Soil heavy metal chromium pollution evaluation method and system based on sensor data

Abstract

The invention discloses a soil heavy metal chromium pollution evaluation method and system based on sensor data, and relates to the technical field of soil heavy metal detection. Selecting one sensor from the chromium ion sensor cluster as a target sensor; according to the sensor position information, the chromium ion concentration value of the position where the target sensor is located is calculated; spatial calculation is conducted on the multiple chromium ion concentration values, and chromium pollution diffusion distribution information is generated; according to the chromium pollution diffusion distribution information, chromium pollution evaluation is carried out, and a chromium pollution evaluation result is obtained. According to the method, the diffusion distribution information can be generated by calculating the chromium ion concentration value, so that chromium pollution evaluation is realized, and the accuracy and food safety are improved.

Description

Technical Field

[0001] This invention relates to the field of soil heavy metal detection technology, and in particular to a method and system for evaluating soil heavy metal chromium pollution based on sensor data. Background Technology

[0002] In the context of environmental protection and sustainable agricultural development, monitoring and assessing the spread of heavy metal pollution in soil is crucial. Existing systems utilize sensors to monitor and assess the spread of chromium in soil in real time. The electrode materials of these sensors are carefully designed to account for the conventional physicochemical properties of farmland soil. However, with long-term operation, the farmland soil environment undergoes a series of continuous changes, exposing the electrode materials to a complex physicochemical microenvironment. This can lead to extremely slow and imperceptible physicochemical passivation or micro-corrosion of the surface active layer of the electrode materials, affecting the sensor's sensitivity to chromium ion signals. This causes deviations in the internal parameters of the conversion function established under laboratory conditions to convert the sensor's raw electrical signal into chromium ion concentration values, resulting in low accuracy in chromium ion concentration identification and heavy metal chromium pollution assessment. Simultaneously, this impacts the long-term health of the farmland ecosystem and the food safety of agricultural products.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a method and system for evaluating soil heavy metal chromium pollution based on sensor data. This method can generate diffusion distribution information by calculating chromium ion concentration values, thereby achieving chromium pollution evaluation and improving accuracy and food safety.

[0005] On one hand, embodiments of the present invention provide a method for evaluating soil heavy metal chromium pollution based on sensor data, comprising the following steps: Acquire sensor location information; Select one sensor from the chromium ion sensor cluster as the target sensor; Based on the sensor location information, calculate the chromium ion concentration value at the location of the target sensor; Spatial extrapolation is performed on multiple chromium ion concentration values ​​to generate chromium pollution diffusion and distribution information; Based on the chromium pollution diffusion and distribution information, a chromium pollution assessment is conducted to obtain the chromium pollution assessment results.

[0006] On the other hand, embodiments of the present invention provide a soil heavy metal chromium pollution assessment system based on sensor data, comprising: The information acquisition module is used to acquire sensor location information; The sensor selection module is used to select one sensor from the chromium ion sensor cluster as the target sensor. The concentration calculation module is used to calculate the chromium ion concentration value at the location of the target sensor based on the sensor location information. The spatial estimation module is used to perform spatial estimation on multiple chromium ion concentration values ​​to generate chromium pollution diffusion and distribution information. The chromium pollution assessment module is used to assess chromium pollution based on the chromium pollution diffusion and distribution information, and to obtain the chromium pollution assessment results.

[0007] The embodiments of this application include at least the following beneficial effects: First, the sensor location information is obtained. Then, a sensor is selected from the chromium ion sensor cluster as the target sensor. Based on the sensor location information, the chromium ion concentration value at the location of the target sensor is calculated. Spatial extrapolation is performed on multiple chromium ion concentration values ​​to generate chromium pollution diffusion distribution information. Finally, based on the chromium pollution diffusion distribution information, chromium pollution evaluation is performed to obtain the chromium pollution evaluation result. Thus, diffusion distribution information can be generated by calculating chromium ion concentration values ​​to achieve chromium pollution evaluation, improving accuracy and food safety.

[0008] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0010] Figure 1 This is a flowchart of a soil heavy metal chromium pollution assessment method based on sensor data, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a soil heavy metal chromium pollution assessment system based on sensor data, according to an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0012] In the context of environmental protection and sustainable agricultural development, particularly in farmland areas near industrial zones, monitoring and assessing the spread of heavy metal pollution in soil is crucial. Traditional soil pollution assessment methods, which typically rely on periodic manual sampling and laboratory analysis, often fail to provide the timely and comprehensive information needed to develop effective remediation strategies. These traditional methods are limited by low data collection frequency and insufficient spatial coverage, making it difficult to accurately track the dynamic movement of pollutants such as chromium in complex soil environments. To overcome these inherent limitations, existing systems deploy sensors to provide real-time insights into soil conditions.

[0013] Specifically, in soil remediation projects for farmland surrounding industrial areas, existing systems typically deploy multiple sensors to monitor the diffusion of the heavy metal chromium in the soil in real time. During the initial deployment of this system, the electrode materials of its sensor probes were carefully designed to fully consider the conventional physicochemical properties of the target farmland soil, such as soil pH, redox potential, and common ion background, ensuring accurate and stable capture of the electrochemical signals of chromium ions in the soil pore water. In the initial stages of system operation, all functions performed normally, providing reliable data support for the preliminary assessment of soil pollution.

[0014] However, with long-term operation of the system, the farmland soil environment undergoes a series of subtle but continuous changes. For example, seasonal rainfall and drought cycles cause repeated fluctuations in soil moisture, which in turn affect the soil's redox state and ion migration rate. Simultaneously, plant roots in the farmland secrete various organic acids during growth, such as citric acid and oxalic acid. These organic acids slowly accumulate in the rhizosphere, altering the local soil chemical environment. Furthermore, the soil microbial community is constantly evolving, and the metabolic products of some microorganisms may affect the sensor electrode materials. The long-term, combined effects of these environmental factors expose the electrode materials to a complex physicochemical microenvironment. This continuous exposure can lead to extremely slow and imperceptible physicochemical passivation or micro-corrosion of the electrode material's surface active layer. For example, a dense oxide film may form on the electrode surface, hindering effective contact of chromium ions; or certain organic acids may react slowly with the electrode material, leading to the gradual loss of active sites. These processes are gradual, and their effects are difficult to manifest in the short term, but long-term accumulation can substantially alter the sensor's performance.

[0015] The slow changes in the surface properties of this electrode material directly affect the sensor's sensitivity to chromium ion signals. The conversion function, originally established under laboratory conditions to convert the sensor's raw electrical signal into a chromium concentration value, will begin to exhibit slight but persistent deviations in its internal parameters. For example, if the electrode surface becomes passivated, the potential signal generated by the sensor at the same chromium concentration may systematically decrease, causing a change in the slope of the conversion function; micro-corrosion may alter the electrode's surface area or charge characteristics, thus affecting the intercept of its potential response. This deviation is not a random fluctuation but a systematic shift, meaning there is a persistent, potentially worsening, fixed deviation between the chromium concentration value output by the sensor and the actual chromium ion concentration in the soil. This results in low accuracy in chromium ion concentration identification and assessment of heavy metal chromium pollution, while simultaneously impacting the long-term health of the farmland ecosystem and the food safety of agricultural products.

[0016] In scenarios with complex and variable soil microenvironments, such as farmland surrounding industrial zones, it is necessary to effectively identify and compensate for the slow and systematic performance degradation of sensor electrode materials caused by the long-term effects of the soil microenvironment. This is to ensure the authenticity of heavy metal chromium pollution diffusion assessment data and the accuracy of chromium pollution diffusion distribution information, so as to avoid significant deviations in remediation decisions.

[0017] The embodiments of this application will be explained in detail below with reference to the accompanying drawings: Figure 1 This is an optional flowchart of a soil heavy metal chromium pollution assessment method based on sensor data provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0018] Step S101: Obtain sensor location information; Step S102: Select one sensor from the chromium ion sensor cluster as the target sensor; Step S103: Calculate the chromium ion concentration at the location of the target sensor based on the sensor location information; Step S104: Spatial extrapolation of multiple chromium ion concentration values ​​to generate chromium pollution diffusion and distribution information; Step S105: Based on the chromium pollution diffusion and distribution information, conduct a chromium pollution assessment to obtain the chromium pollution assessment results.

