Method and device for measuring thickness of litter layer in water conservation forest
By combining real-time data acquisition and regularized least squares method, the litter layer thickness estimation is dynamically adjusted, which solves the accuracy problem of thickness measurement in water conservation forests and improves measurement accuracy.
Patent Information
- Application Number
- CN202511097768.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-06
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2045-08-06
AI Technical Summary
Existing technologies make it difficult to accurately measure the thickness of the litter layer in soil and water conservation forests, as it is greatly affected by environmental factors such as temperature, humidity, and wind speed changes, which impacts ecological benefit assessment and the scientific formulation of soil and water conservation strategies.
By acquiring real-time temperature, humidity and wind speed data in water conservation forests, dividing the strong wind and weak wind periods, and combining the regularized least squares method, the sensitivity and adjustment coefficient of each measuring point are calculated, and the fitting estimate of the litter layer thickness is dynamically adjusted.
The accuracy of litter layer thickness measurement is improved, the interference of environmental factors is reduced, and the measurement accuracy is improved.
Smart Images
Figure CN120593639B_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of thickness measurement, and in particular to a method and device for measuring the thickness of a litter layer in a water conservation forest. Background Art
[0002] As a crucial component of water and soil conservation forest ecosystems, changes in the litter layer's thickness directly reflect vegetation cover, soil and water conservation capabilities, and ecosystem stability. Accurately measuring litter layer thickness is crucial for assessing the ecological benefits of water and soil conservation forests and developing soil and water conservation strategies.
[0003] The existing method of measuring the thickness of the litter layer is difficult to accurately measure the thickness of the litter layer in water conservation forests when measuring with a laser rangefinder. This is because the thickness of the litter layer is greatly affected by the environment. For example, changes in temperature and humidity cause the litter to absorb water and expand or dry and shrink, which in turn leads to dynamic changes in the thickness of the litter layer. In addition, in strong winds, the litter is blown away or accumulated, resulting in large differences in thickness in different areas. This makes it difficult to accurately measure the thickness of the litter layer in water conservation forests, affecting the accurate assessment of the ecological benefits of water conservation forests and the scientific formulation of soil and water conservation strategies. Summary of the Invention
[0004] In order to solve the above technical problems, a method and device for measuring the thickness of the litter layer of water conservation forest are provided to solve the existing problems.
[0005] The solution to the technical problem of this application is to provide a method and device for measuring the thickness of the litter layer of water conservation forest, including the following steps:
[0006] In a first aspect, an embodiment of the present application provides a method for measuring the thickness of a litter layer in a water conservation forest, the method comprising the following steps:
[0007] Real-time acquisition of the temperature, humidity, and litter layer thickness of each measuring point in each area of the water conservation forest at each moment during each monitoring period, as well as the wind speed of each area at each moment during each monitoring period;
[0008] Based on the wind speed in each area during different monitoring periods, each strong wind period and each weak wind period are obtained; the correlation between the periodic characteristics of temperature, humidity and thickness at each measuring point during different weak wind periods is analyzed, and the periodic correlation of each measuring point is calculated. In combination with the differences in the extreme changes in temperature, humidity and thickness during different weak wind periods, the first sensitivity of each measuring point is determined;
[0009] The wind speed fluctuation degree is calculated by analyzing the fluctuation of wind speed in the local area of each region during each strong wind cycle and the deviation of wind speed. The second sensitivity of each measuring point is determined by combining the difference in thickness of each measuring point in the local area during different strong wind cycles and the wind speed of the region to which it belongs.
[0010] According to the wind speed in each area during the current monitoring period and the first sensitivity and second sensitivity of each measuring point in the corresponding area, the coupling influence of each measuring point during the current monitoring period is determined. Combined with the discreteness of the thickness of each measuring point during the current monitoring period, the adjustment coefficient of each measuring point during the current monitoring period is obtained, and the regularization parameter of the regularized least squares method is corrected to determine the corrected regularization parameter corresponding to each measuring point during the current monitoring period. The thickness of the litter layer at each measuring point during the current monitoring period is fitted and estimated using the regularized least squares method.
[0011] Preferably, the process of obtaining each strong wind period and each weak wind period is as follows: select the maximum wind speed of each area at all times in each monitoring period and record it as the maximum wind speed; record the monitoring period in which the maximum wind speed in each area is greater than or equal to the preset wind speed threshold as a strong wind period, otherwise, it is recorded as a weak wind period.
