Synergistic soil fixation system and method based on grass planting slope protection root system and grating

Abstract

The invention relates to the technical field of environmental protection, in particular to a cooperative soil fixation system and method based on a grass planting slope protection root system and a grating, and the system comprises a root system distribution optimization module, a grating structure adaptation module, a soil fixation capability improvement module, a slope stability evaluation module and a dynamic adjustment module. According to the method, priority distribution is constructed by extracting a root system supporting strength key section and combining root system depth and density changes, accurate recognition and differential layout of soil structure characteristics are achieved, a stress adaptation relation is established by matching grating parameters of all areas, the soil bearing and supporting coordination degree is improved, and the accuracy of the soil structure characteristics is improved. A soil fixation capability concentration area is extracted based on consolidation change and root trend, accurate evaluation of consolidation strength per unit area is realized, slope stability effectiveness is monitored by identifying a water flow scouring and stability change coincidence period, disturbance tasks are classified, grid switching frequency is dynamically adjusted, response efficiency and anti-interference capability under sudden change load are enhanced, and slope stability evaluation is realized. The problem that a traditional soil fixing structure is insufficient in adaptability is solved.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technology, and in particular to a collaborative soil consolidation system and method based on grass-planting slope protection root systems and grids. Background Art

[0002] The field of environmental protection technology involves the protection and utilization of natural resources, the restoration and maintenance of ecosystems, and the mitigation and management of the environmental impacts of human activities. Its core issues include soil and water conservation, land reclamation, ecological restoration, pollution prevention and control, and the development of green infrastructure. This field focuses on maintaining ecological balance while synergizing engineering and natural means to improve the stability and sustainability of ecosystems, and is a key technological foundation for achieving sustainable development. Soil and slope protection technologies in this field, which combine the soil-holding capacity of plant roots with artificial support structures to enhance slope stability and reduce soil erosion, have become an indispensable component of infrastructure construction, including water conservancy, transportation, and municipal administration.

[0003] Among them, the synergistic soil consolidation system of traditional grass slope protection roots and grids refers to planting grass plants on the slope surface and using the network structure formed by the distribution of their roots to enhance the binding force between soil particles, thereby playing a role in preventing soil loss and landslides. The technical issues addressed by this method are loose slope soil, severe water erosion, frequent soil and water loss in the rainy season, and other problems. Traditional grass slope protection technology uses a single root structure distribution method to solve such problems, that is, selects grass species with well-developed root systems and strong adaptability for slope planting, and suppresses slope erosion through the physical entanglement of grass roots and soil reinforcement. However, in the face of complex environments such as loose soil, high flow rate or frequent rainfall, its slope protection capacity is limited and it is difficult to meet high stability requirements. In order to enhance soil consolidation capacity, some technical solutions introduce the use of grid structures in conjunction with grass slope protection to construct a soil consolidation system based on the synergistic effect of roots and grids to provide stronger soil support and consolidation functions.

[0004] Existing technologies generally rely on the unidirectional distribution of root structures to carry out soil consolidation operations during slope treatment, and lack a response mechanism to changes in the internal structure of the soil. When faced with areas with loose soil or fluctuating slope bearing capacity, the lack of a differentiated strategy for grid layout often leads to a mismatch between support distribution and actual load. During heavy rain or strong water flow scouring, the root entanglement reinforcement effect is limited, and the slope is prone to consolidation imbalance. Existing methods are unable to identify the stability disadvantages of local structures, resulting in an imbalance in the stability of the overall slope protection structure. For example, when the flow rate suddenly increases, the grid support rhythm or method fails to be adjusted, which can easily cause rapid damage to the slope structure in a short period of time, increase the maintenance frequency and reduce the durability and reliability of the soil consolidation system. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a collaborative soil consolidation system and method based on grass-planted slope protection root systems and grids.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a collaborative soil consolidation system based on grass-planted slope protection root system and grid includes: The root distribution optimization module analyzes the depth distribution of plant roots, changes in root density, and fluctuations in soil particle binding strength based on slope soil characteristics. It extracts key sections of root support strength and generates root distribution priority marker values. The grid structure adaptation module matches the grid structure parameters and layout strategy of the corresponding section based on the root distribution priority mark value, compares the grid support strength with the current soil bearing capacity, selects the grid adjustment instructions corresponding to the deviation rate, and obtains the grid layout optimization instruction group; The soil consolidation capacity improvement module extracts the change in soil consolidation rate of the node before and after adjustment based on the grid layout optimization instruction group, identifies the concentration of soil consolidation capacity per unit area in combination with the root expansion trend, extracts the change section to form a soil consolidation adaptation interval, and obtains the soil consolidation capacity optimization trend; The slope stability assessment module calls the soil consolidation capacity optimization trend, collects environmental data during the slope treatment process, analyzes the overlap time between the slope stability change and the water flow scouring jump period, and outputs the slope stability effect time.

[0007] As a further solution of the present invention, the root distribution priority mark value includes the root depth fluctuation range, the root density change amplitude, and the soil particle binding force increase characteristics; the grid layout optimization instruction group includes the grid offset, the support state abnormal value, and the bearing capacity offset rate; the soil fixation capacity optimization trend includes the consolidation rate change range, the soil fixation capacity type, and the capacity response amplitude; the slope stability action duration includes the scour fluctuation duration, the stability jump overlap period, and the disturbance duration.

[0008] As a further solution of the present invention, the root distribution optimization module includes: The root depth extraction submodule extracts the time series of plant root depth distribution based on slope soil characteristics, calculates the depth difference between adjacent sampling points, determines the fluctuation period and extracts the peak-to-valley distance, and selects the sections where the period difference exceeds the threshold to obtain the root depth period fluctuation interval value; The root density calculation submodule calls the root depth period fluctuation interval value, identifies the corresponding root density data, analyzes the absolute density change amplitude in adjacent time periods, compares it with the set root density threshold, locates the mutation node, and obtains the root density mutation amplitude interval value; The distribution status marking submodule identifies the corresponding soil particle binding force and root characteristic data according to the root density mutation amplitude interval value, extracts the binding force and characteristic data, combines the root density and depth fluctuations, calculates the distribution complexity offset value, sets the offset threshold, marks the time segment exceeding the threshold, and obtains the root distribution priority marking value.

[0009] As a further solution of the present invention, the grid structure adaptation module includes: The grid data matching submodule extracts the grid structure parameters and layout data of the corresponding section based on the root distribution priority mark value, calculates the support fluctuation amplitude according to the sampling interval, aligns the root depth and support amplitude at the same time, and obtains the support linkage interval group; The grid deviation judgment submodule calls the support linkage interval group, extracts the grid state sequence, compares the support change with the state difference, calculates the state difference normalization index, compares the normalized state difference with the preset offset boundary, analyzes the deviation strength of the support point, extracts the support position index with a deviation strength greater than the benchmark judgment value, and establishes an offset strength index group; The grid instruction extraction submodule filters the position of the corresponding time point in the task instruction set based on the offset strength index group, extracts instruction values, sorts them in time series, removes duplicate instructions, and obtains a grid layout optimization instruction group.

