A tobacco field soil acidification early warning and treatment system based on an internet of things

Abstract

The application discloses a tobacco field soil acidification early warning management system based on an Internet of Things, and relates to the technical field of intelligent agricultural Internet of Things, comprising: an Internet of Things sensor network arranged in a tobacco field area and used for collecting soil pH value, temperature, humidity and calcium and magnesium ion concentration data in real time. The tobacco field soil acidification early warning management system based on the Internet of Things solves the problems of early warning lag or false alarm caused by a fixed threshold value by dynamically fusing a tobacco growth stage sensitive coefficient, a regional acidification historical trend and real-time monitoring data, generating an adaptive early warning threshold value through a nonlinear product algorithm; the double verification mechanism of trend deviation inspection and virtual management simulation is combined to screen out abnormal fluctuation interference and predict the feasibility of a strategy through rigid rules, so that the management scheme has both accuracy and robustness, and the problems of management resource mismatch and substandard effect are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart agricultural Internet of Things, and specifically to an Internet of Things-based tobacco field soil acidification early warning and management system. Background Art

[0002] In the field of IoT-based tobacco field soil monitoring and treatment technology, existing systems can deploy sensor networks to collect real-time data on key acidification indicators, such as soil pH, and trigger early warnings and initiate standardized treatment measures based on preset fixed thresholds. However, these systems have significant limitations at the management level. The core issue lies in the lack of intelligence in early warning decision-making and treatment plan formulation, which fails to fully consider the key dynamic factors in tobacco field management. Specifically, the systems generally use a single, static early warning threshold, ignoring the important agronomic principle that tobacco crops exhibit significant differences in sensitivity to soil acidification at different growth and development stages. For example, seedlings may be extremely sensitive to mild acidification, while later in maturity they are relatively tolerant. Fixed thresholds cannot adapt to these stage-by-stage changes in demand.

[0003] Most existing systems focus solely on instantaneous data from real-time monitoring points, lacking the ability to analyze and utilize historical trends in soil acidification in specific fields. Different regions vary in their acidification rates, resilience, and response to treatment measures due to their cropping histories, past fertilization and irrigation patterns, and baseline soil characteristics. This lack of ability to integrate and analyze dynamic information about crop growth stages and regional historical acidification trends leads to a rigid early warning mechanism, potentially generating false alarms or missed alerts. Furthermore, the resulting treatment strategies are overly broad and uniform. Consequently, treatment resources cannot be precisely allocated, leading to insufficient treatment that fails to effectively curb acidification, or excessive treatment that wastes lime and other materials, equipment, and energy, ultimately compromising treatment effectiveness and the economic benefits of tobacco cultivation. Therefore, the pressing technical challenge is how to overcome the rigidity of existing systems' early warning thresholds and the inflexible nature of their treatment strategies. By effectively integrating real-time soil data, information on tobacco crop growth stages, and historical regional soil acidification trends, precise and differentiated early warning decisions and resource-optimized treatment can be achieved. Summary of the Invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a tobacco field soil acidification early warning and control system based on the Internet of Things, comprising:

[0005] An IoT sensor network is deployed in tobacco fields to collect real-time data on soil pH, temperature, humidity, and calcium and magnesium ion concentrations;

[0006] A data fusion module, connected to the IoT sensor network via a data bus, is used to integrate real-time soil data, tobacco crop growth stage information, and historical acidification trend data;

[0007] The third-order dynamic coupling decision engine is connected to the data fusion module through a data interface to generate dynamic warning thresholds and optimize governance strategies;

[0008] A dual verification module, embedded within the three-order dynamic coupling decision engine, is used to perform anomaly detection and strategy feasibility verification on dynamic warning thresholds;

[0009] The execution terminal receives the management instructions output by the double verification module through the wireless communication module and performs precise spraying operations.

[0010] Preferably, the data fusion module includes:

[0011] The growth stage intelligent recognition unit is used to analyze drone aerial images and leaf sensor data, and combine them with the accumulated temperature model to output the regional sensitivity coefficients of tobacco seedlings, clusters, and growing seasons;

[0012] The historical trend analysis unit uses a temporal convolutional network to process the historical acidification data of the target field and outputs the regional acidification slope factor and the governance response attenuation coefficient.

[0013] Preferably, the third-order dynamic coupling decision engine includes:

[0014] A dynamic threshold generation unit is used to perform the following operations:

[0015] Receive the real-time pH value, current regional sensitivity coefficient and acidification slope factor output by the data fusion module;

[0016] Calculating a dynamic warning threshold based on a weighted decision function, wherein the weighted decision function relates a product term of a sensitivity coefficient and an acidification slope factor;

[0017] The strategy generation unit is used to generate the acidification modifier delivery parameters according to the warning level confirmed by the double verification module.

[0018] Preferably, the dual verification module includes:

[0019] Trend deviation detection unit, with built-in long-short-term memory network, is used to compare the deviation between the real-time pH change rate of the tested area and the historical acidification trend. When the deviation exceeds the set tolerance threshold, the threshold correction is triggered;

[0020] The virtual governance verification unit runs Monte Carlo simulation through the digital twin platform to simulate the pH recovery trajectory of the governance plan corresponding to the dynamic threshold. When the number of simulated failures exceeds the set failure threshold, the strategy callback is triggered.

[0021] Preferably, the virtual treatment inspection unit calls the treatment response attenuation coefficient of the historical trend analysis unit during the simulation process to dynamically adjust the simulation parameters of the acidification modifier dosage;

[0022] When the number of simulation failures exceeds the failure number threshold, a threshold callback instruction and correction parameters are sent to the dynamic threshold generation unit.

[0023] Preferably, the strategy generation unit includes:

[0024] Multi-objective optimization processor, used to find the optimal balance between treatment cost, equipment utilization and expected recovery effect;

[0025] Output port for generating a gridded placement map containing the acidifier type zones and a table of spatiotemporal routings for the spray equipment.

[0026] Preferably, it also includes:

[0027] The resource scheduling module, the output port of the physical connection strategy generation unit, is used to plan the movement trajectory and start-stop timing of the sprinkler equipment based on the spatiotemporal scheduling path table.

[0028] Preferably, the execution terminal includes:

[0029] Variable-rate spraying device, equipped with a geo-positioning module and flow controller, for performing differentiated acidification amendment spraying according to a gridded delivery map;

[0030] The status feedback circuit is used to upload the equipment location and operation progress to the dual verification module in real time.

[0031] Preferably, it also includes:

[0032] Closed-loop feedback module, with data input connected to the IoT sensor network and data output connected to the historical trend analysis unit;

[0033] The closed-loop feedback module updates the model through a Bayesian network and uses the post-treatment pH monitoring data to dynamically correct the treatment response attenuation coefficient.

[0034] Preferably, the dynamic threshold generation unit and the dual verification module cooperate in the following manner:

[0035] The dynamic threshold value generating unit outputs the primary threshold value to the trend deviation detecting unit;

[0036] The threshold of the trend deviation test is used to input the virtual governance test unit;

[0037] The verification results output by the virtual governance verification unit are fed back to the strategy generation unit;

[0038] Failure to pass the threshold of any verification link triggers the iterative regeneration process within the engine.

