CN111447038A - Unmanned aerial vehicle defense system based on big data - Google Patents
Unmanned aerial vehicle defense system based on big data Download PDFInfo
- Publication number
- CN111447038A CN111447038A CN202010256174.XA CN202010256174A CN111447038A CN 111447038 A CN111447038 A CN 111447038A CN 202010256174 A CN202010256174 A CN 202010256174A CN 111447038 A CN111447038 A CN 111447038A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- data
- aerial vehicle
- time
- real
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
- H04K3/00—Jamming of communication; Counter-measures
- H04K3/80—Jamming or countermeasure characterized by its function
- H04K3/82—Jamming or countermeasure characterized by its function related to preventing surveillance, interception or detection
- H04K3/825—Jamming or countermeasure characterized by its function related to preventing surveillance, interception or detection by jamming
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses an unmanned aerial vehicle defense system based on big data, which comprises a scanning module, a database, a monitoring module, a judging module, a data calculating module and an interference unit, wherein the scanning module is used for scanning the unmanned aerial vehicle; the scanning module detects objects in a radar range and automatically acquires object information, wherein the object information comprises object position data, object state information and object angle data.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle defense, in particular to an unmanned aerial vehicle defense system based on big data.
Background
An unmanned aircraft, referred to as "drone", is an unmanned aircraft that is operated by a radio remote control device and a self-contained program control device, or is operated autonomously, either completely or intermittently, by an onboard computer. Drones tend to be more suitable for tasks that are too "fool, dirty, or dangerous" than are manned aircraft. Unmanned aerial vehicles can be classified into military and civil applications according to the application field. For military use, unmanned aerial vehicles divide into reconnaissance aircraft and target drone. In the civil aspect, the unmanned aerial vehicle + the industry application is really just needed by the unmanned aerial vehicle; at present, the unmanned aerial vehicle is applied to the fields of aerial photography, agriculture, plant protection, miniature self-timer, express transportation, disaster relief, wild animal observation, infectious disease monitoring, surveying and mapping, news reporting, power inspection, disaster relief, film and television shooting, romantic manufacturing and the like, the application of the unmanned aerial vehicle is greatly expanded, and developed countries actively expand the industrial application and develop the unmanned aerial vehicle technology.
An unmanned aerial vehicle defense system with an authorization notice number of CN205749876U, the unmanned aerial vehicle defense system, according to safe area environment, the flexible configuration of antenna quantity and interval or anti-unmanned aerial vehicle rifle of scope size are equipped, realize effective interference to unmanned aerial vehicle, effectively improve the security in the safe area, start-up system according to the task needs, construct the forbidden zone of dynamic unmanned aerial vehicle, this system transmits anti-navigation signal, realize blocking the interference of black unmanned aerial vehicle remote control link, the effective barrier is the black unmanned aerial vehicle that flies, however, this unmanned aerial vehicle defense system can't be quick discerns unmanned aerial vehicle, and can't be according to unmanned aerial vehicle's relevant data, carry out accurate analysis to unmanned aerial vehicle, for this reason, we propose an unmanned aerial vehicle defense system based on big data.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle defense system based on big data, through the arrangement of a monitoring module and a judging module, the object state and the object shape are analyzed, so that whether an object is an unmanned aerial vehicle is judged, the data is monitored, the accuracy of object identification is increased, errors are reduced, the analysis time is saved, the working efficiency is improved, through the arrangement of a data calculating module, the movement speed, the heating time and the movement distance of the unmanned aerial vehicle are analyzed, interference signals are selected, the unmanned aerial vehicle is interfered according to the interference signals, the accuracy of data calculation is increased, corresponding interference signals are conveniently sent aiming at control signals of the unmanned aerial vehicle, the safety of unmanned aerial vehicle defense is improved, the time for selecting the interference signals is saved, and the working efficiency is improved.
The technical problem to be solved by the invention is as follows:
(1) how to analyze the state of an object monitored by a radar through the arrangement of a monitoring module so as to determine whether the object is static or moving in the state, and analyzing the shape of the monitored object according to a judging module so as to judge whether the object is an unmanned aerial vehicle or not and monitor the data of the unmanned aerial vehicle so as to solve the problem that the unmanned aerial vehicle cannot be identified quickly in the prior art;
(2) how to through the setting of data calculation module, carry out the analysis to unmanned aerial vehicle real-time motion position data, temperature data and time data, thereby calculate unmanned aerial vehicle's velocity of motion, the intensification time, and calculate total movement distance according to it, judge unmanned aerial vehicle's kind, select out interfering signal, and interfere with unmanned aerial vehicle according to it, solve among the prior art can't send corresponding interfering signal's problem to unmanned aerial vehicle.
