Method and device for measuring water content of litter on line

Abstract

The invention belongs to the technical field of forest fire danger monitoring, and provides a litter moisture content on-line measurement method and device, temperature, humidity and wind speed in a forest environment are collected, a fire danger coefficient is calculated according to the collected temperature, humidity and wind speed, when the fire danger coefficient is smaller than a threshold value, no operation is carried out, and when the fire danger coefficient is larger than or equal to the threshold value, no operation is carried out. Transmitting infrared light wavelength matched with the type of the target litter to the target litter, collecting reflected light intensity and transmitted light intensity of the target litter, and normalizing the reflected light intensity and the transmitted light intensity to obtain normalized light intensity; and calculating the moisture content of the target litter through a multiple regression equation according to the normalized illumination intensity, the infrared wavelength and the regression coefficient of the target litter. The device has the advantages of wavelength adjustability, low power consumption management, environment trigger logic and multi-type litter adaptive capability, and realizes rapid, accurate and long-term monitoring of the water content of the litter.

Description

Technical Field

[0001] This invention belongs to the field of forest fire risk monitoring technology, and provides a method and device for online measurement of the moisture content of litter. Background Technology

[0002] As a crucial component of forest ecosystems, the moisture content of litter directly impacts the probability and spread rate of forest fires. Traditional measurement methods primarily rely on gravimetric analysis, requiring manual sample collection and drying to constant weight before calculating moisture content. These methods suffer from limitations such as long processing times, cumbersome manual operations, and the inability to achieve real-time monitoring, making them unsuitable for continuous monitoring of large areas.

[0003] Existing online measurement equipment mostly uses the fixed-wavelength infrared light detection principle, relying solely on reflected or transmitted signals for estimation, resulting in insufficient adaptability to different types of litter. Furthermore, environmental factors (temperature, wind speed) significantly interfere with optical signals, and the lack of an effective compensation mechanism makes measurement results prone to deviation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an online method and apparatus for measuring the moisture content of litter, which features adjustable wavelength, low power consumption management, environmental triggering logic, and adaptability to various types of litter, enabling rapid, accurate, and long-term monitoring of the moisture content of litter.

[0005] The technical solution of the present invention includes the following steps: Collect temperature, humidity, and wind speed data from the forest environment.

[0006] The fire hazard coefficient is calculated based on the collected temperature, humidity and wind speed. When the fire hazard coefficient is less than the threshold, no operation is performed.

[0007] When the fire hazard coefficient is greater than or equal to the threshold, an infrared light wavelength that is compatible with the type of target debris is emitted towards the target debris, and the reflected light intensity and transmitted light intensity of the target debris are collected.

[0008] Normalized light intensity is obtained by normalizing the reflected light intensity and the transmitted light intensity.

[0009] The moisture content of the target litter was calculated using a multiple regression equation based on the normalized light intensity, infrared wavelength, and regression coefficient of the target litter.

[0010] Furthermore, the formula for calculating the fire hazard factor is: , in: F: Environmental fire hazard factor (dimensionless), ranging from 0 to 1; Lower limit of temperature reference; Upper limit of temperature reference; Humidity baseline upper limit; Lower limit of humidity reference; Maximum wind speed; Temperature weighting coefficient, value 0.35, dimensionless; Humidity weighting coefficient, with a value of 0.4, dimensionless; : The weighting coefficient for wind speed, with a value of 0.25, is dimensionless; : Temperature-wind speed interaction coefficient, value 0.3, dimensionless.

[0011] Furthermore, the formula for calculating normalized illuminance is: in: Normalized light intensity, ranging from 0 to 1, dimensionless; : Reflected light intensity measurement value, unit: μW / cm²; Transmitted light intensity measurement value, unit: μW / cm²; Transmitted light intensity weighting coefficient, with a value of 0.3; Initial emitted light intensity of the infrared emitter, unit: μW / cm².

[0012] Furthermore, the formula for calculating moisture content is: in: M: Moisture content of litter, in % % to : Regression coefficients of the target litter; Infrared wavelength.

