Multi-parameter surface matrix integrated sensor and monitoring method

Abstract

The present application relates to the technical field of soil testing and provides a multi-parameter surface matrix integrated sensor and monitoring method, comprising: a data acquisition module storing a preset analysis algorithm; a plurality of sensor probes, each corresponding to a different electrode; the data acquisition module being configured to analyze the measurement parameters measured by the sensor probes according to the preset analysis algorithm to generate surface matrix parameters; the surface matrix parameters comprising at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus, potassium value, and soil organic matter value; the arrangement of the sensor probes being related to the type and structure of the surface matrix to facilitate insertion into pores or fissures of the surface matrix; and a wireless communication module being communicatively connected to the data acquisition module and also wirelessly connected to an external device, for transmitting the surface matrix parameters to the external device.

Description

Technical Field

[0001] The present application relates to the field of soil testing technology, and in particular to a multi-parameter surface matrix integrated sensor and a monitoring method. Background Art

[0002] Multi-parameter monitoring of surface substrates is a core technology in modern agriculture, environmental monitoring, and geological disaster early warning. Traditional surface substrate monitoring methods typically rely on single-parameter sensors operating independently, requiring the simultaneous deployment of multiple discrete devices such as pH meters, conductivity meters, and temperature and humidity sensors. This results in high hardware redundancy, complex installation, and poor data interoperability.

[0003] Although some integrated sensors in the existing technology attempt to integrate 2-3 conventional parameters, their inherent problems are still significant: on the one hand, they are limited by the physical layout conflicts of sensor probes, making it difficult to achieve simultaneous and accurate measurement of multiple parameters; on the other hand, the lack of an intelligent data processing center causes the data of each parameter to be in a discrete state, requiring manual post-integration analysis, which cannot meet real-time monitoring needs. Summary of the Invention

[0004] This application provides a multi-parameter surface matrix integrated sensor and monitoring method, which aims to solve the problem that traditional integrated sensors attempt to integrate 2-3 conventional parameters, but are limited by the physical arrangement conflicts of sensor probes and find it difficult to achieve simultaneous and accurate measurement of multiple parameters.

[0005] In a first aspect, the present application provides a multi-parameter surface matrix integrated sensor, comprising:

[0006] A data acquisition module, wherein the data acquisition module stores a preset analysis algorithm;

[0007] A plurality of sensor probes are connected to the data acquisition module, each of the sensor probes corresponding to a different electrode, the data acquisition module being configured to analyze the measurement parameters measured by the sensor probes according to a preset analysis algorithm to generate surface matrix parameters; the surface matrix parameters comprising at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level, nitrogen, phosphorus, potassium value, and soil organic matter value; the arrangement of the sensor probes is related to the type and structure of the surface matrix to facilitate insertion of the sensor probes into pores or fissures of the surface matrix;

[0008] A wireless communication module is connected to the data acquisition module for communication, and is also connected to an external device for wireless communication, and is used to send the surface matrix parameters to the external device.

[0009] In a second aspect, the present application provides a multi-parameter surface matrix monitoring method, characterized in that it is applied to the multi-parameter surface matrix integrated sensor provided in any embodiment of the present application; the method comprises:

[0010] Obtaining the type of the surface matrix in which the sensor probes are set, and adjusting the arrangement of the sensor probes according to the type of the surface matrix, so that the sensor probes can be inserted into pores or cracks of the surface matrix;

[0011] Acquire multiple analysis algorithms and parameter types corresponding to each analysis algorithm, and acquire target parameters corresponding to each analysis algorithm based on the received measurement parameters measured by the multiple sensor probes and the parameter types corresponding to each analysis algorithm;

[0012] The surface matrix parameters corresponding to each analysis algorithm are calculated according to the target parameters corresponding to each analysis algorithm; the surface matrix parameters include at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value.

[0013] The embodiments of this application provide a multi-parameter integrated surface matrix sensor and monitoring method, primarily for the simultaneous and precise measurement of multiple parameters of the surface matrix. By integrating multiple sensor probes, this sensor system can measure parameters including, but not limited to, moisture content, conductivity, pH, redox potential, temperature, carbon dioxide levels, methane levels, oxygen levels, groundwater levels, nitrogen, phosphorus, potassium, and soil organic matter levels. The core of this system lies in its data acquisition module, which not only stores preset analysis algorithms but also analyzes the data collected by the sensor probes according to these algorithms to generate surface matrix parameters.

[0014] This sensor system can simultaneously measure multiple surface matrix parameters, improving monitoring efficiency and data comprehensiveness. Using pre-set analysis algorithms, the system provides precise measurements, which are crucial for scientific research and environmental monitoring. A wireless communication module enables real-time data transmission, facilitating remote monitoring and rapid response. The system can add or replace sensor probes as needed and adjust their layout to accommodate diverse monitoring needs. Compared to traditional single-parameter sensors, the system's integration of multiple sensor probes reduces deployment costs and maintenance. The system is designed for stability and durability under diverse environmental conditions, enabling it to operate in a wide range of climates and geographic locations. Through its integrated data acquisition module, the system provides deeper data analysis, contributing to an understanding of the dynamics of the surface matrix.

[0015] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 This is a schematic structural diagram of a multi-parameter surface matrix integrated sensor provided in one embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of the installation of a multi-parameter surface matrix integrated sensor provided in one embodiment of the present application;

[0019] Figure 3 This is a front view of a multi-parameter surface matrix integrated sensor provided by an embodiment of the present application;

[0020] Figure 4 This is a schematic structural diagram of a sensor probe provided in one embodiment of the present application;

[0021] Figure 5 This is a schematic flow chart of the steps of a multi-parameter surface matrix monitoring method provided in one embodiment of the present application;

[0022] Figure 6 This is a schematic block diagram of the structure of a surface matrix layered channel monitoring device provided by an embodiment of the present application;

[0023] Figure 7 This is a schematic block diagram of the structure of a data acquisition module provided in one embodiment of the present application.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0027] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0028] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0029] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0030] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0031] The Earth's surface is the fundamental material that nurtures and supports a variety of natural resources, including soil, forests, grasslands, water, and wetlands. The surface matrix is the foundation of ecosystems, supporting plant growth, animal habitats, and microbial activity. It is also a vital resource for human survival and development. The nature and state of the surface matrix directly influence land use, agricultural production, ecological security, the occurrence of natural disasters, and environmental protection.

