A cultivated soil heavy metal gridding monitoring and traceability analysis system

Abstract

The invention discloses to the technical field of cultivated soil, and particularly to a cultivated soil heavy metal gridding monitoring and traceability analysis system, which comprises an adaptive multi-level grid division module, a soil layer layering in-situ multi-parameter acquisition terminal, a field distributed transmission relay module, an encrypted time sequence cloud storage database, a grid heavy metal space-time hierarchical analysis module, a multi-source coupled pollution traceability deduction module, a differentiated partition control output module, a multi-level linkage over-standard early warning unit and a field handheld interactive checking terminal. The invention has the beneficial effect that the simultaneous acquisition equipment can obtain three layers of soil heavy metal data at one time, the traceability module can deduct the original heavy metal interference of the soil, clearly distinguish the two types of pollution paths of surface settlement and underground leakage, greatly reduce the traceability misjudgment probability, the monitoring distribution points are more scientific, and the pollution traceability accuracy is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of arable soil technology, specifically to a grid-based monitoring and source tracing system for heavy metals in arable soil. Background Technology

[0002] Arable land refers to land that has been cultivated and is suitable for farming, and is used year-round to grow various crops such as grains, vegetables, oilseeds, and fruits. It includes paddy fields, dry land, and irrigated land, and is the most basic land resource for agricultural production. This type of soil is affected by irrigation, fertilization, agricultural machinery, and agricultural activities over a long period of time, and is prone to absorbing irrigation water, chemical fertilizers, atmospheric deposition, and heavy metal pollutants from surrounding industrial and mining areas. Once heavy metals accumulate and exceed the standards, they will pollute crops and endanger food safety. Therefore, it is necessary to carry out routine grid-based monitoring and source traceability management.

[0003] Heavy metal monitoring of arable land soil involves routine soil sampling and testing of various types of arable land. The focus is on investigating the content of heavy metals such as cadmium, lead, mercury, chromium, and arsenic in the soil. By using fixed-point sampling and instrumental testing, the distribution and cumulative changes of heavy metals in the soil can be understood. This can not only promptly identify areas of local soil pollution exceeding standards, but also track the long-term development trend of pollution. It provides real data support for subsequent source tracing of pollution, development of soil remediation and arable land management plans, and is a basic regulatory means to safeguard the safety of arable land soil and ensure the quality and safety of agricultural products.

[0004] However, most existing monitoring methods for heavy metals in arable land soil use a uniform sampling method, without adjusting the sampling density based on risk points such as plot topography, irrigation ditches, sewage outlets, and solid waste dumps. Sampling only collects the top layer of soil, ignoring the differences in heavy metal enrichment among the topsoil, plow pan, and subsoil layers. This makes it impossible to distinguish whether the pollution is caused by surface atmospheric deposition, agricultural input application, or underground sewage infiltration, which can easily lead to biased source tracing judgments.

[0005] To this end, we propose a grid-based monitoring and source tracing analysis system for heavy metals in arable land soil. Summary of the Invention

[0006] In view of the problems existing in the above and / or existing grid-based monitoring and source tracing analysis system for heavy metals in arable land soil, this invention is proposed.

[0007] Therefore, the purpose of this invention is to provide a grid-based monitoring and source tracing analysis system for heavy metals in arable land soil. By improving existing methods for monitoring heavy metals in arable land soil, this system can solve the problems mentioned above. Most existing methods for monitoring heavy metals in arable land soil use a uniform sampling method, which does not adjust the sampling density based on risk points such as plot topography, irrigation ditches, sewage outlets, and solid waste dumps. Sampling only collects the topsoil layer, ignoring the differences in heavy metal enrichment among the topsoil, plow pan, and subsoil layers. This makes it impossible to distinguish whether pollution is caused by surface atmospheric deposition, agricultural input application, or underground sewage infiltration, and the source tracing judgment is prone to errors.

