Air-space-ground-well integrated combined pollution in-situ detection method and system

Abstract

The invention provides a space-space-ground-well integrated combined pollution in-situ detection method and system, and the method comprises the steps: obtaining macroscopic and fine spectral data of a target region, and determining a pollution core region in the target region; ground gridding in-situ measurement and underground penetration type in-situ sounding are carried out in the pollution core area, and ground surface and underground multi-mode detection data are obtained; carrying out fusion analysis on the multi-modal detection data and the macroscopic and fine spectral data according to the trained fusion inversion model, and generating a three-dimensional pollution distribution result of the target area; and predicting a pollutant migration trend based on the three-dimensional pollution distribution result and outputting a detection report. According to the method, through cooperative linkage and data fusion of'space-air-ground-well 'multi-dimensional detection means, the problems of limited detection range, single data dimension, fuzzy pollution distribution description, inaccurate migration prediction and the like in a traditional pollution detection method are effectively solved.

Description

Technical Field

[0001] This invention relates to the fields of environmental monitoring and geological exploration technology, specifically to an integrated air-ground-space-well composite pollution in-situ detection method and system. Background Technology

[0002] With the rapid advancement of industrialization and urbanization, the problem of combined pollution of heavy metals and organic matter in soil and groundwater has become increasingly prominent. Under the combined effects of complex geological conditions and human activities, pollutants exhibit characteristics of wide distribution, deep pollution, and complex three-dimensional spatial morphology, posing a serious threat to ecological environment security and human health.

[0003] Current detection technologies for this type of complex pollution still have many technical shortcomings that urgently need to be addressed: ① Single detection dimension: Traditional detection methods mostly focus on single-point sampling on the ground or single-depth monitoring, making it difficult to fully depict the spatial distribution of pollutants; ② Insufficient technology adaptability: Existing detection technologies are mostly designed for single functions, which cannot simultaneously meet the needs of identifying heavy metals and organic matter; ③ Poor detection timeliness: Traditional methods rely on on-site sampling followed by laboratory analysis, which requires cumbersome processes such as sample collection, pretreatment, and instrument testing. The detection cycle usually takes several days to several weeks, which cannot meet the needs of rapid screening of contaminated sites, emergency response to sudden pollution incidents, and real-time monitoring of the remediation process.

[0004] In other words, how to provide an integrated in-situ detection method for complex pollution that combines air, space, ground, and well, enabling rapid screening of contaminated sites and precise capture of complex pollution across the entire space and at multiple scales, from macro to micro and from the surface to the underground, is a technical challenge that urgently needs to be solved in this field. Summary of the Invention

[0005] This invention provides an integrated air-space-ground-well composite pollution in-situ detection method and system to solve at least one of the above-mentioned technical problems.

[0006] In a first aspect, this application provides an integrated air-space-ground-well composite pollution in-situ detection method, the method comprising: Acquire macroscopic and fine spectral data of the target area, and determine the core pollution area within the target area based on the macroscopic and fine spectral data; In the core pollution area, ground grid-based in-situ measurements and underground penetration tests were conducted to obtain multimodal detection data of the surface and underground. The multimodal detection data and the macroscopic and fine spectral data are fused and analyzed based on the trained fusion inversion model to generate the three-dimensional pollution distribution results of the target area. The fusion inversion model is constructed based on the coupling of physicochemical mechanism model and machine learning algorithm. Based on the three-dimensional pollution distribution results, the migration trend of pollutants is predicted and a detection report containing pollutant types, concentration gradients, spatial distribution ranges, and migration paths is output.

[0007] Optionally, acquiring macroscopic and fine spectral data of the target area, and determining the core pollution area within the target area based on the macroscopic and fine spectral data, includes: Macroscopic global spectral data of the target area is acquired by an airborne detection unit, and the macroscopic global spectral data is preliminarily identified using spectral interpretation technology to obtain pollution anomaly areas. The pollution anomaly area is subjected to intensive aerial surveys by a space-based detection unit to obtain finely encrypted spectral data. The finely encrypted spectral data is then spatially registered and fused with the macroscopic global spectral data. Based on a preset pollutant characteristic spectral library, the core pollution area is determined. The pollutant characteristic spectral library includes at least heavy metal characteristic spectra and organic matter characteristic spectra.

[0008] Optionally, the step of conducting ground-based gridded in-situ measurements and underground penetrating in-situ probes within the core pollution area to obtain multimodal surface and subsurface detection data includes: Within the core pollution area, surface detection points are set up according to a preset grid. Multispectral detection is performed on each surface detection point using a ground-based detection unit to obtain surface pollution data for each surface detection point. The surface pollution data includes the types and concentrations of surface pollutants. Based on the surface pollution data of various surface detection points, penetration detection points are selected according to preset conditions. The penetration detection points belong to one or more of the surface detection points. The well-based detection unit conducts in-situ penetration probing at the penetration detection point to obtain underground pollution data and cone tip resistance during the penetration process. The multimodal detection data includes surface pollution data, underground pollution data, and cone tip resistance.

[0009] Optionally, the preset condition is: the concentration of at least one characteristic pollutant at the surface detection point reaches the highest value among all surface detection points.

[0010] Optionally, the pollutants include heavy metals and organic matter. The step of performing multispectral detection at each surface detection point using a ground-based detection unit to obtain surface pollution data for each surface detection point includes: X-rays are emitted to the ground detection points by the ground-based detection unit to collect ground fluorescence signals. The ground fluorescence signals are matched with the pollutant characteristic spectral library to determine the ground heavy metal element type, and the ground heavy metal element concentration is calculated by the characteristic fluorescence intensity inversion formula. The system switches to emit a 532 nm continuous laser and collects the fluorescence spectrum of ground-based organic matter. Based on the fluorescence spectrum of ground-based organic matter, it matches the characteristic spectral library of pollutants to determine the molecular fingerprint information of ground-based organic matter, and calculates the concentration of ground-based organic matter through a standard curve equation. The system switches to emit a 785nm continuous laser and collects ground-based Raman scattering signals of pollutant molecules. The ground-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the ground-based molecular polarizability, the system matches the pollutant characteristic spectral library to determine the ground-based pollutant spectra and calculates the concentrations corresponding to different ground-based pollutant spectra. The ground-based pollutant spectra include ground-based heavy metal element spectra and ground-based organic matter spectra. The surface pollution data consists of the type of heavy metal element in the ground, the concentration of heavy metal element in the ground, the molecular fingerprint information of organic matter in the ground, the concentration of organic matter in the ground, and the form and concentration of pollutants in the ground.

