CN116580329B - Unmanned aerial vehicle heat prediction method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and provides a method, a device, equipment and a medium for predicting the heat of an unmanned aerial vehicle, wherein grid cells corresponding to each detection area can be obtained through grid processing so as to reduce the recognition difficulty of unmanned aerial vehicle coordinates, track data of all unmanned aerial vehicles in each detection area are preprocessed so as to obtain track coordinates of each unmanned aerial vehicle, encoding processing is carried out on the basis of the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid cells corresponding to each detection area, track encoded data of the unmanned aerial vehicle are obtained and stored in a configuration database so as to quickly call and execute subsequent related processing, and the heat level of each unmanned aerial vehicle in a target area is calculated by combining target decoding data and a heat level prediction model of the unmanned aerial vehicle, so that the heat map of the unmanned aerial vehicle is generated and displayed, and the heat of the unmanned aerial vehicle in a designated area can be intuitively displayed so as to assist in carrying out the safety control of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a heat prediction method, device, equipment and medium for an unmanned aerial vehicle.
Background
As the holding amount of civil unmanned aerial vehicles increases, the flying phenomenon of unmanned aerial vehicles is increasingly serious. Low-altitude aircrafts have the characteristics of strong maneuverability, high flexibility, short flight time and low quality, and the conventional means have the pain points of difficult discovery, difficult verification, difficult treatment and difficult penalty. Therefore, the rapid compression of the low-altitude aircraft in the designated area is an important research content, and the phenomenon of flying disorder of the low-altitude aircraft can be thoroughly solved only by assisting a commander in rapidly deciding and integrating the air-ground processing force.
In the prior art, although a certain solution is adopted for the flying phenomenon of the unmanned aerial vehicle, threat degree analysis is not performed on the data of the low-altitude aircraft in a specified time period, the complexity of an analysis algorithm is high, the requirement on the accuracy of the data is high, the technical requirement is high, the efficiency of disposing the low-altitude aircraft cannot be improved, the unmanned aerial vehicle cannot be suitable for multiple unmanned aerial vehicle scenes, and popularization and realization are difficult.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, a device, equipment and a medium for predicting the heat of an unmanned aerial vehicle, which can predict the heat of the unmanned aerial vehicle in a designated area, so as to assist in rapid early warning in the flight process of the unmanned aerial vehicle.
An unmanned aerial vehicle heat prediction method, the unmanned aerial vehicle heat prediction method comprising:
acquiring each detection area, and performing gridding treatment on each detection area to obtain a grid unit corresponding to each detection area;
acquiring track data of all unmanned aerial vehicles in each detection area in real time;
preprocessing the track data of all unmanned aerial vehicles in each detection area to obtain track coordinates of each unmanned aerial vehicle;
acquiring the acquisition time of the track coordinates of each unmanned aerial vehicle;
performing coding processing based on the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid units corresponding to each detection area to obtain unmanned aerial vehicle track coding data corresponding to each detection area, and storing the unmanned aerial vehicle track coding data corresponding to each detection area into a configuration database;
responding to an unmanned aerial vehicle heat prediction instruction of a target area, calling unmanned aerial vehicle track coding data corresponding to the target area from the configuration database to serve as target coding data, and decoding the target coding data to obtain target decoding data;
invoking a pre-trained unmanned aerial vehicle heat level prediction model, and calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model;
And generating an unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area, and displaying the unmanned aerial vehicle heat map.
According to a preferred embodiment of the present invention, the performing gridding processing on each detection area to obtain a grid unit corresponding to each detection area includes:
carrying out grid division of preset rounds on each detection area according to longitude and latitude;
determining a grid division result obtained after the last round of grid division as a grid unit corresponding to each detection area;
and when dividing each round, acquiring a current grid division result, and continuously executing the grid division of the current round according to longitude and latitude on the basis of the current grid division result.
According to a preferred embodiment of the present invention, the real-time acquisition of the trajectory data of all the unmanned aerial vehicles in each detection area includes:
collecting electric signals of all unmanned aerial vehicles in each detection area by using passive radio equipment;
collecting image signals of all unmanned aerial vehicles in each detection area by using photoelectric equipment;
collecting sound signals of all unmanned aerial vehicles in each detection area by using an array microphone;
and integrating the electric signals of all the unmanned aerial vehicles in each detection area, the image signals of all the unmanned aerial vehicles in each detection area and the sound signals of all the unmanned aerial vehicles in each detection area to obtain the track data of all the unmanned aerial vehicles in each detection area.