[0008] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution:

[0009] A grid-based monitoring and source tracing analysis system for heavy metals in arable land soil includes an adaptive multi-level grid division module, a soil layer stratification in-situ multi-parameter acquisition terminal, a field distributed transmission relay module, an encrypted time-series cloud storage database, a grid-based spatiotemporal classification and analysis module for heavy metals, a multi-source coupled pollution source tracing and deduction module, a differentiated zoning control output module, a multi-level linkage exceedance early warning unit, and a field handheld interactive verification terminal.

[0010] The adaptive multi-level grid division module automatically completes grid hierarchical division by coupling farmland topography, water system, and distribution of various external pollution sources. High-risk areas such as sewage outlets and solid waste dumps are automatically densified into secondary micro-grids. After adding pollution sources, the grid can be dynamically re-divided. Each grid is assigned a unique code and bound to the full-cycle archive of the land parcel.

[0011] The in-situ multi-parameter soil layer acquisition terminal is equipped with a retractable layered soil sampling mechanism, which can simultaneously collect multiple sets of heavy metal and physicochemical data of three soil layers: topsoil, plow layer, and subsoil. It also features a self-cleaning anti-cross-contamination structure and a long-term power supply unit for field use.

[0012] The grid-based heavy metal spatiotemporal classification and analysis module accesses multi-layer soil time-series monitoring data, classifies pollution risk levels based on national standard dual thresholds, and combines the longitudinal concentration difference of soil layers and the lateral concentration gradient of adjacent grids to identify the spatiotemporal evolution pattern of long-term accumulation / sudden exceedance of heavy metals.

[0013] The multi-source coupled pollution source tracing and inference module is the core innovative unit of the system. After deducting the background value of native heavy metals in the soil, it calculates the contribution weight of four types of pollution sources: irrigation, agricultural materials, industrial and mining, and air pollution in parallel. It combines the vertical enrichment characteristics of heavy metals in the three soil layers and the topographic water flow diffusion factor to correct the pollution contribution ratio, distinguishes the two types of pollution paths: surface external source deposition and underground sewage infiltration, and quantitatively outputs the proportion of pollution sources in each grid layer.

[0014] The differentiated zoning control output module, in conjunction with the grid pollution risk level and the dominant pollution source determined by source tracing, matches the corresponding layered remediation and source control combination schemes, and automatically generates exportable grid monitoring and source tracing graphic reports.

[0015] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the field distributed transmission relay module is deployed along field roads and irrigation canals to form a mesh communication link, establishes a LoRa low-power wireless connection with each acquisition terminal, and performs outlier removal, missing value interpolation, coordinate calibration, and data encryption preprocessing after receiving cached data packets from the terminal; an offline caching mechanism is enabled in areas of the field without public network coverage, and the interrupted transmission is resumed and batch uploaded to the cloud after the mobile signal is restored; a single relay can link multiple surrounding grid acquisition terminals, reducing the cost of field cabling and base station deployment.

[0016] The encrypted time-series cloud storage database stores data in separate databases and partitions, including a grid-based basic archive database, a layered heavy metal time-series monitoring database, an external pollution source ledger database, an agricultural input record database, and a topographic and hydrological spatial database. All data is linked using grid codes as the primary key. It automatically stores time-series datasets of continuous changes in heavy metals in three soil layers year by year and season by season. It sets hierarchical access encryption permissions to distinguish different viewing permissions for farmers, grassroots supervisors, and municipal platforms.

[0017] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the multi-level linkage over-standard early warning unit and the spatiotemporal classification analysis module are linked in real time. Once the heavy metal content in any soil layer of the grid reaches the moderate or severe risk threshold, a graded early warning is triggered: for mild over-standards, only a notification message is sent to farmers; for moderate over-standards, a notification is sent to both farmers and village-level supervisors; and for severe over-standards, the notification is sent directly to the municipal and county-level soil supervision platform. The early warning message includes the location of the over-standard grid, pollution data of the three soil layers, and preliminary source tracing results, and supports triple push notifications via SMS, system pop-ups, and mobile APP.