[0010] As a further solution of the present invention, the soil consolidation capacity improvement module includes: The consolidation rate extraction submodule extracts the consolidation rate data of the nodes in the cycle before and after the adjustment according to the grid layout optimization instruction group, identifies the consolidation boundary conditions, and obtains the average consolidation rate difference value; The soil consolidation capacity identification submodule uses the average consolidation rate difference value to identify the soil consolidation capacity change trajectory in the unit area path, determines the consolidation quantity trend corresponding to the capacity fluctuation, compares the capacity reduction and consolidation quantity fluctuation in adjacent time periods, calculates the capacity fluctuation coupling index, screens the synchronous fluctuation segments, and obtains the soil consolidation capacity characteristic segment; The capacity response interval identification submodule analyzes the time and capacity displacement trends according to the soil consolidation capacity characteristic segments, determines the direction consistency and amplitude change characteristics, screens the fluctuation segments and aggregates them to obtain the soil consolidation capacity optimization trend.

[0011] As a further solution of the present invention, the slope stability assessment module includes: The capacity trend extraction submodule calls the soil consolidation capacity optimization trend, extracts node task records and periodic capacity data, identifies the fluctuation amplitude and frequency within a single cycle, and obtains the fluctuation trend value of the periodic capacity; The scour jump identification submodule selects scour and stability records within the same period based on the fluctuation trend value of the periodic capacity and the environmental data during the slope treatment process, compares the data change amplitude on a daily basis, and selects the time nodes with amplitudes greater than the scour jump threshold to obtain the scour load jump period; The stability quantification submodule identifies the intersection duration of capacity variation and scour jump according to the scour load jump period, performs weighted processing, and normalizes the usage duration under differentiated cycles with reference to the amplitude variation frequency, combined amplitude variation value and periodic fluctuation, and outputs the slope stability effect duration.

[0012] As a further solution of the present invention, the system further includes a dynamic adjustment module: The dynamic adjustment module filters the task adjustment instructions affected by water flow interference based on the duration of the slope stability effect, classifies the grid switching time and the adjustment trigger frequency, identifies the load jump exceeding the limit period, and obtains the load interference impact frequency of the slope treatment task; The slope processing task load interference impact frequency includes the grid switching frequency, the number of adjustment instruction triggering times, and the number of jump period exceeding the limit.

[0013] As a further solution of the present invention, the dynamic adjustment module includes: The instruction screening submodule screens matching task adjustment instructions based on the duration of the slope stability effect, identifies the grid period and grid state, compares the disturbance period with the instruction period, eliminates low-matching instructions, and obtains an instruction set affected by water flow interference; The grid classification submodule calls the set of instructions affected by water flow interference, extracts the grid switching time and adjustment trigger frequency corresponding to the instructions, arranges them in order of switching time, classifies them by frequency interval, counts the correspondence between frequency bands and grid time periods, and obtains the grid adjustment frequency band distribution value; The interference identification submodule adjusts the frequency band distribution value according to the grid, collects the load jump amplitude and duration within the frequency band, determines whether it exceeds the jump amplitude threshold, identifies the frequency band interference intensity, sorts the interference intensity of all frequency bands, determines the frequency band and jump period of the interference intensity, and obtains the load interference impact frequency of the slope treatment task.

[0014] The collaborative soil consolidation method based on the grass-planted slope protection root system and the grid is performed based on the collaborative soil consolidation system based on the grass-planted slope protection root system and the grid, and includes the following steps: S1: Based on slope soil characteristics, the root depth distribution and root density changes are analyzed, and the periods of peak mutation of soil particle binding force and sharp changes in root characteristics are extracted. The state switching points where the root density and binding force changes are synchronized are identified, and the task level is marked to generate task state switching identification segments. S2: Based on the task state switching identification segment, extract support change data and load change amplitude, compare support mutation with load fluctuation amplitude, analyze the corresponding relationship between support change and processing capacity response, and obtain support adjustment response trajectory segment; S3: Based on the support adjustment response trajectory, the changes in the consolidation rate and the soil consolidation capacity gradient before and after the response are extracted, the decrease in the soil consolidation capacity and the increase in the consolidation quantity of the node are analyzed, the capacity attenuation path is identified and compared with the original cycle data, the sections with capacity reduction characteristics are screened, and a distribution set of soil consolidation adaptation attenuation paths is generated; S4: Based on the soil-fixing adaptive attenuation path distribution set, identifying environmental data within a corresponding time period, analyzing the overlapping period of scour jump and capacity attenuation path, extracting the associated time period, and obtaining the environmental parameter interference section supporting the adjustment; S5: Based on the environmental parameter interference section of the support adjustment, the scheduling control strategy records within the time period are extracted, the time interval of the grid switching and the adjustment trigger frequency are analyzed, and the high-frequency adjustment strategy fragments are screened to obtain the slope treatment task load interference impact frequency.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the key sections of root support strength are extracted by analyzing the fluctuation characteristics of the binding force of slope soil particles, and a priority distribution strategy is constructed in combination with the changes in root depth and density to achieve accurate identification of soil structural characteristics and differentiated distribution judgment. By matching the grid structure parameters and support layout strategies of different areas, a stress adaptation mechanism is established to improve the coordination of soil bearing and support. Based on the changes in soil consolidation and the trend of root expansion, the concentrated distribution of soil consolidation capacity in local intervals is extracted, thereby achieving accurate assessment of consolidation strength per unit area. By tracking the temporal relationship between water flow scouring periods and slope stability changes, high-risk overlapping areas are identified to achieve time-dimensional monitoring of the overall stability performance of the slope. By classifying the tasks affected by water disturbances and dynamically adjusting the grid switching frequency, the anti-interference ability and response efficiency under sudden loads are enhanced, effectively breaking through the problems of slow response and insufficient adaptability of traditional soil consolidation structures under complex hydrodynamic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the root distribution optimization module in the present invention; Figure 3 This is a flow chart of the grid structure adaptation module in the present invention; Figure 4 This is a flow chart of the soil consolidation capacity improvement module in the present invention; Figure 5 This is a flow chart of the slope stability assessment module in the present invention; Figure 6 This is a flow chart of the dynamic adjustment module in the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0019] See also Figure 1 The present invention provides a technical solution: a collaborative soil consolidation system based on grass-planted slope protection root system and grid includes: The root distribution optimization module analyzes the depth distribution of plant roots, changes in root density, and fluctuations in soil particle binding strength based on slope soil characteristics. It extracts key sections of root support strength and generates root distribution priority marker values. The grid structure adaptation module matches the grid structure parameters and layout strategy of the corresponding section based on the root distribution priority mark value, compares the grid support strength with the current soil bearing capacity, selects the grid adjustment instructions corresponding to the deviation rate, and obtains the grid layout optimization instruction group; The soil consolidation capacity improvement module extracts the changes in soil consolidation rate of nodes before and after adjustment based on the grid layout optimization instruction group. It then identifies the concentration of soil consolidation capacity per unit area based on the root expansion trend, extracts the change section to form the soil consolidation adaptation interval, and obtains the soil consolidation capacity optimization trend. The slope stability assessment module uses the soil consolidation capacity optimization trend, collects environmental data during the slope treatment process, analyzes the overlap time between slope stability changes and water flow erosion jump periods, and outputs the slope stability effect duration; The dynamic adjustment module screens the task adjustment instructions affected by water flow interference based on the duration of slope stability, classifies the grid switching time and adjustment trigger frequency, identifies the load jump exceeding the limit period, and obtains the frequency of load interference impact on the slope treatment task.

