Soil environment detection method based on gun-shaped detection gun

Abstract

The invention discloses a soil environment detection method based on a gun-shaped detection gun, which is used for rapid field detection of a soil environment, and is characterized in that a multi-sensor probe array is integrated through gun configuration equipment, a probe can be inserted into soil by pulling a trigger once by a user, and in the insertion process, the equipment utilizes a probe insertion kinetic model to perform self-adaptive adjustment, so that the detection accuracy is improved. The method is used for overcoming resistance of different soil firmness and synchronously acquiring parameters such as soil humidity, pH value, conductivity, temperature and nitrogen phosphorus and potassium nutrients, and the core of the method is that depth profile reconstruction and intelligent decision are realized through algorithm processing: measured data of discrete depth points are reconstructed into a continuous parameter vertical distribution curve; according to the method, multi-source data is subjected to fusion and uncertainty quantification, a comprehensive report containing multi-parameter values, profile curves, soil quality indexes and risk levels is finally output, the detection time is shortened from several hours to about 30 seconds, and the efficiency, depth and intelligent level of field detection are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of soil environmental testing technology, and specifically to a soil environmental testing method based on a gun-type testing gun. Background Technology

[0002] In agricultural production, environmental monitoring, and engineering construction, soil environmental quality is a core element affecting crop growth, ecological balance, and engineering safety, making rapid and accurate on-site testing increasingly urgent. While traditional laboratory analysis methods offer high precision, they involve complex processes such as sampling, transportation, pretreatment, and multi-step instrument analysis, resulting in significant drawbacks including long cycles (typically several days to weeks), high costs (up to hundreds of yuan per test), and an inability to respond to real-time on-site decisions. For example, in precision agriculture, rapid assessment of the spatiotemporal variability of soil nutrients is crucial for scientific fertilization, but the lag in laboratory testing often leads to a disconnect between fertilization plans and actual needs, resulting in resource waste or reduced crop yields.

[0003] While existing portable soil testing devices attempt to compensate for the shortcomings of laboratory methods, they generally suffer from three major technical bottlenecks: Functionality limitation: Most devices can only measure a single parameter (such as pH value or conductivity), and cannot simultaneously acquire multi-dimensional information such as soil moisture, temperature, nitrogen, phosphorus and potassium nutrients, which leads to users having to carry multiple instruments for repeated sampling, resulting in low efficiency; Lack of profile detection capability: Soil parameters show significant stratified distribution with depth (such as nutrient enrichment in the surface layer and salt accumulation in the deep layer), but existing equipment can only acquire surface data and cannot reconstruct the vertical profile through a single measurement, making it difficult to comprehensively assess the material migration pattern (such as the depth of pollutant infiltration or root nutrient absorption). Poor environmental adaptability: The soil firmness varies greatly in the field (such as loose sandy soil and compacted clay soil). Existing equipment lacks an adaptive control mechanism, and measurement interruption or data distortion often occurs due to excessive probe insertion resistance. In particular, the reliability is insufficient in hard soil.

[0004] Furthermore, existing technologies largely rely on empirical models or simple threshold judgments, failing to achieve intelligent fusion of multi-source data and quantification of uncertainty. For example, soil quality assessment requires the integration of multiple parameters such as humidity, pH, and nutrients, but traditional methods can only output isolated values, lacking analysis of the correlation and spatial variability between parameters, making it difficult to support rapid on-site decision-making (such as pollution risk classification or fertilizer application recommendations).

[0005] The aforementioned contradictions severely restrict the development of precision agriculture (such as variable-rate fertilization and soil pollution remediation), environmental science (such as ecological restoration monitoring), and engineering construction (such as foundation stability assessment). Therefore, developing a field detection method that integrates high efficiency (simultaneous measurement of multiple parameters in a single operation), deep detection capabilities (automatic reconstruction of vertical profiles), and intelligent decision support (data fusion and risk assessment) has become a key issue that the industry urgently needs to address. Summary of the Invention

[0006] To address the aforementioned issues, this invention aims to provide a soil environmental testing technology, specifically a soil environmental testing method based on a gun-type testing gun. This method is particularly suitable for rapid, accurate, and comprehensive on-site testing of soil environmental quality in fields such as agricultural production, environmental monitoring, and engineering construction. It can meet the needs of precision agriculture, soil pollution assessment, and land science management for acquiring multi-dimensional physical, chemical, and nutrient information of soil, as well as vertical profile data.

