Unmanned aerial vehicle onboard integrated monitoring system and method for complex flight conditions

Abstract

This invention belongs to the field of UAV testing technology, and relates to an airborne integrated monitoring system and method for UAVs under complex flight conditions. The system includes a signal acquisition unit, a main control processing unit, and a data transmission unit. The signal acquisition unit has at least one normalized physical interface for connecting parameter acquisition modules performing different parameter acquisition tasks, and matches corresponding preprocessing strategies based on the characterization signals of the connected parameter acquisition modules. The main control processing unit receives the electrical signals output by the signal acquisition unit, processes the data using corresponding filtering algorithms according to the parameter type, and generates structured data. The data transmission unit encapsulates the structured data into data packets conforming to the airborne wireless protocol and sends them. This significantly improves the flexible scalability of UAV airborne monitoring, the signal measurement accuracy under complex flight conditions, and the reliability of wireless data transmission.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) testing technology, and relates to an airborne integrated monitoring system and method for UAVs under complex flight conditions. Background Technology

[0002] As a core carrier of the low-altitude economy, the unmanned aerial vehicle (UAV) industry has been widely applied in logistics, agricultural and forestry protection, emergency rescue, and power line inspection. With the iteration of new energy UAV technology, higher requirements are placed on the airborne status monitoring of UAVs under full flight envelope and extreme maneuvering conditions. In order to accurately conduct system assessments and fault diagnosis, it is urgent to carry out high-precision, real-time online monitoring of multiple operating parameters of UAVs, such as voltage, current, and temperature.

[0003] However, existing drone monitoring and testing technologies have revealed the following significant technical shortcomings in practical applications: 1) Closed system architecture and lack of universal adaptability in structure and algorithms: The physical interfaces of existing monitoring equipment are mostly fixed designs, and the acquisition and processing strategies for specific sensors cannot be changed, lacking unified hardware specifications. When facing diverse power systems such as lithium batteries and hydrogen fuel cells, or when new sensors need to be added for complex working conditions, the system cannot perform adaptive identification and software and hardware reconstruction, resulting in extremely poor system scalability and high iteration costs.

[0004] 2) Current signal processing and filtering mechanisms are ill-equipped to handle strong interference and temperature-changing environments: Under complex flight conditions such as high-speed climbs and sharp turns, UAVs experience strong electromagnetic interference in their onboard electrical environment, and the ambient temperature fluctuates drastically. Existing monitoring systems lack dynamic real-time compensation algorithms for temperature drift, and conventional filtering algorithms cannot effectively filter out dynamic high-frequency noise under complex flight conditions, which can easily lead to jumps in measurement data readings and distortion at the last bit, failing to provide high-confidence state parameters.

[0005] 3) Poor synchronization of multi-channel data acquisition and lack of support for full-link systematic analysis: The digital transformation of the industry requires high-dimensional state perception, but when the attitude of the UAV changes drastically or a sudden failure occurs, the time deviation between the acquisition of different parameters in traditional methods makes it impossible to establish the spatiotemporal dynamic coupling relationship between power supply thermal management, power output and external operating conditions, which restricts the diagnostic accuracy of the UAV health management system. Summary of the Invention

[0006] The purpose of this invention is to address the aforementioned problems in existing technologies by proposing an integrated airborne monitoring system for unmanned aerial vehicles (UAVs) oriented towards complex flight conditions.

[0007] The objective of this invention can be achieved through the following technical solution: an airborne integrated monitoring system for unmanned aerial vehicles (UAVs) oriented towards complex flight conditions, comprising: Signal acquisition unit, main control processing unit, data transmission unit; The signal acquisition unit is electrically connected to the main control processing unit, and the main control processing unit is communicatively connected to the data transmission unit; The signal acquisition unit has at least one normalized physical interface for connecting to parameter acquisition modules that perform different parameter acquisition tasks, and matching the corresponding preprocessing strategy through the characterization signal of the connected parameter acquisition module. The main control processing unit is used to receive the electrical signal output by the signal acquisition unit, and to perform data processing using the corresponding filtering algorithm according to the parameter type to generate structured data. The data transmission unit is used to encapsulate the structured data into data packets conforming to the airborne wireless protocol and send them.

[0008] As an optional embodiment of the present invention, the parameter acquisition module includes at least one of a voltage acquisition module, a current acquisition module, and a temperature acquisition module; Each parameter acquisition module has a standard specification output interface, which is equipped with an identification pin for outputting a characteristic signal that represents the parameter type. The signal acquisition unit also includes an analog-to-digital conversion module, which is used to oversample the conditioned raw parameter signal.

[0009] As an optional embodiment of the present invention, the signal acquisition unit has a built-in ambient temperature monitoring module for obtaining the cold junction temperature at the normalized physical interface; When the signal acquisition unit is connected to the temperature acquisition module, the signal acquisition unit is configured to use a nonlinear compensation algorithm to dynamically correct the thermoelectric potential signal generated by the temperature acquisition module in combination with the cold junction temperature, and output a digital temperature signal after amplification and oversampling processing.

[0010] As an optional embodiment of the present invention, the main control processing unit is configured to use a Kalman filter algorithm to denoise the temperature digital signal and to correct the measurement deviation caused by ambient temperature fluctuations in real time according to a preset temperature drift characteristic curve.

[0011] As an optional embodiment of the present invention, when the signal acquisition unit is connected to the current acquisition module, the signal acquisition unit is configured to adjust the amplification factor of the current signal through an adaptive gain control algorithm, and output a digital current signal after sampling processing.

[0012] As an optional embodiment of the present invention, the main control processing unit is configured to perform fusion processing on the digital current signal using a Kalman filter algorithm, and to perform real-time correction of temperature drift during the current detection process according to the temperature drift compensation model of the current acquisition module.

[0013] As an optional embodiment of the present invention, the voltage acquisition module adopts an input stage circuit with a high common-mode rejection ratio, integrates an RC low-pass filter network and an anti-interference transmission component. When the signal acquisition unit is connected to the voltage acquisition module, the signal acquisition unit is configured to automatically switch the voltage division ratio according to the voltage range in which the voltage is located, and outputs a digital voltage signal after sampling processing.