[0018] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the field handheld interactive verification terminal is equipped with a grid code scanning and recognition function. By scanning the data collection terminal and the grid identification QR code on-site, all historical stratified monitoring data and source tracing conclusions of the grid can be retrieved. When inspecting the field, staff can manually record the use of fertilizers, pesticides, and irrigation wastewater in the current season, and update the cloud database in real time. At the same time, it supports the uploading of on-site retest data, and the system automatically compares the monitoring results with historical data to complete manual verification and correction.

[0019] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the adaptive multi-level grid division module supports manual visual adjustment of grid boundaries. Staff can manually add or remove monitoring grids according to the actual cultivation situation of the plot, and the modified grid information will be synchronously updated to the plot archive in the cloud.

[0020] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the soil layer in-situ multi-parameter acquisition terminal integrates on-site shooting components. After soil sampling is completed, the real-time image of the soil profile is captured simultaneously, and the image data, along with the heavy metal and physicochemical data, is transmitted to the relay module and stored in the cloud.

[0021] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the grid-based spatiotemporal classification and analysis module for heavy metals has an abnormal data identification function. When the deviation between two consecutive monitoring values ​​of the same grid is too large, the abnormal points are automatically marked, reminding staff to go to the site for resampling and testing.

[0022] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the multi-source coupled pollution source tracing and deduction module can compare the background values ​​of the unpolluted blank grid, eliminate the interference of heavy metals inherent in the soil itself, and avoid misjudging native soil elements as external pollution inputs.

[0023] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the differentiated zoning control output module has multiple governance scheme materials built-in. If the source tracing determines that the pollution comes from irrigation water, the water purification scheme is pushed first. If the pollution is caused by fertilizers and agricultural inputs, a low-heavy-metal input replacement scheme is matched.

[0024] As a preferred embodiment of the grid-based monitoring and source tracing analysis system for heavy metals in arable land soil described in this invention, the encrypted time-series cloud storage database retains all data modification and on-site verification operation records, and the complete operation log is stored for a long time, which can be used as digital evidence for arable land pollution control and responsibility division.

[0025] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0026] 1. This invention specifically addresses the pain points of traditional monitoring methods, such as uniform and evenly distributed monitoring points, collection of only surface soil samples, and susceptibility to errors in source tracing. When a new pollution source is added, the system automatically updates the grid layout. Simultaneously, the data acquisition device can acquire heavy metal data from three soil layers at once. The source tracing module can deduct interference from native heavy metals in the soil and, by combining the vertical enrichment of soil layers, the horizontal concentration difference of the plot, and topographic and water flow factors, quantify the contribution ratio of various external pollution sources. It clearly distinguishes between two types of pollution pathways: surface subsidence and underground seepage, significantly reducing the probability of source tracing misjudgment. The monitoring point layout is more scientific, and the accuracy of pollution source tracing is significantly improved.

[0027] 2. In this invention, the field relay equipment is adapted to the field environment without network, supports offline caching and breakpoint resume, and stores years of time-series data in the cloud database with access permissions. All operation records are retained as evidence for liability determination. The system can automatically identify abnormal monitoring data and push early warning information in a graded manner after exceeding the standard. Staff can use handheld terminals to enter agricultural records and retest and verify data on-site in rural areas. It can also automatically match the corresponding remediation and control plan according to the pollution level and pollution source. The whole system is adapted to the daily operation of grassroots agricultural supervision, and the workload of manual labor is greatly reduced.