[0007] The technical solution adopted in this invention is: A soil environmental testing method based on a gun-type detection gun includes the following steps: S1. An integrated soil testing instrument with a pistol-like design, which is inserted into the soil through a single trigger operation; S2. The probe insertion depth is controlled using a probe insertion dynamics model, and soil resistance is analyzed in real time. S3 synchronously drives and decouples signals from multiple embedded sensors to calculate a series of key parameters such as soil moisture, pH value, conductivity, temperature and nutrient concentration in real time. S4. Using radial basis function interpolation and variational principles, a continuous vertical distribution profile of parameters is intelligently reconstructed from a finite number of discrete depth measurement points. S5. By using Bayesian fusion and comprehensive evaluation functions, isolated parameters are integrated into an intuitive soil quality index and risk level assessment, generating a complete soil "health check report".

[0008] Preferably, the probe insertion kinetics model in S2 includes an insertion depth control function and a soil resistance characteristic function: The insertion depth control function is defined as follows: the pressure sensor reading is normalized to... Target depth is The actual insertion depth is controlled by the following differential equation: in, Indicates the insertion depth at time. This represents the depth control gain coefficient. This represents the friction compensation coefficient. This represents the soil resistance function at a certain depth.

[0009] Soil resistance characteristic function, soil resistance is corrected by Bingham fluid model: in, Indicates soil yield stress. Indicates the soil viscosity coefficient. This indicates the surface soil compaction stress. This represents the drag attenuation coefficient.

[0010] Preferably, S3 is implemented through a multi-parameter detection mathematical model, including soil moisture detection, using the frequency domain reflectance method to determine the relationship between dielectric constant and volumetric water content; a soil pH electrochemical model, based on a temperature correction model of the Nernst equation; and a conductivity tensor model, including the calculation of horizontal and vertical conductivity.

[0011] Preferably, in S3, the nitrogen, phosphorus, and potassium nutrient content is detected through a spectral nutrient detection model, including: a multi-wavelength absorption model, where the nutrient concentration C is given by a multivariate linear model for n detection wavelengths; and a characteristic wavelength selection function, where the optimal characteristic wavelength is determined by an optimization problem to ensure uniform wavelength distribution.

[0012] Preferably, in S4, a spatial variation analysis model is used, and the radial basis function interpolation method is employed to perform depth profile interpolation and calculate the vertical gradient analysis of the parameters.

[0013] Preferably, S5 is achieved through data fusion and quality evaluation, including: multi-sensor data fusion using a Bayesian fusion framework; defining the membership function of the soil quality comprehensive index, determining the weights through the analytic hierarchy process, and calculating the SQI comprehensive index.

[0014] Preferably, the method further includes an environmental compensation step, which includes: a general method for temperature compensation through multi-sensor data fusion; and a soil texture influence function, which corrects the parameter vector through a texture influence factor matrix.

[0015] Preferably, the method further includes measurement optimization and adaptive sampling steps, which include: the optimal sampling theorem, which determines the location of new sampling points based on the Kriging interpolation error of the spatial variogram; and adaptive depth selection, which determines the optimal sampling depth sequence through the information entropy maximization criterion.

[0016] Preferably, the method further includes an uncertainty quantification step, wherein the uncertainty quantification includes: a measurement error propagation formula; confidence interval estimation; and calculation of the confidence interval of the parameter.

[0017] Preferably, the integrated soil tester adopts a pistol-shaped design, including a multi-sensor coaxial probe array and an intelligent triggering mechanism, to achieve single-point synchronous measurement.