The purpose of the invention can be realized by the following technical scheme: an unmanned aerial vehicle defense system based on big data comprises a scanning module, a database, a monitoring module, a judging module, a data calculating module and an interference unit;
the scanning module detects an object in a radar range, automatically acquires object information, and transmits the object information to the monitoring module, wherein the object information comprises object position data, object state information and object angle data, and the object state information comprises a static object, a moving object and state retention time data;
the monitoring module acquires object information, performs detection operation according to the object information to obtain monitoring information, and transmits the monitoring information to the judging module;
the monitoring information comprises object image information and object moving speed data, unmanned aerial vehicle image data, unmanned aerial vehicle speed range data, unmanned aerial vehicle real-time motion position data, unmanned aerial vehicle real-time temperature data and time data are stored in the database, and the judging module is used for carrying out safety judgment on the object image information, the object moving speed data, the unmanned aerial vehicle image data and the unmanned aerial vehicle speed range data to obtain unmanned aerial vehicle real-time motion position data, unmanned aerial vehicle real-time temperature data and time data and transmitting the unmanned aerial vehicle real-time motion position data, the unmanned aerial vehicle real-time temperature data and the time data to the;
the data calculation module is used for calculating the real-time motion position data of the unmanned aerial vehicle, the real-time temperature data of the unmanned aerial vehicle and the time data to obtain a total movement value of the unmanned aerial vehicle and transmitting the total movement value to the interference unit;
the system comprises a database, a data base and an interference unit, wherein the database is also internally stored with the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data and the control distance range data corresponding to the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data, the control distance data range and the total movement value of the unmanned aerial vehicle of the control equipment, the interference unit is used for carrying out interference operation on the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data, the control distance data range and the total movement value of the.
As a further improvement of the invention: the specific operation process of the detection operation is as follows:
the method comprises the following steps: acquiring object state information, identifying a static object and a moving object in the object state information, and carrying out state judgment on the static object and the moving object together with state retention time data, wherein the state judgment specifically comprises the following steps:
a1: when a static object is identified and the state retention time data is greater than M, the object is determined to be a fixed object and is not monitored, and when the static object is identified and the state retention time data is less than M, the state of the object is determined to be ambiguous and monitoring is continued;
a2: when a moving object is identified and the state retention time data is less than M, determining that the object keeps moving in a short time, continuously monitoring, and when the moving object is identified and the state retention time data is more than M, determining that the object moves actively and immediately monitoring;
step two: and when the continuous monitoring and the immediate monitoring are carried out according to the judgment result in the first step, automatically acquiring object position data and object angle data corresponding to the object, monitoring the object according to the object position data and the object angle data, and automatically acquiring monitoring information.
As a further improvement of the invention: the specific secondary judgment process of the safety judgment is as follows:
k1: acquiring object image information, object movement speed data, unmanned aerial vehicle image data and unmanned aerial vehicle speed range data, and sequentially marking the object image information, the object movement speed data, the unmanned aerial vehicle image data and the unmanned aerial vehicle speed range data as WYi, WSi, RYL and RSl, wherein i is 1,2,3.. No. n1, l is 1,2,3.. No. n2, and WYi and WSi are in one-to-one correspondence, and RYl and RSl are in one-to-one correspondence;
k2: compare object image information and unmanned aerial vehicle image data, specifically do: when it occurs
When WYi ∈ RYl appears, the object image is similar to the unmanned aerial vehicle image, and the object is similar to the unmanned aerial vehicle image;
k3: carry out real-time analysis with object moving speed data to carry out big to little sequencing with the speed at different moments, select wherein the biggest object moving value, and compare it with unmanned aerial vehicle speed range data, specifically do: when the maximum object movement value does not belong to the unmanned aerial vehicle speed range data, judging that the object movement speed does not accord with the unmanned aerial vehicle movement speed, wherein the object is not an unmanned aerial vehicle, and when the maximum object movement value belongs to the unmanned aerial vehicle speed range data, judging that the object movement speed accords with the unmanned aerial vehicle movement speed, wherein the object is similar to the unmanned aerial vehicle;
k4: the result in with above-mentioned K2 and K3 is synthesized and is judged, when two judgement results are the similar unmanned aerial vehicle of this object, then judges that this object is the unmanned aerial vehicle of this type to automatic acquisition and the corresponding unmanned aerial vehicle real-time motion position data of this type unmanned aerial vehicle, the real-time temperature data of unmanned aerial vehicle and time data.