[0011] Optionally, in-situ penetration testing is conducted at the penetration testing points using a well-based detection unit to obtain subsurface contamination data and cone tip resistance at the penetration testing points during the penetration process, including: The well-based detection unit conducts in-situ penetration probing at the penetration detection point. During the penetration process, the cone tip resistance of the well-based detection unit is calculated in real time according to the following formula: q c =N q ·σ' v +N c ·c u ; Where, q c For the cone tip resistance, N q and N c σ' is the bearing capacity coefficient. v For effective overburden stress, c u This refers to the undrained shear strength. During the drilling process, an in-situ underground probe is conducted at each preset interval, and the underground pollution data detected by each in-situ underground probe during the drilling process is obtained.

[0012] Optionally, each of the aforementioned in-situ penetration tests based on the well foundation includes: X-rays are emitted by the well-based detection unit to collect well-based fluorescence signals. Based on the well-based fluorescence signals, the pollutant characteristic spectral library is matched to determine the type of heavy metal elements in the well-based environment. The concentration of heavy metal elements in the well-based environment is calculated using the characteristic fluorescence intensity inversion formula. Switching to emit a 532 nm continuous laser, collecting the fluorescence spectrum of well-based organic matter, matching the pollutant characteristic spectral library based on the fluorescence spectrum of well-based organic matter to determine the molecular fingerprint information of well-based organic matter, and calculating the concentration of well-based organic matter through a standard curve equation; The system switches to emit a 785nm continuous laser to collect well-based Raman scattering signals of pollutant molecules. The well-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the well-based molecular polarizability, the system matches the pollutant characteristic spectral library to determine the well-based pollutant morphology and calculates the concentrations corresponding to different well-based pollutant morphologies. The well-based pollutant morphologies include well-based heavy metal element morphologies and well-based organic matter morphologies. The underground pollution data consists of the type of heavy metal element in the well base, the concentration of heavy metal element in the well base, the molecular fingerprint information of organic matter in the well base, the concentration of organic matter in the well base, and the form and concentration of pollutants in the well base.

[0013] Optionally, the method further includes: During the drilling process, the well-based detection unit monitors the electrochemical signal in real time through a composite electrode array. When the increase of the electrochemical signal exceeds a preset threshold within a preset time period, the well-based detection unit is activated to carry out in-situ penetration probing, and the remediation system is controlled accordingly based on the detection results of pollutants and their concentrations.

[0014] Optionally, the concentration of heavy metal elements in the ground or the concentration of heavy metal elements in the well can be inverted using the following characteristic fluorescence intensity inversion formula: I i =K·C i ·μm(E0)·ω i ·ε i Among them, I i The characteristic X-ray fluorescence intensity of heavy metal element i detected by ground-based or well-based detection units; C i denoted as , where is the concentration of heavy metal element i in the ground-based system or the concentration of heavy metal element i in the well-based system; K is the instrument constant; E0 is the incident X-ray energy of the ground-based detection unit or the well-based detection unit; μ m (E0) is the mass absorption coefficient, ω i For fluorescence yield; ε i For detector efficiency; The concentration of organic matter in the ground or the concentration of organic matter in the well is calculated using the following standard curve equation: C = a·F + b Wherein, F is the fluorescence intensity of the measured fluorescence spectrum of the organic matter, C is the concentration of the organic matter in the ground or the organic matter in the well, and a and b are the model coefficients in the standard curve; Secondly, this application provides an integrated air-space-ground-well composite in-situ pollution detection system, the system comprising: The space-air collaborative detection module is used to acquire macroscopic and fine spectral data of the target area, and to determine the core pollution area within the target area based on the macroscopic and fine spectral data; The well precision detection module is used to conduct in-situ gridded measurements on the ground and in-situ penetration tests in the core pollution area to obtain multimodal detection data of the surface and underground. The intelligent fusion inversion module is used to perform fusion analysis on the multimodal detection data and the macroscopic and fine spectral data based on the trained fusion inversion model, and generate the three-dimensional pollution distribution results of the target area. The fusion inversion model is constructed based on the coupling of a physicochemical mechanism model and a machine learning algorithm. The prediction output module predicts the migration trend of pollutants based on the three-dimensional pollution distribution results and outputs a detection report containing pollutant types, concentration gradients, spatial distribution ranges, and migration paths.

[0015] Beneficial effects: The integrated air-ground-space-well in-situ pollution detection method provided in this application first acquires macroscopic and fine spectral data of the target area. Based on this data, the core pollution zone within the target area is determined, enabling rapid screening and focusing of the pollution zone and providing a clear target for subsequent precise detection. Then, within the core pollution zone, ground-based gridded in-situ measurements and downhole penetrating in-situ probes are conducted to acquire multimodal surface and subsurface detection data. This acquisition of surface and subsurface multimodal data constructs a three-dimensional data dimension for pollution detection, compensating for the deficiencies in data integrity inherent in single surface or subsurface detection methods. Finally, a trained fusion inversion model is used to fuse and analyze the multimodal detection data and macroscopic and fine spectral data, generating a three-dimensional pollution distribution result for the target area. The fusion inversion model is based on physicochemical... The mechanism model and machine learning algorithm are coupled to construct a system where the physicochemical mechanism model provides a solid theoretical foundation for data inversion, ensuring the scientific rigor and rationality of the inversion process. Meanwhile, the machine learning algorithm, with its powerful data mining and fitting capabilities, improves the accuracy and efficiency of the inversion results. The coupling effect of the two enables the three-dimensional pollution distribution results to not only reflect the objective physicochemical laws of pollution but also accurately capture subtle features in the data, significantly improving the accuracy of pollution distribution characterization. Finally, based on the three-dimensional pollution distribution results, the system predicts pollutant migration trends and outputs a detection report containing pollutant types, concentration gradients, spatial distribution ranges, and migration paths. This provides a comprehensive and forward-looking decision-making basis for the formulation of pollution control solutions, significantly improving the pertinence and effectiveness of pollution control and possessing strong engineering practice value. The integrated air-ground-space-well composite pollution in-situ detection method provided in this application constructs a complete technical system from macro-level investigation to precise positioning, from surface detection to underground exploration, and from data inversion to trend prediction through the synergistic linkage and data fusion of multi-dimensional detection methods of "air-ground-space-well". It effectively solves the problems of limited detection range, single data dimension, vague pollution distribution characterization and inaccurate migration prediction in traditional pollution detection methods. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart of an integrated air-space-ground-well composite pollution in-situ detection method provided in this application; Figure 2 A schematic diagram of the architecture for the integrated air-space-ground-well composite pollution in-situ detection provided in this application; Figure 3This is a schematic diagram of a structural design of the integrated air-ground-space-well composite pollution in-situ detection system provided in this application. Detailed Implementation

[0018] This application provides an integrated air-space-ground-well composite pollution in-situ detection method and system to solve at least one of the above-mentioned technical problems.