[0019] Steps S101 to S105 as shown in the embodiments of this application can generate diffusion distribution information by calculating chromium ion concentration values, thereby achieving chromium pollution assessment and improving accuracy and food safety.

[0020] In some embodiments, steps S101-S105 may involve acquiring sensor location information first. During sensor deployment, the GPS coordinates of each sensor can be manually recorded and stored in a database. Alternatively, a GPS (Global Positioning System) module can be integrated into the sensor to automatically acquire and upload its location information. This location information forms the basis for all subsequent spatial analyses, ensuring that the data collected by each sensor is accurately mapped to its corresponding geographical location. It is understood that sensor location information refers to the geographic coordinate data of the chromium ion sensor within the monitoring area, such as latitude and longitude information, which is crucial for subsequent spatial extrapolation and pollution distribution mapping.

[0021] Then, a sensor is selected from the chromium ion sensor cluster as the target sensor. Different sensors can be selected in turn to ensure that all areas are monitored periodically. Alternatively, the target sensor can be selected dynamically based on real-time data quality, sensor operating status, or the level of interest in a specific area. For example, when the chromium ion concentration in a certain area shows abnormal fluctuations, the sensor in that area can be prioritized as the target sensor. It can be understood that a chromium ion sensor cluster refers to a collection of multiple sensors deployed within a specific area for real-time monitoring of chromium ion concentration in the soil. These sensors work together to form a distributed monitoring network.

[0022] Based on the sensor's location information, the chromium ion concentration at the target sensor's location is calculated. The sensor can directly output an internally calibrated chromium ion concentration value. Alternatively, the sensor can output a raw electrical signal, which can then be converted into a chromium ion concentration value using a preset signal-to-chromium concentration conversion function. For example, a standard curve between the electrical signal and chromium ion concentration can be pre-established in the laboratory and then applied to the electrical signals acquired in the field.

[0023] Spatial extrapolation of multiple chromium ion concentration values ​​generates chromium pollution diffusion distribution information, aiming to transform discrete sensor data points into a continuous pollution distribution map. Interpolation algorithms, such as inverse distance weighted (IDW) or Kriging interpolation, can be used. When using the IDW algorithm, the concentration value at the target sensor location can be calculated by weighting the concentration values ​​of other sensors around the target sensor and their distances, and then the concentration distribution of the entire area can be extrapolated. Alternatively, Geographic Information System (GIS) software can be used to import sensor data and utilize its built-in spatial analysis tools to generate a visualized pollution diffusion map.

[0024] Finally, based on the chromium pollution diffusion and distribution information, a chromium pollution assessment is conducted to obtain the assessment results, aiming to transform technical data into actionable environmental management information. The generated chromium pollution diffusion and distribution information can be compared with soil environmental quality standards to identify areas exceeding the standards and the degree of exceedance. For example, different color zones can be set to represent different pollution levels, thus visually displaying the pollution situation.

[0025] This embodiment establishes a complete and efficient monitoring and assessment system by acquiring sensor location information, selecting target sensors, calculating chromium ion concentration values, performing spatial extrapolation, and finally conducting pollution assessment. The method first lays the foundation for all subsequent spatial analyses by accurately acquiring sensor location information. Then, it flexibly selects target sensors from the sensor cluster, ensuring the targeting and comprehensiveness of the monitoring. By calculating the chromium ion concentration value at the location of the target sensor, the raw sensor data is transformed into a meaningful pollution indicator. Furthermore, spatial extrapolation of multiple chromium ion concentration values ​​transforms discrete monitoring point data into continuous pollution diffusion distribution information, thus intuitively showing the scope and degree of pollution. Finally, chromium pollution assessment is conducted based on the generated chromium pollution diffusion distribution information, providing decision-makers with a scientific and real-time basis for formulating effective pollution prevention and remediation strategies. The entire process is interconnected, making dynamic monitoring and assessment of soil chromium pollution possible, significantly improving the efficiency and accuracy of environmental management.

[0026] Through the above technical solution, this embodiment achieves real-time and continuous monitoring of soil chromium ion concentration by deploying a chromium ion sensor cluster, significantly improving the frequency and spatial resolution of data acquisition. Precise acquisition of sensor location information and flexible selection of target sensors ensure the accuracy and relevance of monitoring. Spatial extrapolation of multiple chromium ion concentration values ​​generates detailed information on the diffusion and distribution of chromium pollution. This spatial extrapolation capability makes the identification of pollution sources, the tracking of diffusion paths, and the delineation of pollution extent more accurate. Ultimately, chromium pollution assessment based on this real-time, high-resolution pollution diffusion information can provide timely and comprehensive decision support for relevant management departments, thereby enabling more effective formulation and implementation of pollution prevention and remediation strategies. This embodiment, through sensor data processing and spatial analysis, opens up new avenues for intelligent monitoring and assessment of soil heavy metal pollution.

[0027] In some embodiments, in step S103, calculating the chromium ion concentration value at the location of the target sensor based on the sensor location information may include, but is not limited to, the following steps: Step S201: Identify the reference sensor adjacent to the target sensor based on the sensor location information; Step S202: Acquire the first electrical signal collected by the target sensor, the soil microenvironment parameters at the location of the target sensor, and the second electrical signal collected by the reference sensor; Step S203: Based on the response trend of the first electrical signal and the response trend of the second electrical signal, perform sensor performance degradation analysis on the target sensor to obtain the sensor performance degradation analysis results. Step S204: If the sensor performance degradation analysis result indicates that performance degradation exists, then calculate the signal deviation based on the first electrical signal and the second electrical signal. Step S205: Based on the correlation rules between electrode material performance degradation and environmental parameters, perform correlation analysis on soil microenvironment parameters and signal deviation to obtain the correlation analysis results; Step S206: If the correlation analysis results show a strong correlation, then adjust the signal-chromium concentration conversion function; Step S207: Based on the adjusted signal and chromium concentration conversion function, the first electrical signal is converted to obtain the chromium ion concentration value.

[0028] In some embodiments, reference sensors adjacent to the target sensor can be identified first based on sensor location information. One or more reference sensors that are spatially close to the target sensor or correlated in data acquisition can be determined based on sensor location information, using criteria such as geographical distance, network topology, or signal coverage. These reference sensors can be used to provide a relatively stable reference signal to facilitate the evaluation of the target sensor's performance status.

[0029] Then, the first electrical signal collected by the target sensor, the soil microenvironment parameters at the location of the target sensor, and the second electrical signal collected by the reference sensor are acquired. The target sensor can continuously collect the electrical signal corresponding to the chromium ion concentration at its location, i.e., the first electrical signal; the target sensor or nearby environmental sensors collect soil microenvironment parameters, such as soil moisture, pH, redox potential, and temperature; simultaneously, the reference sensor collects the electrical signal at its location, i.e., the second electrical signal. These data form the basis for subsequent analysis.

[0030] Then, based on the response trends of the first and second electrical signals, a sensor performance degradation analysis is performed on the target sensor to obtain the sensor performance degradation analysis results. The performance degradation can be assessed by comparing the response trends of the first and second electrical signals, for example, through trend line analysis, drift rate calculation, or statistical methods, to determine whether the target sensor exhibits performance degradation phenomena such as signal drift, decreased sensitivity, or prolonged response time. The sensor performance degradation analysis result can be a binary judgment (presence / absence of degradation) or a quantified degradation index.

[0031] If the sensor performance degradation analysis indicates performance degradation, then the signal deviation is calculated based on the first and second electrical signals. Signal deviation refers to the difference between the first and second electrical signals at a specific point in time or over a period of time; for example, it can be calculated as the difference, ratio, or normalized relative deviation between the two. This deviation quantifies the degree of anomaly of the target sensor relative to the reference sensor, reflecting the specific manifestation of performance degradation.