[0014] As an optional embodiment of the present invention, the main control processing unit is configured to perform state estimation of the voltage digital signal based on a dynamic model of voltage change, and to perform end-to-end temperature compensation of the voltage measurement results by combining the temperature drift coefficient of the analog-to-digital conversion module and the voltage conditioning circuit.

[0015] As an optional embodiment of the present invention, a power supply management unit is also included; The power supply management unit includes a low-dropout linear regulator, which is used to provide a stable operating voltage for each unit of the system and suppress the interference of voltage fluctuations in the power supply circuit on signal acquisition.

[0016] This invention also proposes an airborne integrated monitoring method for unmanned aerial vehicles (UAVs) under complex flight conditions, comprising: Acquire the characterization signal of the access parameter acquisition module, identify the parameter type of the parameter acquisition module based on the characterization signal, and match a preprocessing strategy according to the parameter type; The main control processing unit receives the preprocessed electrical signal and performs data processing using the corresponding filtering algorithm according to the parameter type to generate structured data. The structured data is encapsulated into data packets conforming to the airborne wireless protocol and then transmitted.

[0017] Compared with existing technologies, this invention significantly improves the flexible scalability of UAV onboard monitoring, the signal measurement accuracy under complex flight conditions, and the reliability of wireless data transmission through a clever modular architecture and hardware-software decoupling mechanism. The specific technical effects are as follows: 1) By designing the signal acquisition unit to have at least one normalized physical interface and automatically matching the corresponding preprocessing strategy with the characterization signal of the parameter acquisition module, this system achieves "plug-and-play" parameter acquisition and highly reliable "anti-distortion / anti-mistake" identification at the hardware source. This not only enables multi-source heterogeneous parameter acquisition modules performing different monitoring tasks to be flexibly plugged in and adaptively identified using a completely unified physical interface specification, completely avoiding monitoring data anomalies caused by manual wiring errors or misuse of algorithm schemes, but also greatly improves the reusability of airborne hardware baseboards and completely solves the expansion limitations caused by fixed interfaces in traditional monitoring systems.

[0018] 2) The main control processing unit can call the matching filtering processing algorithm to process the electrical signal and generate structured data according to the automatically identified parameter type. This mechanism provides highly targeted dynamic noise reduction and temperature drift suppression capabilities for harsh working conditions such as high-frequency vibration of motors and strong electromagnetic pulses generated by power battery discharge during actual flight of UAVs. While ensuring high signal-to-noise ratio and high authenticity of output signal, it avoids the waste of airborne computing resources caused by global unified filtering and realizes efficient configuration of limited airborne computing power.

[0019] 3) The purified structured data is encapsulated into data packets conforming to the airborne wireless protocol through the data transmission unit and sent. This greatly reduces the bandwidth of airborne telemetry data communication and significantly enhances the anti-interference capability and transmission frame success rate of data packets in complex multipath fading channels in the air. This ensures that the core electrothermal safety parameters of the UAV can still be transmitted back to the remote control node in real time, completely and with high fidelity when the UAV is in beyond visual range and in a highly dynamic flight state. This provides high-precision and high-real-time decision data support for the safety supervision of the UAV throughout its entire life cycle. Attached Figure Description

[0020] Figure 1 This is a block diagram of the overall architecture of the integrated monitoring system according to an embodiment of the present invention; Figure 2 This is a block diagram of the internal structure of the signal acquisition unit in an embodiment of the present invention; Figure 3 This is a block diagram of the internal workflow of the main control processing unit in an embodiment of the present invention; Figure 4 This is a block diagram showing the internal workflow of the data transmission unit in an embodiment of the present invention; Figure 5 This is a schematic diagram of the integrated monitoring system of the present invention mounted on a drone. Detailed Implementation

[0021] The following are specific embodiments of the present invention, which are described in conjunction with the accompanying drawings to further illustrate the technical solutions of the present invention. However, the present invention is not limited to these embodiments.

[0022] Example 1

[0023] Based on the technical problems highlighted in the background, this embodiment proposes an airborne integrated monitoring system 100 for unmanned aerial vehicles (UAVs) operating under complex flight conditions, such as... Figure 1 As shown, it includes: Signal acquisition unit 101, main control processing unit 102, data transmission unit 103; The signal acquisition unit 101 is electrically connected to the main control processing unit 102, and the main control processing unit 102 is communicatively connected to the data transmission unit 103; The signal acquisition unit 101 has at least one normalized physical interface 201 for accessing parameter acquisition modules 200 that perform different parameter acquisition tasks, and matching the corresponding preprocessing strategy through the characterization signal of the accessed parameter acquisition module 200. The main control processing unit 102 is used to receive the electrical signal output by the signal acquisition unit 101, and perform data processing using the corresponding filtering algorithm according to the parameter type to generate structured data. The data transmission unit 103 is used to encapsulate the structured data into data packets conforming to the airborne wireless protocol and send them.

[0024] The airborne integrated monitoring system of this embodiment is used to monitor parameters without interfering with external (such as UAV) control links. Specifically, the system can be mounted on the subject under test via a logically isolated normalized physical interface 201, with the system's power supply and signal processing links independent of the subject under test's native control links. Alternatively, it can be integrated as a functional module into the control system of the subject under test. Taking a UAV as an example, this embodiment does not require modification of the UAV's original underlying control logic, and can collect key parameters in real time within the UAV's entire flight envelope, thus reproducing the UAV's dynamic operating characteristics.

