Multi-parameter surface matrix integrated sensor and monitoring method

Abstract

The invention relates to the technical field of soil testing, and provides a multi-parameter surface matrix integrated sensor and a monitoring method, and the sensor comprises a data collection module which stores a preset analysis algorithm; the electrodes corresponding to the sensor probes are different, and the data acquisition module is used for analyzing measurement parameters measured by the sensor probes according to a preset analysis algorithm to generate surface matrix parameters; the surface matrix parameters at least comprise one or more of moisture content, conductivity, pH value, oxidation-reduction potential, temperature, carbon dioxide value, methane value, oxygen value, underground water level value, nitrogen phosphorus and potassium value and soil organic matter value; the arrangement of the sensor probes is related to the type and the structure of the arranged surface substrate, so that the sensor probes can be inserted into pores or cracks of the surface substrate; and the wireless communication module is in communication connection with the data acquisition module, is also in wireless communication connection with external equipment, and is used for sending the surface matrix parameters to the external equipment.

Description

Technical Field

[0001] This application relates to the technical field of soil testing, and particularly to a multi-parameter surface matrix integrated sensor and a monitoring method. Background Art

[0002] The multi-parameter monitoring technology of surface matrix is one of the core technologies in the fields of modern agriculture, environmental monitoring, geological disaster warning, etc. Traditional surface matrix monitoring methods usually adopt the independent working mode of single-parameter sensors, and multiple discrete devices such as pH meters, conductivity meters, temperature and humidity sensors need to be deployed simultaneously, resulting in high hardware redundancy, large installation complexity, and poor data coordination.

[0003] Although there are some integrated sensors in the prior art that attempt to integrate 2-3 conventional parameters, their inherent problems are still significant: on the one hand, limited by the physical arrangement conflict of the sensor probes, it is difficult to achieve synchronous and accurate measurement of multiple parameters; on the other hand, the lack of an intelligent data processing center results in discrete state of each parameter data, which requires manual post-integration analysis and cannot meet the real-time monitoring requirements. Summary of the Invention

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

[0005] In the first aspect, this application provides a multi-parameter surface matrix integrated sensor, including: A data acquisition module, which stores a preset analysis algorithm; Multiple sensor probes, the sensor probes are connected to the data acquisition module, and the electrodes corresponding to each sensor probe are different. 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 at least include one or more of moisture content, conductivity, pH value, oxidation-reduction potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, soil organic matter value; the arrangement of the sensor probes is related to the type and structure of the set surface matrix, so that the sensor probes can be inserted into the pores or fissures of the surface matrix; A wireless communication module, the wireless communication module is communicatively connected to the data acquisition module, and the wireless communication module is also wirelessly communicatively connected to an external device, and is used to send the surface matrix parameters to the external device.

[0006] Second aspect, the present application provides a multi-parameter surface substrate monitoring method, which is characterized in that it is applied to the multi-parameter surface substrate integrated sensor provided in any embodiment of the present application; the method includes: Obtain the surface substrate type set by the sensor probe, and adjust the arrangement of the sensor probes according to the surface substrate type; so that the sensor probes are inserted into the pores or fissures of the surface substrate; Obtain a plurality of analysis algorithms and the parameter types corresponding to each analysis algorithm, and obtain the target parameters corresponding to each analysis algorithm according to the measurement parameters measured by a plurality of sensor probes received and the parameter types corresponding to each analysis algorithm; Calculate the surface substrate parameters corresponding to each analysis algorithm according to the target parameters corresponding to each analysis algorithm; the surface substrate parameters include at least one or more of water content, conductivity, pH value, oxidation-reduction potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, soil organic matter value.

[0007] A multi-parameter surface substrate integrated sensor and monitoring method provided by an embodiment of the present application are mainly used for synchronously and accurately measuring multiple parameters of the surface substrate. The sensor system can measure parameters including but not limited to water content, conductivity, pH value, oxidation-reduction potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value by integrating multiple sensor probes. The core of the system lies in its data acquisition module, which can not only store preset analysis algorithms, but also analyze the data collected by the sensor probes according to these algorithms to generate surface substrate parameters.

[0008] The sensor system can measure multiple surface substrate parameters simultaneously, improving the monitoring efficiency and data comprehensiveness. Through the preset analysis algorithms, the system can provide accurate measurement results, which is crucial for scientific research and environmental monitoring. The wireless communication module enables real-time data transmission, facilitating remote monitoring and quick response. The system can add or replace sensor probes as needed and adjust the arrangement of the probes to adapt to different monitoring requirements. Compared with traditional single-parameter sensors, the system reduces the deployment cost and maintenance work by integrating multiple sensor probes. The system design takes into account the stability and durability under different environmental conditions, enabling it to work in various climatic and geographical conditions. Through the integrated data acquisition module, the system can provide more in-depth data analysis, helping to understand the dynamic changes of the surface substrate.

[0009] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic structural diagram of a multi-parameter surface matrix integrated sensor provided by an embodiment of the present application; Figure 2 It is a schematic installation diagram of a multi-parameter surface matrix integrated sensor provided by an embodiment of the present application; Figure 3 It is a front view of a multi-parameter surface matrix integrated sensor provided by an embodiment of the present application; Figure 4 It is a schematic structural diagram of a sensor probe provided by an embodiment of the present application; Figure 5 It is a schematic flow chart of the steps of a multi-parameter surface matrix monitoring method provided by an embodiment of the present application; Figure 6 It is a schematic block diagram of the structure of a surface matrix layered channel monitoring device provided by an embodiment of the present application; Figure 7 It is a schematic block diagram of the structure of a data acquisition module provided by an embodiment of the present application.

[0012] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Detailed implementation manners

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0014] The flow chart shown in the drawings is only an example, and does not necessarily include all the contents and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined or partially merged. Therefore, the actual execution order may change according to the actual situation.

[0015] It should be understood that, for the convenience of clearly describing 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 identical or similar items with basically the same functions and roles. Those skilled in the art can understand that the terms such as "first" and "second" do not limit the quantity and execution order, and the terms such as "first" and "second" do not necessarily mean different.

