Intelligent construction method for water conservation forest engineering in loess plateau area

Abstract

The invention provides an intelligent construction method for a water conservation forest project in a loess plateau area, and relates to the field of forestry ecological restoration and water and soil conservation projects. The method comprises the following steps: collecting and carrying out normalization processing on various original physical quantities based on a space-time sliding window to obtain normalized values of the various physical quantities; constructing a composite physical state function based on the normalized values of various physical quantities, introducing a disturbance propagation response function to construct a measurement evaluation function, and performing spatial threshold screening processing based on the measurement evaluation function to generate a planting candidate point set; generating a spatial aggregation planting block set based on the planting candidate point set; and determining planting parameters based on the gradient change condition of the measurement evaluation function in the spatial aggregation planting block so as to execute planting operation. The problems that a traditional water conservation forest engineering construction method lacks modeling of spatial disturbance and neighborhood consistency, construction parameter configuration lacks intelligent adjustment, and a measurement-analysis-decision-execution closed loop structure is not formed in the whole process are solved.

Description

Technical Field

[0001] The present invention relates to the field of forestry ecological restoration and soil and water conservation engineering, and particularly to an intelligent construction method for soil and water conservation forest projects in the Loess Plateau region. Background Art

[0002] As the operation area gradually extends to marginal areas with complex landforms and changing environments such as high slopes, gully bottoms, and the junctions of terraces, the traditional mode based on uniform planning and empirical construction has been difficult to meet the actual needs of heterogeneous environments. The construction of soil and water conservation forests in the Loess Plateau has gradually entered the development stage of "fine site selection, precise configuration, and intelligent execution". There is an urgent need for a method that is based on the physical state of the real environment, integrates multi-source measurement data, and dynamically reflects the ecological suitability of the construction area as the basic support for scientific decision-making and operation implementation. Therefore, it is urgent to construct a physical quantity measurement method system that responds to the actual environment, provides a refined, data-based, and structured operation basis for soil and water conservation forest projects, and promotes their development towards intelligence, high efficiency, and low interference. Summary of the Invention

[0003] The present invention provides an intelligent construction method for soil and water conservation forest projects in the Loess Plateau region to solve the problems that the traditional construction method of soil and water conservation forest projects lacks the modeling of spatial disturbance and neighborhood consistency, resulting in misjudging areas such as high slopes and gully edges as "suitable planting areas" during construction, increasing the risk of tree death; the construction parameter configuration lacks intelligent adjustment, and the matching between tree species selection, planting density and the actual soil conditions is poor, thus causing resource waste and ecological damage; and the overall process does not form a closed-loop structure of measurement - analysis - decision - execution and cannot dynamically correct construction behaviors.

[0004] An intelligent construction method for soil and water conservation forest projects in the Loess Plateau region includes the following steps: S1. Collect various original physical quantities, perform normalization processing on various original physical quantities based on a spatio-temporal sliding window to obtain the normalized values of various physical quantities; based on the normalized values of various physical quantities, construct a composite physical state function; S2. Based on the composite physical state function, introduce a disturbance propagation response function; construct a measurement evaluation function based on the composite physical state function and the disturbance propagation response function, perform spatial threshold screening processing based on the measurement evaluation function to generate a set of candidate planting points; based on the set of candidate planting points, generate a set of spatially aggregated planting block sets; determine the planting parameters based on the gradient change of the measurement evaluation function in the spatially aggregated planting blocks, and thus execute the planting operation.

[0005] Preferably, the S1 specifically includes: Based on various original physical quantities, calculate with the measurement point as the center, The spatial neighborhood with a radius of and the sliding mean and sliding standard deviation within a time window length of are used to obtain the normalized values of various physical quantities.

[0006] Preferably, the S1 specifically includes: Based on the interaction between the non-linear coupling relationship of the normalized physical quantities in space and the time variation trend, a weighted coupling structure is adopted to construct a composite physical state function.

[0007] Preferably, the S1 specifically includes: The complete expression of the composite physical state function is:

[0008] where is the composite physical state function of the measurement point at time; is the coupling weight coefficient of the th type of physical quantity; is the normalized value of the th type of physical quantity; is the non-linear power response coefficient; is the time perturbation sensitivity factor.