[0012] Preferably, the calculating of the periodic correlation of each measurement point includes:
[0013] The thickness, temperature and humidity of each measuring point at all times in each weak wind cycle are trend-decomposed and the periodic intensity is calculated, which are recorded as the first intensity, the second intensity and the third intensity respectively;
[0014] Calculate the correlation between the first intensity and the second intensity and the third intensity at each measuring point in all weak wind periods, and record them as a first correlation degree and a second correlation degree respectively;
[0015] The periodic correlation is the average of the first correlation and the second correlation.
[0016] Preferably, determining the first sensitivity of each measuring point includes:
[0017] Calculate the range of thickness, temperature and humidity at each measuring point at all times during each weak wind cycle, and record them as the first range, second range and third range respectively;
[0018] The distances between the first range, the second range, and the third range of each measuring point in all weak wind cycles are recorded as the first distance and the second distance, respectively; the average of the first distance and the second distance is taken as the fluctuation difference of each measuring point;
[0019] The first sensitivity is a ratio of the period correlation to the fluctuation difference.
[0020] Preferably, the calculating of wind power fluctuation degree includes:
[0021] Divide each area into multiple time periods at all times during each strong wind cycle; perform curve fitting on the wind speed at all times in each time period, obtain the fitting curve, and calculate the fitting error;
[0022] The degree of dispersion of wind speed at all times in each time period is calculated and recorded as the first dispersion. The mean of the product of the fitting error and the first dispersion in all time periods in each strong wind cycle is taken as the wind fluctuation degree of each area in each strong wind cycle.
[0023] Preferably, determining the second sensitivity of each measuring point includes:
[0024] Calculate the difference in thickness between each measuring point in each area and the previous moment in each time period, and record it as thickness difference;
[0025] Downsampling is performed on the fitting values of the fitting curve at all times in the area to which each measurement point belongs in each time period. The distance between all thickness differences of each measurement point in each area in each time period and the fitting value after downsampling is calculated and recorded as the relative distance.
[0026] The reciprocal of the average of the relative distances of each measuring point in all time periods within each strong wind cycle is used as the coordinated change degree of each measuring point in each strong wind cycle;
[0027] The second sensitivity is the average value of the product of the wind fluctuation degree and the coordinated variation degree at each measurement point in all strong wind cycles.
[0028] Preferably, determining the coupling influence degree of each measurement point in the current monitoring period includes:
[0029] For each region, if the current monitoring period is a weak wind period, the first weight of each region in the current monitoring period is the preset first value, and the second weight is the preset second value; otherwise, the first weight is the preset second value, and the second weight is the preset first value, wherein the preset first value is greater than the preset second value;
[0030] Based on the first weight and the second weight, a weighted sum is performed on the first sensitivity and the second sensitivity of each measuring point in each area to obtain the coupling influence of each measuring point in the current monitoring period.
[0031] Preferably, obtaining the adjustment coefficient of each measuring point in the current monitoring period includes:
[0032] Calculate the discrete degree of thickness of each measuring point at all times in the current monitoring period, which is recorded as the second discrete degree;
[0033] The adjustment coefficient is a normalized result of the product of the second dispersion and the coupling influence.
[0034] Preferably, the current monitoring period The measurement points correspond to the corrected regularization parameters The calculation formula is: ,in, To preset the initial parameters, The current monitoring period Adjustment factor for each measuring point.
[0035] In the second aspect, an embodiment of the present application also provides a device for measuring the thickness of the litter layer of a water conservation forest, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for measuring the thickness of the litter layer of a water conservation forest.
