CN112882489A - Unmanned aerial vehicle data acquisition system based on big data - Google Patents
Unmanned aerial vehicle data acquisition system based on big data Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle data acquisition system based on big data, which comprises a camera, an identification unit, a database, an analysis unit, a monitoring unit and a sending unit, wherein the camera is connected with the identification unit; the camera is used for acquiring the flight state of the unmanned aerial vehicle in real time, automatically acquiring the image information of the unmanned aerial vehicle and transmitting the image information to the identification unit; the unmanned aerial vehicle flight state judging system comprises a database, an analyzing unit, an identifying unit, a coordinate analyzing unit and an analyzing unit, wherein the database stores unmanned aerial vehicle image data, the identifying unit acquires the unmanned aerial vehicle image data from the database, performs identifying operation on the unmanned aerial vehicle image data and image information to obtain state image data, and transmits the state image data to the analyzing unit.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle data acquisition, in particular to an unmanned aerial vehicle data acquisition system based on big data.
Background
Unmanned aerial vehicle is called "unmanned aerial vehicle" for short, is the unmanned aerial vehicle that utilizes radio remote control equipment and self-contained program control device to control, or by the vehicle-mounted computer independently operate completely or intermittently, along with the rapid development of science and technology, unmanned aerial vehicle's range of application also increases gradually, people go to solve some things that are difficult to accomplish through unmanned aerial vehicle, but, when unmanned aerial vehicle uses, also can appear unusually often.
At present, unmanned aerial vehicle all controls through a remote controller, and unmanned specific motion data can't be known in detail when unmanned aerial vehicle, consequently, long the condition that can take place some misoperation when controlling unmanned aerial vehicle to lead to unmanned aerial vehicle's damage, for this reason, we propose an unmanned aerial vehicle data acquisition system based on big data.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle data acquisition system based on big data, through the arrangement of an identification unit, relevant data in a database is identified and matched with image data acquired by a camera, so that whether the image information is an unmanned aerial vehicle or not is judged quickly, the time consumed by unmanned aerial vehicle identification is saved, the identification accuracy is increased, the working efficiency is improved, through the arrangement of an analysis unit, coordinate analysis is carried out on the positions of all parts of the unmanned aerial vehicle, the flight state of the unmanned aerial vehicle is judged according to the calculation result of the coordinate analysis, the accurate judgment on the state of the unmanned aerial vehicle is increased, the persuasion force of the data is increased, the time consumed by the data analysis is saved, the working efficiency is improved, through the arrangement of a monitoring unit, data monitoring is carried out according to the relevant data obtained by the analysis of the analysis unit, and the.
The purpose of the invention can be realized by the following technical scheme: an unmanned aerial vehicle data acquisition system based on big data comprises a camera, an identification unit, a database, an analysis unit, a monitoring unit and a sending unit;
the camera is used for acquiring the flight state of the unmanned aerial vehicle in real time, automatically acquiring the image information of the unmanned aerial vehicle and transmitting the image information to the identification unit;
the unmanned aerial vehicle image data are stored in the database, the identification unit acquires the unmanned aerial vehicle image data from the database, carries out identification operation on the unmanned aerial vehicle image data and image information to obtain state image data, and transmits the state image data to the analysis unit;
the analysis unit acquires the unmanned aerial vehicle part image data and the corresponding part name data from the database, analyzes the unmanned aerial vehicle part image data and the corresponding part name data with the state image data to obtain relative flight direction data and flight state data, and respectively transmits the relative flight direction data and the flight state data to the monitoring unit and the sending unit;
the monitoring unit is used for acquiring unmanned aerial vehicle data in real time, monitoring and judging the unmanned aerial vehicle according to the unmanned aerial vehicle data to obtain flight direction data, speed data, longitude and latitude data and height data, and transmitting the flight direction data, the speed data, the longitude and latitude data and the height data to the sending unit;
the transmitting unit is used for transmitting the flight state data, the flight direction data, the speed data, the longitude and latitude data and the altitude data to the control terminal.
