A method and system for landscaping construction supervision

Abstract

The application discloses a kind of for garden greening construction supervision method and system, it is related to forestry management technical field, it includes obtaining root system penetration force index and planting hole three-dimensional coordinate, residual critical resistance threshold is calculated based on root system penetration force index;Original penetration resistance sequence, depth sequence and soil moisture content sequence of each test point vertical path are collected;Original penetration resistance sequence is executed multiplication compensation by extracting corresponding moisture compensation coefficient, and modified resistance sequence is generated;Along the depth difference of modified resistance sequence, derivation is sought, and the depth node of derivative greater than mutation preset value is extracted as the upper and lower endpoints of hardening layer, and the thickness characteristics of hardening residue are calculated;The three-dimensional hardness distribution grid of hole bottom is constructed;When the thickness of hardening residue is greater than the thickness reference and the hardening connectivity rate is greater than the proportion reference, it is determined that there is a risk of hidden water accumulation, a locking instruction is generated to cut off the process of transplanting seedlings and output directional breaking instruction;Otherwise, issue a recovery construction instruction. Precise recognition of hardening is achieved, and hidden water accumulation is prevented.

Description

Technical Field

[0001] This invention relates to the field of forestry management technology, and in particular to a method and system for supervising landscaping construction. Background Technology

[0002] In the hidden engineering phase of landscaping construction, the quality of removing the compacted soil layer at the bottom of planting holes directly affects the survival rate of seedlings. However, traditional construction supervision methods have long relied excessively on subjective experience and visual inspection. Existing technologies often focus on the geometric appearance indicators of the excavation, lacking scientific quantitative detection of the hidden physical state of the soil at the bottom of the hole and an interlocking control mechanism deeply integrated with the construction process. This allows a large amount of compacted soil that has not been completely removed to be easily covered by the subsequent covering process, leading to a hidden water accumulation effect at the bottom of the planting hole, which can easily cause serious quality accidents such as root suffocation and rot.

[0003] Regarding specific testing methods and evaluation standards, traditional techniques have failed to establish a coupled judgment logic between plant physiological characteristics and the soil physical environment. Existing soil compaction assessments typically employ rigid and singular resistance judgment criteria, completely ignoring the inherent physiological differences among different plants in taproot depth tendency and lateral root hardness. This results in test results that fail to accurately reflect the actual penetration requirements of specific target plants. Furthermore, conventional detection processes often fail to effectively isolate environmental interference from on-site climatic conditions. When faced with temporary increases in soil penetration resistance due to natural water deficit, traditional methods lack scientific water compensation correction mechanisms, easily misjudging simply drought-hardened soil as structural compaction layers requiring mechanical demolition, leading to serious distortions in engineering quality assessment standards.

[0004] In terms of spatial feature analysis and closed-loop management of engineering projects, existing technologies only focus on isolated detection at discrete points, failing to transform local detection sequences into a three-dimensional spatial topology reflecting the overall hardness distribution at the bottom of planting pits. This dimensionality-reduced and flattened data processing method cannot quantitatively assess the spatial connectivity and residual thickness characteristics of compacted areas, leading to numerous regulatory blind spots. Furthermore, traditional regulatory models are disconnected from on-site construction procedures, making it difficult to implement mandatory procedural interventions when conditions are substandard. Due to the lack of directional operation guidelines based on in-situ spatial positioning and a physical coverage mechanism for retesting data in related areas, on-site rectification work often descends into chaotic, blind digging, and the quality of the rectified project still lacks rigorous data closed-loop verification, making the supervision of concealed works prone to becoming a mere formality. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for supervising construction in landscaping and greening projects.

[0006] This invention provides a method for supervising landscaping construction, comprising the following steps:

[0007] Obtain the root penetration index of the target plant and the three-dimensional coordinates of each test point pre-arranged in the planting hole;

[0008] The residual critical resistance threshold for the target plant is calculated based on the root penetration index.

[0009] Simultaneously collect the original penetration resistance sequence, corresponding depth sequence, and soil moisture content sequence of each test point along the vertical descent path;

[0010] Read the pre-calibrated baseline moisture mapping table, extract the moisture compensation coefficient corresponding to the soil moisture content sequence from the baseline moisture mapping table, and use the moisture compensation coefficient to perform multiplication compensation operation on the original penetration resistance sequence to generate a corrected resistance sequence that eliminates drought environment interference.

[0011] Perform differential operation on the modified resistance sequence with respect to the depth sequence, extract the depth nodes whose derivative changes are greater than the preset value of the mutation as the upper and lower limit endpoints, and calculate the distance between the upper and lower limit endpoints as the residual thickness feature of the slab at each test point.

[0012] By combining the three-dimensional coordinates with the corrected resistance sequence of each test point, spatial interpolation is performed to establish a three-dimensional hardness distribution grid at the bottom of the planting hole. Target grid cells with node resistance values ​​greater than the residual critical resistance threshold are selected from the three-dimensional hardness distribution grid. The ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid is calculated to generate the compaction connectivity feature.

[0013] When the residual thickness of the compaction exceeds the predetermined thickness benchmark and the connectivity of the compaction exceeds the predetermined ratio benchmark, it is determined that the planting hole has a hidden risk of water accumulation. A program lock command is generated to cut off the seedling planting process, and the three-dimensional coordinates of the target grid cell are extracted to generate a directional breaking command and output it to the mobile control terminal.

[0014] When the residual thickness of the slab is not greater than the predetermined thickness benchmark and the connectivity of the slab is not greater than the predetermined proportion benchmark, an instruction to resume the seedling planting process is generated and issued.

[0015] Furthermore, a construction supervision system for landscaping is proposed to implement the construction supervision method for landscaping as described above, including: a data acquisition module, acquiring the root penetration index of the target plant and the three-dimensional coordinates of each test point preset in the planting hole;

[0016] The threshold generation module calculates and generates the residual critical resistance threshold for the target plant based on the root penetration index.

[0017] The sequence synchronization acquisition module synchronously acquires the original penetration resistance sequence, corresponding depth sequence, and soil moisture content sequence of each test point on the vertical descent path.

[0018] The moisture compensation correction module reads the pre-calibrated baseline moisture mapping table, extracts the moisture compensation coefficient corresponding to the soil moisture content sequence from the baseline moisture mapping table, and performs a multiplicative compensation operation on the original penetration resistance sequence using the moisture compensation coefficient to generate a corrected resistance sequence that eliminates drought environment interference.

[0019] The thickness feature extraction module performs differential calculation on the modified resistance sequence with respect to the depth sequence, extracts depth nodes whose derivative changes are greater than the preset value of abrupt change as upper and lower limit endpoints, and calculates the distance between the upper and lower limit endpoints as the residual thickness feature of the slab at each test point.

[0020] The connectivity feature generation module combines three-dimensional coordinates with the modified resistance sequence of each test point to perform spatial interpolation calculations, establishes a three-dimensional hardness distribution grid at the bottom of the planting hole, selects target grid cells with node resistance values ​​greater than the residual critical resistance threshold in the three-dimensional hardness distribution grid, calculates the ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid, and generates the compaction connectivity feature.