[0036] This application has at least the following beneficial effects:
[0037] This application divides the monitoring period into a strong wind period and a weak wind period according to the size of the wind speed in each area during different monitoring periods. The beneficial effect of this application is that it facilitates the subsequent study of the impact of wind speed on the thickness of the litter layer during the strong wind period, and the impact of temperature and humidity on the thickness of the litter layer during the weak wind period; the periodic correlation of each measuring point is calculated, and the beneficial effect of this application is that it preliminarily evaluates the impact of temperature and humidity changes in the environment on the measurement of litter layer thickness through the correlation between the periodic changes in thickness and the periodic changes in temperature and humidity during the weak wind period; secondly, the first sensitivity of each measuring point is determined. Its beneficial effect is that it takes into account the difference between the thickness variation range and the temperature and humidity variation range under the weak wind cycle, and combines the period correlation to comprehensively evaluate the degree to which the thickness of the litter layer is affected by the temperature and humidity changes under the weak wind cycle; further, the wind fluctuation degree is calculated, which has the beneficial effect of taking into account the fluctuation of wind speed in the local time period under the strong wind cycle, reflecting the short-term frequent fluctuation of wind speed under the strong wind cycle, so as to evaluate the short-term frequent fluctuation characteristics of wind speed changes under the strong wind cycle; the second sensitivity of each measuring point is obtained, which has the beneficial effect of taking into account the fluctuation of wind speed in the strong wind cycle The synergy between the change in thickness and wind speed in a local time period, combined with the wind fluctuation, reflects the influence of the short-term frequent fluctuation of wind speed and gust effect on the thickness of the litter layer, so as to evaluate the influence of wind interference on the thickness of the litter layer; determine the coupling influence of each measuring point in the current monitoring period, which has the beneficial effect of taking into account the strong or weak wind situation in the current monitoring period, and then evaluate the significance of the combined influence of temperature, humidity and wind on the thickness of the litter layer at different measuring points; obtain the adjustment coefficient of each measuring point in the current monitoring period, and adjust the regularized least squares The regularization parameters of the method are corrected to determine the corrected regularization parameters corresponding to each measuring point in the current monitoring period, and the thickness of the litter layer at each measuring point in the current monitoring period is fitted and estimated by the regularized least squares method. Its beneficial effect is that the regularization parameters of the regularized least squares method are dynamically adjusted according to the fluctuation of the thickness of the litter layer and its significant influence by the combined effect of temperature, humidity and wind force, so as to reduce the interference of environmental factors, improve the fitting accuracy of the litter layer thickness at each measuring point, and improve the accuracy of estimating the litter layer thickness at different measuring points in water-protected forests. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] The following is a further detailed description of a method for measuring the thickness of the litter layer of a water conservation forest in accordance with the present application, in conjunction with the accompanying drawings.
[0039] Figure 1 A flowchart of a method for measuring the thickness of a litter layer in a water conservation forest provided in an embodiment of the present application;
[0040] Figure 2A flowchart of the steps of the method for obtaining the corrected regularization parameter provided in an embodiment of the present application. DETAILED DESCRIPTION
[0041] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the following, in conjunction with the accompanying drawings and implementation examples, further describes in detail a method and device for measuring the thickness of the litter layer of a water conservation forest proposed in this application. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.
[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
[0043] See also Figure 1 , which shows a flowchart of a method for measuring the thickness of a litter layer of a water conservation forest provided by an embodiment of the present application, the method comprising the following steps:
[0044] Step 1: Real-time acquisition of the temperature, humidity, and litter layer thickness of each measuring point in each area of the water conservation forest at each moment in each monitoring period, as well as the wind speed of each area at each moment in each monitoring period.
[0045] As a key component of the water conservation forest ecosystem, the litter layer significantly impacts soil protection and water retention. The litter layer mitigates the direct impact of rainwater on the soil, reduces surface runoff velocity, and thus reduces soil erosion. Litter layer thickness is a key indicator of the ecological function of water conservation forests. Measuring litter layer thickness in different areas of water conservation forests can provide a scientific basis for improving soil erosion, forest stand management, and ecological restoration.
[0046] Secondly, litter includes organic matter such as leaves, branches, fruits, and peels that fall naturally from plants. The litter layer is usually composed of three layers, from bottom to top, namely the decomposition layer, the fermentation layer, and the fresh litter layer. During the decomposition process, they will release the nutrients they contain into the soil, which can maintain the growth and renewal of the water conservation forest itself and maintain the stability of the water conservation forest ecosystem.
[0047] Based on the above analysis, the thickness of litter in different areas of the water conservation forest was measured, specifically:
[0048] Multiple measurement points are selected in each area of the water conservation forest, and a laser rangefinder is installed at each measurement point to collect the thickness of the litter layer at each measurement point in each area in real time;
[0049] In this embodiment, based on the installation height of the laser rangefinder at a certain measuring point, that is, the distance from the laser rangefinder to the ground; and measuring the interval distance from the laser rangefinder to the upper surface of the litter layer by the laser rangefinder, the difference between the installation height and the interval distance is the thickness of the litter layer at the measuring point.
[0050] Secondly, when measuring the thickness of the litter layer, it is easily affected by environmental factors such as temperature, humidity, and wind speed. For example, in a high humidity environment, the litter will absorb moisture from the air, and the leaves and branches in the litter will swell and gain weight due to the water absorption. This makes the originally loose litter layer more compact, resulting in the actual measured thickness being smaller than its thickness in its naturally dry state. Conversely, in a dry environment, the litter will lose moisture, becoming dry and brittle. In this case, the litter layer may become relatively loose and its thickness may increase. Secondly, strong winds can blow the litter away, resulting in localized over-thickness or over-thinness, resulting in the measured thickness not accurately reflecting the actual condition of the litter layer in that area.
[0051] Based on the above analysis, temperature and humidity sensors are installed at each measurement point in each area to collect temperature and humidity at each moment in real time. Wind speed sensors are also installed in each area to collect wind speed in each area at different moments in real time.