As a further improvement of the invention: the specific operation process of the identification operation comprises the following steps:
the method comprises the following steps: obtain image information, extract unmanned aerial vehicle's picture data in it to match all the other unmanned aerial vehicle image data, specifically do: when the matching result of the picture data and the image data of the unmanned aerial vehicle is inconsistent, judging that the image information is not the image information of the unmanned aerial vehicle to generate an image error signal, and when the matching result of the picture data and the image data of the unmanned aerial vehicle is consistent, judging that the image information is the image information of the unmanned aerial vehicle to generate an image correct signal;
step two: acquiring the image error signal and the image correct signal in the first step, identifying the image error signal and the image correct signal, and when the image error signal is identified, not performing the operation in the third step on the image information;
step three: and extracting the unmanned aerial vehicle image in the image information, and calibrating the real-time state of the unmanned aerial vehicle into state image data.
As a further improvement of the invention: the specific operation process of the analysis operation is as follows:
k1: acquiring state image data, and matching the state image data with unmanned aerial vehicle part image data, specifically: matching the unmanned aerial vehicle component image data with the unmanned aerial vehicle image part in the state image data, and when the matching result of the unmanned aerial vehicle component image data and the unmanned aerial vehicle image part is consistent, extracting component name data corresponding to the unmanned aerial vehicle component image data, and marking the component name data as unmanned aerial vehicle position data BWi, wherein i is 1,2,3.. n 1;
k2: establishing a virtual space rectangular coordinate system, marking state image data in the virtual space rectangular coordinate system, and marking each corner point of the unmanned aerial vehicle with coordinates, which specifically comprises the following steps: BjDi (Xl, Yl, Zl), j 1,2,3.. No. n1, i 1,2,3.. No. n2, l 1,2,3.. No. n 3;
k3: selecting coordinate data of the central position of the unmanned aerial vehicle at different time points according to unmanned aerial vehicle position data in K1 and coordinates of each corner point of the unmanned aerial vehicle in K2, and marking the coordinate data as a central coordinate point ZXl, (Xl, Yl, Zl), wherein l is 1,2,3.... n2, selecting a coordinate point of each rotor of the unmanned aerial vehicle at different time points, and marking the coordinate point as a rotor coordinate point XYj, (Xj, Yj, Zj), wherein j is 1,2,3.. n3, wherein the setting is that the rotor coordinate and the central coordinate point are in the same horizontal plane under the condition that the unmanned aerial vehicle is horizontally placed;
k4: comparing and calculating the rotor wing coordinates with the central coordinate point to obtain a horizontal flight signal, a curve flight signal, a rising signal, a falling signal, a high-order rotor wing and a low-order rotor wing;
k5: the high-order rotor wing and the low-order rotor wing are calibrated to be relative flight direction data and transmitted to the monitoring unit, and the horizontal flight signal, the curve flight signal, the ascending signal and the descending signal are calibrated to be flight state data and transmitted to the sending unit.
As a further improvement of the invention: the specific operation process of the data monitoring operation is as follows:
h1: acquiring a high-order rotor wing and a low-order rotor wing in the relative flight direction data, marking the high-order rotor wing and the low-order rotor wing, identifying specific position data of the high-order rotor wing and the low-order rotor wing by a monitoring unit according to fact map data, calibrating the flight direction data according to the specific position data, and monitoring longitude and latitude data and height data of the unmanned aerial vehicle according to the specific position data;
h2: and acquiring the flight time and the flight distance of the unmanned aerial vehicle in the unmanned aerial vehicle data, thereby calculating the flight speed data of the unmanned aerial vehicle and marking the flight speed data as speed data.