According to a preferred embodiment of the present invention, preprocessing the trajectory data of all the unmanned aerial vehicles in each detection area to obtain the trajectory coordinates of each unmanned aerial vehicle includes:
carrying out smoothing processing on the track data of all unmanned aerial vehicles in each detection area based on a Kalman filtering smoothing algorithm to obtain data to be processed;
acquiring the electric signal in the data to be processed, and inputting the electric signal into a pre-trained electric signal feature extraction model to obtain an electric signal feature map;
acquiring the image signals in the data to be processed, and inputting the image signals into a pre-trained image signal feature extraction model to obtain an image signal feature map;
acquiring the sound signal in the data to be processed, and inputting the sound signal into a pre-trained MFCC model to obtain a sound signal feature map;
performing feature fusion on the electric signal feature map, the image signal feature map and the sound signal feature map to obtain fusion features;
and performing dimension reduction processing on the fusion characteristics by using a PCA dimension reduction algorithm to obtain the track coordinates of each unmanned aerial vehicle.
According to a preferred embodiment of the present invention, before the invoking the pre-trained unmanned aerial vehicle heat level prediction model, the method further includes:
Acquiring historical unmanned aerial vehicle track data as a training sample;
marking the training sample to obtain a label sample;
training a convolutional neural network model using the label sample;
and stopping training when the accuracy of the convolutional neural network model reaches the configuration accuracy, and obtaining the unmanned aerial vehicle heat level prediction model.
According to a preferred embodiment of the present invention, the calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoded data and the unmanned aerial vehicle heat level prediction model includes:
determining a prediction time period and a target position according to the unmanned aerial vehicle heat prediction instruction;
acquiring the number of unmanned aerial vehicles at each acquisition time point in the prediction time period from the target decoding data, and the linear distance from each unmanned aerial vehicle to the target position;
inputting the predicted time period, the number of unmanned aerial vehicles at each acquisition time point and the linear distance from each unmanned aerial vehicle to the target position as input data into the unmanned aerial vehicle heat level prediction model;
and obtaining output data of the unmanned aerial vehicle heat level prediction model as the heat level of each unmanned aerial vehicle in the target area.
According to a preferred embodiment of the present invention, the generating the unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area includes:
configuring the corresponding relation between the heat level and the display color;
determining a target display color corresponding to the heat level of each unmanned aerial vehicle in the target area according to the corresponding relation;
and marking the heat level of each unmanned aerial vehicle according to the target display color corresponding to the heat level of each unmanned aerial vehicle so as to generate an unmanned aerial vehicle heat map of the target area.
An unmanned aerial vehicle heat prediction device, the unmanned aerial vehicle heat prediction device comprising:
the gridding unit is used for acquiring each detection area, and gridding the detection areas to obtain grid units corresponding to the detection areas;
the acquisition unit is used for acquiring the track data of all unmanned aerial vehicles in each detection area in real time;
the preprocessing unit is used for preprocessing the track data of all the unmanned aerial vehicles in each detection area to obtain track coordinates of each unmanned aerial vehicle;
the acquisition unit is used for acquiring the acquisition time of the track coordinates of each unmanned aerial vehicle;
the encoding unit is used for carrying out encoding processing based on the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid unit corresponding to each detection area to obtain unmanned aerial vehicle track encoded data corresponding to each detection area, and storing the unmanned aerial vehicle track encoded data corresponding to each detection area into the configuration database;
The decoding unit is used for responding to the unmanned aerial vehicle heat prediction instruction of the target area, calling unmanned aerial vehicle track coding data corresponding to the target area from the configuration database to serve as target coding data, and decoding the target coding data to obtain target decoding data;
the calculation unit is used for calling a pre-trained unmanned aerial vehicle heat level prediction model, and calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model;
and the generation unit is used for generating an unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area and displaying the unmanned aerial vehicle heat map.
A computer device, the computer device comprising:
a memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the unmanned aerial vehicle heat prediction method.
A computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the drone heat prediction method.