[0032] Placing sensors in the ground's surface matrix to monitor parameters across various layers is an important environmental monitoring technology. This technique is primarily used to obtain data on moisture, temperature, salinity, pH, redox potential, and chemical composition. This approach is widely used in fields such as agricultural management and environmental protection.

[0033] Traditional surface matrix monitoring methods typically employ single-parameter sensors operating independently, requiring the simultaneous deployment of multiple discrete devices such as pH meters, conductivity meters, and temperature and humidity sensors. This results in high hardware redundancy, complex installation, and poor data interoperability. While some existing integrated sensors attempt to integrate two or three common parameters, these inherent challenges remain. Firstly, physical layout conflicts between sensor probes hinder simultaneous and accurate measurement of multiple parameters. Secondly, the lack of an intelligent data processing hub results in discrete data for each parameter, requiring manual post-processing and integration analysis, making them incapable of meeting real-time monitoring requirements.

[0034] To resolve the above issues, please refer to Figures 1 to 4 The present application provides a multi-parameter surface matrix integrated sensor 100, comprising: a data acquisition module 10, wherein the data acquisition module stores a preset analysis algorithm; a plurality of sensor probes 20, wherein the sensor probes are connected to the data acquisition module, and each sensor probe corresponds to a different electrode, and the data acquisition module is used to analyze the measurement parameters measured by the sensor probes according to the preset analysis algorithm to generate surface matrix parameters; the surface matrix parameters include at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value; the arrangement of the sensor probes is related to the type and structure of the surface matrix to be set, so that the sensor probes can be inserted into the pores or cracks of the surface matrix; a wireless communication module 30, wherein the wireless communication module is communicatively connected to the data acquisition module and is also wirelessly connected to an external device for sending the surface matrix parameters to the external device.

[0035] Specifically, this application relates to a multi-parameter integrated surface matrix sensor and monitoring method, primarily for the simultaneous and precise measurement of multiple surface matrix parameters. By integrating multiple sensor probes, this sensor system can measure parameters including, but not limited to, moisture content, conductivity, pH, redox potential, temperature, carbon dioxide levels, methane levels, oxygen levels, groundwater levels, nitrogen, phosphorus, potassium levels, and soil organic matter. The core of this system lies in its data acquisition module, which not only stores preset analysis algorithms but also analyzes the data collected by the sensor probes according to these algorithms to generate surface matrix parameters.

[0036] Each sensor probe corresponds to a different electrode, and the combination of electrodes is used to monitor one or more specific measurement parameters to avoid physical layout conflicts. For example, a moisture sensor probe may adopt a capacitive or resistive design, while a conductivity sensor probe may use a conductivity electrode. For example, a microelectromechanical system (MEMS) process can be used to manufacture a miniaturized probe set, and a three-dimensional stacking technique can be used to achieve a compact layout with a probe spacing of less than 1 mm. For example, the redox potential probe uses a nanoporous platinum-coated electrode. The pH sensor module integrates a glass electrode and a solid-state reference electrode. The temperature and humidity sensor uses a CMOS-compatible polyimide dielectric layer. At the same time, the probe surface is functionalized, such as the immobilization of a nitrate-selective polymer membrane on the surface of the nitrogen, phosphorus, and potassium sensor probe; the coating of a Pd / WO3 nanocatalytic layer on the methane sensor probe; and the integration of a polyaniline gas-sensitive conductive film into the CO2 probe.

[0037] The data acquisition module is the brain of the sensor system. It not only stores preset analysis algorithms but also receives data from individual sensor probes and processes it according to the algorithms to generate surface matrix parameters. The data acquisition module uses an FPGA to control a 16-bit ADC array, achieving a sampling rate of 1000 times per second. Each sensor channel is equipped with independent signal conditioning circuitry (such as a temperature-compensated instrumentation amplifier). The data acquisition module uses a Kalman filter to fuse multimodal sensor data and establish parameter correlation models (such as a temperature-conductivity coupling correction matrix). The wireless communication module is responsible for wirelessly transmitting the surface matrix parameters generated by the data acquisition module to external devices such as computers, smartphones, or other monitoring devices, enabling remote monitoring and analysis of the data. Preset analysis algorithms are predefined based on the measurement principles and mathematical models of different parameters to ensure data accuracy and reliability. Integrating these modules forms a complete multi-parameter surface matrix integrated sensor system that can be deployed in diverse surface environments, such as farmland, forests, and urban green spaces.

[0038] In some embodiments, moisture content in a matrix is measured using frequency domain reflectometry (FDR) or time domain reflectometry (TDR), leveraging the effect of moisture on the dielectric constant of electromagnetic waves or electrical signals. The probe consists of two to four parallel metal electrodes (e.g., steel needles) that form capacitor plates or an electromagnetic wave transmission path. The data acquisition module generates high-frequency signals (FDR: 10-100 MHz; TDR: nanosecond pulses) and measures the signal phase shift (FDR) or pulse reflection time (TDR). Based on a calibration model for dielectric constant and moisture content (e.g., the Topp equation), combined with matrix temperature compensation (temperature affects dielectric constant), volumetric moisture content is calculated with an accuracy of ±3%.

[0039] In some embodiments, the conductivity method measures the ability of ions in a matrix solution to conduct electrical current, reflecting the total dissolved ion concentration (EC). The probe consists of two stainless steel electrodes (alloy material) with a low-frequency AC voltage applied to prevent electrode polarization. The data acquisition module measures the AC resistance between the two electrodes and converts it into conductivity (EC = K × 1 / R, where K is the cell constant). The cell constant is calibrated using a standard potassium chloride solution to account for temperature effects (conductivity increases by approximately 2% for every 1°C increase in temperature). The output units are mS / cm or μS / cm.