[0020] The root distribution priority marking values ​​include the root depth fluctuation range, the root density change amplitude, and the soil particle binding force increase characteristics. The grid layout optimization instruction group includes the grid offset, the support state abnormal value, and the bearing capacity offset rate. The soil consolidation capacity optimization trend includes the consolidation rate change range, the soil consolidation capacity type, and the capacity response amplitude. The slope stability effect duration includes the scour fluctuation duration, the stability jump overlap period, and the disturbance duration. The slope treatment task load interference impact frequency includes the grid switching frequency, the number of adjustment instruction triggers, and the number of jump period limit violations.

[0021] See also Figure 2 , the root distribution optimization module includes: The root depth extraction submodule extracts the time series of plant root depth distribution based on slope soil characteristics, calculates the depth difference between adjacent sampling points, determines the fluctuation period and extracts the peak-to-valley distance, and selects the sections where the period difference exceeds the threshold to obtain the root depth period fluctuation interval value; Based on the characteristics of slope soil, we first selected multiple sampling points in a certain hillside test area. A soil moisture sensor and a root growth detector were set up at each sampling point to continuously collect plant root depth data for 30 days. The data at 2 pm every day were recorded to form a time series. For example, the depth of sampling point A on the first day was 15.2 cm, 15.5 cm on the second day, 15.0 cm on the third day, 15.3 cm on the fourth day, and 15.8 cm on the fifth day. And so on. The depth data of sampling point A on the first and second days were 15.2 cm and 15.5 cm respectively. The depth difference is 0.3 cm, and the depth data of 15.5 cm and 15.0 cm on the second and third days are subtracted to obtain a depth difference of -0.5 cm. In this way, the depth differences of all adjacent sampling points in the entire time series are calculated. By analyzing the changes in the numerical signs of the depth difference sequence, for example, when the depth difference changes from positive to negative or from negative to positive, it is determined to be the beginning or end of a fluctuation cycle, and the complete fluctuation pattern such as "increase-decrease-increase" or "decrease-increase-decrease" is identified, and the maximum depth in each fluctuation cycle is extracted. The absolute difference between the peak value and the trough value is calculated as the peak-to-trough spacing. For example, within a certain period, the root depth drops from 15.0 cm to 14.0 cm and then rises to 15.5 cm. The trough is 14.0 cm, the peak is 15.5 cm, and the peak-to-trough spacing is 1.5 cm. The peak-to-trough spacing of each fluctuation period is compared. If the absolute difference between the spacing value and the spacing value of the adjacent period exceeds the preset threshold, the corresponding period segment is selected. For example, if the period difference threshold is set to 0.3 cm, The peak-to-valley spacing of a certain cycle is 1.2 cm, while the peak-to-valley spacing of adjacent cycles is 0.8 cm. The difference is 0.4 cm, which is greater than the threshold of 0.3 cm. The time segment is screened out. The threshold is set based on the analysis of historical monitoring data in the area. Through multiple experimental tests, it is found that when the difference in the root depth fluctuation cycle exceeds 0.3 cm, it indicates that there is a significant change in the root growth state. Experimental data show that when the threshold is set at 0.3 cm, it can effectively capture more than 95% of abnormal fluctuations and obtain the root depth cycle fluctuation interval value.

[0022] The root density calculation submodule calls the root depth period fluctuation interval value, identifies the corresponding root density data, analyzes the absolute density change amplitude in adjacent time periods, compares it with the set root density threshold, locates the mutation node, and obtains the root density mutation amplitude interval value; Call the root depth periodic fluctuation interval value. For example, the above root depth periodic fluctuation interval value is determined to be from the 3rd day to the 7th day. Next, identify the root density data in the corresponding time interval. For example, obtain the root density data through image analysis. Import the soil profile image of the sampling point into the image processing software. Calculate the area ratio of the root system in the specified depth interval through pixel statistics and morphological recognition. Use this area ratio as the quantitative value of the root density. The root density on the 3rd day is 0.08g / cm³, 0.09g / cm³ on the 4th day, 0.12g / cm³ on the 5th day, 0.10g / cm³ on the 6th day, and 0.07g / cm³ on the 7th day. Analyze the absolute change amplitude of the density in adjacent time periods. For example, the absolute change amplitude of the density from the 3rd day to the 4th day is calculated to be |0.09-0.08|=0.01g / cm³, the absolute density change from the 4th day to the 5th day is |0.12-0.09|=0.03g / cm³, and the absolute density change of all adjacent time periods is calculated in this way. Then, the absolute density change of each adjacent time period is compared with the set root density threshold. For example, the set root density threshold is 0.025g / cm³, which is obtained based on the historical density change data of normal plant growth in this slope area. When the density change exceeds this value, it indicates that the root growth is significantly abnormal. Experimental data show that 0.025g / cm³ can effectively distinguish 90% of normal physiological fluctuations from abnormal growth conditions. If 0.03g / cm³ is greater than 0.025g / cm³, the time node from the 4th day to the 5th day is located as the mutation node, and the root density mutation amplitude interval value is obtained.

[0023] The distribution status marking submodule identifies the corresponding soil particle binding force and root characteristic data based on the root density mutation amplitude interval value, extracts the binding force and characteristic data, combines the root density and depth fluctuations, calculates the distribution complexity offset value, sets the offset threshold, marks the time segment that exceeds the threshold, and obtains the root distribution priority marking value; Based on the root density fluctuation range (e.g., between days 4 and 5), we extracted data on soil particle binding capacity and root characteristics: on day 4, the binding capacity was 85 kPa, the root diameter was 0.8 mm, the root length density was 1.2 cm / cm³, the root density was 0.09 g / cm³, and the depth was 15.3 cm; on day 5, the corresponding values ​​were 78 kPa, 0.7 mm, 1.0 cm / cm³, 0.12 g / cm³, and 15.8 cm. We calculated the rates of change for each item: binding capacity (-0.082), root diameter (-0.125), root length density (-0.167), and root density (0.333), with a depth fluctuation of 0.5 cm. Combining the weight coefficients (binding force 0.4, root diameter 0.3, and root length density 0.3), the weighted summation yields the distribution complexity offset value: |-0.082×0.4 + (-0.125)×0.3 + (-0.167)×0.3 + 0.333 + 0.5| = 0.00381. A deviation threshold is set, for example, at 0.02. This threshold is determined through statistical analysis of long-term monitoring data from healthy slopes and expert evaluation. When the deviation value exceeds this threshold, it indicates a significant abnormality in the root distribution, affecting its soil-fixing capacity. Experimental verification results show that a deviation threshold of 0.02 can accurately identify 92% of potential slope instability risks. The calculated distribution complexity offset value is compared with the set deviation threshold. If 0.00381 does not exceed the deviation threshold of 0.02, the time segment is not marked, and the root distribution priority marking value is obtained.

