An intelligent construction method for soil and water conservation forest projects in the Loess Plateau region

Abstract

The present invention provides an intelligent construction method for water conservation and forestry projects in the Loess Plateau region, which relates to the fields of forestry ecological restoration and water and soil conservation projects. The content includes: collecting and normalizing various original physical quantities based on a time-space sliding window to obtain normalized values of various physical quantities; constructing a composite physical state function based on the normalized values of various physical quantities, and introducing a disturbance propagation response function to construct a measurement evaluation function, performing spatial threshold screening processing based on the measurement evaluation function to generate a set of planting candidate points; generating a set of spatially aggregated planting blocks based on the set of planting candidate points; determining planting parameters based on the gradient change of the measurement evaluation function in the spatially aggregated planting blocks, and thus executing the planting operation. This method solves the problems of traditional water conservation and forestry project construction methods, such as the lack of modeling of spatial disturbances and neighborhood consistency, the lack of intelligent adjustment of construction parameter configuration, and the failure of the overall process to form a closed-loop structure of measurement-analysis-decision-execution.

Description

Technical Field

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

[0002] As the operation area gradually extends to marginal areas with complex topography and variable environments, such as high slopes, gully bottoms, and the junction of tablelands, the traditional model based on uniform planning and empirical construction has become difficult to adapt to the actual needs of the heterogeneous environment. Water conservation forest construction on the Loess Plateau has gradually entered a development stage of "refined site selection, precise configuration, and intelligent execution." There is an urgent need for a method based on the physical state of the real environment, integrating multi-source measurement data, and dynamically reflecting the ecological suitability of the construction area to serve as the foundation for scientific decision-making and operation implementation. Therefore, it is urgent to establish a physical quantity measurement method system that responds to actual environmental conditions to provide a refined, data-based, and structured operation foundation for water conservation forest projects, and promote their development towards intelligence, high efficiency, and low interference. Summary of the Invention

[0003] The present invention provides an intelligent construction method for water conservation and forestry projects in the Loess Plateau region, so as to solve the problems that traditional water conservation and forestry project construction methods lack modeling of spatial disturbance and neighborhood consistency, resulting in the misjudgment of high slopes, gully edges and other areas as "suitable planting areas" during the construction process, increasing the risk of tree death; the construction parameter configuration lacks intelligent adjustment, and the tree species selection and planting density are poorly matched with the actual soil conditions, thus causing resource waste and ecological damage; and the overall process does not form a closed-loop structure of measurement-analysis-decision-making-execution, and cannot dynamically correct construction behavior.

[0004] An intelligent construction method for a water conservation and forestry project in the Loess Plateau region comprises the following steps:

[0005] S1. Collect various original physical quantities and perform normalization processing on them based on a time-space sliding window to obtain normalized values of the various physical quantities; construct a composite physical state function based on the normalized values of the various physical quantities;

[0006] S2. Based on the composite physical state function, a disturbance propagation response function is introduced; based on the composite physical state function and the disturbance propagation response function, a measurement evaluation function is constructed, and spatial threshold screening is performed based on the measurement evaluation function to generate a set of planting candidate points; based on the set of planting candidate points, a set of spatially aggregated planting blocks is generated; based on the gradient change of the measurement evaluation function in the spatially aggregated planting blocks, the planting parameters are determined to perform the planting operation.

[0007] Preferably, the S1 specifically includes:

[0008] Based on various original physical quantities, calculations are performed based on the measured points. Centered on The spatial neighborhood with radius is and the time window length is The sliding mean and sliding standard deviation within the time period are calculated to obtain the normalized values of various physical quantities.

[0009] Preferably, the S1 specifically includes:

[0010] Based on the interaction between the nonlinear coupling relationship of normalized physical quantities in space and the temporal variation trend, a weighted coupling structure is adopted to construct a composite physical state function.