[0052] In this embodiment, the data collection time interval for the thickness, temperature and humidity of the litter layer is 15s. Since the wind speed changes frequently, the data collection time interval for the wind speed is set to 1s. As other implementation methods, the implementer can set it according to actual conditions.
[0053] All moments in a day are recorded as a monitoring cycle. As other implementation methods, implementers can set them according to actual conditions to obtain the thickness of each measuring point in each area of the water conservation forest at all moments in each monitoring cycle and the wind speed of each area at all moments in each monitoring cycle.
[0054] It should be noted that continuously monitoring the data of each measurement point in each area every day in a quarter is another implementation method that the implementer can set according to actual conditions.
[0055] At this point, the thickness, temperature, and humidity of each measuring point in each area of the water conservation forest at all times during each monitoring period, as well as the wind speed of each area at all times during each monitoring period, are obtained.
[0056] Step 2: Based on the wind speed in each area during different monitoring periods, obtain each strong wind period and each weak wind period; analyze the correlation between the periodic characteristics of temperature, humidity, and thickness at each measuring point during different weak wind periods, calculate the periodic correlation of each measuring point, and determine the first sensitivity of each measuring point based on the differences in the extreme changes in temperature, humidity, and thickness during different weak wind periods.
[0057] Because the surface of litter is usually uneven and easily affected by environmental factors, the measurement results of the litter layer thickness fluctuate greatly and there are large measurement errors. Specifically, when the temperature is high, the cellulose and lignin components in the litter layer will expand due to heat, causing the litter layer thickness to increase. When the temperature drops, the litter layer will contract due to cooling, and the thickness will decrease. Litter has a strong water absorption capacity. When the ambient humidity is high, such as after rain or in foggy conditions, the litter will absorb moisture from the air, expand in volume, and increase in thickness. Conversely, in a dry environment, the moisture in the litter layer will continue to dissipate, shrink in volume, and decrease in thickness. Secondly, strong winds may cause the litter layer to blow away or aggregate, resulting in significant thickness changes.
[0058] Based on the above analysis, the impact of strong wind on litter thickness is more significant than that of temperature and humidity changes. For example, under strong wind weather, the change in litter thickness is mainly affected by wind speed, while the influence of temperature and humidity changes is relatively weak. By analyzing the wind speed in different monitoring periods, the monitoring period is divided according to wind intensity, specifically:
[0059] The maximum wind speed of each area at all times during each monitoring period is selected and recorded as the maximum wind speed;
[0060] The monitoring period in which the maximum wind speed in each area is greater than or equal to the preset wind speed threshold is recorded as a strong wind period, otherwise it is recorded as a weak wind period;
[0061] In this embodiment, the preset wind speed threshold is 8 m / s. As other implementation methods, the implementer can set it according to actual conditions.
[0062] Secondly, during a weak wind cycle, temperature and humidity will fluctuate periodically with the alternation of day and night, causing the overall thickness of the litter layer during the weak wind cycle to also exhibit corresponding periodic variation characteristics. The more significant the periodic variation characteristics between temperature, humidity and litter layer thickness, the greater the degree to which the litter layer thickness during the weak wind cycle is affected by changes in the temperature and humidity environment. Therefore, the periodic correlation is calculated based on the periodic changes in temperature, humidity and litter layer thickness at each measurement point during the weak wind cycle. Specifically,
[0063] The thickness of each measuring point at all times in each weak wind cycle is trend-decomposed, and the periodic intensity is calculated and recorded as the first intensity;
[0064] The temperature trend of each measuring point at all times in each weak wind period is decomposed, and the periodic intensity is calculated and recorded as the second intensity;
[0065] The humidity at each measuring point at all times during each weak wind period is trend-decomposed, and the periodic intensity is calculated and recorded as the third intensity;
[0066] In this embodiment, the STL trend decomposition algorithm (Seasonal and Trend decomposition using Loess) is used for trend decomposition. The STL trend decomposition algorithm and the calculation of periodic intensity are well-known technologies and will not be described in detail here. and the residual , the calculation formula of periodic intensity is: ,in, is the periodic intensity, is the variance of the residual term, is the variance of the seasonal term and the residual term, is the maximum value function.
[0067] Calculating the correlation between the first intensity and the second intensity at each measuring point in all weak wind periods, recorded as a first correlation;
[0068] Calculating the correlation between the first intensity and the third intensity at each measuring point in all weak wind periods, and recording it as a second correlation;
[0069] In this embodiment, the degree of correlation is obtained by calculating the Spearman correlation coefficient between the first intensity of each measuring point and the second intensity and the third intensity in all weak wind cycles, and taking the absolute value of the Spearman correlation coefficient as the first correlation and the second correlation, respectively. The calculation of the Spearman correlation coefficient is a well-known technology and will not be described in detail here. As other implementation methods, the implementer may adopt other methods of the existing technology, such as the Pearson correlation coefficient, etc., and this embodiment does not impose any special restrictions on this.
