A method, device, equipment, medium and product for detecting a signal frequency range boundary of a UAV

Abstract

Embodiments of the present application provide a method, device, equipment, medium and product for detecting the boundary of the signal frequency range of a UAV, which generates a spectrum diagram according to the collected UAV signal, determines the frequency range corresponding to the UAV signal according to the spectrum diagram, determines the total energy of the UAV signal according to the frequency range and the spectrum diagram, and determines the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range. According to the embodiments of the present disclosure, the total energy of the UAV signal is determined according to the frequency range and the spectrum diagram, and the boundary of the frequency range is determined based on the total energy of the UAV signal and the frequency range, which can improve the detection accuracy of the boundary of the signal frequency range of the UAV.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) signal detection technology, and in particular to a method, apparatus, equipment, medium, and product for detecting the frequency range boundary of UAV signals. Background Technology

[0002] Drone technology is developing rapidly and is widely used in many fields such as aerial surveying, logistics delivery, agricultural plant protection, and emergency rescue. Drones mainly operate in the 2.4GHz and 5.8GHz industrial, scientific, and medical (ISM) frequency bands. Furthermore, unauthorized use of spectrum resources by drones not only interferes with important radio communication systems such as civil aviation and military aviation, but also may threaten public safety. Therefore, accurately detecting the frequency range boundaries of drone signals and achieving effective identification, monitoring, and interference avoidance of drone signals has become crucial to ensuring the orderly use of radio spectrum and the healthy development of the drone industry. However, existing technologies have the following shortcomings: Threshold-based detection methods: This method sets a fixed or adaptive power threshold and identifies the portion of the spectrum with power exceeding the threshold as the signal boundary. However, UAV signals are affected by multipath fading and co-channel interference in complex electromagnetic environments, resulting in significant signal power fluctuations. Fixed thresholds are difficult to adapt to dynamically changing environments, easily leading to misjudgments or missed detections of boundary boundaries. For example, when a UAV is in a densely populated urban area with tall buildings, the signal power attenuates severely after multiple reflections. If the threshold is set too high, some valid signals may be identified as noise; if the threshold is set too low, interference signals may be misidentified as UAV signals. Summary of the Invention

[0003] This invention provides a method, apparatus, device, medium, and product for detecting the boundary of the frequency range of UAV signals, which can improve the detection accuracy of the boundary of the frequency range of UAV signals.

[0004] In a first aspect, this embodiment provides a method for detecting the frequency range boundary of a drone signal. The method includes: generating a spectrum diagram based on the acquired drone signal; determining the frequency range corresponding to the drone signal based on the spectrum diagram; determining the total energy of the drone signal based on the frequency range and the spectrum diagram; and determining the target boundary of the frequency range based on the total energy of the drone signal and the frequency range.

[0005] Secondly, this embodiment provides a device for detecting the frequency range boundary of a drone signal. The device includes: a spectrum generation module for generating a spectrum based on the acquired drone signal; a frequency range determination module for determining the frequency range corresponding to the drone signal based on the spectrum; a total energy determination module for determining the total energy of the drone signal based on the frequency range and the spectrum; and a boundary determination module for determining the target boundary of the frequency range based on the total energy of the drone signal and the frequency range.

[0006] Thirdly, this embodiment provides an electronic device, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for detecting the frequency range boundary of a UAV signal according to any embodiment of the present invention.

[0007] Fourthly, this embodiment provides a computer-readable storage medium storing computer instructions that cause a processor to execute a method for detecting the boundary of the UAV signal frequency range as described in any embodiment of the present invention.

[0008] Fifthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the method for detecting the boundary of the UAV signal frequency range as described in any embodiment of the present invention.

[0009] The technical solution of this disclosure includes generating a spectrum diagram based on the collected UAV signal; determining the frequency range corresponding to the UAV signal based on the spectrum diagram; determining the total energy of the UAV signal based on the frequency range and the spectrum diagram; and determining the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range. This disclosure improves the detection accuracy of the frequency range boundary of UAV signals by determining the total energy of the UAV signal based on the frequency range and the spectrum diagram, and by determining the frequency range boundary based on the total energy of the UAV signal and the frequency range.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for detecting the frequency range boundary of a UAV signal provided in an embodiment of the present invention; Figure 2 A schematic diagram of the target boundary effect provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a device for detecting the frequency range boundary of a UAV signal provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Figure 1This is a schematic flowchart illustrating a method for detecting the frequency range boundary of a drone signal according to an embodiment of this application. This disclosure applies to the detection of drone signal frequency range boundaries. The method can be executed by a drone signal frequency range boundary detection device, which can be implemented in software and / or hardware, optionally through an electronic device such as a mobile terminal, PC, or server. Figure 1 As shown, the method includes: S110. Generate a spectrum diagram based on the collected UAV signals.