[0025] The system includes a signal acquisition unit 101, a main control processing unit 102, and a data transmission unit 103. The signal acquisition unit 101 is electrically connected to the main control processing unit 102, and the main control processing unit 102 is electrically connected to the data transmission unit 103. The signal acquisition unit 101 is used to acquire key parameters of UAV test components (such as motors and batteries) throughout their flight envelope, including temperature, current, voltage, and power. To address the technical problem of existing technologies where the hardware physical interface is fixed and cannot adaptively reconfigure the system for monitoring, this embodiment designs at least one normalized physical interface 201 in the signal acquisition unit 101 to connect parameter acquisition modules 200 that perform different parameter acquisition tasks. The parameter acquisition modules 200 can be voltage acquisition modules, current acquisition modules, temperature acquisition modules, etc., and each parameter acquisition module 200 is preferably a corresponding sensor. When used as a voltage acquisition module, it performs voltage parameter acquisition tasks by connecting to the test component through a conductive path (such as wires or cables) to measure the real-time open-circuit voltage of the test component during UAV flight. When used as a current acquisition module, it performs current parameter acquisition tasks by connecting a current sampling element (such as a DC current shunt or a manganese copper shunt) in series with the charging and discharging circuit of the test component, and connecting a conductive path from the current acquisition module to the current sampling element to measure the real-time current of the test component during UAV flight. When used as a temperature acquisition module, it performs temperature parameter acquisition tasks by connecting a temperature acquisition element (such as a thermocouple or a thermistor) to the internal component whose temperature needs to be measured through an opening in the test component, and connecting a conductive path from the temperature acquisition module to the temperature acquisition element to measure the real-time temperature of the test component during UAV flight. The parameter acquisition module 200 is connected to the normalized physical interface 201 and connected to the signal acquisition unit 101. The acquired parameter data is transmitted to the signal acquisition unit 101. The signal acquisition unit 101 matches the preprocessing strategy of the corresponding parameter with the characterization signal of the parameter acquisition module 200 and performs corresponding preprocessing on different parameters.

[0026] In this embodiment, the main control processing unit 102 is a microcontroller (MCU), such as Figure 3As shown, its internal components, from input to output, mainly include a synchronous clock management circuit 301, a Kalman filter denoising module 302, a system-wide temperature drift online correction model 303, and a timestamp appending and data packaging encapsulation layer 304. It employs a high-performance ARM Cortex-M series processor. Upon receiving the digitized quantized signal output from the signal acquisition unit 101, it obtains the parameter type based on the electrical signal and performs data processing using corresponding filtering algorithms based on the parameter type. Data processing includes denoising, error correction, and other operations, generating structured data corresponding to the acquired parameter data. The use of a high-performance processor ensures high-precision monitoring of parameters during UAV flight, minimizing measurement errors and guaranteeing the timeliness of parameter data. Testers can quickly reproduce the dynamic characteristics of UAV flight based on the parameter data.

[0027] The generated structured data is transmitted to the data transmission unit 103. In order to realize the systematic analysis of the UAV's flight attitude, power supply and key parameters, the data transmission unit 103 performs secondary encapsulation of the structured data. The encapsulated data packet conforms to the airborne wireless protocol. Furthermore, the encapsulated data packet also contains a high-precision timestamp based on the hardware RTC clock, with a preferred precision of 1ms. The high-precision timestamp is used to enable the spatiotemporal coupling and alignment of multi-dimensional parameters based on the timestamp when the back-end terminal receives data packets from different sensors (such as voltage, current, temperature, etc.), thereby accurately restoring the electrothermal characteristics of the UAV under specific actions.

[0028] Furthermore, the data packet payload adopts the IEEE 754 single-precision floating-point format and integrates a unique sensor identifier (ID). Simultaneously, by adding a cyclic redundancy check (CRC32) and a message authentication code (HMAC-SHA256) to the packet tail, it ensures that the data is not tampered with or subject to bit errors in flight environments with strong electromagnetic interference, providing a high-confidence data source for fault diagnosis. The aforementioned security mechanisms and data fragmentation are provided by... Figure 4 The data packet slicing and encryption verification engine 402 in the data transmission unit 103 shown is executed.

[0029] In this embodiment, the data transmission unit 103 uses a wireless communication gateway (such as a WIFI gateway) to realize the remote transmission of airborne monitoring data. The wireless communication gateway has a built-in lightweight network protocol stack (such as the LwIP protocol stack). It supports flexible switching between direct connection to the terminal within the local area network and relay via the Internet. When the terminal and the wireless communication gateway are in the same local area network, the wireless communication gateway, as a TCP client, actively initiates a connection to the ground terminal. It obtains structured data from the main control processing unit 102 through the SPI high-speed interface. The wireless communication gateway converts the structured data into TCP data segments and also uses OFDM modulation technology (modulated by the wireless radio frequency transceiver modulator 403) at the physical layer to convert the data signal into a radio frequency signal. The wireless communication gateway dynamically adjusts the transmit power of the radio frequency power amplifier 404 (PA) based on the received signal strength indication (such as RSSI) feedback from the ground terminal, for example, adjusting it within the range of 1-20dBm. In this way, when the drone is far away from the ground terminal, the transmission power is automatically increased to ensure the link gain; when the drone is operating close to the terminal, the power is reduced to optimize energy consumption. Combined with the built-in microstrip antenna transmitter 405 with a gain of ≥2dBi to optimize the radiation efficiency of the 2.4GHz band, and based on the dynamic changes in the drone's flight distance, a dynamic power control strategy based on signal strength is adopted to ensure signal penetration in complex obstructed environments.

[0030] Furthermore, to avoid the transmission impact caused by bandwidth fluctuations in the UAV's onboard wireless link, a lightweight data compression engine 401 is used during communication between the wireless communication gateway and the terminal. High-efficiency compression algorithms (such as LZ4) are employed to process structured data in real time. For onboard monitoring data, the compression rate can reach over 60%, significantly reducing the bandwidth usage of the wireless channel and improving the real-time performance of data transmission. To adapt to the maximum transmission unit (MTU) limitation of wireless transmission, the wireless communication gateway splits large-sized onboard monitoring data into multiple sub-packets, such as each sub-packet ≤ 1400 bytes, and assigns them consecutive sequence numbers. After receiving the data segments as a TCP server, the terminal parses them using the corresponding lightweight network protocol stack, reassembles the data using the sequence numbers, extracts the sensor ID, timestamp, and onboard monitoring data, and displays and stores them in real time.