According to the technical scheme, the method and the device can carry out gridding treatment on each detection area to obtain the grid unit corresponding to each detection area, reduce the recognition difficulty of unmanned aerial vehicle coordinates, acquire and preprocess track data of all unmanned aerial vehicles in each detection area in real time to obtain track coordinates of each unmanned aerial vehicle, encode the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid unit corresponding to each detection area, obtain unmanned aerial vehicle track encoded data corresponding to each detection area, store the unmanned aerial vehicle track encoded data in a configuration database, facilitate subsequent quick call and execute relevant treatment, calculate the heat level of each unmanned aerial vehicle in a target area by combining target decoded data acquired from the configuration database and a heat level prediction model of the unmanned aerial vehicle, generate a heat map and display, and further intuitively display the heat level of the unmanned aerial vehicle in a designated area so as to assist in safety control of the unmanned aerial vehicle.
Drawings
Fig. 1 is a flowchart of a heat prediction method of a drone according to a preferred embodiment of the present invention.
Fig. 2 is a schematic view of an application environment of the unmanned aerial vehicle heat prediction method of the present invention.
Fig. 3 is a functional block diagram of a preferred embodiment of the unmanned aerial vehicle heat prediction apparatus of the present invention.
Fig. 4 is a schematic structural diagram of a computer device for implementing a heat prediction method of an unmanned aerial vehicle according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flowchart of a heat prediction method of an unmanned aerial vehicle according to a preferred embodiment of the present invention. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
The unmanned aerial vehicle heat prediction method is applied to one or more computer devices, wherein the computer device is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the computer device comprises, but is not limited to, a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, an ASIC), a programmable gate array (Field-Programmable Gate Array, FPGA), a digital processor (Digital Signal Processor, DSP), an embedded device and the like.
The computer device may be any electronic product that can interact with a user in a human-computer manner, such as a personal computer, tablet computer, smart phone, personal digital assistant (Personal Digital Assistant, PDA), game console, interactive internet protocol television (Internet Protocol Television, IPTV), smart wearable device, etc.
The computer device may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers. Referring specifically to fig. 2, an application environment schematic diagram of the unmanned aerial vehicle heat prediction method of the present invention is shown. The server collects track data of all unmanned aerial vehicles in each detection area and stores the track data into the database for later use in predicting the heat level of each unmanned aerial vehicle and generating a heat map of the unmanned aerial vehicle.
The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.
Specifically, the unmanned aerial vehicle heat prediction method comprises the following steps:
s10, acquiring each detection area, and performing gridding treatment on each detection area to obtain a grid unit corresponding to each detection area.
It can be appreciated that the flight area of the unmanned aerial vehicle may include a lot, so that for convenience of targeted management, the flight area of the unmanned aerial vehicle may be divided in advance, for example, according to the detected priority, or according to the geographic location, and the like, and specifically may be configured according to the actual detection requirement, which is not limited by the present invention.
Through the division of the detection area, the appointed area can be detected according to the actual detection requirement during subsequent detection, the detection range is reduced, and the accurate detection of the unmanned aerial vehicle is realized.
In this embodiment, the performing gridding processing on each detection area to obtain a grid unit corresponding to each detection area includes:
carrying out grid division of preset rounds on each detection area according to longitude and latitude;
determining a grid division result obtained after the last round of grid division as a grid unit corresponding to each detection area;
and when dividing each round, acquiring a current grid division result, and continuously executing the grid division of the current round according to longitude and latitude on the basis of the current grid division result.
For example: the preset number of rounds may be configured to be 5 times. In the initial dividing process, dividing a detection area A along longitude and latitude respectively to obtain first grid data; and when the second round of grid division is performed, dividing is continuously performed along the longitude and latitude on the basis of the first grid data, and so on until after 5 rounds of grid division, determining the currently obtained grid data as a grid unit of a detection area A, and representing the divided grid units by characters A-Z, wherein the characters of each small unit are used for representing the geographic position of a single grid.
Through carrying out the meshing to each detection region, can improve unmanned aerial vehicle positional information's readability, be convenient for discern.
S11, track data of all unmanned aerial vehicles in each detection area are collected in real time.
In this embodiment, track data of all the unmanned aerial vehicles in each detection area are collected in real time, so that subsequent unmanned aerial vehicle heat prediction is performed according to the track data.
Specifically, the acquiring, in real time, the trajectory data of all the unmanned aerial vehicles in each detection area includes:
collecting electric signals of all unmanned aerial vehicles in each detection area by using passive radio equipment;
collecting image signals of all unmanned aerial vehicles in each detection area by using photoelectric equipment;
collecting sound signals of all unmanned aerial vehicles in each detection area by using an array microphone;
and integrating the electric signals of all the unmanned aerial vehicles in each detection area, the image signals of all the unmanned aerial vehicles in each detection area and the sound signals of all the unmanned aerial vehicles in each detection area to obtain the track data of all the unmanned aerial vehicles in each detection area.