[0040] In some embodiments, the selective response of a glass electrode (or a metal electrode such as a lead-zinc probe) to H+ ions is utilized. The probe consists of an indicator electrode (e.g., a lead-zinc alloy probe, sensitive to H+) and a reference electrode (e.g., a steel probe). The potential difference between the two electrodes is measured (E = E0 + 0.05916 × pH, at 25°C). The pH value is calculated with an accuracy of ±0.1, corrected for temperature using the Nernst equation and combined with the stable potential of the reference electrode (e.g., an Ag / AgCl electrode).

[0041] In some embodiments, the integrated potential of the redox reaction in the matrix is measured to reflect the redox capacity of the medium. The probe uses an inert metal electrode (e.g., platinum) as an indicator electrode and a reference electrode (e.g., steel). The data acquisition module directly measures the equilibrium potential (in the millivolt range) between the two electrodes, eliminating the need for an external power supply. After calibrating the reference electrode potential, the ORP value (in millivolts) is directly output, reflecting the relative strength of the oxidant / reducant in the matrix.

[0042] In some embodiments, the resistance-temperature characteristic of a thermistor or thermocouple is utilized. The probe incorporates an NTC thermistor (buried in the probe tip) or thermocouple wire. The data acquisition module measures the resistance (NTC) or thermoelectromotive force (thermocouple) and converts it to temperature (°C). Through simple linear calibration, the temperature is used to compensate for other parameters (such as pH and conductivity temperature correction).

[0043] In some embodiments, infrared absorption (for gases) or soil profile gas stripping (for dissolved CO2 in solid matrices) is used. A gas sensor, such as a non-dispersive infrared (NDIR) sensor, detects the CO2 absorption spectrum at wavelengths of 2-4 μm. The probe uses a gas-permeable membrane with micropores that allow gas to diffuse into the sensor cavity. The absorbance is converted to CO2 concentration (ppm or mg / L) according to the Beer-Lambert law, distinguishing between CO2 produced by soil respiration and atmospheric diffusion.

[0044] In some embodiments, catalytic combustion (for combustible gases) or photoacoustic spectroscopy are combined. Gas sensors: Catalytic combustion sensors (e.g., platinum wire catalytic elements) use heat released by methane combustion, resulting in a change in resistance. Probes: Anti-clogging probes (e.g., capillary probes) penetrate deep into the matrix pores to collect gas samples. The linear relationship between the heat of combustion and methane concentration is used to output the methane volume fraction (ppm), making it suitable for monitoring anaerobic environments (e.g., wetlands and landfills).

[0045] In some embodiments, oxygen levels are measured electrochemically (galvanic or polarographic) or by fluorescence quenching. Using a polarographic oxygen electrode (gold cathode + lead anode, KCl electrolyte), oxygen diffuses through a permeable membrane, triggering a reduction reaction. The data acquisition module measures the reduction current (proportional to the O2 concentration) and outputs the volume percentage (%) or partial pressure (kPa). This compensates for the effects of temperature and pressure on gas solubility, providing an indication of substrate permeability (e.g., warning of root hypoxia).

[0046] In some embodiments, the groundwater level is converted to depth by measuring the hydrostatic pressure exerted by the groundwater on the probe. The probe incorporates a built-in piezoresistive pressure sensor (accuracy ±0.1% FS), with a hole at the tip of the probe contacting the groundwater. The data acquisition module calculates the water level depth (h = P / (ρg), where ρ is the water density and g is the acceleration due to gravity) using the pressure formula (P = ρgh). Combined with surface elevation data (preset or GPS-derived), the module outputs the absolute groundwater level elevation (m) for flood or drought warnings.

[0047] In some embodiments, nitrogen (N) in the nitrogen, phosphorus and potassium value (N / P / K) is measured primarily by ammonium nitrogen (NH4 + ) or nitrate nitrogen (NO3 - ), using ion selective electrode (ISE) or spectrometry. Phosphorus (P) is measured by phosphate (PO4 3- ), based on electrochemical voltammetry or near infrared spectroscopy (NIRS). Potassium (K) is measured by potassium ions (K + ), using a potassium ion selective electrode.

[0048] The probe collects 400-2500nm spectra through ion-selective electrodes (such as valinomycin membrane with potassium electrode) or hyperspectral sensors.

[0049] The data acquisition module measures electrode potential using the ISE method (consistent with the Nernst equation). Spectroscopy uses the partial least squares (PLS) method to build a model linking spectral reflectance and nutrient concentration. For example, nitrate nitrogen is inverted by UV spectroscopy (220-230nm) absorption peaks, while phosphorus and potassium are analyzed by near-infrared spectral characteristic bands, outputting mg / kg concentrations.

[0050] In some embodiments, soil organic matter values are determined using spectroscopy, leveraging the absorption properties of CH and OH bonds in organic matter in the near-infrared (NIR) band. A hyperspectral sensor covering the 700-2500 nm band collects soil reflectance spectra. The data acquisition module uses partial least squares regression (PLSR) or machine learning models (such as random forests) to establish an inverse relationship between spectral data and organic matter content (g / kg). The calibration model must include a spectral library of different organic matter types (such as humus and plant and animal debris), eliminating interference from soil color and moisture content, and achieve an accuracy of ±5%.

[0051] Dedicated electrodes (e.g., lead-zinc probes for pH, steel probes for conductivity) or integrated gas / spectral sensors are used for different parameters. Frequency domain reflectometry (moisture content), electrochemical methods (pH / ORP), and spectral measurement methods (nitrogen, phosphorus, potassium, and organic matter) are all centrally accessed through the data acquisition module. Retractable and capillary probes ensure contact with the matrix pores in sand and gravel, improving signal reliability.

[0052] Through the above method, the sensor can achieve simultaneous measurement of 11 parameters, covering physical (moisture content, temperature), chemical (pH, conductivity, ORP, nutrients), and biological (gas, organic matter) dimensions, meeting the needs of multi-indicator monitoring of surface matrix.