[0016] The spectrum diagram refers to a two-dimensional frequency domain distribution diagram obtained by Fourier transforming the time-domain UAV signal. The spectrum diagram consists of a frequency horizontal axis and an amplitude vertical axis.

[0017] S120. Determine the frequency range corresponding to the UAV signal based on the spectrum diagram.

[0018] The frequency range can refer to a roughly continuous frequency band that includes only the UAV signal. It is the limiting range for subsequent total energy calculation and target boundary determination, and is used to eliminate interference from large segments of irrelevant frequency bands.

[0019] In this embodiment, the frequency range containing only UAV signals can be roughly screened out based on the spectrum diagram, limiting the effective range for subsequent calculations and eliminating large segments of irrelevant frequency band interference; alternatively, the signal type can be determined based on the spectrum diagram, and the frequency range corresponding to the UAV signal type can be extracted based on the signal type.

[0020] Optionally, determining the frequency range corresponding to the UAV signal based on the spectrum diagram includes: determining the signal type based on the spectrum diagram; and extracting the frequency range corresponding to the UAV signal in the spectrum diagram based on the signal type.

[0021] In this embodiment, the signal type within the spectrum is identified by the frequency domain characteristics of the spectrogram. Various signal types are distinguished, and the frequency band corresponding to the identified drone signal type is extracted as a rough frequency range for subsequent calculations. This embodiment does not limit the signal type; for example, it can be drone signals, Wireless Fidelity (WiFi), Bluetooth, ambient noise, or other wireless signals.

[0022] In this embodiment, if the spectrum diagram contains only one signal, namely the drone signal, the frequency range corresponding to the drone signal can be directly determined based on the spectrum diagram.

[0023] In this embodiment, if other interference signals are mixed in within the frequency range, the total energy will include the energy of the interference, causing a shift in the subsequent energy proportion determination. The final target boundary will extend into the interference frequency band, resulting in a significant error. By accurately intercepting signals after signal type identification, it is ensured from the source that the frequency range only contains UAV signals, and the total energy benchmark is free of impurities, thus improving the accuracy of subsequent target boundary detection.

[0024] S130. Determine the total energy of the UAV signal based on the frequency range and the spectrum diagram.

[0025] In this embodiment, within the frequency range of the coarse screening, the total energy of the UAV signal is determined based on the spectrum diagram, and a unified energy benchmark for target boundary determination is established.

[0026] Optionally, determining the total energy of the UAV signal based on the frequency range and the spectrum diagram includes: integrating the amplitude value of each frequency point in the spectrum diagram along the horizontal frequency axis within the frequency range to obtain the total energy of the UAV signal.

[0027] The spectrum diagram consists of a frequency horizontal axis and an amplitude vertical axis.

[0028] In this embodiment, the total energy is calculated as follows: within the frequency range of the coarse screening, the amplitude values ​​of all frequency points in the spectrum are integrated along the horizontal frequency axis, and the integration result is the total energy of the UAV signal.

[0029] In this embodiment, the total energy is the cumulative integral result of the amplitudes at all frequency points across the entire frequency range. Amplitude fluctuations at a single or a small number of frequency points (noise spikes, signal dips) will only have a minimal impact on the total energy and will not change the overall magnitude of the total energy. Compared to traditional methods that use single-point amplitude as the criterion, the total energy benchmark obtained by integration has stronger anti-interference capabilities and higher stability.

[0030] S140. Determine the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range.

[0031] In this embodiment, the target boundary of the UAV signal frequency range is determined by using the total energy as a benchmark and combining it with the frequency range of a coarse screening. The target boundary can be understood as the two endpoints of the effective signal interval within the frequency range. This embodiment does not impose specific limitations on the energy ratio rule; for example, it can use the total signal energy within the frequency band as a unified benchmark, accumulating signal energy point by point from both ends of the band towards the middle. When the accumulated energy on one side reaches a preset proportion of the total energy, the current frequency point is determined as the target boundary, and finally, the continuous frequency band corresponding to the vast majority of the main energy is retained as the effective signal interval.