Through the embodiment, the electric signal, the image signal and the sound signal are respectively acquired by utilizing different acquisition equipment, and the multi-dimensional signal is used as the track data of the unmanned aerial vehicle, so that the subsequent heat prediction can be more accurate.
S12, preprocessing the track data of all the unmanned aerial vehicles in each detection area to obtain track coordinates of each unmanned aerial vehicle.
In this embodiment, in order to further improve the recognition accuracy of the unmanned aerial vehicle, it is also necessary to pre-process the track data of all unmanned aerial vehicles in each detection area.
Specifically, the preprocessing the track data of all the unmanned aerial vehicles in each detection area to obtain the track coordinates of each unmanned aerial vehicle includes:
carrying out smoothing processing on the track data of all unmanned aerial vehicles in each detection area based on a Kalman filtering smoothing algorithm to obtain data to be processed;
acquiring the electric signal in the data to be processed, and inputting the electric signal into a pre-trained electric signal feature extraction model to obtain an electric signal feature map;
acquiring the image signals in the data to be processed, and inputting the image signals into a pre-trained image signal feature extraction model to obtain an image signal feature map;
acquiring the sound signal in the data to be processed, and inputting the sound signal into a pre-trained MFCC (Mel Frequency Cepstrum Coefficient ) model to obtain a sound signal characteristic diagram;
Performing feature fusion on the electric signal feature map, the image signal feature map and the sound signal feature map to obtain fusion features;
and performing dimension reduction processing on the fusion characteristics by using a PCA (Principal Component Analysis) principle component analysis dimension reduction algorithm to obtain the track coordinates of each unmanned aerial vehicle.
The electric signal feature extraction model and the image signal feature extraction model may be a model obtained by training based on a CNN (Convolutional Neural Networks, convolutional neural network) model.
By preprocessing the track data of all unmanned aerial vehicles in each detection area, the identifiability of the track data can be further improved, and the accuracy and the prediction speed of the subsequent heat prediction are further improved.
S13, acquiring acquisition time of the track coordinates of each unmanned aerial vehicle.
Specifically, the acquisition time of the track coordinates of each unmanned aerial vehicle can be synchronously recorded when the track coordinates of each unmanned aerial vehicle are acquired.
S14, encoding processing is carried out on the basis of the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid units corresponding to each detection area, unmanned aerial vehicle track encoded data corresponding to each detection area are obtained, and the unmanned aerial vehicle track encoded data corresponding to each detection area are stored in a configuration database.
Wherein the configuration database may be a ES (Elastic Search) database. The ES database has higher retrieval efficiency and high reliability.
It should be noted that, the present embodiment does not limit the encoding algorithm used.
S15, responding to an unmanned aerial vehicle heat prediction instruction of a target area, calling unmanned aerial vehicle track coding data corresponding to the target area from the configuration database to serve as target coding data, and decoding the target coding data to obtain target decoding data.
In this embodiment, the target area may be any one of each detection area.
In this embodiment, the unmanned aerial vehicle heat prediction instruction may be triggered by a person having unmanned aerial vehicle security management responsibilities.
In this embodiment, the target encoded data may be subjected to decoding processing using a decoding algorithm corresponding to the encoding algorithm employed previously.
S16, invoking a pre-trained unmanned aerial vehicle heat level prediction model, and calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model.
In this embodiment, before the invoking the pre-trained unmanned aerial vehicle heat level prediction model, the method further includes:
Acquiring historical unmanned aerial vehicle track data as a training sample;
marking the training sample to obtain a label sample;
training a convolutional neural network model using the label sample;
and stopping training when the accuracy of the convolutional neural network model reaches the configuration accuracy, and obtaining the unmanned aerial vehicle heat level prediction model.
Wherein, the marking the training sample may be hot marking the training sample.
The configuration accuracy rate may be configured in a customized manner, for example, 98%.
Through the embodiment, the unmanned aerial vehicle heat level prediction model can be obtained based on the marked historical data training and used for subsequent heat prediction.
In this embodiment, the calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model includes:
determining a prediction time period and a target position according to the unmanned aerial vehicle heat prediction instruction;
acquiring the number of unmanned aerial vehicles at each acquisition time point in the prediction time period from the target decoding data, and the linear distance from each unmanned aerial vehicle to the target position;
Inputting the predicted time period, the number of unmanned aerial vehicles at each acquisition time point and the linear distance from each unmanned aerial vehicle to the target position as input data into the unmanned aerial vehicle heat level prediction model;
and obtaining output data of the unmanned aerial vehicle heat level prediction model as the heat level of each unmanned aerial vehicle in the target area.