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. The naming or numbering of steps appearing in this application does not imply that the steps in the method flow must be performed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical purpose, as long as the same or similar technical effect is achieved.

[0021] The module division described in this application is a logical division. In practical applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between modules shown or discussed may be through some interfaces, and the indirect coupling or communication connection between modules may be electrical or other similar forms, none of which are limited in this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules. Some or all of the modules may be selected to achieve the purpose of the solution in this application according to actual needs.

[0022] Next, please refer to Figure 1-2 , Figure 1This is a flowchart illustrating an integrated space-air-ground-well in-situ detection method for composite pollution according to an embodiment of the present invention. As an embodiment of the integrated space-air-ground-well in-situ detection method for composite pollution provided by the present invention, the method includes the following steps S110 to S140: Step S110: Obtain macroscopic and fine spectral data of the target area, and determine the core pollution area within the target area based on the macroscopic and fine spectral data; As one feasible approach, the acquisition of macroscopic and fine spectral data of the target area in step S110 above, and the determination of the pollution core area within the target area based on the macroscopic and fine spectral data, specifically includes the following: Macroscopic global spectral data of the target area is obtained by using an airborne detection unit. Spectral interpretation technology is then used to preliminarily identify the pollution anomaly area. By conducting intensive aerial surveys of the pollution anomaly area using space-based detection units, fine-grained spectral data is obtained. This fine-grained spectral data is then spatially registered and fused with macroscopic global spectral data. Based on a pre-set pollutant characteristic spectral library, the core pollution area is determined. This pollutant characteristic spectral library includes at least heavy metal characteristic spectra and organic matter characteristic spectra.

[0023] Specifically, macroscopic global spectral data of the target area was acquired and preliminarily identified using airborne detection units. Leveraging the wide coverage and high efficiency of airborne detection, rapid scanning of large target areas and preliminary screening of pollution anomalies were achieved, defining key areas for subsequent detection. Building upon this, spaceborne detection units conducted intensive aerial surveys of pollution anomalies to acquire refined and detailed spectral data. Utilizing the high resolution and flexibility of spaceborne detection, precise detection was carried out in pollution anomalies, significantly improving the precision and information content of the spectral data for this region. Subsequently, the refined and detailed spectral data was spatially registered and fused with the macroscopic global spectral data, achieving data complementarity between "macroscopic coverage" and "refined focus." This preserved the regional integrity of the macroscopic data while incorporating the local accuracy of the refined data, resulting in a dual improvement in both spatial and spectral resolution. Simultaneously, the core pollution area was identified based on a pre-set pollutant characteristic spectral library containing the characteristic spectra of heavy metals and organic matter. Through explicit matching of pollutant spectral features, preliminary identification of pollution types and a preliminary judgment of pollution levels were achieved, avoiding misjudgments caused by relying solely on spectral anomalies and ensuring the scientific accuracy of the pollution core area delineation. This process, through a progressive detection strategy of "macroscopic screening, fine-grained focusing, and data fusion," efficiently and accurately pinpointed the core pollution area, providing precise target areas for subsequent surface and downhole exploration, and significantly improving the efficiency and accuracy of the entire detection process.

[0024] Step S120: Conduct in-situ ground gridding measurements and in-situ penetration tests in the core pollution area to obtain multimodal surface and subsurface detection data; As an achievable method, the above step S120 involves conducting ground gridded in-situ measurements and underground penetration in-situ probes within the core pollution area to obtain multimodal surface and subsurface detection data, specifically including the following sub-steps (1)-(4): (1) Surface detection points are set up in the core pollution area according to the preset grid. Multispectral detection is carried out on each surface detection point through the ground detection unit to obtain surface pollution data of each surface detection point. The surface pollution data includes the types and concentrations of surface pollutants. Specifically, the grid can be laid out in a preset grid size of 5m×5m to 20m×20m, which can be set according to actual needs.

[0025] This application deploys surface detection points in a pre-defined grid within the core pollution area and conducts multispectral detection. The gridded deployment ensures the comprehensiveness and uniformity of surface detection, avoids the existence of blind spots, and enables the acquired surface pollution data to fully reflect the surface pollution distribution characteristics of the core pollution area, providing comprehensive surface data support for subsequent underground detection.

[0026] As an achievable method, pollutants include heavy metals and organic matter. The sub-step (1) above involves multispectral detection of each surface detection point using a ground-based detection unit to obtain surface pollution data for each surface detection point, specifically including the following: X-rays are emitted from ground-based detection units to detect points on the ground surface to collect ground fluorescence signals. Based on the ground fluorescence signals, a pollutant characteristic spectral library is matched to determine the types of heavy metal elements in the ground, and the concentration of heavy metal elements in the ground is calculated by the characteristic fluorescence intensity inversion formula. The system switches to emit a 532 nm continuous laser and collects the fluorescence spectrum of ground-based organic matter. Based on the fluorescence spectrum of ground-based organic matter, it matches the characteristic spectral library of pollutants to determine the molecular fingerprint information of ground-based organic matter, and calculates the concentration of ground-based organic matter through a standard curve equation. The system switches between emitting a 785nm continuous laser and collecting ground-based Raman scattering signals of pollutant molecules. The ground-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the ground-based molecular polarizability, a characteristic spectral library of pollutants is matched to determine the ground-based pollutant spectra, and the concentrations corresponding to different ground-based pollutant spectra are calculated. The ground-based pollutant spectra include the ground-based heavy metal element spectra and the ground-based organic matter spectra. Among them, the types of heavy metal elements in the ground, the concentrations of heavy metal elements in the ground, the molecular fingerprint information of organic matter in the ground, the concentration of organic matter in the ground, and the forms and concentrations of pollutants in the ground constitute the surface pollution data.