As a further improvement of the invention: the specific operation process of the calculation operation is as follows:
g1: acquiring real-time motion position data of the unmanned aerial vehicle, establishing a virtual space rectangular coordinate system by taking monitoring equipment as an origin, marking the real-time position data of the unmanned aerial vehicle in the virtual space rectangular coordinate system, and marking the real-time position as WWj, wherein j is 1,2,3.... times.n 3, and the real-time position coordinate of the unmanned aerial vehicle is WWj (Xj, Yj, Zj);
g2: selecting any two positions of the real-time positions of the unmanned aerial vehicle, and bringing the positions into the calculation formulas of an X axis and a Y axis:
and the coordinate values of the Z axis and the coordinate values are brought into a calculation formula together:
wherein, HjXYExpressed as the distance, Hj, from the intersection of the X and Y axes to the monitoring deviceZXYExpressing the distance data between the unmanned aerial vehicle and the monitoring equipment, bringing the positions of the real-time motion position data of the unmanned aerial vehicle in two different time periods to the monitoring equipment into a difference value calculation formula, solving the difference value of the distances between the unmanned aerial vehicle and the monitoring equipment at two different time points, and marking the difference value as JCj;
g3: acquiring real-time temperature data of the unmanned aerial vehicle corresponding to the real-time motion position data of the unmanned aerial vehicle, marking the real-time temperature data as WDj, and bringing the real-time temperature data and corresponding time data into a calculation formula:
wherein, VWDjExpressed as the frequency of temperature change, WD1 and WD2 are expressed as the temperature data of the drone in two time periods, T, respectivelyDifference (D)The time difference of the real-time temperature data of the two unmanned aerial vehicles corresponding to the real-time motion position data of the unmanned aerial vehicles is represented, u is represented as an influence factor of the external temperature on the temperature change of the unmanned aerial vehicles, and R is represented as a diffusion value of the temperature of the unmanned aerial vehicles;
g4: the difference of the distances between the unmanned aerial vehicle and the monitoring equipment at two different time points is brought into a calculation formula together with time data: vIs not j＝JCj/TDifference (D)Wherein V isIs not jExpressed as the speed of movement of the drone and taken into the mean calculation:
wherein PVIs not jThe average moving speed of the unmanned aerial vehicle is represented, the temperature data of the unmanned aerial vehicle at the final moment is obtained,the temperature data at the final moment specifically refers to the latest monitored temperature data, and is brought into the calculation formula together with the temperature change frequency:
wherein, TGeneral assemblyExpressed as total time-consuming value, r1 is expressed as sudden change influence factor, and is brought into the calculation formula J L together with the average moving speed of the unmanned aerial vehicleGeneral assembly＝TGeneral assembly*PVIs not jR2, wherein J LGeneral assemblyExpressed as the total value of the drone's movement, r2 is expressed as the distance calculation error adjustment factor.
As a further improvement of the invention: the specific operation process of the interference operation is as follows:
e1: match unmanned aerial vehicle's removal total value and control distance range data, the matching result divide into two kinds, is respectively: a. b, the total moving value of the unmanned aerial vehicle does not belong to the control distance range data;
e2: and performing type matching on the matching result of the type b in the E1 and the frequency point data of the unmanned aerial vehicle, thereby obtaining the equipment type corresponding to the matching result, extracting the equipment interference signal corresponding to the equipment type, and marking the interference signal as a defense interference signal.
The invention has the beneficial effects that:
(1) detecting an object in a radar range through a scanning module, automatically acquiring object information, and transmitting the object information to a monitoring module; the monitoring module acquires object information, acquires real-time position data of an object, judges the object to be a static object and a moving object according to the positions of the object at different times and the time of keeping the object at the position, continuously monitors the moving object, acquires monitoring information and transmits the monitoring information to the judging module; the judging module acquires object image information, object moving speed data, unmanned aerial vehicle image data and unmanned aerial vehicle speed range data, carry out the analysis according to its kind to the object, judge whether this object is unmanned aerial vehicle, extract rather than corresponding unmanned aerial vehicle real-time motion position data, unmanned aerial vehicle real-time temperature data and time data, setting through monitoring module, object to monitoring carries out state analysis to the radar, thereby static still when confirming the state that the object was located is the motion, the object shape of monitoring is analyzed according to judging module, thereby judge whether the object is unmanned aerial vehicle, and monitor its data, increase the accuracy of discerning the object, reduce the error, save analysis time, and improve work efficiency.