[0053] In some embodiments, the sensor adopts a modular three-layer architecture to achieve full chain integration from physical perception to data output:

[0054] Sensing layer: It is composed of a multimodal composite probe array, including: Electrochemical detection unit: glass microelectrode (pH measurement, range 0-14, accuracy ±0.05) and solid-state ion selective electrode (K + 、Na + , Ca 2+ Plasma detection, detection limit up to 10 -6 mol / L); Oxidation-reduction potential (ORP) uses a platinum-silver / silver chloride composite electrode with a range of ±2000mV and a resolution of 1mV;

[0055] Physical parameter unit: Water content sensor based on the time domain reflectometry principle (TDR), operating frequency 1GHz, measurement depth 0-50cm, accuracy ±1.5%; platinum thin film temperature sensor (range -40~85℃, accuracy ±0.2℃); MEMS pressure sensor (groundwater level monitoring, range 0-10bar, error <0.1%FS).

[0056] Spectral analysis unit: Near-infrared spectroscopy module (wavelength range 900-1700nm), using the PLS algorithm to analyze soil organic matter (detection limit 0.5%) and nitrogen, phosphorus, and potassium content (N: 0-500mg / kg, P: 0-200mg / kg, K: 0-1000mg / kg); laser methane sensor (TDLAS technology, detection range 0-100%LEL, response time <10s).

[0057] The transmission layer corresponding to the wireless communication module: dual-mode communication design: long-distance: LoRaWAN Class A (frequency band 868MHz, transmission distance 10km); short-distance: BLE 5.2 (transmission rate 2Mbps, support Mesh networking).

[0058] The composite probe design utilizes a coaxial nested structure: outer layer: 316L stainless steel sheath (corrosion-resistant, 0.5mm wall thickness); middle layer: polytetrafluoroethylene insulation (dielectric constant 2.1, temperature resistance -200-260°C); inner layer: functional electrode array (1.5mm spacing to avoid electrochemical interference). The probe tip features a self-sharpening diamond coating (Mohs hardness 10), reducing penetration resistance by 60%. A spiral guide groove structure (3mm pitch) reduces soil compaction during insertion.

[0059] The sensor probe's substrate material is a shape-memory alloy (NiTiNol, phase transition temperature 30°C) combined with a flexible PCB. Deployment modes: At room temperature, the probe operates in a rigid mode (for ease of transportation). During insertion, the probe is heated to 35°C and becomes flexible, allowing it to fit into rock cracks (bending radius up to 5mm). The pressure feedback system features an integrated micro strain gauge (range 0-50N) for real-time adjustment of the insertion angle.

[0060] At the same time, if Figure 2 As shown, the sensor module can be inserted into the surface matrix through the built-in hydraulic jack using the installation tool 200.

[0061] In some embodiments, the sensor probe is made of an alloy material, and the alloy material includes at least one of stainless steel and zinc.

[0062] The use of alloy materials enhances the durability of the sensor probe and extends its service life. At the same time, it improves the corrosion resistance of the probe in harsh environments and is suitable for monitoring a variety of environments.

[0063] The sensor probe is made of a SUS316L stainless steel substrate coated with a zinc-nickel alloy layer (thickness 15-20μm), in which the zinc content is controlled at 12-15wt% to optimize corrosion resistance. The probe tip is designed as a triangular pyramid nanostructure (cone angle 60 ° , surface roughness Ra ≤ 0.8μm), enhanced by electrochemical polishing. The conical geometry reduces probe insertion resistance, ensuring measurement accuracy even in compacted soils. The sacrificial anode effect of zinc extends the probe's service life to over five years.

[0064] In some embodiments, the wireless communication module is connected to the external device via Bluetooth or Star Flash.

[0065] The application of wireless communication technology reduces the complexity of wiring and improves deployment flexibility. It also supports Bluetooth and Star Flash technology, improving compatibility with different external devices.

[0066] The wireless module utilizes a dual-mode communication architecture, integrating a BLE 5.2 module (operating at 2.4 GHz) and a SparkLink module (operating at 5.8 GHz). The device automatically switches transmission modes based on ambient channel quality, enabling SparkLink technology for 1 Gbps high-speed transmission in open areas and switching to BLE to maintain a base rate of 10 Mbps in complex terrain.

[0067] The dual-mode redundant design ensures communication reliability in complex geological environments, with a packet loss rate of less than 0.1%. Star Flash technology reduces the transmission time of large files such as spectral data to 1 / 20 of traditional Zigbee. The mesh networking function in BLE mode supports the construction of a 100-node monitoring network.

[0068] In some embodiments, the analysis algorithm includes at least one or more of frequency domain reflectometry, time domain reflectometry, electrochemistry, conductivity, and spectroscopy.

[0069] The application of multiple analysis algorithms improves the accuracy and reliability of measurement. The appropriate analysis algorithm can be selected according to different measurement requirements, which improves the flexibility of the system.

[0070] The analysis algorithms include the time domain reflectometry (TDR) moisture content analysis algorithm, the cyclic voltammetry redox potential analysis algorithm, and the spectrum inversion algorithm based on Mie scattering theory. The TDR algorithm uses the improved Topp formula:

[0071] ε_r = 3.03×10^(-9)θ^3 - 2.55×10^(-6)θ^2 + 7.89×10^(-4)θ + 1.67, where θ is the volumetric water content.

[0072] In some embodiments, as Figure 4 As shown, the present invention further includes: a base plate 60, in which the sensor probe is retractably arranged; a visual acquisition module 70, which is arranged in the base plate and is used to capture a surface matrix image and send the surface matrix image to the external device via the wireless communication module, and the external device returns the monitored surface matrix parameter type and sensor arrangement mode; a sensor moving mechanism, which determines a target probe among multiple sensor probes based on the surface matrix parameter type, the depth corresponding to the surface matrix, and the electrode corresponding to each sensor probe, adjusts the position of the target probe according to the sensor arrangement mode, and controls the target probe to extend out of the base plate and be arranged in the surface matrix.

[0073] The baseplate can be made of aviation aluminum with an anodized surface. The internal structure can include a honeycomb support frame (60% weight reduction, compressive strength ≥ 200MPa). The baseplate can also include a probe storage compartment (dustproof rating IP6X, built-in desiccant compartment) and a power management system (24V / 5A power supply, support for hot-swappable battery replacement).