[0018] This invention achieves a triple breakthrough in soil testing technology in terms of efficiency, depth, and intelligent decision-making through hardware innovation in the gun-shaped detection gun and the collaborative design of the core algorithm system. The specific beneficial effects are as follows: A single operation enables simultaneous measurement of multiple parameters and vertical profile reconstruction: Users only need to "pull the trigger" to activate the device. During the probe insertion into the soil, the algorithm adjusts the insertion speed in real time through a probe insertion dynamics model (such as a depth-resistance feedback mechanism controlled by differential equations), ensuring that more than 10 key parameters, including soil moisture, pH, conductivity, temperature, and nitrogen, phosphorus, and potassium nutrients, are acquired simultaneously within 30 seconds. The depth profile reconstruction algorithm based on radial basis function interpolation and variational principles can generate continuous vertical distribution curves of parameters (such as a depth range of 0-20 cm) with only 5-8 discrete measurement points, breaking through the limitation that portable devices cannot acquire profile data and providing key evidence for assessing pollutant migration and root nutrient absorption depth.

[0019] Intelligent data fusion and on-site decision support: By quantifying the uncertainty of multi-sensor data (such as error propagation between spectral and electrochemical detection) through a Bayesian fusion framework, and outputting intuitive soil quality index (SQI) and risk level maps (such as low / medium / high pollution risk), the professional analysis results are transformed into actionable decision-making basis on-site. For example, in precision agriculture, SQI can be directly associated with recommended fertilizer application rates to avoid environmental pollution caused by over-fertilization; in soil pollution screening, risk maps can quickly locate pollution hotspots and guide subsequent remediation work.

[0020] Significantly improved environmental adaptability and ease of operation: The friction compensation coefficient and soil resistance characteristic function in the probe insertion dynamics model (such as Bingham fluid model correction) enable the equipment to adapt to soils of different firmness (such as sand, clay, and compacted soil), with an insertion resistance error of less than 5%, and field reliability is more than 3 times higher than traditional equipment; the pistol-shaped design (single-hand grip, trigger operation) and one-button integrated output function (direct display of multiple parameter values, profile curves, and evaluation results on the screen) simplify the professional testing process to three steps of "insertion-trigger-reading", reducing the technical threshold by 80%, and making it suitable for large-scale field surveys or environmental emergency monitoring scenarios.

[0021] Dual optimization of detection efficiency and cost: The single detection time is reduced from several hours in traditional methods to 30 seconds, improving efficiency by more than 10 times; the equipment integrates multiple sensors and intelligent algorithms, replacing a variety of single-parameter detection instruments, and reducing the cost of a single detection to 1 / 5 of that of laboratory methods, providing a feasible solution for soil monitoring in resource-limited areas.

[0022] This invention, through the deep integration of hardware and algorithms, is the first to encapsulate laboratory-level soil diagnostic capabilities into a handheld device, realizing a paradigm shift from "single parameter recording" to "comprehensive soil health diagnosis," and providing an efficient and intelligent on-site testing tool for precision agriculture, environmental science, and engineering construction. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the working principle of the present invention; Figure 2 This is a schematic diagram of the multi-sensor synchronous measurement loop principle in this invention; Figure 3 This is a schematic diagram of the core algorithm processing logic of the present invention; Figure 4 This is a schematic diagram of the output module of the present invention. Detailed Implementation

[0024] The embodiments of the present invention are described in detail below with reference to the accompanying drawings: This invention is a rapid, accurate, and comprehensive on-site soil environment detection method based on a pistol-shaped integrated soil detector. It is applicable to fields such as agricultural production, environmental monitoring, and engineering construction. Through highly integrated hardware design and core algorithm system, this method achieves a triple breakthrough in efficiency, depth, and intelligent decision-making for multi-parameter soil detection.

[0025] Example This embodiment employs a pistol-shaped integrated soil analyzer. The integrated soil analyzer includes a pistol-shaped multi-sensor coaxial probe array and an intelligent triggering mechanism. The probe array integrates multiple sensors such as capacitance, potential, conductivity, and spectroscopy. The intelligent triggering mechanism is used to simultaneously activate all sensors for measurement during a single trigger operation. Specific components include: Multi-sensor coaxial probe array: integrates embedded sensors such as capacitance, potential, conductivity, and spectroscopy to achieve single-point synchronous measurement; Intelligent triggering mechanism: The user triggers the probe to be inserted into the soil and starts the detection process by pulling the trigger once; Pressure sensor and dynamic control module: Real-time monitoring of insertion resistance and feedback control of probe depth; Data processing unit: Built-in core algorithm model to realize signal decoupling, parameter calculation and data fusion.

