Method for realizing remote identification signal acquisition of unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle remote identification signal acquisition implementation method, and belongs to the technical field of unmanned aerial vehicles. According to the realization method of the unmanned aerial vehicle remote identification signal acquisition, a data identification module, a central control module, a parameter adjustment module, a single-chip microcomputer controller module and a serial port server module are included, and generated new signal parameters are identified to the unmanned aerial vehicle through a signal acquisition technology. New signal parameters are generated through a point-to-point communication mechanism between the nodes and distributed to each intelligent node in the system, and when each intelligent node receives the new signal parameters, a parameter integration module controls each node to be responsible for optimization of local parameters, so that each node can be responsible for optimization of the local parameters, and the optimization of the local parameters is facilitated. Therefore, the parallelism and adaptability of the system can be improved, the dependence on the central control unit can be reduced, the load rate of the parameter adjusting system can be reduced, and meanwhile the working efficiency of the parameter adjusting system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles, and more specifically, to a method for realizing remote identification signal collection of unmanned aerial vehicles. Background Art

[0002] In recent years, drone technology has developed rapidly and has been widely used in military, civil and commercial fields. With the continuous advancement of drone technology, ensuring air traffic safety and preventing drones from illegally invading important control areas have become the focus of topics. Drone remote identification technology should become a research hotspot. The existing three drone detection methods, radar detection, optical detection, and radio spectrum detection, all have some problems. For example, radar detection has difficulties in detecting small drones, difficult to identify in complex background noise, and difficult to track multiple targets; optical detection is greatly affected by environmental factors such as light and weather, and is costly; radio spectrum detection cannot detect uncontrolled drones, encrypted radio is difficult to crack, and the spectrum feature library needs to be updated in a timely manner. Therefore, the society and the public are calling for and demanding new drone identification methods. This patent provides a new drone remote identification signal acquisition implementation method to solve the above problems.

[0003] Moreover, the current method for implementing UAV remote identification signal acquisition is mainly to generate new adjustment parameters through the system, and then adjust the parameters of each node in the system through the central control unit. This will cause the central control unit to synchronously adjust the parameters of each node during the process of adjusting the signal parameters, thereby causing the load rate of the adjustment system to increase during the synchronous adjustment period, and then causing the working efficiency of the adjustment system to decrease. Summary of the invention

[0004] The purpose of the present invention is to provide a method for realizing remote identification signal collection of unmanned aerial vehicles to solve the problems raised in the above-mentioned background technology.

[0005] A method for implementing remote identification signal acquisition of a drone, the method comprising a data identification module, a central control module, a parameter adjustment module, a single-chip controller module and a serial port server module;

[0006] The data identification module is used to identify the collected data through signal acquisition technology;

[0007] The central control module is used to receive and process real-time data from sensors, and parse, store and preliminarily analyze the data to prepare for subsequent parameter adjustment;

[0008] The parameter adjustment module is used to make decisions based on real-time data, historical data and parameter range information set by the user, and generate new signal parameter settings, identify the drone through signal acquisition technology, and then distribute the generated new parameters to multiple intelligent nodes in the system, and let each node be responsible for the optimization of local parameters. These parameters will be used to adjust the operating status of the system, which may include the settings of the control equipment and the working mode of the system;

[0009] The single chip controller module is used to periodically optimize system parameters;

[0010] The data identification module identifies the collected data through signal acquisition technology, and then the central control module parses, stores and preliminarily analyzes the received data, and then makes decisions based on the real-time data, historical data and parameter range information set by the user after analysis by the parameter adjustment module, and generates new signal parameter settings. The generated new parameters are then identified to the drone through signal acquisition technology, and parameter adjustment operations are performed. After the parameter adjustment operation, the single-chip controller module is required to periodically optimize the system parameters.

[0011] Preferably, the data identification module includes a data acquisition module and an identification module;

[0012] The data acquisition module is used for sensor nodes to collect real-time data when the system is running, such as temperature, pressure and humidity. The frequency of data acquisition can be configured according to system requirements;

[0013] The identification module is used to encode the collected data and package it according to a pre-defined data format. Common data formats can be selected as JSON and XML, and then the packaged data is identified.

[0014] Preferably, the central control module includes a network layer module and a data processing module;

[0015] The network layer module is used for the network layer of the central control unit to receive data from the sensor node, and then let the network layer unpack and decode the data, and then store the received data in the central control unit;

[0016] The data processing module is used to perform real-time data statistics, filtering and smoothing steps on the data that has undergone unpacking and decoding operations, and to prepare for subsequent parameter adjustment.