[0011] Preferably, the S1 specifically includes:

[0012] The complete expression of the composite physical state function is:

[0013]

[0014] in, It is a measuring point exist Composite physical state function at time; For the Coupling weight coefficient of quasi-physical quantity; It is Normalized value of quasi-physical quantity; is the nonlinear power response coefficient; is the time disturbance sensitivity factor.

[0015] Preferably, the S2 specifically includes:

[0016] The disturbance propagation response function is:

[0017]

[0018] in, It is a measuring point exist The disturbance propagation response function value at time; For measuring points Centered on is the two-dimensional integration neighborhood of radius; It is a measuring point exist Composite physical state function at time; is the spatial scale coefficient of the Gaussian kernel function.

[0019] Preferably, the S2 specifically includes:

[0020] The mathematical structure of the measurement evaluation function contains two sub-items. The first sub-item performs ratio processing on the composite physical state function and the disturbance propagation response function, and then multiplies it with the cosine transform sum 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 direction of space; the two sub-items are combined to form the measurement quality intensity evaluation value.

[0021] Preferably, the S2 specifically includes:

[0022] Based on the measurement evaluation function, spatial threshold screening is performed, and a planting suitability threshold is set. All measurement point sets that meet the measurement quality intensity evaluation value greater than or equal to the planting suitability threshold are recorded as the planting candidate point set.

[0023] Preferably, the S2 specifically includes:

[0024] A threshold is set to determine whether the difference in the measurement quality intensity assessment values of each measuring point in the spatially aggregated planting block is less than the threshold, thereby determining the planting parameters of each measuring point; the construction equipment receives the planting parameters and performs the tree planting operation.

[0025] The beneficial effects of the technical solution of the present invention are:

[0026] 1. By constructing a composite physical state function, the comprehensive influence of various physical quantities on the suitability of the measuring points under unequal weight and nonlinear conditions is described, which effectively expresses the synergistic effect between physical quantities in the real geomorphic environment, making the output results more in line with the actual ecological conditions and improving the accuracy and operability of the suitability assessment.

[0027] 2. By introducing the disturbance propagation response function, common problems such as "isolated point misjudgment" and "small-scale interference expansion" are effectively suppressed, and the stability and robustness of the overall measurement are improved; by comprehensively considering the composite physical state function, the disturbance propagation response function and the periodic response of the physical quantity, a measurement evaluation function is constructed, so that the measurement quality intensity evaluation value of each measuring point can not only reflect its physical adaptability, but also express its state correlation with the surrounding environment, which is convenient for constructing a refined spatial planting strategy.

[0028] 3. By performing spatial threshold screening on the measurement evaluation function and determining the planting parameters of each measuring point based on the gradient change of the measurement evaluation function in the spatially aggregated planting block, the automatic conversion from "physical measurement data" to "intelligent decision-making information" is realized, which significantly reduces the degree of traditional manual dependence and improves construction efficiency, intelligence level and forest survival rate; in terms of planting parameter configuration, according to the change trend of the measurement quality intensity assessment value of each measuring point in different spatially 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 conservation forest project from fixed point, fixed species, fixed quantity to fixed depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of the intelligent construction method for water conservation and forestry projects in the Loess Plateau region described in the present invention. DETAILED DESCRIPTION

[0030] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0031] 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 invention belongs.

[0032] The specific scheme of the intelligent construction method of water conservation and forestry project in the Loess Plateau provided by the present invention is described in detail below with reference to the accompanying drawings.

[0033] Refer to the attached Figure 1 , which shows a flow chart of an intelligent construction method for a water conservation and forestry project in the Loess Plateau region provided by one embodiment of the present invention, the method comprising the following steps:

[0034] S1. Collect various original physical quantities and perform normalization processing on them based on a time-space sliding window to obtain normalized values of various physical quantities; construct a composite physical state function based on the normalized values of various physical quantities.

