Low-power Management Method for Field Telemetry Equipment Based on Multi-channel

Abstract

The present invention relates to the field of device data management, and discloses a low-power management method for field telemetry devices based on multiple channels, which is used to solve the problem that when the environment is not conducive to device data collection and transmission, unnecessary power is consumed by the device. The method includes collecting digital elevation model data of the information area and calculating the terrain complexity coefficient, setting an environmental sensor on the device, collecting environmental data of the environment where the device is located in real time through the environmental sensor, obtaining the vegetation coverage image of the area where the device is located, and obtaining the environmental impact coefficient through the temperature data, humidity data and vegetation coverage image, obtaining the current signal strength, signal-to-noise ratio and packet loss rate, calculating the signal instability coefficient, calculating the waiting index according to the terrain complexity coefficient, environmental impact coefficient and signal instability coefficient, and controlling data collection and transmission according to the waiting index, effectively improving the management efficiency of device power consumption and reducing unnecessary power consumption.

Description

Technical Field

[0001] The present invention relates to the field of device data management, and more particularly to a low-power management method for field telemetry devices based on multiple channels. Background Art

[0002] A multi-channel field telemetry device is a device used for remotely monitoring natural environmental parameters (such as meteorology, geology, ecology, etc.), and has the ability to simultaneously use multiple communication channels for data transmission. These channels can be different frequency bands, communication protocols, or sensor data channels. The device realizes efficient and stable data transmission by dynamically selecting the best communication channel and optimizing data scheduling, and at the same time extends the working time of the device in a power-constrained environment through low-power design and intelligent management. The multi-channel technology enables the device to flexibly cope with complex field environment changes and network conditions, and also makes the power consumption management of field telemetry devices particularly important.

[0003] Existing low-power management methods for telemetry devices usually judge the operating state of the device according to environmental changes and adjust the working mode of the sensor to achieve low-power management of the device. However, if the field environment for data collection and transmission by the field telemetry device is not conducive to data collection and transmission, and power consumption management is still carried out according to the operating state of the device, it may cause the device to consume unnecessary power to collect and transmit inaccurate data.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a low-power management method for field telemetry devices based on multiple channels to solve the problems existing in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A low-power management method for field telemetry devices based on multiple channels includes the following steps:

[0008] Step 1: Obtain digital elevation model data of the area where the device collects information through a remote sensing satellite system, and calculate the terrain complexity coefficient according to the digital elevation model data;

[0009] Step 2: Set environmental sensors on the device, where the environmental sensors include a temperature sensor and a humidity sensor, and collect temperature data and humidity data of the environment where the device is located in real time through the environmental sensors;

[0010] Step 3: Obtain a vegetation coverage image of the area where the device is located through a satellite remote sensing system, and obtain an environmental impact coefficient based on the temperature data, humidity data, and vegetation coverage image;

[0011] Step 4: Obtain the current signal strength, signal-to-noise ratio, and packet loss rate, and calculate the signal instability coefficient;

[0012] Step 5: Calculate the waiting index based on the terrain complexity coefficient, environmental impact coefficient, and signal instability coefficient. The calculation formula is , where WF represents the waiting index, TI represents the terrain complexity coefficient, EF represents the environmental impact coefficient, SI represents the signal instability coefficient, and a1, a2, and a3 represent the weight coefficients of the terrain complexity coefficient, environmental impact coefficient, and signal instability coefficient;

[0013] Step 6: Control data acquisition and transmission according to the waiting index.

[0014] Preferably, the digital elevation model data includes elevation values, spatial resolution, geographic coordinates, and the actual surface area and vertical area.

[0015] Preferably, the steps for calculating the terrain complexity coefficient based on the digital elevation model data are as follows:

[0016] Obtain the elevation difference and grid resolution of adjacent grids from the digital elevation model data, and calculate the slope value for each grid point. The calculation formula is , where S represents the slope value, is the height difference, is the horizontal distance;

[0017] Obtain the maximum and minimum elevations in the grid from the digital elevation model data, and calculate the height change value for each grid point. The calculation formula is , where E represents the height change value, represents the maximum elevation, is the minimum elevation, and a is the area of one grid;

[0018] Obtain the actual surface area in the grid from the digital elevation model data. The actual area includes terrain undulations and the horizontal projection area in the grid, and calculate the terrain roughness value for each grid point. The calculation formula is , where R represents the terrain roughness value, TA represents the actual surface area, and PA represents the horizontal projection area;

[0019] Calculate the terrain complexity coefficient based on the slope value, height change value, and terrain roughness value. The calculation formula is , where TI represents the terrain complexity coefficient, S i represents the slope value of the i-th grid point, R i represents the roughness value of the i-th grid point, E i represents the height change value of the i-th grid point, and n is the total number of grid points.

