Intelligent collection system for accumulated snow data in glacial frozen soil desert area

Abstract

The invention relates to the technical field of data acquisition, in particular to an intelligent acquisition system for accumulated snow data in a glacier, frozen soil or desert area, an ultrasonic snow depth detection module comprises a plurality of ultrasonic snow depth detection sensors used for measuring the accumulated snow depth in the glacier, frozen soil or desert area; the temperature compensation and transmitting power control module is used for monitoring the ambient temperature in real time through a temperature sensor, and calculating the required transmitting power gain by using the relationship between the temperature and the ultrasonic propagation speed and the attenuation relationship; and a low-delay data transmission module for adjusting the transmitting power of the ultrasonic snow depth detection sensor, wherein the low-delay data transmission module comprises an ad hoc network communication protocol. According to the invention, the signal propagation distance and the echo quality are effectively improved, the precision and stability of snow depth measurement in an extreme environment are ensured by the temperature compensation design, and the method is suitable for severe conditions such as glacier, frozen soil and desert areas.

Description

Technical Field

[0001] The present invention relates to the technical field of data collection, and in particular to an intelligent collection system for snow accumulation data in glacier frozen soil desert areas. Background Art

[0002] Snow depth data in glacier, permafrost and desert areas are of great significance for the study of global climate change, water resources management and ecological environment monitoring. The extreme environmental characteristics of these areas, such as severe cold, windy sand, strong radiation and complex terrain, pose great challenges to traditional snow monitoring methods.

[0003] Existing snow depth measurement technologies usually adopt the following methods:

[0004] Manual measurement: Manual detection is carried out on the snow surface, and the snow depth is recorded using measuring rods or other physical tools. This method is time-consuming and labor-intensive, especially in remote glaciers and desert areas. It is extremely difficult to operate and difficult to achieve large-scale monitoring.

[0005] Fixed sensors: such as ultrasonic sensors and laser ranging sensors, which are installed on the ground or fixed brackets to monitor the snow depth in real time. Although these sensors can partially solve the limitations of manual operation, their signals are severely attenuated in low temperature environments. In particular, ultrasonic sensors are prone to reduced measurement accuracy due to increased air density and increased propagation attenuation in extremely low temperatures.

[0006] In glacier, permafrost and desert areas, due to poor signal coverage and insufficient network infrastructure, traditional wireless communication methods are less applicable in these areas, making it difficult to transmit collected data to remote servers or storage centers in real time. At the same time, existing communication technologies usually rely on central nodes and cannot cope with data interruptions caused by node failures or changes in network topology. Summary of the invention

[0007] The invention provides an intelligent collection system for snow accumulation data in glacier frozen soil desert areas.

[0008] An intelligent collection system for snow data in glacier, frozen soil and desert areas, comprising:

[0009] Ultrasonic snow depth detection module: The ultrasonic snow depth detection module includes a plurality of ultrasonic snow depth detection sensors for measuring the depth of snow in glaciers, frozen soil or desert areas;

[0010] Temperature compensation and transmission power control module: Automatically adjust the transmission power of the ultrasonic sensor according to the ambient temperature, monitor the ambient temperature in real time through the temperature sensor, and use the relationship between temperature and ultrasonic propagation speed and attenuation to calculate the required transmission power gain. Dynamically adjust the transmission power of the ultrasonic snow depth detection sensor according to the calculation results to ensure that accurate snow depth data can still be obtained in a low temperature environment;

[0011] Low-latency data transmission module: The low-latency data transmission module includes a self-organizing network communication protocol. The self-organizing network communication protocol adopts a self-organizing network topology structure. When there is no central node, point-to-point direct communication between multiple data acquisition devices automatically selects the optimal path for data transmission based on real-time network topology changes to avoid signal blockage caused by environmental obstacles (such as glacier cracks, sand dunes, permafrost, etc.).