[0016] It should be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0017] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0018] The following will describe in detail some embodiments of this application with reference to the drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0019] The earth's surface layer breeds and supports the basic substances of various natural resources such as soil, forests, grasslands, water, wetlands, etc. The surface substrate is the foundation of the ecosystem, supporting plant growth, animal habitation and microbial activities, and is also an important resource for human survival and development. The nature and state of the surface substrate directly affect aspects such as land use, agricultural production, ecological security, the occurrence of natural disasters, and environmental protection.

[0020] Setting sensors in the surface substrate to monitor the parameters of each stratum is an important environmental monitoring technology, mainly used to obtain data such as moisture, temperature, salinity, pH, redox potential, and chemical composition. This method is widely used in fields such as agricultural management and environmental protection.

[0021] Traditional surface substrate monitoring methods usually adopt the independent working mode of single-parameter sensors, and multiple discrete devices such as pH meters, conductivity meters, and temperature and humidity sensors need to be deployed simultaneously, resulting in high hardware redundancy, high installation complexity, and poor data coordination. Although there are some integrated sensors in the prior art that attempt to integrate 2-3 conventional parameters, their inherent problems are still significant: on the one hand, limited by the physical arrangement conflict of the sensor probes, it is difficult to achieve multi-parameter synchronous and accurate measurement; on the other hand, there is a lack of an intelligent data processing center, resulting in the discrete state of each parameter data, and manual post-integration analysis is required, which cannot meet the real-time monitoring requirements.

[0022] To solve the above problems, please refer toFigures 1 to 4 , this application provides a multi-parameter integrated surface substrate sensor 100, including: a data acquisition module 10, which stores a preset analysis algorithm; a plurality of sensor probes 20, the sensor probes are connected to the data acquisition module, and the electrodes corresponding to each sensor probe are different. 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 substrate parameters; the surface substrate parameters include at least one or more of moisture content, conductivity, pH value, 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 set surface substrate, so that the sensor probes can be inserted into the pores or fissures of the surface substrate; a wireless communication module 30, the wireless communication module is communicatively connected to the data acquisition module, and the wireless communication module is also wirelessly communicatively connected to an external device, and is used to send the surface substrate parameters to the external device.

[0023] Specifically, this application relates to a multi-parameter integrated surface substrate sensor and its monitoring method, which is mainly used for synchronously and accurately measuring multiple parameters of the surface substrate. The sensor system can measure parameters including but not limited to moisture content, conductivity, pH value, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, and soil organic matter value by integrating a plurality of sensor probes. The core of the system lies in its data acquisition module, which can not only store preset analysis algorithms, but also analyze the data collected by the sensor probes according to these algorithms to generate surface substrate parameters.

[0024] The electrodes corresponding to each sensor probe are different, and the combination of the electrodes is used to monitor one or more specific measurement parameters to avoid physical layout conflicts. For example, the moisture content sensor probe may adopt a capacitive or resistive design, while the conductivity sensor probe may adopt a conductivity electrode. If a microelectromechanical system (MEMS) process is used to manufacture a miniaturized probe group, a compact layout with a probe pitch of less than 1 mm can be achieved through a three-dimensional stacking technique. For example: the redox potential probe uses a nanoporous platinum-coated electrode. The pH sensing module integrates a glass electrode and a solid reference electrode. The temperature and humidity sensor uses a CMOS-compatible polyimide dielectric layer. At the same time, the surface of the probe is functionalized, such as a nitrate-selective polymer film immobilized on the surface of the nitrogen, phosphorus and potassium sensor probe; the methane sensor probe is coated with a Pd / WO3 nanocatalytic layer; the CO2 probe integrates a polyaniline gas-sensitive conductive film.

[0025] The data acquisition module is the brain of the sensor system. It not only stores preset analysis algorithms but also receives data from various sensor probes, processes them according to the algorithms, and generates surface substrate parameters. The data acquisition module can use an FPGA to control a 16-bit ADC array to achieve a sampling frequency of 1000 times per second. Each sensor channel is equipped with an independent signal conditioning circuit (such as a temperature-compensated instrumentation amplifier). The data acquisition module can fuse multi-modal sensor data through a Kalman filter and establish a parameter correlation model (such as a temperature-conductivity coupling correction matrix). The wireless communication module is responsible for wirelessly transmitting the surface substrate parameters generated by the data acquisition module to external devices such as computers, smartphones, or other monitoring devices to achieve remote monitoring and analysis of the data. The preset analysis algorithms are preset according to the measurement principles and mathematical models of different parameters, ensuring the accuracy and reliability of the data. Integrating the above modules forms a complete multi-parameter surface substrate integrated sensor system, which can be deployed in different surface environments such as farmland, forests, urban green spaces, etc.

[0026] In some embodiments, by utilizing the influence of moisture on the dielectric constant of electromagnetic waves or electrical signals, the moisture content in the substrate is measured by the frequency domain reflectometry (FDR) or time domain reflectometry (TDR). Probes: 2 - 4 parallel metal electrodes (such as steel needles), forming capacitor plates or electromagnetic wave transmission paths. Data acquisition module: Generates high-frequency signals (FDR: 10 - 100 MHz; TDR: nanosecond-level pulses), measures the signal phase shift (FDR) or pulse reflection time (TDR). According to the calibration model of dielectric constant and water content (such as the Topp formula), combined with substrate temperature compensation (temperature affects dielectric constant), the volumetric water content is calculated with an accuracy of up to ±3%.

[0027] In some embodiments, based on the conductivity method, the ability of ionic conduction current in the substrate solution is measured to reflect the total dissolved ion concentration (EC value). Probes: 2 stainless steel electrodes (alloy materials), applying a low-frequency alternating voltage (to avoid electrode polarization). Data acquisition module: Measures the alternating resistance between the two electrodes and converts it to conductivity (EC = K × 1 / R, where K is the electrode constant). After deducting the influence of temperature (for every 1°C increase in temperature, the conductivity increases by about 2%), the electrode constant is calibrated through a standard potassium chloride solution, and the output unit is mS / cm or μS / cm.

