A precision fertilization and tillage coordination management system in cold and wet black soil region

Abstract

The application discloses a cold and wet black soil area precision fertilization and tillage coordination management system and belongs to the technical field of agricultural technology popularization, which comprises an acquisition module, a generation module, a coordination operation module and an instruction execution module.The acquisition module is used for acquiring soil basic parameters, crop growth parameters and future weather parameters of the cold and wet black soil area.The generation module is used for generating current required fertilization control parameters and tillage control parameters according to the soil basic parameters, the crop growth parameters and the future weather parameters.The coordination operation module is used for generating final fertilization instructions and final tillage instructions.The instruction execution module comprises an execution unit, a popularization data generation unit and a popularization service interface unit, is used for executing the final fertilization instructions and the final tillage instructions, generating a visual agricultural technology operation standard package and converting the visual agricultural technology operation standard package into dynamic operation instructions, and transmitting the dynamic operation instructions to a preset farmer terminal.The current waveform and the stroke trajectory of the execution unit are used for generating a tillage depth thermal map and a fertilization timing diagram, so that the precision execution of the fertilization and tillage coordination operation by the farmer is realized.

Description

Technical Field

[0001] This invention relates to the field of agricultural technology extension technology, and in particular to a precision fertilization and tillage coordination management system for cold and humid black soil regions. Background Technology

[0002] The cold and humid black soil region refers to the alpine black soil belt above 45°N latitude, characterized by low average annual temperatures and a short frost-free period. Its soils possess both high humus content and low temperature and high humidity. Spring snowmelt erosion and delayed permafrost warming significantly slow nutrient release. Under these unique climatic conditions, conventional fertilization is difficult to match with crop nutrient requirements, resulting in severe fertilizer loss and continuous soil structure degradation. While the soil fertility in the Northeast cold and humid black soil region is rich, fertilization efforts face numerous challenges due to the unique climatic conditions of low temperatures, water erosion, and slow spring warming.

[0003] Current fertilization technologies focus on two main approaches: variable-rate control and tillage-linked methods. Variable-rate fertilization devices rely on pre-set models to adjust the application rate, while conservation tillage equipment reduces soil erosion through shallow tillage and mulching. Although some composite devices attempt to integrate tillage and fertilization functions, they merely superimpose mechanical structures and fail to dynamically optimize operating parameters based on the environment, lacking adaptive adjustment methods for low-temperature and high-humidity soil conditions.

[0004] In cold and humid environments, traditional variable-rate fertilization devices rely on preset static models to adjust fertilizer application, but they fail to incorporate dynamic interference factors such as freeze-thaw lag and rainfall erosion in cold and humid black soil regions, leading to decreased fertilizer utilization. Furthermore, traditional agricultural technical services only provide text / image operation specifications, lacking visual guidance tools, resulting in a high rate of misoperation by farmers in coordinating fertilization and tillage operations. Therefore, there is an urgent need to provide a precision fertilization and tillage co-management system for cold and humid black soil regions to address these issues. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a precision fertilization and tillage co-management system for cold and humid black soil areas.

[0006] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is: to provide a precision fertilization and tillage collaborative management system for cold and humid black soil areas, including an acquisition module, a generation module, a collaborative calculation module and an instruction execution module;

[0007] The acquisition module is used to acquire basic soil parameters, crop growth parameters, and future weather parameters in the cold and humid black soil region.

[0008] The generation module generates the required fertilization control parameters and tillage control parameters based on the basic soil parameters, crop growth parameters, and future weather parameters, combined with preset historical tillage operations.

[0009] The method for obtaining the historical tillage amount in the generation module is as follows: the generation module calls the preset black soil area tillage database, extracts the average rotary tillage depth and fertilizer applicator travel speed benchmark value of the same plot in the cold and humid black soil area in the past three years, and uses the extracted results as the historical tillage amount.

[0010] The steps for generating the fertilization control parameters in the generation module are as follows:

[0011] S1. Based on the pH value and nutrient content in the basic soil parameters, and combined with the crop stem height and leaf nitrogen accumulation in the crop growth parameters, a target nutrient requirement range is generated.

[0012] S2. Load the rainfall probability distribution curve and the frozen soil ablation temperature value from the future weather parameters. When the rainfall probability in the rainfall probability distribution curve exceeds the preset first threshold or the frozen soil ablation temperature value does not reach the preset critical value, generate a phased fertilization strategy based on the target nutrient demand range.