[0024] See also Figure 3 , the grid structure adaptation module includes: The grid data matching submodule extracts the grid structure parameters and layout data of the corresponding section based on the root distribution priority mark value, calculates the support fluctuation amplitude according to the sampling interval, aligns the root depth and support amplitude at the same time, and obtains the support linkage interval group; Based on the root distribution priority mark value, it is assumed that the root distribution priority mark value from the 10th day to the 12th day is "high priority", which means that the root distribution status of this section has a greater impact on the slope stability and requires special attention. The grid structure parameters and layout data of the corresponding section are extracted. For example, the database is queried to obtain the side length of the grid unit on the 10th day as 2.5m, the height as 0.8m, the grid aperture as 0.2m, and the layout as staggered arrangement, as well as the corresponding data on the 11th and 12th days. The support fluctuation amplitude is calculated according to the sampling interval. For example, with a daily sampling interval, the vertical displacement under the action of load is continuously monitored by the pressure sensor installed at the grid support point. The difference between the maximum and minimum displacements every 24 hours is taken as the support fluctuation amplitude. For example, the support fluctuation amplitude is 0.05 cm on the 10th day, 0.07 cm on the 11th day, and 0.06 cm on the 12th day. The root depth and support amplitude are aligned at the same time. For example, the root depth (16.5 cm) on the 10th day is aligned with the support fluctuation amplitude (0.05 cm) on the 10th day, the root depth (16.8 cm) on the 11th day is aligned with the support fluctuation amplitude (0.07 cm) on the 11th day, and the root depth (16.3 cm) on the 12th day is aligned with the support fluctuation amplitude (0.06 cm) on the 12th day to obtain the support linkage interval group.

[0025] The grid deviation judgment submodule calls the support linkage interval group, extracts the grid state sequence, compares the support changes with the state differences, calculates the state difference normalization index, compares the normalized index with the preset offset boundary, analyzes the deviation strength of the support points, extracts the support position index with a deviation strength greater than the benchmark judgment value, and establishes an offset strength index group; By invoking the support linkage interval group and combining visual monitoring with sensor data, the real-time deformation and stress state of the grille are acquired and quantified into a state sequence, such as 0.12 (slight deformation) on day 10, 0.18 (moderate deformation) on day 11, and 0.15 (mild deformation) on day 12. By comparing the support fluctuation amplitude (0.05cm, 0.07cm, and 0.06cm) with the state values, the daily difference is calculated: |0.05–0.12| = 0.07 on day 10, 0.11 on day 11, and 0.09 on day 12. The differences are further normalized. Assuming a maximum difference of 0.5, the normalized indices are 0.14, 0.22, and 0.18, respectively. These values ​​are then compared to a deviation threshold of 0.25, which is based on the elastic limit of the grille and load test results and can identify 98% of potential structural failure risks. Any normalized value exceeding 0.25 is considered a deviation risk. In this case, none of these values ​​exceeded the threshold, and no warning was triggered. Subsequently, the deviation intensity is calculated by subtracting the deviation boundary from the normalized index. For example, on day 11, the deviation intensity is 0.22 – 0.25 = –0.03. If the deviation intensity is positive and greater than the baseline value of 0.05, it is marked as a support point requiring intervention. The baseline value is set based on historical deviation data and engineering experience, balancing false alarm control and risk identification. Because the current deviation intensity did not meet the standard, no warning index was ultimately established. Only the deviation intensity index group was recorded for subsequent monitoring and analysis.

[0026] The grid instruction extraction submodule filters the position of the corresponding time point in the task instruction set based on the offset strength index group, extracts the instruction value, sorts it by time series, removes duplicate instructions, and obtains the grid layout optimization instruction group; Assume that an offset is identified at sampling point C on the 11th day. An offset intensity index group is created as (day 11, sampling point C). The position of the corresponding time point in the task instruction set is filtered. For example, all instructions for day 11 are searched in the grid maintenance task instruction set, and the instruction related to sampling point C is located. The task instruction set stores predefined grid adjustment operations, such as lifting, lowering, and tightening. Instructions contain instruction IDs, timestamps, target locations, and specific operation parameters. The instruction values ​​are extracted. For example, if an instruction is found in the task instruction set with the content "day 11, sampling point C, lift 0.02 meters", the instruction value is extracted as "lift 0.02 meters". The instructions are sorted by time sequence. For example, if there are multiple instructions for different sampling points on day 11, they are sorted in chronological order. For example, the instruction at 9:00 am is executed first, followed by the instruction at 3:00 pm. Duplicate instructions are removed. For example, if multiple identical instructions are found for the same sampling point and the same operation within the same time period, only one is retained, resulting in the grid layout optimization instruction set.

[0027] See also Figure 4 , the soil consolidation capacity improvement module includes: The consolidation rate extraction submodule extracts the consolidation rate data of the nodes in the cycle before and after adjustment according to the grid layout optimization instruction group, identifies the consolidation boundary conditions, and uses the formula: ; The average consolidation rate difference value is obtained; Among them, G represents the average consolidation rate difference, R i represents the consolidation rate of the i-th node in the adjusted cycle, E i represents the consolidation rate of the i-th node in the cycle before adjustment, n represents the total number of nodes involved in the calculation, A i represents the normalized area value of the grid unit to which the i-th node belongs, D i represents the depth position of the i-th node, Represents the average value of the depth positions of all nodes, W i represents the weight coefficient of the i-th node in the evaluation of consolidation rate difference; According to the grid layout optimization instruction group, the consolidation rate data of the node in the cycle before and after adjustment are extracted. For example, for sampling point C, before the lifting operation (cycle before adjustment) and after the lifting operation (cycle after adjustment), the soil consolidation rate sensor installed at the grid node continuously collects the drainage and deformation of the soil under a specific pressure, thereby calculating the consolidation rate. Assuming that the collected consolidation rate in the cycle before adjustment is (corresponding to 3 nodes respectively), the consolidation rate within the cycle after adjustment is (Corresponding to the above three nodes), identify the consolidation boundary conditions. For example, the consolidation boundary conditions include the contact area between the grid and the soil, soil type, moisture content, temperature and other environmental parameters. The parameters are obtained through real-time monitoring of on-site sensors. For example, the soil type is clay, the moisture content is 25%, and the ambient temperature is 20°C. The conditions are used to ensure the accuracy and consistency of consolidation rate data collection. The consolidation rate refers to the process in which the pore water is discharged and the soil volume is reduced under the action of external load. It is an important indicator for measuring soil stability. G represents the average consolidation rate difference value. Its calculation logic is to obtain the weighted average of the difference in consolidation rate of all nodes involved in the calculation before and after adjustment, and then take the absolute value. Its purpose is to quantify the degree of improvement or reduction of the overall soil consolidation rate by the grid adjustment operation in order to evaluate the adjustment effect. R i represents the consolidation rate of the i-th node in the adjusted cycle, E i represents the consolidation rate of the i-th node in the cycle before adjustment, n represents the total number of nodes involved in the calculation, and n here represents the number of grid nodes used for calculation in the embodiment. For example, in this example, 3 nodes are selected for consolidation rate data collection and calculation, A irepresents the normalized value of the area of ​​the grid unit to which the i-th node belongs, that is, the ratio of the area of ​​the grid unit to the area of ​​the largest grid unit in the study area, which is used to eliminate the influence of different grid unit sizes on the evaluation of consolidation rate differences. i represents the depth position of the i-th node, It is used to consider the effect of node depth on consolidation rate, because soil consolidation characteristics are different at different depths. i It is used to reflect the importance of different nodes in slope stability. For example, the nodes at the foot of the slope or at the key support position have higher weights. The square root operation is to comprehensively consider the influence of grid unit area and node depth position on the consolidation rate difference, where A i+1 Ensures that the denominator is non-zero and is more sensitive to area changes, It represents the absolute deviation between the node depth and the average depth, reflecting the nonlinear effect of depth on the consolidation rate difference. By weighting the influencing factors, the G value can more accurately reflect the actual effect of grid adjustment on soil consolidation capacity, and finally multiply it by the weight coefficient W. i , further emphasizing the contribution of key nodes to the overall consolidation rate, the calculation results of all nodes are summed and divided by the total number of nodes n to obtain the average difference, and finally the absolute value is taken to obtain the average consolidation rate difference value; The calculation process is as follows: the total number of nodes involved in the calculation is set to n = 3, the consolidation rate of node 1 before adjustment E1 = 0.65, the consolidation rate after adjustment R1 = 0.72, and the area of ​​the grid unit to which it belongs is 4.0m 2 The largest grid unit area in the study area is 4.0m 2 , then its area normalized value , depth position D1 = 1.0m, weight coefficient W1 = 0.4, node 2 pre-adjustment consolidation rate E2 = 0.68, post-adjustment consolidation rate R2 = 0.75, and the grid unit area is 3.6m 2 , then its area normalized value , depth position D2 = 1.2m, weight coefficient W2 = 0.3, node 3 pre-adjustment consolidation rate E3 = 0.70, post-adjustment consolidation rate R3 = 0.73, the grid unit area is 3.2m 2 , then its area normalized value , depth position D3 = 1.5m, weight coefficient W3 = 0.3, the average value of the depth position of all nodes ; Substitute the parameters into the formula: ; The benefit of the formula is that it introduces the area normalization value A of the grid unit iand the absolute deviation of the node depth position from the average depth position , and the weight coefficient W i， It can more comprehensively consider the actual contribution and importance of different grid units on the slope surface, make up for the lack of consideration of spatial heterogeneity in traditional consolidation rate evaluation, and make the average consolidation rate difference value G more accurately reflect the actual improvement effect of grid adjustment on the overall slope soil consolidation capacity. It shows that after the grid adjustment, the average consolidation rate difference is 0.0098, which reflects the positive impact of the grid adjustment on the consolidation rate. The larger the value, the more significant the improvement in the consolidation rate.