[0013] Furthermore, the regression coefficient calibration method for the target litter is as follows: Ten groups of infrared light of different wavelengths were selected, and 30 litter samples with different moisture contents were collected under each group of wavelengths. The actual moisture content, normalized light intensity and ambient temperature measured by weighing method were recorded simultaneously, and the coefficients were obtained by fitting the data using a multiple linear regression algorithm.

[0014] Furthermore, the data processing strategies implemented based on the measured moisture content are as follows: when Adjust the data storage cycle to reduce data redundancy: in: The data storage period is measured in minutes, ranging from 30 minutes to 60 minutes. It is a natural exponential function; when Time: Triggers an alarm mechanism and performs sampling every minute to continuously monitor changes in moisture content; Medium moisture content scenario Time: Data is stored every 30 minutes and uploaded to the server on the hour.

[0015] The present invention also provides an online measuring device for the moisture content of litter, comprising: The front-end data acquisition module, deployed at forest monitoring points, includes temperature sensors, humidity sensors, and wind speed sensors, used to collect forest environmental parameters in real time. The back-end acquisition module, deployed in conjunction with the front-end acquisition module, includes a wavelength-tunable infrared emitter, a dual-channel infrared detector, and an ADC analog-to-digital converter. The wavelength-tunable infrared emitter dynamically adjusts the infrared wavelength according to the type of litter and emits an infrared beam towards the target litter. The dual-channel infrared detector synchronously acquires the reflected and transmitted light intensity of the target litter. The ADC is connected to the dual-channel infrared detector and converts the analog signals of reflected and transmitted light intensity into digital signals. The main control module is connected to each sensor in the front-end acquisition module and the wavelength-tunable infrared transmitter and ADC analog-to-digital converter in the back-end acquisition module. It is used to receive environmental parameters and calculate the fire hazard coefficient. Based on the fire hazard coefficient, it determines whether to start the back-end acquisition module. After starting, it receives the digital signal output by the ADC and calculates the normalized light intensity. It further combines the infrared light wavelength and the regression coefficient corresponding to the target litter type to calculate the moisture content of the litter. The communication module, connected to the main control module, is used to transmit moisture content data, alarm information and device status to a remote terminal or cloud platform. The power module provides power support for the front-end acquisition module, back-end acquisition module, main control module and communication module, and supports solar charging and power consumption management functions to achieve long-term continuous power supply in the field.

[0016] Furthermore, the communication module is equipped with an alarm mechanism. When the moisture content is lower than the minimum threshold, the measurement time, device latitude and longitude, moisture content value, environmental parameters and device status code are transmitted simultaneously when transmitting the moisture content.

[0017] Furthermore, the wavelength of the wavelength-tunable infrared emitter is between 1.3 μm and 2.5 μm.

[0018] The technical solution provided by this invention has the following advantages compared with the prior art: The system collects temperature, humidity, and wind speed data from the forest environment. Based on these data, a fire hazard coefficient is calculated. If the fire hazard coefficient is below a threshold, no action is taken. If the fire hazard coefficient is greater than or equal to the threshold, an infrared light wavelength adapted to the target litter type is emitted towards the litter, and the reflected and transmitted light intensities are collected. The reflected and transmitted light intensities are normalized to obtain a normalized light intensity. Based on the normalized light intensity, infrared wavelength, and regression coefficients of the target litter, the moisture content of the target litter is calculated using a multivariate regression equation. Compared to existing technologies, this invention features adjustable wavelength, low power consumption management, environmental triggering logic, and adaptability to multiple litter types, enabling rapid, accurate, and long-term monitoring of litter moisture content.

[0019] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the judgment logic of an embodiment of the present invention.

[0022] Figure 2 This is a structural layout diagram of an embodiment of the present invention. Detailed Implementation

[0023] The following detailed description of a specific embodiment of the present invention is provided in conjunction with the accompanying drawings. However, it should be understood that the scope of protection of the present invention is not limited to the specific embodiment.

[0024] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the technical solution of this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0025] In the description of the embodiments of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0026] In the description of this invention, relative humidity (RH) is the percentage of actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature, used to indicate how close the air is to saturation. Its calculation formula is: RH = (Actual water vapor pressure / Saturated water vapor pressure) × 100%. Relative humidity is a dimensionless percentage value; the higher the value, the more humid the air, and the lower the value, the drier the air.