[0039] The present invention provides a tobacco field soil acidification early warning and management system based on the Internet of Things. It has the following beneficial effects:

[0040] This tobacco field soil acidification early warning and management system based on the Internet of Things dynamically integrates the sensitivity coefficient of the tobacco growth stage, the historical trend of regional acidification and real-time monitoring data, and generates an adaptive early warning threshold through a nonlinear product algorithm to solve the problem of early warning lag or false alarm caused by fixed thresholds; combined with the dual verification mechanism of trend deviation test and virtual management simulation, it uses rigid rules to screen out abnormal fluctuation interference and predict the feasibility of the strategy, ensuring that the management plan is both accurate and robust, and solving the problems of mismatch of management resources and substandard results.

[0041] This IoT-based tobacco field soil acidification early warning and control system uses a multi-objective optimization engine to achieve the spatiotemporal optimal scheduling of control resources, and automatically triggers terrain adaptive control and emergency mechanisms in complex scenarios such as steep slope operations and sudden rainfall to ensure operation safety and continuity. The closed-loop feedback module dynamically optimizes decision parameters by continuously learning the actual control effects, so that the system's early warning accuracy and resource utilization efficiency continue to improve with operating time, ultimately achieving full-process intelligent management of tobacco field soil acidification control, effectively reducing labor costs and avoiding the environmental burden caused by excessive control. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of module interaction of a tobacco field soil acidification early warning and management system based on the Internet of Things according to the present invention;

[0043] Figure 2 A schematic diagram of a closed-loop solution path for data conflicts according to the present invention;

[0044] Figure 3 A schematic diagram of a multi-level defense system for virtual governance testing of the present invention;

[0045] Figure 4 Schematic diagram of the dual-channel mechanism of interrupt response of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] See also Figures 1 to 4 The present invention provides a technical solution: an Internet of Things-based tobacco field soil acidification early warning and management system, comprising:

[0048] An IoT sensor network is deployed in tobacco fields to collect real-time data on soil pH, temperature, humidity, and calcium and magnesium ion concentrations;

[0049] A data fusion module, connected to the IoT sensor network via a data bus, is used to integrate real-time soil data, tobacco crop growth stage information, and historical acidification trend data;

[0050] The third-order dynamic coupling decision engine is connected to the data fusion module through a data interface to generate dynamic warning thresholds and optimize governance strategies;

[0051] A dual verification module, embedded within the three-order dynamic coupling decision engine, is used to perform anomaly detection and strategy feasibility verification on dynamic warning thresholds;

[0052] The execution terminal receives the management instructions output by the double verification module through the wireless communication module and performs precise spraying operations.

[0053] It should be further explained that in the specific implementation process, the IoT sensor network is deployed in the tobacco field area to collect soil pH, temperature, humidity, and calcium and magnesium ion concentration data in real time, and transmit them to the data fusion module through the data bus. The data fusion module performs the following operations:

[0054] S1. Real-time soil data processing: filter and calibrate sensor data, and mark geographic coordinates and timestamps;

[0055] S2. Crop Growth Stage Determination: When receiving drone aerial images, the system analyzes tobacco leaf morphology and combines the accumulated temperature model with the leaf sensor's humidity curve to dynamically divide the field into growth stages and output the sensitivity coefficient for each area. The field-level growth stages include the seedling stage, the cluster stage, and the vigorous growth stage. The seedling stage has the highest sensitivity coefficient, while the maturity stage has the lowest.

[0056] S3. Historical trend loading: Call the historical database of the target field, extract the acidification rate curve and treatment response records of the past three years, and generate the acidification slope factor and response attenuation coefficient; among them, in the acidification slope factor: a positive direction indicates continuous acidification, and a negative direction indicates recovery.

[0057] After the third-order dynamic coupling decision engine obtains fused data through the data interface, it generates a decision according to the following logic. The process is as follows:

[0058] Dynamic threshold generation stage: When the real-time pH value, current sensitivity coefficient and acidification slope factor are input, the sensitivity coefficient is multiplied by the acidification slope factor and the baseline pH value is superimposed to generate a primary dynamic threshold; if historical data shows that the area's response to lime is significantly attenuated, an additional safety margin offset is added; the acidification modifiers include but are not limited to alkaline soil conditioners such as lime, calcium hydroxide, calcium carbonate, dolomite, and biochar.

[0059] The double verification phase includes trend deviation testing and virtual governance testing; among which:

[0060] Trend deviation test: The real-time pH change rate is input into the embedded long short-term memory network. When the current rate deviates from the historical trend by more than two standard deviations, it is determined to be an abnormal fluctuation and a recalculation instruction is sent to the dynamic threshold generation unit.

[0061] Virtual governance verification: In the digital twin platform, the lime spraying effect is simulated based on the governance response attenuation coefficient, including the following two scenarios:

[0062] Scenario 1: If the simulation shows that the probability of pH returning to a safe range is lower than the preset success rate threshold, the strategy is deemed to have failed and the threshold callback mechanism is triggered;

[0063] Scenario 2: If the simulation is successful but the required lime quantity exceeds the inventory warning line, the low-cost alternative optimization process is initiated.

[0064] After the execution terminal receives the governance instruction that has passed the double verification, it enters the precise execution stage, namely: the variable spray device moves to the target area under the guidance of the positioning module according to the grid deployment map; when the equipment enters the high-sensitivity coefficient area, the spraying accuracy is automatically improved to centimeter-level control; if a sudden change in pH is detected in real time during the operation, the task is interrupted in real time through the state feedback circuit and a strategy update is requested.

[0065] The data fusion module includes:

[0066] The growth stage intelligent recognition unit is used to analyze drone aerial images and leaf sensor data, and combine them with the accumulated temperature model to output the regional sensitivity coefficients of tobacco seedlings, clusters, and growing seasons;

[0067] The historical trend analysis unit uses a temporal convolutional network to process the historical acidification data of the target field and outputs the regional acidification slope factor and the governance response attenuation coefficient.

[0068] It should be further explained that, in the specific implementation process, after the data fusion module receives the real-time data stream from the IoT sensor network, it starts the parallel processing channel. The process is as follows: the growth stage intelligent recognition unit loads the drone aerial image, and when the image clarity meets the analysis requirements, the leaf area index and stem height characteristics of the tobacco plant are extracted. If the leaf wrinkle density is higher than the seedling stage threshold and the plant height is lower than the cluster stage threshold, it is judged to be in the seedling stage and assigned a high sensitivity coefficient; when the accumulated value of the accumulated temperature model reaches the critical point of the vigorous growth period, it automatically switches to the low sensitivity coefficient mode; for the shielded area that cannot be visually identified, the leaf sensor humidity change curve is called, and if the daytime transpiration rate fluctuation meets the maturity characteristics, the image recognition result is overwritten to output the maturity coefficient.

