Multi-channel-based low-power-consumption management method for field telemetering equipment

Abstract

The invention relates to the field of equipment data management, and discloses a multi-channel-based low-power-consumption management method for field telemetering equipment, which is used for solving the problem that the equipment consumes unnecessary power when the environment is unfavorable for equipment data acquisition and transmission, and comprises the following steps of: acquiring digital elevation model data of an information area; an environment sensor is arranged on the equipment, environment data of the environment where the equipment is located is collected in real time through the environment sensor, a vegetation coverage image of the area where the equipment is located is obtained, and an environment influence coefficient is obtained through temperature data, humidity data and the vegetation coverage image; according to the method, the current signal intensity, the signal-to-noise ratio and the packet loss rate are acquired, the signal instability coefficient is calculated, the waiting index is calculated according to the terrain complexity coefficient, the environment influence coefficient and the signal instability coefficient, and data acquisition and transmission control is performed according to the waiting index, so that the management efficiency of equipment power consumption is effectively improved, and unnecessary power consumption is reduced.

Description

Technical Field

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

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

[0003] Existing low-power management methods for telemetry equipment usually judge the operating status of the equipment based on environmental changes and adjust the working mode of the sensor to achieve low-power management of the equipment. However, if the field telemetry equipment collection environment is not conducive to data collection and transmission, power consumption management is still performed based on the operating status of the equipment, which may cause the equipment 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 consumption management method for field telemetry equipment based on multiple channels to solve the problems existing in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The low power consumption management method of field telemetry equipment based on multiple channels includes the following steps: Step 1: Obtain digital elevation model data of the equipment information collection area through the remote sensing satellite system, and calculate the terrain complexity coefficient based on the digital elevation model data; Step 2: Setting an environmental sensor on the device, wherein the environmental sensor includes a temperature sensor and a humidity sensor, and collecting temperature data and humidity data of the environment in which the device is located in real time through the environmental sensor; Step 3: Obtain the vegetation coverage image of the area where the equipment is located through the satellite remote sensing system, and obtain the environmental impact coefficient through temperature data, humidity data and vegetation coverage 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, a1, a2, 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.

[0007] Preferably, the digital elevation model data includes elevation value, spatial resolution, geographic coordinates, and actual surface area and vertical area.

[0008] Preferably, the step of calculating the terrain complexity coefficient according to the digital elevation model data is: The elevation difference and grid resolution of adjacent grids are obtained from the digital elevation model data, and the slope value is calculated for each grid point. The calculation formula is: , where S represents the slope value, is the height difference, is the horizontal distance; Get 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, Expressed as the maximum altitude, is the minimum altitude, a is the area of ​​a grid; The actual area of ​​the ground surface in the grid is obtained from the digital elevation model data. The actual area includes the terrain undulation and the horizontal projection area in the grid. The terrain roughness value is calculated for each grid point. The calculation formula is: , where R represents the terrain roughness value, TA represents the actual area of ​​the surface, and PA represents the horizontal projection area; The terrain complexity coefficient is calculated based on the slope value, height change value and terrain roughness value. The calculation formula is: , where TI is the terrain complexity coefficient, S i Represented as the slope value of the i-th grid point, R i Expressed as the roughness value of the i-th grid point, E i It is expressed as the elevation change value of the i-th grid point, and n is the total number of grid points.

[0009] Preferably, the step of collecting temperature data and humidity data of the environment in which the device is located in real time through an environmental sensor is: Select temperature sensors and humidity sensors, install the temperature and humidity sensors on the equipment, connect the sensors to the equipment's control system, and provide power; After installing the sensor, perform a preliminary calibration by comparing the sensor readings with a standard thermometer and hygrometer; Depending on the sensor type, use the corresponding driver or library for development and set the timer so that the device can collect temperature and humidity data at fixed time intervals; The collected temperature and humidity data are stored in the local storage of the device.

