A method and device for monitoring wind erosion accumulation based on multi-sensor fusion

Abstract

The application provides a kind of wind erosion accumulation dynamic monitoring method and device based on multi-sensor fusion, it is related to wind erosion monitoring technical field, the synchronous measurement data of optical sensor array, temperature sensor array and moisture sensor array arranged along vertical direction in the region to be monitored is collected with preset period;According to the synchronous measurement data, the optical demarcation point candidate position, temperature demarcation point candidate position and moisture demarcation point candidate position in the region to be monitored are determined;Based on the confidence weight corresponding to the optical demarcation point candidate position, temperature demarcation point candidate position and moisture demarcation point candidate position, weighted average calculation is carried out, and the current relative height value corresponding to the above-ground-underground demarcation point position is obtained;The current relative height value is compared with historical relative height value, and the wind erosion thickness or accumulation thickness is obtained.The accurate measurement of wind erosion accumulation can be realized, compared with the monitoring mode in the related art, the monitoring efficiency and precision can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of wind erosion monitoring technology, and in particular to a method and device for dynamic monitoring of wind erosion accumulation based on multi-sensor fusion. Background Technology

[0002] In the field of soil wind erosion and deposition monitoring, real-time and accurate monitoring of dynamic changes in surface thickness is the core foundation for assessing the intensity of regional wind and sand activity and formulating ecological protection measures. This is especially true in wind-erosion sensitive areas such as desert edges and grasslands, where long-term and continuous monitoring data is crucial for understanding wind and sand movement patterns and predicting wind erosion disasters. With the increasing demand for ecological and environmental monitoring, traditional monitoring methods relying on manual intervention are no longer sufficient to meet the requirements of high-frequency and wide-area monitoring. The industry is gradually moving towards automated and intelligent monitoring, urgently requiring monitoring technologies that can operate stably in complex field environments and accurately identify surface locations to achieve dynamic capture of changes in wind erosion and deposition thickness.

[0003] Currently, there are two main types of technical solutions in the industry for monitoring soil wind erosion and deposition thickness. One type is the traditional physical probe technology, which involves vertically burying a rigid component such as a graduated steel rod on the surface of the monitoring area. Staff periodically go to the site to read the height of the probe above the ground, and then calculate the wind erosion or deposition thickness within a specific time period by comparing the two readings. The other type is a monitoring technology based on a single electronic sensor. This technology often uses ultrasonic or laser sensors as the core detection element. It uses the propagation characteristics of sound waves or lasers to measure the distance between the sensor and the ground surface, and then calculates the wind erosion or deposition thickness. This eliminates the need for frequent on-site manual operation and initially achieves semi-automation of the monitoring process.

[0004] However, both of the aforementioned existing technologies have significant limitations. The most critical issue is their inability to achieve long-term, accurate, and stable automated monitoring in complex outdoor sandstorm environments. Traditional physical probe technology relies entirely on periodic manual readings, which is not only inefficient and unable to capture instantaneous thickness changes during sandstorm activity, but also introduces reading errors due to manual operation. Furthermore, it is ill-suited for long-term monitoring in remote and harsh environments. On the other hand, monitoring technologies based on single ultrasonic or laser sensors suffer from significant limitations in high-concentration sandstorm environments. Suspended dust particles in the air strongly absorb and scatter sound waves or laser energy, leading to a shortened effective detection distance, a sharp attenuation of signal strength, and even complete signal loss. Additionally, the reflection of dust particles can create false echoes, distorting the monitoring data and failing to provide reliable evidence for wind erosion and deposition thickness analysis. Summary of the Invention

[0005] This invention provides a method and apparatus for dynamic monitoring of wind erosion deposits based on multi-sensor fusion, which can achieve accurate measurement of wind erosion deposits and effectively improve monitoring efficiency and accuracy compared with monitoring methods in related technologies.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] Firstly, a method for dynamic monitoring of wind erosion deposition based on multi-sensor fusion is provided. The method includes: acquiring synchronous measurement data collected at preset intervals from an optical sensor array, a temperature sensor array, and a moisture sensor array deployed vertically within a monitored area; the measurement data from the optical sensor array is light intensity, the measurement data from the temperature sensor array is temperature, and the measurement data from the moisture sensor array is capacitance; determining candidate locations for optical, temperature, and moisture boundaries within the monitored area based on the synchronous measurement data; assigning corresponding confidence weights to the candidate locations for optical, temperature, and moisture boundaries based on environmental conditions, including day / night conditions determined by the maximum light intensity and thermal activity conditions determined by the maximum temperature change rate; performing a weighted average calculation based on the confidence weights of the candidate locations for optical, temperature, and moisture boundaries to obtain the current relative height value corresponding to the above-ground / underground boundary location; and comparing the current relative height value with historical relative height values ​​to obtain the wind erosion thickness or deposition thickness.

[0008] The beneficial effects of this invention are as follows: The method provided by this invention overcomes the limitations of traditional manual probes that rely on manual readings and cannot monitor in real time by simultaneously collecting data from three types of sensors—optical, temperature, and moisture—deployed along a vertical direction. It also solves the problem of signal attenuation or distortion of a single electronic sensor in complex environments such as high-concentration sandstorms. At the same time, by assigning confidence weights to the candidate locations of the three boundary points based on environmental conditions, adaptive fusion of sensor data is achieved, avoiding the defect of a single sensor failing in a specific environment. Finally, by comparing the current relative height value with historical values ​​to calculate the wind erosion / deposition thickness, it can dynamically capture the continuous changes in surface thickness during sandstorm activity, providing a precise and automated overall solution for wind erosion and deposition monitoring, effectively meeting the needs of long-term dynamic monitoring in complex field environments.

[0009] In one possible implementation of the first aspect, determining the candidate locations of optical boundary points, temperature boundary points, and moisture boundary points within the monitored area based on the synchronous measurement data includes: determining the candidate locations of optical boundary points by normalization processing and spatial gradient calculation based on the measurement data of the optical sensor array; determining the candidate locations of temperature boundary points by temperature change rate calculation and spatial gradient calculation based on the measurement data of the temperature sensor array; and determining the candidate locations of moisture boundary points by spatial gradient calculation based on the measurement data of the moisture sensor array.

[0010] The method provided by this invention matches targeted computational logic for each of the three types of sensors, taking into account their different detection characteristics. Specifically, the variation law of focused light intensity of optical sensors, the difference in the rate of temperature change of focused temperature of temperature sensors, and the abrupt change in the focused capacitance value of moisture sensors ensure that the determination of each candidate boundary point is based on the core detection advantages of the sensor, avoiding candidate position deviations caused by mismatch between computational logic and sensor characteristics. Compared with a general method of determining candidate positions, this refined path can improve the independent accuracy of the three types of candidate positions, laying the foundation for subsequent weighted fusion to obtain reliable above-ground-underground boundary point positions, and further reducing the monitoring errors that may be introduced by a single computational logic.

[0011] In one possible implementation of the first aspect, the optical sensor array includes multiple optical sensors. The step of determining candidate locations for optical boundary points based on measurement data from the optical sensor array through normalization processing and spatial gradient calculation includes: normalizing the illumination intensity values ​​corresponding to the multiple optical sensors according to the maximum and minimum values ​​among the illumination intensity values ​​corresponding to the multiple optical sensors to obtain a normalized illumination intensity value for each optical sensor; determining a spatial gradient value corresponding to each optical sensor based on the normalized illumination intensity values ​​of every two adjacent optical sensors; and determining the location of the optical sensor corresponding to the maximum value among the multiple spatial gradient values ​​as the candidate location for the optical boundary point.