[0069] The historical trend analysis unit searches the target field database and executes the following logic:

[0070] When there are three consecutive years of acidification records: a temporal convolutional network is used to extract acidification acceleration features. If the annual pH drop rate increases, a positive slope factor is output to strengthen the early warning.

[0071] When lime treatment has been implemented: analyze the second-order derivative of the pH recovery curve before and after treatment. When the rebound rate after treatment exceeds the acidification rate before treatment, mark the response attenuation coefficient as a high-risk level;

[0072] When the data of newly reclaimed fields is missing: start transfer learning of similar soil types in the vicinity and output the predicted slope factor with confidence mark.

[0073] When the outputs of two types of units conflict, for example, the visual identification is the seedling stage but the accumulated temperature does not meet the standard, the closed-loop solution path of the data conflict is executed, and the following process is performed: first, the soil ion concentration auxiliary decision is started, and if the calcium ion concentration is lower than the critical value, the high sensitivity coefficient is given priority; then the historical governance record verification is activated. If the historical governance frequency during the same period is higher than the average level, the accumulated temperature model result is adopted.

[0074] The third-order dynamically coupled decision engine includes:

[0075] A dynamic threshold generation unit is used to perform the following operations:

[0076] Receive the real-time pH value, current regional sensitivity coefficient and acidification slope factor output by the data fusion module;

[0077] Calculating a dynamic warning threshold based on a weighted decision function, where the weighted decision function is the product of the correlation sensitivity coefficient and the acidification slope factor;

[0078] The strategy generation unit is used to generate lime delivery parameters according to the warning level confirmed by the double verification module.

[0079] It should be further explained that, in the specific implementation process, the dynamic threshold generation unit continuously receives the real-time pH value, regional sensitivity coefficient and acidification slope factor transmitted by the data fusion module, and executes the threshold generation logic: when the sensitivity coefficient is in the high value range of the seedling stage and the acidification slope factor is positive, the product of the two is added to the baseline pH threshold as the main adjustment item to generate a downward-shifted primary dynamic threshold;

[0080] If the historical trend analysis unit marks the response attenuation coefficient of the area as a high-risk level, an additional safety margin compensation value will be added. The compensation value is taken from the average of successful cases with the same attenuation level in the historical optimal governance parameter pool; when the real-time pH value is sampled below the primary dynamic threshold for three consecutive times, an early warning signal is triggered and the strategy generation unit is activated.

[0081] The strategy generation unit responds to the warning signal and processes it according to the scenario, that is, the classification response logic of the strategy generation. The process is as follows:

[0082] Conventional warning scenario: A multi-objective optimization processor is used to generate a gridded distribution map of lime types, using lime inventory, equipment availability, and expected recovery period as constraints. Nano-calcium carbonate solution is used in high-sensitivity areas to improve response speed.

[0083] Emergency deterioration scenario: When the real-time pH value falls below the safety threshold, the system skips the optimization calculation and directly calls the preset emergency treatment template to start full-area coverage spraying and simultaneously apply for material replenishment.

[0084] Resource conflict scenario: If multiple areas are warned at the same time and there is insufficient equipment, resources will be allocated according to the product of "sensitivity coefficient × acidification slope", and the area with the highest product will be given priority.

[0085] The dual verification module starts parallel verification immediately after the threshold is generated: the trend deviation detection unit compares the real-time pH change rate with the historical trend stored in the long-short-term memory network. When a sudden increase in the rate is detected that exceeds a preset multiple of the historical maximum fluctuation (1.2 times), the current threshold is frozen and a re-test cycle is triggered;

[0086] The virtual governance inspection unit loads the lime delivery plan corresponding to the dynamic threshold for Monte Carlo simulation and initiates a callback when the following situations occur. The callback process includes Scenario A and Scenario B. Among them: Scenario A: pH recovery fails more than 3 times in 20 consecutive simulations, and the plan is judged to be unreliable; Scenario B: Successful but lime consumption exceeds 80% of the inventory, triggering the low-cost alternative solution optimization process.

[0087] The two-factor authentication module includes:

[0088] Trend deviation detection unit, with built-in long-short-term memory network, is used to compare the deviation between the real-time pH change rate of the tested area and the historical acidification trend. When the deviation exceeds the set tolerance threshold, the threshold correction is triggered;

[0089] The virtual governance verification unit runs Monte Carlo simulation through the digital twin platform to simulate the pH recovery trajectory of the governance plan corresponding to the dynamic threshold. When the number of simulated failures exceeds the set failure threshold, the strategy callback is triggered.

[0090] It should be further explained that, in the specific implementation process, after receiving the primary threshold value output by the dynamic threshold value generation unit, the trend deviation detection unit immediately starts the long-short-term memory network analysis channel: extracts the time series data of pH value of the target area for the past 30 days to construct a benchmark fluctuation band. When the pH change rate collected in real time exceeds the upper limit of the fluctuation band, it is judged as an abnormal working condition and the current threshold value is frozen; if five consecutive sampling data show a deviation exceeding twice the standard deviation, a red alarm instruction is sent to the dynamic threshold value generation unit, triggering the threshold regeneration process and starting the high-frequency monitoring mode, and the sampling interval is shortened to 1 minute; for data distortion caused by heavy rain erosion, the soil moisture sensor data is called synchronously, and when the humidity value exceeds the field water holding capacity, the calibration is automatically delayed for 12 hours.

[0091] After the trend deviation test is passed, the virtual governance inspection unit activates the digital twin platform to form a multi-level defense system for virtual governance inspection. The process is as follows:

[0092] Simulation parameter loading phase: Dynamically read the governance response attenuation coefficient of the historical trend analysis unit. If the coefficient is in the high-risk range, the number of Monte Carlo simulations will be automatically increased to 1.5 times the baseline value. Bind the current lime inventory status and activate the alternative material database when the inventory level falls below the safety line.

[0093] During the multi-scenario simulation phase, the conventional treatment plan was first verified: 500 iterations of simulation were performed based on the lime dosage corresponding to the dynamic threshold, and the number of successful pH restorations to a safe range was recorded. Next, an extreme deterioration scenario was tested: when the real-time pH value approached the critical point for crop death, an additional acid rain shock model was added to simulate sudden environmental stress.

[0094] Finally, the result decision and callback mechanism is implemented: if recovery fails more than three times in 20 consecutive simulations, the plan is judged to be unreliable and an orange warning is sent to the strategy generation unit; when the simulation is successful but the material consumption exceeds 80% of the inventory, it switches to low-cost mode and re-simulates; all plans that fail to pass the verification trigger a three-level callback protocol: the first-level callback adjusts the threshold parameters, the second-level callback replaces the treatment materials, and the third-level callback requests manual intervention.