[0017] Preferably, the parameter adjustment module includes an iteration module, a parameter generation module, and an intelligent node module;

[0018] The iteration module performs optimization iterations based on the processed data based on the statistics, filtering and smoothing steps of real-time data, and continuously adjusts the signal parameters based on the real-time data, and the optimization process involves minimization or maximization of the objective function;

[0019] The parameter generation module checks whether the optimization process has converged after each iteration, and generates new signal parameter settings if it has converged;

[0020] The intelligent node module is used to distribute the generated new signal parameter settings to multiple intelligent nodes in the system, and let each node be responsible for the optimization of local parameters.

[0021] Preferably, the intelligent node module includes a node communication module and a parameter integration module;

[0022] The node communication module is used to specify the data format for communication between nodes, and implement the communication interface on each node, including sending and receiving functions, and then establish a communication connection between each node, which may include a handshake protocol to ensure that the nodes can recognize each other and establish a secure communication channel, and set up a point-to-point communication mechanism between each node, and then distribute the generated new signal parameter settings to each intelligent node in the system through the point-to-point communication mechanism;

[0023] The parameter integration module is used to optimize local parameters of each intelligent node in the control system.

[0024] Preferably, the single-chip controller module includes a drone identification module and an antenna module;

[0025] The drone identification module is used to identify the performance of the drone identification system after applying the new parameter settings, including sensor data, output results, system status, and allow users to provide feedback through the interface, which may include adjusting parameter ranges and proposing special requirements. Then, based on user feedback and the results of drone identification, the signal parameters are dynamically adjusted. After adjusting the parameters, drone identification is performed again to generate new monitoring information and reflect the effect of the adjustment;

[0026] The antenna module is used to periodically optimize system parameters and record historical data of each round of optimization, including parameter changes and system performance improvements, which is helpful for subsequent analysis and optimization.

[0027] Preferably, a signal acquisition-based implementation method comprises the following steps:

[0028] S1. Data identification: First, the real-time data of the system during operation is collected through the sensor nodes, and the collected data is encoded, then packaged according to the JSON data format, and then the packaged data is identified through signal acquisition technology;

[0029] S2, data processing: When the central control unit receives the packaged data, it unpacks and decodes the data through the network layer in the central control unit, and then performs statistics, filtering and smoothing of the real-time data;

[0030] S3, generate parameters: after the data has been subjected to statistics, filtering and smoothing operations of real-time data, optimize and iterate according to the processed data, and then continuously adjust the signal parameters according to the real-time data. After the signal parameters are adjusted, check whether the optimization process has converged. If it has converged, generate new signal parameter settings;

[0031] S4, intelligent node: when the new signal parameter settings are generated, the new signal parameters are identified to the drone through signal acquisition technology, and then the new signal parameters are generated through the point-to-point communication mechanism between nodes and distributed to each intelligent node in the system. When each intelligent node receives the new signal parameters, the parameter integration module will control each node to optimize the local parameters;

[0032] S5, single-chip microcomputer controller: After each node has been optimized, the user is allowed to provide feedback through the interface. Then, based on the user feedback and the results of drone identification, the signal parameters are dynamically adjusted. After adjusting the parameters, drone identification is performed again to generate new monitoring information and record the historical data of each round of optimization.

[0033] Compared with the prior art, the advantages of the present invention are:

[0034] 1) In the present invention, the new signal parameters are generated and identified to the UAV through the signal acquisition technology, and then the new signal parameters are generated through the point-to-point communication mechanism between the nodes and distributed to each intelligent node in the system. When each intelligent node receives the new signal parameters, the parameter integration module will control each node to be responsible for the optimization of local parameters. In this way, each node can be responsible for the optimization of local parameters, thereby improving the parallelism and adaptability of the system, and reducing the dependence on the central control unit, thereby reducing the load rate of the parameter adjustment system, and at the same time improving the working efficiency of the parameter adjustment system.

[0035] 2) In the present invention, after the parameters are optimized at each node, the user is allowed to provide feedback through the interface, and then the signal parameters are dynamically adjusted according to the user feedback and the results of drone identification. After adjusting the parameters, the drone is re-identified to generate new monitoring information, and the historical data of each round of optimization is recorded. In this way, the operating status of the drone identification system can be known. The optimization operation can also be performed by regularly repeating the above steps, which can improve the autonomy of the parameter adjustment system, thereby providing convenience for operators and further improving the work efficiency of the parameter adjustment system.

