Soil heavy metal pollution detection and remediation hardware system based on isotopic tracing

Abstract

The invention discloses a soil heavy metal pollution detection and remediation hardware system based on isotopic tracing, which relates to the technical field of soil and comprises an isotopic tracing module, a heavy metal detection module, a remediation execution module, a real-time monitoring module, an intelligent regulation and control module and a data storage and transmission module, the data storage and transmission module is connected with the isotope tracing module, the heavy metal detection module, the repair execution module, the real-time monitoring module and the intelligent regulation and control module, and transmits data to a remote monitoring center in a wired or wireless communication mode, so that further data analysis and remote control are carried out; through multi-source data fusion and intelligent algorithm optimization, precise positioning, dynamic monitoring and efficient repairing of heavy metal pollution are achieved, the isotope tracing technology can position a pollution source and predict a diffusion path, and the plant and microorganism synergistic repairing efficiency is improved; the system is suitable for industrial pollution sites, farmlands and other scenes, and has remarkable environmental and economic benefits.

Description

Technical Field

[0001] This invention relates to the field of soil technology, specifically to a hardware system for detecting and remediating soil heavy metal pollution based on isotope tracing. Background Technology

[0002] Heavy metals are the most prominent inorganic pollutants in soil, mainly because they cannot be decomposed by soil microorganisms and are easily accumulated. They can transform into more toxic methyl compounds, and some can even accumulate in the human body at harmful concentrations through the food chain, seriously endangering human health. Major heavy metal pollutants in soil include mercury, cadmium, lead, copper, chromium, arsenic, nickel, iron, manganese, and zinc. Although arsenic is not classified as a heavy metal, its behavior, sources, and hazards are similar to heavy metals, so it is usually discussed in the heavy metal category. In terms of plant needs, metallic elements can be divided into two categories: ① Elements not needed for plant growth and development but with significant health risks, such as cadmium, mercury, and lead. ② Elements necessary for normal plant growth and development, and which also have certain physiological functions for humans, such as copper and zinc, but excessive amounts can cause pollution and hinder plant growth and development.

[0003] Heavy metal pollution in soil is a challenging issue in current environmental remediation, and traditional detection and remediation methods have the following limitations: Conventional chemical analysis methods are time-consuming and difficult to obtain real-time data on the dynamic migration of heavy metals, and cannot reflect the spatial heterogeneity of pollution. Existing technologies mostly rely on physicochemical remediation (such as rinsing and solidification), which are costly and easily damage the soil ecosystem; The synergistic effect of phytoremediation and microbial remediation lacks a quantitative model, making it difficult to maximize remediation efficiency.

[0004] To address these issues, a hardware system for detecting and remediating heavy metal pollution in soil based on isotope tracing is proposed. Summary of the Invention

[0005] This invention provides a hardware system for soil heavy metal pollution detection and remediation based on isotope tracing. Through multi-source data fusion and intelligent algorithm optimization, it achieves accurate location, dynamic monitoring and efficient remediation of heavy metal pollution, thereby solving the problems of insufficient detection accuracy, single remediation strategy and lack of coordinated regulation mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a hardware system for soil heavy metal pollution detection and remediation based on isotope tracing, comprising the following modules: An isotope tracing module, including an isotope injection device, an isotope detection sensor, and a path analysis unit, is used to track the migration paths of heavy metals in soil through stable isotope labeling technology. The heavy metal detection module integrates a multispectral sensor and an electrochemical analysis unit to collect data on the concentration, type, and distribution of heavy metals in soil in real time. The repair execution module includes an automatically deployed repair plant planting unit and a microbial repair agent release device. The repair plant is a hyperaccumulating plant, and the microbial repair agent contains heavy metal immobilized bacteria. The real-time monitoring module includes optical sensors, electrochemical sensors, X-ray fluorescence spectrometers, and heavy metal dynamic migration imaging units, which are used to monitor the content, location, and speciation parameters of heavy metals in soil in real time and to generate a multi-dimensional environmental parameter matrix. The intelligent control module, based on dynamic migration imaging data and environmental parameter matrix, dynamically adjusts the planting density of remediation plants and the release strategy of microbial remediation agents through an optimized algorithm model. The data storage and transmission module is used to store various data during the entire system operation process, including isotope labeling information, monitoring data, repair strategies and control instructions, and transmit the data to the remote monitoring center for data analysis and remote control.