(2) Analyzing real-time motion position data of the unmanned aerial vehicle, real-time temperature data of the unmanned aerial vehicle and time data through a data calculation module, analyzing motion records of the unmanned aerial vehicle according to the position data of the unmanned aerial vehicle at different time points, analyzing motion time of the unmanned aerial vehicle according to motion distance and motion time, calculating temperature rise speed of the unmanned aerial vehicle according to temperature change of the unmanned aerial vehicle, calculating total time of temperature rise according to the temperature rise speed, calculating total moving distance according to the total time and the motion speed, and transmitting the total moving distance to an interference unit; the interference unit is used for carrying out interference operation together to equipment kind, equipment interference signal, unmanned aerial vehicle frequency point data, control distance data range and unmanned aerial vehicle's removal sum, obtain defense interference signal, and send corresponding real-time interference signal, setting through data calculation module, to unmanned aerial vehicle real-time motion position data, temperature data and time data carry out the analysis, thereby calculate unmanned aerial vehicle's velocity of motion, the rise in temperature time, and calculate total movement distance according to it, judge unmanned aerial vehicle's kind, select out interference signal, and interfere unmanned aerial vehicle according to it, increase the accuracy of calculating data, be convenient for send corresponding interference signal to unmanned aerial vehicle's control signal, the security that unmanned aerial vehicle defended increases, the time of selecting interference signal is saved, and the work efficiency is improved.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a system block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is an unmanned aerial vehicle defense system based on big data, including a scanning module, a database, a monitoring module, a judging module, a data calculating module and an interference unit;
the scanning module detects an object in a radar range and automatically acquires object information, wherein the object information comprises object position data, object state information and object angle data, the object position data refers to relative position data, namely the relative position of the object relative to radar equipment, the object angle data refers to the relative angle of the object relative to the radar equipment, and the object state information comprises a static object, a moving object and state holding time data and is transmitted to the monitoring module;
the monitoring module acquires object information and performs detection operation according to the object information, and specifically comprises the following steps:
the method comprises the following steps: acquiring object state information, identifying a static object and a moving object in the object state information, and carrying out state judgment on the static object and the moving object together with state retention time data, wherein the state judgment specifically comprises the following steps:
a1: when a static object is identified and the state retention time data is greater than M, the object is determined to be a fixed object and is not monitored, and when the static object is identified and the state retention time data is less than M, the state of the object is determined to be ambiguous and monitoring is continued;
a2: when a moving object is identified and the state retention time data is less than M, determining that the object keeps moving in a short time, continuously monitoring, and when the moving object is identified and the state retention time data is more than M, determining that the object moves actively and immediately monitoring;
step two: when the continuous monitoring and the immediate monitoring are carried out according to the judgment result in the first step, automatically acquiring object position data and object angle data corresponding to the object, monitoring the object according to the object position data and the object angle data, and automatically acquiring monitoring information;
step three: transmitting the monitoring information to a judging module;
monitoring information includes object image information and object moving speed data, the storage has unmanned aerial vehicle image data, unmanned aerial vehicle speed range data, unmanned aerial vehicle real-time motion position data, unmanned aerial vehicle real-time temperature data and time data in the database, the judge module is used for carrying out safety judgement to object image information, object moving speed data, unmanned aerial vehicle image data and unmanned aerial vehicle speed range data, and safety judgement's concrete follow the decision-making process is:
k1: acquiring object image information, object movement speed data, unmanned aerial vehicle image data and unmanned aerial vehicle speed range data, and sequentially marking the object image information, the object movement speed data, the unmanned aerial vehicle image data and the unmanned aerial vehicle speed range data as WYi, WSi, RYL and RSl, wherein i is 1,2,3.. No. n1, l is 1,2,3.. No. n2, and WYi and WSi are in one-to-one correspondence, and RYl and RSl are in one-to-one correspondence;
k2: compare object image information and unmanned aerial vehicle image data, specifically do: when it occurs
When WYi ∈ RYl appears, the object image is similar to the unmanned aerial vehicle image, and the object is similar to the unmanned aerial vehicle image;
k3: carry out real-time analysis with object moving speed data to carry out big to little sequencing with the speed at different moments, select wherein the biggest object moving value, and compare it with unmanned aerial vehicle speed range data, specifically do: when the maximum object movement value does not belong to the unmanned aerial vehicle speed range data, judging that the object movement speed does not accord with the unmanned aerial vehicle movement speed, wherein the object is not an unmanned aerial vehicle, and when the maximum object movement value belongs to the unmanned aerial vehicle speed range data, judging that the object movement speed accords with the unmanned aerial vehicle movement speed, wherein the object is similar to the unmanned aerial vehicle;
k4: comprehensively judging the results in the K2 and the K3, judging that the object is the unmanned aerial vehicle of the type when the two judgment results are that the object is similar to the unmanned aerial vehicle, automatically acquiring real-time motion position data, real-time temperature data and time data of the unmanned aerial vehicle corresponding to the unmanned aerial vehicle of the type, and transmitting the real-time motion position data, the real-time temperature data and the time data to a data calculation module through a processor;
the data calculation module is used for calculating the real-time motion position data of the unmanned aerial vehicle, the real-time temperature data of the unmanned aerial vehicle and the time data, and the specific operation process of the calculation operation is as follows:
g1: acquiring real-time motion position data of the unmanned aerial vehicle, establishing a virtual space rectangular coordinate system by taking monitoring equipment as an origin, marking the real-time position data of the unmanned aerial vehicle in the virtual space rectangular coordinate system, and marking the real-time position as WWj, wherein j is 1,2,3.... times.n 3, and the real-time position coordinate of the unmanned aerial vehicle is WWj (Xj, Yj, Zj);
g2: selecting any two positions of the real-time positions of the unmanned aerial vehicle, and bringing the positions into the calculation formulas of an X axis and a Y axis:
and the coordinate values of the Z axis and the coordinate values are brought into a calculation formula together:
wherein, HjXYExpressed as the distance, Hj, from the intersection of the X and Y axes to the monitoring deviceZXYExpressing the distance data between the unmanned aerial vehicle and the monitoring equipment, bringing the positions of the real-time motion position data of the unmanned aerial vehicle in two different time periods to the monitoring equipment into a difference value calculation formula, solving the difference value of the distances between the unmanned aerial vehicle and the monitoring equipment at two different time points, and marking the difference value as JCj;