[0095] Among them, the intelligent fault-tolerant mechanism of trend deviation test satisfies the following table:

[0096] Exception Type Judgment criteria Countermeasures Burst data drift Single sampling exceeds 1.2 times the fluctuation band Start the resampling validation loop Continuous deterioration Five consecutive times exceeding 2σ deviation Activate red alert + high frequency monitoring Environmental interference distortion Soil moisture > field capacity Delayed verification and correlation of meteorological data

[0097] In the construction of the volatility band benchmark, a rolling 30-day window is used to update the historical volatility range to avoid seasonal misjudgment caused by fixed thresholds;

[0098] The three-level callback satisfies the following table:

[0099] Callback Level Trigger Conditions Core Operations Level 1 Simulation failure ≤ 5 times Adjust threshold offset ±0.1pH Level 2 Simulation failed > 5 times Change material type + optimize delivery accuracy Level 3 Material exhaustion or continuous failure Emergency start of manual decision-making channel

[0100] During the simulation process, the virtual governance verification unit calls the governance response attenuation coefficient of the historical trend analysis unit to dynamically adjust the simulation parameters of the lime feeding amount;

[0101] When the number of simulation failures exceeds the failure number threshold, a threshold callback instruction and correction parameters are sent to the dynamic threshold generation unit.

[0102] It should be further explained that during the specific implementation process, when the virtual governance verification unit starts the simulation process, it first requests the governance response attenuation coefficient of the target area from the historical trend analysis unit. When the coefficient is in the high-risk range, that is, the soil pH rebound rate after historical governance continues to be higher than the acidification rate before governance, the number of Monte Carlo simulations is automatically increased to 1.5 times the baseline value, and the following enhanced parameters are loaded into the digital twin platform:

[0103] Call the lime activity attenuation model: reduce the material reaction efficiency proportionally according to the attenuation coefficient value;

[0104] Regional soil buffer capacity correction: When historical data shows that the clay content in the region is lower than the critical value, the expected pH recovery value in the simulation is simultaneously lowered;

[0105] Injection of extreme weather events: If the meteorological module warns of heavy rainfall in the next 48 hours, an acid rain shock scenario is superimposed in the simulation.

[0106] The Monte Carlo simulation process involves two phases of decision making:

[0107] Primary simulation stage: Run 500 basic iterations based on the lime application rate generated by the dynamic threshold, and record the number of successful pH restoration to the lower tolerance limit of the crop. If there are five consecutive simulation failures, immediately initiate adaptive parameter adjustment: if the real-time monitored soil moisture value is higher than the field water holding capacity, the weight of the moisture influencing factor is lowered; if the calcium and magnesium ion concentrations are abnormally low, activate the magnesium supplementation synergistic treatment program.

[0108] In-depth verification phase: When the success rate of the basic iteration does not meet the preset requirements, the historical optimal treatment parameter pool is loaded: successful cases with the same attenuation level are matched, and their lime type ratios and placement timings are extracted. The current solution is replaced with the case parameters and re-simulated. When the success rate improvement exceeds the allowable fluctuation range, the original solution is determined to be defective. For cases that still fail after replacement, cross-regional transfer learning is initiated: neighboring area models with similar soil texture and good treatment response are retrieved; the neighboring area treatment parameters are scaled by the geological difference coefficient and injected into the simulation.

[0109] When making decisions based on simulation results, three levels of response are implemented, including the following:

[0110] Level 1 callback: When material consumption exceeds 80% of the current inventory but the simulation succeeds, a cost optimization instruction is sent to the strategy generation unit, triggering one of the following actions: replacing 30% of calcium hydroxide with limestone powder and re-verifying; narrowing the treatment scope to the core acidification grid;

[0111] Secondary callback: When the number of simulation failures exceeds 5% of the total number, a parameter correction package is sent to the dynamic threshold generation unit, including: threshold offset recommendations based on historical success cases; and a dedicated safety margin coefficient for the governance response attenuation area.

[0112] Level 3 circuit breaker: When three consecutive simulations fail verification, the automatic decision-making process is frozen and the following actions are executed: a red alert and a snapshot of the failure data are sent to the administrator; and the field-wide conservative governance template is enabled to prevent the deterioration from spreading.

[0113] Among them, the simulation enhancement mechanism driven by the attenuation coefficient satisfies the following table:

[0114] Attenuation level Simulation Enhancements High risk (rebound rate > acidification rate) Simulation times × 1.5 + activity decay model Moderate risk (rebound rate ≈ acidification rate) Inject historical optimal parameter case library Low risk (rebound rate < acidification rate) Benchmark 500 simulations

[0115] The failure rescue mechanism of transfer learning includes: the calculation formula of the geological difference coefficient is embedded in the simulation engine, and the parameters of the neighboring area are scaled according to the following rules: when the clay content of the target area is lower than that of the neighboring area, the amount of treatment material is multiplied by 1.3; when the slope of the target area is higher than that of the neighboring area, the spraying coverage rate is multiplied by 0.8. After the migration, the simulation verification adds an acid rain stress test link to ensure the robustness of the scheme.

[0116] The strategy generation unit includes:

[0117] Multi-objective optimization processor, used to find the optimal balance between treatment cost, equipment utilization and expected recovery effect;

[0118] Output port for generating a gridded placement map containing lime type zones and a table of spatiotemporal routings for the sprinkler equipment.

[0119] It should be further explained that, in the specific implementation process, after the strategy generation unit receives the warning level confirmed by the double verification module, the multi-objective optimization processor immediately starts the three-dimensional constraint solving process. The process is as follows:

[0120] When processing routine warnings, lime inventory, available equipment quantity, and crop recovery cycle requirements are simultaneously loaded to construct a cost-effectiveness-time balance model. When inventory falls below the safety reserve line, the alternative material decision tree is automatically activated. When available equipment is insufficient, it is sorted and allocated by the product of regional sensitivity coefficient and acidification slope, with high-precision equipment being prioritized in areas with high product. When the recovery cycle closely follows the flowering period, the fast-acting nano-calcium carbonate solution is forcibly activated.

[0121] If there is a resource conflict in multiple regions, dynamic priority reset is implemented. That is, when the real-time pH value in a region falls below the critical point for seedling death, all available equipment is immediately preempted. A rolling delay mechanism is activated in non-emergency areas, with the maximum delay calculated based on the soil buffer capacity. Specifically, clay areas are extended for up to 48 hours, and sandy areas are extended for no more than 12 hours.

[0122] When generating treatment parameters, spatial differentiation is implemented. That is, the core logic of spatial differentiation treatment includes core acidification grid identification, material-equipment coupling optimization, and dynamic adjustment of spatiotemporal regulation paths.

[0123] Identification of core acidification grids: Grids with pH values ​​below the dynamic threshold and acidification slopes ranked in the top 10% are marked as red high-risk areas and allocated double lime application. When the area of ​​the high-risk area exceeds one-third of the total treatment area, the system switches to a zoned crop rotation treatment mode.