[0027] like Figure 1 and Figure 2 As shown, the present invention provides a method and apparatus for online measurement of the moisture content of litter, comprising the following steps: Collect temperature, humidity, and wind speed data from the forest environment.

[0028] The fire hazard coefficient is calculated based on the collected temperature, humidity and wind speed. When the fire hazard coefficient is less than the threshold (0.6), no operation is performed.

[0029] When the fire hazard coefficient is greater than or equal to the threshold (0.6), an infrared light wavelength that is compatible with the type of target debris is emitted to the target debris, and the reflected light intensity and transmitted light intensity of the target debris are collected.

[0030] Normalized light intensity is obtained by normalizing the reflected light intensity and the transmitted light intensity.

[0031] The moisture content of the target litter was calculated using a multiple regression equation based on the normalized light intensity, infrared wavelength, and regression coefficient of the target litter.

[0032] It is important to note that the threshold value of 0.6 is determined based on logistic regression analysis or ROC curve analysis of historical forest fire data and meteorological data from the same period. For example: "Through retrospective analysis of 1000 forest fire risk cases that occurred in this region over the past ten years, and substituting the meteorological data (T, H, V) of the 24 hours prior to the incident into the fire risk coefficient formula for calculation, statistics show that when F ≥ 0.6, the probability of a fire occurrence significantly increases to over 85%. Therefore, this value is set as the trigger threshold. The specific method for obtaining this value is as follows:"

[0033] Calibrate the regression coefficients of the multiple regression equation used in the online measurement method of litter moisture content. to A quantitative relationship was established between infrared optical signals (reflected light intensity, transmitted light intensity, wavelength) and ambient temperature and litter moisture content, and a dedicated calculation model was established for typical coniferous, broadleaf, and herbaceous litter in the Greater Khingan Mountains forest area.

[0034] The study area is located in the Greater Khingan Mountains forest region of China. Coniferous samples were collected from fallen needles of Dahurian larch and Scots pine; broadleaf samples were collected from fallen leaves of birch and aspen; herbaceous samples were collected from litter of sedges and grasses in forest clearings and forest edges. All samples were manually cleaned of impurities and then equilibrated in a constant temperature and humidity chamber (25℃, 50%RH) for 48 hours as backup samples for the experiment.

[0035] The moisture content of the samples was precisely controlled using a "drying-humidification" method. First, a portion of the initial samples was dried to constant weight at 105℃, and the initial moisture content was calculated. Then, at 5% intervals, a sample gradient with moisture content ranging from 5% to 30% was prepared by micro-spraying and sealing for equilibration. After each optical measurement, the samples were immediately weighed using an electronic balance with an accuracy of 0.001g, and the true moisture content (M_real) was verified by the drying method.

[0036] For optical signal acquisition, 10 characteristic wavelengths (1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.1, 2.3, 2.5) within the range of 1.3 μm to 2.5 μm were selected. The sample was placed in a standard measuring fixture and illuminated with an adjustable infrared emitter. A dual-channel infrared detector simultaneously recorded the reflected light intensity. ) and transmitted light intensity ( The experiment also recorded the ambient temperature (T). A total of 900 sets of valid data were obtained, consisting of 3 types of litter × 10 wavelengths × 30 moisture content gradients. Each set of data included... , , , T and .

[0037] Data Analysis and Model Fitting Normalized light intensity is calculated based on the formula. ), and for each type of litter, use the feature vector The independent variable is the actual moisture content. As the dependent variable, multiple linear regression is used to fit and solve the coefficients. to The model's generalization ability was evaluated using 10-fold cross-validation.

[0038] In the embodiments provided by this invention, the formula for calculating the fire hazard factor is as follows: , in: F: Environmental fire hazard factor (dimensionless), ranging from 0 to 1; The lower limit of the temperature reference is set at 0℃. The upper limit of the temperature reference is set at 40℃. The upper limit of humidity is taken as 100% relative humidity. The lower limit of the humidity benchmark is taken as 20% relative humidity; Maximum wind speed, taken as 40 m / s; Temperature weighting coefficient, value 0.35, dimensionless; Humidity weighting coefficient, with a value of 0.4, dimensionless; : The weighting coefficient for wind speed, with a value of 0.25, is dimensionless; : Temperature-wind speed interaction coefficient, value 0.3, dimensionless.