[0074] Probe types include at least 8 sensors (moisture, temperature, pH, EC, ORP, CO2, O2, groundwater level)

[0075] The sensor movement mechanism is built into the base plate and can be driven by a stepper motor (0.9° step angle, 32 subdivisions). The built-in spiral lifting guide (lead 2mm, repeatability ±5μm) controls the probe extension speed: adjustable from 1-50mm / s (automatically adjusted according to the hardness of the substrate).

[0076] The visual acquisition module can include a multispectral camera: 5-band imaging (RGB + near infrared 850nm + short-wave infrared 1550nm) with a resolution of 2048×1536 pixels and a field of view of 75°.

[0077] At the same time, a corresponding light source system can also be set up: integrated ring LED (color temperature 5600K, adjustable illumination 0-20000lux) and lidar, the lidar includes a ToF ranging module (range 0.1-5m, accuracy ±1mm); scanning frequency 10Hz, generating three-dimensional surface point cloud data.

[0078] The corresponding workflow can be as follows: 1. Start the visual system: collect surface images + 3D point clouds, and upload them to external devices, such as the corresponding cloud analysis platform. 2. The platform uses a deep learning model (ResNet-50 + PointNet) to analyze: matrix type (sand / clay / gravel, etc.); structural characteristics (crack density, stone distribution); historical data comparison (to determine whether special parameter monitoring is required). 3. Generate deployment instructions: select the probe type (for example, saline-alkali land needs to activate EC / pH / Na + probe); plan the probe arrangement pattern (matrix / radial / gradient); and determine the insertion depth. 4. The mobile mechanism executes the action: the linear motor selects the target probe, the XY stage positions it, and the rotation mechanism adjusts the angle. The probe extends along the optimal path (the pressure sensor provides real-time resistance feedback). For example, an A* algorithm is used to optimize the probe movement path to avoid hard obstacles. If resistance > 50N is detected, the probe is retracted 5mm, rotated ±15°, and re-insertion is attempted. If three consecutive failures occur, a backup probe is switched. 5. After deployment, the system enters monitoring mode, performing self-tests and fine-tuning the position every six hours.

[0079] It can also incorporate self-learning mechanisms: establishing a deployment record database containing geographic location, substrate type, probe configuration, and monitoring data. It can also use reinforcement learning (PPO algorithm) to optimize subsequent deployment strategies: automatically selecting the historically optimal solution under similar geological conditions.

[0080] In some embodiments, as Figure 4 As shown, the sensor probe further includes: at least one capillary probe, which is arranged at the tip of the sensor probe and is used for extending the capillary probe into the gaps of the gravel and / or gravel for detection when the sensor probe is arranged in the gravel and / or gravel.

[0081] By integrating one or more independent capillary channels (inner diameter 0.3mm, Teflon FEP lining) inside the probe; the capillary probe assembly uses superelastic nickel-titanium alloy wire (diameter 50μm, breaking strength 2GPa).

[0082] It can correspond to the multi-level extension mechanism shown in the following table:

[0083]

[0084] This embodiment overcomes the monitoring difficulties of traditional equipment in heterogeneous loose matrices by combining the bionic capillary penetration principle with cutting-edge micro-nano technology.

[0085] In some embodiments, as Figure 2 As shown, it also includes: a hyperspectral sensor 40 and a gas sensor 50, which are connected to the data acquisition module and are used to send the measured hyperspectral information and gas information to the data acquisition module. The data acquisition module analyzes the measurement parameters, hyperspectral information and gas information according to a preset analysis algorithm to generate the surface matrix parameters.

[0086] These two sensors connect to the data acquisition module, transmitting the measured hyperspectral and gas information to it. The module analyzes the measured parameters, hyperspectral information, and gas information using a pre-set analysis algorithm to generate surface matrix parameters. The addition of hyperspectral and gas sensors enables the system to monitor a wider range of surface matrix parameters, such as soil organic matter levels. This increases the richness of the data and provides more data support for environmental monitoring and geological disaster warning.

[0087] A hyperspectral imaging module (wavelength range 400-2500nm, spectral resolution 3nm) and a MEMS gas sensor array (including a PID photoionization detector and an NDIR carbon dioxide sensor) are integrated at each end of the probe array. Hyperspectral data is preprocessed using SG smoothing and first-order derivatives before being fed into a random forest model for organic matter content inversion.

[0088] Exemplarily, the hyperspectral sensor and the gas sensor are distributed at both ends of the plurality of sensor probes.

[0089] Hyperspectral sensors and gas sensors are distributed at both ends of multiple sensor probes to achieve more comprehensive monitoring. This sensor distribution optimizes the monitoring layout and improves monitoring efficiency. The sensors distributed at both ends can cover a wider monitoring area, improving the comprehensiveness of monitoring.

[0090] The hyperspectral module and gas sensor are mounted at the north and south poles of the probe array, with a spacing of at least 50 mm. The hyperspectral probe is tilted outward at a 45° angle, and the gas collection port is designed as a cyclonic separation structure to prevent soil particle contamination.

[0091] In some embodiments, the plurality of sensor probes are distributed in a line on the data acquisition module.

[0092] The linear arrangement of sensor probes facilitates more organized monitoring and eases management and maintenance. This optimizes space utilization and reduces the equipment's footprint. The sensor probes are arranged in a straight line along the axial direction, with spacing following the λ / 4 principle (λ is the operating wavelength of each sensor) and a minimum spacing of 15mm. The probe base utilizes a polyetheretherketone (PEEK) insulating frame, with an EMI shielding layer (copper mesh coverage ≥95%) applied to the frame surface. The linear layout keeps the equipment diameter within a range suitable for standard drilling boreholes. The wavelength-adaptive spacing eliminates electromagnetic coupling interference between sensors, improving the signal-to-noise ratio. The shielding structure attenuates external 50Hz power frequency interference to -60dB.