[0027] Specifically, the high sensitivity and specificity of X-ray fluorescence detection technology enable rapid and accurate identification of heavy metal pollutants. By switching to a 532nm continuous laser to collect the fluorescence spectrum of organic matter, the unique fluorescence characteristics of organic matter are used to accurately identify molecular fingerprint information. Combined with standard curve equations to calculate concentration, organic matter detection can not only clearly identify the specific type of pollutant but also achieve quantitative analysis of concentration, solving the problem of "qualitative but not quantitative" or low quantitative accuracy in traditional organic matter detection. By switching to a 785nm continuous laser to collect Raman scattering light signals to analyze pollutant morphology, the gap in traditional detection for characterizing pollutant morphology is filled. The morphology of pollutants directly affects their toxicity, mobility, and bioavailability—for example, different forms of the same heavy metal element (including oxidized and combined states) have vastly different toxicities and migration capabilities. By calculating molecular polarizability using the Raman scattering intensity formula and matching it with a spectral library, different forms of heavy metals and organic matter can be accurately distinguished, and the concentration of each form can be calculated. This makes surface pollution data not only include "what it is" and "how much it is," but also "what state it is in," providing crucial information for subsequent analysis of the environmental risks and migration patterns of pollutants.

[0028] (2) Based on the surface pollution data of each surface detection point, select the penetration detection point according to the preset conditions. The penetration detection point belongs to one or more of the surface detection points. As one possible approach, the preset condition is that the concentration of at least one characteristic pollutant at each surface detection point reaches the highest value among all surface detection points.

[0029] Specifically, surface detection points with the highest concentrations of characteristic pollutants often directly reflect pollution sources or areas of concentrated pollution, and their underground areas are highly likely to contain even more severe pollution or more complex pollution distributions. Using such points as penetration detection points allows underground detection to directly focus on the most severely polluted and representative areas, thereby obtaining the most critical underground pollution information with minimal detection costs. This avoids ineffective detection in areas with lower levels of pollution and significantly improves the utilization efficiency of detection resources.

[0030] (3) Conduct in-situ penetration exploration at the penetration exploration point through the well base exploration unit to obtain underground pollution data and cone tip resistance during the penetration process. The multi-modal exploration data includes surface pollution data, underground pollution data and cone tip resistance.

[0031] As one feasible approach, the sub-step (3) above, which involves conducting in-situ penetration probing at the penetration detection point using a well-based detection unit to obtain underground contamination data and cone tip resistance at the penetration detection point during the penetration process, specifically includes the following: In-situ penetration probing is conducted at the penetration points using a well-based detection unit. During the penetration process, the cone tip resistance of the well-based detection unit is calculated in real time according to the following formula: q c =N q ·σ' v +N c ·c u ; Where, q c For the cone tip resistance, N q and N c σ' is the bearing capacity coefficient. v For effective overburden stress, c u This refers to the undrained shear strength. During the drilling process, an in-situ underground probe is conducted at each preset interval, and the underground pollution data detected by each in-situ underground probe during the drilling process is obtained.

[0032] Among them, cone tip resistance can reflect the density and mechanical properties of the strata, which directly affect the infiltration and migration rate of pollutants, thus making the pollution detection results closer to the actual geological environment and providing more reliable data support for the prediction of pollution migration trends.

[0033] As a feasible approach, each in-situ well-based penetration test can obtain a set of subsurface contamination data. The in-situ well-based penetration test process specifically includes the following: X-rays are emitted by the well-based detection unit to collect well-based fluorescence signals. Based on the well-based fluorescence signals, the pollutant characteristic spectral library is matched to determine the type of heavy metal elements in the well-based system. The concentration of heavy metal elements in the well-based system is calculated by using the characteristic fluorescence intensity inversion formula. The system switches to emit a 532 nm continuous laser to collect the fluorescence spectrum of well-based organic matter. Based on the fluorescence spectrum of well-based organic matter, it matches the characteristic spectral library of pollutants to determine the molecular fingerprint information of well-based organic matter, and calculates the concentration of well-based organic matter through a standard curve equation. The system switches to emit a 785nm continuous laser to collect the well-based Raman scattering signal of pollutant molecules. The well-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the well-based molecular polarizability, a characteristic spectral library of pollutants is matched to determine the well-based pollutant spectra, and the concentrations corresponding to different well-based pollutant spectra are calculated. The well-based pollutant spectra include well-based heavy metal element spectra and well-based organic spectra. Among them, the types of heavy metal elements in the well base, the concentration of heavy metal elements in the well base, the molecular fingerprint information of organic matter in the well base, the concentration of organic matter in the well base, and the form and concentration of pollutants in the well base constitute underground pollution data.

[0034] Specifically, this claim extends the precise analysis technology of ground-based multispectral detection to downhole detection, achieving the homogeneity and comparability of underground pollution data with surface pollution data. This solves the problems of inconsistent data standards and difficulty in integrating analysis results in traditional well-based detection, while ensuring the comprehensiveness and high accuracy of underground pollution data. Through this precise detection, underground pollution data also forms a complete information system of "type-concentration-morphology," echoing surface data and constructing a complete pollution information chain from the surface to the underground in the core pollution area. This provides continuous and accurate data support for the subsequent generation of three-dimensional pollution distribution results. Data detected by ground-based and well-based detection units enables the three-dimensional distribution results to show the distribution and variation patterns of pollutants in both horizontal and vertical directions, significantly improving the three-dimensionality and realism of the pollution distribution characterization.

[0035] As one feasible method, the concentration of heavy metal elements in the ground or well can be inverted using the following characteristic fluorescence intensity inversion formula: I i =K·C i ·μm(E0)·ω i ·ε i Among them, I i The characteristic X-ray fluorescence intensity of heavy metal element i detected by ground-based or well-based detection units; C i denoted as , where is the concentration of heavy metal element i in the ground-based system or the concentration of heavy metal element i in the well-based system; K is the instrument constant of the ground-based detection unit or the well-based detection unit; E0 is the incident X-ray energy of the ground-based detection unit or the well-based detection unit; μ m (E0) is the mass absorption coefficient, ω i The fluorescence yield in ground-based or well-based detection units; ε i This refers to the efficiency of the detectors in the ground-based or well-based detection unit.