[0032] Based on the correlation rules between electrode material performance degradation and environmental parameters, a correlation analysis is performed on soil microenvironment parameters and signal deviation to obtain the correlation analysis results. Statistical regression or expert systems can be used to identify the strength of the relationship between signal deviation and specific soil microenvironment parameters. The correlation analysis results can indicate whether a strong correlation exists, i.e., that certain environmental parameters have a significant impact on signal deviation. The correlation rules are pre-established knowledge bases or models that describe how different environmental parameters (such as high humidity and low pH) affect the degradation process of sensor electrode materials and their specific contribution to signal deviation.

[0033] If the correlation analysis results indicate a strong correlation, the signal-to-chromium concentration conversion function needs adjustment. The signal-to-chromium concentration conversion function is a mathematical model or lookup table used to convert the electrical signal acquired by the sensor into the actual chromium ion concentration value. When a strong correlation is identified, it means that specific environmental parameters are significantly affecting the sensor's response characteristics; therefore, the conversion function needs to be corrected to compensate for errors caused by environmental factors. This adjustment can be achieved by modifying the function's coefficients, introducing new correction terms, or selecting a different conversion model.

[0034] Finally, based on the adjusted signal and the chromium concentration conversion function, the first electrical signal is converted to obtain the chromium ion concentration value. This means that the original first electrical signal collected by the target sensor is input into the conversion function calibrated for environmental parameters and performance degradation, thereby outputting a more accurate and reliable chromium ion concentration value.

[0035] This embodiment effectively solves the problem of inaccurate concentration calculation caused by sensor signal degradation and complex microenvironmental parameters by introducing a dynamic monitoring and calibration mechanism for target sensor performance degradation. Specifically, by identifying adjacent reference sensors and acquiring their electrical signals, a reliable benchmark can be provided for the performance degradation analysis of the target sensor. When performance degradation of the target sensor is detected, the signal deviation between it and the reference sensor is calculated to quantify the degree of degradation. Furthermore, this signal deviation is correlated with soil microenvironmental parameters to reveal the specific impact patterns of environmental factors on sensor degradation. Based on the results of this correlation analysis, the signal-to-chromium concentration conversion function is specifically adjusted to ensure that even under conditions of sensor performance degradation or changes in environmental conditions, the first electrical signal can be accurately converted into the true chromium ion concentration value. Thus, this embodiment fundamentally improves the accuracy and reliability of chromium ion concentration calculation, providing a solid data foundation for subsequent chromium pollution assessment.

[0036] To illustrate this technical solution more clearly, a specific example is used below. Suppose that after long-term deployment, a target sensor's first electrical signal exhibits a gradual drifting trend. Simultaneously, by identifying its neighboring reference sensor, it is found that the reference sensor's second electrical signal is relatively stable. At this point, based on the response trends of the first and second electrical signals, a sensor performance degradation analysis is performed, indicating that the target sensor is experiencing performance degradation. Further, the signal deviation between the first and second electrical signals is calculated. Simultaneously, when performing a correlation analysis between soil microenvironment parameters (e.g., soil moisture, pH) and the signal deviation, a strong correlation is found between the signal deviation and soil pH, indicating that a highly acidic environment accelerates the degradation of the sensor's electrode materials. Based on this strong correlation, the original signal-to-chromium concentration conversion function is adjusted, for example, by introducing a pH-related correction factor. Finally, the adjusted conversion function is used to convert the target sensor's first electrical signal, obtaining a calibrated and more accurate chromium ion concentration value. In this way, even under conditions of sensor performance degradation and complex environments, the accuracy of chromium ion concentration data can be ensured.

[0037] Through the above technical solution, this embodiment can dynamically assess and calibrate the performance degradation of the target sensor and consider the influence of soil microenvironment parameters on the sensor response, thereby significantly improving the accuracy and reliability of chromium ion concentration calculation. This avoids measurement errors caused by sensor aging or environmental changes, making the subsequent generation of chromium pollution diffusion and distribution information and pollution assessment results more accurate, and providing more reliable data support for the precise monitoring and remediation of soil heavy metal pollution.

[0038] In some embodiments, step S206, adjusting the signal-to-chromium concentration conversion function, may include, but is not limited to, the following steps: Step S301: Acquire macroscopic environmental data and the third electrical signal collected by each chromium ion sensor in the chromium ion sensor cluster; Step S302: Based on macroscopic environmental data and multiple third electrical signals, establish a reference line for the overall signal drift of the network; Step S303: Generate a calibration curve based on the overall network signal drift reference line; Step S304: Determine the parameter adjustment amount based on the calibration curve and soil microenvironment parameters; Step S305: Adjust the signal-to-chromium concentration conversion function according to the parameter adjustment amount.

[0039] In some embodiments, since sensor performance degradation is often affected by changes in the macroscopic environment, adjustments based solely on local information from a single sensor may not adequately correct for systematic biases caused by these complex factors, thereby affecting the accuracy and stability of the adjustments.

[0040] To achieve this, we can first acquire macroscopic environmental data and the third electrical signal collected by each chromium ion sensor in the chromium ion sensor cluster. Macroscopic environmental data refers to external environmental factors affecting the entire sensor network or a large area, such as regional temperature, rainfall, atmospheric humidity, and light intensity. This data is typically acquired through weather stations, satellite remote sensing, or regional environmental monitoring systems. The third electrical signal refers to the raw electrical signal collected by all sensors in the chromium ion sensor cluster at a specific point in time. These signals reflect the response of each sensor under the current environment. Acquiring this data aims to comprehensively capture the external macroscopic factors affecting sensor performance and the overall response status of the sensor network.

[0041] Then, based on macroscopic environmental data and multiple third-party electrical signals, a reference line for the overall signal drift of the network is established. By analyzing macroscopic environmental data and multiple third-party electrical signals, the systematic drift trend of the entire sensor network under specific macroscopic environmental conditions can be identified and quantified. Methods such as statistical regression or time series analysis can be used to correlate changes in the macroscopic environment with the overall deviation of sensor signals, thereby constructing a baseline that reflects the common drift behavior of the network. This reference line aims to distinguish between the local degradation of individual sensors and the general drift of the entire network affected by the macroscopic environment.

[0042] Next, a calibration curve is generated based on the overall network signal drift reference line. A curve for correcting sensor signals can be derived based on the established overall network signal drift reference line and a specific calibration algorithm. This calibration curve can transform the overall network drift trend into specific correction parameters, enabling subsequent fine-tuning of individual sensor signals. The generation of the calibration curve can consider nonlinear relationships to more accurately reflect the sensor's response characteristics under different drift states.

[0043] Finally, based on the calibration curve and soil microenvironment parameters, the parameter adjustment amount is determined. The specific adjustment value for the signal-to-chromium concentration conversion function of the target sensor can be calculated using the generated calibration curve and the soil microenvironment parameters at the target sensor's location. By combining the overall network correction information provided by the calibration curve with local microenvironment parameters, personalized and precise adjustment of the conversion function parameters can be achieved. The signal-to-chromium concentration conversion function is then adjusted according to the parameter adjustment amount. The calculated parameter adjustment amount can be applied to the original signal-to-chromium concentration conversion function, for example, by modifying the function's coefficients, intercept, or introducing new correction terms, thereby obtaining a more accurate conversion function that better reflects the current sensor performance and environmental conditions.

[0044] This embodiment establishes a reference line for the overall signal drift of the network by introducing macroscopic environmental data and the third electrical signal of the entire chromium ion sensor cluster. This reference line effectively captures the systematic and universal drift of the sensor network caused by macroscopic environmental factors, thus overcoming the limitations of relying solely on local information from a single sensor for adjustment. By converting this overall network drift information into a calibration curve and combining it with the soil microenvironment parameters of the target sensor, the parameter adjustment amount for that sensor can be determined more comprehensively and accurately. This hierarchical adjustment mechanism—first considering the overall network drift and then combining it with the local microenvironment for fine-tuning—enables the adjustment of the signal-to-chromium concentration conversion function to more accurately reflect the true performance state of the sensor and effectively compensate for errors caused by complex environmental factors.