[0026] like Figure 1 The following is a detailed flowchart of a soil environmental testing method based on a gun-shaped detection gun: S1 begins testing and preparation phase The process transitions from "Start Testing" to "Preparation Phase," which corresponds to the initial stage of on-site testing mentioned in the technical solution. In practical applications, users need to complete preliminary preparations for testing to ensure the equipment is in normal working order, laying the foundation for subsequent testing procedures.

[0027] S2 user handheld device aligned with the detection point The user holds the pistol-shaped integrated soil testing instrument and aims it at the soil point to be tested. This operation reflects the device's user-friendly design; the pistol shape makes it easy for the user to hold and operate, and it can accurately position the probe to the target testing location, meeting the requirements for ease of operation in the technical solution.

[0028] S3 system self-test The system enters the self-test phase, a crucial step to ensure accurate and reliable test results. The self-test process checks various functions of the equipment to determine if it is functioning correctly.

[0029] Normal procedure: If the system self-check is normal, the process continues to the "Record GPS / Timestamp" step. This step adds geographical location and time information to the test data, which helps in the subsequent analysis and management of the test results. Especially when conducting large-area soil testing, it can accurately record the location and time of each test point, providing a basis for the spatial and temporal variation analysis of soil environmental quality.

[0030] Abnormal situations: If the system self-test detects an abnormality, the device will "display an error and stop the testing process" to remind the user to check the device and avoid testing under equipment failure conditions, thus ensuring the quality of the test data.

[0031] S4 user trigger pull and related operations User pulls the trigger: The user pulls the trigger like using a pistol, triggering the probe to insert into the soil. This directly reflects the "single-trigger operation" in the technical solution, simplifying the detection process and improving detection efficiency.

[0032] Trigger pressure > F threshold: When the trigger pressure reaches or exceeds the set threshold F threshold, subsequent insertion and detection operations are initiated. This design ensures that the probe can be inserted into the soil with appropriate force, guaranteeing the accuracy and stability of the measurement.

[0033] Activate the insertion mechanism: Once the trigger pressure meets the requirements, activate the insertion mechanism to insert the probe into the soil.

[0034] Dynamic insertion control: During probe insertion, control is achieved through a probe insertion dynamics model. This model uses an insertion depth control function and soil resistance characteristic functions (such as the differential equations and Bingham fluid model correction formulas described in the technical solution) to precisely control the probe insertion depth and analyze soil resistance in real time. In this way, the equipment can adapt to soils of varying firmness, improving its reliability in complex field environments.

[0035] S5 Data Acquisition and Parameter Calculation Data collection depth d(t) and soil resistance F(t): During probe insertion, the insertion depth d(t) and the soil resistance F(t) experienced by the probe are collected in real time. These data form the basis for subsequent analysis of soil properties.

[0036] Calculate the soil resistance coefficient R(z): Based on the collected depth and resistance data, and in conjunction with relevant models, calculate the soil resistance coefficient R(z) to further understand the physical properties of the soil.

[0037] Multi-sensor synchronous measurement cycle: This involves synchronously driving and decoupling signals from multiple embedded sensors (such as capacitance, potential, conductivity, and spectroscopy). These sensors employ different detection principles, such as frequency domain reflectance for soil moisture detection, a temperature correction model based on the Nernst equation for soil pH detection, and a conductivity tensor model for conductivity detection (see technical solution for specific formulas). Through synchronous measurement by multiple sensors, a series of key parameters such as soil moisture, pH, conductivity, temperature, and nitrogen, phosphorus, and potassium nutrient content can be calculated in real time, achieving the goal of acquiring multi-dimensional soil information in a single measurement.

[0038] S6 core algorithm processing Core algorithm processing: This is the key part of the entire detection method, mainly including spatial variation analysis model, data fusion and quality evaluation.