[0032] Optionally, determining the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range includes: within the frequency range, accumulating the amplitude values ​​of each frequency point in the spectrum from the low-frequency end to the high-frequency end of the frequency horizontal axis to obtain a first amplitude integral accumulation value; when the first amplitude integral accumulation value reaches a first preset proportion of the total energy of the UAV signal, recording the corresponding frequency point on the frequency horizontal axis as the low-frequency boundary of the frequency range; within the frequency range, accumulating the amplitude values ​​of each frequency point in the spectrum from the high-frequency end to the low-frequency end of the frequency horizontal axis to obtain a second amplitude integral accumulation value; when the second amplitude integral accumulation value reaches a second preset proportion of the total energy of the UAV signal, recording the corresponding frequency point on the frequency horizontal axis as the high-frequency boundary of the frequency range; and determining the low-frequency boundary and the high-frequency boundary as the target boundary of the frequency range of the UAV signal.

[0033] In this embodiment, the amplitude value is accumulated point by point from the lowest frequency to the highest frequency in the frequency range of the coarse screening to obtain a dynamic first amplitude integral cumulative value. When the first amplitude integral cumulative value reaches a first preset proportion of the total energy, the accumulation stops, and the current frequency point is the low-frequency boundary. The amplitude value is accumulated point by point from the highest frequency to the lowest frequency in the frequency range of the coarse screening to obtain a dynamic second amplitude integral cumulative value. When the second amplitude integral cumulative value reaches a second preset proportion of the total energy, the accumulation stops, and the current frequency point is the high-frequency boundary.

[0034] In this embodiment, the first preset ratio and the second preset ratio may be the same or different. This embodiment does not limit the specific values ​​of the first preset ratio and the second preset ratio; for example, the first preset ratio may be 2.5%, and the second preset ratio may be 2.5%.

[0035] Optionally, the sum of the first preset ratio and the second preset ratio is less than or equal to a set value.

[0036] In this embodiment, there is no limitation on the set value; for example, it can be 5%.

[0037] For example, if the sum of the first preset ratio and the second preset ratio is greater than 5%, too much effective signal energy will be cut off, resulting in a shrinking boundary and failing to reflect the actual occupied range of the signal; if the sum of the ratios is less than 1%, it is impossible to effectively filter out stray noise at the edge of the signal, and the noise reduction effect is insufficient; the upper limit of 5% can ensure that most edge noise and out-of-band clutter are cut off, while retaining more than 95% of the main signal energy, resulting in the highest boundary accuracy.

[0038] For example, the first preset ratio and the second preset ratio are both set to 2.5%, with a total of 5%, retaining 95% of the main signal energy.

[0039] In this embodiment, the energy proportion of noise and stray radiation at the signal edges is extremely low, concentrated at both ends of the total energy. Bidirectional cumulative cropping of the low-proportion edges essentially filters out edge noise while preserving the main signal energy, balancing noise reduction and signal integrity. The target boundary is determined based on the cumulative proportion of overall energy, not the amplitude of a single point. Therefore, it avoids false boundaries that expand outward due to local noise peaks, and fragmented boundaries that appear due to local signal dips. The target boundary consists of the two endpoints of a continuous and unique stable interval. Symmetrical cropping (e.g., 2.5% at each end) ensures that the main energy interval remains centered, preventing unilateral shifts and better reflecting the spectral characteristics of the real signal.

[0040] In this embodiment, within a roughly evaluated frequency range, the signal energy is integrated from left to right along the frequency axis until the signal energy reaches 2.5% of the total signal energy. The frequency position of the final integrated signal is recorded as f1, that is, the amplitude value of each frequency point in the spectrum is accumulated point by point from the low-frequency end to the high-frequency end of the frequency horizontal axis to obtain the first amplitude integration accumulation value. When the first amplitude integration accumulation value reaches 2.5% of the total signal energy of the UAV, the corresponding frequency point on the frequency horizontal axis is recorded as the low-frequency boundary of the frequency range; for example... Figure 2 As shown, Figure 2 This is a schematic diagram of the target boundary effect provided in an embodiment of the present invention.

[0041] For example, within the roughly evaluated signal frequency range, the signal energy is integrated from right to left along the frequency axis until the signal energy reaches 2.5% of the total signal energy. The frequency position of the final integrated signal is recorded as f2. That is, within the frequency range, the amplitude values ​​of each frequency point in the spectrum are accumulated point by point from the high-frequency end to the low-frequency end of the frequency horizontal axis to obtain the second amplitude integration accumulation value. When the second amplitude integration accumulation value reaches 2.5% of the total signal energy of the UAV, the corresponding frequency point on the frequency horizontal axis is recorded as the high-frequency boundary of the frequency range. Figure 2 As shown, it is determined that 95% of the energy of the UAV signal is located in the frequency range of f1 to f2, that is, the frequency target boundary of the UAV signal is f1 and f2.