As a further improvement of the invention: the specific process of comparing and calculating the rotor coordinate and the central coordinate point is as follows:
s1: when the rotor coordinate is the same as the Z-axis coordinate point of the central coordinate point, the rotor coordinate and the central coordinate point in two different time periods are selected, and the value of the Z-axis is calculated, specifically:
SS 1: marking two different time points as T1 and T2, selecting rotor coordinates and center coordinate points corresponding to the two time points and marking the rotor coordinates and the center coordinate points as XY1 and XY2, and ZX1 and ZX 2;
SS 2: carry into the subtraction formula with the value of the X axle of two different time point rotor coordinates and center coordinate point, Y axle and Z axle respectively to calculate the difference of X axle, Y axle and Z axle, when the difference of rotor coordinate and center coordinate point X axle and Y axle is less than the predetermined threshold value, then judge that this unmanned aerial vehicle is in the elevating movement state, go up and down the judgement according to the difference of Z axle, specifically do: when the Z-axis difference is larger than zero, the unmanned aerial vehicle is judged to be in a descending state, a descending signal is generated, and when the Z-axis difference is smaller than zero, the unmanned aerial vehicle is judged to be in an ascending state, and an ascending signal is generated;
s2: choose the rotor coordinate point of every different rotor, extract the Z axle value of every rotor to carry out the sequencing from big to little with it, select the biggest Z axle value and the minimum Z axle value after the sequencing, extract the center coordinate point that corresponds the time point, and compare the Z axle value of center coordinate point with the biggest Z axle value of rotor coordinate point and minimum Z axle value, specifically do: when the difference between the maximum Z-axis value and the minimum Z-axis value of the rotor coordinate point and the Z-axis value of the central coordinate point is smaller than or equal to a preset threshold value, judging that the aircraft is in horizontal flight, generating a horizontal flight signal, when the difference between the maximum Z-axis value and the minimum Z-axis value of the rotor coordinate point and the Z-axis value of the central coordinate point is larger than the preset threshold value, judging that the aircraft is in curvilinear flight, generating a curvilinear flight signal, wherein the flight directions of the horizontal flight and the curvilinear flight are the rotors corresponding to the maximum Z-axis value and the minimum Z-axis value, marking the rotor corresponding to the maximum Z-axis value as a high-order rotor, and marking the rotor corresponding to the minimum Z-axis value as a low-order rotor.
The invention has the beneficial effects that:
(1) the flight state of the unmanned aerial vehicle is acquired in real time through the camera, image information of the unmanned aerial vehicle is automatically acquired, and the image information is transmitted to the identification unit; the identification unit obtains unmanned aerial vehicle image data from the database to carry out the identification operation with it and image information, through the setting of identification unit, discern the image data that relevant data and camera acquireed in the database and match, thereby judge fast whether image information is unmanned aerial vehicle, save the time that unmanned aerial vehicle discernment consumed, increase the accuracy of discernment, improve work efficiency.
(2) Acquiring unmanned aerial vehicle part image data and corresponding part name data from a database through an analysis unit, and analyzing the unmanned aerial vehicle part image data and the state image data to obtain relative flight direction data and flight state data; through the setting of analysis element, carry out coordinate analysis to each part position of unmanned aerial vehicle to judge unmanned aerial vehicle's flight state according to coordinate analysis's calculated result, increase the accurate judgement to the unmanned aerial vehicle state, thereby increase the persuasion dynamics of data, save the time that data analysis consumed, improve work efficiency.
(3) The monitoring unit acquires unmanned aerial vehicle data in real time, monitors and judges the unmanned aerial vehicle according to the unmanned aerial vehicle data to obtain flight direction data, speed data, longitude and latitude data and height data, and transmits the flight direction data, the speed data, the longitude and latitude data and the height data to the sending unit; the sending unit sends the flight direction data, the speed data, the longitude and latitude data and the altitude data to the control terminal; through the setting of the monitoring unit, data monitoring is carried out according to the related data obtained by analysis of the analysis unit, and the monitoring efficiency is improved.