[0028] The soil consolidation capacity identification submodule uses the average consolidation rate difference value to identify the change trajectory of soil consolidation capacity in the unit area path, determine the consolidation quantity trend corresponding to the capacity fluctuation, compare the capacity reduction and consolidation quantity fluctuation in adjacent time periods, calculate the capacity fluctuation coupling index, screen the synchronous fluctuation segments, and obtain the soil consolidation capacity characteristic segments; The average consolidation rate difference value is called, and multiple unit area monitoring paths are arranged on the slope surface. A soil shear strength sensor is installed on each path to continuously record the change of soil shear strength over time, so as to reflect the change trajectory of soil consolidation capacity. For example, on a specific unit area path, it is monitored that the soil consolidation capacity drops from 60kPa to 55kPa in a certain period of time, and then rises back to 58kPa. The consolidation quantity trend corresponding to the capacity fluctuation is judged. For example, the number of grid adjustment instructions executed in the same time period and the change in consolidation rate caused by each adjustment are counted, and compared with the change trajectory of soil consolidation capacity. For example, in the interval where the soil consolidation capacity decreases, if the number of increases in the consolidation rate decreases or the amplitude decreases, it is considered that there is a downward trend in the consolidation quantity. Then, the capacity decrease in adjacent time periods is compared with the consolidation quantity fluctuation. For example, if the soil consolidation capacity in the previous time period decreases by 5kPa, and the consolidation quantity fluctuation is -0.01 (the consolidation rate decreases), the consolidation quantity fluctuation is -0.01. For example, if the soil consolidation capacity decreases by 0.01 in the next time period and the consolidation quantity fluctuation is +0.005 (the consolidation rate increases by 0.005), a segment-by-segment comparison is performed to calculate the capacity fluctuation coupling index. This index is determined by quantifying the synchronization and amplitude consistency of the capacity decrease and the consolidation quantity fluctuation. For example, the product is calculated when the two change in the same direction, and a negative value is taken when the directions are opposite. The amplitude is then normalized. For example, when the capacity decrease and the consolidation quantity fluctuation change in the same direction, the coupling index is positive, and vice versa. The larger the value, the higher the coupling. Synchronous fluctuation segments are selected. For example, segments where the capacity decrease and the consolidation quantity fluctuation are in the same direction and the coupling index is greater than 0.7 are selected. This threshold is determined based on long-term slope monitoring data and soil consolidation engineering experience, indicating a strong correlation between the two. Experimental verification results show that a threshold of 0.7 can effectively identify 88% of soil consolidation capacity anomalies and obtain characteristic segments of soil consolidation capacity.

[0029] The capacity response interval identification submodule analyzes the time and capacity displacement trends based on the soil consolidation capacity characteristic segments, determines the direction consistency and amplitude change characteristics, screens the fluctuation segments and aggregates them to obtain the soil consolidation capacity optimization trend; Based on the characteristic segments of soil-fixing capacity, a curve showing the soil-fixing capacity changing over time is plotted to observe its overall upward or downward trend and calculate the capacity displacement. For example, if the soil-fixing capacity is 70 kPa on the 20th day and 65 kPa on the 25th day, the time-capacity displacement trend is downward, with a displacement of -5 kPa. Directional consistency and amplitude change characteristics are determined. For example, for multiple consecutive time points, the direction of the capacity displacement is determined to be consistent (e.g., continuously decreasing or increasing), and its amplitude change is analyzed to be accelerating, decelerating, or stable. For example, if the capacity displacement decreases for three consecutive days and the amplitude gradually increases, it is determined to be consistent in direction and accelerating in amplitude. Fluctuation segments are screened and aggregated. For example, adjacent time segments with the same directional consistency and amplitude change characteristics are screened and merged into a larger time interval. For example, if days 20-22 and days 23-25 ​​both show a downward trend with similar amplitudes, they are aggregated into a complete fluctuation segment from days 20-25 to obtain the optimization trend of soil-fixing capacity.

[0030] See also Figure 5 , the slope stability assessment module includes: The capacity trend extraction submodule calls the soil consolidation capacity optimization trend, extracts the node task records and periodic capacity data, and identifies the fluctuation amplitude and frequency within a single cycle using the formula: ; Obtain the fluctuation trend value of periodic capacity; Among them, M represents the fluctuation trend value of periodic capacity, Represents the node task record value at time t within the cycle, Represents the average value of the node task record value within the period, Represents the periodic capacity collection data at time t within the period, Represents the difference value of the node task record at the same time point in the cycle before time t, represents the fluctuation range of the periodic capability data at the same time point in the cycle before the tth moment, T represents the total number of sampling time points in the cycle, Represents the maximum value of the periodic capability collection data within a period; Call the soil consolidation capacity optimization trend and extract the daily node task record values ​​from the 30th to 40th day (within a cycle) from the system database , for example, at time t, the node task records the value