[0021] The risk assessment and instruction output module determines that the planting hole has a hidden risk of water accumulation when the residual thickness of the compacted soil is greater than the predetermined thickness benchmark and the connectivity of the compacted soil is greater than the predetermined ratio benchmark. It then generates a program lock instruction to cut off the seedling planting process and extracts the three-dimensional coordinates of the target grid cell to generate a directional breaking instruction to be output to the mobile control terminal. When the residual thickness of the compacted soil is not greater than the predetermined thickness benchmark and the connectivity of the compacted soil is not greater than the predetermined ratio benchmark, it generates and issues an instruction to resume the seedling planting process.

[0022] In summary, the present invention has at least one of the following beneficial technical effects:

[0023] 1. By acquiring the root penetration index of the target plant and the three-dimensional coordinates of each test point pre-arranged within the planting hole, and simultaneously collecting the original penetration resistance sequence, depth sequence, and soil moisture content sequence along the vertical descent path, a multi-dimensional monitoring model capable of accurately reflecting the true physical state of the planting hole bottom was constructed. The core collaborative mechanism of this model lies in firstly extracting the moisture compensation coefficient using a benchmark moisture mapping table, and then performing a multiplicative compensation operation on the original penetration resistance sequence to generate a corrected resistance sequence, effectively eliminating the interference of artificially high resistance caused by the drought environment on site. Simultaneously, it breaks the limitations of traditional fixed compaction standards, integrating the root penetration index of the target plant itself with the benchmark characteristics of soil compaction, and deriving a personalized residual critical resistance threshold in reverse. Based on this, spatial interpolation is performed using the three-dimensional coordinates and the corrected resistance sequence to establish a three-dimensional hardness distribution grid at the bottom of the planting hole, thereby accurately extracting the residual thickness and connectivity characteristics of the compacted soil. This collaborative processing method, which integrates biological characteristic indicators, environmental moisture compensation, and three-dimensional spatial interpolation depth, not only significantly improves the accuracy of detecting the compaction state of the bottom soil but also thoroughly eliminates the hidden dangers of water accumulation at the bottom, ensuring the healthy respiration of seedling roots.

[0024] 2. This invention utilizes in-depth analysis of a three-dimensional hardness distribution grid. After selecting target grid cells whose nodal resistance values ​​exceed the residual critical resistance threshold, it quantitatively calculates the connectivity and residual thickness characteristics of the hardened area, enabling proactive prediction and intervention of hidden water accumulation hazards. When a water accumulation hazard is detected, the system directly converts physical detection data into engineering control actions. It immediately generates a program lock command to forcibly halt the planting process, preventing substandard planting pits from being covered by soil. Furthermore, it extracts the spatial extrema and infimum data of the target grid cells, generating a directional demolition command containing the three-dimensional work boundary, which is output to the mobile control terminal. More importantly, after guiding precise local rectification on-site, the system receives a re-measured penetration resistance sequence and performs element replacement and overlay operations on the three-dimensional hardness distribution grid, forming a rigorous closed-loop update process. This closed-loop management mechanism, which perfectly coordinates multi-source detection data analysis, interlocked mandatory intervention, spatial orientation correction, and dynamic re-survey coverage, provides a brand-new automated solution for timely intervention and quality confirmation of hidden works in landscaping and greening, making the rigid constraints on construction quality more efficient, accurate, and unavoidable in practical applications. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a method for supervising construction in landscaping according to the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the process of generating the equivalent diameter of the planting hole and the three-dimensional coordinates of the test point according to the present invention.

[0028] Figure 3 This is a schematic diagram of the upper and lower boundary endpoints of the present invention;

[0029] Figure 4 This is a curve comparing the penetration resistance data before and after soil moisture content compensation according to the present invention. Detailed Implementation

[0030] The following description, in conjunction with the implementation of the present invention, is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention, and all such modifications and additions should fall within the protection scope of the present invention.

[0031] Example

[0032] This invention is applicable to the supervision of concealed works during landscaping construction, specifically before planting and covering seedlings with soil. This method does not merely verify external data such as the excavation depth and diameter of the planting hole; instead, it focuses on the hidden quality issue of whether the compacted soil layer at the bottom of the planting hole has been effectively removed. It correlates the target plant's root penetration ability, on-site penetration resistance, depth, soil moisture content, and the spatial location of test points to generate program-locked, program-unlocked, and directional removal commands that can be recognized by the construction system. In this way, the monitoring system can provide location-based, repeatable, and closed-loop data judgments before the compacted layer is covered with soil, avoiding reliance solely on manual experience to determine the quality of soil loosening at the bottom of the planting hole.

[0033] This application discloses a method and system for supervising the construction of landscaping.

[0034] Reference Figure 1-3 A method for supervising landscaping construction, specifically including:

[0035] Obtain the root penetration index of the target plant and the three-dimensional coordinates of each test point pre-arranged in the planting hole;

[0036] In one specific embodiment, the method for monitoring landscaping construction is executed collaboratively by an intelligent penetration terminal, a mobile control terminal, and a monitoring server on-site. The intelligent penetration terminal includes a penetration rod, a resistance sensor, a depth encoder, a soil moisture sensor, and a positioning component. The mobile control terminal receives test point prompts and directional breaking instructions from the monitoring server. The monitoring server is responsible for calculating the root penetration index, residual critical resistance threshold, corrected resistance sequence, residual compaction thickness characteristics, three-dimensional hardness distribution grid, and compaction connectivity characteristics. Data transmission between devices uses data packets with planting hole numbers, test point numbers, and timestamps to avoid data mixing from multiple planting holes within the same construction area.

[0037] The monitoring server first receives the construction task data for the target plant. This data originates from the landscaping construction management system and can be obtained through the seedling entry QR code, planting hole electronic tag, or connection to the construction drawing database. This data includes at least the target plant's species classification tag, root ball height data, designed excavation depth data for the planting hole, and the planting hole number. For example, for a camphor tree seedling with a diameter at breast height (DBH) of 8 cm, the root ball height data could be 60 cm, and the designed excavation depth data for the planting hole could be 90 cm; the system stores both as numerical fields in centimeters. Simultaneously, the mobile control terminal or intelligent penetration terminal collects the three-dimensional coordinates of each test point pre-positioned within the planting hole. These three-dimensional coordinates use a unified coordinate system for the construction site, with the horizontal and vertical coordinates in meters and the elevation coordinate in meters, and the positive elevation direction aligned with the construction site measurement benchmark.

[0038] The residual critical resistance threshold for the target plant is calculated based on the root penetration index.

[0039] In one specific embodiment, after obtaining the root penetration index of the target plant, the monitoring server calculates and generates a residual critical resistance threshold for the target plant based on the root penetration index. The residual critical resistance threshold represents the upper limit of resistance at which the original soil at the bottom of the planting hole can still be considered as permeable by the roots under the current conditions of the target plant and the current planting hole depth. The higher the root penetration index, the stronger the target plant's adaptability to compaction resistance under the same soil compaction conditions, and the higher the residual critical resistance threshold is accordingly; conversely, for target plants with a high proportion of fleshy roots and fibrous roots and weak root compressive strength, the residual critical resistance threshold is set lower. This threshold is not an isolated input, but a direct criterion for subsequent screening of target grid cells, thus forming a one-to-one comparison relationship with the node resistance values ​​in the three-dimensional hardness distribution grid.