[0028] 3. In this invention, the entire system is adapted for long-term unattended use in the field, and is equipped with low-power wireless communication, self-cleaning equipment, and long-term power supply design, which reduces equipment maintenance and site wiring costs. It realizes the digitalization of soil monitoring, pollution analysis, and farmland management throughout the entire process, eliminating the need for a large number of paper records. The data is retained for a long time, which facilitates the comparison of the changing trends of heavy metals in farmland year by year. It is convenient for regulatory personnel to track the evolution of pollution in plots over a long period of time, providing complete digital support for farmland classification management, soil remediation, and pollution source control, and improving the standardization level of long-term management of heavy metals in farmland soil. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the process of the present invention;

[0030] Figure 2 This is a schematic diagram of the adaptive multi-level mesh generation module of the present invention;

[0031] Figure 3 This is a schematic diagram of the multi-source coupled pollution tracing and deduction module of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0033] This invention provides a grid-based monitoring and source tracing analysis system for heavy metals in arable land soil. It specifically addresses the pain points of traditional monitoring methods, such as uniform and uniform point distribution, collection of only surface soil data, and susceptibility to errors in source tracing. The system automatically updates the grid layout when a new pollution source is added. Simultaneously, the data acquisition device can acquire heavy metal data from three soil layers at once. The source tracing module can deduct interference from native heavy metals in the soil and, by combining the vertical enrichment of soil layers, the horizontal concentration difference of plots, and topographic and water flow factors, quantify the contribution ratio of various external pollution sources. It clearly distinguishes between two types of pollution pathways: surface subsidence and groundwater seepage, significantly reducing the probability of source tracing misjudgment. The system offers the advantages of more scientific monitoring point distribution and significantly improved accuracy in pollution source tracing.

[0034] Please see Figure 1-3 A grid-based monitoring and source tracing analysis system for heavy metals in arable land soil includes an adaptive multi-level grid division module, a soil layer stratification in-situ multi-parameter acquisition terminal, a field distributed transmission relay module, an encrypted time-series cloud storage database, a grid-based spatiotemporal classification and analysis module for heavy metals, a multi-source coupled pollution source tracing and deduction module, a differentiated zoning control output module, a multi-level linkage over-standard early warning unit, and a field handheld interactive verification terminal. The adaptive multi-level grid division module automatically completes grid classification and subdivision by coupling arable land topography, water system, and distribution of multiple types of external pollution sources. High-risk areas such as sewage outlets and solid waste dumps are automatically densified with secondary microgrids. The grid can be dynamically re-divided after the addition of pollution sources. Each grid is assigned a unique code and bound to the full-cycle archive of the plot.

[0035] The soil stratification in-situ multi-parameter acquisition terminal is equipped with a retractable stratified soil sampling mechanism, which can simultaneously collect multiple sets of heavy metal and physicochemical data from three soil layers: topsoil, plow pan, and subsoil. It features a self-cleaning structure to prevent cross-contamination and a long-term power supply unit for field use. The grid-based heavy metal spatiotemporal classification and analysis module integrates multi-layer soil time-series monitoring data, classifies pollution risk levels based on national standard dual thresholds, and identifies the spatiotemporal evolution patterns of long-term accumulation / sudden exceedances of heavy metals by combining the longitudinal concentration difference of soil layers and the lateral concentration gradient of adjacent grids. The multi-source coupled pollution tracing and inference module is the core innovative unit of the system. After deducting the background value of native heavy metals in the soil, it calculates the contribution weights of four types of pollution sources: irrigation, agricultural inputs, industrial and mining, and air pollution in parallel. It combines the longitudinal enrichment characteristics of heavy metals in the three soil layers and the topographic and water flow diffusion factors to correct the pollution contribution ratio, distinguishes between two types of pollution pathways: surface external deposition and groundwater infiltration, and quantifies the proportion of pollution sources in each grid layer.