Correspondingly, when the unmanned aerial vehicle heat level prediction model is trained, the prediction time period in the historical unmanned aerial vehicle track data, the number of unmanned aerial vehicles at each acquisition time point and the linear distance from each unmanned aerial vehicle to the target position can be obtained to serve as the training sample, meanwhile, the sample data in the training sample are labeled according to the corresponding heat level, and the label is used as a training target to train to obtain the unmanned aerial vehicle heat level prediction model.
The higher the heat level is, the higher the risk of unmanned aerial vehicle in flight is, the greater the threat to safety is, and at this time, safety protection and flight control are required to be timely carried out so as to avoid safety problems.
The prediction time period is a specific time period for which unmanned aerial vehicle heat prediction is needed, if the prediction time period is 9 to 11 am, the number of unmanned aerial vehicles and the linear distance from each unmanned aerial vehicle to the target position in the time period from 9 to 11 am are obtained, and then subsequent processing is carried out.
The target position is a pre-configured reference position and is used for determining whether the unmanned aerial vehicle flies too close or not and whether the risk degree is too high or not. For example: after the target position is determined, if the distance from the unmanned aerial vehicle to the target position is lower than the safety distance, the risk of the unmanned aerial vehicle flying currently can be determined to be too high.
The unmanned aerial vehicle heat level prediction model adopted in the embodiment combines the linear distance from each unmanned aerial vehicle to the target position in the prediction time period when predicting, so that threat degree analysis can be carried out on the data of the low-altitude aircraft in the appointed time period. Meanwhile, the algorithm complexity of mesh division processing, unmanned aerial vehicle heat level prediction models and the like adopted in the heat prediction process is low, and the requirements on data and technology are not too high, so that the method can be suitable for multiple unmanned aerial vehicle scenes. And as track data is stored in advance, and gridding, preprocessing, coding and other operations are performed in advance, the efficiency of subsequent heat prediction is improved, and the method is convenient to popularize and use.
And S17, generating an unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area, and displaying the unmanned aerial vehicle heat map.
In this embodiment, the generating the unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area includes:
configuring the corresponding relation between the heat level and the display color;
determining a target display color corresponding to the heat level of each unmanned aerial vehicle in the target area according to the corresponding relation;
and marking the heat level of each unmanned aerial vehicle according to the target display color corresponding to the heat level of each unmanned aerial vehicle so as to generate an unmanned aerial vehicle heat map of the target area.
For example: when the heat level is high, medium and low, the high heat level can be configured to be red, the medium heat level can be configured to be orange and the low heat level can be configured to be yellow according to the general cognition of a user, so that the user can intuitively feel the heat level through visual effects, and the unmanned aerial vehicle can be controlled by the user according to different heat levels conveniently.
According to the method, the heat levels (namely, safety threat) of the unmanned aerial vehicles in the appointed area are analyzed, threat levels of the unmanned aerial vehicles in different areas can be displayed through the electronic map (namely, the unmanned aerial vehicle heat map), the levels of threat of the unmanned aerial vehicles in a certain area are intuitively displayed, rapid early warning is achieved, and protection of a target object is enhanced.
According to the technical scheme, the method and the device can carry out gridding treatment on each detection area to obtain the grid unit corresponding to each detection area, reduce the recognition difficulty of unmanned aerial vehicle coordinates, acquire and preprocess track data of all unmanned aerial vehicles in each detection area in real time to obtain track coordinates of each unmanned aerial vehicle, encode the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid unit corresponding to each detection area, obtain unmanned aerial vehicle track encoded data corresponding to each detection area, store the unmanned aerial vehicle track encoded data in a configuration database, facilitate subsequent quick call and execute relevant treatment, calculate the heat level of each unmanned aerial vehicle in a target area by combining target decoded data acquired from the configuration database and a heat level prediction model of the unmanned aerial vehicle, generate a heat map and display, and further intuitively display the heat level of the unmanned aerial vehicle in a designated area so as to assist in safety control of the unmanned aerial vehicle.