[0007] Furthermore, the isotope tracing module uses stable isotopes and radioactive isotopes as tracers, and adds the tracers to the soil sample through a specific isotope labeling device.

[0008] Furthermore, in the isotope tracing module, the stable isotope is at least one of zinc-68, cadmium-114, or lead-206, and the path analysis unit establishes a three-dimensional model of the heavy metal migration path through radioactive decay signals and mass fractionation effects.

[0009] Furthermore, the optical sensor is used to detect the characteristic spectrum emitted by the isotopically labeled heavy metal; The electrochemical sensor is used to measure the electrochemical signals of heavy metals in soil; The X-ray fluorescence spectrometer is used to rapidly determine the content of various heavy metals in soil.

[0010] Furthermore, in the heavy metal detection module, the multispectral sensor operates in the 400-2500nm band. Combined with the differential pulse voltammetry of the electrochemical analysis unit, the heavy metal spectral characteristics and electrochemical signals are fused and analyzed through a convolutional neural network to output the heavy metal pollution level classification results.

[0011] Furthermore, in the remediation execution module, the microbial remediation agent release device is equipped with a microfluidic chip, which dynamically adjusts the flow rate and dosage of the remediation agent according to the soil porosity, and enhances the immobilization efficiency of heavy metals by the microbial community through gene expression regulation technology. The remediation plant planting unit uses a seed ejection device carried by a drone to achieve precise sowing based on the heat map of heavy metal pollution distribution. The sowing density is positively correlated with the pollution concentration.

[0012] Furthermore, the optimization algorithm model of the intelligent control module includes: The migration prediction sub-model uses a long short-term memory network to predict the migration path of heavy metals in time series. The restoration efficiency assessment sub-model analyzes the synergistic effect between restoration plants and microorganisms using the random forest algorithm to generate a restoration efficiency score; The dynamic control sub-model employs a multi-objective particle swarm optimization algorithm, aiming to minimize remediation costs and maximize pollution removal rates, and outputs the optimal combination of remediation parameters.

[0013] Furthermore, in the dynamic regulation sub-model, the constraints include soil pH threshold, the growth cycle of the repair plant, and the temperature range of microbial activity, and the weights are adjusted and optimized in real time through a fuzzy logic controller.

[0014] Furthermore, the optical sensor in the real-time monitoring module employs a high-sensitivity photomultiplier tube and a high-resolution spectral detector, which can detect weak isotope fluorescence signals. By establishing a quantitative relationship model between the intensity of isotope fluorescence signals and the content of heavy metals in the soil, a multiple linear regression algorithm is used for data analysis to determine the content of heavy metals in the soil.

[0015] Furthermore, the data storage and transmission module adopts a distributed storage architecture, distributing data across multiple storage nodes, and its data storage format adopts a unified XML or JSON format.

[0016] Compared with existing technologies, this invention provides a hardware system for soil heavy metal pollution detection and remediation based on isotope tracing, which has the following beneficial effects: This isotope-based hardware system for detecting and remediating heavy metal pollution in soil achieves precise location, dynamic monitoring, and efficient remediation of heavy metal pollution through multi-source data fusion and intelligent algorithm optimization. Isotope tracing technology can locate pollution sources and predict diffusion paths, improving the efficiency of plant and microbial co-remediation. The algorithm achieves multi-objective optimization, shortening the remediation cycle and improving efficiency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overall system of the soil heavy metal pollution detection and remediation hardware system based on isotope tracing of the present invention. Figure 2 This is a schematic diagram of the isotope tracing module of the hardware system for soil heavy metal pollution detection and remediation based on isotope tracing of the present invention. Figure 3 This is a schematic diagram of the real-time monitoring module of the soil heavy metal pollution detection and remediation hardware system based on isotope tracing of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0022] Please see Figure 1-3 This invention discloses a hardware system for soil heavy metal pollution detection and remediation based on isotope tracing, comprising the following modules: An isotope tracing module, comprising an isotope injection device, an isotope detection sensor, and a path analysis unit, is used to track the migration paths of heavy metals in soil using stable isotope labeling technology. The isotope tracing module utilizes stable isotopes and radioactive isotopes as tracers, adding the tracers to soil samples through a specific isotope labeling device. In the isotope tracing module, the stable isotope is at least one of zinc-68, cadmium-114, or lead-206. The path analysis unit establishes a three-dimensional model of the heavy metal migration path through radioactive decay signals and mass fractionation effects.