[0036] The differentiated zoning control output module, combined with the grid pollution risk level and the dominant pollution source identified through source tracing, matches corresponding layered remediation and source control combination schemes, automatically generating exportable grid monitoring and source tracing graphic reports. It specifically addresses the pain points of traditional monitoring, such as uniform and even distribution of monitoring points, collection of only surface soil, and susceptibility to errors in source tracing. When a new pollution source is added, the system automatically updates the grid layout. Simultaneously, the data acquisition equipment can acquire heavy metal data from three soil layers at once. The source tracing module can deduct interference from native heavy metals in the soil and, combined with the vertical enrichment of soil layers, the horizontal concentration difference of the plot, and topographic and water flow factors, quantifies the contribution ratio of various external pollution sources, clearly distinguishes between surface subsidence and groundwater seepage pollution pathways, significantly reduces the probability of source tracing misjudgment, makes monitoring point deployment more scientific, and significantly improves the accuracy of pollution source tracing.

[0037] Among them, the field distributed transmission relay module is deployed along field roads and irrigation canals to form a mesh communication link, establishes a LoRa low-power wireless connection with each data acquisition terminal, and performs outlier removal, missing value interpolation, coordinate calibration, and data encryption preprocessing after receiving data packets from the terminal. In areas of the field without public network coverage, an offline caching mechanism is activated, and the data is uploaded to the cloud in batches after the mobile signal is restored. A single relay can link multiple surrounding grid data acquisition terminals, reducing the cost of field cabling and base station deployment. The encrypted time-series cloud storage database stores data in separate databases and partitions, including a grid basic archive database, a layered heavy metal time-series monitoring database, an external pollution source ledger database, an agricultural input record database, and a topographic and hydrological spatial database. All data is associated with the grid code as the primary key, and the database automatically stores the annual and seasonal three-layer soil heavy metal continuous change time-series datasets. Hierarchical access encryption permissions are set to distinguish the different viewing permissions of farmers, grassroots supervisors, and municipal platforms.

[0038] The multi-level linkage early warning unit and the spatiotemporal classification analysis module work in real time. Once the heavy metal content in any soil layer of the grid reaches the moderate or severe risk threshold, a graded early warning is triggered: mild exceedances only send a notification to farmers, moderate exceedances send a notification to both farmers and village-level supervisors, and severe exceedances send a notification directly to the municipal and county-level soil monitoring platform. The early warning message includes the location of the exceedance grid, pollution data of the three soil layers, and preliminary source tracing results, and supports triple push notifications via SMS, system pop-ups, and mobile APP. The handheld interactive verification terminal in the field is equipped with a grid code scanning and recognition function. By scanning the QR code of the data collection terminal and the grid identification, all historical stratified monitoring data and source tracing conclusions of the grid can be retrieved on-site. When inspecting the field, staff can manually record the use of fertilizers, pesticides, and irrigation wastewater in the current season, and update the cloud database in real time. It also supports the uploading of on-site retest data, and the system automatically compares the monitoring results with historical data to complete manual verification and correction.

[0039] The adaptive multi-level grid division module supports manual and visual adjustment of grid boundaries. Staff can manually add or remove monitoring grids according to the actual cultivation situation of the plot. The modified grid information will be updated synchronously to the plot file in the cloud. The soil layer in-situ multi-parameter acquisition terminal integrates on-site shooting components. After soil sampling, it synchronously shoots the real scene of the soil profile. The image data, along with heavy metal and physicochemical data, is transmitted to the relay module and stored in the cloud. The grid heavy metal spatiotemporal classification and analysis module has an abnormal data identification function. When the difference between two monitoring values ​​of the same grid is too large, it automatically marks the abnormal point and reminds staff to go to the site for resampling and testing.

[0040] The multi-source coupled pollution tracing and simulation module can compare the baseline values ​​of the unpolluted blank grid to eliminate the interference of heavy metals inherent in the soil itself, avoiding misjudging native soil elements as external pollution inputs; the differentiated zoning control output module has built-in materials for various treatment schemes. If the source of pollution is determined to be from irrigation water, water purification schemes will be prioritized. If the pollution is caused by fertilizers and agricultural inputs, low-heavy-metal input replacement schemes will be matched; the encrypted time-series cloud storage database will retain all data modification and on-site verification operation records. The complete operation log is stored for a long time and can be used as digital evidence for farmland pollution control and responsibility division.