[0035] The Loess Plateau region is characterized by large-scale undulating slopes, dense gullies, severe wind erosion, and loose soil structure. Therefore, the collection units are deployed in a distributed manner, with grid measuring points arranged according to landform divisions and slope changes to ensure effective coverage of gully-slope junction areas, wind erosion exposed areas, and local tableland tops.

[0036] Six basic physical quantities are acquired within each acquisition unit and used as raw physical quantities: surface moisture content, soil compaction, slope, wind speed, evaporation rate, and operational error vector. Surface moisture content is measured using a moisture acquisition device; soil compaction is measured using an embedded cone pressure probe, which acquires compressive strength data influenced by loess compaction in the 0–20 cm soil layer. Slope information is measured using a combination of a terrain inclinometer and laser ranging equipment to meet the high-precision measurement requirements of steeply changing areas at the edges of gullies. Wind speed is acquired using an anti-interference ultrasonic velocimeter in areas prone to strong winds on the plateau. Evaporation is obtained by combining an automatic evaporation dish with an existing meteorological correction model, and the evaporation rate is further calculated by combining time. The operational error vector is measured using a laser displacement error acquisition device.

[0037] In order to ensure that multiple physical quantities have uniform computability and coupling analysis capabilities at different time and space scales, all physical quantities are normalized based on the time and space sliding window. Then, according to its historical change trend and local environmental characteristics, the Centered on The spatial neighborhood with radius is and the time window length is The sliding mean and standard deviation within the time period are used to eliminate the dimension differences and sampling scale inconsistencies between different physical quantities. Each original physical quantity is converted into a dimensionless standardized form. The specific formula is as follows:

[0038]

[0039] in, It is Normalized value of quasi-physical quantity; For the Class physical quantities Time and location The measured value at refers to any one of the following: surface moisture content, soil compaction, slope, wind speed, evaporation rate and operation error vector; Indicates the The sliding mean of a quasi-physical quantity within a certain spatial radius and time window; For the Sliding standard deviation of quasi-physical quantities; To prevent division by zero errors, you can use .

[0040] To construct a composite physical state function to describe the current measurement point, it is necessary to fully consider the interaction between the nonlinear spatial coupling relationship and the temporal variation trend of the normalized multiple physical quantities. The composite physical state function should not only accurately express the impact of each physical quantity on the environmental state of the current measurement point, but also reflect the synergistic effect between physical quantities under unequal weights and nonlinear conditions. A weighted coupling structure is used to improve the physical rationality of variable fusion. The complete expression of the composite physical state function is:

[0041]

[0042] in, It is a measuring point exist The composite physical state function at the moment represents the coupling strength of the physical state of the current measuring point; For the The coupling weight coefficient of the quasi-physical quantity is set by historical experience and represents the interaction strength of different physical quantities in a specific measurement context; is the nonlinear power response coefficient, indicating the The sensitivity adjustment factor of the physical quantity to the composite physical state can be set according to the range of change of the physical quantity and the intensity of the measurement fluctuation: for example, if the slope changes drastically, use Smooth its effect; use it when the moisture is stable amplify its response; It is a time disturbance sensitive 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 exist The stability strength of the physical state at a given moment is a quantitative expression of the coupling state under the joint influence of various physical quantities.

[0043] S2. Based on the composite physical state function, a disturbance propagation response function is introduced; based on the composite physical state function and the disturbance propagation response function, a measurement evaluation function is constructed, and spatial threshold screening is performed based on the measurement evaluation function to generate a set of planting candidate points; based on the set of planting candidate points, a set of spatially aggregated planting blocks is generated; based on the gradient change of the measurement evaluation function in the spatially aggregated planting blocks, the planting parameters are determined to perform the planting operation.