[0036] As one feasible method, the concentration of organic matter in the ground or well can be calculated using the following standard curve equation: C = a·F + b Where F is the fluorescence intensity of the organic matter fluorescence spectrum measured by the ground-based detection unit or the well-based detection unit, C is the concentration of organic matter in the ground or the well, and a and b are the model coefficients in the standard curve. As one feasible approach, the molecular polarizability of the ground or well can be inversely calculated using the following Raman scattering intensity formula: I Raman ∝I0·ν 4 ·(∂α / ∂Q) 2 Among them, I RamanI0 is the Raman scattering intensity of the ground-based or well-based detection unit; I0 is the incident laser intensity of the ground-based or well-based detection unit; ν is the Raman scattering frequency or Raman shift of the ground-based or well-based detection unit; α is the molecular polarizability of the ground-based or well-based detection unit; Q is the molecular vibration coordinate; ∂α / ∂Q is the rate of change of molecular polarizability.

[0037] Step S130: Based on the trained fusion inversion model, multimodal detection data and macroscopic and fine spectral data are fused and analyzed to generate three-dimensional pollution distribution results of the target area. The fusion inversion model is constructed by coupling a physicochemical mechanism model and a machine learning algorithm. As one feasible approach, the airborne detection unit is a spaceborne hyperspectral remote sensing device, the space-based detection unit is a UAV equipped with a multispectral or hyperspectral sensor, the ground-based detection unit is a portable in-situ detector integrating an X-ray fluorescence detection module, a laser fluorescence module, and a Raman spectroscopy module, and the well-based detection unit is a penetrating probe integrating an X-ray fluorescence detection module, a laser fluorescence module, a Raman spectroscopy module, and a cone-tip drag sensor.

[0038] Step S140: Based on the three-dimensional pollution distribution results, predict the pollutant migration trend and output a detection report containing pollutant types, concentration gradients, spatial distribution ranges and migration paths.

[0039] As one possible approach, the method provided in this application further includes the following steps: During the penetration process, the well-based detection unit monitors the electrochemical signal in real time using a composite electrode array. When the increase of the electrochemical signal exceeds a preset threshold within a preset time period, the well-based detection unit is activated to carry out in-situ penetration probing, and the remediation system is controlled accordingly based on the detection results of pollutants and their concentrations.

[0040] The ground-based detection unit also integrates an electrochemical module, which can monitor the electrochemical signals at surface detection points in real time. When the increase of the electrochemical signal exceeds a preset threshold within a preset time period, the ground-based detection unit is activated to conduct in-situ detection at the surface detection points. The well-based detection unit also integrates an electrochemical module, used to monitor the electrochemical signals in real time during the penetration process based on a composite electrode array. When the increase of the electrochemical signal exceeds a preset threshold within a preset time period, the well-based detection unit is activated to conduct in-situ penetration testing, and the remediation system is controlled accordingly based on the detection results of pollutants and their concentrations.

[0041] Specifically, by using a composite electrode array to monitor electrochemical signals in real time, these signals can sensitively reflect the electrochemical activity of pollutants in the underground environment. For example, the presence of heavy metal ions can cause changes in the potential or current of the electrochemical signal, and the redox reactions of organic matter can also cause signal fluctuations. By monitoring these signal changes, anomalies in underground pollution can be detected in real time. When the signal increase exceeds a preset threshold, it indicates that there may be high concentrations of pollution or pollution diffusion in the detection area. At this time, penetrating in-situ probing is initiated, realizing the transformation from "timed detection" to "on-demand detection," significantly improving the detection sensitivity and response speed of areas with abnormal pollution, and avoiding missing key pollution information. Based on the detection results of pollutants and their concentrations, a closed-loop linkage mechanism of "detection-remediation" is constructed. Traditional detection and remediation are often separate, with remediation plans formulated after detection, leading to untimely remediation. However, this technology can activate the remediation system based on real-time detection data as soon as an abnormal pollution is detected. For example, a heavy metal chelation remediation module can be activated for areas with high concentrations of heavy metal pollution, and a catalytic degradation remediation module can be activated for areas with organic pollution. This collaborative mechanism enables pollution control to respond quickly to detection results, significantly shortening the "detection-remediation" time interval and improving the efficiency and effectiveness of pollution control, especially suitable for areas with a high risk of pollution spread.

[0042] In summary, the integrated air-ground-space-well in-situ pollution detection method provided in this application first acquires macroscopic and fine spectral data of the target area, and then determines the core pollution area within the target area based on the macroscopic and fine spectral data, achieving rapid screening and focusing of the pollution area and providing a clear target for subsequent precise detection. Next, in-situ gridded ground measurements and in-situ penetrating borehole probes are conducted within the core pollution area to acquire multimodal surface and subsurface detection data, achieving the acquisition of multimodal surface and subsurface data and constructing a three-dimensional data dimension for pollution detection, compensating for the deficiencies in data integrity of single surface or subsurface detection. Finally, a trained fusion inversion model is used to fuse and analyze the multimodal detection data and macroscopic and fine spectral data to generate a three-dimensional pollution distribution result for the target area. The fusion inversion model is based on physical... The chemical mechanism model and machine learning algorithm are coupled to construct a system where the physicochemical mechanism model provides a solid theoretical foundation for data inversion, ensuring the scientific rigor and rationality of the inversion process. Meanwhile, the machine learning algorithm, with its powerful data mining and fitting capabilities, improves the accuracy and efficiency of the inversion results. This coupling effect allows the three-dimensional pollution distribution results to reflect both the objective physicochemical laws of pollution and accurately capture subtle features in the data, significantly improving the accuracy of pollution distribution characterization. Finally, based on the three-dimensional pollution distribution results, the system predicts pollutant migration trends and outputs a detection report containing pollutant types, concentration gradients, spatial distribution ranges, and migration paths. This provides a comprehensive and forward-looking decision-making basis for the formulation of pollution control solutions, significantly improving the targeting and effectiveness of pollution control and possessing strong engineering practice value. The integrated air-ground-space-well composite pollution in-situ detection method provided in this application constructs a complete technical system from macro-level investigation to precise positioning, from surface detection to underground exploration, and from data inversion to trend prediction through the synergistic linkage and data fusion of multi-dimensional detection methods of "air-ground-space-well". It effectively solves the problems of limited detection range, single data dimension, vague pollution distribution characterization and inaccurate migration prediction in traditional pollution detection methods.