[0031] The data transmission unit 103 also supports a remote transmission mode. When the wireless communication gateway detects that the local area network connection is restricted or the terminal has enabled remote mode, the wireless communication gateway uses the domain name resolution mechanism to obtain the access address of the preset cloud server and actively initiates a remote connection request as a TCP client. After receiving the encapsulated structured data packets and completing physical layer modulation and power adaptive adjustment, the wireless communication gateway forwards the data segments to the Internet through an external access point (such as a router or mobile hotspot). This mechanism ensures that even when the UAV is operating at a long distance, the onboard monitoring data it collects can still be transmitted back in real time through the public network link.

[0032] As the core node for data relay, the cloud server is responsible for the efficient distribution of received airborne monitoring data. This embodiment preferably uses the Message Queuing Telemetry Transport (MQTT) protocol or the HTTP / 2 protocol. Utilizing MQTT's publish / subscribe mechanism, the cloud server publishes the received sensor ID, timestamp, and airborne monitoring data to a specific topic. Ground terminals (such as mobile apps or remote monitoring screens) can subscribe to the corresponding topic to obtain real-time operational status of the remote drone. This not only solves the distance limitations of point-to-point transmission but also supports multiple monitoring terminals simultaneously monitoring the same drone, greatly improving regulatory efficiency in the "low-altitude economy" scenario. To enhance the security of public network transmission, this embodiment establishes encrypted tunnels using the SSL / TLS 1.3 protocol between the cloud server and the terminal, and between the cloud server and the wireless communication gateway. Combined with the aforementioned Cyclic Redundancy Check (CRC32) and Message Authentication Code (HMAC-SHA256), the system achieves end-to-end security protection from "airborne sensor" to "cloud server" to "terminal". Even if data segments are intercepted during transit over the public network, they cannot be decrypted or tampered with, ensuring the absolute privacy and authenticity of sensitive data related to flight safety, such as power line inspections and emergency rescue.

[0033] By installing application software compatible with the system on the terminal, after receiving data segments, the application software can display the data curves of various parameters in real time.

[0034] Preferably, the parameter acquisition module 200 includes at least one of a voltage acquisition module, a current acquisition module, and a temperature acquisition module; Each parameter acquisition module 200 has a standard specification output interface, which is provided with an identification pin for outputting a characteristic signal that represents the parameter type. The signal acquisition unit 101 also includes an analog-to-digital conversion module 205, which is used to perform oversampling processing on the conditioned original parameter signal.

[0035] In this embodiment, the parameter acquisition module 200 is at least one of a voltage acquisition module, a current acquisition module, and a temperature acquisition module, used to measure the voltage, current, and temperature of the component under test, respectively. To achieve modular expansion and "plug-and-play" functionality of the system, such as... Figure 2As shown, each parameter acquisition module 200 uses a unified physical connector (such as a specific pin header, female header, or aviation plug) and a predefined pin layout, which is consistent with the normalized physical interface 201 of the signal acquisition unit 101. The standard output interface includes at least a power pin, a ground pin, a signal transmission pin, and a type identification pin. In this way, whether the parameter acquisition module 200 acquires a thermocouple signal, a shunt signal, or other signals, the physical interface specifications of the external output are completely consistent. This allows the hardware baseboard of the signal acquisition unit 101 and the main control processing unit 102 to reserve multiple universal slots without having to set specific interfaces for specific sensors.

[0036] Specifically, the signal acquisition unit 101 has a built-in physical identifier 203 for characterization signals. The characterization signal is an electrical signal present on the type identification pin, used to uniquely indicate the sensor type of the module. The characterization signal can be implemented in analog, digital, communication, or any other way. For example, in analog implementation, a specific resistor can be connected to the type identification pin. When the module is connected to the signal acquisition unit 101, the signal acquisition unit 101 forms a voltage divider with the identification resistor using an internal reference voltage, and the acquired voltage amplitude is the "characterization signal." For example, 0.5V corresponds to a temperature acquisition module, and 1.0V corresponds to a current acquisition module. In digital implementation, a combination of high and low levels from multiple identification pins can be used as the characterization signal. In communication implementation, a miniature memory (such as an I2C interface EEPROM) can be integrated inside each parameter acquisition module 200, storing a unique identifier (UUID) containing the module type, range, and calibration parameters; this identifier is the characterization signal. When the signal acquisition unit 101 detects that a parameter acquisition module 200 has been connected to the normalized physical interface 201, it first reads the characterization signal of the type identification pin. The read characterization signal is then compared with a type mapping table preset in memory. Once the parameter type is determined, for example, a K-type thermocouple temperature module, the matching preprocessing algorithm strategy is automatically invoked. The physical reading and strategy matching logic of the aforementioned characterization signal is executed by the built-in characterization signal physical recognizer 203. This recognition logic achieves deep decoupling between hardware and software. Even if the user places the module in a location other than the preset universal slot, the system can accurately identify it through the characterization signal, avoiding data errors caused by algorithm misuse. Furthermore, when a new type of sensor needs to be added, only a new characterization signal needs to be defined and the corresponding entry added to the software mapping table; no hardware circuitry needs to be modified, greatly improving the iteration efficiency of the UAV monitoring system.

[0037] Specifically, the signal acquisition unit 101 adopts a modular and scalable architecture. It reserves multiple normalized physical interfaces 201, such as analog input interfaces, digital input interfaces, and I2C / SPI communication interfaces, supporting plug-and-play access for various sensors such as heat flow sensors, pressure sensors, and humidity sensors. The expansion interface design in this embodiment meets the requirements of a wide voltage input range (0-5V analog, 4-20mA current loop, mV-level weak signals) and high input impedance (≥10MΩ), ensuring compatibility with the output characteristics of different sensors and solving the expansion limitations caused by fixed interfaces in traditional testing systems. Furthermore, multiple normalized hardware expansion slots are reserved to support plug-and-play access for future new sensors such as fiber optic temperature sensors and MEMS pressure sensors. The firmware of the main control processing unit 102 can be upgraded via OTA or USB to support algorithm updates for new sensors without hardware replacement, achieving full lifecycle scalability of the system and solving the pain point of "requiring replacement of the entire machine or redesign of the circuit" in traditional testing and monitoring methods.