[0045] To illustrate this technical solution more clearly, a specific example is used below. Suppose a cluster of chromium ion sensors is deployed in a farmland area to monitor soil chromium pollution. To adjust the signal-to-chromium concentration conversion function of a specific target sensor, recent macro-environmental data for the farmland area is first acquired, such as daily average temperature, rainfall, and soil surface moisture obtained from a weather station. Simultaneously, the third electrical signals collected by all sensors in the cluster over a past period are also collected. Based on this macro-environmental data and the third electrical signals from all sensors, a reference line reflecting the relationship between the signal drift of the entire sensor network and macro-environmental factors can be established using multiple regression analysis or a neural network model. For example, when the temperature rises or rainfall increases, the baseline signals of all sensors may generally drift upwards or downwards.

[0046] Furthermore, based on this overall network signal drift reference line, a calibration curve can be generated. This curve quantifies the general corrections required for the sensor signal under different macroscopic environmental conditions. For example, if the reference line shows that the signal is generally higher under high temperature and high humidity conditions, the calibration curve will indicate a corresponding negative correction. Subsequently, combined with soil microenvironment parameters at the target sensor's location, such as soil pH, redox potential, and organic matter index near the sensor, these local parameters are integrated with the calibration curve to determine the final parameter adjustment. For example, the calibration curve provides a general correction based on the macroscopic environment, while local soil pH may further fine-tune this correction, as pH directly affects the electrochemical response of chromium ions. Finally, based on this parameter adjustment that comprehensively considers both the overall network drift and the local microenvironment, the signal-to-chromium concentration conversion function of the target sensor is precisely adjusted, thereby ensuring that its output chromium ion concentration value is more accurate and reliable.

[0047] By establishing a network-wide signal drift reference line and generating a calibration curve, this embodiment effectively separates systematic errors from local decay, thereby making the adjustment of the signal-to-chromium concentration conversion function more accurate and stable. This embodiment not only improves the accuracy of calculating chromium ion concentration values ​​from individual sensors but also enhances the consistency and reliability of the entire sensor network data, providing a more solid data foundation for subsequent estimation and evaluation of chromium pollution diffusion distribution.

[0048] In some embodiments, step S303, generating a calibration curve based on the overall network signal drift reference line, may include, but is not limited to, the following steps: Step S401: Collect microenvironmental data, including soil moisture, pH, redox potential, organic matter index, microbial activity index, biofilm index, and chelating agent index. Step S402: Based on micro-environmental data, determine the dominant decline factor and synergistic effect mode; Step S403: Construct a nonlinear correction function based on the dominant decline factor and synergistic effect mode; Step S404: Correct the overall signal drift reference line of the network according to the nonlinear correction function to obtain the calibration curve.

[0049] In some embodiments, sensor performance degradation is often significantly influenced by complex and localized microenvironmental factors, and calibration curves generated solely based on macroscopic environmental data and a network-wide signal drift reference line may not adequately capture these subtle local differences. This can lead to insufficient accuracy in the calibration curves, affecting the accuracy of chromium ion concentration calculations and ultimately potentially reducing the reliability of chromium pollution assessments.

[0050] Therefore, micro-environmental data can be collected first. Micro-environmental data refers to detailed environmental parameters collected at or near the location of the target sensor that directly affect the performance and response characteristics of the sensor's electrode materials. Micro-environmental data includes soil moisture, pH, redox potential, organic matter index, microbial activity index, biofilm index, and chelating agent index. Collecting this data aims to obtain more refined and locally representative information than macro-environmental data, in order to more accurately assess the sensor's degradation under specific micro-environments. For example, soil moisture affects ion migration rates and the hydration layer on the electrode surface; pH directly affects ion exchange and the corrosion rate of electrode materials; redox potential reflects the intensity of redox reactions in the soil; and organic matter index, microbial activity index, biofilm index, and chelating agent index may affect the electrode surface state and activity through physical adsorption, biodegradation, or chemical complexation.

[0051] Then, based on microenvironmental data, the dominant degradation factor and synergistic interaction mode are determined. The dominant degradation factor refers to the environmental parameter or combination thereof that plays a major role in sensor performance degradation under specific microenvironmental conditions. The synergistic interaction mode refers to the way in which multiple environmental parameters interact, jointly accelerating or slowing down sensor degradation. The purpose of determining the dominant degradation factor and synergistic interaction mode is to reveal the deep mechanism of sensor degradation, thereby providing a theoretical basis for subsequent nonlinear correction. For example, high humidity and low acidity / alkalinity may jointly accelerate the corrosion of electrode materials; in this case, humidity and acidity / alkalinity constitute a synergistic interaction mode and may become the dominant degradation factor.

[0052] Then, based on the dominant decay factor and the synergistic effect mode, a nonlinear correction function is constructed. The nonlinear correction function is a mathematical model built based on the determined dominant decay factor and synergistic effect mode, used to describe the nonlinear influence of these micro-environmental factors on sensor signal drift. This function can capture the complex nonlinear relationship between sensor response and environmental parameters, such as threshold effect, saturation effect, or interaction. The purpose of constructing the nonlinear correction function is to provide a precise tool to quantify the specific amount of correction that micro-environmental factors make to the overall network signal drift reference line.

[0053] Finally, the overall signal drift reference line of the network is corrected using a nonlinear correction function to obtain the calibration curve. The correction amount calculated by the nonlinear correction function can be applied to the overall signal drift reference line of the network, thereby generating a calibration curve that better reflects the actual micro-environment conditions of the target sensor. This correction process aims to eliminate or reduce sensor signal drift errors caused by local micro-environment differences, enabling the calibration curve to more accurately reflect the sensor's true response characteristics at a specific location.

[0054] This embodiment introduces micro-environmental data and, based on this data, identifies the dominant degradation factors and synergistic effects, thereby constructing a nonlinear correction function to correct the overall signal drift reference line of the network. This addresses the limitation of calibration curves failing to fully consider the complex influences of local micro-environments. Specifically, firstly, collecting micro-environmental data provides more refined and locally representative information than macro-environmental data, making the assessment of sensor performance degradation more accurate. Secondly, analyzing this micro-environmental data allows for the identification of dominant degradation factors that play a key role in sensor degradation and their synergistic effects, contributing to a deeper understanding of the sensor's degradation mechanism in specific micro-environments. In-depth analysis of the degradation mechanism enables the construction of a correction function that accurately describes the nonlinear influence of micro-environmental factors on sensor signal drift. Finally, this nonlinear correction function is used to correct the overall signal drift reference line of the network, enabling the generated calibration curve to more accurately reflect the true response characteristics of the target sensor in specific micro-environments, thus effectively improving the adjustment accuracy of the signal-to-chromium concentration conversion function.

[0055] To illustrate this technical solution more clearly, a specific example is used below. Suppose a chromium ion sensor is deployed in a farmland area, and its microenvironmental data is continuously collected. This microenvironmental data includes soil moisture, pH, redox potential, organic matter index, microbial activity index, biofilm index, and chelating agent index. Analysis of this data reveals, for example, that when soil moisture is above 80% and pH is below 5.0, the corrosion rate of the sensor electrodes accelerates significantly. This indicates that high humidity and low pH are the dominant degradation factors for the sensor in this area, and that there is a synergistic effect between them. Based on this, a nonlinear correction function, such as a polynomial function, can be constructed. This function takes soil moisture and pH as input and outputs a correction coefficient. This correction coefficient quantifies the deviation of the sensor signal drift relative to the network's overall signal drift reference line under specific humidity and pH conditions. Subsequently, this correction coefficient is applied to the network's overall signal drift reference line for nonlinear correction, thereby generating a more accurate calibration curve specific to the microenvironment of this particular sensor. In this way, even under complex local microenvironment conditions, the accuracy of sensor signal conversion can be ensured, thereby obtaining more reliable chromium ion concentration values.

[0056] Through the above technical solution, this embodiment incorporates micro-environmental data such as soil moisture and pH, and further identifies the dominant degradation factors and synergistic effects, enabling the construction of a nonlinear correction function that better reflects actual conditions. Consequently, the generated calibration curve more accurately reflects the sensor's true performance under specific micro-environments, significantly improving the adjustment accuracy of the signal-to-chromium concentration conversion function, thereby enhancing the accuracy of chromium ion concentration calculations. This refined calibration method effectively avoids measurement errors caused by local micro-environmental differences, making the final chromium pollution assessment results more reliable and accurate, providing strong support for the precise monitoring and remediation of soil heavy metal pollution.