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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 invention relates to an unmanned aerial vehicle data acquisition system based on big data, which comprises a camera, an identification unit, a database, an analysis unit, a monitoring unit and a sending unit;
the camera is used for acquiring the flight state of the unmanned aerial vehicle in real time, automatically acquiring the image information of the unmanned aerial vehicle and transmitting the image information to the identification unit;
the database stores unmanned aerial vehicle image data, the identification unit acquires the unmanned aerial vehicle image data from the database and carries out identification operation on the unmanned aerial vehicle image data and the image information, and the specific operation process of the identification operation is as follows:
the method comprises the following steps: obtain image information, extract unmanned aerial vehicle's picture data in it to match all the other unmanned aerial vehicle image data, specifically do: when the matching result of the picture data and the image data of the unmanned aerial vehicle is inconsistent, judging that the image information is not the image information of the unmanned aerial vehicle to generate an image error signal, and when the matching result of the picture data and the image data of the unmanned aerial vehicle is consistent, judging that the image information is the image information of the unmanned aerial vehicle to generate an image correct signal;
step two: acquiring the image error signal and the image correct signal in the first step, identifying the image error signal and the image correct signal, and when the image error signal is identified, not performing the operation in the third step on the image information;
step three: extracting an unmanned aerial vehicle image in the image information, calibrating the real-time state of the unmanned aerial vehicle into state image data, and transmitting the state image data to an analysis unit;
the data base also stores unmanned aerial vehicle part image data and corresponding part name data, the analysis unit acquires the unmanned aerial vehicle part image data and the corresponding part name data from the data base and carries out analysis operation on the unmanned aerial vehicle part image data and the state image data, and the specific operation process of the analysis operation is as follows:
k1: acquiring state image data, and matching the state image data with unmanned aerial vehicle part image data, specifically: matching the unmanned aerial vehicle component image data with the unmanned aerial vehicle image part in the state image data, and when the matching result of the unmanned aerial vehicle component image data and the unmanned aerial vehicle image part is consistent, extracting component name data corresponding to the unmanned aerial vehicle component image data, and marking the component name data as unmanned aerial vehicle position data BWi, wherein i is 1,2,3.. n 1;
k2: establishing a virtual space rectangular coordinate system, marking state image data in the virtual space rectangular coordinate system, and marking each corner point of the unmanned aerial vehicle with coordinates, which specifically comprises the following steps: BjDi (Xl, Yl, Zl), j 1,2,3.. No. n1, i 1,2,3.. No. n2, l 1,2,3.. No. n 3;
k3: selecting coordinate data of the central position of the unmanned aerial vehicle at different time points according to unmanned aerial vehicle position data in K1 and coordinates of each corner point of the unmanned aerial vehicle in K2, and marking the coordinate data as a central coordinate point ZXl, (Xl, Yl, Zl), wherein l is 1,2,3.... n2, selecting a coordinate point of each rotor of the unmanned aerial vehicle at different time points, and marking the coordinate point as a rotor coordinate point XYj, (Xj, Yj, Zj), wherein j is 1,2,3.. n3, wherein the setting is that the rotor coordinate and the central coordinate point are in the same horizontal plane under the condition that the unmanned aerial vehicle is horizontally placed;
k4: the rotor wing coordinate and the center coordinate point are compared and calculated, and the method specifically comprises the following steps:
s1: when the rotor coordinate is the same as the Z-axis coordinate point of the central coordinate point, the rotor coordinate and the central coordinate point in two different time periods are selected, and the value of the Z-axis is calculated, specifically:
SS 1: marking two different time points as T1 and T2, selecting rotor coordinates and center coordinate points corresponding to the two time points and marking the rotor coordinates and the center coordinate points as XY1 and XY2, and ZX1 and ZX 2;
SS 2: carry into the subtraction formula with the value of the X axle of two different time point rotor coordinates and center coordinate point, Y axle and Z axle respectively to calculate the difference of X axle, Y axle and Z axle, when the difference of rotor coordinate and center coordinate point X axle and Y axle is less than the predetermined threshold value, then judge that this unmanned aerial vehicle is in the elevating movement state, go up and down the judgement according to the difference of Z axle, specifically do: when the Z-axis difference is larger than zero, the unmanned aerial vehicle is judged to be in a descending state, a descending signal is generated, and when the Z-axis difference is smaller than zero, the unmanned aerial vehicle is judged to be in an ascending state, and an ascending signal is generated;