[0020] Preferably, the step of collecting temperature data and humidity data of the environment where the device is located in real time through the environmental sensor is as follows:

[0021] Select a temperature sensor and a humidity sensor, install the temperature and humidity sensors on the device, connect the sensors to the control system of the device, and provide power;

[0022] After installing the sensors, use a standard thermometer and hygrometer to compare the readings of the sensors for preliminary calibration;

[0023] According to the sensor type, use the corresponding driver or library for development, set a timer so that the device can collect temperature and humidity data at fixed time intervals;

[0024] Store the collected temperature and humidity data in the local storage of the device.

[0025] Preferably, the step of obtaining the environmental impact coefficient from the temperature data, humidity data, and vegetation cover image is as follows:

[0026] Obtain the optimal humidity data and optimal temperature data during device operation, calculate the temperature and humidity difference degree based on the temperature data and humidity data, and its calculation formula is , where ws represents the temperature and humidity difference degree, wd represents the temperature data, wd’ represents the optimal temperature data, sd represents the humidity data, and sd’ represents the optimal humidity data;

[0027] Obtain the vegetation coverage rate from the vegetation cover image;

[0028] Calculate the environmental impact coefficient through the temperature and humidity difference degree and the vegetation coverage rate, and its calculation formula is , where EF represents the environmental impact coefficient, ws represents the temperature and humidity difference degree, and vc represents the vegetation coverage rate.

[0029] Preferably, the step of obtaining the vegetation coverage rate from the vegetation cover image is as follows:

[0030] Preprocess the vegetation cover image, and the preprocessing includes atmospheric correction and geometric correction;

[0031] Extract the reflectance data of the red light band and the near-infrared band from the preprocessed vegetation cover image, calculate the normalized difference vegetation index value of each pixel through the normalized difference vegetation index formula, and the numerical range is between -1 and 1;

[0032] Set the threshold of the normalized difference vegetation index, classify the image into vegetation areas and non-vegetation areas, and generate a vegetation cover map;

[0033] Count the number of pixels in the vegetation area and the total number of pixels, and calculate the vegetation coverage rate. The calculation formula is , where vc represents the vegetation coverage rate, and num v represents the number of pixels in the vegetation area, and num 总 represents the total number of pixels.

[0034] Preferably, the steps for calculating the signal instability coefficient are as follows:

[0035] Obtain the current signal strength through the device;

[0036] Obtain the signal-to-noise ratio through a signal measurement tool;

[0037] Collect the ratio of the number of lost data packets to the total number of sent data packets during the monitoring data transmission process as the packet loss rate;

[0038] Calculate the signal instability coefficient based on the signal strength, signal-to-noise ratio, and packet loss rate. The calculation formula is , where SI represents the signal instability coefficient, RSSI represents the signal strength, SNR represents the signal-to-noise ratio, and DBL represents the packet loss rate.

[0039] Preferably, the steps for controlling data collection and transmission according to the waiting index are as follows:

[0040] Compare the waiting index with the waiting threshold. If the waiting index is less than the waiting threshold, it is determined that the current environment is good and data collection and transmission can be performed. If the waiting index is greater than the waiting threshold, it is determined that the current environment is poor, the sleep state is triggered, and data collection and transmission are no longer performed until the waiting index is less than the waiting threshold, and data collection and transmission are restarted.