[0028] In some embodiments, the selective response of H+ ions by a glass electrode (or a metal electrode such as a lead-zinc needle) is utilized. Probes: 1 indicating electrode (such as a lead-zinc alloy needle, sensitive to H+) and 1 reference electrode (such as a steel needle). Measures the potential difference between the two electrodes (E = E0 + 0.05916 × pH at 25°C). Corrects the temperature through the Nernst equation, combined with the stable potential of the reference electrode (such as an Ag / AgCl electrode), and calculates the pH value with an accuracy of ±0.1.

[0029] In some embodiments, the comprehensive potential of the redox reaction in the matrix is measured to reflect the redox ability of the medium. The probe uses an inert metal electrode (such as a platinum needle) as the indicating electrode and a reference electrode (such as a steel needle). The data acquisition module directly measures the equilibrium potential (in mV level) between the two electrodes without an external power supply. After calibrating the potential of the reference electrode, the ORP value (in mV) is directly output to reflect the relative strength of the oxidant / reductant in the matrix.

[0030] In some embodiments, the resistance-temperature characteristics of a thermistor or a thermocouple are utilized. The probe integrates an NTC thermistor (embedded at the tip of the probe) or a thermocouple wire. The data acquisition module measures the resistance value (for NTC) or the thermoelectromotive force (for thermocouple) and converts it into temperature (in °C). Through simple linear calibration, the temperature is used for compensation of other parameters (such as temperature correction of pH and conductivity).

[0031] In some embodiments, it is through the infrared absorption method (for gases) or the soil profile gas extraction method (for dissolved CO2 in solid matrices). Through a gas sensor, such as a non-dispersive infrared sensor (NDIR), the absorption spectrum of CO2 at a wavelength of 2 - 4 μm is detected. The probe: a gas-permeable membrane probe with micropores that allows gas to diffuse into the sensor cavity. According to the Lambert-Beer law, the absorbance is converted into CO2 concentration (in ppm or mg / L) to distinguish the CO2 generated by soil respiration from atmospheric diffusion.

[0032] In some embodiments, it combines the catalytic combustion method (for combustible gases) or photoacoustic spectroscopy. Gas sensor: a catalytic combustion sensor (such as a platinum wire catalytic element), where the combustion heat of methane causes a change in resistance. Probe: an anti-blocking probe (such as a capillary probe) that penetrates into the matrix pores to collect gas samples. Through the linear relationship between the combustion heat and the methane concentration, the volume fraction of methane (in ppm) is output, which is applicable to the monitoring of anaerobic environments (such as wetlands, landfills).

[0033] In some embodiments, the oxygen value is measured through an electrochemical method (galvanic cell or polarography) or fluorescence quenching method. A polarographic oxygen electrode (gold cathode + lead anode, with KCl as the electrolyte), where oxygen diffuses through the gas-permeable membrane to initiate a reduction reaction. The data acquisition module measures the reduction current (proportional to the O2 concentration) and outputs the volume percentage (%) or partial pressure (in kPa). The influence of temperature and air pressure on gas solubility is compensated to reflect the matrix gas permeability (such as early warning of root hypoxia).

[0034] In some embodiments, the groundwater level value is converted to the water depth by measuring the hydrostatic pressure of the groundwater on the probe. A piezoresistive pressure sensor (accuracy ±0.1%FS) is built into the probe, and the tip of the probe is open to contact the groundwater. The data acquisition module calculates the water depth according to the pressure formula (P = ρgh) (h = P / (ρg), where ρ is the water density and g is the acceleration due to gravity). Combining with the surface elevation data (preset or obtained by GPS), the absolute elevation of the groundwater level (m) is output for flood or drought warning.

[0035] In some embodiments, for the nitrogen, phosphorus, and potassium values (N / P / K), nitrogen (N) is mainly measured by measuring ammonium nitrogen (NH4 + ), or nitrate nitrogen (NO3 - ), using the ion selective electrode method (ISE) or spectroscopy. Phosphorus (P) is measured by measuring phosphate (PO4 3- ), based on electrochemical voltammetry or near-infrared spectroscopy (NIRS). Potassium (K) is measured by measuring potassium ions (K + ), using a potassium ion selective electrode.

[0036] The probe collects the 400 - 2500 nm spectrum through an ion selective electrode (such as the valinomycin membrane of a potassium electrode) or a hyperspectral sensor.

[0037] The data acquisition module measures the electrode potential by the ISE method (in line with the Nernst equation); for spectroscopy: a model of spectral reflectance and nutrient concentration is established by partial least squares (PLS). For example, nitrate nitrogen is retrieved from the absorption peak of ultraviolet spectrum (220 - 230 nm), and phosphorus and potassium are analyzed through the characteristic bands of near-infrared spectrum, and the concentration at the mg / kg level is output.

[0038] In some embodiments, for the soil organic matter value, in combination with spectroscopy, the absorption characteristics of C-H and O-H bonds in organic matter in the near-infrared (NIR) band are utilized. Through a hyperspectral sensor: covering the 700 - 2500 nm band, the soil reflection spectrum is collected. The data acquisition module: through partial least squares regression (PLSR) or a machine learning model (such as random forest), an inversion relationship between spectral data and the organic matter content (g / kg) is established. The calibration model needs to include a spectral library of different organic matter types (such as humus, plant and animal residues), excluding the interference of soil color and water content, and the accuracy can reach ±5%.

[0039] Exclusive electrodes are used for different parameters (such as a lead-zinc needle for pH and a steel needle for conductivity), or integrated gas / spectrum sensors are used. The frequency domain reflectometry (water content), electrochemistry (pH / ORP), and spectral measurement (nitrogen, phosphorus, potassium / organic matter) are uniformly called by the data acquisition module. By making the probe retractable and using capillary probes, it is ensured to contact the matrix pores in gravel / gravel, improving the signal reliability.