[0057] In some embodiments, in step S402, determining the dominant decline factor and synergistic mode based on microenvironmental data may include, but is not limited to, the following steps: Step S501: Calculate the dynamic change trend based on the micro-environment data; Step S502: Based on the third electrical signal, perform behavioral pattern anomaly analysis to obtain the behavioral pattern anomaly analysis results; Step S503: If the abnormal behavior pattern analysis result indicates the presence of anomalies, then based on the micro-environment data, identify the combination of environmental parameters associated with the abnormal behavior pattern through cross-correlation analysis. Step S504: Based on the combination of environmental parameters, perform electrode material performance degradation analysis to obtain the electrode material performance degradation analysis results; Step S505: Based on the analysis results and dynamic trends of electrode material performance degradation, determine the dominant degradation factor and synergistic effect mode.

[0058] In some embodiments, dynamic trends can be calculated first based on microenvironmental data. Time series analysis can be performed on the microenvironmental data to identify patterns, periodicity, or long-term trends in their changes over time. For example, methods such as moving averages, exponential smoothing, or Kalman filtering can be used to capture the dynamic evolution characteristics of environmental parameters, with the aim of understanding the inherent volatility and potential long-term changes of the soil microenvironment.

[0059] Then, based on the third electrical signal, anomaly analysis of behavior patterns is performed to obtain the results of the behavior pattern anomaly analysis. The aim is to detect whether there are abnormal situations that deviate from the normal behavior pattern during the operation of the sensor by analyzing the characteristics of the third electrical signal, such as signal drift, increased noise, or slow response. These anomalies may indicate the degradation of sensor performance.

[0060] If the behavioral pattern anomaly analysis indicates the presence of anomalies, then based on the micro-environment data, cross-correlation analysis is used to identify combinations of environmental parameters associated with the behavioral pattern anomalies. Cross-correlation analysis is a statistical method used to measure the similarity between two time series and their time lag relationship. By calculating the cross-correlation function between the anomalous third electrical signal and various micro-environment data, one or more environmental parameters significantly correlated with the sensor's anomalous behavior can be identified, thus forming combinations of environmental parameters. The aim is to accurately identify the environmental causes leading to sensor performance anomalies.

[0061] Next, based on the combination of environmental parameters, an electrode material performance degradation analysis is conducted to obtain the results. This aims to deeply explore how the identified environmental parameter combinations affect the physicochemical properties of sensor electrode materials, leading to performance decline. By consulting a pre-defined degradation mechanism library or conducting simulation experiments, the degradation processes that may occur in electrode materials under specific environmental conditions, such as corrosion, passivation, contamination, or structural changes, can be understood. This yields the electrode material performance degradation analysis results, which detail the type and extent of degradation. Finally, based on the electrode material performance degradation analysis results and dynamic trends, the dominant degradation factors and synergistic action modes are determined. Through comprehensive analysis, a complete understanding of the intrinsic mechanisms and external driving factors of sensor degradation can be achieved.

[0062] This embodiment captures the long-term evolution of the soil environment by calculating the dynamic trends of micro-environment data. Simultaneously, by analyzing the behavioral anomalies of the third electrical signal, potential performance problems of the sensor can be detected in a timely manner. When behavioral anomalies are detected, cross-correlation analysis is used to associate the abnormal behavior with specific combinations of environmental parameters, thereby identifying potential environmental factors leading to sensor performance degradation. Furthermore, based on these combinations of environmental parameters, electrode material performance degradation analysis can be performed to gain a deeper understanding of the specific degradation mechanisms. Finally, by combining the results of electrode material performance degradation analysis and the dynamic trends of micro-environment data, the dominant degradation factors leading to sensor performance decline and the synergistic effects between these factors can be accurately determined, providing a scientific basis for subsequent signal correction and calibration.

[0063] Through the above technical solution, this embodiment can achieve refined identification of the causes of sensor performance degradation. Specifically, by combining the analysis of abnormal behavior patterns of sensor electrical signals with the cross-correlation analysis of microscopic environmental data, this embodiment can accurately locate the key environmental parameter combinations leading to sensor performance degradation. Furthermore, through in-depth analysis of the electrode material performance degradation mechanism, the dominant factors of degradation and their synergistic effects can be revealed, thereby providing a more accurate and reliable basis for subsequent adjustments to the signal-to-chromium concentration conversion function, significantly improving the accuracy and robustness of chromium ion concentration calculation.

[0064] In some embodiments, in step S502, behavioral pattern anomaly analysis is performed based on the third electrical signal to obtain behavioral pattern anomaly analysis results, which may include, but is not limited to, the following steps: A parameter safety check is performed on the third electrical signal to obtain the parameter safety check results. The parameter safety check includes instantaneous rate of change safety check, electrode impedance safety check, and reference electrode potential difference safety check. Based on the results of the parameter safety check, identify the sensor's operating status; If the sensor is operating normally, the trend deviation is calculated based on the current and historical drift trends of the third electrical signal. Based on trend deviations, behavioral patterns are analyzed to obtain abnormal behavioral pattern analysis results.

[0065] In some embodiments, a parameter safety check can be performed on the third electrical signal to obtain the parameter safety check results. The purpose of this check is to ensure the basic quality and reliability of the sensor data. The parameter safety check includes a transient rate of change safety check, an electrode impedance safety check, and a reference electrode potential difference safety check. The transient rate of change safety check aims to detect abnormal transient jumps or drastic fluctuations in the electrical signal, which typically indicates that the sensor may be subject to transient interference or malfunction. The electrode impedance safety check is used to assess the physical state of the sensor electrodes; abnormal changes in electrode impedance may indicate corrosion, contamination, or aging of the electrode material. The reference electrode potential difference safety check is used to monitor the stability of the reference electrode; abnormal drift in its potential difference directly affects measurement accuracy. Through these checks, the validity of the third electrical signal can be comprehensively evaluated.

[0066] Then, based on the results of the parameter safety checks, the sensor's operating status is identified. For example, if any parameter safety check fails, the sensor may be judged as being in an abnormal or faulty state; if all checks pass, the sensor is identified as being in a normal operating state.

[0067] If the sensor is operating normally, the trend deviation is calculated based on the current and historical drift trends of the third electrical signal. The current drift trend refers to the sensor's signal change tendency over a recent period, while the historical drift trend refers to the sensor's inherent drift characteristics over a longer timescale. By comparing these two drift trends, the difference between the sensor's current behavior and its expected or historically stable behavior can be quantified, thereby identifying potential, imperceptible performance degradation or abnormal drift.

[0068] Finally, based on trend deviations, behavioral patterns are analyzed to obtain anomaly analysis results. For example, persistent trend deviations that exceed historical ranges may indicate that the sensor is experiencing slow but continuous performance degradation, while periodic trend deviations may be related to periodic changes in environmental factors or the sensor's own periodic behavior. By analyzing these behavioral patterns, more refined and accurate anomaly analysis results can be obtained.

[0069] This embodiment effectively filters out unreliable data caused by sensor malfunctions or transient interference by introducing multi-dimensional parameter safety checks, thereby ensuring the quality of input data for subsequent behavioral pattern analysis. Simultaneously, by identifying the sensor's operating status, invalid trend analysis can be avoided for sensors with clearly identified anomalies. Furthermore, by calculating the trend deviation between the current drift trend and the historical drift trend of the third electrical signal, this embodiment can capture subtle but persistent performance degradation or drift that may occur in the sensor under normal operating conditions. This rigorous control over data quality and refined analysis of signal drift trends makes the results of behavioral pattern anomaly analysis more accurate and reliable, providing a solid foundation for the subsequent determination of dominant degradation factors and synergistic effects.