[0043] The following describes an embodiment of the integrated air-ground-space-well composite pollution in-situ detection system of the present invention.

[0044] Please see Figure 3 , Figure 3 This is a schematic diagram of an embodiment of the integrated space-air-ground-well in-situ pollution detection system 300 of the present invention. The integrated space-air-ground-well in-situ pollution detection system 300 includes: The space-air collaborative detection module 301 is used to acquire macroscopic and fine spectral data of the target area and determine the core pollution area within the target area based on the macroscopic and fine spectral data; The well precision detection module 302 is used to conduct in-situ gridded measurements on the ground and in-situ penetration tests in the core pollution area to obtain multimodal detection data of the surface and underground. The intelligent fusion inversion module 303 is used to perform fusion analysis on multimodal detection data and macroscopic and fine spectral data based on the trained fusion inversion model to generate three-dimensional pollution distribution results of the target area. The fusion inversion model is constructed based on the coupling of physicochemical mechanism model and machine learning algorithm. The prediction output module 304 predicts the migration trend of pollutants based on the three-dimensional pollution distribution results and outputs a detection report containing pollutant types, concentration gradients, spatial distribution ranges and migration paths.

[0045] Specifically, the integrated air-space-ground-well in-situ pollution detection system provided in this application first acquires macroscopic and fine spectral data of the target area through the air-space collaborative detection module 301. Based on the macroscopic and fine spectral data, the core pollution area within the target area is determined, achieving rapid screening and focusing of the pollution area and providing a clear target for subsequent precise detection. Then, the well precision detection module 302 conducts ground gridded in-situ measurements and downhole penetration in-situ probes within the core pollution area to acquire multimodal detection data of the surface and subsurface, realizing the acquisition of multimodal data of the surface and subsurface, constructing a three-dimensional data dimension for pollution detection, and making up for the deficiencies in data integrity of single surface or subsurface detection. Then, the intelligent fusion inversion module 303 performs fusion analysis on the multimodal detection data and macroscopic and fine spectral data according to the trained fusion inversion model to generate a three-dimensional pollution profile of the target area. The results show that the fusion inversion model is constructed by coupling a physicochemical mechanism model and a machine learning algorithm. The physicochemical mechanism model provides a solid theoretical foundation for data inversion, ensuring the scientific and rational nature of the inversion process, while the machine learning algorithm, with its powerful data mining and fitting capabilities, improves the accuracy and efficiency of the inversion results. The coupling effect of the two enables the three-dimensional pollution distribution results to not only reflect the objective physicochemical laws of pollution, but also accurately capture the subtle features in the data, greatly improving the accuracy of pollution distribution characterization. Finally, the prediction output module 304 predicts the migration trend of pollutants based on the three-dimensional pollution distribution results and outputs a detection report containing pollutant types, concentration gradients, spatial distribution ranges, and migration paths. This provides a comprehensive and forward-looking decision-making basis for the formulation of pollution control solutions, significantly improving the pertinence and effectiveness of pollution control, and has extremely strong engineering practice value. The integrated air-ground-space-well composite pollution in-situ detection method provided in this application constructs a complete technical system from macro-level investigation to precise positioning, from surface detection to underground exploration, and from data inversion to trend prediction through the synergistic linkage and data fusion of multi-dimensional detection methods of "air-ground-space-well". It effectively solves the problems of limited detection range, single data dimension, vague pollution distribution characterization and inaccurate migration prediction in traditional pollution detection methods.

[0046] As an exemplary implementation, the space-air collaborative detection module 301 is also used for: Macroscopic global spectral data of the target area is obtained by using an airborne detection unit. Spectral interpretation technology is then used to preliminarily identify the pollution anomaly area. By conducting intensive aerial surveys of the pollution anomaly area using space-based detection units, fine-grained spectral data is obtained. This fine-grained spectral data is then spatially registered and fused with macroscopic global spectral data. Based on a pre-set pollutant characteristic spectral library, the core pollution area is determined. This pollutant characteristic spectral library includes at least heavy metal characteristic spectra and organic matter characteristic spectra.

[0047] As an exemplary implementation, the well precision detection module 302 is also used for: Within the core pollution area, surface detection points are set up according to a preset grid. Multispectral detection is performed on each surface detection point through ground-based detection units to obtain surface pollution data for each surface detection point. The surface pollution data includes the types and concentrations of surface pollutants. Based on surface pollution data from various surface detection points, penetration detection points are selected according to preset conditions. Each penetration detection point belongs to one or more of the surface detection points. The well-based detection unit is used to conduct in-situ penetration tests at the penetration detection points to obtain underground pollution data and cone tip resistance during the penetration process. The multimodal detection data includes surface pollution data, underground pollution data, and cone tip resistance.

[0048] As one feasible approach, the preset condition is that the concentration of at least one characteristic pollutant at each surface detection point reaches the highest value among all surface detection points.

[0049] As an exemplary implementation, the well precision detection module 302 is also used for: X-rays are emitted from ground-based detection units to detect points on the ground surface to collect ground fluorescence signals. Based on the ground fluorescence signals, a pollutant characteristic spectral library is matched to determine the types of heavy metal elements in the ground, and the concentration of heavy metal elements in the ground is calculated by the characteristic fluorescence intensity inversion formula. The system switches to emit a 532 nm continuous laser and collects the fluorescence spectrum of ground-based organic matter. Based on the fluorescence spectrum of ground-based organic matter, it matches the characteristic spectral library of pollutants to determine the molecular fingerprint information of ground-based organic matter, and calculates the concentration of ground-based organic matter through a standard curve equation. The system switches between emitting a 785nm continuous laser and collecting ground-based Raman scattering signals of pollutant molecules. The ground-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the ground-based molecular polarizability, a characteristic spectral library of pollutants is matched to determine the ground-based pollutant spectra, and the concentrations corresponding to different ground-based pollutant spectra are calculated. The ground-based pollutant spectra include the ground-based heavy metal element spectra and the ground-based organic matter spectra. Among them, the types of heavy metal elements in the ground, the concentrations of heavy metal elements in the ground, the molecular fingerprint information of organic matter in the ground, the concentration of organic matter in the ground, and the forms and concentrations of pollutants in the ground constitute the surface pollution data.