[0070] Taking the average of the first correlation and the second correlation as the period correlation of each measurement point;
[0071] It should be noted that the greater the periodic correlation, the more significant the influence of periodic changes in the thickness of the litter layer at each measurement point during the weak wind period on the periodic changes in temperature and humidity environmental factors, that is, changes in temperature and humidity in the environment have a greater impact on the measurement of the thickness of the litter layer.
[0072] Secondly, under the influence of temperature and humidity during the weak wind period, the extreme variation range of the litter layer thickness is correlated with the temperature and humidity variation range. Combined with the periodic correlation, the first sensitivity is calculated as follows:
[0073] Calculate the range of thickness at each measuring point at all times during each weak wind cycle and record it as the first range;
[0074] Calculate the temperature range of each measuring point at all times during each weak wind cycle and record it as the second range;
[0075] Calculate the range of humidity at each measuring point at all times during each weak wind cycle, and record it as the third range;
[0076] The distance between the first extreme difference and the second extreme difference of each measuring point in all weak wind periods is recorded as a first distance;
[0077] The distance between the first extreme difference and the third extreme difference of each measuring point in all weak wind cycles is recorded as the second distance;
[0078] In this embodiment, the distance is measured by calculating the DTW distance between the first range and the second range and the third range of each measuring point in all weak wind cycles, wherein the DTW distance is a well-known technology and will not be described here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as Euclidean distance, etc., and this embodiment does not impose any special restrictions on this.
[0079] Taking the average of the first distance and the second distance as the fluctuation difference of each measurement point;
[0080] It should be noted that the smaller the fluctuation difference, the more consistent the amplitude change of the litter layer thickness at each measurement point in the weak wind period is with the fluctuation change of temperature and humidity, reflecting that the measurement of the litter layer thickness is greatly affected by temperature and humidity.
[0081] Furthermore, based on the period correlation and the fluctuation difference, a first sensitivity is determined, specifically:
[0082] Using the ratio of the period correlation to the fluctuation difference as the first sensitivity of each measurement point;
[0083] It should be noted that the greater the first sensitivity, the more sensitive the change in the litter layer thickness at the measurement point is to changes in temperature and humidity during the weak wind period, reflecting that the change in the litter layer thickness is greatly affected by changes in temperature and humidity.
[0084] At this point, the first sensitivity of each measurement point is obtained.
[0085] Step 3: Calculate the wind fluctuation degree by analyzing the fluctuation of wind speed in the local area of each region during each strong wind cycle and the deviation of wind speed. Determine the second sensitivity of each measuring point by combining the difference in thickness of each measuring point in the local area during different strong wind cycles and the wind speed of the region to which it belongs.
[0086] Furthermore, during periods of strong winds, wind speed has a greater impact on the measurement of litter layer thickness. In strong winds, the wind can blow away the litter layer, causing the litter layer thickness in different areas to be too thin or to accumulate. Secondly, wind speed changes are characterized by frequent fluctuations in a short period of time, and there is a gust phenomenon, that is, the wind speed increases rapidly in a short period of time and then decreases rapidly. When the wind speed fluctuates more significantly in a short period of time, or the greater the impact of the gust effect, the higher the amplitude of the change in the litter layer thickness at the measurement point, it means that the litter layer thickness is more affected by the strong wind.
[0087] First, the wind speed fluctuation degree is calculated for each region in each strong wind period based on the short-term fluctuation of wind speed in the local area. Specifically,
[0088] Divide all moments of each region in any strong wind cycle into multiple time periods;
[0089] In this embodiment, the duration of a time period is 10 minutes. As for other implementation methods, the implementer can set it according to actual conditions.
[0090] Perform curve fitting on the wind speed at all times in each time period, obtain the fitting curve, and calculate the fitting error;
[0091] In this embodiment, the least squares method is used for curve fitting, and the fitting error is measured by calculating the mean square error. The least squares method and the calculation of the mean square error are well-known technologies and will not be described in detail here. As other implementation methods, the implementer can use other existing methods to measure the fitting error, for example, the root mean square error, the mean absolute error, etc. This embodiment does not impose any special restrictions on this.