[0070] To illustrate this technical solution more clearly, a specific example is used below. Suppose a chromium ion sensor operates continuously in soil. At a certain point in time, the instantaneous rate of change of its third electrical signal suddenly exceeds the preset safety range, indicating that the sensor may have been subjected to transient interference or an internal circuit malfunction. In this case, through a safety check of the instantaneous rate of change, the sensor is marked as abnormal, and its data will not be used for subsequent trend deviation calculations, thus avoiding erroneous analysis. For example, another sensor performs normally in parameter safety checks, but the average daily value of its third electrical signal output over the past month is slightly higher than the average value for the same period of the previous month, and this upward trend persists. By calculating the trend deviation between the current drift trend of the sensor's third electrical signal (e.g., the average drift rate of the most recent week) and the historical drift trend (e.g., the average drift rate of the past six months), it is found that the deviation is consistently positive and exceeds the normal fluctuation range. This indicates that the sensor may be experiencing slow electrode aging or contamination, causing a systematic drift in its response value. Based on this trend deviation, it can be analyzed that the sensor exhibits an abnormal behavior pattern of "slow positive drift," even though its instantaneous readings remain within acceptable range. This refined anomaly analysis can promptly trigger further assessments of the sensor's performance degradation and provide crucial information for subsequent adjustments to the signal and chromium concentration conversion function.

[0071] Through the above technical solution, this embodiment can significantly improve the accuracy and reliability of behavioral pattern anomaly analysis. Parameter safety checks effectively eliminate erroneous data caused by sensor malfunctions or transient interference, ensuring the effectiveness of the analysis. By comparing the current drift trend with historical drift trends, subtle performance degradation or drift that may occur in the sensor under normal operating conditions can be detected in a timely manner, thereby avoiding misjudgments in subsequent chromium pollution assessments due to data quality issues. This refined behavioral pattern analysis provides a more reliable basis for accurately determining the dominant degradation factors and synergistic effects, thereby improving the accuracy of signal-to-chromium concentration conversion function adjustments, ultimately making the soil heavy metal chromium pollution assessment results more accurate.

[0072] In some embodiments, step S503, identifying combinations of environmental parameters associated with abnormal behavioral patterns based on micro-environment data through cross-correlation analysis, may include, but is not limited to, the following steps: Time alignment of microscopic environment data and third electrical signals; Calculate the cross-correlation function between the time-aligned third electrical signal and the microscopic environment data; Analyze the peak width of the cross-correlation function; Based on the peak width of the cross-correlation function, the micro-environment data are filtered to obtain the combination of environmental parameters.

[0073] In some embodiments, the microenvironmental data and the third electrical signal can be time-aligned first. Since microenvironmental data (e.g., soil moisture, pH, redox potential, etc.) and the third electrical signal may be collected at different sampling frequencies or at different time points, these data need to be synchronized along the time axis to ensure the accuracy of subsequent analysis. This can be achieved through techniques such as interpolation, resampling, or timestamp-based matching, so that each microenvironmental data point can establish an accurate time correspondence with its corresponding third electrical signal data point. The purpose is to provide a consistent and synchronized data foundation for subsequent cross-correlation analysis.

[0074] Then, the cross-correlation function between the time-aligned third electrical signal and the micro-environment data is calculated. Cross-correlation analysis, a statistical tool, can be used to quantify the similarity between two time series at different time lags. The cross-correlation function can reveal whether there is a potential correlation between anomalous behavior of the sensor signal and changes in specific micro-environmental parameters, as well as the strength of this correlation and its relationship with time lags. For example, when the cross-correlation function peaks at a certain time lag, it indicates that at that lag time, the change in environmental parameters is strongly correlated with the anomalous behavior of the sensor signal.

[0075] Next, we analyze the peak width of the cross-correlation function. The peak position of the cross-correlation function indicates the time lag of the effect of changes in microscopic environmental parameters on the sensor's electrical signal. For example, a positive lag may mean that changes in environmental parameters precede anomalies in the sensor signal, while a negative lag may indicate that anomalies in the sensor signal precede changes in environmental parameters. The peak width reflects the duration or range of this correlation. Narrow, high peaks usually indicate strong and concentrated correlations, while wide, flat peaks may indicate weaker correlations or longer durations of influence. The aim is to gain a deeper understanding of the dynamic relationship between environmental parameters and abnormal sensor behavior.

[0076] Finally, the micro-environment data is filtered based on the peak width of the cross-correlation function to obtain a combination of environmental parameters. For example, statistical analysis of a large amount of historical peak data can be performed to calculate the average value under anomaly-free conditions as a peak width threshold, selecting micro-environment parameters whose cross-correlation function peak width is less than the threshold. This filtering effectively identifies environmental parameters that are significantly correlated with abnormal sensor behavior patterns, thereby eliminating irrelevant or weakly correlated parameters and forming a refined combination of environmental parameters. The purpose is to provide the most relevant environmental factors for subsequent electrode material performance degradation analysis.

[0077] This embodiment ensures the synchronicity and reliability of data analysis by precisely aligning the microscopic environmental data and the third electrical signal in time. Based on this, calculating the cross-correlation function effectively quantifies and reveals the potential correlation between sensor signal anomalies and changes in microscopic environmental parameters. In-depth analysis of the peak width of the cross-correlation function identifies which environmental parameters exhibit significant temporal lag or synchronization with the abnormal sensor behavior, as well as the strength and persistence of this correlation. This allows for the precise screening of environmental parameter combinations associated with abnormal behavior patterns, providing crucial input for subsequent electrode material performance degradation analysis, thereby more accurately determining the dominant degradation factor and synergistic action mode.

[0078] Through the above technical solution, this embodiment can effectively identify environmental parameters directly related to abnormal sensor behavior patterns from complex microscopic environmental data, avoiding the computational burden and errors caused by blindly analyzing all environmental parameters. This precise screening method based on cross-correlation analysis improves the accuracy of identifying dominant degradation factors and synergistic modes, making the analysis of sensor performance degradation more refined and targeted. This provides a more reliable basis for subsequent adjustments to the signal-to-chromium concentration conversion function, ultimately improving the calculation accuracy of chromium ion concentration values.

[0079] In some embodiments, step S504, based on the combination of environmental parameters, performs an electrode material performance degradation analysis to obtain the electrode material performance degradation analysis results, which may include, but is not limited to, the following steps: Step S601: Identify the dominant parameters and interaction modes based on the combination of environmental parameters; Step S602: Evaluate the strength of the interaction between parameters in the combination of environmental parameters; Step S603: Select multiple decay primitives that match the dominant parameters from the decay mechanism primitive library based on the interaction strength and interaction mode. Step S604: Combine multiple decay primitives to generate a decay mechanism with a hierarchical structure; Step S605: Analyze the degradation mechanism and generate the electrode material performance degradation analysis results.

[0080] In some embodiments, the dominant parameters that have the greatest impact on sensor performance degradation can be identified based on the combination of environmental parameters, and the possible interaction modes among these parameters can be determined. Dominant parameters refer to environmental factors that play a decisive role in the performance degradation of sensor electrode materials under specific microenvironments, such as high humidity, extreme acidity / alkalinity, or specific organic matter concentrations. Interaction modes describe how these dominant parameters synergistically or antagonistically affect the degradation process; for example, high humidity may accelerate corrosion in acidic environments, forming a synergistic effect.

[0081] Then, the strength of interactions between parameters in the combination of environmental parameters is assessed. This can be quantified using statistical methods to determine the contribution of different environmental factors to electrode material degradation and the strength of their interactions. For example, the effects of humidity and pH on electrode oxidation rates can be analyzed to quantify the strength of their interactions. Based on the interaction strength and mode, multiple degradation primitives matching the dominant parameters are selected from a degradation mechanism primitive library. This library is a knowledge base containing various known basic processes of electrode material degradation (such as oxidation, corrosion, passivation, biofilm formation, etc.). Each primitive is associated with a specific environmental parameter and interaction mode. For example, if the dominant parameters are high redox potential and an acidic environment, degradation primitives such as "oxidative corrosion" and "acidic dissolution" might be selected.