[0012] Data acquisition equipment includes:

[0013] Ultrasonic snow depth detection sensor;

[0014] Communication module, used for data transmission between other data acquisition devices;

[0015] Temperature sensor, used to monitor ambient temperature and assist in temperature compensation control;

[0016] The processor unit is used for data preprocessing and controlling the operation of the ad hoc network communication protocol.

[0017] Optionally, the ultrasonic snow depth detection sensor is installed above the snow via a bracket (generally 5 meters from the ground when there is no snow). After installation, the distance d1 between the ultrasonic snow depth detection sensor and the ground when there is no snow is obtained in advance. After the snow accumulates, the ultrasonic snow depth detection sensor is calculated to be d2 based on the propagation time of the ultrasonic wave by emitting an ultrasonic signal and receiving the echo reflected from the snow surface. The snow depth is obtained by subtracting d2 from d1.

[0018] Optionally, the temperature compensation and transmission power control module is based on a mathematical model of the relationship between temperature, ultrasonic propagation velocity and attenuation. It calculates the required ultrasonic transmission power gain by inputting the ambient temperature collected in real time by the temperature sensor, and dynamically adjusts the transmission power of the ultrasonic snow depth detection sensor according to the calculation result. The mathematical model of the relationship between temperature, propagation velocity and attenuation is based on the nonlinear relationship between ambient temperature and ultrasonic propagation velocity, and takes into account the influence of ambient temperature on the sound wave attenuation coefficient. The influence of ambient temperature changes on signal propagation is compensated by dynamically adjusting the transmission power.

[0019] Optionally, the mathematical model of the relationship between temperature, propagation velocity and attenuation includes an ultrasonic propagation velocity model and an attenuation coefficient model.

[0020] Optionally, the ultrasonic wave propagation velocity model is expressed as:

[0021] v(T env )=v0·(1+α·(T env -T0)), where v(T env ) is the ambient temperature T env v0 is the ultrasonic propagation velocity at standard temperature T0 = 20 °C (about 343 m / s), and α is the linear coefficient of ultrasonic propagation velocity to ambient temperature change, which is about 0.6×10 -3 / ℃,T env is the current ambient temperature, T0 is the reference temperature, which is 20℃.

[0022] Optionally, the attenuation coefficient model is expressed as the attenuation coefficient α atten (T env )’s estimate:

[0023] α atten (T env )=β·(1+γ·(T env -T0)), where α atten (T env ) is the temperature T env The ultrasonic attenuation coefficient under the condition of β is the attenuation coefficient constant, which depends on the ultrasonic frequency and the ambient pressure. β can be taken as 0.1 (dB / m). γ is the proportional constant of the effect of temperature on the attenuation coefficient. γ can be taken as 0.03. env Same as T0 definition above.

[0024] Optionally, the ultrasonic transmission power gain G(T env ) is calculated as:

[0025] Among them, G(T env ) is the temperature T env The required transmit power gain, α atten (T0) and α atten (T env ) are the reference temperature T0 and the current temperature T env Ultrasonic attenuation coefficient under env ) and v0 are the ultrasonic propagation velocities at the current temperature and reference temperature respectively.

[0026] Optionally, the dynamically adjusting the transmit power specifically includes:

[0027] According to the calculated transmission power gain G(T env ), dynamically adjust the transmission power P of the ultrasonic snow depth detection sensor through the power control moduletrans (T env ), expressed as: P trans (T env )=P0·G(T env ), where P trans (T env ) is the actual transmit power at the current ambient temperature, and P0 is the initial transmit power at the standard temperature T0.

[0028] Optionally, the self-organizing network communication protocol in the low-latency data transmission module specifically includes:

[0029] Ad hoc network topology: The ad hoc network communication protocol adopts a cellular or star ad hoc network topology to achieve point-to-point direct communication between multiple data acquisition devices;

[0030] Dynamic routing selection: Through dynamic routing selection, the path priority score is calculated according to the real-time network topology changes, and the optimal path is automatically selected for data transmission based on the priority score. The data transmission path is dynamically adjusted according to the number of path hops, distance and load.