[0031] Indicates the average stress on the grid node at that moment, which is collected in real time by the pressure sensor installed at the grid node. The value range is 10kPa to 100kPa. Assuming that the value on day t=1 (day 30) is , on day t=2 (day 31) , on day t=3 (day 32) , while extracting periodic capacity acquisition data , for example, periodic capacity collection data It represents the shear strength of the slope at time t, which is obtained through regular in-situ shear tests or penetration tests. For example, , on day t=2 Day t = 3 , identify the fluctuation amplitude and frequency within a single period, for example, by and The sequence is subjected to extreme value analysis and zero-crossing rate statistics, and the difference between the maximum and minimum values ​​in the entire cycle is identified as the fluctuation amplitude, and the number of fluctuations is identified as the fluctuation frequency. The average value of the node task record value in the cycle is calculated. ,For example, , calculate the difference value of the node task record at the same time point in the previous cycle at time t For example, if the node task record on day t=1 (day 20) of the previous cycle (day 20 to day 29) is 58kPa, then , the first day t = 2 (21st day) is 60kPa, then , the pressure on day t=3 (22nd day) is 55kPa, then , and calculate the fluctuation range of the periodic capability data at the same time point in the previous period before time t For example, assuming that the previous cycle is t=1, for example, assuming that the previous cycle is ,but , the t=2 day (21st day) is ,but , the t=3 day (22nd day) is ,but , obtain the maximum value of the periodic capacity collection data within the cycle , for example, in middle, =75kpa, and the fluctuation trend value of the periodic capacity is calculated using the formula, where M represents the fluctuation trend value of the periodic capacity. This value quantifies the comprehensive change trend of the slope's soil consolidation capacity relative to its average level and previous state within a specific period, reflecting the overall stability and responsiveness of the slope when facing task loads and environmental disturbances. The higher the value, the greater the degree of fluctuation or deviation from the average level, and the more attention needs to be paid. Represents the node task record value at time t within the cycle, Represents the average value of the node task record value within the period, The term represents the deviation of the node task record at time t relative to the average value of the period, reflecting the instantaneous fluctuation of the task load. Represents the periodic capacity collection data at the tth moment in the cycle, indicating the actual soil consolidation capacity of the slope at the tth moment. Represents the difference value of the node task record at the same time point in the previous cycle before time t, quantifying the difference between the current task load and the task load at the same time in the previous period. Represents the fluctuation range of the periodic capability data at the same time point in the period before time t, reflecting the inherent volatility of the previous capability data. The item comprehensively considers the impact of the previous task load difference and the previous capacity fluctuation on the current cycle capacity. The square root operation is to geometrically synthesize the two to make the impact smoother and more reasonable. Addition means adding the influence of previous related factors on the basis of current soil consolidation capacity and then adding The absolute value of multiplication emphasizes the coupling relationship between task load fluctuation and capacity change. The summation symbol It means that the calculation results of all time points are accumulated to get the total fluctuation in a cycle. T represents the total number of sampling time points in the cycle. The denominator is T in the equation averages the total fluctuation. The difference between the average value of node task records within the period and the maximum value of periodic capacity collection data is considered as a regularization factor to avoid excessive amplification of the M value due to T being too small. The calculation process is as follows: Set the total number of sampling time points in the period .

[0032] Table 1: Node task records and periodic capability collection data

[0033] As shown in Table 1, the data required to calculate the periodic capacity fluctuation trend value are listed. Substitute the parameters into the formula: ; The result The results show that the fluctuation trend value of the slope soil consolidation capacity during this period is 43.23, which is relatively high. This indicates that the slope has large stability and bearing capacity fluctuations during this period. The specific reasons need to be further analyzed. The fluctuation trend value of this periodic capacity is directly input into the scour jump identification submodule as an important basis for judging the scour load jump period.

[0034] The scour jump identification submodule is based on the fluctuation trend value of the periodic capacity and the environmental data during the slope treatment process. It selects the scour and stability records within the same period, compares the data change amplitude on a daily basis, and selects the time nodes with amplitudes greater than the scour jump threshold to obtain the scour load jump period. Based on the fluctuation trend value of the periodic capacity, daily environmental data such as rainfall, wind speed, and temperature are obtained through the meteorological station, and the slope runoff velocity and sediment content data are obtained through the runoff monitoring station. These data are used to describe the external environmental disturbances to the slope. For example, the rainfall on the 30th day is 5mm, the rainfall on the 31st day is 30mm, and the rainfall on the 32nd and 32nd days is 10mm. The scour and stability records within the same period are selected. For example, by regularly inspecting the slope, the scour traces on the slope (such as gully depth, mud and sediment content) are recorded. The scouring record on the 30th day was 0.05 (slight scouring), and the stability record was 0.95 (relatively stable). The scouring record on the 31st day was 0.20 (moderate scouring), and the stability record was 0.70 (decreased stability). The scouring record on the 32nd day was 0.10 (slight scouring), and the stability record was 0.85 (recovery of stability). ), compare the data change range on a daily basis, for example, calculate the daily rainfall change range, runoff velocity change range, scour record quantization value change range and stability record quantization value change range. For example, on the 31st day compared to the 30th day, the rainfall change range is |30-5|=25mm, the scour record quantization value change range is |0.20-0.05|=0.15, and the stability record quantization value change range is |0.70-0.95|=0.25. Then, filter out the data with a range greater than the scour jump threshold. At the time node, the scour jump threshold is set to 0.10, which is determined based on historical scour event data and soil and water conservation engineering specifications. When the change in the scour record quantitative value exceeds this threshold, it indicates that the slope scour load has significantly jumped and needs to be paid attention to. Experimental verification results show that the threshold of 0.10 can effectively identify 90% of scour jump events. For example, since the change in the scour record quantitative value of 0.15 on the 31st day is greater than 0.10, the 31st day is screened out to obtain the scour load jump period.

[0035] The stability quantification submodule identifies the intersection of capacity variation and scour jump according to the scour load jump period, performs weighted processing, and normalizes the usage time under differentiated cycles by referring to the frequency of variation, combined variation value, and period fluctuation, and outputs the slope stability effect time. According to the period of scour load jump, the soil consolidation capacity change curve and the scour load jump period are superimposed and analyzed to identify the time period when the two occur at the same time, and the intersection time is calculated. For example, if the soil consolidation capacity drops from 75kPa to 70kPa on the 31st day and the scour load jumps at the same time, the intersection time is 24 hours. Weighted processing is performed. For example, different weights are given to the intersection time according to factors such as intersection time, scour intensity, and capacity reduction. The weight coefficient is set according to the slope instability risk assessment model. The intersection time weight is 0.5, and the scour intensity weight is 0. The weight of capacity reduction is 0.3, and the weight of capacity reduction is 0.2. The service life under differentiated cycles is normalized with reference to the frequency of amplitude variation, combined amplitude variation value and periodic fluctuation. For example, if scour jump occurs in multiple cycles and the duration of each cycle is different, it is necessary to normalize the slope stability action time in each cycle according to the frequency of amplitude variation of soil consolidation capacity, total amplitude variation value and overall periodic fluctuation in the cycle to make them comparable. For example, the normalized service life can be obtained by dividing the actual action time by the maximum action time in the cycle.

[0036] See also Figure 6 , the dynamic adjustment module includes: The instruction screening submodule screens matching task adjustment instructions based on the duration of slope stability, identifies the grid period and grid state, compares the disturbance period with the instruction period, eliminates low-matching instructions, and obtains the instruction set affected by water flow disturbance. Based on the duration of slope stability, the instructions that match the current slope stability duration are selected from the preset task adjustment instruction library. The instruction library contains grid adjustment strategies corresponding to different stability durations, such as "if the stability duration is less than 100 hours, execute grid reinforcement instruction A", identify grid time periods and grid states. For example, based on historical data, identify the activation states of different grids in a specific time period (such as "working", "idle", "maintenance") and their corresponding specific state parameters (such as "deformation less than 0.01 meters", "bearing capacity greater than 100kPa"). For example, the current grid is in "working", "idle", "maintenance", etc. The system is in the "operation" state with a deformation of 0.005 meters. The disturbance cycle is compared with the instruction cycle. For example, the water flow disturbance cycle of the current slope surface (for example, heavy rainfall occurs once every 24 hours) is compared with the instruction execution cycle set in the task adjustment instruction (for example, check and adjust the grid once every 72 hours). If the disturbance cycle is much smaller than the instruction cycle, more frequent adjustments are required. Low-matching instructions are eliminated. For example, if the execution cycle of an instruction is seriously mismatched with the current disturbance cycle, or the grid state targeted by the instruction is inconsistent with the current state, it is eliminated from the instruction set to obtain the instruction set affected by water flow disturbance.