[0012] The formula for the normalization process is as follows:

[0013] L norm [i]=(L[i]-L min ) / (L max -L min );

[0014] L norm [i] represents the normalized light intensity value corresponding to the i-th optical sensor, and L[i] represents the light intensity value corresponding to the i-th optical sensor. max L is the maximum value among the light intensity values ​​corresponding to multiple optical sensors. minIt is the minimum value among the light intensity values ​​corresponding to multiple optical sensors;

[0015] The formula for determining the spatial gradient value of the optical sensor is:

[0016] Grad L [i]=L norm [i]-L norm [i+1];

[0017] Grad L [i] represents the spatial gradient value corresponding to the i-th optical sensor, L norm [i+1] is the normalized value of the light intensity corresponding to the (i+1)th optical sensor.

[0018] The method provided by this invention normalizes light intensity values ​​under different environments to a uniform 0-1 range, effectively eliminating the interference of absolute light intensity fluctuations on the judgment. This solves the problem in traditional optical detection where large differences in absolute light intensity due to day / night and cloudy / sunny conditions make it difficult to stably identify the ground surface. Furthermore, by calculating the spatial gradient of normalized values ​​from adjacent sensors, it accurately captures the abrupt changes in light intensity above and below ground. The location corresponding to the maximum value precisely matches the abrupt change point in the optical characteristics of the ground surface, ensuring that even under drastically changing lighting conditions, the candidate location of the optical boundary point maintains high accuracy and robustness.

[0019] In one possible implementation of the first aspect, the temperature sensor array includes multiple temperature sensors. The step of determining candidate locations for temperature boundary points based on measurement data from the temperature sensor array through temperature change rate calculation and spatial gradient calculation includes: determining the temperature change rate corresponding to each temperature sensor based on historical and current temperature values ​​of each temperature sensor within a preset time period; determining the spatial gradient value corresponding to each temperature sensor based on the temperature change rates of every two adjacent temperature sensors; and determining the location of the temperature sensor corresponding to the maximum value among the multiple spatial gradient values ​​as the candidate location for the temperature boundary point.

[0020] The formula for determining the rate of temperature change is:

[0021] dT_dt[i]=(T new [i]-T old [i]) / Δt;

[0022] Where dT_dt[i] is the temperature change rate corresponding to the i-th temperature sensor, T new [i] represents the current temperature value of the i-th temperature sensor, T old [i] represents the historical temperature value of the i-th temperature sensor, and Δt represents the duration of the preset time period;

[0023] The formula for determining the spatial gradient value of the temperature sensor is:

[0024] Grad T [i] = dT_dt[i] - dT_dt[i+1];

[0025] Among them, Grad T [i] represents the spatial gradient value of the i-th temperature sensor, and dT_dt[i+1] represents the temperature change rate corresponding to the (i+1)-th temperature sensor.

[0026] The method provided by this invention utilizes the difference in thermal inertia between air and soil by measuring the rate of temperature change; then, by calculating the spatial gradient of the rate of temperature change of adjacent sensors, it captures the abrupt change characteristics of this rate of change, and the location corresponding to the maximum value is the point of abrupt change in the thermal properties of the earth's surface; thus, even in scenarios where the absolute temperature values ​​are similar, the earth's surface can still be accurately located by the difference in the rate of change, significantly improving the applicable scenarios and judgment accuracy of the candidate locations for temperature boundary points, and is especially suitable for different environments with large or small diurnal temperature fluctuations.

[0027] In one possible implementation of the first aspect, the moisture sensor array includes multiple moisture sensors, and the step of determining the candidate location of the moisture boundary point by spatial gradient calculation based on the measurement data of the moisture sensor array includes: determining the spatial gradient value corresponding to each moisture sensor according to the capacitance value of every two adjacent moisture sensors; and determining the location of the moisture sensor corresponding to the maximum value among the multiple spatial gradient values ​​as the candidate location of the moisture boundary point.

[0028] The formula for determining the spatial gradient value of the moisture sensor is:

[0029] Grad M [i] = C[i] - C[i+1];

[0030] Among them, Grad M [i] represents the spatial gradient value of the i-th moisture sensor, C[i] represents the capacitance value of the i-th moisture sensor, and C[i+1] represents the capacitance value of the (i+1)-th moisture sensor.

[0031] The method provided by this invention calculates the spatial gradient between adjacent sensors based on the capacitance value of a moisture sensor, accurately capturing abrupt changes in the dielectric properties of air and soil. Even in extremely dry sandy environments such as deserts, there is still a measurable difference between the dielectric constant of dry sand and air. The location corresponding to the maximum gradient value is precisely the surface location where this dielectric property change occurs. This avoids the shortcomings of traditional moisture detection that relies on the absolute value of water content. By directly locking onto the surface through gradient changes, it ensures that the candidate locations of moisture boundary points are stable and accurate under different humidity conditions, such as dry sand and moist soil, providing a reliable moisture dimension reference for multi-sensor fusion.

[0032] In one possible implementation of the first aspect, assigning corresponding confidence weights to the candidate locations of optical boundary points, temperature boundary points, and moisture boundary points based on environmental conditions includes: determining a first confidence weight for the candidate location of an optical boundary point when the maximum light intensity is greater than a preset light intensity threshold; determining a second confidence weight for the candidate location of an optical boundary point when the maximum light intensity is less than or equal to the preset light intensity threshold; determining a third confidence weight for the candidate location of a temperature boundary point when the maximum temperature change rate is greater than a preset temperature change rate threshold; determining a fourth confidence weight for the candidate location of a temperature boundary point when the maximum temperature change rate is less than or equal to the preset temperature change rate threshold; and determining a fifth confidence weight for the candidate location of a moisture boundary point, wherein the first value is greater than the second value, the third value is greater than the fourth value, the fifth value is less than the first value but greater than the second value, and the fifth value is equal to the fourth value.

[0033] The method provided by this invention quantifies the impact of environmental conditions on sensor accuracy by setting a preset threshold, and realizes dynamic adaptive adjustment of weights. This avoids the error caused by fixed weights in the prior art, which leads to a certain type of sensor dominating the judgment in an unsuitable environment. It ensures that the most reliable sensor data in the current environment always dominates the weighted fusion, thereby improving the accuracy of the final boundary point position.

[0034] In one possible implementation of the first aspect, the formula for calculating the weighted average is:

[0035] P surface =(Conf_L*P_light+Conf_T*P_temp+Conf_M*P_moist) / (Conf_L+Conf_T+Conf_M);

[0036] Among them, P surface Conf_L is the current relative height value corresponding to the above-ground / underground boundary point location, P_light is the confidence weight corresponding to the optical boundary point candidate location, Conf_T is the confidence weight corresponding to the temperature boundary point candidate location, P_temp is the relative height value corresponding to the temperature boundary point candidate location, Conf_M is the confidence weight corresponding to the moisture boundary point candidate location, and P_moist is the relative height value corresponding to the moisture boundary point candidate location.

[0037] The step of comparing the current relative height value with the historical relative height value to obtain the wind erosion thickness or the deposition thickness includes: when the current relative height value is less than the historical relative height value, the wind erosion thickness is the difference between the current relative height value and the historical relative height value; when the current relative height value is equal to the historical relative height value, the wind erosion thickness and the deposition thickness are 0; when the current relative height value is greater than the historical relative height value, the deposition thickness is the difference between the current relative height value and the historical relative height value.

[0038] The method provided by this invention integrates three candidate locations according to their confidence weights, highlighting the dominant role of high-confidence candidate locations while also taking into account the auxiliary reference of low-confidence candidate locations. This effectively offsets the random errors of a single candidate location, making the final current relative height value closer to the actual surface location. At the same time, it clarifies three scenarios for thickness calculation, directly obtaining the specific thickness through the difference, solving the problems of ambiguous thickness calculation logic and unintuitive results in existing technologies. This improves the accuracy of the above-ground / underground boundary location and makes the calculation of wind erosion / deposition thickness more direct and quantifiable, providing a clear and reliable basis for the engineering application of monitoring data.