[0092] Calculate the degree of dispersion of wind speed at all times in each time period, recorded as the first dispersion;
[0093] In this embodiment, the degree of dispersion is measured by calculating the standard deviation of the wind speed at all times in each time period. As other implementation methods, the implementer can adopt other methods of the existing technology, such as variance, coefficient of variation, etc., and this embodiment does not impose any special restrictions on this.
[0094] Calculating the mean of the product of the fitting error and the first discreteness in all time periods within any one strong wind cycle as the wind fluctuation degree of any one strong wind cycle;
[0095] It should be noted that, the larger the fitting error, the more frequent the wind speed is above or below the fitting value, reflecting the frequent occurrence of strong gust effects; the larger the first discreteness, the more obvious the alternation between gusts and calm winds, and the drastic change in wind speed; the larger the wind fluctuation, the more severe the mutation and volatility of wind speed changes under the strong wind cycle, and the significant short-term frequent fluctuation characteristics.
[0096] Secondly, under the effect of gusts, the thickness of the litter layer will also change significantly, and this thickness change is more significant after the gusts. Therefore, we analyze the consistency of the trend of thickness change and wind speed change in each time period and calculate the degree of coordinated change, which is specifically:
[0097] Calculate the difference in thickness between each measuring point in each area and the previous moment in each time period, and record it as thickness difference;
[0098] Downsampling is performed on the fitting values of the fitting curve at all times in the area to which each measurement point belongs in each time period. The distance between all thickness differences of each measurement point in each area and the fitting value after downsampling is calculated and recorded as the relative distance.
[0099] It should be noted that the downsampling process is a well-known technology. Since the sampling time intervals of wind speed and thickness are different, in this embodiment, the wind speed collection time interval is 1s, and the thickness collection time interval is 15s. Therefore, one thickness corresponds to 15 wind speeds. By downsampling the fitting values at all moments on the fitting curve corresponding to each time period, the mode of all fitting values within every 15s is used as the wind speed corresponding to the thickness at one moment, so that the number of all thickness differences in each time period is consistent with the number of fitting values after downsampling.
[0100] In this embodiment, the SBD (Shape Based Distance) distance between all thickness differences in each time period and the fitting value after downsampling is calculated and recorded as the relative distance. The calculation of the SBD distance is a well-known technology and will not be repeated here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as DTW distance, etc., and this embodiment does not impose any special restrictions on this.
[0101] The reciprocal of the average of the relative distances of each measuring point in all time periods within any strong wind cycle is used as the coordinated variation degree of each measuring point in any strong wind cycle;
[0102] It should be noted that the larger the relative distance, the more inconsistent the change in the thickness of the litter layer is with the changing trend of the wind speed. Conversely, if the change in the thickness of the litter layer is consistent with the changing trend of the wind speed, the greater the obtained coordinated change degree, indicating that the wind speed has a greater impact on the thickness of the litter layer under this strong wind cycle.
[0103] Furthermore, based on the wind power fluctuation degree and the coordinated variation degree, a second sensitivity is determined, specifically:
[0104] The average value of the product of the wind fluctuation degree and the coordinated variation degree at each measuring point in all strong wind periods is used as the second sensitivity of each measuring point;
[0105] It should be noted that the greater the second sensitivity, the more the measured thickness of the litter layer at the measurement point is affected by the short-term frequent fluctuations in wind speed and the gust effect, reflecting that the measured thickness of the litter layer at the measurement point is greatly affected by wind interference.
[0106] At this point, the second sensitivity of each measurement point is obtained.
[0107] Step 4: Determine the coupling influence of each measuring point in the current monitoring period based on the wind speed in each area and the first sensitivity and second sensitivity of each measuring point in the corresponding area during the current monitoring period. Combined with the discreteness of the thickness of each measuring point during the current monitoring period, obtain the adjustment coefficient of each measuring point during the current monitoring period, correct the regularization parameter of the regularized least squares method, determine the corrected regularization parameter corresponding to each measuring point during the current monitoring period, and use the regularized least squares method to fit and estimate the thickness of the litter layer at each measuring point during the current monitoring period.
[0108] Furthermore, since the change in litter layer thickness is mainly affected by changes in temperature and humidity during weak wind periods, and is mainly affected by changes in wind speed during strong wind periods, the coupling influence is calculated by combining the first sensitivity and the second sensitivity based on the wind speed of each area in the current monitoring period. Specifically,
[0109] For each region, if the current monitoring period is a weak wind period, the first weight of each region in the current monitoring period is the preset first value, and the second weight is the preset second value; otherwise, the first weight of each region in the current monitoring period is the preset second value, and the second weight is the preset first value, wherein the preset first value is greater than the preset second value;
[0110] In this embodiment, the preset first value is 0.8, and the preset second value is 0.2. As other implementation methods, the implementer can set them according to actual conditions.