[0041] The technical effects and advantages of the present invention:

[0042] Collect digital elevation model data of the information collection area and calculate the terrain complexity coefficient. Set an environmental sensor on the device, and collect environmental data of the environment where the device is located in real time through the environmental sensor. Obtain the vegetation coverage image of the area where the device is located, and obtain the environmental impact coefficient through temperature data, humidity data, and the vegetation coverage image. Obtain the current signal strength, signal-to-noise ratio, and packet loss rate, calculate the signal instability coefficient, calculate the waiting index based on the terrain complexity coefficient, environmental impact coefficient, and signal instability coefficient, and control data collection and transmission according to the waiting index, effectively improving the management efficiency of device power consumption and reducing unnecessary power consumption. Brief Description of the Drawings

[0043] Figure 1 is the overall flowchart of the present invention. Detailed Embodiments

[0044] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. In addition, the forms of each structure described in the following embodiments are merely examples, and the low-power management method for multi-channel field telemetry devices according to the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0045] The present invention provides a low-power management method for multi-channel field telemetry devices, including the following steps:

[0046] Step 1: Obtain digital elevation model data of the device collection information area through a remote sensing satellite system. The digital elevation model data is raster data representing the surface height, and each pixel represents the surface elevation of a fixed area. Calculate the terrain complexity coefficient based on the digital elevation model data;

[0047] The remote sensing satellite system is a system that conducts long-distance observation and data collection of the Earth's surface through sensors carried on satellites. This system uses electromagnetic waves of different bands, such as visible light, infrared, microwave, etc., to obtain images and relevant information of the Earth's surface from space, and is widely used in environmental monitoring, resource exploration, agricultural assessment, meteorological observation, disaster warning and other fields. The satellite remote sensing system can provide large-scale surface information all-weather and all-round, helping scientists and decision-makers analyze the impacts of natural phenomena and human activities

[0048] The digital elevation model data includes elevation values, spatial resolution, geographic coordinates, horizontal and vertical reference planes, and the actual surface area and vertical area of the surface;

[0049] Each pixel or grid cell contains an elevation value, usually in meters or feet, representing the elevation of the surface at that point relative to sea level;

[0050] The spatial resolution refers to the actual surface area covered by each grid cell. The higher the resolution, the smaller the grid, and the richer the details;

[0051] The spatial position of each digital elevation model data grid cell is located by a geographic coordinate system (usually longitude and latitude, or a projected coordinate system) to ensure the accurate positioning of the digital elevation model data on the Earth's surface;

[0052] The horizontal reference plane is used to locate the geographic coordinate system of the data and defines the position of the data on the Earth's surface. The vertical reference plane is used to define the reference plane for elevation data, usually sea level.

[0053] In this embodiment, it should be specifically noted that the steps for calculating the terrain complexity coefficient based on the digital elevation model data are as follows:

[0054] Obtain the elevation difference between adjacent grids and the grid resolution from the digital elevation model data, and calculate the slope value for each grid point. The calculation formula is , where S represents the slope value, is the height difference, that is, the elevation difference between adjacent grids, is the horizontal distance, that is, the grid resolution. The larger the slope, the more severe the terrain undulation in this area and the higher the complexity;

[0055] Obtain the maximum and minimum elevations in the grid from the digital elevation model data, and calculate the height change value for each grid point. The calculation formula is , where E represents the height change value, represents the maximum elevation, is the minimum elevation, a is the area of one grid. An area with a large elevation change usually indicates high terrain complexity;

[0056] Obtain the actual area of the ground surface in the grid from the digital elevation model data. The actual area includes the terrain undulation and the horizontal projection area in the grid, and calculate the terrain roughness value for each grid point. The calculation formula is , where R represents the terrain roughness value, TA represents the actual area of the ground surface, PA represents the horizontal projection area. The larger the roughness, the more complex the terrain;

[0057] Calculate the terrain complexity coefficient based on the slope value, height change value, and terrain roughness value. The calculation formula is , where TI represents the terrain complexity coefficient, S i represents the slope value of the i-th grid point, R i represents the roughness value of the i-th grid point, E i represents the height change value of the i-th grid point, and n is the total number of grid points.

[0058] By comprehensively considering slope, height changes, and terrain roughness, terrain features can be more comprehensively quantified to obtain a comprehensive terrain complexity coefficient. This quantification method can effectively capture the complexity of the terrain and provide reliable basic data for subsequent analysis. In applications such as field telemetry and wireless communication, understanding the terrain complexity coefficient can help optimize the layout and deployment of equipment. Areas with a higher terrain complexity coefficient may cause signal attenuation, interference, and unstable connections. By analyzing this coefficient, engineers can select more appropriate equipment locations to reduce obstacles to signal propagation. Based on the calculated terrain complexity coefficient, the quality of the signal propagation path can be better evaluated. Identifying areas with high-complexity terrain allows corresponding technical measures to be taken in advance, such as increasing signal emission points and using stronger signal enhancement devices, to ensure signal stability and reliability.