[0023] Path analysis unit: A three-dimensional model of heavy metal migration is constructed using radioactive decay signals and mass fractionation effects. The formula is as follows: ; Where D is the diffusion coefficient, C is the concentration, λ is the decay constant, and S is the source term.

[0024] The heavy metal detection module integrates a multispectral sensor and an electrochemical analysis unit to collect data on the concentration, type, and distribution of heavy metals in soil in real time. In the heavy metal detection module, the multispectral sensor operates in the 400-2500 nm wavelength range. Combined with the differential pulse voltammetry of the electrochemical analysis unit, the heavy metal spectral characteristics and electrochemical signals are fused and analyzed using a convolutional neural network (CNN) to output the heavy metal pollution level classification results.

[0025] A multispectral sensor (400-2500 nm band) works in conjunction with an electrochemical analysis unit (differential pulse voltammetry) to fuse spectral and electrochemical signals via a convolutional neural network (CNN) and output pollution level classification results. .

[0026] The repair execution module includes an automatically deployed repair plant planting unit and a microbial repair agent release device. The repair plant is a hyperaccumulating plant, and the microbial repair agent contains heavy metal immobilized bacteria. Microbial remediation agent release device: It uses a microfluidic chip to dynamically adjust the flow rate of the remediation agent, and combines gene regulation technology to improve the efficiency of microbial community immobilization; Drone seeding unit: Based on a pollution heat map, hyperaccumulating plants (such as centipede grass) are precisely seeded using a catapult device. The planting density ρ satisfies the following relationship with the pollution concentration C: ; Where k is the adjustment coefficient and C0 is the reference concentration.

[0027] The real-time monitoring module includes optical sensors, electrochemical sensors, X-ray fluorescence spectrometers, and heavy metal dynamic migration imaging units, which are used to monitor the content, location, and speciation parameters of heavy metals in soil in real time and to generate a multi-dimensional environmental parameter matrix. The intelligent control module, based on dynamic migration imaging data and environmental parameter matrix, dynamically adjusts the planting density of remediation plants and the release strategy of microbial remediation agents through an optimized algorithm model. Migration prediction sub-model: A Long Short-Term Memory (LSTM) network is used to predict heavy metal migration trends. The hidden state update formula is as follows: .

[0028] Dynamic regulation sub-model: Based on the multi-objective particle swarm optimization algorithm (MOPSO), using remediation cost F1 and clearance rate F2 as objective functions, optimizes planting density and remediation agent dosage. ; The constraints include soil pH (5.5-7.5) and temperature for microbial activity (15-35℃).

[0029] The data storage and transmission module, connected to the isotope tracing module, heavy metal detection module, repair execution module, real-time monitoring module, and intelligent control module, stores various data during the entire system operation, including isotope labeling information, monitoring data, repair strategies, and control instructions. It transmits this data to a remote monitoring center via wired or wireless communication for further data analysis and remote control. Data transmission employs encryption protocols, such as the AES encryption algorithm, to ensure data security and integrity.

[0030] The system further includes a user interaction terminal, which provides a visual interface to display pollution maps, remediation progress and analysis results of algorithm models, and supports the input of manual intervention commands and priority settings.