[0042] This invention overcomes the difficulty of using thresholds to manually determine signal boundaries in existing technologies, where such thresholds cannot adapt to changes in the target environment in real time. This invention provides a method for determining the boundaries of UAV signals by observing the main energy in the spectrum, focusing on the primary energy and eliminating secondary energy. It calculates the cumulative distribution of signal energy within a known frequency range, finds the minimum frequency band containing 95% of the signal energy, and defines the upper and lower boundaries of this frequency band as the target boundary (spectral boundary) of the UAV signal. This approach fully utilizes the concentrated characteristics of signal energy, effectively eliminating noise and interference signals. Furthermore, this method enables automatic monitoring of signal boundaries without human intervention.

[0043] The main idea of ​​this embodiment is to capture the main energy of the UAV signal and determine the target boundary of the UAV signal based on the energy distribution of the main signal energy. The threshold of the main signal energy (e.g., 95% of the main energy) can be set according to actual requirements. The method of obtaining the boundary of the main energy in this embodiment is to first determine the secondary energy distribution through integration, and then obtain the main energy distribution to finally determine the target boundary of the UAV signal. This method is simple and reliable. By acquiring the spectrum and integrating on the frequency axis, the target boundary of the UAV signal can be easily determined without complex calculations. Good signal boundary values ​​can be obtained without human intervention. It does not require attention to the amplitude characteristics of the UAV signal, thus having strong anti-interference ability.

[0044] In this embodiment of the invention, the existing technology calculates the target boundary of the UAV signal by using a threshold. However, when the frequency range of the target signal fluctuates greatly, some points are below the threshold, which leads to multiple unstable boundaries for dividing the UAV signal. In contrast, the target boundary determined by this embodiment of the invention is unique and captures the energy of the main UAV signal, making the target boundary of the UAV signal more reliable.

[0045] In this embodiment of the invention, the boundary of the UAV signal can be obtained without dividing the signal amplitude threshold. This eliminates the need for the detection equipment to pre-set the signal power threshold, reducing the problem of misjudgment caused by human intervention.

[0046] The technical solution of this disclosure includes generating a spectrum diagram based on the collected UAV signal; determining the frequency range corresponding to the UAV signal based on the spectrum diagram; determining the total energy of the UAV signal based on the frequency range and the spectrum diagram; and determining the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range. This disclosure improves the detection accuracy of the frequency range boundary of UAV signals by determining the total energy of the UAV signal based on the frequency range and the spectrum diagram, and by determining the frequency range boundary based on the total energy of the UAV signal and the frequency range.

[0047] Figure 3 This is a schematic diagram of a device for detecting the boundary of a drone signal frequency range, provided in an embodiment of the present invention. This device is applicable to the detection of the boundary of a drone signal frequency range. The device can be implemented in hardware and / or software and is generally integrated into an electronic device. Figure 3 As shown, the device includes: a spectrum generation module 301, a frequency range determination module 302, a total energy determination module 303, and a boundary determination module 304, wherein... The spectrum generation module 301 is used to generate a spectrum based on the collected UAV signals; The frequency range determination module 302 is used to determine the frequency range corresponding to the UAV signal based on the spectrum diagram. Total energy determination module 303 is used to determine the total energy of the UAV signal based on the frequency range and the spectrum diagram; Boundary determination module 304 is used to determine the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range.

[0048] The technical solution of this disclosure includes: a spectrum generation module generating a spectrum based on the acquired UAV signal; a frequency range determination module determining the frequency range corresponding to the UAV signal based on the spectrum; a total energy determination module determining the total energy of the UAV signal based on the frequency range and the spectrum; and a boundary determination module determining the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range. This disclosure improves the detection accuracy of the UAV signal frequency range boundary by determining the total energy of the UAV signal based on the frequency range and the spectrum, and by determining the frequency range boundary based on the total energy of the UAV signal and the frequency range.

[0049] Optionally, the frequency range determination module is specifically used to: determine the signal type based on the spectrum diagram; and extract the frequency range corresponding to the UAV signal in the spectrum diagram based on the signal type.

[0050] The spectrum diagram consists of a frequency horizontal axis and an amplitude vertical axis.

[0051] Optionally, the total energy determination module is specifically used to: within the frequency range, integrate the amplitude value of each frequency point in the spectrum along the horizontal axis of the frequency to obtain the total energy of the UAV signal.