[0111] Based on the first weight and the second weight, performing a weighted summation of the first sensitivity and the second sensitivity of each measuring point in each area to obtain a coupling influence degree of each measuring point in the current monitoring period;
[0112] In this embodiment, the calculation formula for the coupling influence degree of each measurement point in the current monitoring period is:
[0113] ;
[0114] in, is the coupling influence of the mth measurement point in the current monitoring period, is the first weight, is the second weight, is the first sensitivity of the mth measurement point, is the second sensitivity of the mth measurement point.
[0115] It should be noted that the coupling influence reflects the comprehensive influence of temperature, humidity and wind speed on the thickness. The greater the coupling influence, the more significant the change in the thickness of the litter layer at the measuring point during the current monitoring period is affected by the comprehensive influence of temperature, humidity and wind force, reflecting that there is a large error in the measurement of the thickness of the litter layer.
[0116] Secondly, the thickness of the litter layer at each measuring point will also change to a certain extent during the current monitoring period. The adjustment coefficient is calculated based on the fluctuation of the thickness at each measuring point during the current monitoring period and the degree of influence of environmental factors. Specifically, it is:
[0117] Calculate the discrete degree of thickness of each measuring point at all times in the current monitoring period, which is recorded as the second discrete degree;
[0118] In this embodiment, the degree of dispersion is measured by calculating the information entropy of the thickness of each measuring point at all times in the current monitoring period, wherein the calculation of information entropy is a well-known technology and will not be repeated here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as coefficient of variation, variance, etc., and this embodiment does not impose any special restrictions on this.
[0119] Normalizing a product of the second dispersion and the coupling influence as an adjustment coefficient for each measurement point in a current monitoring period;
[0120] In this embodiment, the tanh function is used for normalization processing, wherein the tanh function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the softmax function, the sigmoid function, etc., and this embodiment does not impose any special restrictions on this.
[0121] It should be noted that the larger the second discreteness, the more complex the fluctuation of the litter layer thickness and the more drastic the fluctuation; the larger the obtained adjustment coefficient, the more obvious the fluctuation degree of the litter layer thickness and the degree of environmental influence, and the more significant the interference of environmental factors on the measurement of the litter layer thickness.
[0122] Furthermore, if the fluctuation of litter layer thickness is more severe and the interference by environmental factors is more significant, then when fitting and correcting the litter layer thickness at each measurement point using the regularized least squares method, a larger regularization parameter should be set to reduce the interference effect of environmental factors and improve fitting accuracy. Conversely, a smaller regularization parameter should be set to avoid overfitting. Therefore, the regularization parameter of the regularized least squares method is corrected by the adjustment coefficient, specifically:
[0123] ;
[0124] in, The current monitoring period The measurement points correspond to the corrected regularization parameters, To preset the initial parameters, The current monitoring period Adjustment factor for each measuring point.
[0125] In this embodiment, the initial parameters are preset The value is 0.1, so The value range is As other implementation methods, implementers can set them according to actual conditions.
[0126] Based on the corrected regularization parameter, the regularized least squares method is used to fit the thickness of each measurement point at all times in the current monitoring period, so as to more accurately estimate the thickness of the litter layer and improve the accuracy of the litter layer thickness estimation.
[0127] It should be noted that the regularized least squares method is a well-known technology and will not be described in detail here.
[0128] Furthermore, the flowchart of the method for obtaining the modified regularization parameter provided in the embodiment of the present application is as follows: Figure 2 shown.
[0129] Based on the same inventive concept as the above method, an embodiment of the present application also provides a device for measuring the thickness of the litter layer of a water conservation forest, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned methods for measuring the thickness of the litter layer of a water conservation forest are implemented.
[0130] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.
[0131] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0132] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the present application. It should be noted that a person skilled in the art can make various modifications and improvements without departing from the spirit of the present application. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiments made in accordance with the technical essence of the present application without departing from the content of the present application's technical solution fall within the scope of protection of the present application's technical solution.