[0038] The signal acquisition unit 101 not only receives heterogeneous parameter data from multiple sources such as voltage, current, and temperature through a preset normalized physical interface 201, but also performs high-precision signal conditioning and digital quantization through a built-in pre-conditioning circuit 202, a programmable gain amplifier 204, and an integrated analog-to-digital converter module 205. The analog-to-digital converter module 205 dynamically adjusts the sampling rate according to the parameter type, and dynamically triggers oversampling processing for input signals with high precision requirements or weak signals. The parameter data after oversampling quantization is converted into high-resolution digital signals, such as a voltage quantization resolution of 0.001mV and a temperature quantization resolution of 0.001℃.

[0039] Furthermore, the analog-to-digital converter module 205 incorporates an internal precision reference voltage source 207 to address gain temperature drift and zero-point drift in the analog conditioning circuit caused by complex flight conditions of the UAV. Its accuracy is preferably ±0.1%. Through a real-time calibration mechanism triggered every 100ms (executed within the analog-to-digital converter module 205), the zero-point offset and gain error of the analog-to-digital converter module 205 are dynamically corrected with reference to the internal reference voltage. This eliminates measurement errors caused by device aging or sudden changes in ambient temperature, ensuring that the long-term measurement error of the multi-channel parameter signals during the digitization process remains stable within the range of ≤±0.1%. This achieves high-precision quantization of parameter data and indirectly reduces the computing power consumption of the main control processing unit 102 in processing signals.

[0040] Preferably, the signal acquisition unit 101 has a built-in ambient temperature monitoring module 206 for acquiring the cold junction temperature at the normalized physical interface 201. When the signal acquisition unit 101 is connected to the temperature acquisition module, the signal acquisition unit 101 is configured to use a nonlinear compensation algorithm to dynamically correct the thermoelectric potential signal generated by the temperature acquisition module in combination with the cold junction temperature, and output a digital temperature signal after amplification and oversampling processing.

[0041] The signal acquisition unit 101 also integrates an ambient temperature monitoring module 206. When the signal is characterized to indicate that the module connected to the signal acquisition unit 101 is a temperature acquisition module, the temperature acquisition element collects the temperature data and converts it into a thermoelectric potential signal. For example, a K-type thermocouple, based on the Seebeck effect, converts the temperature of UAV test components such as motors and batteries into a microvolt-level thermoelectric potential signal. The ambient temperature monitoring module 206 is preferably a MAX6675 thermocouple signal conditioning chip. The chip has a built-in high-precision platinum resistance temperature sensor (such as PT1000) to monitor the cold junction temperature in real time. When the chip receives the microvolt-level thermoelectric potential signal, it combines the cold junction temperature and uses a nonlinear compensation algorithm based on the thermocouple calibration table, i.e., piecewise cubic spline interpolation based on the national standard calibration curve of the K-type thermocouple, to dynamically correct the influence of the cold junction temperature on the microvolt-level thermoelectric potential signal and eliminate measurement errors caused by ambient temperature fluctuations. Meanwhile, the chip preferably employs a low-noise instrumentation amplifier with an input impedance ≥10MΩ and a common-mode rejection ratio ≥100dB to pre-amplify the weak thermoelectric potential signal. The gain range is preferably 1-128 times, increasing the amplitude of the weak thermoelectric potential signal to a range recognizable by the analog-to-digital converter module 205 (e.g., 0-3.3V) and suppressing common-mode interference, such as electromagnetic noise generated by the drone motor. The amplified thermoelectric potential signal is oversampled (e.g., 64 times oversampling) by a 24-bit Σ-Δ ADC, and high-frequency noise is filtered out by a Sinc³ digital filter, converting the analog thermoelectric potential into a high-resolution digital temperature signal with a resolution of up to 0.001℃. This ensures high accuracy and high stability in temperature measurement.

[0042] Preferably, the main control processing unit 102 is configured to use a Kalman filter algorithm to denoise the temperature digital signal and to correct the measurement deviation caused by ambient temperature fluctuations in real time according to a preset temperature drift characteristic curve.

[0043] The main control processing unit 102 reads high-resolution digital temperature signals via the I2C interface and uses a Kalman filter algorithm to fuse the temperature data after cold junction compensation, further suppressing environmental and circuit noise. Furthermore, the main control processing unit 102 incorporates a temperature drift compensation model corresponding to the ambient temperature monitoring module 206. For example, based on the temperature drift characteristic curve of the aforementioned MAX6675 thermocouple signal conditioning chip, the chip's own temperature drift coefficient is obtained through pre-calibration. The temperature drift compensation model corrects measurement deviations caused by changes in the chip's operating temperature in real time, ensuring that the temperature measurement error is ≤ ±0.5℃ within the full operating temperature range (e.g., -40℃ to 85℃). In addition, the system supports multi-channel synchronous temperature acquisition, such as simultaneously connecting multiple temperature acquisition modules. The sampling time of each channel is synchronized via multiple thermocouples and a high-precision clock, with a preferred synchronization error ≤ 1μs, avoiding temperature correlation errors caused by asynchronous sampling (e.g., the time difference between motor winding and casing temperatures). This provides a synchronous data foundation for thermal management analysis (e.g., heat dissipation efficiency assessment, overheat warning). Through closed-loop processing of "thermocouple signal - cold junction compensation - signal amplification - high-precision ADC conversion - digital filtering - temperature drift correction", high-precision and interference-resistant acquisition of temperature parameters is achieved, solving the pain points of traditional temperature measurement such as cold junction fluctuation, weak signal and noise interference, and providing reliable data support for thermal characteristic analysis and fault diagnosis of UAV power system.