[0124] Material-equipment coupling optimization: High-precision spray equipment is only deployed in areas with a sensitivity coefficient greater than 1.5; when the equipment needs to cross steep slopes, the liquid load is automatically reduced and the frequency of round trips is increased;

[0125] Dynamic adjustment of the spatiotemporal routing: The basic path generates the shortest trajectory according to the Hamiltonian circuit; if a new warning area is added during the operation: the new point is immediately inserted into the current task sequence when the sensitivity coefficient is greater than 1.8; if the new point is within 300 meters of the current path, the road management protocol is activated;

[0126] The output port generates an execution file according to the following rules, including a gridded deployment map and a spatiotemporal routing table. The gridded deployment map includes: red high-risk areas marked with nano-calcium carbonate logos and deployment values ​​of 120% of the baseline dosage; the edge buffer zone uses limestone powder and the dosage decreases to 80% of the baseline value; the spatiotemporal routing table includes: equipment trajectories with speed curves, such as a speed limit of 60% on ramps to 60% of that on flat ground; and meteorological data associated with the control window period, such as prohibiting operations 4 hours before rainfall.

[0127] Also includes:

[0128] The resource scheduling module, the output port of the physical connection strategy generation unit, is used to plan the movement trajectory and start-stop timing of the sprinkler equipment based on the spatiotemporal scheduling path table.

[0129] It should be further explained that, in the specific implementation process, the resource scheduling module obtains the spatiotemporal scheduling path table output by the policy generation unit in real time through the physical connection port and starts the three-dimensional space resource mapping process. The mapping process includes the following two stages:

[0130] During the dynamic optimization phase of the equipment trajectory, when the basic path includes a steep slope with a gradient greater than 15 degrees, the trajectory is automatically divided into three sections: climbing, operating, and downhill. The liquid volume in the climbing section is limited to 60% of the rated capacity, and the electromagnetic braking anti-skid program is activated in the downhill section. If the meteorological module warns that the probability of rainfall exceeds the critical value during the operation window, the strategy generation unit is requested to shorten the treatment time, triggering one of the following adjustments:

[0131] Multi-device parallel mode: Split the continuous path into sub-region synchronous operations;

[0132] Fast-acting material substitution: Switching the lime type in high-risk areas to nano-calcium carbonate can increase the speed by 2 times.

[0133] Material supply chain coordination stage: Total lime demand is calculated based on a gridded deployment map. When the inventory gap exceeds the safety threshold, a warehouse allocation protocol is initiated to search available warehouses within 50 kilometers to generate a supply route. If the supply cycle exceeds the maximum delay, the cross-field transfer channel is activated to temporarily transfer reserve materials from low-sensitivity areas. For calcium hydroxide materials that are prone to hardening, pipeline flushing intervals are embedded in the schedule, and a 5-minute flushing cycle is automatically inserted after completing three high-risk area operations.

[0134] The execution process responds to emergencies in real time, including the following three scenarios:

[0135] Equipment failure scenario: When a device reports a motor overheating alarm, its task sequence is immediately frozen. If the sensitivity coefficient of the area covered by the faulty device is greater than 1.5, a neighboring device relay mechanism is activated: the device closest to the fault point suspends marginal operations and shifts to core area coverage. Non-emergency areas in the original faulty device's task sequence are delayed until repairs are complete.

[0136] New warning point insertion scenario: The resource scheduling module receives the coordinates of the newly added warning point confirmed by the double verification module and executes the following actions: When the sensitivity coefficient of the newly added point is greater than 1.8 and it is within 500 meters of the current operating equipment, the original path of the equipment is interrupted and a new task is inserted; if all equipment is overloaded, a mobile backup spray tanker is activated to perform rapid point treatment;

[0137] Sudden environmental change scenario: In the event of unexpected rainfall, soil moisture sensors are used for real-time monitoring. When the humidity exceeds the field water holding capacity, operations are forced to suspend and the equipment rain cover is opened. The sprayed area is marked as pending for testing, and the pH value is retested as a priority after the rain stops.

[0138] The execution terminal includes:

[0139] Variable-speed spraying system equipped with geo-positioning module and flow controller for differentiated lime spraying according to gridded delivery map;

[0140] The status feedback circuit is used to upload the equipment location and operation progress to the dual verification module in real time.

[0141] It should be further explained that, during the specific implementation process, the variable spray device starts the operation process according to the gridded delivery map issued by the resource scheduling module, and the geographic positioning module compares the offset distance between the device coordinates and the target grid center point in real time, and triggers the automatic correction program when the offset exceeds 1 meter. The spraying operation implements graded precision control, including: in the core area with a sensitivity coefficient greater than 1.5, centimeter-level positioning and upgraded flow control are enabled, and non-core areas are switched to meter-level positioning and upgraded flow control to reduce energy consumption. The flow controller receives the material type and baseline dosage value marked in the delivery map, and performs the following differentiated operations, including: for red high-risk grids with pH values ​​below the dynamic threshold and acidification slopes in the top 10%, nano-calcium carbonate solution is sprayed at 120% of the baseline dosage, and the nozzle pressure is increased to 1.3 times the standard value to ensure atomization penetration; when applying limestone powder in the edge buffer zone, a fan-shaped nozzle mode is used and the dosage is gradually reduced to 80% of the baseline value, and the anti-drifting wind curtain is activated at the same time;

[0142] The state feedback circuit continuously collects equipment operating conditions and soil response data, including data on routine operation phases, sudden interruption scenarios, and dynamic path adjustments. During the routine operation phase, the progress of each spraying operation is uploaded to the dual verification module after every five grids are sprayed. A calibration protocol is automatically triggered when actual lime consumption exceeds the planned value by 15%.

[0143] In the case of sudden interruptions, the dual-channel mechanism for interrupt response includes both device-side and environment-side anomalies. For device-side anomalies, if the motor temperature exceeds the safety threshold or the pipeline pressure is abnormal, the spraying process will be immediately stopped and a fault code will be sent. For environment-side anomalies, if the built-in soil pH sensor detects that the pH value after spraying does not meet the expected recovery curve, and the deviation is within the allowable fluctuation range, the grid will be marked as a re-inspection area. If the deviation exceeds 1.2 times the historical maximum recovery deviation, the current task chain will be interrupted and the decision engine will be requested to re-evaluate.

[0144] Dynamic path adjustment: When the resource scheduling module inserts a new warning point, the positioning module calculates the shortest entry path in real time. If the new point is less than 300 meters from the current position and the sensitivity coefficient is greater than 1.8, it will immediately turn to execute. If the new point is located on the reverse extension line of the current path, U-turn optimization will be initiated after completing the current grid.

[0145] Among them, the special working condition response measures include two working conditions: heavy rain sudden scene and equipment power criticality; heavy rain sudden scene: when the rain sensor detects that the hourly rainfall is greater than 5 mm: if the proportion of the sprayed area is less than 50%, open the rain cover and suspend the operation; start the emergency neutralization procedure for the highly sensitive area that has just been completed, and spray the pH stabilizer; equipment power criticality: when the battery capacity is less than 20%, give priority to completing the current high-risk grid operation; non-emergency tasks are delayed until charging is completed, and a relay request is sent to the adjacent equipment at the same time.

[0146] Also includes:

[0147] Closed-loop feedback module, with data input connected to the IoT sensor network and data output connected to the historical trend analysis unit;

[0148] The closed-loop feedback module updates the model through the Bayesian network and uses the post-treatment pH monitoring data to dynamically correct the treatment response attenuation coefficient.