[0040] Simultaneously collect the original penetration resistance sequence, corresponding depth sequence, and soil moisture content sequence of each test point along the vertical descent path;

[0041] In one specific embodiment, the intelligent penetration terminal sequentially performs vertical descent sampling at each test point according to the test point location displayed on the mobile control terminal. The vertical descent path starts from the bottom surface of the planting hole and penetrates downwards to a detection depth range of 30 to 40 centimeters. In this embodiment, the depth sampling interval is 1 centimeter, and the penetration speed is controlled between 1 centimeter per second and 3 centimeters per second to avoid abnormally high original penetration resistance sequences caused by rapid manual impact. At each depth node, the resistance sensor records the axial resistance experienced by the penetration rod at that depth node, the depth encoder records the corresponding depth value, and the soil moisture sensor records the volumetric moisture content of the soil near the same depth node. Thus, each test point forms an original penetration resistance sequence, a corresponding depth sequence, and a soil moisture content sequence. The three types of sequences are bound using the same sampling sequence number. For example, the fifteenth sampling sequence number of the same test point simultaneously corresponds to a depth of 15 centimeters, the original penetration resistance at that depth, and the soil moisture content at that depth, ensuring that subsequent compensation calculations will not result in depth misalignment.

[0042] Read the pre-calibrated baseline moisture mapping table, extract the moisture compensation coefficient corresponding to the soil moisture content sequence from the baseline moisture mapping table, and use the moisture compensation coefficient to perform multiplication compensation operation on the original penetration resistance sequence to generate a corrected resistance sequence that eliminates drought environment interference.

[0043] In one specific embodiment, even without the formation of a true compaction layer, dry soil can exhibit high penetration resistance. To avoid misjudging drought as a compaction layer, the monitoring server reads a pre-calibrated baseline moisture mapping table. This table is formed by calibrating representative soil samples from the same target area under different moisture contents, recording the correspondence between soil moisture content ranges and moisture compensation coefficients. Each moisture content data point in the soil moisture content sequence is matched with a corresponding moisture compensation coefficient. When the moisture content is significantly lower than the standard saturation moisture content, the moisture compensation coefficient is a positive number less than one to reduce the artificially high resistance caused by drying. When the moisture content is close to the standard saturation moisture content, the moisture compensation coefficient is close to one. When the moisture content exceeds the upper limit set in the field calibration, the system uses the moisture compensation coefficient within the upper limit range to prevent the compensation from exceeding the calibration range. The monitoring server performs a multiplicative compensation operation on the moisture compensation coefficients and the discrete resistance data points with the same sampling sequence number in the original penetration resistance sequence to generate a corrected resistance sequence. The modified resistance sequence is still expressed in Newtons, and its physical meaning is the penetration resistance used to identify the residual compaction after removing the interference of arid environment.

[0044] Perform differential operation on the modified resistance sequence with respect to the depth sequence, extract the depth nodes whose derivative changes are greater than the preset value of the mutation as the upper and lower limit endpoints, and calculate the distance between the upper and lower limit endpoints as the residual thickness feature of the slab at each test point.

[0045] In one specific embodiment, after obtaining the corrected resistance sequence, the monitoring server performs a differential operation on the corrected resistance sequence with respect to the depth sequence. The purpose of this process is not simply to find the point of maximum resistance, but rather to find the location where the resistance abruptly changes along the depth direction. For each test point, the monitoring server calculates the resistance difference between adjacent depth nodes in the sequence and performs a differential operation based on the corresponding depth node spacing. The specific calculation formula is as follows: The definitions and calculation constraints for each parameter in the formula are as follows: Here is the first derivative of the resistance data corresponding to the j-th depth interval, in Newtons per centimeter; , These are the corrected resistance values ​​for the j-th and (j+1)-th depth nodes in the corrected resistance sequence, respectively. , These represent the soil depth values ​​corresponding to the two nodes. To avoid calculation anomalies, a mandatory verification mechanism is set before the calculation to strictly determine the depth spacing. If sensor malfunctions cause abnormalities such as no depth change or duplicate data points at the same depth, the abnormal data will be skipped and removed to completely eliminate data overflow errors caused by a zero denominator, ultimately obtaining valid and reliable first-order derivative data of resistance for each depth range. Since the depth node spacing is positive and each depth node increases from the bottom of the hole downwards, the sign of the first-order derivative data reflects the upward and downward trend of resistance with depth. The system extracts depth nodes whose derivative change is greater than a preset abrupt change value from the first-order derivative data as upper and lower boundary endpoints. Positive abrupt changes typically correspond to the upper boundary endpoint of entering the slab layer, while negative abrupt changes typically correspond to the lower boundary endpoint of leaving the slab layer. The monitoring server calculates the distance between the upper and lower boundary endpoints and uses this distance as the residual slab thickness characteristic for each test point. The residual slab thickness characteristic is in centimeters and directly reflects the thickness of the suspected unbroken slab layer on the vertical test path.

[0046] By combining the three-dimensional coordinates with the corrected resistance sequence of each test point, spatial interpolation is performed to establish a three-dimensional hardness distribution grid at the bottom of the planting hole. Target grid cells with node resistance values ​​greater than the residual critical resistance threshold are selected from the three-dimensional hardness distribution grid. The ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid is calculated to generate the compaction connectivity feature.

[0047] In one specific embodiment, a single test point can only reflect the state of a local location, while the compacted layer at the bottom of planting holes often exhibits sheet-like distribution and residual edge characteristics. Therefore, the monitoring server performs spatial interpolation calculations by combining the three-dimensional coordinates of each test point with the corrected resistance sequence of each test point to establish a three-dimensional hardness distribution grid at the bottom of the planting hole. The grid spacing of the three-dimensional hardness distribution grid can be set to five to ten centimeters depending on the diameter of the planting hole, with each grid node corresponding to a spatial location and an interpolated node resistance value. The monitoring server traverses the three-dimensional hardness distribution grid, filtering out grid cells with node resistance values ​​greater than the residual critical resistance threshold as candidate compacted areas. Within these candidate compacted areas, interconnected sheet-like regions are identified based on the adjacency relationship of grid cells, and target grid cells are determined from these. Subsequently, the sum of the projected areas of the target grid cells is calculated, and then the ratio of this sum of projected areas to the total projected area of ​​the grid is calculated to generate a compaction connectivity feature. This compaction connectivity feature is a dimensionless data point between zero and one; the larger the value, the larger the area of ​​continuous compaction at the bottom of the hole, and the higher the risk of water accumulation at the bottom.

[0048] When the residual thickness of the compaction exceeds the predetermined thickness benchmark and the connectivity of the compaction exceeds the predetermined ratio benchmark, it is determined that the planting hole has a hidden risk of water accumulation. A program lock command is generated to cut off the seedling planting process, and the three-dimensional coordinates of the target grid cell are extracted to generate a directional breaking command and output it to the mobile control terminal.