[0093] For example, if multiple sensor probes are arranged in a straight line on the data acquisition module, and there are five sensor probes, the first, second, fourth, and fifth sensor probes, from top to bottom, are steel needles, and the third sensor probe is a lead-zinc needle. The first and third sensor probes are used to measure pH; the second and fourth sensor probes are used to measure conductivity; and the fourth and fifth sensor probes are used to measure humidity. Nitrogen, phosphorus, and potassium are converted using an algorithm; and temperature is measured using a thermistor within the substrate.

[0094] The five sensor probes are arranged in a straight line vertically (or horizontally, depending on the application scenario) on the data acquisition module. From top to bottom (or left to right), they are numbered 1, 2, 3, 4, and 5. The spacing between the probes is adjusted based on the pore size of the surface substrate (e.g., soil, gravel, etc.) to ensure that the probes can be inserted into the pores or cracks of the substrate and avoid mutual interference.

[0095] Probes 1, 2, 4, and 5 use steel needles (e.g., stainless steel) with strong corrosion resistance and stable conductivity, making them suitable as reference electrodes or general-purpose electrodes for electrochemical measurements. Probe 3 uses a lead-zinc needle (e.g., a lead-zinc alloy), leveraging the electrochemical activity of lead and zinc. It is suitable for measuring the concentration of specific ions (e.g., H+) and provides a sensitive electrode for pH detection.

[0096] pH measurement (probes #1 and #3) is based on the glass electrode method (or metal electrode method). Probe #1 serves as the reference electrode (providing a stable reference potential), and Probe #3 serves as the indicator electrode (responsive to H+ ions). When the two electrodes are inserted into the surface matrix, they form a galvanic cell. The potential difference is related to the H+ concentration in the matrix (i.e., pH) by the Nernst equation. The data acquisition module calculates the pH value using a pre-set electrochemical algorithm (such as the Nernst equation).

[0097] Conductivity measurements (Probes 2 and 4) are based on the conductivity method, calculating the conductivity of the matrix by measuring the AC resistance between two electrodes. Probes 2 and 4 act as conductive electrodes. A low-frequency AC voltage is applied, and the loop current is measured. The resistance is calculated using Ohm's law. The conductivity is converted based on the known electrode spacing and area to reflect the total ion concentration in the matrix.

[0098] Humidity measurement (Probes 4 and 5) is based on capacitance or resistance methods, exploiting changes in the dielectric constant or resistivity caused by changes in the moisture content of the substrate. Probes 4 and 5 serve as capacitor plates, and the substrate acts as the dielectric. Higher moisture content increases the dielectric constant, and the change in capacitance is positively correlated with humidity. The resistance between the two electrodes is directly measured. Higher moisture content indicates a higher concentration of conductive ions (dissolved in water), resulting in lower resistance. This is then converted to humidity using a calibration curve.

[0099] Each probe is connected to a data acquisition module via a wire. The module features a built-in multi-channel analog switch that time-selects different electrode pairs (e.g., pairs 1+3, pairs 2+4, and pairs 4+5). For conductivity and humidity, voltage / current or capacitance signals are collected. The data acquisition module processes the signals from different electrode pairs according to pre-set analysis algorithms (e.g., electrochemical or conductivity methods). For pH, the Nernst equation is used to correct for temperature effects and calculate H+ activity. For conductivity, electrode polarization effects are deducted to calculate equivalent conductivity. For humidity, capacitance / resistance signals are converted to moisture content using a calibrated empirical formula or machine learning model. These processed parameters are transmitted to an external device via a wireless communication module (e.g., Bluetooth) or stored locally.

[0100] For different matrices such as soil and gravel, the probe insertion depth and spacing (such as "arrangement is related to the type of surface matrix") are adjusted to ensure that the potential difference signal between the lead-zinc needle (No. 3) and the steel needle is stable.

[0101] The steel probes (except the tip) are insulated to prevent stray current interference, while the lead-zinc probes feature an anti-oxidation coating for extended service life. Five probes can be combined to measure three parameters, reducing probe count and hardware costs, making them suitable for portable or distributed monitoring devices.

[0102] The probe arrangement (straight line) and material (steel, lead-zinc) are specific implementations of the "sensor probe arrangement is related to the type and structure of the surface substrate in which it is installed" principle, ensuring insertion into pores or fissures. pH is measured using electrochemical methods, conductivity using conductivity methods, and humidity using frequency domain reflectometry or resistivity methods, all of which are specific types of pre-defined analysis algorithms. The probe material (steel, lead-zinc) meets the requirement that "alloy materials include at least one of stainless steel and zinc."

[0103] By limiting the material and functional distribution of five straight-line distributed probes, integrated measurement of pH, conductivity, and humidity is achieved. The core of this method is to utilize the electrochemical properties of different metal electrodes and combine and reuse electrode pairs to complete multi-parameter detection in a compact structure. It combines cost-effectiveness and measurement accuracy, and is suitable for rapid in-situ monitoring of surface matrices.

[0104] In some embodiments, the plurality of sensor probes are distributed in a ring shape on the data acquisition module.

[0105] The circular arrangement of probes can achieve coverage without blind spots, and is particularly suitable for scenarios such as: groundwater flow tracking (through multi-directional conductivity difference analysis); pollutant diffusion path modeling (constructing a three-dimensional concentration gradient field) and geological structure tomography (combined with ERT resistivity tomography technology).

[0106] In some embodiments, the system further includes: a wireless charging module, wherein the distance between the wireless charging module and the sensor module is less than a preset distance, and the wireless charging module is used to wirelessly charge the sensor module.

[0107] The application of wireless charging technology makes charging more convenient and reduces maintenance work. The wireless charging module ensures the continuous operation of the sensor module and improves the stability of the system.

[0108] The wireless charging module utilizes the Qi 1.3 standard. The transmitting coil (40mm diameter, 150μH) is built into the bottom of the protective housing, with a vertical spacing of ≤10mm from the receiving coil (35mm diameter, 135μH) inside the device. The charging management IC integrates an MPPT algorithm, achieving a conversion efficiency of 85% at an input power of 5W.

[0109] In some embodiments, the data acquisition module stores multiple analysis algorithms and parameter types corresponding to each analysis algorithm. The data acquisition module obtains the target parameters corresponding to each analysis algorithm based on the received multiple measurement parameters and the parameter types corresponding to each analysis algorithm, and calculates the surface matrix parameters corresponding to each analysis algorithm based on the target parameters corresponding to each analysis algorithm.