[0044] Based on the composite physical state function, in order to further evaluate its spatial consistency and the degree of local interference, it is necessary to introduce a disturbance propagation response function to measure the aggregated influence of state interference on the measuring point within its spatial neighborhood. The disturbance propagation response function not only avoids the interference of isolated values in 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 results of the current measuring point are stable and reliable. The disturbance propagation response function is:

[0045]

[0046] in, It is a measuring point exist The disturbance propagation response function value at the moment is used to measure the intensity of the influence of the state of the neighboring points on the current measuring point at the current moment; the integral area For measuring points Centered on A two-dimensional integral neighborhood with a radius of , which is used to capture the influence of all measurement points in the local neighborhood on the state of the center point; is the coordinate of the integral variable, which indicates the position of the adjacent spatial point in the integral domain that has a disturbing effect on the current measuring point; It is a measuring point exist Composite physical state function at time; is the spatial scale coefficient of the Gaussian kernel function, which controls spatial weight decay and represents the rate of spatial perturbation diffusion. The perturbation propagation response function measures the weighted influence of the state values of neighboring points on the current point. It provides the ability to correct the effectiveness of local perturbations on the measurement of the point, reflecting spatial correlation and local consistency.

[0047] The final output measurement evaluation function is constructed based on the composite physical state function and the disturbance propagation response function. This function aims to quantify the measurement validity of the current measurement point under the integrated 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 the ratio of the composite physical state function to the disturbance propagation response function, reflecting the independent significance of the physical state of the measurement point in the current spatiotemporal environment. A larger ratio indicates that the measurement point information is less affected by neighboring interference and more stable and reliable. This ratio is then multiplied by the sum of the cosine transforms of the normalized values of various physical quantities. This incorporates the periodic response of each normalized physical quantity (e.g., wind speed, evaporation rate) into the measurement process, thereby enhancing the measurement point's ability to detect disturbances. The second sub-term is the absolute value of the second-order mixed partial derivative of the composite physical state function in two spatial directions. This sub-term characterizes the gradient strength of the physical state changes in the region where the measurement point is located. A larger absolute value of the second-order mixed partial derivative indicates that the measurement point is in a critical region of physical variable variation, reflecting the measurement complexity or potential anomalies at the current measurement point. The combination of the two sub-items constitutes the measurement quality strength evaluation value of a single measurement point in the global coupling tensor measurement field, which can be used as an important indicator for screening high-reliability points or eliminating abnormal interference in actual measurements. The measurement evaluation function is in the form of:

[0048]

[0049] in, is the measurement evaluation function, which represents the measurement point At the moment The measurement quality strength assessment value is used to determine the validity of the measurement point within the current physical environment. The measurement evaluation function consists of two terms: the first term represents the physical structural integrity of the measurement point; the second term reflects the severity of structural changes surrounding the measurement point. Overall, the measurement quality strength assessment value determines whether the measurement point has measurement stability and environmental structural uniformity based on the current state of the measurement point and its surroundings.

[0050] Measurement evaluation function It represents the "composite physical state credibility" or "planting suitability" level of each measuring point in the target construction area. The higher the final measurement quality strength assessment value, the more the measuring point has the natural conditions and operation guarantee capabilities of being "suitable for soil and water conservation forest planting operations" in multiple physical dimensions such as soil moisture, slope stability, wind speed disturbance, evaporation risk, soil hardness and operation error.

[0051] The spatial threshold screening process is performed based on the measurement evaluation function, and the suitability threshold is set by the expert experience method. . Will satisfy The set of all measurement points is recorded as the candidate planting point set. Each point in the candidate planting point set meets the conditions of high suitability and high stability of composite physical quantities and has the natural conditions to support afforestation. The points in the candidate planting point set are further subjected to spatial consistency analysis to eliminate sporadic isolated points or overly dense areas. The existing adjacent pixel clustering algorithm is used to form a set of spatially aggregated planting blocks, which is the final planting operation planning area.