[0149] It should be further explained that during the specific implementation process, the closed-loop feedback module continuously obtains the post-treatment soil pH, temperature, and ion concentration data uploaded by the IoT sensor network through the data input terminal. When it detects that the pH recovery trajectory of a certain area deviates from the expected, it activates the Bayesian network update model to execute the following optimization process:

[0150] Regular data update phase: pH values ​​are collected every two hours within 24 hours after treatment. If the data points fall within the expected recovery curve ±0.2pH range for three consecutive times, the treatment is determined to be effective and a success signal is sent to the historical trend analysis unit. If the actual recovery rate falls below the lower limit of the expected value, the area is automatically marked as an attenuation monitoring area, and the data collection frequency is increased to once an hour.

[0151] Parameter correction triggering phase: When the actual recovery rate is found to be continuously lower than the expected value for more than 12 hours, the core correction program of the Bayesian network is activated: the historical optimal treatment parameter pool is retrieved to match the lime dosage ratio of successful cases under the same soil type and meteorological conditions; if a matching case exists, the difference coefficient between the current dosage and the successful case is calculated to generate a correction recommendation value;

[0152] When there are no direct matching cases, cross-regional transfer learning is initiated: parameters from neighboring regions with the highest geological similarity are selected, scaled by clay content differences, and then injected into the model. For areas with high-risk response attenuation coefficients, i.e., where the historical rebound rate exceeds the acidification rate, the following additional checks are added: calcium and magnesium ion concentrations are re-measured on the third day after treatment, and if they do not reach the critical threshold, a supplementary treatment protocol is triggered. Acid rain monitoring records are loaded, and the slope of the expected recovery curve is adjusted when heavy rainfall occurs after treatment.

[0153] The model output transmits two key types of updates to the historical trend analysis unit, including:

[0154] Recalibration of the attenuation coefficient in response to treatment: When the actual recovery rate exceeds expectations, the attenuation coefficient level will be lowered; if an accelerated rebound occurs after treatment, that is, the pH drops by more than 50% within 3 days, the attenuation coefficient of the area will be marked as the highest risk level;

[0155] Optimizing the decision engine weight parameters: For areas that have been successfully treated three times in a row, the weight of their sensitivity coefficient in the dynamic threshold generation will be increased; when the virtual treatment inspection unit frequently calls back to a certain area's plan, the decision weight of the acidification slope factor in that area will be reduced;

[0156] Special scenario processing rules include the cold start scenario of newly reclaimed tobacco fields and the extreme climate interference scenario; among them: the cold start scenario of newly reclaimed tobacco fields: the first three treatments do not involve parameter correction, and only the basic database is established; starting from the fourth treatment, the initial value of the attenuation coefficient of the adjacent mature tobacco fields is used for weighted calculation; the extreme climate interference scenario: when a rainstorm that occurs once in fifty years occurs after treatment, the parameter update is frozen and manual review is started; if the temperature is continuously high during the data collection period, that is, the temperature is greater than 35°C, the expected time of the recovery curve will be automatically extended to 1.5 times the standard value.

[0157] The dynamic threshold generation unit and the double verification module work in the following way:

[0158] The dynamic threshold value generating unit outputs the primary threshold value to the trend deviation detecting unit;

[0159] The threshold of the trend deviation test is used to input the virtual governance test unit;

[0160] The verification results output by the virtual governance verification unit are fed back to the strategy generation unit;

[0161] Failure to pass the threshold of any verification link triggers the iterative regeneration process within the engine.

[0162] It's important to note that, during implementation, when the third-order dynamic coupling decision engine activates, the dynamic threshold generation unit first integrates the current regional sensitivity coefficient and the acidification slope factor, generating a primary dynamic threshold through a nonlinear product calculation. When the sensitivity coefficient is in the high seedling stage range and the acidification slope is accelerating, the threshold generation amplitude is increased to 1.8 times that of the conventional scenario. If the historical trend analysis unit marks the regional response attenuation coefficient as high-risk, an additional safety margin compensation value is applied.

[0163] The primary threshold is immediately transmitted to the trend deviation detection unit of the double verification module, which performs a comparison of the real-time data stream and the historical fluctuation band through an embedded long short-term memory network: if the pH change rate is detected to exceed 1.2 times the historical maximum fluctuation, the threshold is frozen and a high-frequency re-detection cycle is started; when the deviation of five consecutive samples is less than two standard deviations, the threshold enters the virtual governance verification link.

[0164] The virtual governance verification unit loads the governance plan corresponding to the threshold into the digital twin platform for Monte Carlo simulation, and processes the scenarios as follows:

[0165] Conventional verification scenario: Run 500 basic iterations. When pH recovery fails three times in a row, adaptive parameter adjustment is triggered. If soil moisture is abnormally high, the moisture factor weight is reduced. If the ion concentration is insufficient, magnesium is injected as a synergistic solution.

[0166] High-risk area scenario: The response attenuation coefficient high-risk area automatically increases the number of simulations to 750 and loads the acid rain impact model;

[0167] Resource-constrained scenario: When the simulation shows that material consumption exceeds inventory by 80%, the limestone powder alternative is switched and re-verified.

[0168] Verify the results of the decision-making process and enforce rigid rules, including:

[0169] If the trend test fails, a red alert is sent to the dynamic threshold generation unit, triggering a regeneration process based on historical optimal parameters;

[0170] Virtual inspection failure: Initiate a three-level callback protocol. The first-level callback adjusts the threshold offset by ±0.1pH. The second-level callback changes the material type. The third-level callback requests manual intervention.

[0171] Double verification passed: The strategy generation unit immediately outputs the gridded deployment map and the timely scheduling path.

[0172] When a new warning point is inserted or a device failure causes the process to be interrupted, the engine starts a hot recovery mechanism: the threshold value that has completed verification remains valid for 12 hours; the last valid data point before the interruption is used as the restart benchmark, skipping the repeated verification step.

[0173] It should be further explained that during the specific implementation process, the IoT sensor network continuously collects tobacco field soil data, including pH, temperature and humidity, and calcium and magnesium ion concentrations, and transmits them to the data fusion module via the data bus. This module simultaneously processes three types of key information: first, it filters and calibrates the real-time soil data and binds it to geographic coordinates; second, it analyzes drone aerial images and leaf sensor data, dynamically divides tobacco growth stages in combination with the accumulated temperature model, and outputs regional differentiated sensitivity coefficients, with the seedling stage assigned the highest sensitivity level; finally, it loads the historical database of the target field and uses a special algorithm to extract soil acidification rate characteristics and treatment response records to generate the acidification slope factor and response attenuation coefficient. When the growth stage determination conflicts with the accumulated temperature model, the calcium and magnesium ion concentrations are used as the arbitration basis. If the ion concentration is below the critical value, the seedling stage sensitivity coefficient is applied.