[0039] In the embodiments provided by this invention, the formula for calculating normalized light intensity is: in: Normalized light intensity, ranging from 0 to 1, dimensionless; : Reflected light intensity measurement value, unit: μW / cm²; Transmitted light intensity measurement value, unit: μW / cm²; Transmitted light intensity weighting coefficient, with a value of 0.3; Initial emitted light intensity of the infrared emitter, unit: μW / cm².

[0040] In the embodiments provided by this invention, the formula for calculating the moisture content is: in: M: Moisture content of litter, in % % to : Regression coefficients of the target litter; Infrared wavelength.

[0041] In the embodiments provided by this invention, the method for calibrating the regression coefficient of the target litter is as follows: Ten groups of different wavelengths (1.3μm, 1.5μm, ..., 2.5μm) were selected, and 30 litter samples with different moisture contents (5% to 30%) were collected at each wavelength.

[0042] Simultaneously record the actual moisture content, normalized light intensity, and ambient temperature measured by the gravimetric method.

[0043] Constructing the feature matrix and target vector: Feature matrix Each row represents a sample, and the columns correspond to feature terms: Target vector The true moisture content for each sample.

[0044] The coefficients were obtained by fitting the data using a multiple linear regression algorithm. The least squares method was then used to solve the problem. in .

[0045] The regression coefficients for different types of litter are shown in Table 1.

[0046] Table 1 Regression coefficients of different types of litter In the embodiments provided by this invention, the data processing strategies executed based on the measured moisture content are as follows: when Adjust the data storage cycle to reduce data redundancy: in: The data storage period is measured in minutes, ranging from 30 minutes to 60 minutes. It is a natural exponential function; when Time: Triggers an alarm mechanism and performs sampling every minute to continuously monitor changes in moisture content; Medium moisture content scenario Time: Data is stored every 30 minutes and uploaded to the server on the hour.

[0047] The present invention also provides an online measuring device for the moisture content of litter, comprising: The front-end data acquisition module, deployed at forest monitoring points, includes temperature, humidity, and wind speed sensors. It is used to collect real-time data on temperature (T), relative humidity (H), and wind speed (V) parameters in the atmospheric environment, providing data support for fire risk factor calculation. The temperature sensor has an accuracy of ±0.5℃ and a measurement range of -30℃ to 60℃; the humidity sensor has an accuracy of ±3%RH and a measurement range of 0 to 100%RH; and the wind speed sensor has a measurement range of 0 to 30 m / s and a resolution of 0.1 m / s.

[0048] The back-end acquisition module, deployed in conjunction with the front-end acquisition module, includes a wavelength-tunable infrared emitter, a dual-channel infrared detector, and an ADC (analog-to-digital converter). The wavelength-tunable infrared emitter dynamically adjusts the infrared wavelength according to the type of litter and emits an infrared beam towards the target litter. The dual-channel infrared detector synchronously acquires the reflected and transmitted light intensity of the target litter. The ADC is connected to the dual-channel infrared detector and converts the analog signals of reflected and transmitted light intensity into digital signals. The wavelength-tunable infrared emitter has a wavelength adjustment range of 1.3μm to 2.5μm and an adjustment accuracy of 0.01μm. The dual-channel infrared detector has a light intensity detection accuracy of 0.001μW / cm². The ADC has a sampling rate of 1kHz and a resolution of 16 bits.

[0049] The main control module is connected to each sensor in the front-end acquisition module and the wavelength-tunable infrared transmitter and ADC analog-to-digital converter in the back-end acquisition module. It is used to receive environmental parameters and calculate the fire hazard coefficient. Based on the fire hazard coefficient, it determines whether to start the back-end acquisition module. After starting, it receives the digital signal output by the ADC and calculates the normalized light intensity. It further combines the infrared light wavelength and the regression coefficient corresponding to the target litter type to calculate the moisture content of the litter. The communication module, connected to the main control module, is used to transmit moisture content data, alarm information and device status to a remote terminal or cloud platform. The power module provides power support for the front-end acquisition module, back-end acquisition module, main control module and communication module, and supports solar charging and power consumption management functions to achieve long-term continuous power supply in the field.