[0010] Preferably, the step of obtaining the environmental impact coefficient through temperature data, humidity data and vegetation coverage image is: Get the best humidity data and temperature data when the equipment is running, and calculate the temperature and humidity difference based on the temperature and humidity data. The calculation formula is: , where ws represents the degree of temperature and humidity difference, wd represents the temperature data, wd' represents the optimal temperature data, sd represents the humidity data, and sd' represents the optimal temperature data; Obtaining vegetation coverage rate according to the vegetation coverage image; The environmental impact coefficient is calculated by the temperature and humidity difference and the vegetation coverage rate. The 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.

[0011] Preferably, the step of obtaining the vegetation coverage rate according to the vegetation coverage image is: Preprocessing the vegetation coverage image, wherein the preprocessing includes atmospheric correction and geometric correction; Extract the reflectance data of the red light band and the near infrared band from the preprocessed vegetation cover image, and calculate the normalized vegetation index value of each pixel by the normalized vegetation index formula, with the value range between -1 and 1; The threshold of the normalized vegetation index is used to classify the image into vegetation areas and non-vegetation areas, and a vegetation coverage map is generated; The number of pixels in the vegetation area and the total number of pixels are counted to calculate the vegetation coverage rate. The calculation formula is: , where vc represents the vegetation coverage rate, num v Represented as the number of pixels in the vegetation area, num 总 Expressed as total number of pixels.

[0012] Preferably, the step of calculating the signal instability coefficient is: Get the current signal strength through the device; Get the signal-to-noise ratio through signal measurement tools; The ratio of the number of data packets lost during the monitoring data transmission to the total number of data packets sent is collected as the packet loss rate; The signal instability coefficient is calculated based on the signal strength, signal-to-noise ratio and packet loss rate, and its 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.

[0013] Preferably, the control steps of collecting and transmitting data according to the waiting index are: The waiting index is compared with the waiting threshold. If the waiting index is less than the waiting threshold, the current environment is determined to be good and data collection and transmission can be performed. If the waiting index is greater than the waiting threshold, the current environment is determined to be 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 performed again.

[0014] Technical effects and advantages of the present invention: Collect digital elevation model data of the information area and calculate the terrain complexity coefficient. Set environmental sensors on the equipment to collect environmental data of the equipment in real time through the environmental sensors, obtain the vegetation coverage image of the equipment area, and obtain the environmental impact coefficient through temperature data, humidity data and vegetation coverage image. Get 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 based on the waiting index, which effectively improves the management efficiency of equipment power consumption and reduces unnecessary power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The low-power consumption management method for multi-channel field telemetry equipment involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] The present invention provides a low power consumption management method for field telemetry equipment based on multiple channels, comprising the following steps: Step 1: Acquire digital elevation model data of the area where the equipment collects information through a remote sensing satellite system. The digital elevation model data is used to represent raster data of the surface height, and each pixel represents the surface altitude of a fixed area. The terrain complexity coefficient is calculated based on the digital elevation model data; The remote sensing satellite system is a system that uses sensors on board satellites to conduct long-distance observation and data collection of the earth's surface. The system uses electromagnetic waves of different bands, such as visible light, infrared, and microwaves, to obtain images and related information of the earth's surface from space. It is widely used in environmental monitoring, resource exploration, agricultural assessment, meteorological observation, and disaster warning. The satellite remote sensing system can provide a wide range of surface information around the clock and in all directions, helping scientists and decision makers analyze the impact of natural phenomena and human activities. The digital elevation model data includes elevation values, spatial resolution, geographic coordinates, horizontal and vertical reference planes, and actual surface area and vertical area; Each pixel or raster cell contains an elevation value, usually in meters or feet, representing the elevation of the ground surface at that point relative to sea level; 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; 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), ensuring that the digital elevation model data is accurately located on the earth's surface; The horizontal datum is used to locate the geographic coordinate system of the data and defines the location of the data on the earth's surface. The vertical datum is used to define the reference surface of the elevation data, which is usually the sea level.