[0013] S3. Convert the phased fertilization strategy into fertilization control parameters, which include fertilizer type ratio and fertilizer applicator travel speed reference value.

[0014] The generation steps of the tillage control parameters in the generation module are as follows: the vertical distribution density of crop roots in the crop growth parameters is compared with a preset second threshold, and a rotary tillage depth adjustment command is generated according to the comparison result. The rotary tillage depth adjustment command is corrected by combining the average rotary tillage depth in the historical tillage operation volume. The corrected rotary tillage depth adjustment command is bound to the fertilizer applicator travel speed reference value to generate tillage control parameters.

[0015] The collaborative computing module converts the fertilization control parameters into fertilization operation volume benchmark values ​​and simultaneously converts the tillage control parameters into tillage operation volume benchmark values. Based on the fertilization operation volume benchmark values ​​and the tillage operation volume benchmark values, it generates the final fertilization instruction and the final tillage instruction.

[0016] The instruction execution module includes an execution unit, a promotion data generation unit, and a promotion service interface unit. The execution unit is used to execute the final fertilization instruction and the final tillage instruction. The promotion data generation unit is used to parse the execution results of the execution unit and generate a visual agricultural technology operation standard package based on the preset planting characteristics of the cold and humid black soil area. The promotion service interface unit is used to convert the visual agricultural technology operation standard package into dynamic operation guidance and transmit it to the preset farmer terminal.

[0017] The present invention is further configured such that: the basic soil parameters in the acquisition module are collected by a soil moisture sensor, and the basic soil parameters include pH value and nutrient content;

[0018] The crop growth parameters are obtained by a combination of near-ground temperature and humidity sensors and a multispectral imager. The crop growth parameters include crop stem height, leaf nitrogen accumulation, and crop root vertical distribution density.

[0019] The future weather parameters are based on weather radar and satellite cloud imagery predictions, and include precipitation probability distribution curves, predicted snowfall thickness, and accumulated temperature values ​​for permafrost ablation.

[0020] The present invention is further configured such that the method for generating the final fertilization instruction in the collaborative computing module is as follows:

[0021] Q1. Extract the fertilizer type ratio from the fertilizer control parameters as a reference parameter, and extract the reference value of the fertilizer applicator's travel speed as an operating parameter. Input the reference parameter, the operating parameter, and the pre-applied fertilizer reference amount into a preset prediction model. Dynamically simulate the scenario in the prediction model that matches the future weather parameters to generate the first simulation result.

[0022] Q2. Load the rainfall probability distribution curve and the accumulated temperature of frozen soil thawing in the future weather parameters. When the future weather parameters meet the weather conditions in the phased fertilization strategy, reduce the fertilization benchmark amount according to the first preset ratio, take the reduced fertilization benchmark amount as the actual application amount, and output the final fertilization command after binding it with the operation parameters.

[0023] The present invention is further configured such that the generation steps of the final tillage instruction in the collaborative computing module are as follows:

[0024] W1. Generate an initial rotary tillage depth value based on the modified rotary tillage depth adjustment command in the tillage control parameters;

[0025] W2. Load the accumulated temperature value of frozen soil layer thawing in the future weather parameters. When the accumulated temperature value of frozen soil layer thawing does not reach the preset critical value, compress the initial rotary tillage depth value according to the second preset ratio.

[0026] W3. Call the rainfall probability distribution curve in the future weather parameters. When the rainfall probability in the rainfall probability distribution curve exceeds the preset first threshold, link the fertilizer machine travel speed benchmark value in the fertilizer control parameters to generate the tillage speed optimization value. Bind the corrected rotary tillage depth value with the tillage speed optimization value and output the final tillage command.

[0027] The present invention is further configured such that: the execution unit in the instruction execution module includes an instruction parsing subunit, a multi-channel control subunit, and a device driver subunit;

[0028] The instruction parsing subunit is used to receive the final fertilization instruction and the final tillage instruction sent by the collaborative computing module, and decode them into fertilization control pulse sequence and tillage control pulse sequence, respectively.