[0174] After receiving the fused data, the third-order dynamic coupling decision engine starts the dynamic threshold generation: the real-time pH, current sensitivity coefficient and acidification slope factor are input into the weighted decision function, where the product of the sensitivity coefficient and the acidification slope factor constitutes the core adjustment item. If the historical response attenuation coefficient of the area is marked as a high-risk level, the safety margin compensation value is superimposed. The generated primary threshold immediately enters the double verification link: the trend deviation test unit compares the real-time pH change rate of the tested area with the historical fluctuation range through the embedded analysis model. When it detects that the rate suddenly increases by a certain proportion of the historical maximum fluctuation range, it immediately freezes the threshold and starts a high-frequency re-test cycle; the threshold that passes the trend test is transferred to the virtual governance test unit, which simulates the lime spraying effect in the digital twin environment and calls the governance response attenuation coefficient to dynamically adjust the simulation parameters. If recovery fails or material overconsumption occurs in multiple consecutive simulations, a multi-level callback mechanism is triggered.

[0175] The strategy generation unit initiates a multi-objective optimization calculation based on verified thresholds. In conventional early warning scenarios, a gridded deployment map is generated, constrained by lime inventory, equipment availability, and crop recovery period. Nano-scale active materials are used in highly sensitive areas, with increased deployment intensity. In emergency deterioration scenarios, emergency templates are implemented for comprehensive remediation. In the event of resource conflicts in multiple regions, equipment resources are allocated based on the product of sensitivity coefficient and acidification slope. The resulting remediation plan includes a spatial distribution map of material type and a spatiotemporal scheduling path for equipment.

[0176] The variable-speed spraying system performs the execution phase: centimeter-level positioning and enhanced flow control are activated in core sensitive areas, while meter-level precision is switched to reduce energy consumption in peripheral areas. The actual treatment effect is monitored in real time during the operation. If the soil pH recovery trajectory after spraying deviates from the expected value and exceeds the allowable fluctuation range, the task is immediately interrupted and the decision engine is requested to re-evaluate. The resource scheduling module dynamically optimizes equipment movement paths, automatically segmenting work sections and limiting liquid loads when encountering steep slopes. New warning points are intelligently inserted into the task sequence based on sensitivity level and spatial location.

[0177] A closed-loop feedback module continuously tracks post-treatment soil data. Using a self-learning model, it analyzes deviations between actual recovery and predicted values, dynamically adjusting the treatment response attenuation coefficient and the decision engine weighting parameters. Initially, newly reclaimed tobacco fields utilize weighted calculations based on neighboring regional parameters. In the event of extreme weather conditions, model updates are frozen and manual review initiated. Through continuous iterative optimization, the system improves early warning accuracy and resource utilization efficiency.

[0178] Special rules for dynamic threshold generation include: when tobacco is in the seedling growth stage and the acidification slope shows an accelerating deterioration trend, the system automatically enables the product amplification mechanism, making the threshold offset significantly higher than the conventional linear weighted result. For areas that have experienced rapid rebound after treatment, additional safety margin values ​​based on historical success cases are injected. After the threshold is generated, it must pass a double rigid verification: when the trend test finds an abnormal surge in the rate of change of pH, the threshold is immediately frozen and correlated with soil moisture data to determine whether there is environmental interference; virtual testing automatically increases the simulation intensity of high-risk areas and loads the acid rain impact model to test the robustness of the solution. Failure in any verification link will trigger multi-level corrections. The first failure will fine-tune the threshold parameters, the second failure will change the material type, and continuous failure will result in manual decision-making.

[0179] Deep optimizations have been made to virtual remediation verification, including dynamically binding material inventory status to the simulation process. When predicted consumption approaches the inventory limit, a low-cost alternative solution is automatically switched for revalidation. For areas with historical remediation failures, the system forcibly applies an attenuation coefficient correction model: The system retrieves parameters from successful cases with the same soil type. If no matching cases exist, cross-regional transfer learning is initiated, and remediation parameters in neighboring areas are proportionally scaled based on clay content differences. Simulation results use a rigid pass criterion; a solution is deemed infeasible after a set number of consecutive failures.

[0180] Adaptive control of the execution terminal includes: When operating in highly sensitive core areas, the equipment automatically improves positioning accuracy and spray pressure to ensure deep penetration of active materials. After spraying, soil response is monitored in real time. If the pH value does not reach the expected recovery curve and the deviation exceeds the historical maximum deviation, the process is immediately interrupted and the area is marked as failure risk. New warning point insertion logic follows the principles of spatial proximity and sensitivity priority. High-risk areas within the set range of the operation point are directly inserted into the current task. In the event of overload, mobile backup equipment is activated to implement point-to-point treatment.

[0181] The closed-loop learning mechanism uses a progressive approach: data from the first three treatments of newly reclaimed tobacco fields is used only to build the local model. Starting from the fourth treatment, parameters from neighboring areas are integrated to generate the initial attenuation coefficient. If the actual treatment effect exceeds expectations, the attenuation level is lowered, and if a rapid rebound occurs, the area is marked as the most vulnerable. The decision engine automatically records the accuracy of the generated solutions for each area and continuously optimizes the weighting of the sensitivity coefficient and the acidification slope.

[0182] It should be further explained that, in the specific implementation process, a tobacco field soil acidification early warning and control method based on the Internet of Things includes the following steps:

[0183] Step S1: The IoT sensor network continuously collects data on soil pH, temperature and humidity, and calcium and magnesium ion concentrations in tobacco fields, binds the data to geographic coordinates and timestamps, and transmits the data to the data fusion module through the data bus.

[0184] Step S2: Analyze drone aerial images and leaf sensor data, combine the accumulated temperature model to divide tobacco growth stages and output regional sensitivity coefficients; load the historical database to extract the acidification slope factor and governance response attenuation coefficient; when there is a conflict in growth stage determination, use calcium and magnesium ion concentrations as the arbitration basis.

[0185] Step S3: The real-time pH, sensitivity coefficient, and acidification slope factor are integrated to generate a primary dynamic threshold through nonlinear product calculation; if the regional response attenuation coefficient is at a high-risk level, the safety margin compensation value of historical successful cases is superimposed.

[0186] Step S4: Compare the real-time pH change rate of the tested area with the historical fluctuation range through the embedded analysis model: if the rate suddenly increases and exceeds the historical maximum fluctuation range, freeze the threshold and start high-frequency re-testing; after five consecutive samplings are stable, hand over to virtual inspection.

[0187] Step S5: Load the threshold corresponding scheme in the digital twin environment for simulation: run the benchmark iteration number in the conventional area, double the simulation intensity in the high-risk area and load the acid rain model; when the recovery fails for the set number of consecutive times or the material is over-consumed, the three-level callback mechanism is triggered.

[0188] Step S6: Activate multi-objective optimization through verified thresholds, generate gridded deployment maps for conventional scenarios, strengthen governance in highly sensitive areas, call emergency templates for emergency scenarios, and allocate equipment in order of sensitivity coefficient and acidification slope multiplication when resources conflict.