[0031] Specifically, the optical sensor is used to detect the characteristic spectrum emitted by isotopically labeled heavy metals; The electrochemical sensor is used to measure the electrochemical signals of heavy metals in soil; The X-ray fluorescence spectrometer is used to rapidly determine the content of various heavy metals in soil. Its working principle is based on the fact that each heavy metal element has a unique fluorescence response to X-rays of a specific wavelength. By performing algorithmic analysis on the monitored spectral data, the real-time content and existing form of each heavy metal in the soil can be determined.

[0032] Specifically, in the remediation execution module, the microbial remediation agent release device is equipped with a microfluidic chip, which dynamically adjusts the flow rate and dosage of the remediation agent according to the soil porosity, and enhances the immobilization efficiency of heavy metals by the microbial community through gene expression regulation technology. The remediation plant planting unit uses a seed ejection device carried by a drone to achieve precise sowing based on the heat map of heavy metal pollution distribution. The sowing density is positively correlated with the pollution concentration.

[0033] Specifically, the optimization algorithm model of the intelligent control module includes: The migration prediction sub-model uses a long short-term memory network (LSTM) to predict the migration path of heavy metals in time series. The restoration efficiency assessment sub-model analyzes the synergistic effect between restoration plants and microorganisms using the random forest algorithm to generate a restoration efficiency score; The dynamic regulation sub-model employs a multi-objective particle swarm optimization algorithm (MOPSO) to minimize remediation costs and maximize pollution removal rates, outputting the optimal combination of remediation parameters. Constraints in the dynamic regulation sub-model include soil pH threshold, remediation plant growth cycle, and microbial activity temperature range, with optimization weights adjusted in real-time using a fuzzy logic controller.

[0034] Specifically, the optical sensor in the real-time monitoring module uses a high-sensitivity photomultiplier tube and a high-resolution spectral detector, which can detect weak isotope fluorescence signals. By establishing a quantitative relationship model between the intensity of isotope fluorescence signals and the content of heavy metals in the soil, a multiple linear regression algorithm is used for data analysis to determine the content of heavy metals in the soil. The process of establishing this quantitative relationship model involves measuring multiple soil heavy metal standard samples with known concentrations, collecting corresponding fluorescence signal data, performing regression analysis using statistical software, obtaining regression equations, and thus achieving rapid and accurate determination of heavy metal content in unknown soil samples.

[0035] Specifically, the data storage and transmission module adopts a distributed storage architecture, distributing data across multiple storage nodes and ensuring data security and reliability through a redundancy backup mechanism. Its data storage format uses a unified XML or JSON format for easy data reading, parsing, and transmission. During data transmission, in addition to using the AES encryption algorithm, digital signature technology is incorporated to ensure the reliability and integrity verification of the data source. The digital signature uses an asymmetric encryption algorithm, such as RSA, to sign the transmitted data. Upon receiving the data, the receiving end verifies the digital signature to confirm that the data has not been tampered with, thus ensuring the security and reliability of the entire data transmission process and providing strong support for the stable operation of the system and the accurate utilization of data.

[0036] In summary, this isotope-tracing-based hardware system for detecting and remediating heavy metal pollution in soil achieves precise location, dynamic monitoring, and efficient remediation of heavy metal pollution through multi-source data fusion and intelligent algorithm optimization. Isotope tracing technology can locate pollution sources and predict diffusion paths, improving the efficiency of phytoremediation and microbial co-remediation. The algorithm achieves multi-objective optimization, shortening the remediation cycle and increasing efficiency. This invention integrates isotope labeling, multispectral sensing, intelligent algorithms, and bioremediation technology. It uses an LSTM network to predict pollution migration paths and the MOPSO algorithm to optimize remediation parameters, achieving precise detection and efficient treatment of heavy metal pollution. This system is applicable to industrial contaminated sites, farmland, and other scenarios, offering significant environmental and economic benefits.