[0039] In one possible implementation of the first aspect, after comparing the current relative height value with the historical relative height value to obtain the wind erosion thickness or deposition thickness, the method further includes: in response to an input reset operation, updating the historical relative height value according to the current relative height value, and storing the timestamp corresponding to the current relative height value.

[0040] The method provided by this invention can respond to input and update historical values ​​through a reset operation, ensuring that subsequent thickness calculations are based on a new benchmark and avoiding monitoring data errors caused by benchmark deviation. At the same time, storing the timestamp corresponding to the current relative height value allows for clear tracing of the time node of each benchmark update, facilitating subsequent analysis of wind erosion deposition patterns at different monitoring stages. This solves the problems of benchmark inability to be updated and data tracing difficulties after device relocation in existing monitoring methods, improving the flexibility of the method and the traceability of data, and adapting to the needs of complex monitoring scenarios in the field.

[0041] Secondly, the present invention provides a dynamic monitoring device for wind erosion deposition based on multi-sensor fusion, applied to the dynamic monitoring method for wind erosion deposition based on multi-sensor fusion described in any of the first aspects above. The device includes: a measurement data acquisition module, used to acquire synchronous measurement data collected at a preset period by an optical sensor array, a temperature sensor array, and a moisture sensor array arranged vertically within a monitored area, wherein the measurement data of the optical sensor array is a light intensity value, the measurement data of the temperature sensor array is a temperature value, and the measurement data of the moisture sensor array is a capacitance value; and a candidate position determination module, used to determine, based on the synchronous measurement data, candidate positions of optical boundary points and temperature boundary points within the monitored area. The system includes candidate locations for optical, temperature, and moisture boundary points; a weighting module for assigning corresponding confidence weights to these candidate locations based on environmental conditions, including day / night conditions determined by maximum light intensity and thermal activity conditions determined by maximum temperature change rate; a boundary point location determination module for performing a weighted average calculation based on the confidence weights of the candidate locations for optical, temperature, and moisture boundary points to obtain the current relative height value corresponding to the above-ground / underground boundary point location; and a thickness determination module for comparing the current relative height value with historical relative height values ​​to obtain the wind erosion thickness or deposition thickness.

[0042] Thirdly, the present invention also provides a dynamic monitoring device for wind erosion deposition based on multi-sensor fusion, the device comprising a mechanical structure component, a sensor system component, a power supply component, and a main control unit; the mechanical structure component comprising a frame and a transparent outer shell fitted over the frame; the sensor system component comprising an optical sensor array, a temperature sensor array, and a moisture sensor array arranged at predetermined intervals along the length of the frame, wherein the optical sensor array, temperature sensor array, and moisture sensor array are disposed on the side of the frame; the power supply component is used to provide power to the sensor system component and the main control unit; the main control unit is used to execute the dynamic monitoring method for wind erosion deposition based on multi-sensor fusion as described in any one of the first aspects above.

[0043] Understandably, the beneficial effects that the apparatus of the second aspect and the apparatus of the third aspect provided above can achieve can be referred to the beneficial effects of the first aspect and any of its possible design embodiments, which will not be repeated here. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention;

[0045] Figure 2A flowchart of a dynamic monitoring method for wind erosion deposition based on multi-sensor fusion provided in an embodiment of the present invention;

[0046] Figure 3 A flowchart of another dynamic monitoring method for wind erosion deposition based on multi-sensor fusion provided in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of the structure of a dynamic monitoring device for wind erosion deposition provided in an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of another dynamic monitoring device for wind erosion deposition provided in an embodiment of the present invention;

[0049] Figure 6 Three-view drawing of another dynamic monitoring device for wind erosion deposition provided in an embodiment of the present invention;

[0050] Figure 7 This is a top view of another dynamic monitoring device for wind erosion deposition provided in an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. The "or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A or B can represent: A alone, A and B simultaneously, and B alone. A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.

[0052] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0053] In this embodiment of the invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this embodiment of the invention should not be construed as superior or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0054] In the field of soil wind erosion and deposition monitoring, real-time and accurate monitoring of dynamic changes in surface thickness is the core foundation for assessing the intensity of regional wind and sand activity and formulating ecological protection measures. This is especially true in wind-erosion sensitive areas such as desert edges and grasslands, where long-term and continuous monitoring data is crucial for understanding wind and sand movement patterns and predicting wind erosion disasters. With the increasing demand for ecological and environmental monitoring, traditional monitoring methods relying on manual intervention are no longer sufficient to meet the requirements of high-frequency and wide-area monitoring. The industry is gradually moving towards automated and intelligent monitoring, urgently requiring monitoring technologies that can operate stably in complex field environments and accurately identify surface locations to achieve dynamic capture of changes in wind erosion and deposition thickness.

[0055] Currently, there are two main types of technical solutions in the industry for monitoring soil wind erosion and deposition thickness. One type is the traditional physical probe technology, which involves vertically burying a rigid component such as a graduated steel rod on the surface of the monitoring area. Staff periodically go to the site to read the height of the probe above the ground, and then calculate the wind erosion or deposition thickness within a specific time period by comparing the two readings. The other type is a monitoring technology based on a single electronic sensor. This technology often uses ultrasonic or laser sensors as the core detection element. It uses the propagation characteristics of sound waves or lasers to measure the distance between the sensor and the ground surface, and then calculates the wind erosion or deposition thickness. This eliminates the need for frequent on-site manual operation and initially achieves semi-automation of the monitoring process.

[0056] However, both of the aforementioned existing technologies have significant limitations. The most critical issue is their inability to achieve long-term, accurate, and stable automated monitoring in complex outdoor sandstorm environments. Traditional physical probe technology relies entirely on periodic manual readings, which is not only inefficient and unable to capture instantaneous thickness changes during sandstorm activity, but also introduces reading errors due to manual operation. Furthermore, it is ill-suited for long-term monitoring in remote and harsh environments. On the other hand, monitoring technologies based on single ultrasonic or laser sensors suffer from significant limitations in high-concentration sandstorm environments. Suspended dust particles in the air strongly absorb and scatter sound waves or laser energy, leading to a shortened effective detection distance, a sharp attenuation of signal strength, and even complete signal loss. Additionally, the reflection of dust particles can create false echoes, distorting the monitoring data and failing to provide reliable evidence for wind erosion and deposition thickness analysis.

[0057] In view of this, embodiments of the present invention provide a method and apparatus for dynamic monitoring of wind erosion deposition based on multi-sensor fusion. The method includes: acquiring synchronous measurement data collected at a preset period by an optical sensor array, a temperature sensor array, and a moisture sensor array arranged vertically in a monitoring area, wherein the measurement data of the optical sensor array is light intensity value, the measurement data of the temperature sensor array is temperature value, and the measurement data of the moisture sensor array is capacitance value; determining candidate positions of optical boundary points, temperature boundary points, and moisture boundary points in the monitoring area based on the synchronous measurement data; assigning corresponding confidence weights to the candidate positions of optical boundary points, temperature boundary points, and moisture boundary points according to environmental conditions, wherein the environmental conditions include day and night conditions determined based on the maximum light intensity and thermal activity conditions determined based on the maximum temperature change rate; performing a weighted average calculation based on the confidence weights corresponding to the candidate positions of optical boundary points, temperature boundary points, and moisture boundary points to obtain the current relative height value corresponding to the above-ground / underground boundary point position; and comparing the current relative height value with historical relative height values ​​to obtain the wind erosion thickness or deposition thickness.