[0044] Preferably, when the signal acquisition unit 101 is connected to the current acquisition module, the signal acquisition unit 101 is configured to adjust the amplification factor of the current signal through an adaptive gain control algorithm, and output a digital current signal after sampling processing.

[0045] When the input signal to the signal acquisition unit 101 is determined to be a current acquisition module, the current acquisition element collects the current data and then converts it. Preferably, the current acquisition element is a high-precision DC shunt made of manganese copper alloy with a temperature coefficient ≤10ppm / ℃ and an accuracy of 0.1%. It converts large current data (e.g., 0-100A) into a microvolt-level voltage signal proportionally; for example, 100A corresponds to 10mV. The signal acquisition unit 101 also integrates a built-in instrumentation amplifier with a preferred common-mode rejection ratio ≥120dB and an input impedance ≥1GΩ. This amplifier incorporates an adaptive gain control algorithm that dynamically adjusts the amplification factor based on the current amplitude, with an adjustment range of 10-1000 times, amplifying the microvolt-level voltage signal to ensure it remains within the range recognizable by the analog-to-digital conversion module 205 (e.g., 0-3.3V) and maintains a high signal-to-noise ratio. The instrumentation amplifier also integrates a low-pass filter with a preferred cutoff frequency of 10Hz, effectively suppressing high-frequency electromagnetic interference such as switching noise generated by the PWM drive of the UAV motor. The amplified voltage signal is oversampled (e.g., 128x oversampling) by a 24-bit Σ-Δ ADC, and then passed through Sinc.4 Digital filters remove high-frequency noise and convert analog voltage into high-resolution digital voltage signals, with a resolution of up to 0.001mV, enabling high-precision quantization of current measurement.

[0046] Preferably, the main control processing unit 102 is configured to use a Kalman filter algorithm to perform fusion processing on the digital current signal, and to perform real-time correction of temperature drift during the current detection process according to the temperature drift compensation model of the current acquisition module.

[0047] The main control processing unit 102 reads high-resolution digital voltage signals via the I2C interface and uses a Kalman filter algorithm to fuse the current signals, eliminating environmental and circuit noise. Furthermore, the main control processing unit 102 incorporates a temperature drift compensation model corresponding to the current acquisition element. It preferably uses a PT1000 sensor to monitor the shunt temperature in real time and corrects the measurement based on the shunt's temperature coefficient curve, ensuring that the current measurement error is ≤±0.2% across the entire operating temperature range (e.g., -40℃ to 85℃). In addition, the system supports multi-channel synchronous current acquisition, such as simultaneously connecting multiple current acquisition modules. The sampling time of each channel is synchronized via a multi-channel shunt and a high-precision clock, with a preferred synchronization error of ≤1μs. This avoids current correlation errors caused by asynchronous sampling (e.g., time difference of three-phase motor current), providing a synchronous data foundation for power calculations (e.g., three-phase power). By implementing a closed-loop process of "shunt converter conversion - instrument amplification - high-precision ADC conversion - digital filtering - temperature drift compensation", the pain points of traditional current measurement, such as shunt temperature drift, weak signal, and electromagnetic interference, are solved. This achieves high-precision and interference-resistant acquisition of current parameters, providing reliable data support for the current characteristic analysis and fault diagnosis of UAV power systems (such as overcurrent protection and efficiency calculation).

[0048] Preferably, the voltage acquisition module adopts an input stage circuit with a high common-mode rejection ratio, and integrates an RC low-pass filter network and an anti-interference transmission component. When the signal acquisition unit 101 is connected to the voltage acquisition module, the signal acquisition unit 101 is configured to automatically switch the voltage division ratio according to the voltage range in which the voltage is located, and output a digital voltage signal after sampling processing.

[0049] When the signal is characterized to be connected to the signal acquisition unit 101, the voltage acquisition module is determined. The voltage acquisition element acquires the voltage signal of the test component (such as the output voltage of the power battery, the motor drive voltage, etc.). The voltage acquisition module integrates a built-in high common-mode rejection ratio input stage circuit. This input stage circuit meets the requirements of input impedance ≥10MΩ and common-mode rejection ratio ≥120dB. Specifically, it is designed with an RC low-pass filter network with a cutoff frequency ≤10Hz and shielded twisted pair cable, which can effectively suppress high-frequency electromagnetic interference (such as switching noise generated by motor PWM drive) and common-mode noise in the initially acquired voltage data, ensuring the purity of the voltage data.

[0050] The voltage range is pre-set according to the power supply voltage requirements of the components to be monitored inside the UAV. The voltage range includes a high voltage range and a low voltage range. The high voltage range can be pre-set with a specific numerical range, preferably set according to the voltage source. For example, if the power battery is the main power source of the UAV, its voltage often cannot be directly input to the gain instrumentation amplifier. Therefore, the voltage corresponding to the power battery (e.g., 12V-48V) is used as the high voltage range. The voltage corresponding to some physical devices inside the UAV that support the entire control link is used as the low voltage range, such as the power supply voltage of the sensors. For the 12V-48V power battery voltage, the voltage acquisition element integrates a voltage conditioning circuit, preferably a high-precision voltage divider circuit. This high-precision voltage divider circuit uses low-temperature drift resistors with a temperature coefficient ≤5ppm / ℃ and a voltage division ratio error ≤0.05%, proportionally attenuating the voltage to the range that the analog-to-digital conversion module 205 can recognize (e.g., 0-3.3V). For voltages in the low voltage range, such as the sensor power supply voltage, the signal is directly input to the built-in adaptive gain instrumentation amplifier integrated in the signal acquisition unit 101. The gain range is preferably 1-100 times. The instrumentation amplifier monitors the voltage signal amplitude according to the instructions of the main control unit and dynamically adjusts the gain. The instructions are to adjust the gain multiple according to the input voltage of the instrumentation amplifier so that the output voltage is a preset voltage. For example, if the preset voltage is 3.3V, and the input voltage is 0.5V, the gain is set to 6.6 times, ensuring that the output voltage is within the input range of the analog-to-digital conversion module 205 and maintains a high signal-to-noise ratio (e.g., signal-to-noise ratio ≥ 80dB). The voltage signal after the above conditioning process is oversampled (e.g., 128 times oversampling) by a 24-bit Σ-Δ ADC and then processed by Sinc. 4 A digital filter removes high-frequency noise and converts the analog voltage into a high-resolution digital voltage signal, with a resolution of up to 0.001mV, enabling high-precision quantization of voltage measurements. The 24-bit Σ-Δ ADC incorporates an internal reference voltage source with an accuracy of ±0.1%. Through a optimized real-time calibration mechanism triggered every 100ms, using the internal reference voltage as a reference, it dynamically corrects the ADC's zero-point offset and gain error, ensuring long-term stable measurement accuracy with an error ≤ ±0.1%.