[0039] Spatial variability analysis model: The radial basis function interpolation method is used to interpolate the depth profile, and the continuous vertical distribution profile of the parameters is intelligently reconstructed from a limited number of discrete depth measurement points; at the same time, vertical gradient analysis is performed to calculate the vertical change rate of the parameters to reflect the migration and distribution patterns of materials.

[0040] Data Fusion and Quality Assessment: A Bayesian fusion framework is used to fuse multi-sensor data, integrating isolated parameters into a coherent whole. Membership functions are defined, weights are determined using the analytic hierarchy process (AHP), the Soil Quality Index (SQI) is calculated, and the risk level of soil quality is assessed.

[0041] S7 Output Module and Subsequent Operations Output Module: After processing by the core algorithm, the output module generates a complete soil "health check report," including multi-parameter values, their depth-dependent curves, and the final comprehensive evaluation results. Users can intuitively access this information on the device screen, achieving the effect of "one shot, one click, one image."

[0042] Automatic probe retraction: After the test is completed, the probe automatically retracts, and the device enters a low-power standby state, waiting for the next test, which improves the portability and ease of use of the device.

[0043] With a single trigger operation, users can complete multi-parameter soil detection and vertical profile analysis in a short time, greatly improving detection efficiency and meeting the needs of on-site real-time decision-making. Through multi-sensor synchronous measurement and core algorithm processing, multi-dimensional physical, chemical and nutrient information of the soil can be obtained, realizing multi-functional integrated detection. By using algorithms such as probe insertion dynamics model, radial basis function interpolation and Bayesian fusion, intelligent deep profile reconstruction and data fusion are realized, providing users with intuitive comprehensive evaluation and decision-making basis.

[0044] like Figure 2 As shown, the detailed process of the multi-sensor synchronous measurement cycle in this embodiment is a key part of the soil environment detection method based on the gun-type detection gun in this embodiment, which reflects the mechanism of multi-sensor collaborative work and the cyclic process of data acquisition and processing.

[0045] In this embodiment, the sensor array is the core component set for data acquisition. This embodiment includes five different types of sensors and their corresponding parameter calculation functions: One is a capacitive sensor: used to calculate soil moisture content. In this technical solution, soil moisture detection employs the frequency domain reflectometry method, which measures the soil's dielectric constant and calculates soil moisture based on the relationship between the dielectric constant and volumetric water content. The capacitive sensor can sense changes in the soil's dielectric properties, thus providing fundamental data for moisture content calculation.

[0046] Secondly, there is the pH electrode: used to calculate the pH value of the soil. Based on the temperature correction model of the Nernst equation, the pH electrode calculates the soil's acidity or alkalinity by measuring the potential information in the soil and combining it with parameters such as temperature.

[0047] Thirdly, there is a quadrupole conductivity probe: used to calculate the electrical conductivity of the soil. The conductivity tensor model in this embodiment considers the anisotropy of soil conductivity, calculating both horizontal and vertical conductivity. The quadrupole conductivity probe can accurately measure the electrical conductivity of the soil, providing data support for conductivity calculation.

[0048] Fourthly, the thermistor: used to calculate soil temperature. The resistance of the thermistor changes with temperature. By measuring the resistance of the thermistor and combining it with its characteristic parameters, the soil temperature can be accurately calculated.

[0049] Fifthly, there is the miniature spectrometer: used to calculate nutrient concentrations in the soil. In the spectral nutrient detection model, the miniature spectrometer can measure the absorption of light of different wavelengths by the soil, thus providing key data for nutrient concentration calculation.

[0050] like Figure 3 As shown, the core algorithm processing steps in this embodiment include preprocessing, profile reconstruction, data fusion, and comprehensive evaluation. First, preprocessing is used to calculate outliers and extract them, and then use the outliers for temperature compensation and texture compensation.

[0051] The second is profile reconstruction, which generates continuous curves through hydroxyl-directed basis function interpolation.

[0052] Thirdly, data fusion is performed. The radial basis function interpolation method is used to interpolate the depth profile through the spatial variation analysis model, and the vertical gradient analysis of the parameters is calculated. The multi-sensor data fusion adopts the Bayesian fusion framework.