[0189] Step S7: Analyze the spatiotemporal path table: divide the operation section into steep slope sections and limit the liquid load; split the path and perform parallel operations when a rain warning occurs; and insert new warning points into the task sequence based on the principles of spatial proximity and sensitivity priority.

[0190] Step S8: The variable-speed spraying device uses centimeter-level positioning and high-pressure atomization in the core sensitive area, and switches to meter-level accuracy in the edge area; the soil response after spraying is monitored in real time. If the deviation from the expected value exceeds the historical maximum deviation, the task is interrupted and a reassessment is requested.

[0191] Step S9: When a device fails, the neighboring device relay mechanism is activated to prioritize coverage of highly sensitive areas; in the event of a sudden rainstorm, the rain cover is opened to spray stabilizers on the high-risk areas that have just been treated.

[0192] Step S10: Tracking soil data after treatment: When the actual recovery rate continues to be lower than expected, retrieve historical successful cases to correct parameters; newly reclaimed tobacco fields use weighted parameters of neighboring areas, and extreme climate freeze updates and transfer to manual review.

[0193] Step S11: When the governance effect exceeds expectations, the response attenuation level is lowered; if a rapid rebound occurs, it is marked as the highest risk area; the decision weight is dynamically adjusted according to the accuracy of the plan for each area.

[0194] Step S12: When the process is interrupted, the verified threshold setting time is retained; restart based on the last valid data point and skip the repeated verification step.

[0195] By dynamically integrating the sensitivity coefficients of tobacco growth stages, regional acidification historical trends and real-time monitoring data, an adaptive warning threshold is generated through a nonlinear product algorithm to solve the problem of warning lag or false alarm caused by fixed thresholds; combining the dual verification mechanism of trend deviation test and virtual governance simulation, rigid rules are used to screen out abnormal fluctuation interference and predict the feasibility of the strategy, ensuring that the governance plan is both accurate and robust, and solving the problems of mismatch of governance resources and substandard results.

[0196] Based on the multi-objective optimization engine, the spatiotemporal optimal scheduling of governance resources is achieved, and terrain adaptive control and emergency mechanisms are automatically triggered in complex scenarios such as steep slope operations and sudden rainfall to ensure operation safety and continuity; the closed-loop feedback module dynamically optimizes decision parameters by continuously learning the actual governance effects, so that the system's early warning accuracy and resource utilization efficiency continue to improve with the operating time, and ultimately achieve full-process intelligent management of tobacco field soil acidification governance, effectively reducing labor costs and avoiding the environmental burden caused by excessive governance.

[0197] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0198] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A tobacco field soil acidification early warning and management system based on the Internet of Things, characterized by: include: An IoT sensor network is deployed in tobacco fields to collect real-time data on soil pH, temperature, humidity, and calcium and magnesium ion concentrations; A data fusion module, connected to the IoT sensor network via a data bus, is used to integrate real-time soil data, tobacco crop growth stage information, and historical acidification trend data; The third-order dynamic coupling decision engine is connected to the data fusion module through a data interface to generate dynamic warning thresholds and optimize governance strategies; A dual verification module, embedded within the three-order dynamic coupling decision engine, is used to perform anomaly detection and strategy feasibility verification on dynamic warning thresholds; The execution terminal receives the management instructions output by the double verification module through the wireless communication module and performs precise spraying operations; The third-order dynamic coupling decision engine includes: A dynamic threshold generation unit is used to perform the following operations: Receive the real-time pH value, current regional sensitivity coefficient and acidification slope factor output by the data fusion module; Calculating a dynamic warning threshold based on a weighted decision function, wherein the weighted decision function relates a product term of a sensitivity coefficient and an acidification slope factor; A strategy generation unit, used to generate acidification modifier delivery parameters according to the warning level confirmed by the double verification module; The dual verification module includes: Trend deviation detection unit, with built-in long-short-term memory network, is used to compare the deviation between the real-time pH change rate of the tested area and the historical acidification trend. When the deviation exceeds the set tolerance threshold, the threshold correction is triggered; The virtual governance verification unit runs Monte Carlo simulation on the digital twin platform to simulate the pH recovery trajectory of the governance plan corresponding to the dynamic threshold. When the number of simulated failures exceeds the set failure threshold, the policy callback is triggered; The dynamic threshold generation unit and the dual verification module cooperate in the following manner: The dynamic threshold value generating unit outputs the primary threshold value to the trend deviation detecting unit; The threshold of the trend deviation test is used to input the virtual governance test unit; The verification results output by the virtual governance verification unit are fed back to the strategy generation unit; Failure to pass the threshold of any verification link triggers the iterative regeneration process within the engine; Dynamic threshold generation stage: When the real-time pH value, current sensitivity coefficient and acidification slope factor are input, the sensitivity coefficient and the acidification slope factor are multiplied and the reference pH value is superimposed to generate the primary dynamic threshold.

2. The tobacco field soil acidification early warning and control system based on the Internet of Things according to claim 1 is characterized by: The data fusion module includes: The growth stage intelligent recognition unit is used to analyze drone aerial images and leaf sensor data, and combine the accumulated temperature model to output the regional sensitivity coefficients of tobacco seedling stage, cluster stage, and growth stage; The historical trend analysis unit uses a temporal convolutional network to process the historical acidification data of the target field and outputs the regional acidification slope factor and the governance response attenuation coefficient.

3. The tobacco field soil acidification early warning and control system based on the Internet of Things according to claim 2, characterized in that: During the simulation process, the virtual treatment inspection unit calls the treatment response attenuation coefficient of the historical trend analysis unit to dynamically adjust the simulation parameters of the acidification modifier dosage; When the number of simulation failures exceeds the failure number threshold, a threshold callback instruction and correction parameters are sent to the dynamic threshold generation unit.

4. The tobacco field soil acidification early warning and control system based on the Internet of Things according to claim 3 is characterized by: The strategy generation unit includes: Multi-objective optimization processor, used to find the optimal balance between treatment cost, equipment utilization and expected recovery effect; Output port for generating a gridded placement map containing the acidifier type zones and a table of spatiotemporal routings for the spray equipment.

5. The tobacco field soil acidification early warning and control system based on the Internet of Things according to claim 4 is characterized by: Also includes: The resource scheduling module, the output port of the physical connection strategy generation unit, is used to plan the movement trajectory and start-stop timing of the sprinkler equipment based on the spatiotemporal scheduling path table.

6. The tobacco field soil acidification early warning and control system based on the Internet of Things according to claim 5, characterized in that: The execution terminal includes: Variable-rate spraying device, equipped with a geo-positioning module and flow controller, for performing differentiated acidification amendment spraying according to a gridded delivery map; The status feedback circuit is used to upload the equipment location and operation progress to the dual verification module in real time.

7. The tobacco field soil acidification early warning and control system based on the Internet of Things according to claim 1 is characterized by: Also includes: Closed-loop feedback module, with data input connected to the IoT sensor network and data output connected to the historical trend analysis unit; The closed-loop feedback module updates the model through a Bayesian network and uses the post-treatment pH monitoring data to dynamically correct the treatment response attenuation coefficient.

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