[0029] The multi-channel control subunit is connected to the fertilizer type ratio regulator, travel speed controller and rotary tillage depth hydraulic valve of the pre-fertilizing implement, and generates the equipment analog voltage control signal of the fertilizing implement according to the fertilization control pulse sequence and the tillage control pulse sequence.

[0030] The device drive subunit converts the device analog voltage control signal into drive current through a power amplifier circuit, thereby controlling the operation of the fertilizer discharge motor of the fertilizer applicator and the hydraulic actuator of the tillage implement.

[0031] The present invention is further configured such that the generation step of the visual agricultural technology operation standard package in the instruction execution module is as follows:

[0032] H1. Based on the current waveform of the fertilizer discharge motor of the execution unit and the stroke trajectory of the hydraulic actuator, extract the actual fertilizer application deviation value, rotary tillage depth offset and mechanical operation time sequence.

[0033] H2. Based on the offset of the rotary tillage depth, the spatial distribution of nutrient content in the basic soil parameters is associated with the offset of the rotary tillage depth. A layered color model is generated by combining the vertical distribution density of crop roots. The actual fertilizer application deviation value is superimposed to construct a tillage depth heat map with fertility markers.

[0034] H3. Based on the time series of mechanical operations, calculate the discrete points of the fertilizer applicator's travel speed, integrate the rainfall probability distribution curve in the future weather parameters, and generate a fertilizer application time series diagram, which contains a rainfall risk warning.

[0035] H4. Based on the areas marked as low fertility in the tillage depth heat map, associate the gaps in the target nutrient requirement range and generate soil improvement suggestions, which include the organic matter replenishment ratio and deep tillage cycle.

[0036] H5 spatially aligns the tillage depth heat map, the fertilization time sequence map, and the soil improvement suggestions, adds agronomic operation annotations based on the planting characteristics of the cold and humid black soil area, and generates a visual agricultural technology operation standard package.

[0037] The present invention is further configured such that the specific content of the promotion service interface unit in the instruction execution module is as follows:

[0038] F1. Extract the tillage depth heat map and the fertilization time sequence map from the visual agricultural technology operation standard package, and integrate the mechanical operation time sequence to generate dynamic instruction metadata;

[0039] F2. Based on the type of the preset farmer terminal, call the preset device resource profile and decompose the dynamic instruction metadata into text operation summary, heat map compressed slice and risk time axis sequence;

[0040] F3. Based on the agricultural operation cycle in the planting characteristics of the black soil area, the peak period of the rainfall probability distribution curve is superimposed to generate a rain shelter scenario snapshot and bind it to the risk time axis sequence.

[0041] F4. Embed the first verification interface of the soil improvement suggestion in the text operation summary, and embed the second verification interface of the fertilization time series diagram in the heat map compressed slice, and integrate the text operation summary, the heat map compressed slice, the risk time axis sequence and the rain shelter scenario snapshot into a dynamic operation guide adapted to the preset farmer terminal.

[0042] The beneficial effects of this invention are as follows:

[0043] 1. This invention corrects the phased fertilization strategy in real time by loading the accumulated temperature value of frozen soil layer and the probability distribution curve of rainfall, incorporates the freeze-thaw lag and rainfall erosion interference into the decision-making closed loop, and dynamically generates the fast-acting fertilizer window period and slow-release fertilizer safety period adapted to the hydrothermal environment of cold and humid black soil area, which breaks through the defects of traditional static fertilization model and significantly improves fertilizer utilization efficiency.

[0044] 2. This invention generates a tillage depth heat map and a fertilization timing map by analyzing the current waveform and travel trajectory of the execution unit. It intuitively marks low fertility risk areas and rainfall warning periods. Combined with dynamic operation guidance adapted to the terminal, it transforms agricultural technology standards, solves the problem of the disconnect between traditional text guidance and on-site operation, and enables farmers to accurately execute fertilization and tillage coordination operations. Attached Figure Description

[0045] Figure 1 This is a flowchart of the present invention;

[0046] Figure 2 This is a flowchart illustrating the steps for generating fertilization control parameters according to the present invention. Detailed Implementation

[0047] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0048] Please see Figure 1 - Figure 2 A precision fertilization and tillage collaborative management system for cold and humid black soil areas, characterized in that it includes an acquisition module, a generation module, a collaborative calculation module and an instruction execution module;

[0049] The acquisition module is used to acquire basic soil parameters, crop growth parameters, and future weather parameters in the cold and humid black soil region.