[0018] In this embodiment, it should be specifically explained that the step of calculating the terrain complexity coefficient according to the digital elevation model data is: The elevation difference and grid resolution of adjacent grids are obtained from the digital elevation model data, and the slope value is calculated 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, i.e., grid resolution. The larger the slope, the more drastic the terrain undulation in the area and the higher the complexity. Get 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, Expressed as the maximum altitude, is the minimum elevation, a is the area of ​​a grid, and areas with large elevation changes usually indicate high terrain complexity; The actual area of ​​the ground surface in the grid is obtained from the digital elevation model data. The actual area includes the terrain undulation and the horizontal projection area in the grid. The terrain roughness value is calculated 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. The greater the roughness, the more complex the terrain; The terrain complexity coefficient is calculated based on the slope value, height change value and terrain roughness value. The calculation formula is: , where TI is the terrain complexity coefficient, S i Represented as the slope value of the i-th grid point, R i Expressed as the roughness value of the i-th grid point, E i It is expressed as the elevation change value of the i-th grid point, and n is the total number of grid points.

[0019] By comprehensively considering the slope, height change and terrain roughness, the terrain characteristics can be quantified more comprehensively and a comprehensive terrain complexity coefficient can be obtained. 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 high terrain complexity coefficients may cause signal attenuation, interference and unstable connections. By analyzing this coefficient, engineers can choose more suitable equipment locations and reduce obstacles to signal propagation; based on the calculated terrain complexity coefficient, the quality of the signal propagation path can be better evaluated. By identifying areas with high-complexity terrain, corresponding technical measures can be taken in advance, such as increasing signal transmission points and using stronger signal enhancement equipment, to ensure signal stability and reliability.

[0020] Step 2: Setting an environmental sensor on the device, wherein the environmental sensor includes a temperature sensor and a humidity sensor, and collecting temperature data and humidity data of the environment in which the device is located in real time through the environmental sensor; In this embodiment, it should be specifically explained that the step of collecting the temperature data and humidity data of the environment in which the device is located in real time through the environmental sensor is as follows: According to the working environment and requirements of the equipment, select suitable temperature sensors and humidity sensors, install the temperature and humidity sensors in the appropriate position of the equipment, ensure that the sensors are exposed to the real environment and are not disturbed by the heat or moisture of the equipment itself, correctly connect the sensors to the control system of the equipment (such as microcontroller, data acquisition module, etc.), and provide power. If the sensor is connected to the equipment through wireless communication, configure the wireless communication module; After installing the sensor, perform a preliminary calibration to 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 sensor readings to ensure their accuracy; Write code in the device's control system to read temperature and humidity data in real time. Depending on the sensor type, use the corresponding driver or library for development and 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.); The collected temperature and humidity data are stored in the local storage of the device (such as SD card, EEPROM, etc.) for subsequent analysis.

[0021] Step 3: Obtain the vegetation coverage image of the area where the equipment is located through the satellite remote sensing system, and obtain the environmental impact coefficient through temperature data, humidity data and vegetation coverage image; In this embodiment, it should be specifically explained that the step of obtaining the environmental impact coefficient through temperature data, humidity data and vegetation coverage image is: Get the best humidity data and temperature data when the equipment is running, and calculate the temperature and humidity difference based on the temperature and humidity data. The calculation formula is: , where ws represents the degree of temperature and humidity difference, wd represents the temperature data, wd' represents the optimal temperature data, sd represents the humidity data, and sd' represents the optimal temperature data; The vegetation coverage rate is obtained from the vegetation coverage image. Dense vegetation will absorb and reflect radio waves, weaken the signal, and lead to poor network coverage. In forests 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 hindered; The environmental impact coefficient is calculated by the temperature and humidity difference and the vegetation coverage rate. The 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.