[0051] Preferably, the main control processing unit is configured to perform state estimation of the voltage digital signal based on a dynamic model of voltage change, and to perform end-to-end temperature compensation of the voltage measurement results by combining the temperature drift coefficient of the analog-to-digital conversion module 205 and the voltage conditioning circuit.

[0052] The main control processing unit 102 reads the high-resolution voltage digital signal through the SPI interface, uses a Kalman filter algorithm for noise reduction, and preferably uses a dynamic model based on the state equation to estimate the state of the high-resolution voltage digital signal, thereby eliminating environmental and circuit noise. The state equation is based on voltage changes, and the covariance matrix of the measured environmental and circuit noise is updated in real time. Furthermore, the main control processing unit 102 has a built-in temperature drift compensation model corresponding to the voltage acquisition element. Preferably, the temperature of the voltage acquisition element is monitored in real time using a PT1000 sensor, and the temperature drift coefficients of the analog-to-digital converter module 205 and the voltage conditioning circuit are combined to correct the high-resolution voltage digital signal, ensuring that the voltage measurement error of the UAV is preferably ≤±0.2% within the full operating temperature range (e.g., -40℃ to 85℃). In addition, the system supports multi-channel synchronous voltage acquisition, for example, simultaneously connecting multiple voltage acquisition modules to measure the voltage of multiple test components, such as battery voltage and motor phase voltage. The sampling time of each channel is synchronized using a high-precision clock, preferably with a synchronization error ≤1μs, to avoid power calculation errors caused by asynchronous sampling. By employing a closed-loop processing mechanism of "differential input - voltage divider / amplifier - high-precision ADC - real-time calibration - Kalman filtering - temperature drift compensation", the system addresses the pain points of traditional voltage measurement, such as high input impedance requirements, common-mode interference, and temperature drift. This enables high-precision and interference-resistant acquisition of voltage parameters, making it particularly suitable for scenarios such as drones where voltage measurement accuracy and stability are extremely critical.

[0053] Preferably, it also includes a power supply management unit 104; The power supply management unit 104 includes a low-dropout linear regulator, which is used to provide a stable operating voltage for each unit of the system and suppress the interference of voltage fluctuations in the power supply circuit on signal acquisition.

[0054] The power supply management unit 104 is used to provide a stable operating voltage for each unit of the system, preferably as follows: Figure 5 The power bank 9 shown provides 5V DC power. Specifically, its built-in battery management system uses a power management algorithm to monitor the power supply voltage, current, and load status in real time. Then, a low-dropout linear regulator performs secondary voltage regulation to output a stable low-voltage DC power (e.g., 5V). By dynamically adjusting the output voltage of the low-dropout linear regulator, it is ensured that each unit of the power bank 9 receives stable power during voltage fluctuations (e.g., 4.5V-5.5V) or sudden load changes, avoiding signal acquisition errors and system stability degradation caused by power supply fluctuations.

[0055] Preferably, the system also includes a display module, particularly an LCD display module, which displays the monitoring status of parameter data synchronously on the LCD display module and the terminal.

[0056] like Figure 5As shown, the system in this embodiment can be mounted on the body of the drone 1. The drone body is ensured to operate normally through the drone battery 2 (preferably a lithium battery), the battery negative wire 3, and the battery positive wire 4.

[0057] The signal acquisition unit 101 is specifically integrated inside the data acquisition unit 11. It is connected to the power supply 9 via the acquisition unit-power connection cable 8. The power supply 9 is connected to the drone 1 to provide working voltage for each unit of the system and to establish a connection with the communication control link of the drone 1.

[0058] The current acquisition module can use a shunt or a Hall sensor 5. When using a Hall sensor 5, it is connected to the data acquisition unit 11 via a sensor-acquisition unit connection cable 7 and to the power supply 9 via a sensor-power connection cable 6. The UAV power bus 10 is connected to the data acquisition unit 11. Based on the parallel connection between the UAV power bus 10 and the battery 2, the voltage data of the battery 2 is measured. After the Hall sensor 5 is connected to the data acquisition unit 11, the current data of the battery 2 can be measured by measuring a single power line.

[0059] The data acquisition unit 11 can collect various types of parameters through the signal acquisition unit 101, which integrates multiple parameter acquisition modules 200 or reserves an extended normalized physical interface 201. Then, the parameter data is transmitted to the terminal through the main control processing unit 102 and the data transmission unit 103 integrated inside the data acquisition unit 11.

[0060] This embodiment provides an integrated airborne monitoring system for UAVs operating under complex flight conditions. Through a clever modular architecture and hardware / software decoupling mechanism, it significantly improves the flexible scalability of UAV airborne monitoring, the signal measurement accuracy under complex flight conditions, and the reliability of wireless data transmission. The specific technical effects are as follows: 1) By designing the signal acquisition unit 101 to have at least one normalized physical interface 201, and by automatically matching the corresponding preprocessing strategy with the characterization signal of the parameter acquisition module 200, this system achieves "plug-and-play" parameter acquisition and highly reliable "anti-distortion / anti-mistake" identification at the hardware source. This not only enables the multi-source heterogeneous parameter acquisition module 200 performing different monitoring tasks to perform flexible plug-and-play access and adaptive identification using a completely unified physical interface specification, completely avoiding monitoring data anomalies caused by manual wiring errors or misuse of algorithm schemes, but also greatly improves the reusability of the airborne hardware baseboard and completely solves the expansion limitations caused by fixed interfaces in traditional monitoring systems.