[0050] The generation module generates the required fertilizer control parameters and tillage control parameters based on basic soil parameters, crop growth parameters, and future weather parameters, combined with preset historical tillage operations.

[0051] The collaborative computing module converts fertilization control parameters into fertilization operation volume benchmark values ​​and simultaneously converts tillage control parameters into tillage operation volume benchmark values. Based on the fertilization operation volume benchmark values ​​and tillage operation volume benchmark values, it generates the final fertilization instruction and the final tillage instruction.

[0052] The instruction execution module includes an execution unit, a promotion data generation unit, and a promotion service interface unit. The execution unit is used to execute the final fertilization instruction and the final tillage instruction. The promotion data generation unit is used to parse the execution results of the execution unit and generate a visual agricultural technology operation standard package based on the preset planting characteristics of the cold and humid black soil area. The promotion service interface unit is used to convert the visual agricultural technology operation standard package into dynamic operation guidance and transmit it to the preset farmer terminal.

[0053] The instruction execution module also includes a feedback optimization unit, which collects data on farmers' adoption rate of dynamic operation guidelines and operational effectiveness; combines historical tillage operations to generate adaptive promotion strategies and feeds them back to the generation module.

[0054] The system comprehensively collects soil, crop, and weather parameters in the cold and humid black soil region through an acquisition module. A generation module integrates multi-source data with historical tillage data to accurately generate fertilization and tillage control parameters. A collaborative computing module efficiently converts these control parameters into baseline values ​​for the operating volume and outputs the final instructions. The instruction execution module's execution unit drives the equipment to complete the operation, while the data generation unit analyzes the execution results to generate a visual agricultural technology operation standard package. This package is then converted into dynamic operation guidance and pushed to farmers' terminals by the extension service interface unit. Simultaneously, a feedback optimization unit collects farmer adoption rates and operational effectiveness data, combines them with historical tillage data to generate adaptive extension strategies, and feeds them back to the generation module. This achieves closed-loop optimization throughout the entire process, providing farmers with visual guidance tools and reducing the rate of misoperation in fertilization and tillage coordination operations.

[0055] One embodiment of the present invention is as follows: the basic soil parameters in the acquisition module are collected by a soil moisture sensor. The basic soil parameters include pH value and nutrient content. The soil moisture sensor is buried at a depth of 20-35cm in the tillage layer.

[0056] Crop growth parameters were obtained by combining near-ground temperature and humidity sensors with a multispectral imager. The crop growth parameters included crop stem height, leaf nitrogen accumulation, and vertical distribution density of crop roots. The near-ground temperature and humidity sensors and the multispectral imager were carried by a drone.

[0057] Future weather parameters are based on forecasts from weather radar and satellite cloud images. These parameters include the probability distribution curve of precipitation, the predicted snowfall thickness, and the accumulated temperature of permafrost ablation.

[0058] By accurately collecting pH values ​​and nutrient content using soil moisture sensors buried 20–35 cm deep in the topsoil, and by efficiently acquiring crop stem height, leaf nitrogen accumulation, and root vertical distribution density using near-ground temperature and humidity sensors and multispectral imagers mounted on drones, the system combines rainfall probability distribution curves, snowfall thickness predictions, and frozen soil ablation accumulation temperatures predicted by meteorological radar and satellite cloud images to achieve multi-dimensional data fusion in the cold and humid black soil region and improve the accuracy of agricultural decision-making.

[0059] One embodiment of the present invention is as follows: the method for obtaining the historical tillage operation volume in the generation module is as follows: the generation module calls the preset black soil area tillage database, extracts the average rotary tillage depth and the benchmark value of fertilizer machine travel speed of the same plot in the cold and humid black soil area in the past three years, and uses the extracted results as the historical tillage operation volume.

[0060] Specifically, the steps for generating fertilization control parameters in the generation module are as follows:

[0061] S1. Based on the pH value and nutrient content in the basic soil parameters, and combined with the crop stem height and leaf nitrogen accumulation in the crop growth parameters, the target nutrient requirement range is generated.

[0062] The steps for generating the target nutrient requirement range are as follows:

[0063] S11. Based on crop stem height and leaf nitrogen accumulation in crop growth parameters, and combined with nutrient content in basic soil parameters, an initial nutrient requirement range is generated.