[0037] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0038] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0039] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A hardware system for soil heavy metal pollution detection and remediation based on isotope tracing, characterized in that, Includes the following modules: An isotope tracing module, including an isotope injection device, an isotope detection sensor, and a path analysis unit, is used to track the migration paths of heavy metals in soil through stable isotope labeling technology. The heavy metal detection module integrates a multispectral sensor and an electrochemical analysis unit to collect data on the concentration, type, and distribution of heavy metals in soil in real time. The repair execution module includes an automatically deployed repair plant planting unit and a microbial repair agent release device. The repair plant is a hyperaccumulating plant, and the microbial repair agent contains heavy metal immobilized bacteria. The real-time monitoring module includes optical sensors, electrochemical sensors, X-ray fluorescence spectrometers, and heavy metal dynamic migration imaging units, which are used to monitor the content, location, and speciation parameters of heavy metals in soil in real time and to generate a multi-dimensional environmental parameter matrix. The intelligent control module, based on dynamic migration imaging data and environmental parameter matrix, dynamically adjusts the planting density of remediation plants and the release strategy of microbial remediation agents through an optimized algorithm model. The data storage and transmission module is used to store various data during the entire system operation process, including isotope labeling information, monitoring data, repair strategies and control instructions, and transmit the data to the remote monitoring center for data analysis and remote control.

2. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 1, characterized in that: The isotope tracing module uses stable isotopes and radioactive isotopes as tracers, and adds the tracers to the soil sample through a specific isotope labeling device.

3. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 1, characterized in that: In the isotope tracing module, the stable isotope is at least one of zinc-68, cadmium-114, or lead-206, and the path analysis unit establishes a three-dimensional model of the heavy metal migration path through radioactive decay signals and mass fractionation effects.

4. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 1, characterized in that: The optical sensor is used to detect the characteristic spectrum emitted by isotopically labeled heavy metals. The electrochemical sensor is used to measure the electrochemical signals of heavy metals in soil; The X-ray fluorescence spectrometer is used to rapidly determine the content of various heavy metals in soil.

5. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 1, characterized in that: In the heavy metal detection module, the multispectral sensor operates in the 400-2500 nm band. Combined with the differential pulse voltammetry of the electrochemical analysis unit, the heavy metal spectral characteristics and electrochemical signals are fused and analyzed by a convolutional neural network to output the heavy metal pollution level classification results.

6. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 1, characterized in that: In the remediation execution module, the microbial remediation agent release device is equipped with a microfluidic chip, which dynamically adjusts the flow rate and dosage of the remediation agent according to the soil porosity, and enhances the immobilization efficiency of heavy metals by the microbial community through gene expression regulation technology. The remediation plant planting unit uses a seed ejection device carried by a drone to achieve precise sowing based on the heat map of heavy metal pollution distribution. The sowing density is positively correlated with the pollution concentration.

7. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 1, characterized in that: The optimization algorithm model of the intelligent control module includes: The migration prediction sub-model uses a long short-term memory network to predict the migration path of heavy metals in time series. The restoration efficiency assessment sub-model analyzes the synergistic effect between restoration plants and microorganisms using the random forest algorithm to generate a restoration efficiency score; The dynamic control sub-model employs a multi-objective particle swarm optimization algorithm, aiming to minimize remediation costs and maximize pollution removal rates, and outputs the optimal combination of remediation parameters.

8. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 7, characterized in that: In the dynamic regulation sub-model, the constraints include soil pH threshold, the growth cycle of the repaired plant, and the temperature range of microbial activity, and the weights are adjusted and optimized in real time through a fuzzy logic controller.

9. The hardware system for soil heavy metal pollution detection and remediation based on isotope tracing according to claim 1, characterized in that: The optical sensor in the real-time monitoring module uses a high-sensitivity photomultiplier tube and a high-resolution spectral detector, which can detect weak isotope fluorescence signals. By establishing a quantitative relationship model between the intensity of isotope fluorescence signals and the content of heavy metals in the soil, a multiple linear regression algorithm is used for data analysis to determine the content of heavy metals in the soil.

10. The soil heavy metal pollution detection and remediation hardware system based on isotope tracing according to claim 1, characterized in that: The data storage and transmission module adopts a distributed storage architecture, which disperses data across multiple storage nodes, and its data storage format adopts a unified XML or JSON format.

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