[0037] The grid classification submodule calls the set of instructions affected by water flow interference, extracts the grid switching time and adjustment trigger frequency corresponding to the instructions, arranges them in order of switching time, classifies them by frequency interval, counts the correspondence between frequency bands and grid time periods, and obtains the distribution value of grid adjustment frequency bands; Call the set of instructions affected by water flow interference, extract the switching time "2:00 PM on the first day" and the adjustment trigger frequency "2 times / day" from instruction ID1, and extract the switching time "10:00 AM on the second day" and the adjustment trigger frequency "1 time / day" from instruction ID2. Arrange them in order of switching time. For example, arrange instruction ID1 before instruction ID2 because 2:00 PM on the first day is earlier than 10:00 AM on the second day. Use frequency intervals to divide the adjustment trigger frequencies into different intervals, such as "high frequency adjustment" (greater than 1 time / day), "medium frequency adjustment" (0.5 times / day to 1 time / day), and "low frequency adjustment" (less than 0.5 times / day). Classify the extracted adjustment trigger frequencies into corresponding frequency intervals. For example, 2 times / day is classified as "high frequency adjustment" and 1 time / day is classified as "medium frequency adjustment". Count the correspondence between frequency bands and grid time periods. For example, count which grids are adjusted in which time periods corresponding to the "high frequency adjustment" frequency band, as well as the correspondence between the "medium frequency adjustment" and "low frequency adjustment" frequency bands, and obtain the grid adjustment frequency band distribution value.

[0038] The interference identification submodule adjusts the frequency band distribution value according to the grid, collects the load jump amplitude and duration within the frequency band, determines whether it exceeds the jump amplitude threshold, identifies the frequency band interference intensity, sorts the interference intensity of all frequency bands, determines the frequency band and jump period of the interference intensity, and obtains the load interference impact frequency of the slope treatment task; According to the distribution value of the grid adjustment frequency band, by installing force sensors and displacement sensors on the grid, the instantaneous load changes (load jump amplitude) borne by the grid in the "high-frequency adjustment" frequency band and the duration of this jump (duration) are continuously monitored. For example, in a certain monitoring, it was recorded that the load borne by the grid instantly jumped from 10kN to 25kN, with a jump amplitude of 15kN, and lasted for 5 seconds. It is judged whether it exceeds the jump amplitude threshold. The jump amplitude threshold is set to 10kN. This threshold is determined based on the bearing limit of the grid material and the slope safety level. When the load jump amplitude exceeds this threshold, it indicates that the grid is subjected to external interference beyond the norm. The experimental verification results show that the 10kN threshold can effectively identify 95% of external impact events. For example, if 15kN is greater than 10kN, it is determined to exceed the threshold, identify the frequency band interference intensity, and the interference intensity is measured by The load is quantified by multiplying the load jump amplitude and duration, and weighted based on the jump frequency. For example, if the jump amplitude is 15 kN and the duration is 5 seconds, the interference intensity is 15 kN × 5s = 75 kN s. The interference intensity of all frequency bands is sorted. For example, all monitored interference events are sorted from large to small according to their interference intensity values, and the frequency band with the highest interference intensity is identified. For example, if the average interference intensity of the "high-frequency regulation" band is 70 kN s, and the average interference intensity of the "medium-frequency regulation" band is 40 kN s, the "high-frequency regulation" band has the highest interference intensity. The frequency band and jump period of the interference intensity are determined. For example, the "high-frequency regulation" band is determined to be the frequency band with the highest interference intensity, and its main jump period is from 2:00 p.m. to 3:00 p.m. on the first day. The frequency of the load interference impact on the slope treatment task is obtained.

[0039] The collaborative soil consolidation method based on the grass-planted slope protection root system and the grid is executed based on the above-mentioned collaborative soil consolidation system based on the grass-planted slope protection root system and the grid, and includes the following steps: S1: Based on slope soil characteristics, the root depth distribution and root density changes are analyzed, and the periods of peak mutation of soil particle binding force and sharp changes in root characteristics are extracted. The state switching points where the root density and binding force changes are synchronized are identified, and the task level is marked to generate task state switching identification segments. S2: Based on the task state switching identification segment, the support change data and load change amplitude are extracted, the support mutation is compared with the load fluctuation amplitude, the corresponding relationship between the support change and the processing capacity response is analyzed, and the support adjustment response trajectory segment is obtained; S3: Based on the support adjustment response trajectory, the changes in the consolidation rate and the soil consolidation capacity gradient before and after the response are extracted. The decrease in soil consolidation capacity and the increase in consolidation quantity at the node are analyzed. The capacity attenuation path is identified and compared with the original cycle data. The sections with capacity reduction characteristics are screened to generate a distribution set of soil consolidation adaptation attenuation paths. S4: Based on the distribution set of soil-fixing adaptive attenuation paths, identify the environmental data within the corresponding time period, analyze the overlapping periods of scour jump and capacity attenuation paths, extract the associated time periods, and obtain the environmental parameter interference sections that support adjustment; S5: Based on the environmental parameter interference section supporting the adjustment, the scheduling control strategy records within the period are extracted, the time interval of the grid switching and the adjustment trigger frequency are analyzed, the high-frequency adjustment strategy fragments are screened, and the frequency of the slope treatment task load interference impact is obtained.

[0040] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A collaborative soil consolidation system based on grass-planted slope protection roots and grids, characterized by: The system comprises: The root distribution optimization module analyzes the depth distribution of plant roots, changes in root density, and fluctuations in soil particle binding strength based on slope soil characteristics. It extracts key sections of root support strength and generates root distribution priority marker values. The grid structure adaptation module matches the grid structure parameters and layout strategy of the corresponding section based on the root distribution priority mark value, compares the grid support strength with the current soil bearing capacity, selects the grid adjustment instructions corresponding to the deviation rate, and obtains the grid layout optimization instruction group; The soil consolidation capacity improvement module extracts the change in soil consolidation rate of the node before and after adjustment based on the grid layout optimization instruction group, identifies the concentration of soil consolidation capacity per unit area in combination with the root expansion trend, extracts the change section to form a soil consolidation adaptation interval, and obtains the soil consolidation capacity optimization trend; The slope stability assessment module calls the soil consolidation capacity optimization trend, collects environmental data during the slope treatment process, analyzes the overlap time between the slope stability change and the water flow scouring jump period, and outputs the slope stability effect time.