[0058] The method provided by this invention overcomes the limitations of traditional manual probes that rely on manual readings and cannot monitor in real time by simultaneously collecting data from three types of sensors—optical, temperature, and moisture—deployed vertically. It also solves the problem of signal attenuation or distortion of a single electronic sensor in complex environments such as high-concentration sandstorms. Furthermore, by assigning confidence weights to the candidate locations of the three boundary points based on environmental conditions, it achieves adaptive fusion of sensor data, avoiding the defect of a single sensor failing under specific conditions. Finally, by comparing the current relative height value with historical values, it calculates the wind erosion / deposition thickness, dynamically capturing the continuous changes in surface thickness during sandstorm activity. This provides a precise and automated overall solution for wind erosion and deposition monitoring, effectively meeting the needs of long-term dynamic monitoring in complex field environments.

[0059] In some embodiments, the wind erosion deposition dynamic monitoring method based on multi-sensor fusion provided in this invention can be executed by a wind erosion deposition dynamic monitoring device 100 based on multi-sensor fusion (hereinafter referred to as wind erosion deposition dynamic monitoring device 100).

[0060] As an example, the wind erosion deposition dynamic monitoring device 100 can be any electronic device 200 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer. The specific implementation method of the wind erosion deposition dynamic monitoring device 100 is not limited here.

[0061] Figure 1A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown. The electronic device 200 includes a processor 210, a memory 220, and a communication interface 230.

[0062] Processor 210 may include one or more processing cores. Processor 210 connects to various parts within electronic device 200 using various interfaces and lines, and performs various functions and processes data of electronic device 200 by running or executing instructions, programs, code sets, or instruction sets stored in memory 220, and by calling data stored in memory 220. Optionally, processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0063] The memory 220 may include random access memory (RAI) or read-only memory (ROI). Optionally, the memory 220 may include non-transitory computer-readable storage ledger. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a stored program area. The stored program area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc.

[0064] Communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing devices or Ethernet, wireless access network (RAN), wireless local area network (WLAN), etc.

[0065] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, and communication interface 230) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be located within the same device, i.e., as different devices within the same device, similar to the deployment of devices or components in a distributed system.

[0066] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 200. In other embodiments of the present invention, the electronic device 200 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0067] The following description, in conjunction with the accompanying drawings, illustrates a dynamic monitoring method for wind erosion deposition based on multi-sensor fusion provided by an embodiment of the present invention.

[0068] Figure 2 This is a flowchart illustrating a dynamic monitoring method for wind erosion deposition based on multi-sensor fusion, provided as an embodiment of the present invention. Optionally, this method can be... Figure 1 The illustrated electronic device 200 performs this operation. The method may include the following steps:

[0069] S1. Acquire synchronous measurement data from the optical sensor array, temperature sensor array, and moisture sensor array arranged vertically within the area to be monitored at a preset cycle.

[0070] The measurement data of the optical sensor array is the light intensity value, the measurement data of the temperature sensor array is the temperature value, and the measurement data of the moisture sensor array is the capacitance value.

[0071] It should be noted that the preset cycle is every minute / time, which means that the optical sensor array, temperature sensor array and moisture sensor array are woken up (equivalent to timed wake-up) at preset time intervals (such as every minute) to obtain synchronous measurement data, and the synchronization accuracy of the synchronous measurement data is ±10ms.

[0072] It should be understood that the moisture sensor array measures at a frequency of 1 Hz.

[0073] In one example, the optical sensor is either AMS's TSL2591 or Vishay's VEML7700. These offer high accuracy and a wide measurement range. They feature an I2C interface, a size of approximately 2mm x 2mm, and built-in infrared response, allowing for better differentiation between sunlight and artificial light. The moisture sensor is a standard capacitive digital humidity sensor, such as Sensirion's SHT35, for air humidity. For soil moisture, a frequency domain reflectance (FDR) sensor is required. However, for miniaturization and arraying, a miniature needle probe solution from MPS-6 (Decagon Devices, now part of Meter Group) can be used, or a custom-designed miniature FDR probe can be employed. It should be noted that the moisture sensor requires different measurement modes and calibration curves for air and soil environments. The temperature sensor is either Maxim Integrated's DS18B20 or TI's TMP117. Features: The DS18B20 is a single-bus digital sensor, facilitating the serial connection of multiple sensors with a single cable, reducing costs. The TMP117 offers extremely high accuracy (±0.1°C), making it suitable for measuring minute temperature gradients. It can be used in combination with other sensors: the TMP117 for high-precision applications (such as near expected boundary points), and the DS18B20 for other applications.

[0074] It should be noted that the above sensor selection is only an example, and the specific models of optical sensor arrays, temperature sensor arrays and moisture sensor arrays are not particularly limited in the embodiments of the present invention.

[0075] S2. Determine the candidate locations of the optical boundary, temperature boundary, and moisture boundary within the area to be monitored based on the synchronous measurement data.

[0076] In some embodiments, see Figure 3 The above S2 includes:

[0077] S21. Based on the measurement data of the optical sensor array, the candidate positions of the optical boundary points are determined through normalization processing and spatial gradient calculation.

[0078] In one possible implementation, the optical sensor array includes multiple optical sensors, and S21 includes: normalizing the illumination intensity values ​​corresponding to the multiple optical sensors according to the maximum and minimum values ​​among the illumination intensity values ​​corresponding to the multiple optical sensors to obtain a normalized illumination intensity value corresponding to each optical sensor; determining the spatial gradient value corresponding to each optical sensor according to the normalized illumination intensity values ​​of every two adjacent optical sensors; and determining the position of the optical sensor corresponding to the maximum value among the multiple spatial gradient values ​​as the candidate position of the optical boundary point.

[0079] The formula for the normalization process is as follows:

[0080] L norm [i]=(L[i]-L min ) / (L max -L min );

[0081] L norm [i] represents the normalized light intensity value corresponding to the i-th optical sensor, and L[i] represents the light intensity value corresponding to the i-th optical sensor. max L is the maximum value among the light intensity values ​​corresponding to multiple optical sensors. min It is the minimum value among the light intensity values ​​corresponding to multiple optical sensors;

[0082] The formula for determining the spatial gradient value of the optical sensor is:

[0083] Grad L [i]=L norm [i]-L norm [i+1];

[0084] Grad L [i] represents the spatial gradient value corresponding to the i-th optical sensor, L norm [i+1] is the normalized value of the light intensity corresponding to the (i+1)th optical sensor.

[0085] Specifically, the maximum light intensity value among the multiple optical sensor values ​​is the light intensity value obtained by the topmost optical sensor, and the minimum light intensity value among the multiple optical sensor values ​​is the light intensity value obtained by the bottommost optical sensor. Here, "top" and "bottom" refer to the top and bottom in the vertical direction of space.

[0086] It should be noted that the absolute value is taken when the gradient value is negative.

[0087] The method provided by this invention normalizes light intensity values ​​under different environments to a uniform 0-1 range, effectively eliminating the interference of absolute light intensity fluctuations on the judgment. This solves the problem in traditional optical detection where large differences in absolute light intensity due to day / night and cloudy / sunny conditions make it difficult to stably identify the ground surface. Furthermore, by calculating the spatial gradient of normalized values ​​from adjacent sensors, it accurately captures the abrupt changes in light intensity above and below ground. The location corresponding to the maximum value precisely matches the abrupt change point in the optical characteristics of the ground surface, ensuring that even under drastically changing lighting conditions, the candidate location of the optical boundary point maintains high accuracy and robustness.

[0088] S22. Based on the measurement data of the temperature sensor array, determine the candidate location of the temperature boundary point by calculating the rate of temperature change and the spatial gradient of the rate of change.

[0089] In one possible implementation, the temperature sensor array includes multiple temperature sensors, and the above-mentioned S22 includes: determining the temperature change rate corresponding to each temperature sensor based on the historical temperature value and the current temperature value of each temperature sensor within a preset time period; determining the spatial gradient value corresponding to each temperature sensor based on the temperature change rate of every two adjacent temperature sensors; and determining the position of the temperature sensor corresponding to the maximum value among the multiple spatial gradient values ​​as the candidate position of the temperature boundary point.