Fig. 3 is a functional block diagram of a heat prediction device for an unmanned aerial vehicle according to a preferred embodiment of the present invention. The unmanned aerial vehicle heat prediction apparatus 11 includes a gridding unit 110, an acquisition unit 111, a preprocessing unit 112, an acquisition unit 113, an encoding unit 114, a decoding unit 115, a calculation unit 116, and a generation unit 117. The module/unit referred to in the present invention refers to a series of computer program segments, which are stored in a memory, capable of being executed by a processor and of performing a fixed function. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
The gridding unit 110 is configured to obtain each detection area, and gridding each detection area to obtain a grid unit corresponding to each detection area;
the acquisition unit 111 is configured to acquire track data of all the unmanned aerial vehicles in each detection area in real time;
the preprocessing unit 112 is configured to preprocess trajectory data of all the unmanned aerial vehicles in each detection area, so as to obtain trajectory coordinates of each unmanned aerial vehicle;
the acquiring unit 113 is configured to acquire an acquisition time of a track coordinate of each unmanned aerial vehicle;
the encoding unit 114 is configured to perform encoding processing based on the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle, and the grid unit corresponding to each detection area, obtain unmanned aerial vehicle track encoded data corresponding to each detection area, and store the unmanned aerial vehicle track encoded data corresponding to each detection area into the configuration database;
the decoding unit 115 is configured to, in response to an unmanned aerial vehicle heat prediction instruction for a target area, call unmanned aerial vehicle track encoded data corresponding to the target area from the configuration database as target encoded data, and perform decoding processing on the target encoded data to obtain target decoded data;
The calculating unit 116 is configured to invoke a pre-trained unmanned aerial vehicle heat level prediction model, and calculate a heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model;
the generating unit 117 is configured to generate an unmanned aerial vehicle heat map of the target area according to a heat level of each unmanned aerial vehicle in the target area, and display the unmanned aerial vehicle heat map.
According to the technical scheme, the method and the device can carry out gridding treatment on each detection area to obtain the grid unit corresponding to each detection area, reduce the recognition difficulty of unmanned aerial vehicle coordinates, acquire and preprocess track data of all unmanned aerial vehicles in each detection area in real time to obtain track coordinates of each unmanned aerial vehicle, encode the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid unit corresponding to each detection area, obtain unmanned aerial vehicle track encoded data corresponding to each detection area, store the unmanned aerial vehicle track encoded data in a configuration database, facilitate subsequent quick call and execute relevant treatment, calculate the heat level of each unmanned aerial vehicle in a target area by combining target decoded data acquired from the configuration database and a heat level prediction model of the unmanned aerial vehicle, generate a heat map and display, and further intuitively display the heat level of the unmanned aerial vehicle in a designated area so as to assist in safety control of the unmanned aerial vehicle.
Fig. 4 is a schematic structural diagram of a computer device according to a preferred embodiment of the present invention for implementing the heat prediction method of the unmanned aerial vehicle.
The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as a drone heat prediction program, stored in the memory 12 and executable on the processor 13.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation of the computer device 1, the computer device 1 may be a bus type structure, a star type structure, the computer device 1 may further comprise more or less other hardware or software than illustrated, or a different arrangement of components, for example, the computer device 1 may further comprise an input-output device, a network access device, etc.
It should be noted that the computer device 1 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
The memory 12 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, such as a removable hard disk of the computer device 1. The memory 12 may in other embodiments also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 may be used not only for storing application software installed in the computer device 1 and various types of data, such as codes of unmanned aerial vehicle heat prediction programs, etc., but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the computer device 1, connects the respective components of the entire computer device 1 using various interfaces and lines, executes various functions of the computer device 1 and processes data by running or executing programs or modules stored in the memory 12 (for example, executing an unmanned aerial vehicle heat prediction program, etc.), and calls data stored in the memory 12.
The processor 13 executes the operating system of the computer device 1 and various types of applications installed. The processor 13 executes the application program to implement the steps of the various unmanned aerial vehicle heat prediction method embodiments described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a gridding unit 110, an acquisition unit 111, a preprocessing unit 112, an acquisition unit 113, an encoding unit 114, a decoding unit 115, a calculation unit 116, a generation unit 117.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to execute portions of the unmanned aerial vehicle heat prediction method according to the embodiments of the present invention.
The modules/units integrated in the computer device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one straight line is shown in fig. 4, but not only one bus or one type of bus. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the computer device 1 may further comprise a power source (such as a battery) for powering the various components, preferably the power source may be logically connected to the at least one processor 13 via a power management means, whereby the functions of charge management, discharge management, and power consumption management are achieved by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
Further, the computer device 1 may also comprise a network interface, optionally comprising a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the computer device 1 and other computer devices.
The computer device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the computer device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
Fig. 4 shows only a computer device 1 with components 12-13, it will be understood by those skilled in the art that the structure shown in fig. 4 is not limiting of the computer device 1 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In connection with fig. 1, the memory 12 in the computer device 1 stores a plurality of instructions to implement a method of unmanned aerial vehicle heat prediction, the processor 13 being executable to implement:
acquiring each detection area, and performing gridding treatment on each detection area to obtain a grid unit corresponding to each detection area;
acquiring track data of all unmanned aerial vehicles in each detection area in real time;
preprocessing the track data of all unmanned aerial vehicles in each detection area to obtain track coordinates of each unmanned aerial vehicle;
acquiring the acquisition time of the track coordinates of each unmanned aerial vehicle;
performing coding processing based on the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid units corresponding to each detection area to obtain unmanned aerial vehicle track coding data corresponding to each detection area, and storing the unmanned aerial vehicle track coding data corresponding to each detection area into a configuration database;
Responding to an unmanned aerial vehicle heat prediction instruction of a target area, calling unmanned aerial vehicle track coding data corresponding to the target area from the configuration database to serve as target coding data, and decoding the target coding data to obtain target decoding data;
invoking a pre-trained unmanned aerial vehicle heat level prediction model, and calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model;
and generating an unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area, and displaying the unmanned aerial vehicle heat map.
Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
The data in this case were obtained legally.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The invention is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. The units or means stated in the invention may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. The unmanned aerial vehicle heat prediction method is characterized by comprising the following steps of:
acquiring each detection area, and performing gridding treatment on each detection area to obtain a grid unit corresponding to each detection area;
track data of all unmanned aerial vehicles in every detection area is gathered in real time, includes: collecting electric signals of all unmanned aerial vehicles in each detection area by using passive radio equipment; collecting image signals of all unmanned aerial vehicles in each detection area by using photoelectric equipment; collecting sound signals of all unmanned aerial vehicles in each detection area by using an array microphone; integrating the electric signals of all the unmanned aerial vehicles in each detection area, the image signals of all the unmanned aerial vehicles in each detection area and the sound signals of all the unmanned aerial vehicles in each detection area to obtain track data of all the unmanned aerial vehicles in each detection area;
Preprocessing the track data of all unmanned aerial vehicles in each detection area to obtain track coordinates of each unmanned aerial vehicle;
acquiring the acquisition time of the track coordinates of each unmanned aerial vehicle;
performing coding processing based on the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid units corresponding to each detection area to obtain unmanned aerial vehicle track coding data corresponding to each detection area, and storing the unmanned aerial vehicle track coding data corresponding to each detection area into a configuration database;
responding to an unmanned aerial vehicle heat prediction instruction of a target area, calling unmanned aerial vehicle track coding data corresponding to the target area from the configuration database to serve as target coding data, and decoding the target coding data to obtain target decoding data;
invoking a pre-trained unmanned aerial vehicle heat level prediction model, and calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model;
and generating an unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area, and displaying the unmanned aerial vehicle heat map.
2. The unmanned aerial vehicle heat prediction method of claim 1, wherein the gridding each detection area to obtain a grid cell corresponding to each detection area comprises:
carrying out grid division of preset rounds on each detection area according to longitude and latitude;
determining a grid division result obtained after the last round of grid division as a grid unit corresponding to each detection area;
and when dividing each round, acquiring a current grid division result, and continuously executing the grid division of the current round according to longitude and latitude on the basis of the current grid division result.
3. The method for predicting the heat of the unmanned aerial vehicle according to claim 1, wherein preprocessing the trajectory data of all unmanned aerial vehicles in each detection area to obtain the trajectory coordinates of each unmanned aerial vehicle comprises:
carrying out smoothing processing on the track data of all unmanned aerial vehicles in each detection area based on a Kalman filtering smoothing algorithm to obtain data to be processed;
acquiring the electric signal in the data to be processed, and inputting the electric signal into a pre-trained electric signal feature extraction model to obtain an electric signal feature map;
acquiring the image signals in the data to be processed, and inputting the image signals into a pre-trained image signal feature extraction model to obtain an image signal feature map;
Acquiring the sound signal in the data to be processed, and inputting the sound signal into a pre-trained MFCC model to obtain a sound signal feature map;
performing feature fusion on the electric signal feature map, the image signal feature map and the sound signal feature map to obtain fusion features;
and performing dimension reduction processing on the fusion characteristics by using a PCA dimension reduction algorithm to obtain the track coordinates of each unmanned aerial vehicle.
4. The unmanned aerial vehicle heat prediction method of claim 1, wherein before invoking the pre-trained unmanned aerial vehicle heat level prediction model, the method further comprises:
acquiring historical unmanned aerial vehicle track data as a training sample;
marking the training sample to obtain a label sample;
training a convolutional neural network model using the label sample;
and stopping training when the accuracy of the convolutional neural network model reaches the configuration accuracy, and obtaining the unmanned aerial vehicle heat level prediction model.
5. The unmanned aerial vehicle heat prediction method of claim 1, wherein the calculating the heat level of each unmanned aerial vehicle within the target area based on the target decoded data and the unmanned aerial vehicle heat level prediction model comprises:
Determining a prediction time period and a target position according to the unmanned aerial vehicle heat prediction instruction;
acquiring the number of unmanned aerial vehicles at each acquisition time point in the prediction time period from the target decoding data, and the linear distance from each unmanned aerial vehicle to the target position;
inputting the predicted time period, the number of unmanned aerial vehicles at each acquisition time point and the linear distance from each unmanned aerial vehicle to the target position as input data into the unmanned aerial vehicle heat level prediction model;
and obtaining output data of the unmanned aerial vehicle heat level prediction model as the heat level of each unmanned aerial vehicle in the target area.
6. The unmanned aerial vehicle heat prediction method of claim 1, wherein the generating the unmanned aerial vehicle heat map of the target area from the heat level of each unmanned aerial vehicle within the target area comprises:
configuring the corresponding relation between the heat level and the display color;
determining a target display color corresponding to the heat level of each unmanned aerial vehicle in the target area according to the corresponding relation;
and marking the heat level of each unmanned aerial vehicle according to the target display color corresponding to the heat level of each unmanned aerial vehicle so as to generate an unmanned aerial vehicle heat map of the target area.
7. Unmanned aerial vehicle heat prediction device, its characterized in that, unmanned aerial vehicle heat prediction device includes:
the gridding unit is used for acquiring each detection area, and gridding the detection areas to obtain grid units corresponding to the detection areas;
the acquisition unit is used for acquiring the track data of all unmanned aerial vehicles in each detection area in real time, and comprises: collecting electric signals of all unmanned aerial vehicles in each detection area by using passive radio equipment; collecting image signals of all unmanned aerial vehicles in each detection area by using photoelectric equipment; collecting sound signals of all unmanned aerial vehicles in each detection area by using an array microphone; integrating the electric signals of all the unmanned aerial vehicles in each detection area, the image signals of all the unmanned aerial vehicles in each detection area and the sound signals of all the unmanned aerial vehicles in each detection area to obtain track data of all the unmanned aerial vehicles in each detection area;
the preprocessing unit is used for preprocessing the track data of all the unmanned aerial vehicles in each detection area to obtain track coordinates of each unmanned aerial vehicle;
the acquisition unit is used for acquiring the acquisition time of the track coordinates of each unmanned aerial vehicle;
The encoding unit is used for carrying out encoding processing based on the track coordinates of each unmanned aerial vehicle, the acquisition time of the track coordinates of each unmanned aerial vehicle and the grid unit corresponding to each detection area to obtain unmanned aerial vehicle track encoded data corresponding to each detection area, and storing the unmanned aerial vehicle track encoded data corresponding to each detection area into the configuration database;
the decoding unit is used for responding to the unmanned aerial vehicle heat prediction instruction of the target area, calling unmanned aerial vehicle track coding data corresponding to the target area from the configuration database to serve as target coding data, and decoding the target coding data to obtain target decoding data;
the calculation unit is used for calling a pre-trained unmanned aerial vehicle heat level prediction model, and calculating the heat level of each unmanned aerial vehicle in the target area based on the target decoding data and the unmanned aerial vehicle heat level prediction model;
and the generation unit is used for generating an unmanned aerial vehicle heat map of the target area according to the heat level of each unmanned aerial vehicle in the target area and displaying the unmanned aerial vehicle heat map.
8. A computer device, the computer device comprising:
A memory storing at least one instruction; and
A processor executing instructions stored in the memory to implement the unmanned aerial vehicle heat prediction method of any one of claims 1 to 6.
9. A computer-readable storage medium, characterized by: the computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the unmanned aerial vehicle heat prediction method of any of claims 1 to 6.
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