g3: acquiring real-time temperature data of the unmanned aerial vehicle corresponding to the real-time motion position data of the unmanned aerial vehicle, marking the real-time temperature data as WDj, and bringing the real-time temperature data and corresponding time data into a calculation formula:
wherein, VWDjExpressed as the frequency of temperature change, WD1 and WD2 are expressed as the temperature of the drone during two periods of time, respectivelyData, TDifference (D)The time difference of the real-time temperature data of the two unmanned aerial vehicles corresponding to the real-time motion position data of the unmanned aerial vehicles is represented, u is represented as an influence factor of the external temperature on the temperature change of the unmanned aerial vehicles, and R is represented as a diffusion value of the temperature of the unmanned aerial vehicles;
g4: the difference of the distances between the unmanned aerial vehicle and the monitoring equipment at two different time points is brought into a calculation formula together with time data: vIs not j＝JCj/TDifference (D)Wherein V isIs not jExpressed as the speed of movement of the drone and taken into the mean calculation:
wherein PVIs not jThe average moving speed of the unmanned aerial vehicle is represented, the temperature data of the unmanned aerial vehicle at the final moment is obtained, the temperature data at the final moment specifically refers to the latest monitored temperature data, and the latest monitored temperature data and the temperature change frequency are brought into a calculation formula together:
wherein, TGeneral assemblyExpressed as total time-consuming value, r1 is expressed as sudden change influence factor, and is brought into the calculation formula J L together with the average moving speed of the unmanned aerial vehicleGeneral assembly＝TGeneral assembly*PVIs not jR2, wherein J LGeneral assemblyExpressed as the total value of the movement of the drone, r2 is expressed as a distance calculation error adjustment factor;
g5: transmitting the total movement value of the unmanned aerial vehicle to an interference unit;
still store the equipment kind, equipment interference signal, unmanned aerial vehicle frequency point data of controlgear and rather than the control distance range data that correspond in the database, interference unit is used for carrying out the interference operation together to equipment kind, equipment interference signal, unmanned aerial vehicle frequency point data, control distance data range and unmanned aerial vehicle's removal total value, and the concrete operation process of interference operation is:
e1: match unmanned aerial vehicle's removal total value and control distance range data, the matching result divide into two kinds, is respectively: a. b, the total moving value of the unmanned aerial vehicle does not belong to the control distance range data;
e2: performing type matching on the matching result of the type b in the E1 and the frequency point data of the unmanned aerial vehicle, thereby obtaining the type of equipment corresponding to the type of the equipment, extracting equipment interference signals corresponding to the type of the equipment, and marking the interference signals as defense interference signals;
e3: and sending out a corresponding real-time interference signal according to the defense interference signal in E2.
When the radar monitoring device works, the scanning module detects objects in a radar range, automatically acquires object information and transmits the object information to the monitoring module; the monitoring module acquires object information and carries out detection operation according to the object information, acquires real-time position data of the object, judges the object to be a static object and a moving object according to the positions of the object at different times and the time of keeping the object at the positions, continuously monitors the moving object, obtains monitoring information and transmits the monitoring information to the judging module; the judging module acquires object image information, object moving speed data, unmanned aerial vehicle image data and unmanned aerial vehicle speed range data, analyzes the type of the object according to the object, judges whether the object is an unmanned aerial vehicle, extracts corresponding unmanned aerial vehicle real-time motion position data, unmanned aerial vehicle real-time temperature data and time data, and transmits the data to the data calculating module; the data calculation module analyzes real-time motion position data of the unmanned aerial vehicle, real-time temperature data and time data of the unmanned aerial vehicle, analyzes motion records of the unmanned aerial vehicle according to the position data of the unmanned aerial vehicle at different time points, analyzes motion time of the unmanned aerial vehicle according to motion distance and motion time, calculates temperature rise speed of the unmanned aerial vehicle according to temperature change of the unmanned aerial vehicle, calculates total time of temperature rise according to the temperature rise speed, calculates total moving distance according to the total time and the motion speed, and transmits the total moving distance to the interference unit; the database also stores the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data of the control equipment and the control distance range data corresponding to the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data, the control distance data range and the total movement value of the unmanned aerial vehicle, the interference unit is used for carrying out interference operation on the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data, the control distance data range and the total movement value of the unmanned aerial vehicle together to obtain a defense interference signal, and sends out a corresponding.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. An unmanned aerial vehicle defense system based on big data is characterized by comprising a scanning module, a database, a monitoring module, a judging module, a data calculating module and an interference unit;
the scanning module detects an object in a radar range, automatically acquires object information, and transmits the object information to the monitoring module, wherein the object information comprises object position data, object state information and object angle data, and the object state information comprises a static object, a moving object and state retention time data;
the monitoring module acquires object information, performs detection operation according to the object information to obtain monitoring information, and transmits the monitoring information to the judging module;
the monitoring information comprises object image information and object moving speed data, unmanned aerial vehicle image data, unmanned aerial vehicle speed range data, unmanned aerial vehicle real-time motion position data, unmanned aerial vehicle real-time temperature data and time data are stored in the database, and the judging module is used for carrying out safety judgment on the object image information, the object moving speed data, the unmanned aerial vehicle image data and the unmanned aerial vehicle speed range data to obtain unmanned aerial vehicle real-time motion position data, unmanned aerial vehicle real-time temperature data and time data and transmitting the unmanned aerial vehicle real-time motion position data, the unmanned aerial vehicle real-time temperature data and the time data to the;
the data calculation module is used for calculating the real-time motion position data of the unmanned aerial vehicle, the real-time temperature data of the unmanned aerial vehicle and the time data to obtain a total movement value of the unmanned aerial vehicle and transmitting the total movement value to the interference unit;
the system comprises a database, a data base and an interference unit, wherein the database is also internally stored with the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data and the control distance range data corresponding to the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data, the control distance data range and the total movement value of the unmanned aerial vehicle of the control equipment, the interference unit is used for carrying out interference operation on the equipment type, the equipment interference signal, the unmanned aerial vehicle frequency point data, the control distance data range and the total movement value of the.