[0064] S12. Load the accumulated temperature value of frozen soil layer thawing from future weather parameters. When the accumulated temperature value of frozen soil layer thawing does not reach the preset critical value, generate a slow-release nitrogen fertilizer enhancement strategy based on the initial nutrient demand range.

[0065] S13. Based on the fluctuation range of pH value in the basic soil parameters, adjust the proportion of ammonium nitrogen in the slow-release nitrogen fertilizer enhancement strategy to generate the target nutrient demand range, which is adapted to the thawing environment of the cold and humid black soil area.

[0066] In step S13, when the pH value fluctuates within the range of pH < 6.5, the conversion rate of ammonium nitrogen decreases by ≥ 40%, while the utilization rate of nitrate nitrogen increases by 35%.

[0067] When pH > 7.0, the volatilization loss rate of ammonium nitrogen is > 25%, and the proportion of ammonium nitrogen is limited to 25%–50%.

[0068] S2. Load the rainfall probability distribution curve and the frozen soil ablation temperature value from the future weather parameters. When the rainfall probability in the rainfall probability distribution curve exceeds the preset first threshold or the frozen soil ablation temperature value does not reach the preset critical value, generate a phased fertilization strategy based on the target nutrient demand range.

[0069] The preferred range for the first threshold is 60% to 70%, and the preferred range for the critical value is 5℃d to 7℃d.

[0070] The method for generating a phased fertilization strategy is as follows:

[0071] S21. Based on the duration of the rainfall probability distribution curve exceeding the preset first threshold, divide the application window period of quick-acting fertilizer and the safe period of slow-release fertilizer, and simultaneously load the thawing delay mark for the frozen soil layer thawing accumulated temperature value that has not reached the critical value.

[0072] S22. During the application window of quick-acting fertilizer, the proportion of quick-acting nitrogen in the target nutrient requirement range is increased to the preset safe upper limit, and the high-penetration fertilization sequence is generated by associating it with the benchmark value of fertilizer machine travel speed in historical tillage operations; during the safe period of slow-release fertilizer, the ratio of organic and inorganic compound fertilizer in the slow-release nitrogen fertilizer enhancement strategy is bound to the thawing delay marker to generate the anti-leaching fertilization sequence.

[0073] S23. Based on the peak time position of the rainfall probability distribution curve, insert a preset rain shelter operation gap into the high permeability fertilization sequence, and at the same time superimpose the salinization avoidance instruction of pH fluctuation range into the anti-leaching fertilization sequence, output a phased fertilization strategy, and the phased fertilization strategy is adapted to the hydrothermal coupling environment of the cold and humid black soil area.

[0074] Rain shelter operation intervals: Interval duration: 1.5–3 hours (dynamically adjusted according to rainfall intensity);

[0075] Interval insertion location: the peak period of the rainfall probability distribution curve (probability > 80%);

[0076] Salinization avoidance instructions: Based on the pH fluctuation range in step S13, dynamically adjust the fertilizer form. Specific operation instructions include:

[0077] Ammonium nitrogen ban instruction: When the pH value is <6.5, the fertilizer type must be switched to nitrate nitrogen fertilizer (such as calcium nitrate) to avoid the accumulation of ammonium salts in acidic soil;

[0078] Ammonium nitrogen ratio limit directive: When pH value > 7.0, the ammonium nitrogen ratio is limited to ≤ 50% (such as ammonium sulfate + urea compound) to reduce ammonia volatilization in alkaline soils;

[0079] S3. Convert the phased fertilization strategy into fertilization control parameters, which include fertilizer type ratio and the reference value of fertilizer applicator travel speed.

[0080] Specifically, the generation steps of the tillage control parameters in the generation module are as follows: the vertical distribution density of crop roots is compared with a preset second threshold, and a rotary tillage depth adjustment command is generated based on the comparison result. The rotary tillage depth adjustment command is corrected by combining the average rotary tillage depth in the historical tillage operation volume. The corrected rotary tillage depth adjustment command is bound to the fertilizer applicator travel speed reference value to generate tillage control parameters. The preferred range of the second threshold is 20cm to 40cm.