s2: choose the rotor coordinate point of every different rotor, extract the Z axle value of every rotor to carry out the sequencing from big to little with it, select the biggest Z axle value and the minimum Z axle value after the sequencing, extract the center coordinate point that corresponds the time point, and compare the Z axle value of center coordinate point with the biggest Z axle value of rotor coordinate point and minimum Z axle value, specifically do: when the difference value between the maximum Z-axis value and the minimum Z-axis value of the rotor coordinate point and the Z-axis value of the central coordinate point is smaller than or equal to a preset threshold value, judging that the aircraft is in horizontal flight, generating a horizontal flight signal, when the difference value between the maximum Z-axis value and the minimum Z-axis value of the rotor coordinate point and the Z-axis value of the central coordinate point is larger than the preset threshold value, judging that the aircraft is in curvilinear flight, generating a curvilinear flight signal, wherein the flight directions of the horizontal flight and the curvilinear flight are the rotor corresponding to the maximum Z-axis value and the rotor corresponding to the minimum Z-axis value, marking the rotor corresponding to the maximum Z-axis value as a high-order rotor, and the rotor corresponding to the minimum Z-axis value as a low-order rotor;
k5: calibrating a high-order rotor wing and a low-order rotor wing into relative flight direction data, transmitting the relative flight direction data to a monitoring unit, calibrating a horizontal flight signal, a curve flight signal, a rising signal and a falling signal into flight state data, and transmitting the flight state data to a sending unit;
the monitoring unit is used for acquiring unmanned aerial vehicle data in real time and monitoring and judging the unmanned aerial vehicle according to the unmanned aerial vehicle data, and the specific operation process of the data monitoring operation is as follows:
h1: acquiring a high-order rotor wing and a low-order rotor wing in the relative flight direction data, marking the high-order rotor wing and the low-order rotor wing, identifying specific position data of the high-order rotor wing and the low-order rotor wing by a monitoring unit according to fact map data, calibrating the flight direction data according to the specific position data, and monitoring longitude and latitude data and height data of the unmanned aerial vehicle according to the specific position data;
h2: acquiring the flight time and the flight distance of the unmanned aerial vehicle in the unmanned aerial vehicle data, thereby calculating the flight speed data of the unmanned aerial vehicle and marking the flight speed data as speed data;
h3: transmitting the flight direction data, the speed data, the longitude and latitude data and the altitude data to a sending unit;
the transmitting unit is used for transmitting the flight state data, the flight direction data, the speed data, the longitude and latitude data and the altitude data to the control terminal.
When the unmanned aerial vehicle identification system works, the camera acquires the flight state of the unmanned aerial vehicle in real time, automatically acquires the image information of the unmanned aerial vehicle, and transmits the image information to the identification unit; the identification unit obtains unmanned aerial vehicle image data from the database to carry out identification operation with image information, specifically: obtain image information, extract unmanned aerial vehicle's picture data in it to match all the other unmanned aerial vehicle image data, specifically do: when the matching result of the picture data and the image data of the unmanned aerial vehicle is inconsistent, judging that the image information is not the image information of the unmanned aerial vehicle, generating an image error signal, when the matching result of the picture data and the image data of the unmanned aerial vehicle is consistent, judging that the image information is the image information of the unmanned aerial vehicle, generating an image correct signal, extracting data according to the generated corresponding signal, and transmitting the data to an analysis unit; the analysis unit acquires the unmanned aerial vehicle part image data and the corresponding part name data from the database, analyzes the unmanned aerial vehicle part image data and the state image data to obtain relative flight direction data and flight state data, and respectively transmits the relative flight direction data and the flight state data to the monitoring unit and the sending unit; the monitoring unit acquires unmanned aerial vehicle data in real time, monitors and judges the unmanned aerial vehicle according to the unmanned aerial vehicle data to obtain flight direction data, speed data, longitude and latitude data and height data, and transmits the flight direction data, the speed data, the longitude and latitude data and the height data to the sending unit; the sending unit sends the flight direction data, the speed data, the longitude and latitude data and the altitude data to the control terminal.