[0041] The workflow of this invention is as follows: In the early stage of the operation, the adaptive multi-level grid division module completes the grid planning of the entire cultivated land area. The staff imports the geographical information such as the boundary of the entire cultivated land, topographic elevation, irrigation system, industrial and mining sewage discharge points, solid waste dumps, and roads. The system automatically completes the hierarchical grid division. Ordinary cultivated land far away from pollution sources uses conventional grids, while high-risk areas such as downstream of sewage outlets and around sewage dumps are automatically divided into smaller, more encrypted micro-grids. The system generates an independent code for each grid and simultaneously records the crops planted on the plot, the agricultural inputs used in previous years, and the irrigation methods to form a unique plot file. If sewage discharge points or solid waste dumping areas are added to the area later, the system will automatically readjust the surrounding grid range. The staff can also manually drag and modify the grid boundary and add or remove monitoring points through the visual interface. The modified grid file is saved to the cloud database in real time.

[0042] After the grid planning is completed, the in-situ multi-parameter soil layer acquisition terminal is deployed at each grid monitoring point. The equipment is equipped with a retractable layered soil sampling mechanism, which can extract soil samples from the topsoil, plow pan, and subsoil layers during operation. It can simultaneously detect the content of various heavy metals, soil pH, organic matter, and other physicochemical indicators. After each sampling, the equipment automatically blows away the soil sampling tube and detection probe to avoid cross-interference between soil samples from previous and subsequent points. The equipment is equipped with a long-term field energy storage power supply, which can be placed in the field for a long time without human intervention for sampling. At the same time as sampling, the equipment's built-in imaging component takes pictures of the soil profile. All detection values, images, and positioning information are cached locally on the equipment.

[0043] Distributed transmission relay devices are deployed along field roads and irrigation canals, establishing low-power wireless connections with multiple surrounding data acquisition terminals. They periodically receive cached data packets from the terminals, automatically removing obvious abnormal values, filling in missing parameters, calibrating positioning coordinates, and encrypting the data. In remote farmland without mobile networks, the relay devices first store all data locally, and then upload it in batches to an encrypted time-series cloud storage database once they reach an area with signal coverage. The database is organized into grid files, layered monitoring data, pollution source ledgers, agricultural input records, and topographic data. All data is integrated using grid codes as a correlation thread, automatically saving continuous monitoring time-series data for many years and different seasons. At the same time, it distinguishes the different data viewing permissions for farmers, village-level, and city-level regulatory personnel. Every data entry, modification, and on-site verification generates an undeletable operation log that is stored long-term and can serve as a digital certificate for pollution liability determination.

[0044] After the data is entered into the database, the grid-based heavy metal spatiotemporal classification and analysis module automatically retrieves multi-layer monitoring data from the same grid over many years. It classifies the pollution risks as light, moderate, and heavy according to the national soil heavy metal risk standards. The system compares the heavy metal concentration gradients of adjacent grids horizontally and the differences in the content of the three soil layers vertically. It identifies different trends of heavy metal accumulation, sudden exceedance, and slow decline in the plot. When the difference between two monitoring values ​​in the same grid is too large, it automatically marks the abnormal points and reminds the staff to resample and retest on site.

[0045] After the data analysis is completed, it is automatically sent to the multi-source coupled pollution source tracing and inference module to carry out quantitative source tracing work. The system first retrieves the soil background data of the unpolluted blank grid, deducts the numerical interference caused by the native heavy metals of the plot, and then calculates the contribution weight of four types of external pollution sources in parallel: irrigation water, fertilizers and pesticides, industrial and mining exhaust gas, and atmospheric dust. It distinguishes between surface deposition pollution and underground sewage infiltration pollution by combining the heavy metal enrichment characteristics of the three soil layers. Then, it corrects the calculation error caused by cross-grid diffusion based on the plot topography, annual water flow, and wind direction. Finally, it outputs a quantitative proportion report of various pollution sources for each grid and each soil layer.