[0110] The data acquisition module establishes a parameter-algorithm mapping table, automatically linking nitrogen, phosphorus, and potassium measurements to an ICP-OES calibration model, for example, and triggering an NDIR iterative algorithm using carbon dioxide data. When an anomaly in a multi-parameter correlation is detected (e.g., a sudden change in conductivity without a change in ion concentration), a Bayesian network is automatically activated to assess data credibility.

[0111] The above implementation methods integrate multidisciplinary technologies to build an intelligent sensing system with autonomous diagnostic capabilities. Compared with existing technologies, they have achieved breakthrough progress in monitoring dimensions (expanding from the conventional 3 parameters to 15 parameters), response speed (from minutes to sub-seconds), and environmental adaptability (operating temperature range of -40°C to 85°C).

[0112] See also Figure 5 , Figure 5 This is a schematic flow chart of a multi-parameter surface matrix monitoring method provided in one embodiment of the present application. This multi-parameter surface matrix monitoring method can be implemented using the data acquisition module of the multi-parameter surface matrix integrated sensor provided in any embodiment of the present application. The data acquisition module can be deployed on a single server or a server cluster. Alternatively, it can be deployed on a handheld terminal, laptop computer, wearable device, or robot.

[0113] like Figure 5 As shown, the multi-parameter surface matrix monitoring method provided includes steps S101 to S103. The details are as follows:

[0114] Step S101. Acquire the type of the surface substrate on which the sensor probes are set, and adjust the arrangement of the sensor probes according to the type of the surface substrate;

[0115] The sensor's visual acquisition module captures images of the surface matrix and sends them to an external device (such as a server or terminal). The device uses an image recognition algorithm (such as a convolutional neural network) to determine the matrix type (e.g., sandy soil, clay, or gravel) and returns an appropriate probe arrangement (e.g., probe spacing, insertion depth, and combination). For example, the sensor's motion mechanism controls the retractable probes based on the arrangement (adjusting their position, for example, by extending the probe in a gravel matrix and inserting it through a gap using a capillary probe to ensure full contact between the electrode and the matrix).

[0116] Step S102. Acquire multiple analysis algorithms and parameter types corresponding to each analysis algorithm, and acquire target parameters corresponding to each analysis algorithm based on the received measurement parameters measured by multiple sensor probes and the parameter types corresponding to each analysis algorithm;

[0117] Sensor probes (such as steel needles and lead-zinc needles) collect electrical signals (potential, resistance, capacitance, etc.) in real time, and the data acquisition module summarizes the measured parameters according to a preset protocol (such as Modbus). The module calls the corresponding algorithm from the algorithm library based on the parameter type label (such as electrochemical method for pH value and frequency domain reflectometry for humidity). For example: for conductivity parameters, the conductivity method algorithm is called to calculate the ion concentration based on the electrode spacing; for spectral data (from hyperspectral sensors), the spectral measurement method is called to invert the soil organic matter content. Cross-parameter fusion: If multiple algorithms require the same type of parameters (such as temperature affecting both pH and conductivity), the module automatically allocates shared parameters to avoid duplicate collection.

[0118] Step S103. Calculate the surface matrix parameters corresponding to each analysis algorithm based on the target parameters corresponding to each analysis algorithm; the surface matrix parameters include at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value.

[0119] The generated matrix parameters are sent to external devices in real time via wireless communication modules (Bluetooth / Star Flash) for use in scenarios such as agricultural soil moisture monitoring, geological disaster warning, or triggering threshold alarms (such as warnings when groundwater levels are abnormal).

[0120] By dynamically adjusting probe placement based on matrix type (e.g., using capillary probes in gravel), this approach addresses the poor contact issues of traditional sensors in complex matrices and improves detection accuracy, particularly in fractured matrices. Utilizing a single probe array and multiple algorithms, it simultaneously acquires over 10 matrix parameters (such as moisture content, nitrogen, phosphorus, and potassium), eliminating the need for multiple devices and reducing hardware costs by 30%-50%. Automatic parameter matching based on a pre-set algorithm reduces manual configuration steps and supports real-time online analysis, meeting the needs of unmanned, long-term field monitoring. Probe placement optimization (e.g., electrode combination) and algorithm fusion reduce single-sensor errors. For example, by cross-calibrating conductivity and humidity parameters, the moisture content measurement error can be reduced to ±2%. This system is suitable for a variety of scenarios, including agricultural soils, mining substrates, and ecological wetlands. Dynamically updating the placement scheme through external devices supports rapid deployment across regions and matrix types.

[0121] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the multi-parameter surface matrix monitoring method described above and the specific working process of each step can refer to the corresponding process in the multi-parameter surface matrix integrated sensor embodiment described in the above embodiments, and will not be repeated here.

[0122] See also Figure 6 As shown, Figure 6Schematic diagram of the structure of a surface matrix layered channel monitoring device 300 provided in an embodiment of the present application. The surface matrix layered channel monitoring device 300 is used to execute the steps of the multi-parameter surface matrix monitoring method described in the above embodiments. The surface matrix layered channel monitoring device 300 can be a single server or a server cluster, or the surface matrix layered channel monitoring device 300 can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0123] like Figure 6 As shown, the surface matrix layer channel monitoring device 300 includes:

[0124] A type acquisition unit 301 is used to acquire the type of the surface substrate on which the sensor probes are arranged, so as to adjust the arrangement of the sensor probes according to the surface substrate type;

[0125] A target acquisition unit 302 is configured to acquire multiple analysis algorithms and parameter types corresponding to each analysis algorithm, and acquire target parameters corresponding to each analysis algorithm based on the received measurement parameters measured by multiple sensor probes and the parameter types corresponding to each analysis algorithm;

[0126] The parameter analysis unit 303 is used to calculate the surface matrix parameters corresponding to each analysis algorithm based on the target parameters corresponding to each analysis algorithm; the surface matrix parameters include at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value.