[0059] Step 2: Set up environmental sensors on the device. The environmental sensors include a temperature sensor and a humidity sensor, and the temperature data and humidity data of the environment where the device is located are collected in real time through the environmental sensors.

[0060] In this embodiment, it should be specifically noted that the step of collecting the temperature data and humidity data of the environment where the device is located in real time through the environmental sensors is as follows:

[0061] According to the working environment and requirements of the device, select suitable temperature sensors and humidity sensors, install the temperature and humidity sensors at appropriate positions on the device, ensure that the sensors are exposed to the real environment and are not interfered by the heat or moisture of the device itself, correctly connect the sensors to the control system of the device (such as a microcontroller, data acquisition module, etc.), and provide power. If the sensors are connected to the device through wireless communication, configure the wireless communication module.

[0062] After installing the sensors, perform preliminary calibration. Ensure that the temperature and humidity sensors can accurately collect data and eliminate errors during the installation process. If necessary, use a standard thermometer and hygrometer to compare the readings of the sensors to ensure their accuracy.

[0063] Write code in the control system of the device to read the temperature and humidity data in real time. According to the type of sensor, use the corresponding driver or library for development, set a timer so that the device can collect temperature and humidity data at fixed time intervals (such as every minute, every 5 minutes, etc.).

[0064] Store the collected temperature and humidity data in the local storage of the device (such as an SD card, EEPROM, etc.) for subsequent analysis.

[0065] Step 3: Obtain the vegetation coverage image of the area where the device is located through a satellite remote sensing system, and obtain the environmental impact coefficient based on the temperature data, humidity data, and vegetation coverage image.

[0066] In this embodiment, it should be specifically noted that the steps of obtaining the environmental impact coefficient from temperature data, humidity data, and vegetation coverage images are as follows:

[0067] Obtain the optimal humidity data and optimal temperature data during device operation, and calculate the temperature-humidity difference degree based on the temperature data and humidity data. The calculation formula is , where ws represents the temperature-humidity difference degree, wd represents the temperature data, wd' represents the optimal temperature data, sd represents the humidity data, and sd' represents the optimal humidity data;

[0068] Obtain the vegetation coverage rate from the vegetation coverage image. Dense vegetation will absorb and reflect radio waves, weakening the signal and resulting in poor network coverage. In forested areas or areas with high vegetation coverage, the signal strength may be significantly reduced, especially the propagation of high-frequency signals (such as cellular signals) is more obviously blocked;

[0069] Calculate the environmental impact coefficient through the temperature-humidity difference degree and the vegetation coverage rate. The calculation formula is , where EF represents the environmental impact coefficient, ws represents the temperature-humidity difference degree, and vc represents the vegetation coverage rate.

[0070] Taking into account the three key environmental factors of temperature, humidity, and vegetation coverage rate provides a comprehensive index to evaluate the impact of environmental conditions on device performance. This comprehensive evaluation can more accurately reflect the actual impact of environmental changes on signal quality and device operation; it can identify the challenges that the device may face under different environmental conditions. This enables the design and deployment of the device to be optimized according to the actual environmental conditions, thereby improving the stability and reliability of the device; the environmental impact coefficient can help determine the effectiveness of signal propagation under different environmental conditions. It can guide the adoption of more appropriate signal transmission strategies under adverse weather or complex terrain conditions to ensure reliable data transmission; it can help researchers and decision-makers better understand the impact of different environmental factors on the ecosystem and climate change, and support the formulation of sustainable development strategies.

[0071] In this embodiment, it should be specifically noted that the steps of obtaining the vegetation coverage rate from the vegetation coverage image are as follows:

[0072] Preprocess the vegetation coverage image. The preprocessing includes atmospheric correction and geometric correction. The atmospheric correction can remove the atmospheric influence and ensure the accuracy of the ground object reflectivity. The geometric correction can ensure the geometric alignment of the image and eliminate geometric distortion caused by reasons such as satellite orbits and terrain;

[0073] Extract the reflectance data of the red light band and the near-infrared band from the preprocessed vegetation cover image, and calculate the normalized difference vegetation index (NDVI) value for each pixel through the NDVI formula. The value range is between -1 and 1;

[0074] According to the threshold of the NDVI, classify the image into vegetation areas and non-vegetation areas. Usually, pixels with an NDVI greater than 0.2 are used as vegetation areas, and areas below this value are considered non-vegetation, and generate a vegetation cover map;

[0075] Count the number of pixels in the vegetation area and the total number of pixels, and calculate the vegetation coverage rate. The calculation formula is , where vc represents the vegetation coverage rate, num v represents the number of pixels in the vegetation area, num 总 represents the total number of pixels.