[0052] Planting parameters for each measuring point, such as tree species selection, planting density, planting depth, and orientation, are determined based on the gradient of the measurement evaluation function within each spatially aggregated planting block. If the difference in the measurement quality intensity assessment values of each measuring point within a spatially aggregated planting block is less than a set threshold (derived from expert experience), deep-rooted, soil-stabilizing tree species, such as Robinia pseudoacacia, are selected, with a high density value, such as 1.8 plants / m². Otherwise, shallow-rooted, wind-erosion-resistant tree species, such as Caragana korshinskii, are selected, with a low density value, such as 1.0 plants / m², to reduce root system interference.

[0053] Ultimately, the construction equipment receives information such as point coordinates, planting density, tree species selection parameters, and planting depth (determined by soil compaction and slope), and executes the tree planting operation according to a pre-set path sequence. The entire process is centered around a physical quantity measurement and evaluation function, and each planting decision is based on the fusion of multidimensional environmental information. This is a water conservation forest construction implementation method centered on environmental adaptability and based on data measurement.

[0054] In summary, an intelligent construction method for water conservation and forestry projects in the Loess Plateau was completed.

[0055] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0057] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. An intelligent construction method for water conservation and forestry projects in the Loess Plateau, characterized in that: The following steps are involved: S1. Collect various raw physical quantities, including surface moisture content, soil compaction, slope, wind speed, evaporation rate, and operation error vector. Perform normalization processing on these raw physical quantities based on a spatiotemporal sliding window to obtain normalized values of these physical quantities. Based on the interaction between the nonlinear spatial coupling relationship and the temporal variation trend of the normalized physical quantities, a weighted coupling structure is used to construct a composite physical state function. The specific expression is: ； in, It is a measuring point exist Composite physical state function at time; is the coupling weight coefficient of the 𝑖th type of physical quantity; is the normalized value of the 𝑖th physical quantity; is the nonlinear power response coefficient; is the time disturbance sensitivity factor; S2. Based on the composite physical state function, the disturbance propagation response function is introduced. The specific formula is as follows: ； in, It is a measuring point exist The disturbance propagation response function value at time; For measuring points Centered on is the two-dimensional integration neighborhood of radius; It is a measuring point exist Composite physical state function at time; is the spatial scale coefficient of the Gaussian kernel function; a measurement evaluation function is constructed based on the composite physical state function and the disturbance propagation response function, and spatial threshold screening is performed based on the measurement evaluation function to generate a set of planting candidate points; based on the set of planting candidate points, a set of spatially aggregated planting blocks is generated; based on the gradient change of the measurement evaluation function in the spatially aggregated planting blocks, the planting parameters are determined to perform the planting operation.

2. The intelligent construction method for water conservation and forestry projects in the Loess Plateau according to claim 1 is characterized in that: Said S1 specifically includes: Based on various original physical quantities, calculations are performed based on the measured points. Centered on The spatial neighborhood with radius is and the time window length is The sliding mean and sliding standard deviation within the time period are calculated to obtain the normalized values of various physical quantities.

3. The intelligent construction method for water conservation and forestry projects in the Loess Plateau according to claim 1 is characterized in that: Said S2 specifically includes: The mathematical structure of the measurement evaluation function contains two sub-items. The first sub-item performs ratio processing on the composite physical state function and the disturbance propagation response function, and then multiplies it with the cosine transform sum 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 direction of space; the two sub-items are combined to form the measurement quality intensity evaluation value.

4. The intelligent construction method for water conservation and forestry projects in the Loess Plateau according to claim 3 is characterized in that: Said S2 specifically includes: Based on the measurement evaluation function, spatial threshold screening is performed, and a planting suitability threshold is set. All measurement point sets that meet the measurement quality intensity evaluation value greater than or equal to the planting suitability threshold are recorded as the planting candidate point set.

5. The intelligent construction method for water conservation and forestry projects in the Loess Plateau according to claim 4 is characterized in that: Said S2 specifically includes: A threshold is set to determine whether the difference in the measurement quality intensity assessment values of each measuring point in the spatially aggregated planting block is less than the threshold, thereby determining the planting parameters of each measuring point; the construction equipment receives the planting parameters and performs the tree planting operation.

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