[0040] Through the above method, the sensor realizes synchronous measurement of 11 parameters, covering physical (water content, temperature), chemical (pH, conductivity, ORP, nutrients), and biological (gas, organic matter) dimensions, meeting the multi-index monitoring requirements of surface substrates.

[0041] In some embodiments, the sensor adopts a modular three-layer architecture to achieve full-chain integration from physical perception to data output: Perception layer: Composed of a multi-modal composite probe array, including: Electrochemical detection unit: Using glass microelectrodes (pH measurement, range 0 - 14, accuracy ±0.05) and solid-state ion-selective electrodes (for detection of K + , Na + , Ca 2+ and other ions, detection limit up to 10 -6 mol / L); Redox potential (ORP) uses a platinum-silver / silver chloride composite electrode, range ±2000 mV, resolution 1 mV; Physical parameter unit: A water content sensor based on the time domain reflectometry (TDR) principle, operating frequency 1 GHz, measurement depth 0 - 50 cm, accuracy ±1.5%; Platinum thin film temperature sensor (range -40~85 °C, accuracy ±0.2 °C); MEMS pressure sensor (for groundwater level monitoring, range 0 - 10 bar, error <0.1% FS).

[0042] Spectral analysis unit: Near-infrared spectroscopy module (wavelength range 900 - 1700 nm), analyzing soil organic matter (detection limit 0.5%) and nitrogen, phosphorus, and potassium contents (N: 0 - 500 mg / kg, P: 0 - 200 mg / kg, K: 0 - 1000 mg / kg) through the PLS algorithm; Laser methane sensor (TDLAS technology, detection range 0 - 100% LEL, response time <10 s).

[0043] Transmission layer corresponding to the wireless communication module: Dual-mode communication design: Long distance: LoRaWAN Class A (frequency band 868 MHz, transmission distance 10 km); Short distance: BLE 5.2 (transmission rate 2 Mbps, supporting Mesh networking).

[0044] The composite probe design can adopt a coaxial nested structure: Outer layer: 316L stainless steel sheath (corrosion-resistant, wall thickness 0.5 mm); Middle layer: Polytetrafluoroethylene insulation layer (dielectric constant 2.1, temperature resistance -200~260 °C); Inner layer: Functional electrode array (spacing 1.5 mm, avoiding electrochemical interference). The probe tip is coated with self-sharpening diamond (Mohs hardness 10), reducing the penetration resistance by 60%; Spiral groove structure (pitch 3 mm), reducing the soil compaction effect during insertion.

[0045] Substrate material of the sensor probe: Shape memory alloy (NiTiNol, phase transition temperature 30°C) combined with flexible PCB; Deployment mode: At normal temperature: Rigid mode (for easy transportation); When heated to 35°C during insertion: Turns into flexible state, can fit into rock crevices (bending radius reaches 5 mm); Pressure feedback system: Integrated with a micro strain gauge (range 0 - 50 N), adjusts the insertion angle in real time.

[0046] Meanwhile, as Figure 2 shown, the sensor module can be inserted into the surface substrate through the built-in hydraulic jack by the installation tool 200.

[0047] In some embodiments, the sensor probe is made of alloy material, and the alloy material includes at least any one of stainless steel and zinc.

[0048] The use of alloy material 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 various environmental monitoring.

[0049] The sensor probe adopts a SUS316L stainless steel substrate with a zinc-nickel alloy coating (thickness 15 - 20 μm), and the zinc content is controlled at 12 - 15 wt% to optimize the corrosion resistance. The probe tip is designed as a triangular pyramid nano-structure (cone angle 60 ° , surface roughness Ra ≤ 0.8 μm), and the surface activity is enhanced through the electrochemical polishing process. The conical geometric design reduces the insertion resistance of the probe and can still ensure the measurement accuracy in compacted soil; the sacrificial anode effect of zinc extends the service life of the probe to more than 5 years.

[0050] In some embodiments, the wireless communication module is communicatively connected to an external device via Bluetooth or SparkLink.

[0051] The application of wireless communication technology reduces the complexity of wiring and improves the flexibility of deployment. At the same time, it supports both Bluetooth and SparkLink technologies, improving the compatibility with different external devices.

[0052] The wireless module adopts a dual-mode communication architecture, integrating a BLE5.2 module (operating frequency band 2.4 GHz) and a SparkLink module (operating frequency band 5.8 GHz). The device automatically switches the transmission mode according to the environmental channel quality, enables the SparkLink technology to achieve high-speed transmission of 1 Gbps in an open field, and switches to BLE to maintain a basic rate of 10 Mbps in complex terrain.

[0053] The dual-mode redundant design ensures the communication reliability in complex geological environments, with a packet loss rate < 0.1%; The SparkLink technology shortens the transmission time of large files such as spectral data to 1 / 20 of that of traditional Zigbee; The mesh networking function in BLE mode supports the construction of a monitoring network with up to 100 nodes.

[0054] In some embodiments, the analysis algorithms include at least one or more of frequency domain reflectometry, time domain reflectometry, electrochemistry, conductivity measurement, and spectroscopy measurement.

[0055] The application of multiple analysis algorithms improves the measurement accuracy and reliability. Appropriate analysis algorithms can be selected according to different measurement requirements, improving the flexibility of the system.

[0056] The analysis algorithms include a time domain reflectometry (TDR) water content analysis algorithm, a cyclic voltammetry redox potential analysis algorithm, and a spectral inversion algorithm based on Mie scattering theory. Among them, the TDR algorithm uses an improved Topp formula: 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.

[0057] In some embodiments, as Figure 4 shown, it further includes: a bottom plate 60, in which the sensor probe is telescopically arranged; a visual acquisition module 70, which is arranged in the bottom plate and is used to capture the surface matrix image, send the surface matrix image to the external device through the wireless communication module, and the external device returns the monitored surface matrix parameter types and the sensor arrangement mode; a sensor moving mechanism, which determines a target probe among multiple sensor probes according to the surface matrix parameter types, the depth corresponding to the surface matrix, and the electrodes 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 bottom plate and be arranged in the surface matrix.