[0049] When the residual thickness of the slab is not greater than the predetermined thickness benchmark and the connectivity of the slab is not greater than the predetermined proportion benchmark, an instruction to resume the seedling planting process is generated and issued.

[0050] In one specific embodiment, after completing the aforementioned data processing, the monitoring server proceeds to the construction process judgment. The predetermined thickness benchmark can be set to five centimeters based on engineering quality requirements, and the predetermined proportion benchmark can be set to 15% to 30% based on the planting hole diameter and the target plant's waterlogging tolerance. When the residual thickness of the compacted soil exceeds the predetermined thickness benchmark and the compaction connectivity exceeds the predetermined proportion benchmark, the monitoring server determines that the planting hole has a hidden risk of water accumulation and immediately generates a program lock command. The program lock command is written into the corresponding planting hole number field in the construction process control data table. After receiving this data, the mobile control terminal changes the seedling placement construction process status of that planting hole from an executable state to a truncated state, preventing on-site personnel from submitting seedling placement confirmation data under that planting hole number. Simultaneously, the monitoring server extracts the three-dimensional coordinates of the target grid unit, generates a directional breaking command, and outputs it to the mobile control terminal, enabling construction personnel to perform secondary breaking according to spatial location and depth range, rather than blindly digging up the entire bottom of the hole. When the residual thickness of the compaction is not greater than the predetermined thickness benchmark and the connectivity of the compaction is not greater than the predetermined proportion benchmark, the monitoring server generates a program unlocking command, the status of the corresponding planting hole number in the construction process control data table is restored to the executable state, and the mobile control terminal allows the input of seedling planting construction process data.

[0051] Furthermore, obtaining the three-dimensional coordinates of each test point pre-arranged within the planting hole includes:

[0052] Collect the set of edge contour coordinates of the planting hole and the coordinates of the origin center;

[0053] Calculate the average distance from each coordinate in the edge contour coordinate set to the center coordinate of the origin, and determine the average distance as the equivalent aperture size;

[0054] Generate a corresponding test point distribution topology map based on the equivalent aperture size mapping;

[0055] Based on the positional relationships indicated by the test point distribution topology map and the coordinates of the origin center, calculate the relative coordinates of multiple planes on the same horizontal reference plane;

[0056] Read the design excavation depth data of the planting pit, add the design excavation depth data to the elevation dimension of the relative coordinates of each plane, and generate the three-dimensional coordinates of each test point that is preset to be arranged in the planting pit.

[0057] As a further step, based on the above complete process, the three-dimensional coordinates of each test point pre-arranged within the planting hole can be automatically generated using the geometric data of the planting hole. In practical applications, the mobile control terminal uses positioning components or vision measurement components to collect the edge contour coordinate set and origin center coordinates of the planting hole. The edge contour coordinate set consists of multiple sampling points on the edge of the planting hole, usually no less than eight sampling points; for circular or near-circular planting holes, sampling can be uniformly collected along the circumference; for planting holes formed by irregular excavation, sampling points are added according to the edge turning points. The origin center coordinates can be determined by the geometric center of the edge contour coordinate set, or corrected from the design center point of the planting hole in the construction drawing. The monitoring server calculates the average distance from each coordinate in the edge contour coordinate set to the origin center coordinates, and determines this average distance as the equivalent diameter size. The specific calculation formula is as follows: In the formula, This is the equivalent aperture size, in meters. The total number of sampling points in the edge contour coordinate set, ( , ) represents the x and y coordinates of the i-th sampling point, ( , ) represents the x and y coordinates of the origin center. Since the mean distance is calculated only on positive distance data, and the sampling points in the edge contour coordinate set do not coincide with the origin center coordinates, the equivalent aperture size is a positive value, which can be stably used for subsequent test point layout.

[0058] After the equivalent diameter is determined, the monitoring server generates a corresponding test point distribution topology map based on the equivalent diameter. This topology map is stored in the point layout rule database, recording the planar positional relationship of the test points relative to the origin center coordinates. For example, when the equivalent diameter is less than 80 cm, a topology using the center point plus three circumferential points can be used; when the equivalent diameter is not less than 80 cm, a topology using the center point plus four circumferential points can be used; for larger tree planting pits, outer ring test points can be added. The monitoring server calculates multiple planar relative coordinates on the same horizontal reference plane according to the positional relationship indicated by the test point distribution topology map and the origin center coordinates. At this point, the planar relative coordinates only contain the horizontal and vertical coordinates, representing the position of the test point within the pit bottom plane. Next, the designed excavation depth data of the planting pit is read and added to the elevation dimension of each planar relative coordinate, generating the three-dimensional coordinates of each test point pre-arranged within the planting pit. This results in test points that cover both the central area of ​​the pit bottom and the edge areas where hard masses may remain, leading to a more balanced spatial interpolation input for the subsequent three-dimensional hardness distribution grid.

[0059] Furthermore, obtaining the root penetration index of the target plant includes:

[0060] Read the species classification label of the target plant;

[0061] The corresponding vertical taproot length feature value and horizontal lateral root hardness feature value were extracted from the plant attribute database using species classification tags.

[0062] The initial root energy characteristics are generated by multiplying the vertical taproot length characteristic value with the horizontal lateral root hardness characteristic value.

[0063] Extract the soil compaction baseline characteristics of the target area where the planting hole is located;

[0064] Calculate the ratio parameter between the initial root energy characteristics and the soil density baseline characteristics, and determine the ratio parameter as the root penetration index of the target plant.

[0065] Specifically, in obtaining the root penetration index of the target plant, this implementation method does not use manual subjective grading; instead, the monitoring server calculates it uniformly based on the plant attribute database. The mobile control terminal reads the species classification tag of the target plant, which can come from the seedling QR code, electronic seedling list, or construction task sheet. The monitoring server uses the species classification tag to extract the corresponding vertical taproot length characteristic value and horizontal lateral root hardness characteristic value from the plant attribute database. The vertical taproot length characteristic value is used to characterize the target plant's root system's need and potential for downward growth, and can be converted into a dimensionless grade based on standardized root data of mature plants or transplanted seedlings; the horizontal lateral root hardness characteristic value is used to characterize the ability of lateral roots to penetrate denser soil, and also uses a dimensionless grade from the same database. To avoid dimensional confusion caused by different data sources, the plant attribute database has already normalized the vertical taproot length characteristic value and the horizontal lateral root hardness characteristic value to the same grade system during the data entry stage, and all values ​​are positive.

[0066] The monitoring server will use the vertical principal root length feature value. Characteristic value of horizontal lateral root hardness Substitute into the formula: Perform the quadrature operation to obtain the initial root system energy characteristics. In the formula: The initial root system energy characteristics, This is the characteristic value of the vertical principal root length. This represents the characteristic value of horizontal lateral root hardness. , , All three are dimensionless normalized positive value parameters. The soil compaction benchmark characteristics of the target area corresponding to the planting hole are extracted and denoted as... , Similarly, the parameter is a dimensionless level parameter greater than 0. The soil compaction benchmark characteristic comes from early soil sample testing data of the construction area or historical penetration data from multiple qualified planting pits in the same area. When entered into the database, it is also converted to a positive level, so that it will not result in a zero value when used as the denominator in the calculation. Then, according to the formula: Calculate the initial root energy characteristics Compared with soil compaction benchmark characteristics The ratio, the obtained ratio parameter As an index of the root penetration ability of the target plant; this calculation is limited. A value always greater than 0 can avoid calculation errors caused by a denominator of 0. The higher the soil density baseline characteristic, the more difficult it is to penetrate the original soil in the target area. Under the same initial root energy characteristics, the root penetration index decreases accordingly. The treatment allows the subsequent residual critical resistance threshold to change synchronously with plant and site conditions.