[0009] Preferably, the S2 specifically includes: The perturbation propagation response function is:

[0010] where is the value of the perturbation propagation response function of the measurement point at time; is a two-dimensional integral neighborhood centered on the measurement point with a radius of ; is the composite physical state function of the measurement point at time; is the spatial scale coefficient of the Gaussian kernel function.

[0011] Preferably, the S2 specifically includes: The mathematical structure of the measurement evaluation function contains two sub-items. The first sub-item processes the ratio of the composite physical state function to the perturbation propagation response function, and then multiplies the sum of the cosine transforms of the normalized values of various physical quantities; the second sub-item is the absolute value of the second-order mixed partial derivative of the composite physical state function in the two-dimensional spatial direction; after the two sub-items are combined, the measurement quality intensity evaluation value is formed.

[0012] Preferably, the step S2 specifically includes: Based on the measurement evaluation function, perform spatial threshold screening processing, set the suitability threshold, and record all the measurement points that meet the condition that the measurement quality intensity evaluation value is greater than or equal to the suitability threshold as the planting candidate point set.

[0013] Preferably, the step S2 specifically includes: Set a threshold, determine whether the difference in the measurement quality intensity evaluation values of each measurement point in the spatial aggregated planting block is less than the threshold, so as to determine the planting parameters of each measurement point; the construction equipment receives the planting parameters and performs the tree planting operation.

[0014] The beneficial effects of the technical solution of the present invention are: 1. By constructing a composite physical state function, it describes the comprehensive influence of various physical quantities on the suitability of measurement points under non-equal weight and non-linear conditions, effectively expresses the synergistic effect between physical quantities in the real geomorphic environment, makes the output result more in line with the actual ecological conditions, and improves the accuracy and operability of the suitability evaluation.

[0015] 2. By introducing the disturbance propagation response function, it effectively suppresses common problems such as "isolated point misjudgment" and "expansion of small-scale interference", and improves the stability and robustness of the overall measurement; comprehensively considering the composite physical state function, the disturbance propagation response function and the periodic response of physical quantities, a measurement evaluation function is constructed, so that the measurement quality intensity evaluation value of each measurement point can not only reflect its physical adaptability, but also express its state relevance with the surrounding environment, which is convenient for constructing a refined spatial planting strategy.

[0016] 3. By performing spatial threshold screening processing on the measurement evaluation function and determining the planting parameters of each measurement point according to the gradient change of the measurement evaluation function in the spatial aggregated planting block, the automatic conversion from "physical measurement data" to "intelligent decision-making information" is realized, significantly reducing the traditional manual dependence, improving the construction efficiency, intelligent level and forest survival rate; in terms of planting parameter configuration, according to the change trend of the measurement quality intensity evaluation values of each measurement point in different spatial aggregated planting blocks, the most suitable tree species type, density configuration and planting depth and other information are automatically matched, realizing the precise control of the water and soil conservation forest project from fixed point, fixed species, fixed quantity to fixed depth. Brief Description of the Drawings

[0017] Figure 1 It is a flowchart of an intelligent construction method for a water and soil conservation forest project in the Loess Plateau area of the present invention. Detailed Embodiments

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solution of an intelligent construction method for a water and soil conservation forest project in the Loess Plateau region provided by the present invention in conjunction with the accompanying drawings.

[0021] Refer to the attached Figure 1 , which shows a flowchart of an intelligent construction method for a water and soil conservation forest project in the Loess Plateau region provided by an embodiment of the present invention. The method includes the following steps: S1. Collect various types of original physical quantities, perform normalization processing on various types of original physical quantities based on a spatio-temporal sliding window to obtain the normalized values of various physical quantities; based on the normalized values of various physical quantities, construct a composite physical state function.

[0022] The Loess Plateau region has the characteristics of large-scale slope undulations, dense gullies, severe wind erosion, and loose soil structure. Therefore, the acquisition unit adopts a distributed deployment method, and grid measurement points are arranged according to geomorphic zoning and slope direction changes to ensure effective coverage in the gully-slope junction area, wind erosion exposed area, and local tableland top surface.