[0031] Optionally, the path priority score S(P i ) is used to select the optimal path P optimal , expressed as:

[0032] S(P i )=w1·D(P i )+w2·L(P i )+w3·H(P i ), where S(P i ) represents the path P i The smaller the value, the higher the priority. i ) represents the path P i The total distance is the sum of the physical distances between the nodes in the path, L(P i ) represents the path P i The total load of the path represents the sum of the communication loads of each node, H(P i ) represents the path P i The total number of hops indicates the number of relay nodes required from the source node to the target node. w1, w2, and w3 are weight parameters, indicating the influence of each indicator on the path selection, which can be adjusted according to actual needs.

[0033] Beneficial effects of the present invention:

[0034] The present invention solves the problem of ultrasonic signal attenuation caused by increased air density in low-temperature environments through temperature compensation and transmission power control modules, combined with temperature sensors and dynamic power regulation. The system can monitor the ambient temperature in real time and dynamically adjust the sensor's transmission power based on a mathematical model of the relationship between temperature and ultrasonic propagation speed and attenuation, effectively improving signal propagation distance and echo quality. The temperature compensation design ensures the accuracy and stability of measuring snow depth in extreme environments, and is suitable for harsh conditions such as glaciers, permafrost and desert areas.

[0035] The present invention solves the problem of data transmission in extreme environments with no central node and unstable signals by adopting cellular or star-shaped self-organizing network communication protocols. The system is based on dynamic routing selection and can automatically select the optimal path for data transmission according to real-time network topology changes, reducing communication interruptions caused by environmental obstacles (such as sand dunes, glacier cracks) or node failures. In addition, the point-to-point direct communication mechanism makes the network highly flexible when the number of nodes changes, ensuring the reliability and low latency of data transmission, and greatly improving the practicality of the collection system in remote areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 This is a schematic diagram of the functional modules of the acquisition system according to an embodiment of the present invention;

[0038] Figure 2 Schematic diagram of a data acquisition device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0040] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0041] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0042] like Figure 1-Figure 2 As shown, an intelligent collection system for snow data in glacier frozen soil desert areas includes:

[0043] Ultrasonic snow depth detection module: The ultrasonic snow depth detection module includes multiple ultrasonic snow depth detection sensors, which are used to measure the snow depth in glaciers, frozen soil or desert areas;

[0044] Temperature compensation and transmission power control module: Automatically adjust the transmission power of the ultrasonic sensor according to the ambient temperature, monitor the ambient temperature in real time through the temperature sensor, and use the relationship between temperature and ultrasonic propagation speed and attenuation to calculate the required transmission power gain. Dynamically adjust the transmission power of the ultrasonic snow depth detection sensor according to the calculation results to ensure that accurate snow depth data can still be obtained in a low temperature environment;

[0045] Low-latency data transmission module: The low-latency data transmission module includes a self-organizing network communication protocol, which solves the signal transmission problem in extreme environments such as glaciers, permafrost and desert areas. Specifically, the self-organizing network communication protocol adopts a self-organizing network topology structure. When there is no central node, multiple data acquisition devices communicate directly point-to-point. According to the real-time network topology changes, the optimal path is automatically selected for data transmission to avoid signal blockage caused by environmental obstacles (such as glacier cracks, sand dunes, permafrost, etc.).

[0046] Data collection equipment refers to a comprehensive device with an ultrasonic snow depth detection sensor and a communication module. Each data collection device collects snow depth data in glaciers, frozen soil or desert areas through an ultrasonic snow depth detection sensor, and transmits the collected data to other devices or storage centers through a communication module. Data collection equipment includes:

[0047] Ultrasonic snow depth detection sensor;

[0048] Communication module, used for data transmission between other data acquisition devices;

[0049] Temperature sensor, used to monitor ambient temperature and assist in temperature compensation control;

[0050] The processor unit is used for data preprocessing and controlling the operation of the ad hoc network communication protocol.