[0022] Taking into account the three key environmental factors of temperature, humidity and vegetation coverage, a comprehensive indicator is provided 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 devices may face under different environmental conditions. This allows the design and deployment of equipment to be optimized according to actual environmental conditions, thereby improving the stability and reliability of the equipment; 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 in severe 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 ecosystems and climate change, and support the formulation of sustainable development strategies.

[0023] In this embodiment, it should be specifically explained that the step of obtaining the vegetation coverage rate according to the vegetation coverage image is: Preprocessing the vegetation cover image, the preprocessing includes atmospheric correction and geometric correction. The atmospheric correction can remove the atmospheric influence and ensure the accuracy of the reflectivity of the ground objects. The geometric correction can ensure the geometric alignment of the image and eliminate the geometric distortion caused by satellite orbit, terrain and other reasons. Extract the reflectance data of the red band and the near-infrared band from the preprocessed vegetation cover image, and calculate the normalized vegetation index value of each pixel using the normalized vegetation index formula, with the value range between -1 and 1; According to the threshold of the normalized vegetation index, the image is classified into vegetation area and non-vegetation area. Usually, pixels with a normalized vegetation index greater than 0.2 are used as vegetation areas, and areas below this value are considered to be non-vegetation, and a vegetation coverage map is generated; The number of pixels in the vegetation area and the total number of pixels are counted to calculate the vegetation coverage rate. The calculation formula is: , where vc represents the vegetation coverage rate, num v Represented as the number of pixels in the vegetation area, num 总 Expressed as total number of pixels.

[0024] Satellite remote sensing systems can cover large areas of the surface and are not affected by ground obstacles. This makes it possible to obtain vegetation information over a large area in a short period of time, which is suitable for large-scale ecological monitoring and land use planning; satellites can regularly obtain image data to reflect vegetation changes in real time or near real time. This high timeliness makes vegetation monitoring more flexible and facilitates timely response to environmental changes; modern satellite remote sensing systems can provide high-resolution images that can clearly capture the spatial distribution characteristics of vegetation. This helps to accurately calculate vegetation coverage and provide more detailed environmental information; satellite remote sensing technology is usually equipped with multispectral or hyperspectral sensors that can obtain data in different bands. These data can help identify vegetation types, health conditions, and growth conditions, thereby improving the accuracy of vegetation coverage.

[0025] Satellite remote sensing systems can provide long-term accumulation of historical data, which is convenient for time series analysis and observation of vegetation coverage trends. 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. Through automated processing and analysis of satellite images, vegetation coverage data can be quickly obtained, reducing survey costs.

[0026] Step 4: Obtain the current signal strength, signal-to-noise ratio, and packet loss rate, and calculate the signal instability coefficient; In this embodiment, it should be specifically explained that the step of calculating the signal instability coefficient is: Get the current signal strength through the device; The signal-to-noise ratio (in dB) is obtained through the signal measurement tool, which represents the ratio between the effectiveness of the signal and the noise; Monitor the ratio of the number of data packets lost during data transmission to the total number of data packets sent to obtain the packet loss rate; The signal instability coefficient is calculated based on the signal strength, signal-to-noise ratio and packet loss rate, and its 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. It reflects the influence of signal strength. The lower the RSSI value, the higher the signal instability. The nonlinear effect of the signal-to-noise ratio was simulated, and the lower the signal-to-noise ratio, the greater the signal instability; It is used to aggravate the impact of high packet loss rate on signal instability, especially when the packet loss rate is high, the impact is significantly increased; This further demonstrates the interaction between packet loss rate and signal-to-noise ratio. When the packet loss rate increases and the signal-to-noise ratio decreases, the signal instability coefficient increases dramatically.