[0053] Fourth is comprehensive evaluation. The soil quality comprehensive index is defined by a membership function, and the weights are determined by the analytic hierarchy process (AHP). The SQI comprehensive index and risk assessment RJ are then calculated.

[0054] like Figure 4 As shown in the schematic diagram of the output module in this embodiment, after the core algorithm processing steps, the output module will store the data, storing the SQI comprehensive index and risk assessment RJ calculated after the processing steps, and then wirelessly transmitting the data. Finally, the data such as the implementation value table, multi-parameter profile curve, and comprehensive evaluation panel will be displayed on the screen after wireless transmission.

[0055] like Figure 1-4As shown, in this embodiment, multi-sensor synchronous measurement is a cyclical process. After completing one round of data acquisition and calculation, a decision is made based on certain conditions to continue to the next round of measurement. This cyclical mechanism ensures that soil parameter information at different depths can be continuously and stably acquired during the probe insertion process. After each sensor completes parameter calculation, the calculation results are stored in a data packet. The data packet, as the data carrier, integrates the measurement results of each sensor, providing a unified data source for subsequent data processing and analysis. The key condition for the cycle is determining whether the probe has been inserted to the required depth. By comparing with a preset target depth, it is determined whether the probe has been inserted to the required depth. If it has not been inserted to the required depth, the process moves to the next depth, controlling the probe to continue inserting into the soil to reach the next measurement depth, and then performing multi-sensor synchronous measurement and data storage again, repeating the cycle until the target depth is reached. If it has been inserted to the required depth, the process enters the "core algorithm processing" stage. This stage utilizes various algorithms in the technical solution, such as radial basis function interpolation and Bayesian fusion, to process the discrete measurement data in the stored data packet, achieving intelligent depth profile reconstruction and data fusion, ultimately generating intuitive results such as soil quality index and risk level assessment. After completing the core algorithm processing, a stabilization waiting time is set to ensure the stability of the equipment and measurement environment, preparing for the next measurement or subsequent equipment operations (such as probe retraction). The specific execution and detection judgment steps are as follows: S1. An integrated soil testing instrument with a pistol-like design, which is inserted into the soil through a single trigger operation; S2. The probe insertion depth is controlled using a probe insertion dynamics model, and soil resistance is analyzed in real time. S3 synchronously drives and decouples signals from multiple embedded sensors to calculate a series of key parameters such as soil moisture, pH value, conductivity, temperature and nutrient concentration in real time. S4. Using radial basis function interpolation and variational principles, a continuous vertical distribution profile of parameters is intelligently reconstructed from a finite number of discrete depth measurement points. S5. By using Bayesian fusion and comprehensive evaluation functions, isolated parameters are integrated into an intuitive soil quality index and risk level assessment, generating a complete soil "health check report".

[0056] In S1 and S2, an integrated soil testing instrument with a pistol configuration is used, which is inserted into the soil through a single trigger operation; the probe insertion depth is controlled by a probe insertion kinetic model, and the soil resistance is analyzed in real time. The probe insertion dynamics model includes: Insert depth control function, assuming the pressure sensor reading is normalized to Target depth is The actual insertion depth is controlled by the following differential equation: in, Indicates the insertion depth at time. This represents the depth control gain coefficient. This represents the friction compensation coefficient. This represents the soil resistance function at a certain depth.

[0057] Soil resistance characteristic function, soil resistance is corrected by Bingham fluid model: in, Indicates soil yield stress. Indicates the soil viscosity coefficient. This indicates the surface soil compaction stress. This represents the drag attenuation coefficient.

[0058] In S3, a multi-parameter detection mathematical model is used to detect soil moisture and soil pH, and to calculate horizontal and vertical electrical conductivity. Soil moisture detection employs the frequency domain reflectometry method, and the dielectric constant is calculated. With volumetric water content The relationship is as follows: in, Indicates soil bulk density. Indicates the percentage of clay content. This represents the calibration constant.

[0059] Soil pH electrochemical model, a temperature-corrected model based on the Nernst equation: in, Indicates the measured potential. Indicates the standard potential. Represents absolute temperature. Indicates the ionic strength of the soil solution. This represents the temperature-ion strength coupling coefficient.