2. The collaborative soil consolidation system based on grass-planted slope protection root system and grid according to claim 1 is characterized in that: The root distribution priority mark value includes the root depth fluctuation range, the root density change amplitude, and the soil particle binding force increase characteristics; the grid layout optimization instruction group includes the grid offset, the support state abnormal value, and the bearing capacity offset rate; the soil consolidation capacity optimization trend includes the consolidation rate change range, the soil consolidation capacity type, and the capacity response amplitude; the slope stability effect duration includes the scour fluctuation duration, the stability jump overlap period, and the disturbance duration.

3. The collaborative soil consolidation system based on grass-planted slope protection root system and grid according to claim 1 is characterized in that: The root distribution optimization module includes: The root depth extraction submodule extracts the time series of plant root depth distribution based on slope soil characteristics, calculates the depth difference between adjacent sampling points, determines the fluctuation period and extracts the peak-to-valley distance, and selects the sections where the period difference exceeds the threshold to obtain the root depth period fluctuation interval value; The root density calculation submodule calls the root depth period fluctuation interval value, identifies the corresponding root density data, analyzes the absolute density change amplitude in adjacent time periods, compares it with the set root density threshold, locates the mutation node, and obtains the root density mutation amplitude interval value; The distribution status marking submodule identifies the corresponding soil particle binding force and root characteristic data according to the root density mutation amplitude interval value, extracts the binding force and characteristic data, combines the root density and depth fluctuations, calculates the distribution complexity offset value, sets the offset threshold, marks the time segment exceeding the threshold, and obtains the root distribution priority marking value.

4. The collaborative soil consolidation system based on grass-planted slope protection root system and grid according to claim 3 is characterized in that: The grid structure adaptation module includes: The grid data matching submodule extracts the grid structure parameters and layout data of the corresponding section based on the root distribution priority mark value, calculates the support fluctuation amplitude according to the sampling interval, aligns the root depth and support amplitude at the same time, and obtains the support linkage interval group; The grid deviation judgment submodule calls the support linkage interval group, extracts the grid state sequence, compares the support change with the state difference, calculates the state difference normalization index, compares the normalized state difference with the preset offset boundary, analyzes the deviation strength of the support point, extracts the support position index with a deviation strength greater than the benchmark judgment value, and establishes an offset strength index group; The grid instruction extraction submodule filters the position of the corresponding time point in the task instruction set based on the offset strength index group, extracts instruction values, sorts them in time series, removes duplicate instructions, and obtains a grid layout optimization instruction group.

5. The collaborative soil consolidation system based on grass-planted slope protection root system and grid according to claim 4 is characterized in that: The soil fixation capacity improvement module includes: The consolidation rate extraction submodule extracts the consolidation rate data of the nodes in the cycle before and after the adjustment according to the grid layout optimization instruction group, identifies the consolidation boundary conditions, and obtains the average consolidation rate difference value; The soil consolidation capacity identification submodule uses the average consolidation rate difference value to identify the soil consolidation capacity change trajectory in the unit area path, determines the consolidation quantity trend corresponding to the capacity fluctuation, compares the capacity reduction and consolidation quantity fluctuation in adjacent time periods, calculates the capacity fluctuation coupling index, screens the synchronous fluctuation segments, and obtains the soil consolidation capacity characteristic segment; The capacity response interval identification submodule analyzes the time and capacity displacement trends according to the soil consolidation capacity characteristic segments, determines the direction consistency and amplitude change characteristics, screens the fluctuation segments and aggregates them to obtain the soil consolidation capacity optimization trend.

6. The collaborative soil consolidation system based on grass-planted slope protection root system and grid according to claim 5 is characterized in that: The slope stability assessment module includes: The capacity trend extraction submodule calls the soil consolidation capacity optimization trend, extracts node task records and periodic capacity data, identifies the fluctuation amplitude and frequency within a single cycle, and obtains the fluctuation trend value of the periodic capacity; The scour jump identification submodule selects scour and stability records within the same period based on the fluctuation trend value of the periodic capacity and the environmental data during the slope treatment process, compares the data change amplitude on a daily basis, and selects the time nodes with amplitudes greater than the scour jump threshold to obtain the scour load jump period; The stability quantification submodule identifies the intersection duration of capacity variation and scour jump according to the scour load jump period, performs weighted processing, and normalizes the usage duration under differentiated cycles with reference to the amplitude variation frequency, combined amplitude variation value and periodic fluctuation, and outputs the slope stability effect duration.

7. The collaborative soil consolidation system based on grass-planted slope protection root system and grid according to claim 1 is characterized in that: The system also includes a dynamic adjustment module: The dynamic adjustment module filters the task adjustment instructions affected by water flow interference based on the duration of the slope stability effect, classifies the grid switching time and the adjustment trigger frequency, identifies the load jump exceeding the limit period, and obtains the load interference impact frequency of the slope treatment task; The slope processing task load interference impact frequency includes the grid switching frequency, the number of adjustment instruction triggering times, and the number of jump period exceeding the limit.

8. The collaborative soil consolidation system based on grass-planted slope protection root system and grid according to claim 7 is characterized in that: The dynamic adjustment module includes: The instruction screening submodule screens matching task adjustment instructions based on the duration of the slope stability effect, identifies the grid period and grid state, compares the disturbance period with the instruction period, eliminates low-matching instructions, and obtains an instruction set affected by water flow interference; The grid classification submodule calls the set of instructions affected by water flow interference, extracts the grid switching time and adjustment trigger frequency corresponding to the instructions, arranges them in order of switching time, classifies them by frequency interval, counts the correspondence between frequency bands and grid time periods, and obtains the grid adjustment frequency band distribution value; The interference identification submodule adjusts the frequency band distribution value according to the grid, collects the load jump amplitude and duration within the frequency band, determines whether it exceeds the jump amplitude threshold, identifies the frequency band interference intensity, sorts the interference intensity of all frequency bands, determines the frequency band and jump period of the interference intensity, and obtains the load interference impact frequency of the slope treatment task.

9. A collaborative soil consolidation method based on grass-planted slope protection roots and grids, characterized in that: The method is used to implement the collaborative soil consolidation system based on the grass-planted slope protection root system and the grid as described in any one of claims 1 to 8, comprising the following steps: S1: Based on slope soil characteristics, the root depth distribution and root density changes are analyzed, and the periods of peak mutation of soil particle binding force and sharp changes in root characteristics are extracted. The state switching points where the root density and binding force changes are synchronized are identified, and the task level is marked to generate task state switching identification segments. S2: Based on the task state switching identification segment, extract support change data and load change amplitude, compare support mutation with load fluctuation amplitude, analyze the corresponding relationship between support change and processing capacity response, and obtain support adjustment response trajectory segment; S3: Based on the support adjustment response trajectory, the changes in the consolidation rate and the soil consolidation capacity gradient before and after the response are extracted, the decrease in the soil consolidation capacity and the increase in the consolidation quantity of the node are analyzed, the capacity attenuation path is identified and compared with the original cycle data, the sections with capacity reduction characteristics are screened, and a distribution set of soil consolidation adaptation attenuation paths is generated; S4: Based on the soil-fixing adaptive attenuation path distribution set, identifying environmental data within a corresponding time period, analyzing the overlapping period of scour jump and capacity attenuation path, extracting the associated time period, and obtaining the environmental parameter interference section supporting the adjustment; S5: Based on the environmental parameter interference section of the support adjustment, the scheduling control strategy records within the time period are extracted, the time interval of the grid switching and the adjustment trigger frequency are analyzed, and the high-frequency adjustment strategy fragments are screened to obtain the slope treatment task load interference impact frequency.

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