[0076] Satellite remote sensing systems can cover large areas of the earth's surface and are not affected by ground obstacles. This makes it possible to obtain vegetation information over a large area in a short time, which is suitable for large-scale ecological monitoring and land use planning; satellites can regularly obtain image data, reflecting vegetation changes in real-time or near real-time. This high timeliness makes the monitoring of vegetation more flexible and facilitates timely response to environmental changes; modern satellite remote sensing systems can provide high-resolution images, which can clearly capture the spatial distribution characteristics of vegetation. This helps to accurately calculate the vegetation coverage rate and provide more detailed environmental information; satellite remote sensing technology is usually equipped with multi-spectral or hyperspectral sensors, which can obtain data in different bands. These data can help identify vegetation types, health conditions, and growth situations, thereby improving the accuracy of the vegetation coverage rate.

[0077] Satellite remote sensing systems can provide long-term accumulation of historical data, facilitating time series analysis and observing the change trend of the vegetation coverage rate. This is very important for ecological restoration, climate change research, and environmental management; compared with traditional ground survey methods, satellite remote sensing greatly reduces the investment of manpower and time. By automatically processing and analyzing satellite images, vegetation coverage rate data can be obtained quickly, reducing the survey cost.

[0078] Step 4: Obtain the current signal strength, signal-to-noise ratio, and packet loss rate, and calculate the signal instability coefficient;

[0079] In this embodiment, it should be specifically noted that the step of calculating the signal instability coefficient is as follows:

[0080] Obtain the current signal strength through the device;

[0081] Obtain the signal-to-noise ratio (in dB) through a signal measurement tool, which represents the ratio between the effectiveness of the signal and the noise;

[0082] Monitor the ratio of the number of lost data packets to the total number of transmitted data packets during the data transmission process to obtain the packet loss rate;

[0083] Calculate the signal instability coefficient based on the signal strength, signal-to-noise ratio, and packet loss rate. The calculation formula is , where SI represents the signal instability coefficient, RSSI represents the signal strength, SNR represents the signal-to-noise ratio, and DBL represents the packet loss rate. reflects the influence of the signal strength. The lower the RSSI value, the higher the signal instability; simulates the non-linear influence of the signal-to-noise ratio. The lower the signal-to-noise ratio, the increase in signal instability; is used to exacerbate the influence of a high packet loss rate on signal instability, especially when the packet loss rate is high, its influence increases significantly; further reflects the interaction between the packet loss rate and the signal-to-noise ratio. When the packet loss rate rises and the signal-to-noise ratio drops, the signal instability coefficient increases sharply.

[0084] Step 5: Calculate the waiting index based on the terrain complexity coefficient, environmental impact coefficient, and signal instability coefficient. The calculation formula is , where WF represents the waiting index, TI represents the terrain complexity coefficient. The terrain complexity coefficient quantifies the complexity of the terrain by evaluating terrain features such as slope, undulation, and the number of obstacles, while the waiting index reflects the delay and waiting time of the device when performing tasks or receiving signals. When the terrain complexity coefficient increases, it means the device faces more complex environmental conditions, which may cause signal propagation obstruction, unstable connections, or increased computational resource requirements, resulting in longer waiting times. It emphasizes that when conducting field telemetry and signal transmission, it is necessary to fully consider terrain factors to optimize the performance and reliability of the device. Therefore, understanding the proportional relationship between the terrain complexity coefficient and the waiting index helps improve system design, enhance communication and data processing efficiency, and ensure more effective monitoring and control in a changing natural environment. EF represents the environmental impact coefficient. When the environmental impact coefficient increases, the device may encounter more interference and obstacles when performing operations or receiving signals, leading to an increase in the waiting index, indicating that the device needs more time to complete tasks or respond to instructions. This relationship highlights that when designing and deploying field telemetry devices, it is necessary to consider the impact of environmental factors on system performance. It can help optimize device configuration, enhance its anti-interference ability and adaptability to achieve more efficient data acquisition and processing. In addition, in practical applications, if the change of the environmental impact coefficient can be predicted in advance, corresponding preventive measures can be taken to reduce the waiting time and improve the reliability and efficiency of the overall system. SI represents the signal instability coefficient. When the signal instability coefficient increases, it indicates that the device may face more interference and connection interruptions when receiving and transmitting data, which will cause delays when the device processes information or executes instructions, resulting in an increase in the waiting index. It emphasizes the importance of signal quality to device performance in wireless communication and remote monitoring systems. To ensure the efficient operation of the system, engineers and system designers need to pay attention to the signal instability coefficient and take measures such as optimizing the signal transmission path, enhancing signal amplifiers, or adjusting communication protocols to reduce signal instability and thus reduce the waiting time. This understanding helps improve the reliability and response speed of the system, especially in complex or interference-rich environments. a1, a2, and a3 represent the weight coefficients of the terrain complexity coefficient, the environmental impact coefficient, and the signal instability coefficient, respectively, and , the specific values of a1, a2, and a3 are determined by professionals according to the actual situation. For example, a1, a2, and a3 can be 0.4, 0.2, and 0.4;