[0081] The beneficial effects of this example are as follows: pH value and nutrient content are accurately collected by soil moisture sensors; crop stem height, leaf nitrogen accumulation and root vertical distribution density are obtained by UAV multispectral imager; combined with the rainfall probability distribution curve, snow thickness and frozen soil ablation accumulated temperature value predicted by weather radar; the generation module dynamically adjusts the fertilizer type ratio and fertilizer applicator travel speed benchmark value based on the target nutrient demand range (including slow-release nitrogen fertilizer enhancement strategy) and the phased fertilization strategy (fast-acting fertilizer window period / slow-release fertilizer safety period); the tillage control parameters generate rotary tillage depth adjustment instructions by comparing root density with the 20-40cm threshold, and bind the travel speed benchmark value after correction by combining the historical average rotary tillage depth, so as to achieve precise adaptation to the water and heat coupling environment in the cold and humid black soil area, reduce fertilizer efficiency loss and improve tillage safety.

[0082] One embodiment of the present invention is as follows: the method for generating the final fertilization instruction in the collaborative computing module is as follows:

[0083] Q1. Extract the fertilizer type ratio from the fertilizer control parameters as the baseline parameter, and extract the baseline value of the fertilizer applicator's travel speed as the operating parameter. Input the baseline parameter, operating parameter, and pre-applied fertilizer baseline amount into the preset prediction model. Dynamically simulate the scenario in the prediction model that matches the future weather parameters to generate the first simulation result.

[0084] The fertilization baseline is dynamically calculated based on the application window of quick-acting fertilizer and the safe period of slow-release fertilizer in the phased fertilization strategy: During the application window of quick-acting fertilizer, the baseline amount of quick-acting fertilizer is generated based on the proportion of quick-acting nitrogen in the target nutrient requirement range being increased to the preset safe upper limit; During the safe period of slow-release fertilizer, the baseline amount of slow-release fertilizer is generated based on the ratio of organic and inorganic compound fertilizer in the slow-release nitrogen fertilizer enhancement strategy and the binding of the thaw delay marker. Finally, the baseline amounts of quick-acting fertilizer and slow-release fertilizer are integrated into the pre-fertilization baseline amount; The prediction model is a neural network learning model in the existing technology.

[0085] Q2. Load the rainfall probability distribution curve and the accumulated temperature of frozen soil thawing in the future weather parameters. When the future weather parameters meet the weather conditions in the phased fertilization strategy, reduce the fertilization benchmark amount according to the first preset ratio, take the reduced fertilization benchmark amount as the actual application amount, and output the final fertilization command after binding it with the operation parameters.

[0086] The first preset ratio in step Q2 is 20% to 40%. If the probability of rainfall is >60% or the accumulated temperature of soil melting is <5℃d, the base amount of fertilizer should be reduced by 20% to 40%.

[0087] Specifically, the steps for generating the final tillage instruction in the collaborative computing module are as follows:

[0088] W1. Generate the initial rotary tillage depth value based on the modified rotary tillage depth adjustment command in the tillage control parameters;

[0089] W2. Load the accumulated temperature value of frozen soil layer thawing from future weather parameters. When the accumulated temperature value of frozen soil layer thawing does not reach the preset critical value, compress the initial rotary tillage depth value according to the second preset ratio.

[0090] The second preset ratio is 20% to 30%. If the accumulated temperature of frozen soil thawing is less than 5℃d, the initial rotary tillage depth should be 20% to 30%.

[0091] W3. Call the rainfall probability distribution curve in the future weather parameters. When the rainfall probability in the rainfall probability distribution curve exceeds the preset first threshold, link the fertilizer machine travel speed benchmark value in the fertilizer control parameters to generate the tillage speed optimization value, bind the corrected rotary tillage depth value with the tillage speed optimization value, and output the final tillage command.

[0092] The beneficial effects of this example are as follows: the soil moisture sensor in the acquisition module collects pH value and nutrient content, the UAV multispectral imager acquires crop stem height, leaf nitrogen accumulation and root vertical distribution density, and the meteorological radar predicts rainfall probability distribution curve, snow thickness and frozen soil ablation accumulated temperature value; the generation module dynamically calculates the baseline amount of quick-acting fertilizer and slow-release fertilizer based on the target nutrient demand range (including slow-release nitrogen fertilizer enhancement strategy) and the phased fertilization strategy (quick-acting fertilizer window period / slow-release fertilizer safety period); the collaborative calculation module reduces the fertilization baseline amount by 20% to 40% (rainfall > 60% or accumulated temperature < 5℃d) and compresses the rotary tillage depth value by 20% to 30% (accumulated temperature < 5℃d), and finally outputs the correction depth value by binding the tillage speed optimization value, thereby realizing the precise control of fertilization and tillage in cold and humid black soil areas.