[0090] The formula for determining the rate of temperature change is:

[0091] dT_dt[i]=(T new [i]-T old [i]) / Δt;

[0092] Where dT_dt[i] is the temperature change rate corresponding to the i-th temperature sensor, T new [i] represents the current temperature value of the i-th temperature sensor, T old [i] represents the historical temperature value of the i-th temperature sensor, and Δt represents the duration of the preset time period;

[0093] In one example, the preset time period is 5 minutes long.

[0094] The formula for determining the spatial gradient value of the temperature sensor is:

[0095] Grad T [i] = dT_dt[i] - dT_dt[i+1];

[0096] Among them, Grad T [i] represents the spatial gradient value of the i-th temperature sensor, and dT_dt[i+1] represents the temperature change rate corresponding to the (i+1)-th temperature sensor.

[0097] The method provided by this invention utilizes the difference in thermal inertia between air and soil by measuring the rate of temperature change; then, by calculating the spatial gradient of the rate of temperature change of adjacent sensors, it captures the abrupt change characteristics of this rate of change, and the location corresponding to the maximum value is the point of abrupt change in the thermal properties of the earth's surface; thus, even in scenarios where the absolute temperature values ​​are similar, the earth's surface can still be accurately located by the difference in the rate of change, significantly improving the applicable scenarios and judgment accuracy of the candidate locations for temperature boundary points, and is especially suitable for different environments with large or small diurnal temperature fluctuations.

[0098] S23. Based on the measurement data of the moisture sensor array, determine the candidate location of the moisture boundary point through spatial gradient calculation.

[0099] In one possible implementation, the moisture sensor array includes multiple moisture sensors, and the above-mentioned S23 includes: determining the spatial gradient value corresponding to each moisture sensor based on the capacitance values ​​of every two adjacent moisture sensors; and determining the position of the moisture sensor corresponding to the maximum value among the multiple spatial gradient values ​​as the candidate position of the moisture boundary point.

[0100] The formula for determining the spatial gradient value of the moisture sensor is:

[0101] Grad M [i] = C[i] - C[i+1];

[0102] Among them, Grad M [i] represents the spatial gradient value of the i-th moisture sensor, C[i] represents the capacitance value of the i-th moisture sensor, and C[i+1] represents the capacitance value of the (i+1)-th moisture sensor.

[0103] The method provided by this invention calculates the spatial gradient between adjacent sensors based on the capacitance value of a moisture sensor, accurately capturing abrupt changes in the dielectric properties of air and soil. Even in extremely dry sandy environments such as deserts, there is still a measurable difference between the dielectric constant of dry sand and air. The location corresponding to the maximum gradient value is precisely the surface location where this dielectric property change occurs. This avoids the shortcomings of traditional moisture detection that relies on the absolute value of water content. By directly locking onto the surface through gradient changes, it ensures that the candidate locations of moisture boundary points are stable and accurate under different humidity conditions, such as dry sand and moist soil, providing a reliable moisture dimension reference for multi-sensor fusion.

[0104] In summary, the method provided by this invention matches targeted computational logic for each of the three types of sensors, taking into account their different detection characteristics. Specifically, the variation in focused light intensity of the optical sensor, the difference in the rate of temperature change of the focused temperature sensor, and the abrupt change in the focused capacitance value of the moisture sensor ensure that the determination of each candidate boundary point is based on the core detection advantages of the sensor, avoiding candidate position deviations caused by mismatches between computational logic and sensor characteristics. Compared to a general method of determining candidate positions, this refined path can improve the independent accuracy of the three types of candidate positions, laying the foundation for subsequent weighted fusion to obtain reliable above-ground / underground boundary point locations, and further reducing monitoring errors that may be introduced by a single computational logic.

[0105] S3. Assign corresponding confidence weights to the candidate locations of optical boundary points, temperature boundary points, and moisture boundary points based on the environmental conditions.

[0106] The environmental state includes the day-night state determined based on the maximum light intensity and the thermally active state determined based on the maximum temperature change rate.

[0107] It should be noted that the environmental conditions also include the weather conditions (cloudy or sunny, rainy, etc.) determined based on the maximum light intensity, and the embodiments of the present invention do not impose any special restrictions on this.

[0108] In some embodiments, S3 includes: when the maximum light intensity is greater than a preset light intensity threshold, determining the confidence weight corresponding to the candidate optical boundary point position as a first value; when the maximum light intensity is less than or equal to the preset light intensity threshold, determining the confidence weight corresponding to the candidate optical boundary point position as a second value; when the maximum temperature change rate is greater than a preset temperature change rate threshold, determining the confidence weight corresponding to the candidate temperature boundary point position as a third value; when the maximum temperature change rate is less than or equal to the preset temperature change rate threshold, determining the confidence weight corresponding to the candidate temperature boundary point position as a fourth value; and determining the confidence weight corresponding to the candidate moisture boundary point position as a fifth value, wherein the first value is greater than the second value, the third value is greater than the fourth value, the fifth value is less than the first value and greater than the second value, and the fifth value is equal to the fourth value.

[0109] Specifically, when the maximum light intensity is greater than a preset light intensity threshold, it indicates daytime; when the maximum light intensity is less than or equal to the preset light intensity threshold, it indicates nighttime. When the maximum temperature change rate is greater than a preset temperature change rate threshold, it indicates a large diurnal temperature range.

[0110] In one example, the preset light intensity threshold is 500 lux, the preset temperature change rate threshold is 0.5℃ / min, the first value is 0.9, the second value is 0.1, the third value is 0.9, the fourth value is 0.5, and the fifth value is 0.5.

[0111] It should be understood that the fifth value is a preset value, but the fifth value can also be dynamically fine-tuned according to the sensor data quality. For example, when the humidity is greater than the preset humidity, the confidence weight corresponding to the candidate position of the moisture boundary point is reduced. The embodiments of the present invention do not impose any special restrictions on the specific implementation of the fifth value.

[0112] The method provided by this invention quantifies the impact of environmental conditions on sensor accuracy by setting a preset threshold, and realizes dynamic adaptive adjustment of weights. This avoids the error caused by fixed weights in the prior art, which leads to a certain type of sensor dominating the judgment in an unsuitable environment. It ensures that the most reliable sensor data in the current environment always dominates the weighted fusion, thereby improving the accuracy of the final boundary point position.

[0113] S4. Based on the confidence weights of the candidate optical boundary point, the candidate temperature boundary point, and the candidate moisture boundary point, a weighted average is calculated to obtain the current relative height value corresponding to the above-ground-underground boundary point.

[0114] In one possible implementation, the formula for calculating the weighted average is:

[0115] P surface =(Conf_L*P_light+Conf_T*P_temp+Conf_M*P_moist) / (Conf_L+Conf_T+Conf_M);

[0116] Among them, P surface Conf_L represents the current relative height value corresponding to the above-ground / underground boundary point location, in mm; Conf_L represents the confidence weight corresponding to the optical boundary point candidate location; P_light represents the relative height value of the optical boundary point candidate location; Conf_T represents the confidence weight corresponding to the temperature boundary point candidate location; P_temp represents the relative height value of the temperature boundary point candidate location; Conf_M represents the confidence weight corresponding to the moisture boundary point candidate location; and P_moist represents the relative height value of the moisture boundary point candidate location.

[0117] The method provided by this invention integrates three candidate locations according to their confidence weights, which not only highlights the dominant role of high-confidence candidate locations, but also takes into account the auxiliary reference of low-confidence candidate locations, effectively offsetting the random errors of a single candidate location, and making the final current relative height value closer to the true surface location.