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 data acquisition system based on big data is characterized by comprising a camera, an identification unit, a database, an analysis unit, a monitoring unit and a sending unit;
the camera is used for acquiring the flight state of the unmanned aerial vehicle in real time, automatically acquiring the image information of the unmanned aerial vehicle and transmitting the image information to the identification unit;
the unmanned aerial vehicle image data are stored in the database, the identification unit acquires the unmanned aerial vehicle image data from the database, carries out identification operation on the unmanned aerial vehicle image data and image information to obtain state image data, and transmits the state image data to the analysis unit;
the analysis unit acquires the unmanned aerial vehicle part image data and the corresponding part name data from the database, analyzes the unmanned aerial vehicle part image data and the corresponding part name data with the state image data to obtain relative flight direction data and flight state data, and respectively transmits the relative flight direction data and the flight state data to the monitoring unit and the sending unit;
the monitoring unit is used for acquiring unmanned aerial vehicle data in real time, monitoring and judging the unmanned aerial vehicle according to the unmanned aerial vehicle data to obtain flight direction data, speed data, longitude and latitude data and height data, and transmitting the flight direction data, the speed data, the longitude and latitude data and the height data to the sending unit;
the transmitting unit is used for transmitting the flight state data, the flight direction data, the speed data, the longitude and latitude data and the altitude data to the control terminal.
2. The big data-based unmanned aerial vehicle data acquisition system according to claim 1, wherein the specific operation process of the identification operation is as follows:
the method comprises the following steps: obtain image information, extract unmanned aerial vehicle's picture data in it to match all the other unmanned aerial vehicle image data, specifically do: when the matching result of the picture data and the image data of the unmanned aerial vehicle is inconsistent, judging that the image information is not the image information of the unmanned aerial vehicle to generate an image error signal, and when the matching result of the picture data and the image data of the unmanned aerial vehicle is consistent, judging that the image information is the image information of the unmanned aerial vehicle to generate an image correct signal;
step two: acquiring the image error signal and the image correct signal in the first step, identifying the image error signal and the image correct signal, and when the image error signal is identified, not performing the operation in the third step on the image information;
step three: and extracting the unmanned aerial vehicle image in the image information, and calibrating the real-time state of the unmanned aerial vehicle into state image data.
3. The big data-based unmanned aerial vehicle data acquisition system according to claim 1, wherein the specific operation process of the analysis operation is as follows:
k1: acquiring state image data, and matching the state image data with unmanned aerial vehicle part image data, specifically: matching the unmanned aerial vehicle component image data with the unmanned aerial vehicle image part in the state image data, and when the matching result of the unmanned aerial vehicle component image data and the unmanned aerial vehicle image part is consistent, extracting component name data corresponding to the unmanned aerial vehicle component image data, and marking the component name data as unmanned aerial vehicle position data BWi, wherein i is 1,2,3.. n 1;
k2: establishing a virtual space rectangular coordinate system, marking state image data in the virtual space rectangular coordinate system, and marking each corner point of the unmanned aerial vehicle with coordinates, which specifically comprises the following steps: BjDi (Xl, Yl, Zl), j 1,2,3.. No. n1, i 1,2,3.. No. n2, l 1,2,3.. No. n 3;
k3: selecting coordinate data of the central position of the unmanned aerial vehicle at different time points according to unmanned aerial vehicle position data in K1 and coordinates of each corner point of the unmanned aerial vehicle in K2, and marking the coordinate data as a central coordinate point ZXl, (Xl, Yl, Zl), wherein l is 1,2,3.... n2, selecting a coordinate point of each rotor of the unmanned aerial vehicle at different time points, and marking the coordinate point as a rotor coordinate point XYj, (Xj, Yj, Zj), wherein j is 1,2,3.. n3, wherein the setting is that the rotor coordinate and the central coordinate point are in the same horizontal plane under the condition that the unmanned aerial vehicle is horizontally placed;
k4: comparing and calculating the rotor wing coordinates with the central coordinate point to obtain a horizontal flight signal, a curve flight signal, a rising signal, a falling signal, a high-order rotor wing and a low-order rotor wing;
k5: the high-order rotor wing and the low-order rotor wing are calibrated to be relative flight direction data and transmitted to the monitoring unit, and the horizontal flight signal, the curve flight signal, the ascending signal and the descending signal are calibrated to be flight state data and transmitted to the sending unit.