[0036] 3) In the present invention, when the operator is proficient in the specific steps of operating the parameter adjustment system in the 4G gateway module, the connection module is used to perform simulated operation identification. If an operation error occurs during the operation identification, the system will prompt the operation error. When the system prompts the operation error, a demonstration step will pop up to let the operator understand the role of operating this step until the operator passes the connection module. This allows the operator to quickly use and operate the system, thereby effectively shortening the time for the operator to become familiar with the parameter adjustment system, and at the same time improving the work efficiency of the parameter adjustment system on a certain basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the overall flow of an implementation method based on signal acquisition of the present invention;

[0038] Figure 2 A schematic flow chart of an implementation method based on signal acquisition of the present invention;

[0039] Figure 3 A schematic diagram of a system structure block of a signal acquisition-based implementation method of the present invention;

[0040] Figure 4 A schematic diagram of data flow of wireless data recognition based on signal acquisition implementation method of the present invention;

[0041] Figure 5 A schematic diagram of a wired Ethernet identification_local serial port assistant viewing data flow direction for an implementation method based on signal acquisition of the present invention;

[0042] Figure 6 The present invention is a schematic diagram of remote upgrade data flow based on a signal acquisition implementation method. DETAILED DESCRIPTION

[0043] Example: See Figure 1 ,A method for implementing remote identification signal acquisition of unmanned aerial vehicles,A method for implementing remote identification signal acquisition of unmanned aerial vehicles includes a data identification module, a central control module, a parameter adjustment module, a single-chip controller module and a serial port server module;

[0044] The data identification module is used to identify the collected data through signal acquisition technology to ensure the real-time and reliability of the data;

[0045] The central control module is used to receive and process real-time data from sensors, and parse, store and preliminarily analyze the data to prepare for subsequent parameter adjustment;

[0046] The parameter adjustment module is used to make decisions based on real-time data, historical data and parameter range information set by the user, and generate new signal parameter settings. The drone is identified through signal acquisition technology, and then the generated new parameters are distributed to multiple intelligent nodes in the system, and each node is responsible for the optimization of local parameters. These parameters will be used to adjust the operating status of the system, which may include the settings of the control equipment and the working mode of the system;

[0047] The single-chip controller module is used to periodically optimize the system parameters;

[0048] The data identification module identifies the collected data through signal acquisition technology, and then the central control module parses, stores and preliminarily analyzes the received data. It makes decisions based on the real-time data, historical data and parameter range information set by the user after analysis by the parameter adjustment module, and generates new signal parameter settings. The generated new parameters are then identified to the drone through signal acquisition technology, and parameter adjustment operations are performed. After the parameter adjustment operation, the microcontroller controller module needs to periodically optimize the system parameters.

[0049] Specifically, new signal parameters are generated and identified to the drone through signal acquisition technology, and then new signal parameters are generated through the point-to-point communication mechanism between nodes and distributed to each intelligent node in the system. When each intelligent node receives the new signal parameters, the parameter integration module will control each node to be responsible for the optimization of local parameters. In this way, each node can be responsible for the optimization of local parameters, thereby improving the parallelism and adaptability of the system, and reducing dependence on the central control unit, thereby reducing the load rate of the parameter adjustment system, and at the same time improving the working efficiency of the parameter adjustment system.

[0050] The data identification module includes a data acquisition module and an identification module;

[0051] The data acquisition module is used by sensor nodes to collect real-time data during system operation, such as temperature, pressure and humidity. The frequency of data acquisition can be configured according to system requirements;

[0052] The recognition module is used to encode the collected data and package it according to a pre-defined data format. Common data formats can be selected as JSON and XML, and then the packaged data is recognized.

[0053] The central control module includes a network layer module and a data processing module;

[0054] The network layer module is used for the network layer of the central control unit to receive data from the sensor nodes, and then let the network layer unpack and decode the data to ensure that the data can be correctly understood by the central control unit, and then store the received data in the central control unit;

[0055] The data processing module is used to perform real-time data statistics, filtering and smoothing on the data that has undergone unpacking and decoding operations, and to prepare for subsequent parameter adjustment.

[0056] The parameter adjustment module includes an iteration module, a parameter generation module, and an intelligent node module;

[0057] The iteration module performs optimization iterations based on the processed data based on the statistics, filtering and smoothing steps of real-time data, and continuously adjusts the signal parameters based on the real-time data. The optimization process involves minimization or maximization of the objective function to achieve optimal adjustment of the system.

[0058] The parameter generation module checks whether the optimization process has converged after each iteration. If it has converged, it generates new signal parameter settings.

[0059] The intelligent node module is used to distribute the generated new signal parameter settings to multiple intelligent nodes in the system, and let each node be responsible for the optimization of local parameters.