[0118] S5. Compare the current relative height value with the historical relative height value to obtain the wind erosion thickness or deposition thickness.

[0119] Specifically, S5 includes: when the current relative height value is less than the historical relative height value, the wind erosion thickness is the difference between the current relative height value and the historical relative height value; when the current relative height value is equal to the historical relative height value, the wind erosion thickness and the accumulation thickness are 0; when the current relative height value is greater than the historical relative height value, the accumulation thickness is the difference between the current relative height value and the historical relative height value.

[0120] The method provided by this invention clarifies three scenarios for thickness calculation, directly obtaining the specific thickness through the difference, solving the problems of ambiguous thickness calculation logic and unintuitive results in the prior art; thereby improving the accuracy of the above-ground-underground boundary location, and making the calculation of wind erosion / deposition thickness more direct and quantifiable, providing a clear and reliable basis for the engineering application of monitoring data.

[0121] To facilitate understanding of this solution, the following example illustrates the method provided by the embodiments of the present invention.

[0122] For example, the wind erosion dynamic monitoring device synchronously reads the measurement values ​​of an array of optical, temperature, and moisture sensors deployed vertically at a preset cycle. The device detects abrupt changes in the spatial gradient of light intensity: it normalizes the readings of the light intensity sensor array, calculates the difference between the normalized values ​​of adjacent sensors, and uses the position with the largest difference as a candidate optical boundary point. It also detects abrupt changes in the temporal gradient of temperature change rate: it calculates the temperature change rate of each temperature sensor within a predetermined time window, forming a temperature change rate array; it calculates the gradient of this array along the spatial dimension, and uses the position with the largest absolute gradient value as a candidate temperature boundary point. Finally, it detects abrupt changes in the spatial gradient of the dielectric constant of the medium: it calculates the spatial gradient of the moisture sensor (capacitance value) array readings, and uses the position with the largest gradient value as a candidate moisture boundary point. The device assigns a confidence weight to each candidate position based on environmental conditions (e.g., determining day / night based on top light intensity, and determining thermal activity based on the overall temperature change rate). The wind erosion and deposition dynamic monitoring device uses a weighted average algorithm to merge three candidate locations into a final surface boundary point. The device compares this boundary point with historical benchmarks to determine the wind erosion / deposition thickness. When the monitoring device is reset, the current boundary point is recorded as the new benchmark.

[0123] As can be seen from S1-S5 above, the method provided by this embodiment of the invention overcomes the limitations of traditional manual probes that rely on manual readings and cannot monitor in real time by simultaneously collecting data from three types of sensors—optical, temperature, and moisture—deployed along the vertical direction. It also solves the problem of signal attenuation or distortion of a single electronic sensor in complex environments such as high-concentration sandstorms. At the same time, by assigning confidence weights to the candidate locations of the three boundary points based on the environmental conditions, adaptive fusion of sensor data is achieved, avoiding the defect of a single sensor failing in a specific environment. Finally, by comparing the current relative height value with historical values ​​to calculate the wind erosion / deposition thickness, the continuous changes in surface thickness during sandstorm activity can be dynamically captured, providing a precise and automated overall solution for wind erosion and deposition monitoring, effectively meeting the needs of long-term dynamic monitoring in complex field environments.

[0124] This can also be understood as follows: The beneficial effects of the embodiments of this invention are: Multi-sensor collaborative verification: Through data fusion and mutual verification of light, temperature, and water sensors, it adapts to different environmental conditions and achieves all-weather monitoring. Strong algorithm robustness: By using the gradient mutation detection method, it fundamentally avoids the interference of absolute environmental changes (such as light intensity, temperature, and humidity), focusing on identifying the relative differences in physical properties between above-ground and underground media, making the boundary point determination accurate and reliable even under complex natural conditions. High precision: Combining array-type measurement and algorithm processing, it can achieve millimeter-level resolution, far exceeding traditional methods. Adaptive fusion: Introducing the concept of credibility weight, it enables the system to intelligently and dynamically adjust the dependence on different sensors according to the current environment (day / night, sunny / cloudy), achieving optimal determination in all weather conditions. Good long-term stability: From structural sealing and material selection to circuit design, it comprehensively considers the challenges of harsh outdoor environments, ensuring the reliability of long-term monitoring. High degree of automation: It automatically collects, analyzes, and transmits data, and intelligently records reset events, greatly reducing manual intervention.

[0125] In some embodiments, after S5 described above, the method provided by the present invention further includes:

[0126] In response to the input reset operation, the historical relative height value is updated according to the current relative height value, and the timestamp corresponding to the current relative height value is stored.

[0127] In one example, the device has a reset button (which can be triggered externally via magnetic attraction). When the instrument is reset (re-embedded), pressing and holding the button will cause the device to record the current relative height value as the new reference point (historical relative height value) and store the reset event along with a timestamp in the Flash memory.

[0128] The method provided by this invention can respond to input and update historical values ​​through a reset operation, ensuring that subsequent thickness calculations are based on a new benchmark and avoiding monitoring data errors caused by benchmark deviation. At the same time, storing the timestamp corresponding to the current relative height value allows for clear tracing of the time node of each benchmark update, facilitating subsequent analysis of wind erosion deposition patterns at different monitoring stages. This solves the problems of benchmark inability to be updated and data tracing difficulties after device relocation in existing monitoring methods, improving the flexibility of the method and the traceability of data, and adapting to the needs of complex monitoring scenarios in the field.

[0129] In one possible implementation, the method provided by the embodiments of the present invention further includes: packaging data such as timestamp, boundary point position (current relative height value), wind erosion / accumulation thickness, and battery voltage, and sending them to the cloud platform through a LoRa module.

[0130] The above mainly describes the solutions of the embodiments of the present invention from a methodological perspective. It is understood that, in order to achieve the above functions, the wind erosion deposition dynamic monitoring device 100 includes at least one of the hardware structures and software modules corresponding to each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present invention.

[0131] In this embodiment of the invention, the wind erosion deposition dynamic monitoring device 100 can be divided into functional units according to the above method example. For example, the wind erosion deposition dynamic monitoring device 100 can be divided into functional units corresponding to various functions, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or software functional units. It should be noted that the unit division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0132] For example, Figure 4This diagram illustrates the hardware structure of a dynamic monitoring device for wind erosion deposition based on multi-sensor fusion, provided by an embodiment of the present invention. Applied to the dynamic monitoring method for wind erosion deposition based on multi-sensor fusion described in any of the first aspects above, the dynamic monitoring device 100 for wind erosion deposition includes: a measurement data acquisition module 110, used to acquire synchronous measurement data collected at a preset period by an optical sensor array, a temperature sensor array, and a moisture sensor array arranged vertically within a monitored area; the measurement data of the optical sensor array is light intensity, the measurement data of the temperature sensor array is temperature, and the measurement data of the moisture sensor array is capacitance; and a candidate position determination module 120, used to determine optical boundary points within the monitored area based on the synchronous measurement data. The system includes candidate locations for optical, temperature, and moisture boundaries; a weight allocation module 130, which assigns corresponding confidence weights to the candidate locations based on environmental conditions, including day / night conditions determined by the maximum light intensity and thermal activity conditions determined by the maximum temperature change rate; a boundary location determination module 140, which performs a weighted average calculation based on the confidence weights of the candidate locations for optical, temperature, and moisture boundaries to obtain the current relative height value corresponding to the above-ground / underground boundary location; and a thickness determination module 150, which compares the current relative height value with historical relative height values ​​to obtain the wind erosion thickness or deposition thickness.

[0133] In some embodiments, see Figure 5 This invention also provides a dynamic monitoring device for wind erosion deposition. The device includes: a mechanical structure component 510, a sensor system component 520, a power supply component 530, and a main control unit 540. The mechanical structure component 510 includes a frame and a transparent outer shell fitted over the frame. The sensor system component 520 includes an optical sensor array, a temperature sensor array, and a moisture sensor array arranged at predetermined intervals along the length of the frame, wherein the optical sensor array, temperature sensor array, and moisture sensor array are disposed on the side of the frame.