[0082] Multiple decay primitives can then be combined to generate a hierarchical decay mechanism. The selected primitives can be organized according to their logical order and interdependencies in the actual decay process, forming a complete decay path diagram from macroscopic phenomena to microscopic mechanisms. For example, a decay mechanism can be constructed where "biofilm formation" is the initial primitive, leading to "local pH changes," which in turn triggers "electrode material corrosion." This hierarchical structure helps to more comprehensively understand complex decay processes.

[0083] Finally, the degradation mechanism is analyzed to generate electrode material performance degradation analysis results. The constructed hierarchical degradation mechanism can be analyzed in depth to quantify its impact on sensor performance and predict future degradation trends. This can include simulating the degradation process, calculating the degradation rate, identifying key degradation nodes, and ultimately outputting a detailed performance degradation report or predictive model.

[0084] This embodiment, through in-depth analysis of environmental parameter combinations, first identifies the dominant parameters and their interaction modes that decisively influence the performance degradation of electrode materials. This identification process allows degradation analysis to focus on the most critical environmental factors, avoiding the averaging of all parameters, thereby improving the specificity and accuracy of the analysis. By evaluating the interaction strength between parameters, the contribution of different environmental factors to degradation can be quantified, providing data support for subsequent mechanism construction. Furthermore, by selecting degradation primitives matching the dominant parameters from the degradation mechanism primitive library and combining them into a hierarchical degradation mechanism, this application can systematically and comprehensively simulate and understand the complex degradation process of electrode materials. This method based on primitive combination and hierarchical construction makes the generation of degradation mechanisms no longer a simple linear superposition, but can reflect the multi-factor, multi-stage degradation dynamics in the real world. For example, by identifying how biofilm formation induces local microenvironmental changes to accelerate electrode corrosion, complex degradation paths can be revealed. Finally, by analyzing the generated degradation mechanisms, accurate results of electrode material performance degradation analysis can be obtained. This analysis not only predicts the degradation trend of sensor performance but also identifies key factors and potential risks leading to degradation, providing a scientific basis for sensor maintenance, calibration, and lifespan prediction. Consequently, it enables a more accurate assessment of sensor reliability and data accuracy in specific soil microenvironments.

[0085] Through the above technical solution, this embodiment, by identifying dominant parameters, assessing interaction strength, combining degradation primitives, and constructing a hierarchical degradation mechanism, can reveal the intrinsic mechanism of electrode material degradation more deeply. This makes the obtained electrode material performance degradation analysis results more accurate and interpretable, helping to accurately predict sensor lifetime and performance drift, thereby providing more reliable raw data for soil heavy metal chromium pollution assessment and improving the accuracy and credibility of the overall assessment.

[0086] In some embodiments, step S605, analyzing the degradation mechanism and generating electrode material performance degradation analysis results, may include, but is not limited to, the following steps: Identify the nonlinear characteristics of the influence of each decay element in the decay mechanism on the sensor electrical signal; Based on the nonlinear characteristics, the response of the combination of environmental parameters is simulated to generate a multidimensional response surface; Perform topological analysis on the multidimensional response surface to identify feature points, including saddle points or inflection points. Based on the location and number of feature points, the results of electrode material performance degradation analysis are generated.

[0087] In some embodiments, failure to fully consider the complex nonlinear characteristics and interactions of the effects of each element on the sensor's electrical signal in the degradation mechanism may result in inaccurate or incomplete analysis of the degradation of electrode material performance, thereby affecting the accuracy of subsequent calibration and concentration calculations.

[0088] To this end, we can first identify the nonlinear characteristics of the impact of each decay element in the decay mechanism on the sensor's electrical signal. By analyzing experimental data, we can quantify and characterize how each decay element (e.g., oxidation, corrosion, adsorption) affects the sensor's electrical signal output in a nonlinear manner. For example, changes in certain environmental parameters may only significantly affect sensor performance after reaching a specific threshold, or their effects may not be a simple linear superposition but rather involve complex synergistic or antagonistic effects. The aim is to more accurately understand and model the intrinsic mechanism of the decay process.

[0089] Then, based on the nonlinear characteristics, response simulations are performed on combinations of environmental parameters to generate a multidimensional response surface. A nonlinear characteristic model can be constructed using the identified nonlinear features, taking combinations of environmental parameters as input to simulate the sensor's electrical signal response under different environmental conditions. By plotting these simulated responses in multidimensional space, a multidimensional response surface can be generated, which visually demonstrates the complex relationship between sensor performance and multiple environmental parameters. The aim is to visualize and quantify the comprehensive impact of environmental parameters on sensor performance.

[0090] Next, topological analysis is performed on the multidimensional response surface to identify characteristic points, including saddle points and inflection points. Mathematical geometry methods can be used to analyze the shape and structure of the multidimensional response surface to identify points of special significance. Saddle points typically represent a critical state where performance improves in some directions but deteriorates in others, while inflection points typically represent points where the rate of performance degradation changes significantly. These characteristic points embody key turning points or sensitive regions in the degradation mechanism. The aim is to reveal the key sensitive regions and critical conditions in the degradation mechanism.

[0091] Finally, based on the location and number of feature points, the electrode material performance degradation analysis results are generated. The performance degradation status of the electrode material can be comprehensively evaluated based on feature points such as saddle points and inflection points identified through topological analysis, combined with their specific location distribution and number in the multidimensional response surface. For example, the appearance of a specific feature point may indicate the activation of a certain degradation mode, while the number and location distribution of feature points may reflect the severity or complexity of the degradation. The aim is to provide a quantitative and physically meaningful degradation analysis report.

[0092] This embodiment, through in-depth analysis of the nonlinear effects of each degradation element on the sensor's electrical signal within the degradation mechanism, can more accurately capture the complex dynamics of sensor performance degradation. Specifically, identifying nonlinear characteristics makes the modeling of the degradation process more realistic, avoiding errors that may arise from linear models. Based on this, by simulating the response to combinations of environmental parameters and generating a multidimensional response surface, the variation law of sensor performance under the influence of multiple factors can be comprehensively displayed, providing an intuitive and rich data foundation for subsequent analysis. Furthermore, topological analysis of the multidimensional response surface identifies feature points such as saddle points or inflection points, enabling this embodiment to accurately locate key sensitive regions and critical conditions in the degradation mechanism. These feature points represent key thresholds or turning points in the transition of sensor performance from one state to another. For example, when a certain environmental parameter exceeds a specific value, the degradation rate increases sharply, or under the combined effect of multiple environmental factors, the sensor performance will undergo irreversible degradation. Identifying these key feature points allows for a more detailed and in-depth analysis of the performance degradation of electrode materials, thereby generating more instructive analytical results.

[0093] To illustrate this technical solution more clearly, a specific example is used below. Assume that a hierarchical degradation mechanism has been generated during the electrode material performance degradation analysis of a chromium ion sensor. To further analyze this degradation mechanism, firstly, the nonlinear characteristics of the influence of each degradation primitive (e.g., electrode surface oxidation, organic matter adsorption, ion exchange membrane swelling, etc.) on the sensor's electrical signal are identified. For example, experimental data shows that when the soil pH is below 5, the electrode oxidation rate increases exponentially rather than linearly. Secondly, based on these nonlinear characteristics, a mathematical model is constructed using the identified combinations of environmental parameters (e.g., soil moisture, pH, redox potential, etc.) to simulate the sensor's electrical signal response under different combinations of environmental parameters, generating a three-dimensional or higher-dimensional response surface. For example, this surface can show the drift of the sensor's output electrical signal under different combinations of moisture and pH. Next, topological analysis is performed on this multidimensional response surface to identify saddle points and inflection points. For example, a saddle point was found on the response surface, corresponding to the combination of high humidity and low pH. Near this point, the sensor performance becomes extremely sensitive to humidity changes, while remaining relatively stable to pH changes. Simultaneously, an inflection point was identified, indicating that when the organic matter index exceeds a certain threshold, the biofilm formation rate suddenly accelerates, leading to accelerated electrical signal drift. Finally, based on the location and number of these feature points, electrode material performance degradation analysis results were generated. For instance, the analysis indicates that the sensor faces a significant risk of accelerated oxidation in acidic, high-humidity environments, and that when organic matter accumulates to a certain level, the biofilm effect becomes the dominant degradation mechanism, providing a corresponding degradation rate prediction model. These analysis results provide precise guidance for subsequent sensor maintenance strategies and calibration parameter adjustments.