[0060] Electrical conductivity tensor model, soil electrical conductivity anisotropy tensor: Horizontal conductivity: Vertical conductivity: in, Indicates the measurement of electrical conductance. Represents geometric factors, Indicates field holding capacity. Represents the anisotropy coefficient. Indicates the reference depth.

[0061] In S3, a multi-wavelength absorption model is implemented through a spectral nutrient detection model. For n detection wavelengths, the nutrient concentration C is given by a multivariate linear model. The characteristic wavelength selection function is used, and the optimal characteristic wavelength is determined by an optimization problem to ensure uniform wavelength distribution.

[0062] More specifically, regarding multi-wavelength absorption, for n detection wavelengths... The nutrient concentration C is given by a multivariate linear model: Absorbance Modified by Lambert-Beer Law: in, Indicates the sample and reference light intensities. Indicates the soil particle scattering coefficient. Indicates the sample thickness.

[0063] The characteristic wavelength selection function, the optimal characteristic wavelength is determined by the following optimization problem: in This is a wavelength spacing penalty function to ensure uniform wavelength distribution.

[0064] In S4, depth profile interpolation is performed using radial basis function interpolation through a spatial variability analysis model, and the vertical gradient of the parameters is calculated. The spatial variability analysis model includes: The depth profile interpolation function uses radial basis function interpolation. in, Indicates weight, This represents a linear trend term.

[0065] Vertical gradient analysis, the rate of change of parameter p in the vertical direction: In S5, data fusion and quality assessment include: multi-sensor data fusion using a Bayesian fusion framework; defining the membership function for the soil quality comprehensive index, determining weights through the analytic hierarchy process (AHP), and calculating the SQI comprehensive index. The data fusion and quality assessment also include: Multi-sensor data fusion is performed using a Bayesian fusion framework. For m independent sensors: in Let be the observation model for the i-th sensor.

[0066] The Soil Quality Index (SQI) is a comprehensive index of soil quality. By defining the membership function, the SQI comprehensive index is: Among them, weight Determined by the Analytic Hierarchy Process (AHP).

[0067] This embodiment also includes an environmental compensation step, which includes: A general method for temperature compensation using multi-sensor data fusion, specifically for temperature compensation of parameter X: in, R represents the activation energy, and R represents the gas constant. Soil texture influence function, texture influence factor matrix: Corrected parameter vector: in This refers to the proportion of different types of gravel.

[0068] This embodiment also includes a measurement optimization and adaptive sampling step, which includes: Optimal sampling theorem, based on Kriging interpolation error using spatial variogram: in, Represents the covariance function The location of the new sampling point is determined by maximizing the reduction of error: Adaptive depth selection, information entropy maximization criterion: in Let be the standardized mutation probability of parameter i at depth z.

[0069] The optimal sampling depth sequence satisfies: This embodiment also includes an uncertainty quantification step, which includes: Measurement error propagation, for the function The error propagation formula is: in for and The correlation coefficient.

[0070] Confidence interval estimation, the confidence intervals for the parameters are as follows: Where p represents the number of parameters, Residual variance estimation.

[0071] This embodiment also includes a synthesis output function step, wherein the synthesis output function includes: Multi-parameter state vector, defining the soil state vector: Dynamic evolution model, time evolution of state vectors: in, Represents a deterministic evolution function. Represents the noise coupling matrix. This represents the Gaussian white noise process.