[0050] As an example implementation, each in-situ penetration test of the well base includes: X-rays are emitted by the well-based detection unit to collect well-based fluorescence signals. Based on the well-based fluorescence signals, the pollutant characteristic spectral library is matched to determine the type of heavy metal elements in the well-based system. The concentration of heavy metal elements in the well-based system is calculated by using the characteristic fluorescence intensity inversion formula. The system switches to emit a 532 nm continuous laser to collect the fluorescence spectrum of well-based organic matter. Based on the fluorescence spectrum of well-based organic matter, it matches the characteristic spectral library of pollutants to determine the molecular fingerprint information of well-based organic matter, and calculates the concentration of well-based organic matter through a standard curve equation. The system switches to emit a 785nm continuous laser to collect the well-based Raman scattering signal of pollutant molecules. The well-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the well-based molecular polarizability, a characteristic spectral library of pollutants is matched to determine the well-based pollutant spectra, and the concentrations corresponding to different well-based pollutant spectra are calculated. The well-based pollutant spectra include well-based heavy metal element spectra and well-based organic spectra. Among them, the types of heavy metal elements in the well base, the concentration of heavy metal elements in the well base, the molecular fingerprint information of organic matter in the well base, the concentration of organic matter in the well base, and the form and concentration of pollutants in the well base constitute underground pollution data.

[0051] As one possible approach, the well precision detection module is also used for: During the penetration process, the electrochemical signal is monitored in real time by a composite electrode array. When the increase of the electrochemical signal exceeds the preset threshold within a preset time period, the well-based detection unit is activated to carry out in-situ penetration probing, and the remediation system is controlled accordingly based on the detection results of pollutants and their concentrations.

[0052] As one feasible method, the concentration of heavy metal elements in the ground or well can be inverted using the following characteristic fluorescence intensity inversion formula: I i =K·C i ·μm(E0)·ω i ·ε i Among them, I i The characteristic X-ray fluorescence intensity of heavy metal element i detected by ground-based or well-based detection units; C i denoted as , where is the concentration of heavy metal element i in the ground-based system or the concentration of heavy metal element i in the well-based system; K is the instrument constant; E0 is the incident X-ray energy of the ground-based detection unit or the well-based detection unit; μ m (E0) is the mass absorption coefficient, ωi For fluorescence yield; ε i For detector efficiency; The concentration of organic matter in the ground or the concentration of organic matter in the well can be calculated using the following standard curve equation: C = a·F + b Where F is the fluorescence intensity of the measured organic matter fluorescence spectrum, C is the concentration of organic matter in the ground or the concentration of organic matter in the well, and a and b are the model coefficients in the standard curve. The molecular polarizability of the ground or well can be inversely calculated using the following Raman scattering intensity formula: I Raman ∝I0·ν 4 ·(∂α / ∂Q) 2 Among them, I Raman I0 is the Raman scattering intensity of the ground-based or well-based detection unit; I0 is the incident laser intensity of the ground-based or well-based detection unit; ν is the Raman scattering frequency or Raman shift of the ground-based or well-based detection unit; α is the molecular polarizability of the ground-based or well-based detection unit; Q is the molecular vibration coordinate; ∂α / ∂Q is the rate of change of molecular polarizability.

[0053] As one feasible approach, the airborne detection unit is a spaceborne hyperspectral remote sensing device, the space-based detection unit is a UAV equipped with a multispectral or hyperspectral sensor, the ground-based detection unit is a portable in-situ detector integrating an X-ray fluorescence detection module, a laser fluorescence module, and a Raman spectroscopy module, and the well-based detection unit is a penetrating probe integrating an X-ray fluorescence detection module, a laser fluorescence module, a Raman spectroscopy module, and a cone-tip drag sensor.

[0054] The ground-based detection unit also integrates an electrochemical module, which can monitor the electrochemical signals at surface detection points in real time. When the increase of the electrochemical signal exceeds a preset threshold within a preset time period, the ground-based detection unit is activated to conduct in-situ detection at the surface detection points. The well-based detection unit also integrates an electrochemical module, used to monitor the electrochemical signals in real time during the penetration process based on a composite electrode array. When the increase of the electrochemical signal exceeds a preset threshold within a preset time period, the well-based detection unit is activated to conduct in-situ penetration testing, and the remediation system is controlled accordingly based on the detection results of pollutants and their concentrations.

[0055] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0056] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0057] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0058] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for in-situ detection of pollution integrating air, space, ground, and well, characterized in that, The method includes: Acquire macroscopic and fine spectral data of the target area, and determine the core pollution area within the target area based on the macroscopic and fine spectral data; In the core pollution area, ground grid-based in-situ measurements and underground penetration tests were conducted to obtain multimodal detection data of the surface and subsurface. The multimodal detection data and the macroscopic and fine spectral data are fused and analyzed based on the trained fusion inversion model to generate the three-dimensional pollution distribution results of the target area. The fusion inversion model is constructed based on the coupling of physicochemical mechanism model and machine learning algorithm. Based on the three-dimensional pollution distribution results, the migration trend of pollutants is predicted and a detection report containing pollutant types, concentration gradients, spatial distribution ranges, and migration paths is output.

2. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 1, characterized in that, The process of acquiring macroscopic and fine spectral data of the target area, and determining the core pollution area within the target area based on the macroscopic and fine spectral data, includes: Macroscopic global spectral data of the target area is acquired by an airborne detection unit, and the macroscopic global spectral data is preliminarily identified using spectral interpretation technology to obtain pollution anomaly areas. The pollution anomaly area is subjected to intensive aerial surveys by a space-based detection unit to obtain finely encrypted spectral data. The finely encrypted spectral data is then spatially registered and fused with the macroscopic global spectral data. Based on a preset pollutant characteristic spectral library, the core pollution area is determined. The pollutant characteristic spectral library includes at least heavy metal characteristic spectra and organic matter characteristic spectra.

3. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 2, characterized in that, The process involves conducting in-situ ground-based gridded measurements and underground in-situ penetration tests within the core pollution area to acquire multimodal surface and subsurface data, including: Within the core pollution area, surface detection points are set up according to a preset grid. Multispectral detection is performed on each surface detection point using a ground-based detection unit to obtain surface pollution data for each surface detection point. The surface pollution data includes the types and concentrations of surface pollutants. Based on the surface pollution data of various surface detection points, penetration detection points are selected according to preset conditions. The penetration detection points belong to one or more of the surface detection points. The well-based detection unit conducts in-situ penetration probing at the penetration detection point to obtain underground pollution data and cone tip resistance during the penetration process. The multimodal detection data includes surface pollution data, underground pollution data, and cone tip resistance.

4. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 3, characterized in that, The preset condition is that the concentration of at least one characteristic pollutant at each of the surface detection points reaches the highest value among all surface detection points.

5. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 3, characterized in that, The pollutants include heavy metals and organic matter. The method involves using a ground-based detection unit to perform multispectral detection at each surface detection point to obtain surface pollution data for each point, including: X-rays are emitted to the ground detection points by the ground-based detection unit to collect ground fluorescence signals. The ground fluorescence signals are matched with the pollutant characteristic spectral library to determine the ground heavy metal element type, and the ground heavy metal element concentration is calculated by the characteristic fluorescence intensity inversion formula. The system switches to emit a 532 nm continuous laser and collects the fluorescence spectrum of ground-based organic matter. Based on the fluorescence spectrum of ground-based organic matter, it matches the characteristic spectral library of pollutants to determine the molecular fingerprint information of ground-based organic matter, and calculates the concentration of ground-based organic matter through a standard curve equation. The system switches to emit a 785nm continuous laser and collects ground-based Raman scattering signals of pollutant molecules. The ground-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the ground-based molecular polarizability, the system matches the pollutant characteristic spectral library to determine the ground-based pollutant spectra and calculates the concentrations corresponding to different ground-based pollutant spectra. The ground-based pollutant spectra include ground-based heavy metal element spectra and ground-based organic matter spectra. The surface pollution data consists of the type of heavy metal element in the ground, the concentration of heavy metal element in the ground, the molecular fingerprint information of organic matter in the ground, the concentration of organic matter in the ground, and the form and concentration of pollutants in the ground.

6. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 5, characterized in that, The well-based detection unit conducts in-situ penetration probing at the penetration detection points to obtain subsurface contamination data and cone tip resistance during the penetration process, including: The well-based detection unit conducts in-situ penetration probing at the penetration detection point. During the penetration process, the cone tip resistance of the well-based detection unit is calculated in real time according to the following formula: q c =N q ·in v +N c ·c u ; Where, q c For the cone tip resistance, N q and N c σ' is the bearing capacity coefficient. v For effective overburden stress, c u This refers to the undrained shear strength. During the drilling process, an in-situ underground probe is conducted at each preset interval, and the underground pollution data detected by each in-situ underground probe during the drilling process is obtained.

7. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 6, characterized in that, Each in-situ penetration test of the well base includes: X-rays are emitted by the well-based detection unit to collect well-based fluorescence signals. Based on the well-based fluorescence signals, the pollutant characteristic spectral library is matched to determine the type of heavy metal elements in the well-based environment. The concentration of heavy metal elements in the well-based environment is calculated using the characteristic fluorescence intensity inversion formula. Switching to emit a 532 nm continuous laser, collecting the fluorescence spectrum of well-based organic matter, matching the pollutant characteristic spectral library based on the fluorescence spectrum of well-based organic matter to determine the molecular fingerprint information of well-based organic matter, and calculating the concentration of well-based organic matter through a standard curve equation; The system switches to emit a 785nm continuous laser to collect well-based Raman scattering signals of pollutant molecules. The well-based molecular polarizability is calculated using the Raman scattering intensity formula. Based on the well-based molecular polarizability, the system matches the pollutant characteristic spectral library to determine the well-based pollutant morphology and calculates the concentrations corresponding to different well-based pollutant morphologies. The well-based pollutant morphologies include well-based heavy metal element morphologies and well-based organic matter morphologies. The underground pollution data consists of the type of heavy metal element in the well base, the concentration of heavy metal element in the well base, the molecular fingerprint information of organic matter in the well base, the concentration of organic matter in the well base, and the form and concentration of pollutants in the well base.

8. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 7, characterized in that, The method further includes: During the drilling process, the well-based detection unit monitors the electrochemical signal in real time through a composite electrode array. When the increase of the electrochemical signal exceeds a preset threshold within a preset time period, the well-based detection unit is activated to carry out in-situ penetration probing, and the remediation system is controlled accordingly based on the detection results of pollutants and their concentrations.

9. The integrated air-space-ground-well composite pollution in-situ detection method according to claim 7, characterized in that, The concentration of heavy metal elements in the ground or the concentration of heavy metal elements in the well can be inverted using the following characteristic fluorescence intensity inversion formula: I i =K·C i ·μm(E0)·ω i ·e i Among them, I i The characteristic X-ray fluorescence intensity of heavy metal element i detected by ground-based or well-based detection units; C i denoted as , where is the concentration of heavy metal element i in the ground-based system or the concentration of heavy metal element i in the well-based system; K is the instrument constant; E0 is the incident X-ray energy of the ground-based detection unit or the well-based detection unit; μ m (E0) is the mass absorption coefficient, ω i For fluorescence yield; ε i For detector efficiency; The concentration of organic matter in the ground or the concentration of organic matter in the well is calculated using the following standard curve equation: C = a·F + b Wherein, F is the fluorescence intensity of the measured fluorescence spectrum of the organic matter, C is the concentration of the organic matter in the ground or the organic matter in the well, and a and b are the model coefficients in the standard curve.

10. An integrated air-space-ground-well composite in-situ pollution detection system, characterized in that, The system includes: The space-air collaborative detection module is used to acquire macroscopic and fine spectral data of the target area, and to determine the core pollution area within the target area based on the macroscopic and fine spectral data; The well precision detection module is used to conduct in-situ gridded measurements on the ground and in-situ penetration tests in the core pollution area to obtain multimodal detection data of the surface and underground. The intelligent fusion inversion module is used to perform fusion analysis on the multimodal detection data and the macroscopic and fine spectral data based on the trained fusion inversion model, and generate the three-dimensional pollution distribution results of the target area. The fusion inversion model is constructed based on the coupling of a physicochemical mechanism model and a machine learning algorithm. The prediction output module predicts the migration trend of pollutants based on the three-dimensional pollution distribution results and outputs a detection report containing pollutant types, concentration gradients, spatial distribution ranges, and migration paths.

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