[0052] Optionally, the boundary determination module is specifically used for: within the frequency range, accumulating the amplitude values ​​of each frequency point in the spectrum graph point by point from the low-frequency end to the high-frequency end of the frequency horizontal axis to obtain a first amplitude integral accumulation value; when the first amplitude integral accumulation value reaches a first preset proportion of the total energy of the UAV signal, recording the corresponding frequency point on the frequency horizontal axis as the low-frequency boundary of the frequency range; within the frequency range, accumulating the amplitude values ​​of each frequency point in the spectrum graph point by point from the high-frequency end to the low-frequency end of the frequency horizontal axis to obtain a second amplitude integral accumulation value; when the second amplitude integral accumulation value reaches a second preset proportion of the total energy of the UAV signal, recording the corresponding frequency point on the frequency horizontal axis as the high-frequency boundary of the frequency range; and determining the low-frequency boundary and the high-frequency boundary as the target boundary of the UAV signal frequency range.

[0053] Wherein, the sum of the first preset ratio and the second preset ratio is less than or equal to a set value.

[0054] The drone signal frequency range boundary detection device provided in this embodiment of the invention can execute the drone signal frequency range boundary detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Figure 4 This is a schematic diagram of an electronic device provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0056] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0057] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0058] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as methods for detecting the frequency range boundaries of UAV signals.

[0059] In some embodiments, the method for detecting the frequency range boundary of a drone signal can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the method for detecting the frequency range boundary of a drone signal described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the method for detecting the frequency range boundary of a drone signal by any other suitable means (e.g., by means of firmware).

[0060] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0061] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0062] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0063] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0064] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0065] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0066] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for detecting the frequency range boundary of a UAV signal as provided in any embodiment of this invention.

[0067] In implementing a computer program product, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0068] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0069] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting the boundary of a UAV signal frequency range, characterized in that, include: Generate a spectrum diagram based on the collected drone signals; The frequency range corresponding to the UAV signal is determined based on the spectrum diagram. The total energy of the UAV signal is determined based on the frequency range and the spectrum diagram. The target boundary of the frequency range is determined based on the total energy of the UAV signal and the frequency range.

2. The method according to claim 1, characterized in that, Determining the frequency range corresponding to the UAV signal based on the spectrum diagram includes: The signal type is determined based on the spectrum diagram; The frequency range corresponding to the UAV signal in the spectrum is extracted based on the signal type.

3. The method according to claim 1, characterized in that, in, The spectrum diagram consists of a frequency horizontal axis and an amplitude vertical axis; the total energy of the UAV signal is determined based on the frequency range and the spectrum diagram, including: Within the specified frequency range, the amplitude value of each frequency point in the spectrum is integrated along the horizontal axis of the frequency to obtain the total energy of the UAV signal.

4. The method according to claim 3, characterized in that, Determining the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range includes: Within the frequency range, the amplitude values ​​of each frequency point in the spectrum are accumulated point by point from the low-frequency end to the high-frequency end of the frequency horizontal axis to obtain the first amplitude integral accumulation value; When the first amplitude integral cumulative value reaches a first preset proportion of the total energy of the UAV signal, the frequency point corresponding to the frequency horizontal axis is recorded as the low frequency boundary of the frequency range. Within the frequency range, the amplitude values ​​of each frequency point in the spectrum are accumulated point by point from the high-frequency end to the low-frequency end of the frequency horizontal axis to obtain the second amplitude integral accumulation value; When the second amplitude integral cumulative value reaches the second preset proportion of the total energy of the UAV signal, the frequency point corresponding to the frequency horizontal axis is recorded as the high-frequency boundary of the frequency range; The low-frequency boundary and the high-frequency boundary are defined as the target boundaries of the UAV signal frequency range.

5. The method according to claim 4, characterized in that, include: The sum of the first preset ratio and the second preset ratio is less than or equal to a set value.

6. The method according to claim 1, characterized in that, include: The set value is 5%.

7. A device for detecting the boundary of a UAV signal frequency range, characterized in that, include: The spectrum generation module is used to generate spectrum diagrams based on the collected UAV signals; A frequency range determination module is used to determine the frequency range corresponding to the UAV signal based on the spectrum diagram. A total energy determination module is used to determine the total energy of the UAV signal based on the frequency range and the spectrum diagram. A boundary determination module is used to determine the target boundary of the frequency range based on the total energy of the UAV signal and the frequency range.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for detecting the frequency range boundary of the UAV signal as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a method for detecting the boundary of the frequency range of a UAV signal as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for detecting the frequency range boundary of a UAV signal as described in any one of claims 1-6.

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