[0051] The data acquisition device processes and stores the data collected by the sensor and transmits the data to other devices through the self-organizing network communication protocol module. Each data acquisition device can work independently and communicate with other devices in the network, ensuring that the entire system can form a dynamic and flexible data acquisition and transmission network without a central node.

[0052] The ultrasonic snow depth detection sensor is installed above the snow through a bracket (generally 5 meters from the ground when there is no snow). After installation, the distance d1 between the ultrasonic snow depth detection sensor and the ground when there is no snow is obtained in advance. After snow accumulation, the ultrasonic snow depth detection sensor is calculated to be d2 from the snow surface according to the propagation time of the ultrasonic wave by emitting ultrasonic signals and receiving echoes reflected from the snow surface. The snow depth is obtained by subtracting d2 from d1.

[0053] The temperature compensation and transmission power control module is based on the mathematical model of the relationship between temperature, ultrasonic propagation velocity and attenuation. By inputting the ambient temperature collected in real time by the temperature sensor, the required ultrasonic transmission power gain is calculated. According to the calculation results, the transmission power of the ultrasonic snow depth detection sensor is dynamically adjusted. The mathematical model of the relationship between temperature, propagation velocity and attenuation is based on the nonlinear relationship between ambient temperature and ultrasonic propagation velocity, and takes into account the influence of ambient temperature on the sound wave attenuation coefficient. The influence of ambient temperature changes on signal propagation is compensated by dynamically adjusting the transmission power.

[0054] The mathematical model of the relationship between temperature, propagation velocity and attenuation includes an ultrasonic propagation velocity model and an attenuation coefficient model.

[0055] The propagation speed of ultrasound in air v(T env )Affected by the ambient temperature, the ultrasonic propagation velocity model is expressed as:

[0056] v(T env )=v0·(1+α·(T env -T0)), where v(T env ) is the ambient temperature T envv0 is the ultrasonic propagation velocity at standard temperature T0 = 20 °C (about 343 m / s), and α is the linear coefficient of ultrasonic propagation velocity to ambient temperature change, which is about 0.6×10 -3 / ℃,T env is the current ambient temperature, T0 is the reference temperature, which is 20℃.

[0057] The attenuation rate of ultrasound in air is related to ambient temperature, frequency and air density. The density of air increases as the ambient temperature decreases, which intensifies the attenuation of ultrasonic signals. The attenuation coefficient model is expressed as the attenuation coefficient α atten (T env )’s estimate:

[0058] α atten (T env )=β·(1+γ·(T env -T0)), where α atten (T env ) is the temperature T env The ultrasonic attenuation coefficient under the condition of β is the attenuation coefficient constant, which depends on the ultrasonic frequency and the ambient pressure. β can be taken as 0.1 (dB / m). γ is the proportional constant of the effect of temperature on the attenuation coefficient. γ can be taken as 0.03. env Same as T0 definition above.

[0059] Automatically adjust the transmission power P of the ultrasonic sensor according to the ambient temperature trans First, the transmission power gain is analyzed and calculated. The ultrasonic transmission power gain G(T env ) is calculated as:

[0060] Among them, G(T env ) is the temperature T env The required transmit power gain, α atten (T0) and α atten (T env ) are the reference temperature T0 and the current temperature T env Ultrasonic attenuation coefficient under env ) and v0 are the ultrasonic propagation velocities at the current temperature and reference temperature respectively.

[0061] Dynamically adjusting the transmit power specifically includes:

[0062] According to the calculated transmission power gain G(T env ), dynamically adjust the transmission power P of the ultrasonic snow depth detection sensor through the power control module trans (T env ), ensuring that the ultrasonic signal has sufficient propagation distance in a low temperature environment, expressed as: P trans(T env )=P0·G(T env ), where P trans (T env ) is the actual transmit power at the current ambient temperature, P0 is the initial transmit power at the standard temperature T0;

[0063] Transmit power P trans (T env ) is dynamically adjusted to ensure that even if the ultrasonic signal attenuation increases in extremely low temperature environments, the ultrasonic snow depth detection sensor can still measure effective echo signals, thereby ensuring the accuracy of the snow depth data.