[0067] As further content,

[0068] Furthermore, the calculation of the residual critical resistance threshold for the target plant based on the root penetration index includes:

[0069] Read the soil ball height data of the target plant and the design excavation depth data of the planting hole;

[0070] Calculate the difference between the designed excavation depth data and the soil ball height data to generate the root penetration space feature;

[0071] The product parameter of the root penetration index and the predetermined adjustment coefficient is calculated, and the product parameter is summed with the root penetration space feature to generate the residual critical resistance threshold for the target plant.

[0072] In another optional embodiment, during the calculation of the residual critical resistance threshold, the monitoring server also incorporates the target plant's root ball height data and the designed excavation depth data of the planting hole. The root ball height data is derived from the seedling arrival and acceptance records, while the designed excavation depth data of the planting hole is derived from the construction drawing database; both are stored in the same unit of length. The monitoring server first calculates the difference between the designed excavation depth data and the root ball height data to generate a root-planting reserved space feature. This feature represents the vertical space between the bottom of the root ball and the bottom of the designed hole, allowing for soil loosening, drainage, and root planting. A positive difference indicates that the planting hole depth can accommodate the root ball and provide sufficient space below; if the difference is less than the lower limit allowed by construction data, the system will issue a data anomaly warning before proceeding with the threshold calculation, requiring a review of the root ball height data and the designed excavation depth data to avoid incorporating unreasonable excavation data into subsequent monitoring decisions.

[0073] To ensure that the root penetration index and the root penetration space characteristics work together to affect the residual critical resistance threshold, the monitoring server sets a predetermined adjustment coefficient during the engineering calibration phase. This predetermined adjustment coefficient is not a simple mathematical ratio, but a conversion factor determined based on the target area's soil type, commonly used seedling root data, and penetrator calibration data. This ensures that the root penetration index, after being included in the calculation, forms a product parameter that matches the resistance determination. When the root penetration space characteristics are included in the summation calculation, the resistance correction field corresponding to that length unit in the construction database is used. Its physical meaning is the contribution of the vertical reserved space to the correction of the allowable residual resistance. The monitoring server substitutes the root penetration index, the predetermined adjustment coefficient, and the root penetration space characteristics into the threshold generation formula: The residual critical resistance threshold for generating the target plant is calculated. The parameters in the formula are defined as follows: The residual critical resistance threshold for the target plant is expressed in Newtons. It is a dimensionless root penetration index; The predetermined adjustment coefficient has the physical meaning of a basic resistance conversion factor. It is set according to the soil type of the target area and is measured in Newtons. The typical value range is 200 to 500. Features indicating space reserved for root growth, measured in centimeters; This is a resistance correction coefficient pre-stored in the construction database, measured in Newtons per centimeter. Its core function is to convert the spatial length dimension into the resistance contribution dimension. This is achieved by introducing... The coefficient ensures strict dimensional consistency on both sides of the equation. This threshold is ultimately stored in the same measurement system as the penetration resistance, allowing for direct comparison with the nodal resistance values ​​in the three-dimensional hardness distribution grid, thus avoiding any dimensional inconsistencies.

[0074] Furthermore, the step of performing a multiplicative compensation operation on the original penetration resistance sequence using a moisture compensation coefficient to generate a corrected resistance sequence that eliminates drought environmental interference includes:

[0075] The difference between the soil moisture content sequence and the standard saturated moisture content is calculated to generate a water deficit sequence;

[0076] A nonlinear mapping operation is performed on the water deficit sequence using a predetermined exponential decay function to calculate and generate a nonlinear water compensation coefficient.

[0077] By using the moisture compensation coefficient, a product operation is performed on each discrete resistance data point in the original penetration resistance sequence to generate a corrected resistance sequence.

[0078] Specifically, regarding a more detailed implementation of moisture compensation, the monitoring server can perform non-linear processing on the soil moisture content sequence in addition to reading the baseline moisture mapping table. The soil moisture content sequence collected by the smart penetration terminal is the volumetric moisture content, typically ranging from 5% to 45%, with the specific range influenced by the type of soil (sandy, loam, or clay). The standard saturated moisture content is given by soil sample tests in the target area; for example, a calibration value around 35% can be used for loam. The monitoring server calculates the difference between the soil moisture content sequence and the standard saturated moisture content, generating a moisture deficit sequence. In this implementation, the moisture deficit sequence is calculated by subtracting the soil moisture content sequence from the standard saturated moisture content; lower moisture content corresponds to a larger moisture deficit. When the soil moisture content reaches or exceeds the standard saturated moisture content, the moisture deficit is treated as a boundary value of zero or close to zero to prevent the compensation coefficient from being negatively amplified.

[0079] According to the formula for calculating water deficit The difference between the standard saturated moisture content and the measured soil moisture content is calculated to generate a water deficit sequence. Where, Water deficit is expressed as a percentage. This refers to the standard saturated moisture content of the soil. This represents the measured water content values ​​at each point in the soil moisture content sequence. After obtaining the water deficit sequence, the monitoring server uses a predetermined exponential decay function formula. Nonlinear mapping operations were performed on the water deficit sequence to obtain the dimensionless water compensation coefficient. The parameters in the formula are defined and their values ​​are determined according to the following rules: This is a dimensionless moisture compensation coefficient. The values ​​corresponding to the water deficit sequence are measured as a percentage. The value of the attenuation factor for the influence of moisture is determined based on calibration tests of similar soils, with the conventional value range for loam being 1.5–3.0. To avoid calculation anomalies, specific constraint rules are set: when… A negative value is forcibly assigned when the soil moisture content is in a supersaturated state. ,at this time This effectively avoids a moisture compensation coefficient greater than 1, preventing the abnormal amplification of soil resistance under flooded conditions. After calculating the moisture compensation coefficient, the resistance correction formula is applied. The corrected resistance sequence is obtained by performing a product correction operation on each discrete resistance data point within the original penetration resistance sequence. In the formula, This is the original penetration resistance data. The corrected target resistance data is used to generate a complete soil corrected resistance sequence. The advantage of performing the product operation item by item is that the resistance data of each depth node is corrected using the moisture content data of the same depth node, avoiding the use of the surface moisture content at the bottom of the pit to compensate for the deep resistance, thereby reducing misjudgments caused by the difference in moisture content between layers.