[0085] Step 6: Control data acquisition and transmission according to the waiting index.

[0086] In this embodiment, it should be specifically noted that the step of controlling data acquisition and transmission according to the waiting index is as follows:

[0087] Compare the waiting index with the waiting threshold. If the waiting index is less than the waiting threshold, it is determined that the current environment is good and data collection and transmission can be carried out. If the waiting index is greater than the waiting threshold, it is determined that the current environment is poor, triggering the sleep state and no longer performing data collection and transmission until the waiting index is less than the waiting threshold and data collection and transmission are restarted.

[0088] Triggering the sleep state can significantly reduce the power consumption of the device and extend the battery life. This is particularly important for field telemetry devices and Internet of Things sensors because these devices usually operate in environments where it is difficult to obtain power; by monitoring the waiting index, the frequency of data collection and transmission can be dynamically adjusted. When the waiting index is high, the device can choose to enter the sleep state, reducing unnecessary data processing and communication, thereby improving the overall efficiency of the system; in the case of network congestion or poor signal, the device entering the sleep state can avoid unnecessary communication attempts, reduce data conflicts and packet loss phenomena, and improve the success rate of data transmission.

[0089] The device can wake up when the network conditions are good, centrally perform data collection and transmission, and ensure the real-time and accuracy of the data. This strategy can optimize the timing of data transmission and improve the effectiveness of information; reducing the running time of the device in the high-load state can reduce the wear and failure rate of the device, thereby extending the service life of the device; in the sleep state, the device does not occupy network resources, and other devices may have better signal quality when transmitting data, thereby improving the performance of the entire network.

[0090] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0091] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or replacements, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A low-power management method for field telemetry equipment based on multiple channels, characterized in that, It includes the following steps: Step 1: Obtain the digital elevation model data of the area where the device collects information through a remote sensing satellite system, and calculate the terrain complexity coefficient based on the digital elevation model data; Step 2: Set up environmental sensors on the device. The environmental sensors include a temperature sensor and a humidity sensor, and collect the temperature data and humidity data of the environment where the device is located in real time through the environmental sensors; Step 3: Obtain the vegetation cover image of the area where the device is located through a satellite remote sensing system, and obtain the environmental impact coefficient through the temperature data, humidity data, and vegetation cover image; Step 4: Obtain the current signal strength, signal-to-noise ratio, and packet loss rate, and calculate the signal instability coefficient; Step 5: Calculate the waiting index based on the terrain complexity coefficient, environmental impact coefficient, and signal instability coefficient. The calculation formula is , where WF represents the waiting index, TI represents the terrain complexity coefficient, EF represents the environmental impact coefficient, SI represents the signal instability coefficient, and a1, a2, and a3 represent the weight coefficients of the terrain complexity coefficient, environmental impact coefficient, and signal instability coefficient; Step 6: Control data collection and transmission according to the waiting index; The step of calculating the terrain complexity coefficient based on the digital elevation model data is as follows: Obtain the elevation difference between adjacent grids and the grid resolution from digital elevation model data, and calculate the slope value for each grid point. The calculation formula is , where S represents the slope value, is the height difference, is the horizontal distance; Obtain the maximum and minimum elevations in the raster from digital elevation model data, and calculate the height change value for each raster point. The calculation formula is , where E represents the height change value, represents the maximum elevation, is the minimum elevation, and a is the area of a raster; Obtain the actual area of the ground surface in the grid from digital elevation model data. The actual area includes the terrain undulation and the horizontal projected area in the grid. Calculate the terrain roughness value for each grid point, and its calculation formula is , where R represents the terrain roughness value, TA represents the actual area of the ground surface, and PA represents the horizontal projected area; The terrain complexity coefficient is calculated based on the slope value, height change value, and terrain roughness value, and its calculation formula is , where TI represents the terrain complexity coefficient, S i represents the slope value of the i-th grid point, R i represents the roughness value of the i-th grid point, E i represents the elevation change value of the i-th grid point, and n is the total number of grid points; The step of obtaining the environmental impact coefficient through the temperature data, humidity data, and vegetation cover image is as follows: Obtain the optimal humidity data and optimal temperature data during the operation of the device, and calculate the temperature and humidity difference degree based on the temperature data and humidity data. The calculation formula is , where ws represents the temperature and humidity difference degree, wd represents the temperature data, wd’ represents the optimal temperature data, sd represents the humidity data, and sd’ represents the optimal humidity data; Obtain the vegetation coverage rate based on the vegetation cover image; The environmental impact coefficient is calculated through the degree of temperature and humidity difference and the vegetation coverage rate, and its calculation formula is , where EF represents the environmental impact coefficient, ws represents the degree of temperature and humidity difference, and vc represents the vegetation coverage rate.