4. The big data based unmanned aerial vehicle data acquisition system according to claim 1, wherein the specific operation process of the data monitoring operation is as follows:
h1: acquiring a high-order rotor wing and a low-order rotor wing in the relative flight direction data, marking the high-order rotor wing and the low-order rotor wing, identifying specific position data of the high-order rotor wing and the low-order rotor wing by a monitoring unit according to fact map data, calibrating the flight direction data according to the specific position data, and monitoring longitude and latitude data and height data of the unmanned aerial vehicle according to the specific position data;
h2: and acquiring the flight time and the flight distance of the unmanned aerial vehicle in the unmanned aerial vehicle data, thereby calculating the flight speed data of the unmanned aerial vehicle and marking the flight speed data as speed data.
5. The big data-based unmanned aerial vehicle data acquisition system according to claim 3, wherein the specific process of comparing and calculating the rotor coordinate and the central coordinate point is as follows:
s1: when the rotor coordinate is the same as the Z-axis coordinate point of the central coordinate point, the rotor coordinate and the central coordinate point in two different time periods are selected, and the value of the Z-axis is calculated, specifically:
SS 1: marking two different time points as T1 and T2, selecting rotor coordinates and center coordinate points corresponding to the two time points and marking the rotor coordinates and the center coordinate points as XY1 and XY2, and ZX1 and ZX 2;
SS 2: carry into the subtraction formula with the value of the X axle of two different time point rotor coordinates and center coordinate point, Y axle and Z axle respectively to calculate the difference of X axle, Y axle and Z axle, when the difference of rotor coordinate and center coordinate point X axle and Y axle is less than the predetermined threshold value, then judge that this unmanned aerial vehicle is in the elevating movement state, go up and down the judgement according to the difference of Z axle, specifically do: when the Z-axis difference is larger than zero, the unmanned aerial vehicle is judged to be in a descending state, a descending signal is generated, and when the Z-axis difference is smaller than zero, the unmanned aerial vehicle is judged to be in an ascending state, and an ascending signal is generated;
s2: choose the rotor coordinate point of every different rotor, extract the Z axle value of every rotor to carry out the sequencing from big to little with it, select the biggest Z axle value and the minimum Z axle value after the sequencing, extract the center coordinate point that corresponds the time point, and compare the Z axle value of center coordinate point with the biggest Z axle value of rotor coordinate point and minimum Z axle value, specifically do: when the difference between the maximum Z-axis value and the minimum Z-axis value of the rotor coordinate point and the Z-axis value of the central coordinate point is smaller than or equal to a preset threshold value, judging that the aircraft is in horizontal flight, generating a horizontal flight signal, when the difference between the maximum Z-axis value and the minimum Z-axis value of the rotor coordinate point and the Z-axis value of the central coordinate point is larger than the preset threshold value, judging that the aircraft is in curvilinear flight, generating a curvilinear flight signal, wherein the flight directions of the horizontal flight and the curvilinear flight are the rotors corresponding to the maximum Z-axis value and the minimum Z-axis value, marking the rotor corresponding to the maximum Z-axis value as a high-order rotor, and marking the rotor corresponding to the minimum Z-axis value as a low-order rotor.
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| CN116343532A (en) * | 2023-05-26 | 2023-06-27 | 优选空天装备技术(北京)有限公司 | Intelligent combined unmanned aerial vehicle management and control system based on data analysis |
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