2. The big data-based unmanned aerial vehicle defense system according to claim 1, wherein the specific operation process of the detection operation is as follows:
the method comprises the following steps: acquiring object state information, identifying a static object and a moving object in the object state information, and carrying out state judgment on the static object and the moving object together with state retention time data, wherein the state judgment specifically comprises the following steps:
a1: when a static object is identified and the state retention time data is greater than M, the object is determined to be a fixed object and is not monitored, and when the static object is identified and the state retention time data is less than M, the state of the object is determined to be ambiguous and monitoring is continued;
a2: when a moving object is identified and the state retention time data is less than M, determining that the object keeps moving in a short time, continuously monitoring, and when the moving object is identified and the state retention time data is more than M, determining that the object moves actively and immediately monitoring;
step two: and when the continuous monitoring and the immediate monitoring are carried out according to the judgment result in the first step, automatically acquiring object position data and object angle data corresponding to the object, monitoring the object according to the object position data and the object angle data, and automatically acquiring monitoring information.
3. The big data-based unmanned aerial vehicle defense system according to claim 1, wherein the specific slave decision processes of the safety decision are as follows:
k1: acquiring object image information, object movement speed data, unmanned aerial vehicle image data and unmanned aerial vehicle speed range data, and sequentially marking the object image information, the object movement speed data, the unmanned aerial vehicle image data and the unmanned aerial vehicle speed range data as WYi, WSi, RYL and RSl, wherein i is 1,2,3.. No. n1, l is 1,2,3.. No. n2, and WYi and WSi are in one-to-one correspondence, and RYl and RSl are in one-to-one correspondence;
k2: compare object image information and unmanned aerial vehicle image data, specifically do: when it occurs
When WYi ∈ RYl appears, the object image is similar to the unmanned aerial vehicle image, and the object is similar to the unmanned aerial vehicle image;
k3: carry out real-time analysis with object moving speed data to carry out big to little sequencing with the speed at different moments, select wherein the biggest object moving value, and compare it with unmanned aerial vehicle speed range data, specifically do: when the maximum object movement value does not belong to the unmanned aerial vehicle speed range data, judging that the object movement speed does not accord with the unmanned aerial vehicle movement speed, wherein the object is not an unmanned aerial vehicle, and when the maximum object movement value belongs to the unmanned aerial vehicle speed range data, judging that the object movement speed accords with the unmanned aerial vehicle movement speed, wherein the object is similar to the unmanned aerial vehicle;
k4: the result in with above-mentioned K2 and K3 is synthesized and is judged, when two judgement results are the similar unmanned aerial vehicle of this object, then judges that this object is the unmanned aerial vehicle of this type to automatic acquisition and the corresponding unmanned aerial vehicle real-time motion position data of this type unmanned aerial vehicle, the real-time temperature data of unmanned aerial vehicle and time data.