[0134] The power supply component 530 is used to provide power to the sensor system components and the main control unit; the main control unit 540 is used to execute the dynamic monitoring method for wind erosion deposition based on multi-sensor fusion as described above.

[0135] In one example, see Figure 6 , Figure 6The following is a three-view drawing of a wind erosion deposition dynamic monitoring device 100 according to an embodiment of the present invention. The wind erosion deposition dynamic monitoring device 100 includes: a mechanical structure component comprising a hollow cylindrical frame and a shell (highly transparent, wear-resistant cylindrical protective shell) fitted over the frame. The frame has three equally spaced protrusions along its length on its sides, and the shell has three corresponding grooves along its length inside, allowing the frame and shell to be tightly joined together and preventing relative movement between them. Simultaneously, three equally spaced flexible sensor circuit boards are also arranged on the sides of the cylindrical frame. These flexible circuit boards serve as carriers for sensor system components, enabling their installation. The sensor system components include multiple optical sensors, multiple temperature sensors, and multiple moisture sensors arranged at predetermined intervals along the length of the frame. A main control unit is used to execute the monitoring method described above. A power supply component includes a battery and a solar charging circuit. A communication component includes a remote wireless communication module and a near-field communication module.

[0136] Furthermore, the internal framework of the mechanical structure components is a cylinder made of high-strength, corrosion-resistant materials (such as anodized aluminum or PEEK engineering plastics). A channel can be pre-reserved in the center of the cylinder for wiring. The sensor system components consist of three long, flexible PCBs, each integrating its own photosensitive, humidity-sensitive, and temperature-sensitive elements. The PCBs are embedded and bonded to the sides of the framework, with the sensor sensing surfaces flush with or slightly protruding from the outer surface of the housing. The transparent housing is a 1-meter-long, 20mm-diameter transparent cylindrical tube. The preferred materials are wear-resistant polycarbonate (PC) or sapphire glass tubing to ensure light transmission and resist wind and sand abrasion. Both ends of the protective housing are sealed with metal end caps. The upper end cap integrates a solar panel and a LoRa / Bluetooth antenna. The lower end cap is a completely sealed structure. O-rings are used for radial sealing between the end caps and the housing, ensuring an IP67 or higher protection rating.

[0137] Optionally, the sensors are arranged in an array at certain intervals (e.g., every 1 mm or 2 mm) along the length of the frame to achieve positioning accuracy at the 1 mm level. The dividing point is determined by judging the abrupt change in the values ​​of adjacent sensors.

[0138] It should be noted that when the sensor array includes large-sized sensors, for any sensor array, multiple rows of sensors can be arranged in an alternating manner (for example, for an optical sensor array, three rows of optical sensors can be arranged vertically, with a 6mm interval between adjacent optical sensors in each row and a decreasing height of 2mm between adjacent sensors in each row). This allows the sensor array to obtain measurement values ​​at different heights through multiple sensors, thereby achieving a positioning accuracy of 1mm. Technicians can also flexibly set the interval between the sensors in the sensor array according to the actual measurement accuracy. The embodiments of the present invention do not impose any special restrictions on the sensor setting interval.

[0139] To address the need for sensors to directly contact the medium, miniature ventilation windows are provided in the area of ​​the protective shell corresponding to each sensor. These windows are covered with a hydrophobic and breathable membrane (such as an ePTFE membrane), allowing water vapor to pass through while preventing liquid water and dust from entering.

[0140] In some embodiments, the main control unit 540 uses an ultra-low power ARM Cortex-M series MCU, such as STMicroelectronics' STM32L4 series or TI's MSP432 series, so that the main control unit 540 has a multi-channel high-precision ADC (for reading sensor analog signals), sufficient I / O ports, multiple low-power modes, and supports communication protocols such as UART / I2C / SPI.

[0141] In one example, the power supply component 530 is located at the bottom of the hollow area inside the aforementioned frame and includes: a battery: a built-in polymer lithium-ion battery with a capacity of approximately 2000mAh; a charging IC: a solar charging chip supporting maximum power point tracking (MPPT), such as TI's BQ25570, optimized for harvesting microwatt to milliwatt-level energy; and a solar panel: a flexible amorphous silicon solar panel integrated into the upper cover, with an area of ​​approximately 5cm x 5cm and an output power of approximately 0.5W.

[0142] In another example, the communication modules include: Primary communication: a LoRaWAN module, such as Semtech's SX1276 / SX1262 chip solution. It offers long transmission distances and extremely low power consumption, making it ideal for outdoor IoT applications. Near-field communication: a Bluetooth Low Energy (BLE) module, such as Nordic's nRF52832, for field device debugging and manual data download.

[0143] In one possible implementation, see Figure 7 , Figure 7 An embodiment of the present invention is shown.

[0144] A top view of the wind erosion deposition dynamic monitoring device. A reset button and an optical sensor are installed on the top surface of the device. The reset button is used to send a reset operation to the main control unit 540 based on the user's click, so that the main control unit 540 updates the historical relative height value according to the current relative height value and stores the timestamp corresponding to the current relative height value.

[0145] An optical sensor mounted on the top surface is used to acquire light intensity values. The main control unit 540 is also used to compare the light intensity values ​​acquired by the optical sensor on the top surface with a preset light intensity threshold, and adjust the confidence weight corresponding to the candidate position of the optical boundary point according to the comparison structure. For details of the implementation, please refer to the description of the above embodiments, which will not be repeated here. This invention also provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of the above embodiments. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments above, which will not be repeated here.

[0146] This invention also provides a chip. This chip integrates a control circuit for implementing the functions of the aforementioned wind erosion deposition dynamic monitoring device 100 and one or more ports. Optionally, the functions supported by this chip are as described above and will not be repeated here.

[0147] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0148] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0149] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0150] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for dynamic monitoring of wind erosion deposition based on multi-sensor fusion, characterized in that, The method includes: The synchronous measurement data collected by the optical sensor array, temperature sensor array, and moisture sensor array arranged vertically in the area to be monitored at a preset period is obtained. The measurement data of the optical sensor array is the light intensity value, the measurement data of the temperature sensor array is the temperature value, and the measurement data of the moisture sensor array is the capacitance value. Based on the synchronous measurement data, determine the candidate locations of the optical boundary point, temperature boundary point, and moisture boundary point within the area to be monitored; Based on the environmental conditions, corresponding confidence weights are assigned to the candidate locations of optical boundary points, temperature boundary points, and moisture boundary points. The environmental conditions include the day and night state determined based on the maximum light intensity and the thermally active state determined based on the maximum temperature change rate. The current relative height value corresponding to the above-ground-underground boundary point is obtained by weighting the confidence weights of the candidate optical boundary point, candidate temperature boundary point, and candidate moisture boundary point. The current relative height value is compared with the historical relative height value to obtain the wind erosion thickness or deposition thickness. The formula for calculating the weighted average is: P surface =(Conf_L*P_light+Conf_T*P_temp+Conf_M*P_moist) / (Conf_L+Conf_T+Conf_M); Among them, P surface Conf_L is the current relative height value corresponding to the above-ground / underground boundary point location, P_light is the confidence weight corresponding to the optical boundary point candidate location, Conf_T is the confidence weight corresponding to the temperature boundary point candidate location, P_temp is the relative height value corresponding to the temperature boundary point candidate location, Conf_M is the confidence weight corresponding to the moisture boundary point candidate location, and P_moist is the relative height value corresponding to the moisture boundary point candidate location. The step of comparing the current relative height value with historical relative height values ​​to obtain the wind erosion thickness or deposition thickness includes: When the current relative height value is less than the historical relative height value, the wind erosion thickness is the difference between the current relative height value and the historical relative height value; When the current relative height value is equal to the historical relative height value, the wind erosion thickness and the deposition thickness are 0; When the current relative height value is greater than the historical relative height value, the stacking thickness is the difference between the current relative height value and the historical relative height value.

