to claim 1 is characterized in that: The state information of the low-altitude moving target includes but is not limited to the target's mass m, speed v0, position (x0, y0), and height z0; the environmental information of the falling area includes but is not limited to the population density, number of buildings, and falling restricted area; the real-time meteorological data includes but is not limited to atmospheric pressure P, air humidity H and temperature T, wind direction and wind speed v w .

4. The intelligent prediction system for low-altitude moving target disposal landing point according to claim 1 is characterized in that: The situations judged in the analysis and evaluation module include but are not limited to: whether the landing point initially calculated by the calculation module allows the target to fall, whether the kinetic energy of the target falling is within the tolerance range, and the difficulty of recovering the target after falling.

5. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the low-altitude moving target handling landing point intelligent prediction algorithm according to claim 1 is executed.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor executes the low-altitude moving target landing point intelligent prediction algorithm according to claim 1 through the computer program.

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