Claims
1. A method for measuring the thickness of the litter layer of a water conservation forest, characterized in that: The method comprises the following steps: Real-time acquisition of the temperature, humidity, and litter layer thickness of each measuring point in each area of the water conservation forest at each moment during each monitoring period, as well as the wind speed of each area at each moment during each monitoring period; Based on the wind speed in each area during different monitoring periods, each strong wind period and each weak wind period are obtained; the correlation between the periodic characteristics of temperature, humidity and thickness at each measuring point during different weak wind periods is analyzed, and the periodic correlation of each measuring point is calculated. In combination with the differences in the extreme changes in temperature, humidity and thickness during different weak wind periods, the first sensitivity of each measuring point is determined; The wind speed fluctuation degree is calculated by analyzing the fluctuation of wind speed in the local area of each region during each strong wind cycle and the deviation of wind speed. The second sensitivity of each measuring point is determined by combining the difference in thickness of each measuring point in the local area during different strong wind cycles and the wind speed of the region to which it belongs. According to the wind speed in each area during the current monitoring period and the first sensitivity and second sensitivity of each measuring point in the corresponding area, the coupling influence of each measuring point during the current monitoring period is determined. Combined with the discreteness of the thickness of each measuring point during the current monitoring period, the adjustment coefficient of each measuring point during the current monitoring period is obtained, and the regularization parameter of the regularized least squares method is corrected to determine the corrected regularization parameter corresponding to each measuring point during the current monitoring period. The thickness of the litter layer at each measuring point during the current monitoring period is fitted and estimated using the regularized least squares method.
2. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 1, wherein: The process of obtaining each strong wind period and each weak wind period is as follows: the maximum wind speed of each area at all times in each monitoring period is selected and recorded as the maximum wind speed; the monitoring period in which the maximum wind speed in each area is greater than or equal to the preset wind speed threshold is recorded as a strong wind period, otherwise it is recorded as a weak wind period.
3. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 1, wherein: Calculating the periodic correlation of each measurement point includes: The thickness, temperature and humidity of each measuring point at all times in each weak wind cycle are trend-decomposed and the periodic intensity is calculated, which are recorded as the first intensity, the second intensity and the third intensity respectively; Calculate the correlation between the first intensity and the second intensity and the third intensity at each measuring point in all weak wind periods, and record them as a first correlation degree and a second correlation degree respectively; The periodic correlation is the average of the first correlation and the second correlation.
4. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 1, wherein: Determining the first sensitivity of each measuring point includes: Calculate the range of thickness, temperature and humidity at each measuring point at all times during each weak wind cycle, and record them as the first range, second range and third range respectively; The distances between the first range, the second range, and the third range of each measuring point in all weak wind cycles are recorded as the first distance and the second distance, respectively; the average of the first distance and the second distance is taken as the fluctuation difference of each measuring point; The first sensitivity is a ratio of the period correlation to the fluctuation difference.
5. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 1, wherein: The calculation of wind fluctuation degree includes: Divide each area into multiple time periods at all times during each strong wind cycle; perform curve fitting on the wind speed at all times in each time period, obtain the fitting curve, and calculate the fitting error; The degree of dispersion of wind speed at all times in each time period is calculated and recorded as the first dispersion. The mean of the product of the fitting error and the first dispersion in all time periods in each strong wind cycle is taken as the wind fluctuation degree of each area in each strong wind cycle.
6. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 5, wherein: Determining the second sensitivity of each measuring point includes: Calculate the difference in thickness between each measuring point in each area and the previous moment in each time period, and record it as thickness difference; Downsampling is performed on the fitting values of the fitting curve at all times in the area to which each measurement point belongs in each time period. The distance between all thickness differences of each measurement point in each area in each time period and the fitting value after downsampling is calculated and recorded as the relative distance. The reciprocal of the average of the relative distances of each measuring point in all time periods within each strong wind cycle is used as the coordinated change degree of each measuring point in each strong wind cycle; The second sensitivity is the average value of the product of the wind fluctuation degree and the coordinated variation degree at each measurement point in all strong wind cycles.
7. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 1, wherein: Determining the coupling influence of each measurement point in the current monitoring period includes: For each region, if the current monitoring period is a weak wind period, the first weight of each region in the current monitoring period is the preset first value, and the second weight is the preset second value; otherwise, the first weight is the preset second value, and the second weight is the preset first value, wherein the preset first value is greater than the preset second value; Based on the first weight and the second weight, a weighted sum is performed on the first sensitivity and the second sensitivity of each measuring point in each area to obtain the coupling influence of each measuring point in the current monitoring period.
8. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 1, wherein: The adjustment coefficient of each measuring point in the current monitoring period is obtained, including: Calculate the discrete degree of thickness of each measuring point at all times in the current monitoring period, which is recorded as the second discrete degree; The adjustment coefficient is a normalized result of the product of the second dispersion and the coupling influence.
9. The method for measuring the thickness of the litter layer of a water conservation forest according to claim 1, wherein: The current monitoring period The measurement points correspond to the corrected regularization parameters The calculation formula is: ,in, To preset the initial parameters, The current monitoring period Adjustment factor for each measuring point.
10. A device for measuring the thickness of a litter layer in a water conservation forest, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for measuring the thickness of the litter layer of a water conservation forest as described in any one of claims 1 to 9 are implemented.