[0061] 2) The main control processing unit 102 can call the matching filtering processing algorithm to process the electrical signal and generate structured data according to the automatically identified parameter type. This mechanism provides highly targeted dynamic noise reduction and temperature drift suppression capabilities for harsh working conditions such as high-frequency vibration of motors and strong electromagnetic pulses generated by power battery discharge during actual flight of UAV. While ensuring high signal-to-noise ratio and high authenticity of output signal, it avoids the waste of airborne computing resources caused by global unified filtering and realizes efficient configuration of limited airborne computing power.

[0062] 3) The purified structured data is encapsulated into data packets conforming to the airborne wireless protocol through the data transmission unit 103 and sent. While greatly compressing the bandwidth of airborne telemetry data communication, it significantly enhances the anti-interference capability and transmission frame success rate of data packets in complex multipath fading channels in the air. This ensures that the core electrothermal safety parameters of the UAV can still be transmitted back to the remote control node in real time, completely and with high fidelity when the UAV is in beyond visual range and in a highly dynamic flight state. This provides high-precision and high-real-time decision data support for the safety supervision of the UAV throughout its entire life cycle.

[0063] Example 2

[0064] Based on the principles described in Example 1, a comprehensive airborne monitoring method for UAVs under complex flight conditions is also proposed. Based on the system implementation described in Example 1, it includes: The system acquires the characterization signal of the access parameter acquisition module 200, identifies the parameter type of the parameter acquisition module 200 based on the characterization signal, and matches a preprocessing strategy based on the parameter type. The main control processing unit 102 receives the preprocessed electrical signal and performs data processing using the corresponding filtering algorithm according to the parameter type to generate structured data. The structured data is encapsulated into data packets conforming to the airborne wireless protocol and then transmitted.

[0065] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0066] Furthermore, it should be noted that the use of terms such as "first," "second," and "a" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. The terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two elements or the interaction between two elements, unless otherwise explicitly specified. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0067] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0068] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A UAV-borne integrated monitoring system for complex flight conditions, characterized in that, include: Signal acquisition unit, main control processing unit, data transmission unit; The signal acquisition unit is electrically connected to the main control processing unit, and the main control processing unit is communicatively connected to the data transmission unit; The signal acquisition unit has at least one normalized physical interface for connecting to parameter acquisition modules that perform different parameter acquisition tasks, and matching the corresponding preprocessing strategy through the characterization signal of the connected parameter acquisition module. The main control processing unit is used to receive the electrical signal output by the signal acquisition unit, and to perform data processing using the corresponding filtering algorithm according to the parameter type to generate structured data. The data transmission unit is used to encapsulate the structured data into data packets conforming to the airborne wireless protocol and send them.

2. The system according to claim 1, characterized in that, The parameter acquisition module includes at least one of a voltage acquisition module, a current acquisition module, and a temperature acquisition module; Each parameter acquisition module has a standard specification output interface, which is equipped with an identification pin for outputting a characteristic signal that represents the parameter type. The signal acquisition unit also includes an analog-to-digital conversion module, which is used to oversample the conditioned raw parameter signal.

3. The system according to claim 2, characterized in that, The signal acquisition unit has a built-in ambient temperature monitoring module for obtaining the cold junction temperature at the normalized physical interface. When the signal acquisition unit is connected to the temperature acquisition module, the signal acquisition unit is configured to use a nonlinear compensation algorithm to dynamically correct the thermoelectric potential signal generated by the temperature acquisition module in combination with the cold junction temperature, and output a digital temperature signal after amplification and oversampling processing.

4. The system according to claim 3, characterized in that, The main control processing unit is configured to use a Kalman filter algorithm to denoise the temperature digital signal and to correct the measurement deviation caused by ambient temperature fluctuations in real time according to a preset temperature drift characteristic curve.

5. The system according to claim 2, characterized in that, When the signal acquisition unit is connected to the current acquisition module, the signal acquisition unit is configured to adjust the amplification factor of the current signal through an adaptive gain control algorithm, and output a digital current signal after sampling processing.

6. The system according to claim 5, characterized in that, The main control processing unit is configured to use a Kalman filter algorithm to fuse the digital current signal and to correct the temperature drift during the current detection process in real time according to the temperature drift compensation model of the current acquisition module.

7. The system according to claim 2, characterized in that, The voltage acquisition module adopts a high common-mode rejection ratio input stage circuit, integrates an RC low-pass filter network and an anti-interference transmission component. When the signal acquisition unit is connected to the voltage acquisition module, the signal acquisition unit is configured to automatically switch the voltage division ratio according to the voltage range in which the voltage is located, and outputs a digital voltage signal after sampling processing.

8. The system according to claim 7, characterized in that, The main control processing unit is configured to perform state estimation of the voltage digital signal based on a dynamic model of voltage change, and to perform end-to-end temperature compensation of the voltage measurement results by combining the temperature drift coefficient of the analog-to-digital conversion module and the voltage conditioning circuit.

9. The system according to claim 1, characterized in that, It also includes a power supply management unit; The power supply management unit includes a low-dropout linear regulator, which is used to provide a stable operating voltage for each unit of the system and suppress the interference of voltage fluctuations in the power supply circuit on signal acquisition.

10. A method for integrated airborne monitoring of unmanned aerial vehicles (UAVs) under complex flight conditions, characterized in that: Based on any one of claims 1-9, the system implementation includes: Acquire the characterization signal of the access parameter acquisition module, identify the parameter type of the parameter acquisition module based on the characterization signal, and match a preprocessing strategy according to the parameter type; The main control processing unit receives the preprocessed electrical signal and performs data processing using the corresponding filtering algorithm according to the parameter type to generate structured data. The structured data is encapsulated into data packets conforming to the airborne wireless protocol and then transmitted.

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