[0080] Furthermore, the step of performing differential calculation on the modified resistance sequence with respect to the depth sequence, extracting depth nodes whose derivative changes are greater than a predetermined abrupt change value as upper and lower limit endpoints, and calculating the distance between the upper and lower limit endpoints as the residual thickness feature of the slab at each test point, includes:

[0081] Calculate the resistance difference between adjacent depth nodes in the corrected resistance sequence, and the quotient of the resistance difference and the distance between the corresponding depth nodes to generate the first derivative sequence of each depth node;

[0082] In the first derivative sequence, select the node positions whose values ​​are greater than the preset value of positive mutation and determine the node positions as the upper limit endpoints;

[0083] In the first derivative sequence, select the node positions whose values ​​are less than the preset value of negative mutation, and determine the node positions as the lower limit endpoints;

[0084] The absolute value of the elevation difference between the upper and lower limit endpoints of the same vertical test path is used as a feature of the residual thickness of the slab.

[0085] Specifically, in the implementation of differential calculation, the monitoring server first performs a sampling consistency check on the modified resistance sequence and the depth sequence. If the depth sequence of a certain test point has missing sampling points, the system decides whether to retain the test point's data based on whether the distance between adjacent depth nodes is still within the allowable range. For data packets with a depth node distance of zero or negative values, they are directly marked as invalid data and do not participate in the calculation of the first derivative sequence, because such data does not conform to the physical process of a vertical descent path. For valid data, the monitoring server calculates the resistance difference between adjacent depth nodes in the modified resistance sequence, and then calculates the quotient of the resistance difference and the corresponding depth node distance to generate the first derivative sequence for each depth node. The unit of the first derivative sequence is resistance per centimeter, used to characterize the rate of change of resistance along the depth direction.

[0086] After the first derivative sequence is formed, the monitoring server filters out node locations with values ​​greater than the preset positive mutation value, and determines these node locations as upper boundary endpoints. The preset positive mutation value can be determined by historical qualified pit bottom data and known compaction sample data, for example, it can be set in the range of 100 Newtons to 300 Newtons per centimeter, with the specific value corresponding to the diameter of the penetration rod, soil type, and sampling interval. Subsequently, the monitoring server filters out node locations with values ​​less than the preset negative mutation value, and determines these node locations as lower boundary endpoints. The preset negative mutation value is negative, and its absolute value is usually close to the preset positive mutation value, used to identify the location where the penetration path leaves the high-resistivity layer. If there are multiple candidate upper and lower boundary endpoints for the same vertical test path, the system pairs them according to depth order, prioritizing the selection of the nearest lower boundary endpoint after the upper boundary endpoint, and requiring that the average corrected resistance between the two is higher than the residual critical resistance threshold to avoid mistaking accidental noise peaks for compaction layers. Finally, the monitoring server calculates the absolute value of the elevation difference between the upper and lower boundary endpoints of the same vertical test path as the residual thickness characteristic of the slab. Taking the absolute value is to eliminate the difference in the positive direction setting of the elevation coordinates, and the output is still represented by the actual thickness.

[0087] Furthermore, the process of selecting target mesh cells with nodal resistance values ​​greater than the residual critical resistance threshold from the three-dimensional hardness distribution mesh, calculating the ratio of the sum of the projected areas of the target mesh cells to the total projected area of ​​the mesh, and generating a compaction connectivity feature includes:

[0088] The three-dimensional coordinates of each test point and the corresponding residual thickness features of the slab are extracted and input into the Kriging interpolation algorithm model to perform variogram fitting calculation.

[0089] A three-dimensional hardness distribution grid containing the expected thickness values ​​of unmeasured points is established using the predicted data from the variogram fitting operation.

[0090] Traverse all grid cells in the three-dimensional hardness distribution grid, extract grid cells whose nodal resistance values ​​are greater than the residual critical resistance threshold and are in an adjacent connection state, and divide them into the same connected region.

[0091] The connected region containing the most grid cells is marked as the target grid cell. The sum of the projected areas of the target grid cells is calculated as the quotient of the total projected area of ​​the three-dimensional hardness distribution grid to generate the compaction connectivity feature.

[0092] In another optional embodiment, the construction of the three-dimensional hardness distribution grid can be accomplished using a Kriging interpolation algorithm model. The monitoring server extracts the three-dimensional coordinates of each test point and the corresponding residual thickness features of the hardened material, and inputs this data into the Kriging interpolation algorithm model to perform variogram fitting calculations. Here, the Kriging interpolation algorithm model does not directly provide conclusions in an opaque manner, but rather fits spatial correlations based on the differences in spatial distance between test points and residual thickness features of the hardened material. The variogram fitting calculation outputs predicted data on thickness changes under different spatial intervals, reflecting the spatial continuity of the hardened material residue at the bottom of the pit. If the number of test points is small, the system uses smoother variogram parameters to prevent a single abnormal test point from causing excessive expansion of the entire pit bottom area; if the number of test points is large and the coverage is uniform, stronger local variation information is retained, allowing the residual hardened material at the edges to be displayed in the grid.

[0093] Using the predicted data from the variogram fitting calculation, the monitoring server establishes a three-dimensional hardness distribution grid containing the expected thickness values ​​of unmeasured points. Each grid cell in this three-dimensional hardness distribution grid contains spatial coordinates, nodal resistance values, and expected thickness values. The nodal resistance values ​​are derived from the spatial interpolation of the corrected resistance sequence at each test point, and the expected thickness values ​​are derived from the spatial interpolation of the residual thickness characteristics of the slab; both support the selection of target grid cells. The monitoring server traverses all grid cells in the three-dimensional hardness distribution grid, extracting grid cells with nodal resistance values ​​greater than the residual critical resistance threshold and in an adjacent connection state, and assigns them to the same connected region. The adjacent connection state can be determined by shared boundaries, or by a combination of shared boundaries and shared corner points, but consistency must be maintained within the same project. Subsequently, the monitoring server selects the connected region with the largest number of grid cells and marks it as the target grid cell, substituting the relevant area parameters into the proportional calculation formula: The dimensionless connectivity characteristics of the compaction were calculated. The parameters in the formula are defined as follows: This is the final dimensionless connectivity characteristic of the platen; This represents the projected area of ​​a single independent grid cell within the target grid cell. The sum of the total projected areas of all target mesh cells; This represents the total projected area of ​​the three-dimensional hardness distribution grid. Since the sum of the projected areas of the target grid cells will not exceed the total projected area of ​​the grid, and the total projected area of ​​the grid is a positive value, the compaction connectivity characteristic stably falls between zero and one, making it suitable for comparison with a predetermined scale benchmark.

[0094] Furthermore, the step of extracting the three-dimensional coordinates of the target mesh cell to generate a directional breaking command and outputting it to the mobile control terminal includes:

[0095] Extract the x-coordinate set and y-coordinate set corresponding to the target mesh cell;

[0096] Calculate the extreme value range of the x-axis set and the extreme value range of the y-axis set, and determine the corresponding rectangular operation boundary parameters;

[0097] Extract the elevation coordinate set corresponding to the target grid cell, and calculate the infimum data of the elevation coordinate set as the lower limit parameter of the breaking depth;

[0098] The rectangular operation boundary parameters and the lower limit parameter of the breaking depth are concatenated to generate a directional breaking command containing the three-dimensional operation boundary, which is then output to the mobile control terminal.