[0027] 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 stands for the waiting index and TI stands for the terrain complexity factor. The terrain complexity factor quantifies the complexity of the terrain by evaluating terrain features such as slope, undulations, 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 factor increases, it means that the device faces more complex environmental conditions, which may lead to blocked signal propagation, unstable connections, or increased computing resource requirements, resulting in longer waiting times. It emphasizes that when performing field telemetry and signal transmission, the terrain factors need to be fully considered in order to optimize the performance and reliability of the equipment. Therefore, understanding the proportional relationship between the terrain complexity factor and the waiting index will help improve system design, enhance communication and data processing efficiency, and ensure more effective monitoring and control in a changing natural environment. EF stands for the environmental impact factor. When the environmental impact factor increases, the device may encounter more interference and obstacles when performing operations or receiving signals, resulting in 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 equipment, it is necessary to consider the impact of environmental factors on system performance. It can help optimize the configuration of equipment, enhance its anti-interference ability and adaptability, so as to achieve more efficient data collection and processing. In addition, in practical applications, if the changes in the environmental impact coefficient can be predicted in advance, corresponding preventive measures can be taken to reduce waiting time and improve the reliability and efficiency of the overall system. SI is expressed as the signal instability coefficient. When the signal instability coefficient increases, it indicates that the device may face more interference and connection interruption when receiving and transmitting data, which will cause the device to have delays in processing information or executing instructions, thereby increasing the waiting index. The importance of signal quality to equipment performance in wireless communication and remote monitoring systems is emphasized. In order 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 the signal amplifier or adjusting the communication protocol to reduce signal instability and thus reduce waiting time. This understanding helps to improve the reliability and response speed of the system, especially in complex environments or when there is a lot of interference. a1, a2, and a3 are expressed as weight coefficients of the terrain complexity coefficient, environmental impact coefficient, and signal instability coefficient, and , the specific values ​​of a1, a2, and a3 are determined by professionals according to actual conditions. For example, a1, a2, and a3 can be 0.4, 0.2, and 0.4; Step 6: Control data collection and transmission according to the waiting index.

[0028] In this embodiment, it should be specifically explained that the control steps of collecting and transmitting data according to the waiting index are: The waiting index is compared with the waiting threshold. If the waiting index is less than the waiting threshold, the current environment is determined to be good and data collection and transmission can be performed. If the waiting index is greater than the waiting threshold, the current environment is determined to be 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 performed again.

[0029] Triggering the sleep state can significantly reduce the power consumption of the device and extend the battery life. This is especially important for field telemetry equipment and IoT sensors, as these devices usually operate in environments where power is difficult to obtain. 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 to reduce unnecessary data processing and communication, thereby improving the overall efficiency of the system. In the case of network congestion or poor signal, the device enters the sleep state to avoid unnecessary communication attempts, reduce data conflicts and packet loss, and improve the success rate of data transmission.

[0030] The device can wake up when the network conditions are good, collect and transmit data in a centralized manner, 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; reduce the operating time of the device under high load, reduce the wear and failure rate of the device, and thus extend the service life of the device; in the dormant state, the device will not occupy network resources, and other devices may have better signal quality when transmitting data, thereby improving the performance of the entire network.

[0031] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0032] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A low power consumption management method for field telemetry equipment based on multiple channels, characterized in that: The following steps are involved: Step 1: Obtain digital elevation model data of the equipment information collection area through the remote sensing satellite system, and calculate the terrain complexity coefficient based on the digital elevation model data; Step 2: Setting an environmental sensor on the device, wherein the environmental sensor includes a temperature sensor and a humidity sensor, and collecting temperature data and humidity data of the environment in which the device is located in real time through the environmental sensor; Step 3: Obtain the vegetation coverage image of the area where the equipment is located through the satellite remote sensing system, and obtain the environmental impact coefficient through temperature data, humidity data and vegetation coverage 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, a1, a2, 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.

2. The low power consumption management method for field telemetry equipment based on multi-channel according to claim 1, characterized in that: The digital elevation model data includes elevation value, spatial resolution, geographic coordinates, and actual surface area and vertical area.