[0060] The intelligent node module includes a node communication module and a parameter integration module;

[0061] The node communication module is used to specify the data format for communication between nodes and implement the communication interface on each node, including sending and receiving functions, ensuring that the interface can correctly package and unpack data, and handle possible errors in communication, and then establish a communication connection between each node. This can include a handshake protocol to ensure that the nodes can recognize each other and establish a secure communication channel, and set up a point-to-point communication mechanism between each node. Then, the generated new signal parameter settings are distributed to each intelligent node in the system through the point-to-point communication mechanism;

[0062] The parameter integration module is used to optimize the local parameters of each intelligent node in the control system.

[0063] The single-chip controller module includes a drone identification module and an antenna module;

[0064] The drone identification module is used to identify the performance of the drone identification system after applying new parameter settings, including sensor data, output results, and system status, and allows users to provide feedback through the interface, which may include adjusting parameter ranges and making special requests. Then, based on user feedback and drone identification results, the signal parameters are dynamically adjusted. After adjusting the parameters, drone identification is performed again to generate new monitoring information and reflect the effect of the adjustment;

[0065] The antenna module is used to periodically optimize the system parameters and record the historical data of each round of optimization, including parameter changes and system performance improvements, which helps with subsequent analysis and optimization.

[0066] Specifically, after the parameters of each node are optimized, the user is allowed to provide feedback through the interface, and then the signal parameters are dynamically adjusted according to the user feedback and the results of drone identification. After adjusting the parameters, the drone is identified again, new monitoring information is generated, and the historical data of each round of optimization is recorded. In this way, the operating status of the drone identification system can be known. The optimization operation can also be performed by regularly repeating the above steps, which can improve the autonomy of the parameter adjustment system, thereby providing convenience for operators and thus improving the work efficiency of the parameter adjustment system.

[0067] Specifically, the serial port server is used for data conversion. In the detection mode, it receives the message sent by the single-chip microcomputer controller through the serial port, and then transmits it to the 4G gateway through the network port. In the remote upgrade mode, it receives the upgrade data sent by the 4G gateway through the network port, and then sends it to the single-chip microcomputer controller through the serial port.

[0068] Specifically, the operators will regularly use the 4G gateway module to guide themselves through the specific steps of operating the parameter adjustment system. When the operators are proficient in operating the specific steps of the parameter adjustment system in the 4G gateway module, the connection module will be used to perform simulated operation identification. If an operation error occurs during the operation identification, the system will prompt an operation error. When the system prompts an operation error, a demonstration step will pop up to let the operator understand the role of this step until the operator passes the connection module. This allows the operator to quickly use and operate the system, thereby effectively shortening the time for the operators to become familiar with the parameter adjustment system, and at the same time improving the work efficiency of the parameter adjustment system on a certain basis.

[0069] See also Figure 2 , an implementation method based on signal acquisition, comprising the following steps:

[0070] S1. Data identification: First, the real-time data of the system during operation is collected through the sensor nodes, and the collected data is encoded, then packaged according to the JSON data format, and then the packaged data is identified through signal acquisition technology;

[0071] S2, data processing: When the central control unit receives the packaged data, it unpacks and decodes the data through the network layer in the central control unit, and then performs statistics, filtering and smoothing of the real-time data;

[0072] S3, generate parameters: after the data has been subjected to statistics, filtering and smoothing operations of real-time data, optimize and iterate according to the processed data, and then continuously adjust the signal parameters according to the real-time data. After the signal parameters are adjusted, check whether the optimization process has converged. If it has converged, generate new signal parameter settings;

[0073] S4, intelligent node: when the new signal parameter settings are generated, the new signal parameters are identified to the drone through signal acquisition technology, and then the new signal parameters are generated through the point-to-point communication mechanism between nodes and distributed to each intelligent node in the system. When each intelligent node receives the new signal parameters, the parameter integration module will control each node to optimize the local parameters;

[0074] S5, single-chip microcomputer controller: After each node has been optimized, the user is allowed to provide feedback through the interface. Then, based on the user feedback and the results of drone identification, the signal parameters are dynamically adjusted. After adjusting the parameters, drone identification is performed again to generate new monitoring information and record the historical data of each round of optimization.