4. The big data-based unmanned aerial vehicle defense system according to claim 1, wherein the specific operation process of the calculation operation is as follows:
g1: acquiring real-time motion position data of the unmanned aerial vehicle, establishing a virtual space rectangular coordinate system by taking monitoring equipment as an origin, marking the real-time position data of the unmanned aerial vehicle in the virtual space rectangular coordinate system, and marking the real-time position as WWj, wherein j is 1,2,3.... times.n 3, and the real-time position coordinate of the unmanned aerial vehicle is WWj (Xj, Yj, Zj);
g2: selecting any two positions of the real-time positions of the unmanned aerial vehicle, and bringing the positions into an X axis and a Y axisThe calculation formula (c) is as follows:
and the coordinate values of the Z axis and the coordinate values are brought into a calculation formula together:
wherein, HjXYExpressed as the distance, Hj, from the intersection of the X and Y axes to the monitoring deviceZXYExpressing the distance data between the unmanned aerial vehicle and the monitoring equipment, bringing the positions of the real-time motion position data of the unmanned aerial vehicle in two different time periods to the monitoring equipment into a difference value calculation formula, solving the difference value of the distances between the unmanned aerial vehicle and the monitoring equipment at two different time points, and marking the difference value as JCj;
g3: acquiring real-time temperature data of the unmanned aerial vehicle corresponding to the real-time motion position data of the unmanned aerial vehicle, marking the real-time temperature data as WDj, and bringing the real-time temperature data and corresponding time data into a calculation formula:
wherein, VWDjExpressed as the frequency of temperature change, WD1 and WD2 are expressed as the temperature data of the drone in two time periods, T, respectivelyDifference (D)The time difference of the real-time temperature data of the two unmanned aerial vehicles corresponding to the real-time motion position data of the unmanned aerial vehicles is represented, u is represented as an influence factor of the external temperature on the temperature change of the unmanned aerial vehicles, and R is represented as a diffusion value of the temperature of the unmanned aerial vehicles;
g4: the difference of the distances between the unmanned aerial vehicle and the monitoring equipment at two different time points is brought into a calculation formula together with time data: vIs not j＝JCj/TDifference (D)Wherein V isIs not jExpressed as the speed of movement of the drone and taken into the mean calculation:
wherein PVIs not jThe average moving speed of the unmanned aerial vehicle is represented, the temperature data of the unmanned aerial vehicle at the final moment is obtained, the temperature data at the final moment specifically refers to the latest monitored temperature data, and the latest monitored temperature data and the temperature change are obtainedThe frequencies are brought together into the calculation:
wherein, TGeneral assemblyExpressed as total time-consuming value, r1 is expressed as sudden change influence factor, and is brought into the calculation formula J L together with the average moving speed of the unmanned aerial vehicleGeneral assembly＝TGeneral assembly*PVIs not jR2, wherein J LGeneral assemblyExpressed as the total value of the drone's movement, r2 is expressed as the distance calculation error adjustment factor.
5. The big data-based unmanned aerial vehicle defense system according to claim 1, wherein the specific operation process of the interference operation is as follows:
e1: match unmanned aerial vehicle's removal total value and control distance range data, the matching result divide into two kinds, is respectively: a. b, the total moving value of the unmanned aerial vehicle does not belong to the control distance range data;
e2: and performing type matching on the matching result of the type b in the E1 and the frequency point data of the unmanned aerial vehicle, thereby obtaining the equipment type corresponding to the matching result, extracting the equipment interference signal corresponding to the equipment type, and marking the interference signal as a defense interference signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010256174.XA CN111447038B (en) | 2020-04-02 | 2020-04-02 | Unmanned aerial vehicle defense system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010256174.XA CN111447038B (en) | 2020-04-02 | 2020-04-02 | Unmanned aerial vehicle defense system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111447038A true CN111447038A (en) | 2020-07-24 |
| CN111447038B CN111447038B (en) | 2021-02-19 |
Family
ID=71651031
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010256174.XA Active CN111447038B (en) | 2020-04-02 | 2020-04-02 | Unmanned aerial vehicle defense system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111447038B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860416A (en) * | 2020-07-29 | 2020-10-30 | 郑刚 | Unmanned aerial vehicle image monitoring control device and control method thereof |
| CN111912488A (en) * | 2020-08-04 | 2020-11-10 | 郑刚 | Water network monitoring device and monitoring method based on unmanned aerial vehicle image sensor |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205679761U (en) * | 2016-05-27 | 2016-11-09 | 南京博驰光电科技有限公司 | A kind of unmanned plane integrated campaign system |
| CN106842179A (en) * | 2016-12-23 | 2017-06-13 | 成都赫尔墨斯科技有限公司 | A kind of anti-UAS based on acoustic detection |
| CN107846258A (en) * | 2017-09-07 | 2018-03-27 | 新疆美特智能安全工程股份有限公司 | A kind of unmanned plane system of defense |