[0094] Through the above technical solution, this embodiment, by identifying nonlinear characteristics, constructing multidimensional response surfaces, and performing topological analysis, can more comprehensively and accurately reveal the degradation mechanism of electrode materials, especially the performance change patterns under complex microenvironments. Consequently, the generated electrode material performance degradation analysis results are not only more accurate but also provide in-depth insights into degradation-sensitive regions and critical conditions. This provides a more reliable theoretical basis and data support for subsequent sensor calibration, lifetime prediction, and material improvement, significantly enhancing the overall accuracy and reliability of soil heavy metal chromium pollution assessment.

[0095] The beneficial effects of implementing the embodiments of the present invention include: First, the sensor location information is obtained. Then, a sensor is selected from the chromium ion sensor cluster as the target sensor. Based on the sensor location information, the chromium ion concentration value at the location of the target sensor is calculated. Spatial extrapolation is performed on multiple chromium ion concentration values ​​to generate chromium pollution diffusion distribution information. Finally, based on the chromium pollution diffusion distribution information, chromium pollution evaluation is performed to obtain the chromium pollution evaluation result. Thus, diffusion distribution information can be generated by calculating chromium ion concentration values ​​to achieve chromium pollution evaluation, improving accuracy and food safety.

[0096] like Figure 2 As shown, this embodiment of the invention also provides a soil heavy metal chromium pollution assessment system based on sensor data, comprising: Information acquisition module 701 is used to acquire sensor location information; The sensor selection module 702 is used to select a sensor from the chromium ion sensor cluster as the target sensor. The concentration calculation module 703 is used to calculate the chromium ion concentration value at the location of the target sensor based on the sensor location information. The spatial estimation module 704 is used to spatially estimate multiple chromium ion concentration values ​​and generate information on the diffusion and distribution of chromium pollution. The chromium pollution assessment module 705 is used to assess chromium pollution based on chromium pollution diffusion and distribution information, and to obtain chromium pollution assessment results.

[0097] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0098] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for evaluating soil heavy metal chromium pollution based on sensor data, characterized in that, The method comprises the following steps: obtaining sensor position information; selecting a sensor as a target sensor from a cluster of chromium ion sensors; calculating a chromium ion concentration value at a position of the target sensor according to the sensor position information; spatially extrapolating a plurality of chromium ion concentration values to generate chromium pollution diffusion distribution information; evaluating chromium pollution according to the chromium pollution diffusion distribution information to obtain a chromium pollution evaluation result.

2. The method of claim 1, wherein, The method of calculating the chromium ion concentration value at the position of the target sensor according to the sensor position information comprises: identifying a reference sensor adjacent to the target sensor according to the sensor position information; obtaining a first electric signal collected by the target sensor, a soil microenvironment parameter at the position of the target sensor, and a second electric signal collected by the reference sensor; performing sensor performance degradation analysis on the target sensor according to response trends of the first electric signal and the second electric signal to obtain a sensor performance degradation analysis result; if the sensor performance degradation analysis result indicates that there is performance degradation, calculating a signal deviation according to the first electric signal and the second electric signal; performing correlation analysis on the soil microenvironment parameter and the signal deviation according to an association rule between electrode material performance degradation and environmental parameters to obtain a correlation analysis result; if the correlation analysis result indicates that there is strong correlation, adjusting a signal-chromium concentration conversion function; converting the first electric signal according to the adjusted signal-chromium concentration conversion function to obtain a chromium ion concentration value.

3. The method of claim 2, wherein, The method of adjusting the signal-chromium concentration conversion function comprises: obtaining macro-environmental data and a third electric signal collected by each chromium ion sensor in the cluster of chromium ion sensors; establishing a network overall signal drift reference line according to the macro-environmental data and a plurality of third electric signals; generating a calibration curve according to the network overall signal drift reference line; determining a parameter adjustment amount according to the calibration curve and the soil microenvironment parameter; adjusting the signal-chromium concentration conversion function according to the parameter adjustment amount.

4. The method of claim 3, wherein, The method of generating a calibration curve according to the network overall signal drift reference line comprises: collecting micro-environmental data, which includes soil humidity, pH, oxidation-reduction potential, organic matter index, microbial activity index, biofilm index, and chelator index; determining a dominant degradation factor and a synergistic action mode according to the micro-environmental data; constructing a non-linear correction function according to the dominant degradation factor and the synergistic action mode; correcting the network overall signal drift reference line according to the non-linear correction function to obtain the calibration curve.

5. The method of claim 4, wherein, The method of determining a dominant degradation factor and a synergistic action mode according to the micro-environmental data comprises: calculating a dynamic change trend according to the micro-environmental data; performing behavior pattern anomaly analysis according to the third electric signal to obtain a behavior pattern anomaly analysis result; if the behavior pattern anomaly analysis result indicates that there is an anomaly, identifying an environmental parameter combination associated with the behavior pattern anomaly through cross-correlation analysis according to the micro-environmental data. According to the environmental parameter combination, electrode material performance degradation analysis is performed to obtain electrode material performance degradation analysis results; According to the electrode material performance degradation analysis results and the dynamic change trend, the dominant degradation factor and the synergistic mode are determined.

6. The method of claim 5, wherein, According to the third electric signal, behavior mode anomaly analysis is performed to obtain behavior mode anomaly analysis results, including: Performing parameter safety check on the third electric signal to obtain parameter safety check results, the parameter safety check including instantaneous change rate safety check, electrode impedance safety check and reference electrode potential difference safety check; According to the parameter safety check results, the sensor operating state is identified; If the sensor operating state is normal operation, the trend deviation is calculated according to the current drift trend and the historical drift trend of the third electric signal; According to the trend deviation, the behavior mode is analyzed to obtain the behavior mode anomaly analysis results.

7. The method of claim 5, wherein, According to the micro-environmental data, the environmental parameter combination associated with the behavior mode anomaly is identified through cross-correlation analysis, including: Time alignment is performed on the micro-environmental data and the third electric signal; The cross-correlation function between the time-aligned third electric signal and the micro-environmental data is calculated; The peak width of the cross-correlation function is analyzed; According to the peak width of the cross-correlation function, the micro-environmental data is screened to obtain the environmental parameter combination.

8. The method of claim 5, wherein, According to the environmental parameter combination, electrode material performance degradation analysis is performed to obtain electrode material performance degradation analysis results, including: According to the environmental parameter combination, the dominant parameter and the interaction mode are identified; The interaction strength between the parameters in the environmental parameter combination is evaluated; According to the interaction strength and the interaction mode, a plurality of degradation primitives matching the dominant parameter are selected from a degradation mechanism primitive library; The plurality of degradation primitives are combined to generate a hierarchical degradation mechanism; The degradation mechanism is analyzed to generate the electrode material performance degradation analysis results.

9. The method of claim 8, wherein, The degradation mechanism is analyzed to generate the electrode material performance degradation analysis results, including: Identify the nonlinear characteristics of each degradation primitive in the degradation mechanism on the sensor electric signal; According to the nonlinear characteristics, response simulation is performed on the environmental parameter combination to generate a multi-dimensional response surface; Topological structure analysis is performed on the multi-dimensional response surface to identify feature points, including saddle points or inflection points; According to the positions and quantities of the feature points, the electrode material performance degradation analysis results are generated.

10. A soil heavy metal chromium pollution evaluation system based on sensor data, characterized in that, Including: An information acquisition module for acquiring sensor location information; A sensor selection module for selecting a sensor from a chromium ion sensor cluster as a target sensor; A concentration calculation module for calculating the chromium ion concentration value at the location of the target sensor according to the sensor location information; A spatial calculation module for spatially calculating a plurality of chromium ion concentration values to generate chromium pollution diffusion distribution information; A chromium pollution evaluation module for performing chromium pollution evaluation according to the chromium pollution diffusion distribution information to obtain chromium pollution evaluation results.

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