[0127] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working processes of the surface matrix layered channel monitoring device and each module described above can refer to the corresponding processes in the multi-parameter surface matrix integrated sensor embodiments described in the above embodiments, and will not be repeated here.

[0128] The above multi-parameter surface matrix monitoring method can be implemented in the form of a computer program. Figure 6 Run on the device shown.

[0129] See also Figure 7 , Figure 7 1 is a schematic block diagram of the structure of a data acquisition module provided in an embodiment of the present application. The data acquisition module includes a processor, a memory and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0130] The storage medium can store an operating device and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the multi-parameter surface matrix monitoring methods.

[0131] The processor is used to provide computing and control capabilities to support the operation of the entire data acquisition module.

[0132] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any multi-parameter surface matrix monitoring method.

[0133] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific data acquisition module may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0134] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0135] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0136] Get the type of electrode monitored by each sensor probe;

[0137] obtaining a surface substrate type on which the sensor probes are arranged, so as to adjust the arrangement of the sensor probes according to the surface substrate type;

[0138] Acquire multiple analysis algorithms and parameter types corresponding to each analysis algorithm, and acquire target parameters corresponding to each analysis algorithm based on the received measurement parameters measured by the multiple sensor probes and the parameter types corresponding to each analysis algorithm;

[0139] The surface matrix parameters corresponding to each analysis algorithm are calculated according to the target parameters corresponding to each analysis algorithm; the surface matrix parameters include at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value.

[0140] The present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the steps of the multi-parameter surface matrix monitoring method as described above.

[0141] The computer-readable storage medium may be an internal storage unit of the data acquisition module described in the aforementioned embodiment, such as a hard disk or memory of the data acquisition module. The computer-readable storage medium may also be an external storage device of the data acquisition module, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc., equipped on the data acquisition module.

[0142] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A multi-parameter surface matrix integrated sensor, characterized in that: include: A data acquisition module, wherein the data acquisition module stores a preset analysis algorithm; A plurality of sensor probes are connected to the data acquisition module, each of the sensor probes corresponding to a different electrode, the data acquisition module being configured to analyze the measurement parameters measured by the sensor probes according to a preset analysis algorithm to generate surface matrix parameters; the surface matrix parameters comprising at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level, nitrogen, phosphorus, potassium value, and soil organic matter value; the arrangement of the sensor probes is related to the type and structure of the surface matrix to facilitate insertion of the sensor probes into pores or fissures of the surface matrix; A wireless communication module, wherein the wireless communication module is in communication with the data acquisition module and is also in wireless communication with an external device, and is used to send the surface matrix parameters to the external device; and further comprising: a bottom plate, wherein the sensor probe is retractably disposed in the bottom plate; A visual acquisition module is provided in the base plate and is used to capture a surface matrix image and transmit the surface matrix image to the external device via the wireless communication module, and the external device returns the sensor arrangement mode of monitoring; A sensor moving mechanism, wherein the sensor moving mechanism determines a target probe among a plurality of sensor probes according to a surface matrix parameter type, a depth corresponding to the surface matrix, and an electrode corresponding to each of the sensor probes, adjusts a position of the target probe according to the sensor arrangement, and controls the target probe to extend out of the base plate and be disposed in the surface matrix.

2. The sensor according to claim 1, characterized in that The sensor probe is made of an alloy material, and the alloy material includes at least one of stainless steel and zinc; and / or, The wireless communication module is connected to the external device via Bluetooth or Star Flash.

3. The sensor according to claim 1, wherein The sensor probe further comprises: At least one capillary probe is arranged at the tip of the sensor probe, and is used for extending the capillary probe into the gaps of the gravel and / or gravel for detection when the sensor probe is arranged in the gravel and / or gravel.

4. The sensor according to claim 1, characterized in that The analysis algorithm includes at least one or more of frequency domain reflectometry, time domain reflectometry, electrochemical method, conductivity method and spectroscopy measurement method.

5. The sensor according to claim 1, wherein Also includes: A hyperspectral sensor and a gas sensor are connected to the data acquisition module and are used to send the measured hyperspectral information and gas information to the data acquisition module. The data acquisition module analyzes the measurement parameters, hyperspectral information and gas information according to a preset analysis algorithm to generate the surface matrix parameters. The hyperspectral sensor and the gas sensor are distributed at both ends of the multiple sensor probes.

6. The sensor according to claim 1, characterized in that The plurality of sensor probes are distributed in a straight line on the data acquisition module; or, The plurality of sensor probes are distributed in a ring shape on the data acquisition module.

7. The sensor according to claim 6, characterized in that If the plurality of sensor probes are distributed in a straight line on the data acquisition module, and the number of the sensor probes is 5; From top to bottom, the 1st, 2nd, 4th, and 5th sensor probes are steel needles, and the 3rd sensor probe is a lead-zinc needle; The first and third sensor probes are used to measure pH; the second and fourth sensor probes are used to measure conductivity; and the fourth and fifth sensor probes are used to measure humidity.

8. The sensor according to claim 1, wherein The data acquisition module stores multiple analysis algorithms and parameter types corresponding to each analysis algorithm. The data acquisition module obtains the target parameters corresponding to each analysis algorithm based on the received multiple measurement parameters and the parameter types corresponding to each analysis algorithm, and calculates the surface matrix parameters corresponding to each analysis algorithm based on the target parameters corresponding to each analysis algorithm.

9. A multi-parameter surface matrix monitoring method, characterized in that: A multi-parameter surface matrix integrated sensor according to any one of claims 1 to 8; the method comprising: Obtaining the type of the surface matrix in which the sensor probes are set, and adjusting the arrangement of the sensor probes according to the type of the surface matrix, so that the sensor probes can be inserted into pores or cracks of the surface matrix; Acquire multiple analysis algorithms and parameter types corresponding to each analysis algorithm, and acquire target parameters corresponding to each analysis algorithm based on the received measurement parameters measured by the multiple sensor probes and the parameter types corresponding to each analysis algorithm; The surface matrix parameters corresponding to each analysis algorithm are calculated according to the target parameters corresponding to each analysis algorithm; the surface matrix parameters include at least one or more of moisture content, conductivity, pH, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value.

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