3. The low power consumption management method for field telemetry equipment based on multi-channel according to claim 1, characterized in that: The steps of calculating the terrain complexity coefficient according to the digital elevation model data are as follows: The elevation difference and grid resolution of adjacent grids are obtained from the digital elevation model data, and the slope value is calculated for each grid point. The calculation formula is: , where S represents the slope value, is the height difference, is the horizontal distance; Get 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, Expressed as the maximum altitude, is the minimum altitude, a is the area of ​​a grid; The actual area of ​​the ground surface in the grid is obtained from the digital elevation model data. The actual area includes the terrain undulation and the horizontal projection area in the grid. The terrain roughness value is calculated for each grid point. The calculation formula is: , where R represents the terrain roughness value, TA represents the actual area of ​​the surface, and PA represents the horizontal projection area; The terrain complexity coefficient is calculated based on the slope value, height change value and terrain roughness value. The calculation formula is: , where TI is the terrain complexity coefficient, S i Represented as the slope value of the i-th grid point, R i Expressed as the roughness value of the i-th grid point, E i It is expressed as the elevation change value of the i-th grid point, and n is the total number of grid points.

4. The low power consumption management method for field telemetry equipment based on multi-channel according to claim 1, characterized in that: The step of collecting the temperature data and humidity data of the environment in which the device is located in real time through the environmental sensor is: Select temperature sensors and humidity sensors, install the temperature and humidity sensors on the equipment, connect the sensors to the equipment's control system, and provide power; After installing the sensor, perform a preliminary calibration by comparing the sensor readings with a standard thermometer and hygrometer; Depending on the sensor type, use the corresponding driver or library for development and set the timer so that the device can collect temperature and humidity data at fixed time intervals; The collected temperature and humidity data are stored in the local storage of the device.

5. The low power consumption management method for field telemetry equipment based on multi-channel according to claim 1, characterized in that: The steps of obtaining the environmental impact coefficient through temperature data, humidity data and vegetation coverage image are as follows: Get the best humidity data and temperature data when the equipment is running, and calculate the temperature and humidity difference based on the temperature and humidity data. The calculation formula is: , where ws represents the degree of temperature and humidity difference, wd represents the temperature data, wd' represents the optimal temperature data, sd represents the humidity data, and sd' represents the optimal temperature data; The vegetation coverage rate is obtained according to the vegetation coverage image; The environmental impact coefficient is calculated by the temperature and humidity difference and the vegetation coverage rate. The 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.

6. The low power consumption management method for field telemetry equipment based on multiple channels according to claim 5 is characterized in that: The step of obtaining the vegetation coverage rate according to the vegetation coverage image is as follows: Preprocessing the vegetation coverage image, wherein the preprocessing includes atmospheric correction and geometric correction; Extract the reflectance data of the red light band and the near infrared band from the preprocessed vegetation cover image, and calculate the normalized vegetation index value of each pixel by the normalized vegetation index formula, with the value range between -1 and 1; The threshold of the normalized vegetation index is used to classify the image into vegetation areas and non-vegetation areas, and a vegetation coverage map is generated; The number of pixels in the vegetation area and the total number of pixels are counted to calculate the vegetation coverage rate. The calculation formula is: , where vc represents the vegetation coverage rate, num v Represented as the number of pixels in the vegetation area, num 总 Expressed as total number of pixels.

7. The low power consumption management method for field telemetry equipment based on multiple channels according to claim 1, characterized in that: The steps of calculating the signal instability coefficient are: Get the current signal strength through the device; Get the signal-to-noise ratio through signal measurement tools; The ratio of the number of data packets lost during the monitoring data transmission to the total number of data packets sent is collected as the packet loss rate; The signal instability coefficient is calculated based on the signal strength, signal-to-noise ratio and packet loss rate, and its 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.

8. The low power consumption management method for field telemetry equipment based on multiple channels according to claim 1, characterized in that: The control steps for data collection and transmission according to the waiting index are: The waiting index is compared with the waiting threshold. If the waiting index is less than the waiting threshold, the current environment is determined to be good and data collection and transmission can be performed. If the waiting index is greater than the waiting threshold, the current environment is determined to be 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 performed again.

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