[0050] In the embodiments provided by the present invention, the communication module is equipped with an alarm mechanism. When the moisture content is lower than the minimum threshold, the measurement time, device latitude and longitude, moisture content value, environmental parameters and device status code are transmitted simultaneously when transmitting the moisture content.

[0051] In the embodiments provided by the present invention, the wavelength of the wavelength-tunable infrared emitter is between 1.3 μm and 2.5 μm.

[0052] The wavelength-tunable infrared emitter uses a wavelength-tunable laser diode. Continuous wavelength adjustment is achieved through a PWM (Pulse Width Modulation) signal output from the main control module. The relationship between wavelength and PWM signal voltage satisfies the following formula: in: : Actual output wavelength of the infrared emitter (unit: μm); Initial wavelength, valued at 1.3μm (corresponding to the wavelength when the PWM voltage is 0V). k: Wavelength adjustment coefficient, calibrated to 0.006μm / V (meaning that for every 1V increase in PWM voltage, the wavelength increases by 0.006μm). UPWM: The PWM signal voltage (unit: V) output by the main control module, with an adjustment range of 0 to 200V and a corresponding wavelength adjustment range of 1.3μm to 2.5μm.

[0053] Example (using fallen needles as an example) Environmental parameter collection and fire hazard coefficient calculation The front-end data acquisition module measured the following: temperature T=28℃, relative humidity H=35%, and wind speed V=5m / s.

[0054] Calculate the fire hazard coefficient according to formula (1): At this point, F < threshold The subsequent acquisition module remains in sleep mode.

[0055] Post-acquisition startup and moisture content calculation If the subsequent environmental parameters change to: T=35℃, H=25%, V=8m / s, recalculate the fire hazard factor: The triggering condition is still not met; when the environmental parameters change to T=38℃, H=22%, V=12m / s: Continue waiting; when the environmental parameters change to T=40℃, H=20%, V=18m / s: Still not triggered; when the environmental parameters change to T=40℃, H=20%, V=25m / s: Until the environmental parameters become T=40℃, H=20%, V=30m / s: Once the triggering conditions are met, the subsequent data acquisition module will be started.

[0056] The main control module adjusts the wavelength of the infrared transmitter. (Needle leaf litter), measured by infrared detector =32μW / cm², =12μW / cm², =100μW / cm², calculate the normalized light intensity: Given an ambient temperature T=40℃, the regression coefficient of coniferous litter Substitute into formula (3) to calculate the moisture content: at this time The alarm is triggered, the communication module uploads the alarm information, and high-frequency sampling is started at 1-minute intervals.

[0057] Data storage cycle calculation (high moisture content example) If the moisture content is measured Calculate the storage cycle: The device stores data at approximately 52-minute intervals.

[0058] It should be noted that any parts not disclosed or specifically described in this invention are existing technology or conventional configurations, and their specific structures and working principles will not be elaborated further. In this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0059] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily implemented by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A method for online measurement of moisture content in litter, characterized in that, Includes the following steps: Collect temperature, humidity, and wind speed data from the forest environment; The fire hazard coefficient is calculated based on the collected temperature, humidity and wind speed. When the fire hazard coefficient is less than the threshold, no operation is performed. When the fire hazard factor is greater than or equal to the threshold, an infrared light wavelength that is compatible with the type of target debris is emitted towards the target debris, and the reflected light intensity and transmitted light intensity of the target debris are collected. The normalized light intensity is obtained by normalizing the reflected light intensity and the transmitted light intensity. The moisture content of the target litter was calculated using a multiple regression equation based on the normalized light intensity, infrared wavelength, and regression coefficient of the target litter.