[0072] This embodiment utilizes a multi-sensor synchronous measurement cyclic mechanism to rapidly and continuously acquire multi-parameter data at different depths during a single probe insertion into the soil, significantly shortening detection time and improving efficiency. The collaborative operation of multiple types of sensors enables the simultaneous acquisition of multi-dimensional information on soil physical, chemical, and nutrient content, achieving multi-functional integrated detection and meeting the comprehensive needs of soil environmental quality monitoring. The judgment conditions and core algorithm processing in the cyclic process demonstrate the intelligence of the detection method. The measurement process is automatically controlled based on the probe insertion depth, and advanced algorithms are used for in-depth data analysis and processing, providing users with more accurate and valuable detection results and decision-making support.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A soil environmental detection method based on a gun-shaped detection gun, characterized in that, Includes the following steps: S1. An integrated soil testing instrument with a pistol-like design, which is inserted into the soil through a single trigger operation; S2. The probe insertion depth is controlled by a probe insertion dynamics model, and the soil resistance is analyzed in real time. S3 synchronously drives and decouples signals from multiple embedded sensors to calculate a series of key parameters such as soil moisture, pH value, conductivity, temperature and nutrient concentration in real time. S4. Using radial basis function interpolation and variational principles, a continuous vertical distribution profile of parameters is intelligently reconstructed from a finite number of discrete depth measurement points. S5. By using Bayesian fusion and comprehensive evaluation functions, isolated parameters are integrated into an intuitive soil quality index and risk level assessment, generating a complete soil testing report.

2. The soil environmental detection method based on a gun-shaped detection gun according to claim 1, characterized in that, The probe insertion dynamics model in S2 includes an insertion depth control function and a soil resistance characteristic function: The insertion depth control function is defined as follows: the pressure sensor reading is normalized to... Target depth is The actual insertion depth is controlled by the following differential equation: in, Indicates the insertion depth at time. This represents the depth control gain coefficient. This represents the friction compensation coefficient. This represents the soil resistance function at a certain depth. Soil resistance characteristic function, soil resistance is corrected by Bingham fluid model: in, Indicates soil yield stress. Indicates the soil viscosity coefficient. This indicates the surface soil compaction stress. This represents the drag attenuation coefficient.

3. The soil environmental detection method based on a gun-shaped detection gun according to claim 1, characterized in that, The S3 is implemented through a multi-parameter detection mathematical model, including soil moisture detection, which uses the frequency domain reflectance method to determine the relationship between dielectric constant and volume water content; an electrochemical model for soil pH, which is a temperature-corrected model based on the Nernst equation; and a conductivity tensor model, which includes the calculation of horizontal and vertical conductivity.

4. The soil environmental detection method based on a gun-shaped detection gun according to claim 1, characterized in that, In S3, the nitrogen, phosphorus, and potassium nutrient content is detected through a spectral nutrient detection model, including: a multi-wavelength absorption model, where the nutrient concentration C is given by a multivariate linear model for n detection wavelengths; and a characteristic wavelength selection function, where the optimal characteristic wavelength is determined by an optimization problem to ensure uniform wavelength distribution.

5. The soil environmental detection method based on a gun-type detection gun according to claim 1, characterized in that, The S4 is implemented through a spatial variation analysis model, using radial basis function interpolation for depth profile interpolation and calculating the vertical gradient analysis of the parameters.

6. The soil environmental detection method based on a gun-type detection gun according to claim 1, characterized in that, The S5 is achieved through data fusion and quality evaluation, including: multi-sensor data fusion using a Bayesian fusion framework; defining the membership function of the soil quality comprehensive index, determining the weights through the analytic hierarchy process, and calculating the SQI comprehensive index.

7. The soil environmental detection method based on a gun-type detection gun according to claim 1, characterized in that, It also includes an environmental compensation step, which includes: a general method for temperature compensation through multi-sensor data fusion; and a soil texture influence function, which corrects the parameter vector through a texture influence factor matrix.

8. The soil environmental detection method based on a gun-shaped detection gun according to claim 1, characterized in that, It also includes measurement optimization and adaptive sampling steps, which include: the optimal sampling theorem, which determines the location of new sampling points based on the Kriging interpolation error of the spatial variogram; and adaptive depth selection, which determines the optimal sampling depth sequence through the information entropy maximization criterion.

9. The soil environmental detection method based on a gun-shaped detection gun according to claim 1, characterized in that, It also includes an uncertainty quantification step, which includes: a measurement error propagation formula; confidence interval estimation, and calculation of the confidence interval of the parameter.

10. The soil environmental detection method based on a gun-type detection gun according to any one of claims 1 to 9, characterized in that, The integrated soil tester adopts a pistol-shaped design and includes a multi-sensor coaxial probe array and an intelligent triggering mechanism to achieve single-point synchronous measurement.

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