[0075] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for collecting remote identification signals of unmanned aerial vehicles, characterized in that: The method for implementing remote identification signal acquisition of a drone includes a data identification module, a central control module, a parameter adjustment module, a single-chip controller module and a serial port server module; The data identification module is used to identify the collected data through signal acquisition technology; The central control module is used to receive and process real-time data from sensors, and parse, store and preliminarily analyze the data to prepare for subsequent parameter adjustment; The parameter adjustment module is used to make decisions based on real-time data, historical data and parameter range information set by the user, and generate new signal parameter settings, identify the drone through signal acquisition technology, and then distribute the generated new parameters to multiple intelligent nodes in the system, and let each node be responsible for the optimization of local parameters; The single chip controller module is used to periodically optimize system parameters; The data identification module identifies the collected data through signal acquisition technology, and then the central control module parses, stores and preliminarily analyzes the received data, and then makes decisions based on the real-time data, historical data and parameter range information set by the parameter adjustment module, and generates new signal parameter settings, and then identifies the generated new parameters to the drone through signal acquisition technology, and performs parameter adjustment operations. After the parameter adjustment operation, the single-chip controller module needs to periodically optimize the system parameters; The serial port server is used for data conversion, including detection mode and remote upgrade mode. In the detection mode, the message sent by the single-chip microcomputer controller is received through the serial port, and then transmitted to the 4G gateway through the network port; in the remote upgrade mode, the upgrade data sent by the 4G gateway is received through the network port, and then sent to the single-chip microcomputer controller through the serial port.

2. The method for realizing remote identification signal collection of a drone according to claim 1, characterized in that: The data identification module includes a data acquisition module and an identification module; The data acquisition module is used for sensor nodes to collect real-time data when the system is running; The identification module is used to encode the collected data, package it according to a pre-defined data format, and then identify the packaged data.

3. The method for realizing remote identification signal collection of a drone according to claim 1, characterized in that: The central control module includes a network layer module and a data processing module; The network layer module is used for the network layer of the central control unit to receive data from the sensor node, and then let the network layer unpack and decode the data, and then store the received data in the central control unit; The data processing module is used to perform real-time data statistics, filtering and smoothing steps on the data that has undergone unpacking and decoding operations, and to prepare for subsequent parameter adjustment.

4. The method for realizing remote identification signal collection of a drone according to claim 1, characterized in that: The parameter adjustment module includes an iteration module, a parameter generation module and an intelligent node module; The iteration module performs optimization iteration based on the processed data based on the statistics, filtering and smoothing steps of real-time data, and continuously adjusts the signal parameters based on the real-time data; The parameter generation module checks whether the optimization process has converged after each iteration, and generates new signal parameter settings if it has converged; The intelligent node module is used to distribute the generated new signal parameter settings to multiple intelligent nodes in the system, and let each node be responsible for the optimization of local parameters.

5. The method for realizing remote identification signal collection of a drone according to claim 4, characterized in that: The intelligent node module includes a node communication module and a parameter integration module; The node communication module is used to specify the data format for communication between nodes, and implement the communication interface on each node, including sending and receiving functions, then establish a communication connection between each node, and set up a point-to-point communication mechanism between each node, and then distribute the generated new signal parameter settings to each intelligent node in the system through the point-to-point communication mechanism; The parameter integration module is used to optimize local parameters of each intelligent node in the control system.

6. The method for realizing remote identification signal collection of a drone according to claim 1, characterized in that: The single-chip controller module includes a drone identification module and an antenna module; The drone identification module is used to improve the performance of the drone identification system after applying the new parameter settings, and allow the user to provide feedback through the interface, and then dynamically adjust the signal parameters based on the user feedback and the drone identification results, and then re-identify the drone after adjusting the parameters, generate new monitoring information, and reflect the effect of the adjustment; The antenna module is used to periodically optimize system parameters and record historical data of each round of optimization.

7. A signal acquisition-based implementation method, which relates to a method for remote identification signal acquisition of a drone as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Data identification: First, the real-time data of the system during operation is collected through the sensor nodes, and the collected data is encoded, then packaged according to the JSON data format, and then the packaged data is identified through signal acquisition technology; S2, data processing: When the central control unit receives the packaged data, it unpacks and decodes the data through the network layer in the central control unit, and then performs statistics, filtering and smoothing of the real-time data; S3, generate parameters: after the data has been subjected to statistics, filtering and smoothing operations of real-time data, optimize and iterate according to the processed data, and then continuously adjust the signal parameters according to the real-time data. After the signal parameters are adjusted, check whether the optimization process has converged. If it has converged, generate new signal parameter settings; S4, intelligent node: when the new signal parameter settings are generated, the new signal parameters are identified to the drone through signal acquisition technology, and then the new signal parameters are generated through the point-to-point communication mechanism between nodes and distributed to each intelligent node in the system. When each intelligent node receives the new signal parameters, the parameter integration module will control each node to optimize the local parameters; S5, single-chip microcomputer controller: After each node has been optimized, the user is allowed to provide feedback through the interface. Then, based on the user feedback and the results of drone identification, the signal parameters are dynamically adjusted. After adjusting the parameters, drone identification is performed again to generate new monitoring information and record the historical data of each round of optimization.

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