[0046] Once the system identifies any soil layer as having reached the medium or heavy pollution standards for heavy metals, the multi-level linkage warning unit immediately sends out graded alerts. For minor exceedances, only the farmers in the corresponding plot are notified; for moderate exceedances, both farmers and village administrators are notified simultaneously; and for severe pollution, the alert is directly sent to the municipal and county-level soil monitoring platforms. The warning message includes the location of the exceedance grid, the pollution data for the three layers, and preliminary source tracing conclusions, and can be delivered simultaneously through three channels: SMS, system pop-ups, and mobile APP.

[0047] When grassroots staff go to the countryside for inspections, they can use a handheld interactive verification terminal in the field to scan the grid and collect the QR code of the equipment to quickly retrieve all historical monitoring data and source analysis results of the grid over the years. During the inspection, they can also record the use of fertilizers, pesticides and sewage irrigation in the current year on site, and upload the test data of the soil retest on site. The system automatically compares the historical records to complete the manual verification and correction of the data, and all on-site operations are updated in real time to the cloud database.

[0048] Finally, the system generates a complete governance plan through the differentiated zoning control output module. It matches corresponding measures based on the grid pollution risk level and the main pollution sources identified through source tracing. For irrigation pollution, it prioritizes water purification plans; for agricultural input pollution, it matches low-heavy-metal fertilizer alternatives; and for heavily polluted plots, it provides fallow and ex-situ remediation plans. At the same time, it automatically generates complete graphic monitoring and source tracing reports that can be exported and printed. The entire process is completed in a closed loop, which facilitates staff to carry out subsequent farmland remediation and long-term management work.

[0049] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A grid-based monitoring and source tracing analysis system for heavy metals in arable land soil, characterized in that, It includes an adaptive multi-level grid division module, a soil layer in-situ multi-parameter acquisition terminal, a field distributed transmission relay module, an encrypted time-series cloud storage database, a grid-based heavy metal spatiotemporal classification and analysis module, a multi-source coupled pollution tracing and deduction module, a differentiated zoning control output module, a multi-level linkage over-standard early warning unit, and a field handheld interactive verification terminal. The adaptive multi-level grid division module automatically completes grid hierarchical division by coupling farmland topography, water system, and distribution of various external pollution sources. High-risk areas such as sewage outlets and solid waste dumps are automatically densified into secondary micro-grids. After adding pollution sources, the grid can be dynamically re-divided. Each grid is assigned a unique code and bound to the full-cycle archive of the land parcel. The in-situ multi-parameter soil layer acquisition terminal is equipped with a retractable layered soil sampling mechanism, which can simultaneously collect multiple sets of heavy metal and physicochemical data of three soil layers: topsoil, plow layer, and subsoil. It also features a self-cleaning anti-cross-contamination structure and a long-term power supply unit for field use. The grid-based heavy metal spatiotemporal classification and analysis module accesses multi-layer soil time-series monitoring data, classifies pollution risk levels based on national standard dual thresholds, and combines the longitudinal concentration difference of soil layers and the lateral concentration gradient of adjacent grids to identify the spatiotemporal evolution pattern of long-term accumulation / sudden exceedance of heavy metals. The multi-source coupled pollution source tracing and inference module is the core innovative unit of the system. After deducting the background value of native heavy metals in the soil, it calculates the contribution weight of four types of pollution sources: irrigation, agricultural materials, industrial and mining, and air pollution in parallel. It combines the vertical enrichment characteristics of heavy metals in the three soil layers and the topographic water flow diffusion factor to correct the pollution contribution ratio, distinguishes the two types of pollution paths: surface external source deposition and underground sewage infiltration, and quantitatively outputs the proportion of pollution sources in each grid layer. The differentiated zoning control output module, in conjunction with the grid pollution risk level and the dominant pollution source determined by source tracing, matches the corresponding layered remediation and source control combination schemes, and automatically generates exportable grid monitoring and source tracing graphic reports.