[0023] In each acquisition unit, six types of basic physical quantities in the current area are obtained and used as original physical quantities, including surface water content, soil compactness, slope, wind speed, evaporation rate, and operation error vector. The surface water content is measured by a water collection device; the soil compactness is measured by an embedded cone pressure probe to obtain the compressive data affected by the degree of loess compaction in the soil layer at a depth of 0–20 cm; the slope information is jointly measured by a terrain inclinometer and a laser ranging device to meet the high-precision measurement requirements in the steep change zone of the gully edge; the wind speed is obtained by an anti-interference ultrasonic anemometer in the area with frequent strong winds on the plateau; the evaporation rate is obtained by combining an automatic evaporation pan with an existing meteorological correction model and further combining time; the operation error vector is measured by a laser displacement error acquisition device.

[0024] To ensure the unified computability and coupling analysis ability of various physical quantities at different time and space scales, normalization processing based on a spatio-temporal sliding window is performed on all physical quantities. After obtaining each physical quantity After that, according to its historical change trend and local environmental characteristics, calculate the spatial neighborhood centered on the measurement point and with a radius of and the moving average and standard deviation within a time window length of

[0025] to eliminate the dimensional differences between different physical quantities and the problem of inconsistent sampling scales. Convert each original physical quantity into a dimensionless standardized form. The specific formula is as follows: where is the normalized value of the th type of physical quantity; is the measured value of the th type of physical quantity at time and position and refers to any one of the surface moisture content, soil compaction, slope, wind speed, evaporation rate, and operation error vector; represents the moving average of the th type of physical quantity within a certain spatial radius and time window; is the moving standard deviation of the th type of physical quantity; is a constant to prevent division by zero error and can be taken as

[0026] Construct a composite physical state function for describing the current measurement point, which needs to fully consider the interaction between the non-linear coupling relationship in space and the time change trend of multiple normalized physical quantities; the composite physical state function should not only accurately express the influence intensity of each physical quantity on the environmental state of the current measurement point, but also reflect the synergy effect between physical quantities under non-equal weight and non-linear conditions. A weighted coupling structure is adopted to enhance the physical rationality of variable fusion. The complete expression of the composite physical state function is:

[0027] where is the composite physical state function of the measurement point at time and represents the coupling strength of the physical state of the current measurement point; is the coupling weight coefficient of the th type of physical quantity, which is set by the historical experience method and represents the action intensity of different physical quantities under a specific measurement background; is the non-linear power response coefficient, which represents the sensitivity adjustment factor of the th type of physical quantity to the composite physical state and can be set according to the change range and measurement fluctuation intensity of the physical quantity: for example, if the slope changes violently, then use Smooth its influence; use it when the moisture is stable Amplify its response; It is a time perturbation sensitivity factor, used to control the response intensity of short-term violent fluctuations, and is obtained through experiments. The composite physical state function represents the measurement point At The physical state stability intensity at the moment is a quantitative expression of the coupling state under the combined influence of various physical quantities.

[0028] S2. Based on the composite physical state function, introduce the perturbation propagation response function; construct the measurement evaluation function based on the composite physical state function and the perturbation propagation response function, perform spatial threshold screening processing based on the measurement evaluation function to generate a set of planting candidate points; generate a set of spatially aggregated planting block sets based on the set of planting candidate points; determine the planting parameters based on the gradient change of the measurement evaluation function in the spatially aggregated planting blocks, and thus perform the planting operation.

[0029] Based on the composite physical state function, to further evaluate its spatial consistency and the degree of local interference influence, it is necessary to introduce the perturbation propagation response function to measure the aggregated influence of the state interference received by the measurement point within its spatial neighborhood range; the perturbation propagation response function not only avoids the interference of isolated values during the measurement process, but also provides a mathematical measurement framework for state consistency in a continuous space, which is the key to judging whether the measurement result of the current measurement point is stable and reliable. The perturbation propagation response function is:

[0030] Among them, Is the measurement point At The value of the perturbation propagation response function at the moment, used to measure the intensity of the influence of the neighboring point state on the current measurement point at the current moment; the integration region Is centered on the measurement point For the center, Is a two-dimensional integration neighborhood with a radius, used to capture the influence of all measurement points in the local neighborhood on the state of the center point; Is the integration variable coordinate, representing the position of the neighboring space point within the integration domain that has a perturbation effect on the current measurement point; Is the measurement point At The composite physical state function at the moment; Is the spatial scale coefficient of the Gaussian kernel function, used to control the spatial weight attenuation, representing the spatial perturbation diffusion rate. The perturbation propagation response function measures the weighted influence of the state values of neighboring measurement points at the current moment on this measurement point, provides the correction ability of local perturbation to the measurement effectiveness of the measurement point, and reflects the spatial correlation and local consistency.