2. The method according to claim 1, characterized in that, The step of determining the candidate locations of the optical boundary point, temperature boundary point, and moisture boundary point within the monitored area based on the synchronous measurement data includes: Based on the measurement data from the optical sensor array, candidate locations for optical boundary points are determined through normalization processing and spatial gradient calculation. Based on the measurement data from the temperature sensor array, candidate locations for temperature boundary points are determined through calculation of the rate of temperature change and the spatial gradient of the rate of change. Based on the measurement data from the moisture sensor array, candidate locations for moisture boundary points are determined through spatial gradient calculation.

3. The method according to claim 2, characterized in that, The optical sensor array includes multiple optical sensors. The determination of candidate locations for optical boundary points based on measurement data from the optical sensor array, through normalization processing and spatial gradient calculation, includes: The light intensity values ​​corresponding to the plurality of optical sensors are normalized based on the maximum and minimum values ​​among the light intensity values ​​corresponding to the plurality of optical sensors to obtain the normalized light intensity value corresponding to each optical sensor. The spatial gradient value corresponding to each optical sensor is determined based on the normalized value of the illumination intensity of each pair of adjacent optical sensors. The position of the optical sensor corresponding to the maximum value among multiple spatial gradient values ​​is determined as the candidate position of the optical boundary point; The formula for the normalization process is as follows: L norm [i]=(L[i]-L min ) / (L max -L min ); L norm [i] represents the normalized light intensity value corresponding to the i-th optical sensor, and L[i] represents the light intensity value corresponding to the i-th optical sensor. max L is the maximum value among the light intensity values ​​corresponding to multiple optical sensors. min It is the minimum value among the light intensity values ​​corresponding to multiple optical sensors; The formula for determining the spatial gradient value of the optical sensor is: Degree L [i]=L norm [i]-L norm [i+1]; Grad L [i] represents the spatial gradient value corresponding to the i-th optical sensor, L norm [i+1] is the normalized value of the light intensity corresponding to the (i+1)th optical sensor.

4. The method according to claim 3, characterized in that, The temperature sensor array includes multiple temperature sensors. The determination of candidate locations for temperature boundary points based on measurement data from the temperature sensor array, through calculation of the rate of temperature change and spatial gradient of the rate of change, includes: The temperature change rate corresponding to each temperature sensor is determined based on the historical temperature value and the current temperature value of each temperature sensor within a preset time period. The spatial gradient value corresponding to each temperature sensor is determined based on the rate of temperature change between every two adjacent temperature sensors. The location of the temperature sensor corresponding to the maximum value among multiple spatial gradient values ​​is determined as the candidate location of the temperature boundary point; The formula for determining the rate of temperature change is: dT_dt[i]=(T new [i]-T old [i]) / Δt; Where dT_dt[i] is the temperature change rate corresponding to the i-th temperature sensor, T new [i] represents the current temperature value of the i-th temperature sensor, T old [i] represents the historical temperature value of the i-th temperature sensor, and Δt represents the duration of the preset time period; The formula for determining the spatial gradient value of the temperature sensor is: Grad T [i]=dT_dt[i]-dT_dt[i+1]; Among them, Grad T [i] represents the spatial gradient value of the i-th temperature sensor, and dT_dt[i+1] represents the temperature change rate corresponding to the (i+1)-th temperature sensor.

5. The method according to claim 4, characterized in that, The moisture sensor array includes multiple moisture sensors. The step of determining candidate locations for moisture boundary points based on measurement data from the moisture sensor array using spatial gradient calculation includes: The spatial gradient value corresponding to each moisture sensor is determined based on the capacitance values ​​of every two adjacent moisture sensors. The location of the moisture sensor corresponding to the maximum value among multiple spatial gradient values ​​is determined as the candidate location of the moisture boundary point. The formula for determining the spatial gradient value of the moisture sensor is: Grad M [i]=C[i]-C[i+1]; Among them, Grad M [i] represents the spatial gradient value of the i-th moisture sensor, C[i] represents the capacitance value of the i-th moisture sensor, and C[i+1] represents the capacitance value of the (i+1)-th moisture sensor.

6. The method according to claim 5, characterized in that, The assignment of corresponding confidence weights to candidate optical boundary points, candidate temperature boundary points, and candidate moisture boundary points based on environmental conditions includes: When the maximum light intensity is greater than a preset light intensity threshold, the confidence weight corresponding to the candidate position of the optical boundary point is determined to be the first value; When the maximum light intensity is less than or equal to a preset light intensity threshold, the confidence weight corresponding to the candidate position of the optical boundary point is determined to be the second value; If the maximum temperature change rate is greater than the preset temperature change rate threshold, the confidence weight corresponding to the candidate position of the temperature boundary point is determined to be the third value. If the maximum temperature change rate is less than or equal to a preset temperature change rate threshold, the confidence weight corresponding to the candidate position of the temperature boundary point is determined to be the fourth value. The credibility weight corresponding to the candidate position of the moisture boundary point is determined as the fifth value, wherein the first value is greater than the second value, the third value is greater than the fourth value, the fifth value is less than the first value but greater than the second value, and the fifth value is equal to the fourth value.

7. The method according to claim 1, characterized in that, After comparing the current relative height value with historical relative height values ​​to obtain the wind erosion thickness or deposition thickness, the method further includes: In response to the input reset operation, the historical relative height value is updated according to the current relative height value, and the timestamp corresponding to the current relative height value is stored.

8. A dynamic monitoring device for wind erosion deposition based on multi-sensor fusion, characterized in that, The apparatus, applied to the dynamic monitoring method for wind erosion deposition based on multi-sensor fusion as described in any one of claims 1-7, comprises: The measurement data acquisition module is used to acquire synchronous measurement data collected at a preset period by an optical sensor array, a temperature sensor array, and a moisture sensor array arranged vertically in the area to be monitored. The measurement data of the optical sensor array is the light intensity value, the measurement data of the temperature sensor array is the temperature value, and the measurement data of the moisture sensor array is the capacitance value. The candidate location determination module is used to determine the candidate locations of optical boundary points, temperature boundary points, and moisture boundary points within the area to be monitored based on the synchronous measurement data. The weight allocation module is used to assign corresponding confidence weights to the candidate locations of optical boundary points, temperature boundary points, and moisture boundary points according to the environmental conditions. The environmental conditions include day and night states determined based on the maximum light intensity and thermally active states determined based on the maximum temperature change rate. The boundary point location determination module is used to perform a weighted average calculation based on the confidence weights of the candidate optical boundary point location, the candidate temperature boundary point location, and the candidate moisture boundary point location to obtain the current relative height value corresponding to the above-ground-underground boundary point location. The thickness determination module is used to compare the current relative height value with the historical relative height value to obtain the wind erosion thickness or the deposition thickness.

9. A dynamic monitoring device for wind erosion deposition based on multi-sensor fusion, characterized in that, The device includes mechanical structure components, sensor system components, power supply components, and a main control unit; The mechanical structure component includes a frame and a transparent outer shell fitted over the frame; The sensor system components include an optical sensor array, a temperature sensor array, and a moisture sensor array arranged at predetermined intervals along the length of the skeleton, wherein the optical sensor array, temperature sensor array, and moisture sensor array are disposed on the side of the skeleton. The power supply component is used to provide power to the sensor system components and the main control unit; The main control unit is used to execute the dynamic monitoring method for wind erosion deposition based on multi-sensor fusion as described in any one of claims 1-7.

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