[0058] The bottom plate can be made of aviation aluminum and its surface is anodized. The internal structure can include a honeycomb support frame (weight reduction of 60%, compressive strength ≥ 200 MPa); the bottom plate can also include a probe storage bin (dustproof level IP6X, with a built-in desiccant bin) and a power management system (power supply of 24V / 5A, supporting hot-swappable battery replacement).

[0059] The probe types include at least 8 kinds of sensors (moisture, temperature, pH, EC, ORP, CO2, O2, groundwater level) The sensor moving mechanism is built into the bottom plate and can be driven by a stepping motor (step angle of 0.9°, microstepping drive of 32 subdivisions); a built-in spiral lifting guide rail (lead of 2 mm, repeat positioning accuracy of ±5 μm) is used to control the probe extension speed: adjustable from 1 - 50 mm / s (automatically adjusted according to the matrix hardness).

[0060] The visual acquisition module may include a multispectral camera: 5-band imaging (RGB + near-infrared 850nm + short-wave infrared 1550nm), resolution: 2048×1536 pixels, field of view angle 75°.

[0061] Meanwhile, a light source system can also be correspondingly set: integrated ring LED (color temperature 5600K, illuminance adjustable 0 - 20000lux) and lidar. The lidar includes a ToF ranging module (range 0.1 - 5m, accuracy ±1mm); scanning frequency 10Hz, generating 3D point cloud data of the ground surface.

[0062] The corresponding workflow can be as follows: 1. Visual system startup: Collect ground surface images + 3D point clouds and upload them to an external device, such as a corresponding analysis platform in the cloud. 2. The platform analyzes through a deep learning model (ResNet-50 + PointNet): substrate type (sand / clay / gravel, etc.); structural features (crack density, stone distribution); comparison with historical data (judge whether special parameter monitoring is required). 3. Generate deployment instructions: Select the probe type (such as activating EC / pH / Na + probe for saline-alkali land); plan the probe arrangement pattern (matrix type / radiation type / gradient type); determine the insertion depth. 4. The moving mechanism executes actions: The linear motor selects the target probe, the XY platform positions, the rotating mechanism adjusts the angle, and the probe extends along the optimal path (the pressure sensor gives real-time feedback on the resistance). For example, the A* algorithm is used to optimize the probe moving path to avoid hard obstacles. When the detected resistance > 50N, the probe retracts 5mm, rotates the angle ±15° and tries to insert again. If the cumulative failure reaches 3 times, switch to the backup probe. 5. After deployment, enter the monitoring state and perform self-check and fine-tune the position every 6 hours.

[0063] A self-learning mechanism can also be combined: Establish a deployment record database, including: geographical location, substrate type, probe configuration, monitoring data. Use reinforcement learning (PPO algorithm) to optimize subsequent deployment strategies: automatically select the historical optimal solution under similar geological conditions.

[0064] In some embodiments, as Figure 4 shown, the sensor probe further includes: at least one capillary probe, and the capillary probe is arranged at the tip of the sensor probe for, when the sensor probe is arranged in sand and / or gravel, the capillary probe extends into the gaps of the sand and / or gravel for detection.

[0065] By integrating one or more independent capillary channels (inner diameter 0.3mm, Teflon FEP lining) inside the probe; the capillary probe assembly uses superelastic nitinol wire (diameter 50μm, fracture strength 2GPa).

[0066] There can be a multi-level extension mechanism as shown in the following table:

[0067] In this embodiment, by combining the principle of bionic capillary penetration and tip micro-nano technology, the monitoring problem of traditional equipment in heterogeneous loose matrices is overcome.

[0068] In some embodiments, as Figure 2 shown, it further includes: a hyperspectral sensor 40 and a gas sensor 50. The hyperspectral sensor and the 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 measured parameters, hyperspectral information and gas information according to a preset analysis algorithm to generate the surface matrix parameters.

[0069] These two sensors 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 measured parameters, hyperspectral information and gas information according to a preset analysis algorithm to generate the surface matrix parameters. The addition of the hyperspectral sensor and the gas sensor enables the system to monitor more surface matrix parameters, such as soil organic matter values, etc. It increases the richness of data and provides more data support for environmental monitoring and geological disaster warning.

[0070] A hyperspectral imaging module (wavelength range 400 - 2500 nm, spectral resolution 3 nm) and a MEMS gas sensor array (including a PID photoionization detector and an NDIR carbon dioxide sensor) are respectively integrated at both ends of the probe array. The hyperspectral data is preprocessed by SG smoothing - first derivative and then input into a random forest model for organic matter content inversion.

[0071] Exemplarily, the hyperspectral sensor and the gas sensor are distributed at both ends of a plurality of the sensor probes.

[0072] The hyperspectral sensor and the gas sensor are distributed at both ends of a plurality of sensor probes to achieve more comprehensive monitoring. The distribution of the sensors optimizes the monitoring layout and improves the monitoring efficiency. The sensors distributed at both ends can cover a wider monitoring area and improve the comprehensiveness of monitoring.

[0073] The hyperspectral module and the gas sensor are respectively installed at the north and south poles of the probe array, and the distance is kept more than 50 mm. The hyperspectral probe faces outward at an inclination angle of 45°, and the gas collection port is designed as a cyclone separation structure to avoid soil particle pollution.

[0074] In some embodiments, a plurality of the sensor probes are distributed in a straight line on the data acquisition module.

[0075] The sensor probes distributed in a linear pattern make the monitoring more orderly, facilitating management and maintenance. It optimizes space utilization and reduces the floor area of the equipment. The sensor probes distributed in a linear pattern are arranged in a straight line along the axis, and the spacing follows the principle such as λ / 4 (λ is the working wavelength of each sensor), with a minimum spacing of 15 mm. The probe base uses a polyetheretherketone (PEEK) insulating frame, and an EMI shielding layer (copper mesh coverage rate ≥ 95%) is set on the frame surface. The linear layout controls the equipment diameter within the range applicable to the standard drilling aperture; the wavelength-adapted spacing eliminates the electromagnetic coupling interference between sensors and improves the signal-to-noise ratio; the shielding structure attenuates the external 50 Hz power frequency interference to -60 dB.