2. The method for online measurement of moisture content in litter as described in claim 1, characterized in that, The formula for calculating the fire hazard factor is: , in: F: Environmental fire hazard factor (dimensionless), ranging from 0 to 1; Lower limit of temperature reference; Upper limit of temperature reference; Humidity baseline upper limit; Lower limit of humidity reference; Maximum wind speed; Temperature weighting coefficient, value 0.35, dimensionless; Humidity weighting coefficient, with a value of 0.4, dimensionless; : The weighting coefficient for wind speed, with a value of 0.25, is dimensionless; : Temperature-wind speed interaction coefficient, value 0.3, dimensionless.

3. The method for online measurement of moisture content in litter as described in claim 1, characterized in that, The formula for calculating normalized light intensity is: in: Normalized light intensity, ranging from 0 to 1, dimensionless; : Reflected light intensity measurement value, unit: μW / cm²; Transmitted light intensity measurement value, unit: μW / cm²; Transmitted light intensity weighting coefficient, with a value of 0.3; Initial emitted light intensity of the infrared emitter, unit: μW / cm².

4. The method for online measurement of moisture content in litter as described in claim 1, characterized in that, The formula for calculating moisture content is: in: M: Moisture content of litter, in % % to : Regression coefficients of the target litter; Infrared wavelength.

5. The method for online measurement of moisture content in litter as described in claim 4, characterized in that, The method for calibrating the regression coefficients of the target litter is as follows: Ten groups of infrared light of different wavelengths were selected, and 30 litter samples with different moisture contents were collected under each group of wavelengths. The actual moisture content, normalized light intensity and ambient temperature measured by weighing method were recorded simultaneously, and the coefficients were obtained by fitting the data using a multiple linear regression algorithm.

6. The method for online measurement of moisture content in litter as described in claim 4, characterized in that, The data processing strategies based on the measured moisture content are as follows: when Adjust the data storage cycle to reduce data redundancy: in: The data storage period is measured in minutes, ranging from 30 minutes to 60 minutes. It is a natural exponential function; when Time: Triggers an alarm mechanism and performs sampling every minute to continuously monitor changes in moisture content; Medium moisture content scenario Time: Data is stored every 30 minutes and uploaded to the server on the hour.

7. An online measurement device for the moisture content of litter, using the online measurement method for the moisture content of litter as described in any one of claims 1 to 6, characterized in that, include: The front-end data acquisition module, deployed at forest monitoring points, includes temperature sensors, humidity sensors, and wind speed sensors, used to collect forest environmental parameters in real time. The back-end acquisition module, deployed in conjunction with the front-end acquisition module, includes a wavelength-tunable infrared emitter, a dual-channel infrared detector, and an ADC analog-to-digital converter. The wavelength-tunable infrared emitter is used to dynamically adjust the infrared light wavelength according to the type of fallen debris and emit an infrared beam towards the target fallen debris; the dual-channel infrared detector is used to simultaneously collect the reflected light intensity and transmitted light intensity of the target fallen debris. The ADC analog-to-digital converter is connected to the dual-channel infrared detector and is used to convert analog signals of reflected light intensity and transmitted light intensity into digital signals. The main control module is connected to each sensor in the front-end acquisition module and the wavelength-tunable infrared transmitter and ADC analog-to-digital converter in the back-end acquisition module. It is used to receive environmental parameters and calculate the fire hazard coefficient. Based on the fire hazard coefficient, it determines whether to start the back-end acquisition module. After starting, it receives the digital signal output by the ADC and calculates the normalized light intensity. It further combines the infrared light wavelength and the regression coefficient corresponding to the target litter type to calculate the moisture content of the litter. The communication module, connected to the main control module, is used to transmit moisture content data, alarm information and device status to a remote terminal or cloud platform. The power module provides power support for the front-end acquisition module, back-end acquisition module, main control module and communication module, and supports solar charging and power consumption management functions to achieve long-term continuous power supply in the field.

8. The online moisture content measurement device for litter as claimed in claim 7, characterized in that, The communication module is equipped with an alarm mechanism. When the moisture content is lower than the minimum threshold, the measurement time, device latitude and longitude, moisture content value, environmental parameters and device status code are transmitted simultaneously when transmitting the moisture content.

9. The online moisture content measurement device for litter as claimed in claim 7, characterized in that, The wavelength of the wavelength-tunable infrared emitter is between 1.3 μm and 2.5 μm.

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