[0031] Construct a measurement evaluation function for the final output based on the composite physical state function and the perturbation propagation response function. The measurement evaluation function aims to quantitatively express the measurement effectiveness of the current measurement point under the comprehensive physical state, and quantitatively describe the credibility and physical integrity of the data at the current measurement point. Its mathematical structure consists of two highly coupled sub - terms. The first sub - term is obtained by taking the ratio of the composite physical state function and the perturbation propagation response function to reflect the independent significance of the physical state of the measurement point in the current spatio - temporal environment. The larger the ratio, the less the measurement point information is affected by neighborhood interference, and the more stable and reliable the state. Then, the ratio is multiplied by the sum of the cosine transforms of the normalized values of various physical quantities, which means incorporating the periodic responses of each type of normalized physical quantity (such as wind speed, evaporation rate) into the modulation term of the measurement process, thereby enhancing the measurement point's ability to identify perturbations. The second sub - term is the absolute value of the second - order mixed partial derivative of the composite physical state function in the two - dimensional spatial direction, which is used to characterize the gradient intensity of the physical state change in the area where the measurement point is located. The larger the absolute value of the second - order mixed partial derivative, the more the measurement point is in the critical area of physical variable variation, reflecting the measurement complexity or potential abnormality of the current measurement point. After the two sub - terms are combined, they constitute the measurement quality intensity evaluation value of a single measurement point in the global coupled tensor measurement field, which can be used as an important index basis for screening high - reliable points or excluding abnormal interferences in actual measurements. The form of the measurement evaluation function is:

[0032] Where, is the measurement evaluation function, indicating the measurement point at time The measurement quality intensity evaluation value is used to judge whether the measurement point is valid in the current physical environment. The measurement evaluation function consists of two items: the first item represents the physical structure integrity of the current measurement point; the second item reflects the severity of the structural changes around the measurement point. The overall expression is: based on the state of the current measurement point and its neighborhood, judge whether the measurement point has measurement stability and environmental structure uniformity through the measurement quality intensity evaluation value.

[0033] The measurement evaluation function Characterizes the "composite physical state credibility" or "planting suitability" level of each measurement point in the target construction area. The higher the final measurement quality intensity evaluation value, the more the measurement point has the natural conditions and operation guarantee capabilities of "being suitable for carrying out the planting operation of soil and water conservation forests" in multiple physical dimensions such as soil moisture, slope stability, wind speed perturbation, evaporation risk, soil hardness, and operation error.

[0034] Based on the measurement evaluation function, perform spatial threshold screening processing, and set the suitability threshold . by the expert experience method. Those that meet All the measurement points are recorded as the planting candidate point set. Each point in the planting candidate point set meets the conditions of high suitability and high stability of the composite physical quantity and has the natural condition support to be used as an afforestation site. Further spatial consistency analysis is carried out on the points in the planting candidate point set to eliminate scattered isolated points or overcrowded areas, and a set of spatially aggregated planting blocks is formed through the existing adjacent pixel clustering algorithm, which is the final planting operation planning area.

[0035] According to the gradient change of the measurement evaluation function in each spatially aggregated planting block, the planting parameters of each measurement point are determined, such as tree species selection, planting density, planting depth and orientation. If the difference between the measurement quality intensity evaluation values of each measurement point in a spatially aggregated planting block is less than the set threshold (obtained by the expert experience method), then a deep-rooted soil-fixing forest species, such as Robinia pseudoacacia, is selected and a high density value, such as 1.8 plants / m², is set; otherwise, a shallow-rooted wind erosion-resistant tree species, such as Caragana korshinskii, is selected and a low density value, such as 1.0 plants / m², is configured to reduce the mutual interference of root systems.