[0099] Specifically, when the monitoring server determines that a directional demolition command needs to be output, the three-dimensional coordinates of the target grid cell are further processed into a three-dimensional work boundary that construction personnel can understand. The monitoring server first extracts the x-coordinate and y-coordinate sets corresponding to the target grid cell. The x-coordinate set represents the position range of the target grid cell on the horizontal axis of the construction site, and the y-coordinate set represents the position range of the target grid cell on the vertical axis of the construction site. Subsequently, the monitoring server calculates the extreme value ranges of the x-coordinate and y-coordinate sets to determine the corresponding rectangular work boundary parameters. The rectangular work boundary parameters include the minimum horizontal coordinate, the maximum horizontal coordinate, the minimum vertical coordinate, and the maximum vertical coordinate. This transforms potentially irregularly shaped target grid cells into work areas that are easy to lay out on-site. This transformation does not reduce monitoring accuracy; rather, it converts continuous hardened areas into boundary data that can be executed by construction machinery and manual tools, giving the demolition operation clear spatial constraints.

[0100] After the rectangular operation boundary parameters are determined, the monitoring server extracts the elevation coordinate set corresponding to the target grid cell and calculates the lower limit data of the elevation coordinate set as the lower limit parameter for the demolition depth. The elevation coordinate set comes from the depth layer corresponding to the target grid cell in the three-dimensional hardness distribution grid, reflecting the downward extension of the compacted residue. The lower limit data is used to give the minimum depth boundary that the demolition operation should reach, avoiding only loosening the surface layer while retaining the deep compacted layer. The monitoring server concatenates the rectangular operation boundary parameters and the lower limit parameter for demolition depth to generate a directional demolition command containing the three-dimensional operation boundary, which is then output to the mobile control terminal. After receiving the directional demolition command, the mobile control terminal can display the lateral offset, longitudinal offset, and lower limit parameter for demolition depth relative to the center of the planting hole on the interface, or convert them into on-site layout marks to guide workers to carry out secondary demolition within the designated area.

[0101] Furthermore, after outputting the targeted breaking command, the method also includes a closed-loop data update process, including:

[0102] Receive the retested penetration resistance sequence returned after physical demolition of the target grid cell;

[0103] The original node resistance values ​​in the three-dimensional hardness distribution grid within the rectangular working boundary parameter range are replaced and covered by the retested penetration resistance sequence to generate an updated three-dimensional hardness distribution grid.

[0104] Using the updated 3D hardness distribution grid, the step of calculating the ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid is performed again, and the compaction connectivity feature is regenerated for comparison to determine whether it meets the predetermined ratio benchmark.

[0105] In one optional embodiment, when the monitoring server determines that a directional demolition command needs to be output, the three-dimensional coordinates of the target grid cell are further organized into a three-dimensional work boundary that construction personnel can understand. The monitoring server first extracts the set of horizontal and vertical coordinates corresponding to the target grid cell. The horizontal coordinate set represents the position range of the target grid cell on the horizontal coordinate axis of the construction site, and the vertical coordinate set represents the position range of the target grid cell on the vertical coordinate axis of the construction site. Subsequently, the monitoring server calculates the extreme value ranges of the horizontal and vertical coordinate sets to determine the corresponding rectangular work boundary parameters. The rectangular work boundary parameters include the minimum horizontal coordinate, the maximum horizontal coordinate, the minimum vertical coordinate, and the maximum vertical coordinate. This transforms the potentially irregularly shaped target grid cell into a work area that is easy to lay out on-site. This transformation does not reduce monitoring accuracy but rather converts the continuous hardened area into boundary data that can be executed by construction machinery and manual tools, giving the demolition operation clear spatial constraints.

[0106] After the rectangular operation boundary parameters are determined, the monitoring server extracts the elevation coordinate set corresponding to the target grid cell and calculates the lower limit data of the elevation coordinate set as the lower limit parameter for the demolition depth. The elevation coordinate set comes from the depth layer corresponding to the target grid cell in the three-dimensional hardness distribution grid, reflecting the downward extension of the compacted residue. The lower limit data is used to give the minimum depth boundary that the demolition operation should reach, avoiding only loosening the surface layer while retaining the deep compacted layer. The monitoring server concatenates the rectangular operation boundary parameters and the lower limit parameter for demolition depth to generate a directional demolition command containing the three-dimensional operation boundary, which is then output to the mobile control terminal. After receiving the directional demolition command, the mobile control terminal can display the lateral offset, longitudinal offset, and lower limit parameter for demolition depth relative to the center of the planting hole on the interface, or convert them into on-site layout marks to guide workers to carry out secondary demolition within the designated area.

[0107] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0108] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0109] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for supervising construction in landscaping and greening projects, characterized in that, include: Obtain the root penetration index of the target plant and the three-dimensional coordinates of each test point pre-arranged in the planting hole; The residual critical resistance threshold for the target plant is calculated based on the root penetration index. Simultaneously collect the original penetration resistance sequence, corresponding depth sequence, and soil moisture content sequence of each test point along the vertical descent path; Read the pre-calibrated baseline moisture mapping table, extract the moisture compensation coefficient corresponding to the soil moisture content sequence from the baseline moisture mapping table, and use the moisture compensation coefficient to perform multiplication compensation operation on the original penetration resistance sequence to generate a corrected resistance sequence that eliminates drought environment interference. Perform differential operation on the modified resistance sequence with respect to the depth sequence, extract the depth nodes whose derivative changes are greater than the preset value of the mutation as the upper and lower limit endpoints, and calculate the distance between the upper and lower limit endpoints as the residual thickness feature of the slab at each test point. By combining the three-dimensional coordinates with the corrected resistance sequence of each test point, spatial interpolation is performed to establish a three-dimensional hardness distribution grid at the bottom of the planting hole. Target grid cells with node resistance values ​​greater than the residual critical resistance threshold are selected from the three-dimensional hardness distribution grid. The ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid is calculated to generate the compaction connectivity feature. When the residual thickness of the compaction exceeds the predetermined thickness benchmark and the connectivity of the compaction exceeds the predetermined ratio benchmark, it is determined that the planting hole has a hidden risk of water accumulation. A program lock command is generated to cut off the seedling planting process, and the three-dimensional coordinates of the target grid cell are extracted to generate a directional breaking command and output it to the mobile control terminal. When the residual thickness of the slab is not greater than the predetermined thickness benchmark and the connectivity of the slab is not greater than the predetermined proportion benchmark, an instruction to resume the seedling planting process is generated and issued.

2. The method for supervising landscaping construction according to claim 1, characterized in that, Obtain the three-dimensional coordinates of each test point pre-arranged within the planting hole, including: Collect the set of edge contour coordinates of the planting hole and the coordinates of the origin center; Calculate the average distance from each coordinate in the edge contour coordinate set to the center coordinate of the origin, and determine the average distance as the equivalent aperture size; Generate a corresponding test point distribution topology map based on the equivalent aperture size mapping; Based on the positional relationships indicated by the test point distribution topology map and the coordinates of the origin center, calculate the relative coordinates of multiple planes on the same horizontal reference plane; Read the design excavation depth data of the planting pit, add the design excavation depth data to the elevation dimension of the relative coordinates of each plane, and generate the three-dimensional coordinates of each test point that is preset to be arranged in the planting pit.