[0064] The ad hoc network communication protocol in the low-latency data transmission module specifically includes:

[0065] Ad hoc network topology: Ad hoc network communication protocol adopts cellular or star ad hoc network topology to achieve point-to-point direct communication between multiple data acquisition devices;

[0066] Dynamic routing selection: Through dynamic routing selection, the path priority score is calculated according to the real-time network topology changes, and the optimal path is automatically selected for data transmission based on the priority score. The data transmission path is dynamically adjusted according to the number of path hops, distance and load to ensure the minimization of communication delay and the reliability of signal transmission.

[0067] Path priority score S(P i ) is used to select the optimal path P optimal , expressed as:

[0068] S(P i )=w1·D(P i )+w2·L(P i )+w3·H(P i ), where S(P i ) represents the path P i The smaller the value, the higher the priority. i ) represents the path P i The total distance is the sum of the physical distances between the nodes in the path, L(P i ) represents the path P i The total load of the path represents the sum of the communication loads of each node, H(P i ) represents the path P i The total number of hops represents the number of relay nodes required from the source node to the target node. w1, w2, and w3 are weight parameters, which represent the influence of each indicator on the path selection. They are adjusted according to actual needs, and w1 = 0.5, w2 = 0.3, and w3 = 0.2 are taken.

[0069] The total path distance D(Pi ) is calculated as: Among them, d(N j ,N j+1 ) represents the adjacent node N in the path j and N j+1 The physical distance between nodes is calculated by the signal strength between nodes, and n is the path P i The total number of nodes contained in;

[0070] Path load calculation: Total path load L(P i ) is calculated as: Among them, u(N j ) represents node N j The current communication usage, c(N j ) represents node N j The communication capacity represents the maximum amount of communication that a node can carry.

[0071] Path hop count calculation: Path hop count H(P i ) is the number of relay nodes from the source node to the target node in the path, defined as: H(P i ) = n-1, where n is the path P i The total number of nodes in .

[0072] The optimal path is selected among all candidate path sets {P1,P2,...,P k}, select the priority score S(P i )The smallest path is taken as the optimal path P i ∈{P1,P2,...,P k}.

[0073] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0074] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent collection system for snow data in glacier, frozen soil and desert areas, characterized in that: include: Ultrasonic snow depth detection module: The ultrasonic snow depth detection module includes a plurality of ultrasonic snow depth detection sensors for measuring the depth of snow in glaciers, frozen soil or desert areas; Temperature compensation and transmission power control module: monitors the ambient temperature in real time through the temperature sensor, and uses the relationship between temperature and ultrasonic propagation speed and attenuation to calculate the required transmission power gain, and dynamically adjusts the transmission power of the ultrasonic snow depth detection sensor according to the calculation results; Low-latency data transmission module: The low-latency data transmission module includes an ad hoc network communication protocol, which adopts an ad hoc network topology structure. When there is no central node, multiple data acquisition devices directly communicate point-to-point, and automatically select the optimal path for data transmission according to the real-time network topology changes; Data acquisition equipment includes: Ultrasonic snow depth detection sensor; Communication module, used for data transmission between other data acquisition devices; Temperature sensor, used to monitor ambient temperature and assist in temperature compensation control; The processor unit is used for data preprocessing and controlling the operation of the ad hoc network communication protocol.

2. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 1 is characterized in that: The ultrasonic snow depth detection sensor is installed above the snow through a bracket. After installation, the distance d1 between the ultrasonic snow depth detection sensor and the ground when there is no snow is obtained in advance. After snow accumulates, the ultrasonic snow depth detection sensor is emitted and the echo reflected from the snow surface is received. The distance d2 between the ultrasonic snow depth detection sensor and the snow surface is calculated according to the propagation time of the ultrasonic wave. The snow depth is obtained by subtracting d2 from d1.

3. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 1 is characterized in that: The temperature compensation and transmission power control module is based on a mathematical model of the relationship between temperature, ultrasonic propagation velocity and attenuation. By inputting the ambient temperature collected in real time by the temperature sensor, the required ultrasonic transmission power gain is calculated, and the transmission power of the ultrasonic snow depth detection sensor is dynamically adjusted according to the calculation result. The mathematical model of the relationship between temperature, propagation velocity and attenuation is based on the nonlinear relationship between ambient temperature and ultrasonic propagation velocity, and takes into account the influence of ambient temperature on the sound wave attenuation coefficient. The influence of ambient temperature changes on signal propagation is compensated by dynamically adjusting the transmission power.

4. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 3 is characterized in that: The mathematical model of the relationship between temperature, propagation velocity and attenuation includes an ultrasonic wave propagation velocity model and an attenuation coefficient model.

5. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 4 is characterized in that: The ultrasonic wave propagation velocity model is expressed as: v(T env )=v0·(1+α·(T env -T0)), where v(T env ) is the ambient temperature T env v0 is the ultrasonic propagation velocity at standard temperature T0 = 20°C, α is the linear coefficient of ultrasonic propagation velocity to ambient temperature change, T env is the current ambient temperature, and T0 is the reference temperature.

6. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 5 is characterized in that: The attenuation coefficient model is expressed as the attenuation coefficient α atten (T env )’s estimate: α atten (T env )=β·(1+γ·(T env -T0)), where α atten (T env ) is the temperature T env The ultrasonic attenuation coefficient under the condition of ,β is the attenuation coefficient constant, which depends on the ultrasonic frequency and the ambient pressure, γ is the proportional constant of the effect of temperature on the attenuation coefficient, T env Same as T0 definition above.

7. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 6 is characterized in that: The ultrasonic transmission power gain G(T env ) is calculated as: Among them, G(T env ) is the temperature T env The required transmit power gain, α atten (T0) and α atten (T env ) are the reference temperature T0 and the current temperature T env Ultrasonic attenuation coefficient under env ) and v0 are the ultrasonic propagation velocities at the current temperature and reference temperature respectively.

8. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 7 is characterized in that: The dynamic adjustment of the transmission power specifically includes: According to the calculated transmission power gain G(T env ), dynamically adjust the transmission power P of the ultrasonic snow depth detection sensor through the power control module trans (T env ), expressed as: P trans (T env )=P0·G(T env ), where P trans (T env ) is the actual transmit power at the current ambient temperature, and P0 is the initial transmit power at the standard temperature T0.

9. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 1 is characterized in that: The self-organizing network communication protocol in the low-latency data transmission module specifically includes: Ad hoc network topology: The ad hoc network communication protocol adopts a cellular or star ad hoc network topology to achieve point-to-point direct communication between multiple data acquisition devices; Dynamic routing selection: Through dynamic routing selection, the path priority score is calculated according to the real-time network topology changes, and the optimal path is automatically selected for data transmission based on the priority score. The data transmission path is dynamically adjusted according to the number of path hops, distance and load.

10. The intelligent collection system for snow data in glacier frozen soil desert areas according to claim 9 is characterized in that: The path priority score S(P i ) is used to select the optimal path P optimal , expressed as: S(P i )=w1·D(P i )+w2·L(P i )+w3·H(P i ), where S(P i ) represents the path P i The smaller the value, the higher the priority. i ) represents the path P i The total distance is the sum of the physical distances between the nodes in the path, L(P i ) represents the path P i The total load of the path represents the sum of the communication loads of each node, H(P i ) represents the path P i The total number of hops indicates the number of relay nodes required from the source node to the target node. w1, w2, and w3 are weight parameters, indicating the influence of each indicator on the path selection, which can be adjusted according to actual needs.

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