[0036] Finally, the construction equipment receives information such as point coordinates, planting density, tree species selection parameters, planting depth (determined by soil compactness and slope), etc., and performs tree planting operations according to the preset path sequence. The entire process revolves around the measurement evaluation function of physical quantities from beginning to end, and each planting decision made is based on the result of multi-dimensional environmental information fusion. It is a soil and water conservation forest construction implementation method with environmental adaptability as the core and data measurement as the basis.

[0037] In summary, an intelligent construction method for soil and water conservation forest projects in the Loess Plateau region has been completed.

[0038] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0039] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0040] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An intelligent construction method for water and soil conservation forest projects in the Loess Plateau region, characterized in that, It includes the following steps: S1. Collect various original physical quantities, perform normalization processing on various original physical quantities based on a spatio-temporal sliding window to obtain the normalized values of various physical quantities; construct a composite physical state function based on the normalized values of various physical quantities; S2. Based on the composite physical state function, introduce a perturbation propagation response function; construct a measurement evaluation function based on the composite physical state function and the perturbation propagation response function, perform spatial threshold screening processing based on the measurement evaluation function to generate a set of planting candidate points; generate a set of spatially aggregated planting block sets based on the set of planting candidate points; Determine the planting parameters based on the gradient change of the measurement evaluation function in the spatially aggregated planting block, and thus perform the planting operation.

2. The intelligent construction method of the soil and water conservation forest project in the Loess Plateau region according to claim 1, wherein, The specific content of S1 includes: Based on various original physical quantities, calculate the moving average and moving standard deviation within the spatial neighborhood centered at the measurement point and with a radius of and within a time period with a time window length of to obtain the normalized values of various physical quantities.

3. The intelligent construction method of the water and soil conservation forest project in the Loess Plateau region according to claim 1, characterized in that, The specific content of S1 includes: Based on the interaction between the non-linear coupling relationship of the normalized physical quantities in space and the time variation trend, adopt a weighted coupling structure to construct a composite physical state function.

4. The intelligent construction method of the water and soil conservation forest project in the Loess Plateau region according to claim 3, characterized in that, The specific content of S1 includes: The complete expression of the composite physical state function is: ； Among them, is the composite physical state function of the measurement point at moment; is the coupling weight coefficient of the th type of physical quantity; is the normalized value of the th type of physical quantity; is the nonlinear power response coefficient; is the time perturbation sensitivity factor.

5. The intelligent construction method of the water and soil conservation forest project in the Loess Plateau region according to claim 1, characterized in that, The specific content of S2 includes: The perturbation propagation response function is: ； Among them, is the disturbance propagation response function value of the measurement point at moment; is a two-dimensional integral neighborhood centered on the measurement point and with as the radius; is the composite physical state function of the measurement point at moment; is the spatial scale coefficient of the Gaussian kernel function.

6. The intelligent construction method of the water and soil conservation forest project in the Loess Plateau region according to claim 5, characterized in that, The specific content of S2 includes: The mathematical structure of the measurement evaluation function contains two sub-items. The first sub-item processes the ratio of the composite physical state function and the perturbation propagation response function, and then multiplies the sum of the cosine transforms of the normalized values of various physical quantities; the second sub-item is the absolute value of the second-order mixed partial derivative of the composite physical state function in the two-dimensional spatial direction; after the two sub-items are combined, they constitute the measurement quality intensity evaluation value.

7. The intelligent construction method of the water and soil conservation forest project in the Loess Plateau region according to claim 6, characterized in that, The specific content of S2 includes: Perform spatial threshold screening processing based on the measurement evaluation function, set an adaptability threshold, and record all the measurement point sets that satisfy the measurement quality intensity evaluation value being greater than or equal to the adaptability threshold as the set of planting candidate points.

8. The intelligent construction method of the soil and water conservation forest project in the Loess Plateau region according to claim 7, characterized in that The specific content of S2 includes: Set a threshold, judge whether the difference in the measurement quality intensity evaluation values of each measurement point in the spatially aggregated planting block is less than the threshold, so as to determine the planting parameters of each measurement point; the construction equipment receives the planting parameters and performs the tree planting operation.

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