[0076] Exemplarily, if multiple said sensor probes are distributed in a linear pattern on the data acquisition module, and the number of the sensor probes is 5; the 1st, 2nd, 4th, and 5th sensor probes from top to bottom are steel needles, and the 3rd sensor probe is a lead-zinc needle; among them, the 1st and 3rd sensor probes are used to measure the pH value; the 2nd and 4th sensor probes are used to measure the conductivity; the 4th and 5th sensor probes are used to measure the humidity. The nitrogen, phosphorus, and potassium are calculated through an algorithm; the temperature is measured through the thermistor inside the substrate.

[0077] The 5 sensor probes are linearly distributed on the data acquisition module along the vertical direction (or horizontal direction, according to the actual application scenario), and the numbers are sequentially the 1st, 2nd, 3rd, 4th, and 5th probes from top to bottom (or from left to right). The spacing between the probes is adjusted according to the pore size of the surface matrix type (such as soil, gravel, etc.) to ensure that the probes can be inserted into the pores or cracks of the matrix and avoid mutual interference.

[0078] By using steel needles (such as stainless steel material) for the 1st, 2nd, 4th, and 5th probes, they have the characteristics of strong corrosion resistance and stable conductivity, and are suitable as reference electrodes or general electrodes for electrochemical measurement. The 3rd probe uses a lead-zinc needle (such as lead-zinc alloy material), and utilizes the electrochemical activity of lead-zinc, which is suitable for measuring the concentration of specific ions (such as H+), and provides a sensitive electrode for pH value detection.

[0079] The pH value measurement (the 1st + the 3rd probe) is based on the glass electrode method (or metal electrode method). The 1st steel needle serves as the reference electrode (providing a stable reference potential), and the 3rd lead-zinc needle serves as the indicating electrode (responding to H+ ions). After the two electrodes are inserted into the surface matrix, a primary battery is formed, and the potential difference has a relationship with the H+ concentration (i.e., pH value) in the matrix according to the Nernst equation. The data acquisition module calculates the pH value through a preset electrochemical algorithm (such as the Nernst equation).

[0080] The conductivity measurement (using Probe No. 2 + Probe No. 4) is based on the conductivity method. The conductivity of the substrate is calculated by measuring the alternating current resistance between two electrodes. The No. 2 and No. 4 steel needles serve as conductive electrodes. A low-frequency alternating voltage is applied, and the loop current is measured. The resistance is calculated according to Ohm's law, and the conductivity is converted by combining the electrode spacing and area (known parameters), reflecting the total ion concentration in the substrate.

[0081] The humidity measurement (using Probe No. 4 + Probe No. 5) is based on the capacitance method or the resistance method, taking advantage of the change in dielectric constant or resistivity caused by the change in the moisture content of the substrate. The No. 4 and No. 5 steel needles serve as capacitor plates, and the substrate serves as the dielectric. The higher the moisture content, the greater the dielectric constant, and the change in capacitance value is positively correlated with humidity; the resistance between the two electrodes is directly measured. The higher the moisture content, the higher the concentration of conductive ions (dissolved in water), and the lower the resistance. The humidity is converted through a calibration curve.

[0082] Each probe is connected to the data acquisition module through a wire. The module is built-in with a multiplex analog switch, which selectively gates different electrode pairs (such as No. 1 + No. 3, No. 2 + No. 4, No. 4 + No. 5) at different times. For conductivity and humidity, voltage / current or capacitance signals are collected. The data acquisition module processes the signals of different electrode pairs according to the preset analysis algorithms (such as the electrochemical method, the conductivity method): the pH value corrects the temperature effect through the Nernst equation to calculate the H+ activity; the conductivity deducts the electrode polarization effect to calculate the equivalent conductivity; the humidity converts the capacitance / resistance signal into the moisture content through a calibrated empirical formula or a machine learning model. The processed parameters are sent to an external device through a wireless communication module (such as Bluetooth) or stored locally.

[0083] For different substrates such as soil and gravel, the insertion depth and spacing of the probes are adjusted (such as "the arrangement is related to the type of surface substrate"), ensuring the stability of the potential difference signal between the lead-zinc needle (No. 3) and the steel needle.

[0084] The surface of the steel needle is coated with an insulating layer (except for the tip) to avoid stray current interference; the lead-zinc needle is coated with an anti-oxidation coating to extend its service life. The 5 probes can achieve 3-parameter measurements through different combinations, reducing the number of probes and the hardware cost, which is suitable for portable or distributed monitoring devices.

[0085] The probe arrangement (linear) and materials (steel, lead-zinc) are specific implementations of "the arrangement of the sensor probes and the type and structure of the surface substrate to be set are related", ensuring insertion into pores or fissures. The electrochemical method is used to measure the pH value, the conductivity method is used for conductivity, and the frequency domain reflectometry or the resistance method is used for humidity, all of which are specific types of the preset analysis algorithms. The probe materials (steel, lead-zinc) meet the limitation of "the alloy material includes at least any one of stainless steel and zinc".

[0086] By defining the materials and functional allocations of five linear probes, the integrated measurement of pH value, conductivity, and humidity is achieved. The core lies in leveraging the electrochemical characteristics of different metal electrodes and completing multi-parameter detection in a compact structure through electrode pair multiplexing, which combines cost-effectiveness and measurement accuracy and is suitable for rapid in-situ monitoring of surface matrixes.

[0087] In some embodiments, multiple of the sensor probes are distributed in a circular pattern on the data acquisition module.

[0088] The circular arrangement of the probes can achieve a dead-angle-free coverage, which is particularly suitable for scenarios such as underground water flow direction tracking (through multi-directional conductivity difference analysis), pollutant diffusion path modeling (constructing a three-dimensional concentration gradient field), and geological structure tomography (combining with ERT resistivity tomography technology).

[0089] In some embodiments, it further includes: a wireless charging module, where 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.

[0090] The application of wireless charging technology makes charging more convenient and reduces maintenance work. The wireless charging module ensures that the sensor module can operate continuously and improves the stability of the system.