| CN207528914U (en) * | 2017-09-29 | 2018-06-22 | 中航天元防务技术（北京）有限公司 | A kind of unmanned plane positioning and disposal system |
| CN109873669A (en) * | 2019-04-04 | 2019-06-11 | 北京环境特性研究所 | A kind of unmanned plane detection method and unmanned plane detection system |
| CN110133573A (en) * | 2019-04-23 | 2019-08-16 | 四川九洲电器集团有限责任公司 | A kind of autonomous low latitude unmanned plane system of defense based on the fusion of multielement bar information |
| CN110190924A (en) * | 2019-05-17 | 2019-08-30 | 中科融通物联科技无锡有限公司 | A kind of frequency hopping unmanned plane defence method and device |
| WO2020058340A1 (en) * | 2018-09-19 | 2020-03-26 | Rheinmetall Air Defence Ag | Signal interference device and a method for operating a signal interference device for protecting unmanned aerial vehicles (uav), in particular drones |
-
2020
- 2020-04-02 CN CN202010256174.XA patent/CN111447038B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205679761U (en) * | 2016-05-27 | 2016-11-09 | 南京博驰光电科技有限公司 | A kind of unmanned plane integrated campaign system |
| CN106842179A (en) * | 2016-12-23 | 2017-06-13 | 成都赫尔墨斯科技有限公司 | A kind of anti-UAS based on acoustic detection |
| CN107846258A (en) * | 2017-09-07 | 2018-03-27 | 新疆美特智能安全工程股份有限公司 | A kind of unmanned plane system of defense |
| CN207528914U (en) * | 2017-09-29 | 2018-06-22 | 中航天元防务技术（北京）有限公司 | A kind of unmanned plane positioning and disposal system |
| WO2020058340A1 (en) * | 2018-09-19 | 2020-03-26 | Rheinmetall Air Defence Ag | Signal interference device and a method for operating a signal interference device for protecting unmanned aerial vehicles (uav), in particular drones |
| CN109873669A (en) * | 2019-04-04 | 2019-06-11 | 北京环境特性研究所 | A kind of unmanned plane detection method and unmanned plane detection system |
| CN110133573A (en) * | 2019-04-23 | 2019-08-16 | 四川九洲电器集团有限责任公司 | A kind of autonomous low latitude unmanned plane system of defense based on the fusion of multielement bar information |
| CN110190924A (en) * | 2019-05-17 | 2019-08-30 | 中科融通物联科技无锡有限公司 | A kind of frequency hopping unmanned plane defence method and device |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860416A (en) * | 2020-07-29 | 2020-10-30 | 郑刚 | Unmanned aerial vehicle image monitoring control device and control method thereof |
| CN111860416B (en) * | 2020-07-29 | 2021-07-20 | 深圳康桥信息科技有限责任公司 | Unmanned aerial vehicle image monitoring control device and control method thereof |
| CN111912488A (en) * | 2020-08-04 | 2020-11-10 | 郑刚 | Water network monitoring device and monitoring method based on unmanned aerial vehicle image sensor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111447038B (en) | 2021-02-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20180032042A1 (en) | System And Method Of Dynamically Controlling Parameters For Processing Sensor Output Data | |
| US10368392B2 (en) | Wireless sensor network architecture based on multifunctional and compound sensors | |
| CN111447038B (en) | Unmanned aerial vehicle defense system based on big data | |
| CN104537898A (en) | Air-ground coordination unmanned aerial vehicle sensing and avoiding system and method | |
| CN113971697B (en) | Air-ground cooperative vehicle positioning and orientation method | |
| CN112904358B (en) | Laser positioning method based on geometric information | |
| CN108286959A (en) | A kind of O-E Payload for UAV is detectd to be calculated and display methods according to region | |
| CN210377164U (en) | Air-ground cooperative operation system | |
| CN115649501B (en) | Unmanned aerial vehicle night lighting system and method | |
| CN110997488A (en) | System and method for dynamically controlling parameters for processing sensor output data | |
| CN113504784A (en) | Unmanned aerial vehicle descending system based on computer vision | |
| CN115932834A (en) | Anti-unmanned aerial vehicle system target detection method based on multi-source heterogeneous data fusion | |
| CN113532471B (en) | Processing method, equipment and medium for multi-source flight trajectory data fusion | |
| CN115790610B (en) | Unmanned aerial vehicle accurate positioning system and method | |
| JP7089926B2 (en) | Control system | |
| CN117749314A (en) | Unmanned aerial vehicle invasion countering method and system | |
| CN114200961A (en) | Air-ground cooperative system based on unmanned aerial vehicle and intelligent wearable equipment | |
| Chen et al. | Remote safety system for a robot tractor using a monocular camera and a YOLO-based method | |
| Laurito et al. | Airborne localisation of small UAS using visual detection: A field experiment | |
| CN117850465A (en) | Unmanned aerial vehicle flight control system and method for forest fire prevention | |
| SE2050258A1 (en) | Machine learning based system, methods, and control arrangement for positioning of an agent | |
| KR20240031835A (en) | Unmanned aerial vehicle clusture context watching system and method | |
| EP4220580A1 (en) | Method for vehicle driving assistance within delimited area | |
| Li et al. | Research on the intelligent obstacle avoidance and path planning strategy of UAV based on multi-sensor fusion | |
| CN108346252A (en) | A kind of integrated forest fire protection information system excavated based on big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 230088 808-810, north area, 8th floor, academician building, intersection of Xiyou road and Yulan Avenue, high tech Zone, Hefei City, Anhui Province Applicant after: Anhui Weidun safety equipment Technology Co., Ltd Address before: 230088 14-401, Xishu Mingyuan, 502 Wangjiang West Road, high tech Zone, Hefei City, Anhui Province Applicant before: Anhui Weidun safety equipment Technology Co., Ltd |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |