Method for monitoring drone positioning using air pressure wake-up
By installing a processor and an electronic barometer on the drone, combining the heat accumulation prediction model and the convolutional network, the problem of high power consumption for the black flight monitoring of the drone is solved, and efficient power management and accurate flight status judgment are achieved.
Patent Information
- Application Number
- CN202411500577.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-25
- Publication Date
- 2024-12-27
- Estimated Expiration
- 2044-10-25
AI Technical Summary
The existing drone black flight monitoring technology requires real-time communication between positioning equipment and communication equipment, resulting in excessive power consumption and difficulty in monitoring for a long time.
Install monitors on the drone, including processors, electronic barometers and active radio frequency tags, electronic barometers detect air pressure information, processors calculate air pressure fluctuations data to determine flight status, activate active radio frequency tags and positioning equipment to send information to management Platform, combining heat accumulation prediction model and convolutional network to reduce power consumption.
While achieving black flight monitoring of drones, it effectively reduces monitoring power consumption, improves monitoring time, and reduces the risk of misjudgment.
Smart Images
Figure CN119012136B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and in particular to a method for monitoring the positioning of an unmanned aerial vehicle by waking up the unmanned aerial vehicle using air pressure. Background Art
[0002] Standardizing the flight activities of drones is an important part of promoting the healthy development of the drone industry. In the existing technology, in order to prevent illegal drone flights, positioning equipment and communication equipment are installed on drones. The communication equipment feeds the positioning information of the drones back to the drone control platform in real time. The flight status of the drone can be determined based on the positioning information, and by comparing it with the registered flight time, it can be judged whether it is illegal flight.
[0003] However, the above existing methods require the positioning device and the communication device to be in real-time communication state, which will consume a lot of power of the drone and is not conducive to long-term illegal flight control and monitoring of the drone. The present invention aims to improve this technical problem. Summary of the invention
[0004] In order to solve the above technical problems, the present invention provides a method, system, electronic device, computer storage medium and computer program product for monitoring the positioning of unmanned aerial vehicles by using air pressure wake-up, so as to achieve the purpose of completing the unauthorized flying monitoring of unmanned aerial vehicles while effectively reducing the monitoring power consumption.
[0005] The invention discloses a method for monitoring the positioning of an unmanned aerial vehicle by waking up air pressure. The method comprises the following steps: installing a monitor on the unmanned aerial vehicle, the monitor comprising a processor, an electronic barometer, an active radio frequency tag, and a positioning device, wherein the active radio frequency tag and the positioning device are usually in a low-power dormant state; the electronic barometer continuously detects a plurality of groups of air pressure information in a region, and transmits each group of the air pressure information to the processor; the processor calculates air pressure fluctuation data according to each group of the air pressure information, and judges whether the unmanned aerial vehicle is switched to a flight state according to the air pressure fluctuation data; if so, activating the active radio frequency tag and the positioning device, the active radio frequency tag sends the unmanned aerial vehicle ID and the positioning information detected by the positioning device to an unmanned aerial vehicle management platform; the unmanned aerial vehicle management platform compares the unmanned aerial vehicle ID, the positioning information, and the flight record information corresponding to the unmanned aerial vehicle ID stored internally, and judges whether the unmanned aerial vehicle is an illegal flight.
[0006] Optionally, the processor is also used to: communicate with a flight recording device of the UAV to obtain the latest set of flight recording data, extract flight duration and flight speed from the flight recording data; predict a value of heat to be dissipated based on the flight duration and the flight speed, compare and analyze the value of heat to be dissipated with a preset comparison table to obtain a silence time; wherein the silence time in the preset comparison table is positively correlated with the value of heat to be dissipated; when the silence time is reached, receive each set of air pressure information sent by the electronic barometer.
[0007] Optionally, predicting the value of heat to be dissipated based on the flight duration and the flight speed includes: inputting the flight duration, the flight speed, basic parameters of the drone and the ambient temperature into a heat accumulation prediction model based on a large model; wherein the basic parameters include motor parameters, electric control parameters, battery parameters and propeller parameters; and generating the value of heat to be dissipated based on the prediction result of the heat accumulation prediction model.
[0008] Optionally, the generating of the value of heat to be dissipated based on the prediction result of the heat accumulation prediction model includes: the heat accumulation prediction model outputs its predicted preliminary value of heat to be dissipated; the drone management platform sends information of other drones in the drone parking area to the processor, the other drone information includes the number of drones whose parking time is less than a predetermined time; the processor calculates an optimization coefficient based on the number of drones, uses the optimization coefficient to adjust the preliminary value of heat to be dissipated, and obtains the value of heat to be dissipated.
[0009] Optionally, the parking area is a circular area based on the position of the drone, and the radius of the circular area is determined based on the preliminary heat value to be dissipated. Specifically, the radius is negatively correlated with the preliminary heat value to be dissipated.
[0010] Optionally, the processor calculates air pressure fluctuation data based on each group of air pressure information, and determines whether the UAV switches to a flight state based on the air pressure fluctuation data, including: the processor calculates air pressure fluctuation data based on each group of air pressure information, the air pressure fluctuation data including a number of air pressure fluctuation amplitudes sorted in chronological order, each of the air pressure fluctuation amplitudes having a positive or negative sign, the positive sign representing that the subsequent air pressure becomes larger than the adjacent previous air pressure, and the negative sign representing that the subsequent air pressure becomes smaller than the adjacent previous air pressure; using a convolutional network to extract air pressure fluctuation features from the air pressure fluctuation data, inputting the air pressure fluctuation features into a classifier, the classifier outputting a probability that the UAV switches to a flight state; if the probability is higher than a threshold, it is determined that the UAV switches to a flight state, otherwise it is determined that the UAV does not switch to a flight state.
[0011] The present invention also provides a system for monitoring the positioning of a drone using air pressure wake-up, the system comprising a processing module and a storage module, the storage module storing a computer program, the processing module calling the computer program in the storage module to implement the following method steps: installing a monitor on a drone, the monitor comprising a processor, an electronic barometer, an active radio frequency tag, and a positioning device, the active radio frequency tag and the positioning device being in a low-power sleep state at ordinary times; the electronic barometer continuously detects several groups of air pressure information in the area in which it is located, and transmits each group of the air pressure information to the processor; the processor calculates air pressure fluctuation data according to each group of the air pressure information, and determines whether the drone is switched to a flight state according to the air pressure fluctuation data; if so, the active radio frequency tag and the positioning device are activated, and the active radio frequency tag sends the drone ID and the positioning information detected by the positioning device to a drone management platform; the drone management platform compares the drone ID, the positioning information, and the flight filing information corresponding to the drone ID stored internally, and makes a judgment on whether the drone is an illegal flight.
[0012] The present invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the aforementioned method.
[0013] The present invention also discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned method.
[0014] The present invention also discloses a computer program product. When the computer program product is called and executed by a processor of an electronic device, the method described above is implemented during execution.
[0015] The solution of the present invention not only realizes the monitoring and control of illegal UAV flights, but also effectively reduces the problem of power consumption during the monitoring process. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0017] Figure 1 The present invention discloses a method for monitoring the positioning of a drone by waking up air pressure.
[0018] Figure 2 The present invention discloses a system for monitoring the positioning of a drone using air pressure wake-up. DETAILED DESCRIPTION
[0019] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.
[0020] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.
[0021] like Figure 1 As shown, an embodiment of the present invention discloses a method for monitoring the positioning of a drone using air pressure wake-up, the method comprising the following steps: installing a monitor on a drone, the monitor comprising a processor, an electronic barometer, an active radio frequency tag, and a positioning device, the active radio frequency tag and the positioning device being in a low-power sleep state at ordinary times; the electronic barometer continuously detects several groups of air pressure information in the area in which it is located, and transmits each group of the air pressure information to the processor; the processor calculates air pressure fluctuation data according to each group of the air pressure information, and determines whether the drone is switched to a flight state according to the air pressure fluctuation data; if so, the active radio frequency tag and the positioning device are activated, and the active radio frequency tag sends the drone ID and the positioning information detected by the positioning device to a drone management platform; the drone management platform compares the drone ID, the positioning information, and the flight filing information corresponding to the drone ID stored internally, and determines whether the drone is an illegal flight.
[0022] Compared with the existing methods, the active radio frequency tags and positioning devices in the monitor of the present invention are usually in a low-power sleep state, and only the electronic barometer and processor are in a normally open state, which is conducive to reducing the power consumption of the monitor and thus increasing the duration of illegal flight monitoring of drones.
[0023] Specifically, the electronic barometer detects the air pressure in the area where the drone is located to obtain several groups of air pressure information. The processor calculates the air pressure fluctuation data based on each group of air pressure information, and determines whether the drone has switched to the flight state based on the air pressure fluctuation data. If so, the active radio frequency tag and the positioning device are activated. The positioning device detects the positioning information of the drone in real time. The active radio frequency tag sends the drone ID and positioning information to the drone management platform. The drone management platform compares the positioning information of the drone with the flight filing information of the drone stored internally (flight request at a specific time). If there is a match, for example, the filed flight time is not the current time, the drone is judged to be an illegal flight; otherwise, it is judged to be a normal flight.
[0024] The present invention determines whether the drone is in flight by detecting the air pressure information of the area where the drone is located, and triggers the active radio frequency tag and positioning device in the low-power sleep state to switch to the enabled state, so as to send the drone ID and positioning information to the drone management platform in real time, so that the drone management platform can make a judgment on whether the drone is flying illegally. It can be seen that the solution of the present invention not only realizes the monitoring and control of illegal drone flights, but also effectively reduces the problem of power consumption during the monitoring process.
[0025] It should be noted that the drone management platform can be a local platform. For example, the distance between the deployment location of the drone management platform and the storage location of each drone is within 100 meters. In this way, the drone management platform can receive the drone ID and positioning information sent by each drone through radio frequency communication technology.
[0026] Optionally, the processor is also used to: communicate with a flight recording device of the UAV to obtain the latest set of flight recording data, extract flight duration and flight speed from the flight recording data; predict a value of heat to be dissipated based on the flight duration and the flight speed, compare and analyze the value of heat to be dissipated with a preset comparison table to obtain a silence time; wherein the silence time in the preset comparison table is positively correlated with the value of heat to be dissipated; when the silence time is reached, receive each set of air pressure information sent by the electronic barometer.
[0027] In the embodiment of the present invention, the motor, electronic speed regulator, power battery, propeller, etc. of the drone will generate a certain amount of heat during flight, and the friction between the drone body and the air will also generate more heat. This heat will gradually dissipate to the surrounding area within a period of time after the drone lands, thereby causing the air temperature in the area to rise. The change in air temperature will cause the air pressure to change, and the electronic barometer may detect a set of air pressure information with a large fluctuation range, which may easily cause the processor to misjudge that the drone has entered the flight state, that is, flying without permission.
[0028] In view of the above technical problems, the present invention is arranged that after the monitor is installed on the drone or the drone stops on the ground (at this time, the monitor is fixedly installed on the drone), the processor of the monitor communicates with the flight recording device of the drone in a timely manner to obtain the latest set of flight record data of the drone, including flight time and flight speed. The processor can predict the heat generated by the most recent flight based on the flight time and flight speed, that is, predict the heat value to be dissipated at this time, compare and analyze the heat value to be dissipated with the preset comparison table, and obtain the silent time. Finally, the control processor receives multiple sets of air pressure information detected by the electronic barometer after the silent time. Since the heat accumulated during the flight of the drone has been dissipated, the air temperature in the area has returned to normal. At this time, based on the air pressure information in the area, it is judged whether the drone is switched to the flight state, and there is basically no misjudgment of black flight caused by this factor.
[0029] Among them, a comparison table is preset, which contains multiple groups of [silent time, heat value to be dissipated], and the corresponding silent time can be determined by looking up the table.
[0030] Optionally, predicting the value of heat to be dissipated based on the flight duration and the flight speed includes: inputting the flight duration, the flight speed, basic parameters of the drone and the ambient temperature into a heat accumulation prediction model based on a large model; wherein the basic parameters include motor parameters, electric control parameters, battery parameters and propeller parameters; and generating the value of heat to be dissipated based on the prediction result of the heat accumulation prediction model.
[0031] In the embodiment of the present invention, the heat generated by the drone during flight comes from two aspects: 1) the heat generated by the motor, electronic speed regulator, power battery, propeller and other equipment during operation; 2) the heat generated by the friction between the drone and the air during flight. At the same time, the amount of heat generated is positively correlated with the flight time and flight speed, that is, the longer the flight time and the faster the flight speed, the more heat is generated; and, since the faster the flight speed and the lower the ambient temperature, the faster the heat dissipation speed, correspondingly, the heat value to be dissipated is negatively correlated with the flight speed and positively correlated with the ambient temperature. It can be seen that the relationship between the heat value to be dissipated and the flight time, flight speed, basic parameters of the drone and the ambient temperature is very complicated, and it is difficult to construct a corresponding conversion function for it.
[0032] In view of the above technical problems, the present invention constructs a heat accumulation prediction model based on a large model, wherein the large model refers to a machine learning model with large-scale parameters and complex computing structure, which is usually constructed by a deep neural network and has billions or even hundreds of billions of parameters. These models use large-scale data for self-adjustment and optimization during training and optimization to achieve intelligent data analysis and processing. The present invention preferably adopts the latest existing large models, such as Qwen2.5-Math-72B, Llama3.1-405B, GPT-4o mini, Gemma2-9B, etc., and the present invention does not specifically limit this. At the same time, some records of drones of different models (corresponding to different basic parameters) flying at different flight speeds for various durations in environments with various temperatures are collected, and the temperature dissipation after landing is detected. These data constitute a small sample fine-tuning data set, and the small sample fine-tuning data set is used to fine-tune any of the above existing large models to obtain a heat accumulation prediction model suitable for the prediction task of the heat value to be dissipated of the present invention.
[0033] Optionally, the generating of the value of heat to be dissipated based on the prediction result of the heat accumulation prediction model includes: the heat accumulation prediction model outputs its predicted preliminary value of heat to be dissipated; the drone management platform sends information of other drones in the drone parking area to the processor, the other drone information includes the number of drones whose parking time is less than a predetermined time; the processor calculates an optimization coefficient based on the number of drones, uses the optimization coefficient to adjust the preliminary value of heat to be dissipated, and obtains the value of heat to be dissipated.
[0034] In the embodiment of the present invention, the heat accumulation prediction model that has been fine-tuned and trained can preliminarily predict the heat value to be dissipated of the drone based on the flight duration, flight speed, basic parameters of the drone and ambient temperature. The preliminary heat value to be dissipated does not take into account the environment in the drone parking area. There will be multiple drones placed in the parking area corresponding to the drone management platform, some of which may have just completed flight and have not yet completely dissipated heat. The heat dissipated by these drones will also cause changes in the air pressure in the area where the drone is located.
[0035] In response to the above technical problems, the present invention is arranged that after the drone lands and docks, the drone management platform sends the number of drones in the drone parking area whose parking time is less than a predetermined time to the processor of the monitor, and the processor calculates the corresponding optimization coefficient according to the number of such drones, and then uses the optimization coefficient to adjust the preliminary heat value to be dissipated predicted by the above-mentioned heat accumulation prediction model to obtain the final heat value to be dissipated.
[0036] The predetermined time is an empirical value, which means that after the predetermined time, the drone that has landed and landed has completed the dissipation of the accumulated heat. The present invention does not specifically limit the conversion formula between the optimization coefficient and the number of drones, but the two should obviously be adapted to a conversion formula with a positive correlation, such as optimization coefficient = benchmark optimization coefficient + f (number of drones). The optimization coefficient should be a value greater than 1.
[0037] Optionally, the parking area is a circular area based on the position of the drone, and the radius of the circular area is determined based on the preliminary heat value to be dissipated. Specifically, the radius is negatively correlated with the preliminary heat value to be dissipated.
[0038] In an embodiment of the present invention, after the drone lands and docks, a circular area is drawn based on its location, and the radius of the circular area is negatively correlated with the initial heat value to be dissipated predicted by the aforementioned heat accumulation prediction model. The reason for this setting is that: when the initial heat value to be dissipated predicted by the aforementioned heat accumulation prediction model is larger, it means that the heat generated by the drone itself is higher. At this time, the air temperature change in the area is less affected by the heat dissipated by other surrounding drones, so the circular area is reduced to appropriately reduce the number of drones whose parking time is less than the predetermined time; on the contrary, when the initial heat value to be dissipated predicted by the aforementioned heat accumulation prediction model is smaller, it means that the heat generated by the drone itself is lower. At this time, the air temperature change in the area is relatively more affected by the heat dissipated by other surrounding drones, so the circular area is increased to appropriately increase the number of drones whose parking time is less than the predetermined time.
[0039] By adjusting the size of the circular area, excessive consideration of heat dissipation of other surrounding drones can be reduced.
[0040] Optionally, the processor calculates air pressure fluctuation data based on each group of air pressure information, and determines whether the UAV switches to a flight state based on the air pressure fluctuation data, including: the processor calculates air pressure fluctuation data based on each group of air pressure information, the air pressure fluctuation data including a number of air pressure fluctuation amplitudes sorted in chronological order, each of the air pressure fluctuation amplitudes having a positive or negative sign, the positive sign representing that the subsequent air pressure becomes larger than the adjacent previous air pressure, and the negative sign representing that the subsequent air pressure becomes smaller than the adjacent previous air pressure; using a convolutional network to extract air pressure fluctuation features from the air pressure fluctuation data, inputting the air pressure fluctuation features into a classifier, the classifier outputting a probability that the UAV switches to a flight state; if the probability is higher than a threshold, it is determined that the UAV switches to a flight state, otherwise it is determined that the UAV does not switch to a flight state.
[0041] In the embodiment of the present invention, after the processor receives each group of air pressure information detected by the electronic barometer, the air pressure information can be sorted according to the order of collection, and the difference between adjacent air pressure information, that is, the air pressure fluctuation amplitude, is calculated. The air pressure fluctuation amplitude has positive fluctuation and negative fluctuation, which are distinguished by positive and negative signs. The correspondingly sorted air pressure fluctuation amplitudes constitute air pressure fluctuation data. Then, a convolutional network is used to extract the features of the air pressure fluctuation amplitude from the air pressure fluctuation data, and the extracted air pressure fluctuation features represent the characteristics of the air pressure fluctuation at this stage.
[0042] When the height of the drone changes up and down, the electronic barometer can detect the fluctuation of air pressure, but the height change of the drone may be caused by the drone switching to the flight state or by the relevant personnel, so it is necessary to distinguish between them. In this regard, the present invention constructs a classifier, which classifies the air pressure fluctuation characteristics extracted by the convolutional network and outputs the probability of the drone switching to the flight state. When the probability is lower than the threshold, it means that the classifier believes that the air pressure fluctuation characteristics correspond to the air pressure fluctuation characteristics caused by the relevant personnel manually picking up and placing the drone, and when the probability is higher than the threshold, it means that the classifier believes that the air pressure fluctuation characteristics correspond to the air pressure fluctuation characteristics caused by the drone switching to the flight state.
[0043] It should be noted that when the relevant personnel manually pick up and put down the drone, the altitude of the drone changes. At this time, the electronic barometer will indeed detect the change in air pressure, but this is quite different from the pressure fluctuation characteristics (fluctuation amplitude, fluctuation frequency, etc.) corresponding to the situation when the drone switches to the flight state. Because when the drone switches to the flight state, in addition to the change in altitude, the airflow caused by the rotor will also directly cause the air pressure detected by the electronic barometer to fluctuate greatly. The pressure fluctuation characteristics at this time are completely different from those in the case of manual pick-up and placement, and the classifier can distinguish them relatively easily.
[0044] In addition, the classifier may be any one of SVM, decision tree, naive Bayes, etc., and the present invention is not limited to this.
[0045] like Figure 2As shown, an embodiment of the present invention also discloses a system for monitoring the positioning of a drone using air pressure wake-up, the system comprising a processing module and a storage module, the storage module storing a computer program, the processing module calling the computer program in the storage module to implement the following method steps: installing a monitor on a drone, the monitor comprising a processor, an electronic barometer, an active radio frequency tag, and a positioning device, the active radio frequency tag and the positioning device being in a low-power dormant state at ordinary times; the electronic barometer continuously detects several groups of air pressure information in the area in which it is located, and transmits each group of the air pressure information to the processor; the processor calculates air pressure fluctuation data according to each group of the air pressure information, and determines whether the drone is switched to a flight state according to the air pressure fluctuation data; if so, the active radio frequency tag and the positioning device are activated, and the active radio frequency tag sends the drone ID and the positioning information detected by the positioning device to a drone management platform; the drone management platform compares the drone ID, the positioning information, and the flight filing information corresponding to the drone ID stored internally, and makes a judgment on whether the drone is an illegal flight.
[0046] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.
[0047] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.
[0048] The embodiment of the present invention further discloses a computer program product. When the computer program product is called and executed by a processor of an electronic device, the method described in the above embodiment is implemented during execution.
[0049] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.
Claims
1. A method for monitoring the positioning of a drone using air pressure wake-up, characterized in that The method comprises the following steps: installing a monitor on a drone, the monitor comprising a processor, an electronic barometer, an active radio frequency tag, and a positioning device, the active radio frequency tag and the positioning device are usually in a low-power sleep state; the electronic barometer continuously detects several groups of air pressure information in the area, and transmits each group of air pressure information to the processor; The processor calculates air pressure fluctuation data according to each group of air pressure information, and determines whether the drone is switched to a flight state according to the air pressure fluctuation data; If yes, the active radio frequency tag and the positioning device are activated, and the active radio frequency tag sends the drone ID and the positioning information detected by the positioning device to the drone management platform; The drone management platform compares the drone ID, the positioning information, and the flight record information corresponding to the drone ID stored internally to determine whether the drone is an illegal flight; The processor is also used to: communicate with the flight recording device of the UAV to obtain the latest set of flight recording data, extract the flight time and flight speed from the flight recording data; predict the heat value to be dissipated according to the flight time and the flight speed, compare and analyze the heat value to be dissipated with a preset comparison table to obtain the silence time; wherein the silence time in the preset comparison table is positively correlated with the heat value to be dissipated; When the silence time is reached, each group of the air pressure information sent by the electronic barometer is received.
2. The method for monitoring the positioning of a drone using air pressure wake-up according to claim 1, characterized in that: The heat value to be dissipated is predicted according to the flight duration and the flight speed, including: inputting the flight duration, the flight speed, basic parameters of the UAV and the ambient temperature into a heat accumulation prediction model based on a large model; wherein the basic parameters include motor parameters, electric control parameters, battery parameters and propeller parameters; and generating the heat value to be dissipated based on the prediction result of the heat accumulation prediction model.
3. The method for monitoring the positioning of a drone using air pressure wake-up according to claim 2, characterized in that: The heat value to be dissipated is generated based on the prediction result of the heat accumulation prediction model, including: the heat accumulation prediction model outputs its predicted preliminary heat value to be dissipated; the drone management platform sends other drone information in the drone parking area to the processor, and the other drone information includes the number of drones whose parking time is less than a predetermined time; the processor calculates an optimization coefficient based on the number of drones, and uses the optimization coefficient to adjust the preliminary heat value to be dissipated to obtain the heat value to be dissipated.
4. The method for monitoring the positioning of a drone using air pressure wake-up according to claim 3 is characterized in that: The parking area is a circular area based on the position of the UAV, and the radius of the circular area is determined according to the preliminary heat value to be dissipated. Specifically, the radius is negatively correlated with the preliminary heat value to be dissipated.
5. The method for monitoring the positioning of a drone by using air pressure wake-up according to claim 4, characterized in that: The processor calculates the air pressure fluctuation data according to each group of the air pressure information, and determines whether the drone switches to the flight state according to the air pressure fluctuation data, including: the processor calculates the air pressure fluctuation data according to each group of the air pressure information, the air pressure fluctuation data includes a number of air pressure fluctuation amplitudes sorted in chronological order, each of the air pressure fluctuation amplitudes has a positive and negative sign, the positive sign represents that the subsequent air pressure is larger than the adjacent previous air pressure, and the negative sign represents that the subsequent air pressure is smaller than the adjacent previous air pressure; using a convolutional network to extract the air pressure fluctuation characteristics from the air pressure fluctuation data, the air pressure fluctuation characteristics are input into a classifier, and the classifier outputs the probability of the drone switching to the flight state; if the probability is higher than a threshold, it is determined that the drone switches to the flight state, otherwise it is determined that the drone does not switch to the flight state.
6. A system for monitoring the positioning of a drone using air pressure wake-up, the system being used to implement the method according to any one of claims 1 to 5, the system comprising a processing module and a storage module, the storage module storing a computer program, characterized in that: The processing module calls the computer program in the storage module to implement the following method steps: installing a monitor on the drone, the monitor comprising a processor, an electronic barometer, an active radio frequency tag, and a positioning device, the active radio frequency tag and the positioning device are usually in a low-power dormant state; the electronic barometer continuously detects several groups of air pressure information in the area, and transmits each group of air pressure information to the processor; The processor calculates air pressure fluctuation data according to each group of air pressure information, and determines whether the drone is switched to a flight state according to the air pressure fluctuation data; If yes, the active radio frequency tag and the positioning device are activated, and the active radio frequency tag sends the drone ID and the positioning information detected by the positioning device to the drone management platform; The drone management platform compares the drone ID, the positioning information and the internally stored flight record information corresponding to the drone ID to determine whether the drone is an illegal flight.
7. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 5.
8. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 5.
9. A computer program product, when the computer program product is called and executed by a processor of an electronic device, characterized in that: When executed, the method according to any one of claims 1 to 5 is implemented.