[0091] The wireless charging module adopts standards such as Qi 1.3. The transmitting coil (with a diameter of 40 mm and 150 μH) is built into the bottom of the protective shell, and the vertical distance from the receiving coil (with a diameter of 35 mm and 135 μH) inside the device is ≤ 10 mm. The charging management IC integrates the MPPT algorithm, and the conversion efficiency can reach 85% when the input power is 5W.

[0092] In some embodiments, the data acquisition module stores multiple analysis algorithms and the parameter types corresponding to each analysis algorithm. The data acquisition module obtains the target parameters corresponding to each analysis algorithm according to 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 according to the target parameters corresponding to each analysis algorithm.

[0093] The data acquisition module establishes a parameter-algorithm mapping table. For example, it automatically associates the nitrogen, phosphorus, and potassium detection with the ICP-OES calibration model, and the carbon dioxide data triggers the NDIR iterative algorithm. When detecting an abnormality in multi-parameter association (such as a sudden change in conductivity but no change in ion concentration), it automatically enables the Bayesian network to evaluate the data credibility.

[0094] Through the integration of multidisciplinary technologies, the above embodiments have constructed an intelligent sensing system with autonomous diagnosis capabilities. Compared with the prior art, breakthrough progress has been achieved in terms of monitoring dimensions (expanded from conventional 3 parameters to 15 parameters), response speed (upgraded from minute level to sub-second level), environmental adaptability (operating temperature range of -40°C to 85°C), etc.

[0095] Please refer to Figure 5 , Figure 5 which is a schematic flowchart of a multi-parameter surface matrix monitoring method provided by an embodiment of the present application. This multi-parameter surface matrix monitoring method can be implemented by the data acquisition module of the multi-parameter surface matrix integrated sensor provided by any embodiment of the present application. The data acquisition module can be deployed on a single server or a server cluster, or can also be deployed on a handheld terminal, a laptop, a wearable device, a robot, etc.

[0096] As Figure 5 shown, the provided multi-parameter surface matrix monitoring method includes steps S101 to S103. Details are as follows: Step S101. Obtain the type of surface matrix set by the sensor probe, and adjust the arrangement of the sensor probe according to the type of surface matrix; The visual acquisition module of the sensor takes a picture of the surface matrix image and sends it to an external device (such as a server or a terminal). The device judges the matrix type (such as sandy soil, clay, gravel layer) based on an image recognition algorithm (such as a convolutional neural network) and returns an adapted probe arrangement scheme (such as probe spacing, insertion depth, combination method). For example, the sensor moving mechanism controls the retractable probe according to the arrangement scheme (adjust the position, for example, extend the probe in the gravel matrix and insert it through the capillary probe from the gap to ensure full contact between the electrode and the matrix).

[0097] Step S102. Obtain a plurality of analysis algorithms and the parameter types corresponding to each analysis algorithm, and obtain the target parameters corresponding to each analysis algorithm according to the measurement parameters measured by a plurality of sensor probes received and the parameter types corresponding to each analysis algorithm; The sensor probe (such as a steel needle, a lead-zinc needle) collects electrical signals (such as electric potential, resistance, capacitance, etc.) in real time, and the data acquisition module summarizes the measurement parameters according to a preset protocol (such as Modbus). The module retrieves the corresponding algorithm from the algorithm library according to the parameter type label (such as pH value corresponding to the electrochemical method, humidity corresponding to the frequency domain reflectometry method). For example: for the conductivity parameter, call the conductivity method algorithm and calculate the ion concentration in combination with the electrode spacing; for the spectral data (from the hyperspectral sensor), call the spectral measurement method to invert the soil organic matter content. Cross-parameter fusion: If multiple algorithms require the same type of parameter (such as temperature affecting both pH value and conductivity), the module automatically allocates and shares the parameter to avoid repeated acquisition.

[0098] Step S103. Calculate the surface substrate parameters corresponding to each analysis algorithm according to the target parameters corresponding to each analysis algorithm; the surface substrate parameters include at least one or more of moisture content, conductivity, pH value, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, soil organic matter value.

[0099] The generated substrate parameters are sent to an external device in real time through a wireless communication module (Bluetooth / NearLink), and are used in scenarios such as agricultural soil moisture monitoring and geological disaster warning, or trigger threshold alarms (such as warnings when the groundwater level is abnormal).

[0100] Dynamically adjust the probe arrangement by identifying the substrate type (such as using capillary probes in gravel) to solve the problem of poor contact of traditional sensors in complex substrates and improve the detection accuracy (especially for fissured substrates). Use the same set of probe arrays and multiplex multiple algorithms to synchronously obtain more than 10 substrate parameters (such as moisture content, nitrogen, phosphorus and potassium), avoid stacking multiple devices, and reduce the hardware cost by 30%-50%. Automatically match the parameter types based on preset algorithms, reduce manual configuration steps, support real-time online analysis, and meet the unmanned needs of long-term field monitoring. Through probe arrangement optimization (such as electrode combination) and algorithm fusion, reduce the error of a single sensor. For example, calibrate the conductivity and humidity parameters with each other to reduce the moisture content measurement error to ±2%. It is applicable to multiple scenarios such as agricultural soil, mining area substrates, and ecological wetlands. Dynamically update the arrangement scheme through an external device, and support rapid deployment across regions and substrate types.

[0101] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described multi-parameter surface substrate monitoring method and each step can refer to the corresponding processes in the multi-parameter surface substrate integrated sensor embodiments described in the above embodiments, and will not be repeated here.

[0102] Please refer to Figure 6 as shown in Figure 6 FIG. 300 is a schematic structural diagram of a surface substrate layered channel monitoring device 300 provided by an embodiment of the present application. The surface substrate layered channel monitoring device 300 is used to execute the steps of the multi-parameter surface substrate monitoring method shown in the above embodiments. The surface substrate layered channel monitoring device 300 may be a single server or a server cluster, or the surface substrate layered channel monitoring device 300 may be a terminal, and the terminal may be a handheld terminal, a laptop computer, a wearable device or a robot, etc.