2. The grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 1, characterized in that, The field distributed transmission relay module is deployed along field roads and irrigation canals to form a network communication link, establishes a LoRa low-power wireless connection with each data acquisition terminal, and performs outlier removal, missing value interpolation, coordinate calibration, and data encryption preprocessing after receiving data packets from the terminal. In areas of the field without public network coverage, an offline caching mechanism is activated. Once the mobile signal is restored, the data can be resumed and uploaded to the cloud in batches. A single relay can link multiple surrounding grid collection terminals, reducing the cost of field cabling and base station deployment. The encrypted time-series cloud storage database stores data in separate databases and partitions, including a grid-based basic archive database, a layered heavy metal time-series monitoring database, an external pollution source ledger database, an agricultural input record database, and a topographic and hydrological spatial database. All data is linked using grid codes as the primary key. It automatically stores time-series datasets of continuous changes in heavy metals in three soil layers year by year and season by season. It sets hierarchical access encryption permissions to distinguish different viewing permissions for farmers, grassroots supervisors, and municipal platforms.

3. The grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 2, characterized in that, The multi-level linkage over-standard early warning unit is linked in real time with the spatiotemporal classification and analysis module. Once the heavy metal in any soil layer of the grid reaches the medium or severe risk threshold, a graded early warning is triggered: mild over-standards only push notification information to farmers, moderate over-standards simultaneously push to farmers and village-level supervisors, and severe over-standards directly reach the municipal and county-level soil supervision platform. The early warning message includes the location of the over-standard grid, pollution data of the three soil layers, and preliminary source tracing results, and supports triple push via SMS, system pop-ups, and mobile APP.

4. The grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 3, characterized in that, The field handheld interactive verification terminal is equipped with a grid code scanning and recognition function. By scanning the data collection terminal and the grid identification QR code on site, all historical stratified monitoring data and traceability conclusions of the grid can be retrieved. When inspecting the field, staff can manually record the use of fertilizers, pesticides and irrigation wastewater in the current season, and update the cloud database in real time. At the same time, it supports the uploading of on-site retest data, and the system automatically compares the monitoring results with historical data to complete manual verification and correction.

5. The grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 4, characterized in that, The adaptive multi-level grid division module supports manual visual adjustment of grid boundaries. Staff can manually add or remove monitoring grids according to the actual cultivation situation of the plot, and the modified grid information will be updated synchronously to the plot archive in the cloud.

6. The grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 5, characterized in that, The in-situ multi-parameter acquisition terminal for soil stratification integrates on-site shooting components. After soil sampling is completed, it simultaneously captures real-time images of the soil profile. The image data, along with heavy metal and physicochemical data, are transmitted to the relay module and stored in the cloud.

7. The grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 6, characterized in that, The grid-based heavy metal spatiotemporal classification and analysis module has an abnormal data identification function. When the difference between two consecutive monitoring values ​​of the same grid is too large, it automatically marks the abnormal points and reminds the staff to go to the site for resampling and testing.

8. The grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 7, characterized in that, The multi-source coupled pollution tracing and inference module can compare the baseline values ​​of the unpolluted blank grid, eliminate the interference of heavy metals inherent in the soil, and avoid misjudging native soil elements as external pollution inputs.

9. A grid-based monitoring and source tracing system for heavy metals in arable land soil according to claim 8, characterized in that, The differentiated zoning control output module has multiple governance scheme materials built in. If the source of pollution is determined to be irrigation water, the water purification scheme will be pushed first. If the pollution is caused by fertilizers and agricultural inputs, a low-heavy-metal input replacement scheme will be matched.

10. A gridded monitoring and source tracing analysis system for heavy metals in arable land soil according to claim 9, characterized in that, The encrypted time-series cloud storage database will retain all data modification and on-site verification operation records. The complete operation log will be stored for a long time and can be used as digital evidence for farmland pollution control and responsibility division.

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