3. The method for supervising landscaping construction according to claim 1, characterized in that, Obtain the root penetration index of the target plant, including: Read the species classification label of the target plant; The corresponding vertical taproot length feature value and horizontal lateral root hardness feature value were extracted from the plant attribute database using species classification tags. The initial root energy characteristics are generated by multiplying the vertical taproot length characteristic value with the horizontal lateral root hardness characteristic value. Extract the soil compaction baseline characteristics of the target area where the planting hole is located; Calculate the ratio parameter between the initial root energy characteristics and the soil density baseline characteristics, and determine the ratio parameter as the root penetration index of the target plant.

4. A method for supervising landscaping construction according to claim 1, characterized in that, The residual critical resistance threshold for the target plant is calculated based on the root penetration index, including: Read the soil ball height data of the target plant and the design excavation depth data of the planting hole; Calculate the difference between the designed excavation depth data and the soil ball height data to generate the root penetration space feature; The product parameter of the root penetration index and the predetermined adjustment coefficient is calculated, and the product parameter is summed with the root penetration space feature to generate the residual critical resistance threshold for the target plant.

5. A method for supervising landscaping construction according to claim 1, characterized in that, A multiplicative compensation operation is performed on the original penetration resistance sequence using a moisture compensation coefficient to generate a corrected resistance sequence that eliminates drought environmental interference, including: The difference between the soil moisture content sequence and the standard saturated moisture content is calculated to generate a water deficit sequence; A nonlinear mapping operation is performed on the water deficit sequence using a predetermined exponential decay function to calculate and generate a nonlinear water compensation coefficient. By using the moisture compensation coefficient, a product operation is performed on each discrete resistance data point in the original penetration resistance sequence to generate a corrected resistance sequence.

6. A method for supervising landscaping construction according to claim 1, characterized in that, Perform differential calculation on the modified resistance sequence with respect to the depth sequence, extract the depth nodes where the change in derivative is greater than the preset value of abrupt change as the upper and lower limit endpoints, and calculate the distance between the upper and lower limit endpoints as the residual thickness characteristics of the slab at each test point, including: Calculate the resistance difference between adjacent depth nodes in the corrected resistance sequence, and the quotient of the resistance difference and the distance between the corresponding depth nodes to generate the first derivative sequence of each depth node; In the first derivative sequence, select the node positions whose values ​​are greater than the preset value of positive mutation and determine the node positions as the upper limit endpoints; In the first derivative sequence, select the node positions whose values ​​are less than the preset value of negative mutation, and determine the node positions as the lower limit endpoints; The absolute value of the elevation difference between the upper and lower limit endpoints of the same vertical test path is used as a feature of the residual thickness of the slab.

7. A method for supervising landscaping construction according to claim 1, characterized in that, In a three-dimensional hardness distribution grid, target grid cells with nodal resistance values ​​greater than the residual critical resistance threshold are selected. The ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid is calculated to generate the compaction connectivity feature, including: The three-dimensional coordinates of each test point and the corresponding residual thickness features of the slab are extracted and input into the Kriging interpolation algorithm model to perform variogram fitting calculation. A three-dimensional hardness distribution grid containing the expected thickness values ​​of unmeasured points is established using the predicted data from the variogram fitting operation. Traverse all grid cells in the three-dimensional hardness distribution grid, extract grid cells whose nodal resistance values ​​are greater than the residual critical resistance threshold and are in an adjacent connection state, and divide them into the same connected region. The connected region containing the most grid cells is marked as the target grid cell. The sum of the projected areas of the target grid cells is calculated as the quotient of the total projected area of ​​the three-dimensional hardness distribution grid to generate the compaction connectivity feature.

8. A method for supervising landscaping construction according to claim 1, characterized in that, Extract the 3D coordinates of the target mesh cells to generate directional breaking commands, which are then output to the motion control terminal, including: Extract the x-coordinate set and y-coordinate set corresponding to the target mesh cell; Calculate the extreme value range of the x-axis set and the extreme value range of the y-axis set, and determine the corresponding rectangular operation boundary parameters; Extract the elevation coordinate set corresponding to the target grid cell, and calculate the infimum data of the elevation coordinate set as the lower limit parameter of the breaking depth; The rectangular operation boundary parameters and the lower limit parameter of the breaking depth are concatenated to generate a directional breaking command containing the three-dimensional operation boundary, which is then output to the mobile control terminal.

9. The method according to claim 8, characterized in that, After outputting the targeted breaking command, the method also includes a closed-loop data update process, including: Receive the retested penetration resistance sequence returned after physical demolition of the target grid cell; The original node resistance values ​​in the three-dimensional hardness distribution grid within the rectangular working boundary parameter range are replaced and covered by the retested penetration resistance sequence to generate an updated three-dimensional hardness distribution grid. Using the updated 3D hardness distribution grid, the step of calculating the ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid is performed again, and the compaction connectivity feature is regenerated for comparison to determine whether it meets the predetermined ratio benchmark.

10. A system for monitoring construction in landscaping and greening projects, characterized in that, The method for supervising construction of landscaping as described in any one of claims 1-9 includes: a data acquisition module for acquiring the root penetration index of the target plant and the three-dimensional coordinates of each test point preset in the planting hole; The threshold generation module calculates and generates the residual critical resistance threshold for the target plant based on the root penetration index. The sequence synchronization acquisition module synchronously acquires the original penetration resistance sequence, corresponding depth sequence, and soil moisture content sequence of each test point on the vertical descent path. The moisture compensation correction module reads the pre-calibrated baseline moisture mapping table, extracts the moisture compensation coefficient corresponding to the soil moisture content sequence from the baseline moisture mapping table, and performs a multiplicative compensation operation on the original penetration resistance sequence using the moisture compensation coefficient to generate a corrected resistance sequence that eliminates drought environment interference. The thickness feature extraction module performs differential calculation on the modified resistance sequence with respect to the depth sequence, extracts depth nodes whose derivative changes are greater than the preset value of abrupt change as upper and lower limit endpoints, and calculates the distance between the upper and lower limit endpoints as the residual thickness feature of the slab at each test point. The connectivity feature generation module combines three-dimensional coordinates with the modified resistance sequence of each test point to perform spatial interpolation calculations, establishes a three-dimensional hardness distribution grid at the bottom of the planting hole, selects target grid cells with node resistance values ​​greater than the residual critical resistance threshold in the three-dimensional hardness distribution grid, calculates the ratio of the sum of the projected areas of the target grid cells to the total projected area of ​​the grid, and generates the compaction connectivity feature. The risk assessment and instruction output module determines that the planting hole has a hidden risk of water accumulation when the residual thickness of the compacted soil is greater than the predetermined thickness benchmark and the connectivity of the compacted soil is greater than the predetermined ratio benchmark. It then generates a program lock instruction to cut off the seedling planting process and extracts the three-dimensional coordinates of the target grid cell to generate a directional breaking instruction to be output to the mobile control terminal. When the residual thickness of the compacted soil is not greater than the predetermined thickness benchmark and the connectivity of the compacted soil is not greater than the predetermined ratio benchmark, it generates and issues an instruction to resume the seedling planting process.

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