2. The low-power management method for field telemetry devices based on multiple channels according to claim 1, characterized in that: The digital elevation model data includes elevation values, spatial resolution, geographic coordinates, and the actual surface area and vertical area; 3. The low-power management method for field telemetry devices based on multiple channels according to claim 1, characterized in that: The step of collecting the temperature data and humidity data of the environment where the device is located in real time through the environmental sensors is as follows: Select the temperature sensor and humidity sensor, install the temperature and humidity sensors on the device, connect the sensors to the control system of the device, and provide power; After installing the sensors, use a standard thermometer and hygrometer to compare the readings of the sensors for preliminary calibration; According to the sensor type, develop using the corresponding driver or library, set a timer so that the device can collect temperature and humidity data at fixed time intervals; Store the collected temperature and humidity data in the local storage of the device; 4. The low-power management method for a multi-channel field telemetry device according to claim 1, characterized in that: The step of obtaining the vegetation coverage rate based on the vegetation cover image is as follows: Preprocess the vegetation cover image. The preprocessing includes atmospheric correction and geometric correction; Extract the reflectance data of the red light band and near-infrared band from the preprocessed vegetation cover image, and calculate the normalized difference vegetation index value of each pixel through the normalized difference vegetation index formula. The numerical range is between -1 and 1; Determine the threshold of the normalized difference vegetation index, classify the image into vegetation areas and non-vegetation areas, and generate a vegetation cover map; The number of pixels in the vegetation area and the total number of pixels are counted, and the vegetation coverage rate is calculated. The calculation formula is , where vc represents the vegetation coverage rate, and num v represents the number of pixels in the vegetation area, and num 总 represents the total number of pixels.

5. The low-power management method for a multi-channel field telemetry device according to claim 1, characterized in that: The step of calculating the signal instability coefficient is as follows: Obtain the current signal strength through the device; Obtain the signal-to-noise ratio through a signal measurement tool; Collect the ratio of the number of lost data packets to the total number of sent data packets during the monitoring data transmission process as the packet loss rate; The signal instability coefficient is calculated based on the signal strength, signal-to-noise ratio, and packet loss rate. The calculation formula is , where SI represents the signal instability coefficient, RSSI represents the signal strength, SNR represents the signal-to-noise ratio, and DBL represents the packet loss rate.

6. The low-power management method for field telemetry equipment based on multiple channels according to claim 1, characterized in that: The step of controlling data collection and transmission according to the waiting index is as follows: Compare the waiting index with the waiting threshold. If the waiting index is less than the waiting threshold, it is determined that the current environment is good and data collection and transmission can be carried out. If the waiting index is greater than the waiting threshold, it is determined that the current environment is poor, trigger the sleep state, and no longer carry out data collection and transmission until the waiting index is less than the waiting threshold, and then resume data collection and transmission.

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