[0103] As Figure 6 shown, the surface substrate layered channel monitoring device 300 includes: A type acquisition unit 301 is configured to acquire the surface matrix type set by the sensor probe, so as to adjust the arrangement of the sensor probe according to the surface matrix type; A target acquisition unit 302 is configured to acquire a plurality of analysis algorithms and the parameter types corresponding to each analysis algorithm, and acquire the target parameters corresponding to each analysis algorithm according to the measurement parameters measured by a plurality of received sensor probes and the parameter types corresponding to each analysis algorithm; A parameter analysis unit 303 is configured to calculate the surface matrix parameters corresponding to each analysis algorithm 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 value, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, soil organic matter value.

[0104] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described surface matrix stratification channel monitoring device and each module can refer to the corresponding processes in the multi-parameter surface matrix integrated sensor embodiments described in the above embodiments, and will not be elaborated herein.

[0105] The above multi-parameter surface matrix monitoring method can be implemented in the form of a computer program, and the computer program can run on a device as shown in Figure 6 shown.

[0106] Please refer to Figure 7 , Figure 7 which is a schematic block diagram of the structure of the data acquisition module provided by the embodiment of the present application. The data acquisition module includes a processor, a memory and a network interface connected through a device bus. Among them, the memory may include a storage medium and an internal memory.

[0107] 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 multi-parameter surface matrix monitoring method.

[0108] The processor is used to provide computing and control capabilities to support the operation of the entire data acquisition module.

[0109] 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.

[0110] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 7The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the terminals to which the solution of this application is applied. Specifically, the data acquisition module may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0112] Among them, in one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps: Obtain the electrode types monitored by each sensor probe; Obtain the surface matrix types set by the sensor probes to adjust the arrangement of the sensor probes according to the surface matrix types; Obtain a plurality of analysis algorithms and the parameter types corresponding to each analysis algorithm, and obtain the target parameters corresponding to each analysis algorithm according to the measurement parameters measured by the received plurality of sensor probes and the parameter types corresponding to each analysis algorithm; Calculate the surface matrix parameters corresponding to each analysis algorithm 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 value, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, soil organic matter value.

[0113] This application also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to implement the steps of the multi-parameter surface matrix monitoring method described above.

[0114] Among them, the computer-readable storage medium may be an internal storage unit of the data acquisition module described in the foregoing embodiments, such as the 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 equipped on the data acquisition module, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0115] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A multi-parameter surface matrix integrated sensor, characterized in that, Including: A data acquisition module, which stores a preset analysis algorithm; Multiple sensor probes, the sensor probes are connected to the data acquisition module, and the electrodes corresponding to each sensor probe are different. 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 at least include one or more of moisture content, conductivity, pH value, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, soil organic matter value; the arrangement of the sensor probes is related to the type and structure of the set surface matrix, so that the sensor probes can be inserted into the pores or fissures of the surface matrix; A wireless communication module, the wireless communication module is communicatively connected to the data acquisition module, and the wireless communication module is also wirelessly communicatively connected to an external device, and is used to send the surface matrix parameters to the external device.

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

3. The sensor according to claim 1, characterized in that, It further includes: A bottom plate, and the sensor probe is telescopically arranged in the bottom plate; A visual acquisition module, which is arranged in the bottom plate, is used to capture an image of the surface matrix, and send the surface matrix image to the external device through the wireless communication module, and the external device returns the monitored sensor arrangement method; A sensor moving mechanism, which determines a target probe according to the type of surface matrix parameters, the depth corresponding to the surface matrix, and the electrodes corresponding to each sensor probe among the multiple sensor probes, adjusts the position of the target probe according to the sensor arrangement method, and controls the target probe to extend out of the bottom plate and be arranged in the surface matrix.

4. The sensor according to claim 1, characterized in that, The sensor probe further includes: At least one capillary probe, which is arranged at the tip of the sensor probe, and is used to extend into the gaps of the gravel and / or stone gravel for detection when the sensor probe is arranged in the gravel and / or stone gravel.

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

6. The sensor according to claim 1, characterized in that, It further includes: A hyperspectral sensor and a gas sensor, the hyperspectral sensor and the 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 the preset analysis algorithm to generate the surface matrix parameters, and the hyperspectral sensor and the gas sensor are distributed at both ends of the multiple sensor probes.

7. The sensor according to claim 1, characterized in that, The multiple sensor probes are linearly distributed on the data acquisition module; or, The multiple sensor probes are circularly distributed on the data acquisition module.

8. The sensor according to claim 7, characterized in that, If the multiple sensor probes are linearly distributed on the data acquisition module and the number of the sensor probes is 5; The 1st, 2nd, 4th, and 5th sensor probes from top to bottom are steel needles, and the 3rd sensor probe is a lead-zinc needle; Among them, the 1st and 3rd sensor probes are used to measure the pH value; the 2nd and 4th sensor probes are used to measure the conductivity; the 4th and 5th sensor probes are used to measure the humidity.

9. The sensor according to claim 1, characterized in that, The data acquisition module stores multiple analysis algorithms and the parameter types corresponding to each analysis algorithm. The data acquisition module obtains the target parameters corresponding to each analysis algorithm according to 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 according to the target parameters corresponding to each analysis algorithm.

10. A multi-parameter surface matrix monitoring method, characterized in that, Applied to the multi-parameter surface matrix integrated sensor according to any one of claims 1-9; the method includes: Obtain the type of surface matrix set by the sensor probe, so as to adjust the arrangement of the sensor probe according to the type of surface matrix; so that the sensor probe can be inserted into the pores or cracks of the surface matrix; Obtain multiple analysis algorithms and the parameter types corresponding to each analysis algorithm, and obtain the target parameters corresponding to each analysis algorithm according to the received measurement parameters measured by multiple sensor probes and the parameter types corresponding to each analysis algorithm; Calculate the surface matrix parameters corresponding to each analysis algorithm 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 value, redox potential, temperature, carbon dioxide value, methane value, oxygen value, groundwater level value, nitrogen, phosphorus and potassium value, soil organic matter value.

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