[0093] One embodiment of the present invention is as follows: the execution unit in the instruction execution module includes an instruction parsing subunit, a multi-channel control subunit, and a device driver subunit;

[0094] The instruction parsing subunit is used to receive the final fertilization instruction and the final tillage instruction sent by the collaborative computing module, and decode them into fertilization control pulse sequence and tillage control pulse sequence, respectively.

[0095] The fertilization control pulse sequence and the tillage control pulse sequence are obtained by decoding the pre-set decoder in the collaborative computing module. The decoder uses a field-programmable gate array (FPGA) chip.

[0096] The multi-channel control subunit connects the fertilizer type ratio regulator, travel speed controller, and rotary tillage depth hydraulic valve of the pre-fertilized fertilizer to generate the equipment analog voltage control signal of the fertilized fertilizer based on the fertilization control pulse sequence and the tillage control pulse sequence.

[0097] The equipment drive subunit converts the equipment analog voltage control signal into drive current through a power amplifier circuit, which controls the fertilizer discharge motor of the fertilizer applicator and the hydraulic actuator of the tillage implement, respectively.

[0098] Specifically, the steps for generating the visual agricultural technology operation standard package in the instruction execution module are as follows:

[0099] H1. Based on the current waveform of the fertilizer discharge motor of the execution unit and the stroke trajectory of the hydraulic actuator, extract the actual fertilizer application deviation, rotary tillage depth offset and mechanical operation time sequence.

[0100] The specific steps for extracting the deviation value of actual fertilizer application, the offset of rotary tillage depth, and the time series of mechanical operation are as follows:

[0101] H11. Based on the peak range of the current waveform of the fertilizer discharge motor and the preset relationship curve between motor torque and fertilizer discharge amount, calculate the actual instantaneous application amount and compare it with the fertilizer type ratio benchmark value in the fertilizer control parameters to generate the actual fertilizer application amount deviation value.

[0102] H12. Based on the displacement of the stroke trajectory of the hydraulic actuator and the preset mapping table of hydraulic cylinder thrust and tillage depth, the real-time rotary tillage depth value is inverted, and the rotary tillage depth offset is calculated in combination with the rotary tillage depth adjustment command in the tillage control parameters.

[0103] H13. Synchronously acquire the rising edge timestamp of the current waveform and the starting displacement coordinate of the travel trajectory, and generate a mechanical operation time series based on the preset mechanical action response delay threshold. The mechanical operation time series includes the coordinated start and stop time nodes of fertilization and tillage equipment.

[0104] H2. Based on the spatial distribution of nutrient content in the basic soil parameters associated with the rotary tillage depth offset, and combined with the vertical distribution density of crop roots, a layered color model is generated, and the actual fertilizer application deviation value is superimposed to construct a tillage depth heat map with fertility markers.

[0105] H3. Based on the time series of mechanical operations, the discrete points of the fertilizer applicator's travel speed are calculated, and the rainfall probability distribution curve in the future weather parameters is integrated to generate a fertilizer application time series diagram, which contains a rainfall risk warning.

[0106] H4. Based on the areas marked as low fertility in the tillage depth heat map, associate the gaps in the target nutrient requirement range and generate soil improvement suggestions, which include the organic matter replenishment ratio and deep tillage cycle.

[0107] The H5 interface spatially aligns the tillage depth heatmap, fertilization timeline map, and soil improvement recommendations, adds agronomic operation annotations based on the planting characteristics of the cold and humid black soil region, and generates a visual agricultural technology operation standard package.

[0108] The specific characteristics of cultivation include:

[0109] Soil moisture characteristics: based on the pH fluctuation range (5.5-7.2) and the spatial distribution gradient of nutrient content in basic soil parameters;

[0110] Crop root characteristics: The proportion of active crop roots in the 20-40cm tillage layer;

[0111] Climate risk characteristics: the distribution of critical values ​​(5℃d) of accumulated temperature for permafrost thawing and peak periods of rainfall probability in future weather parameters.

[0112] Agronomic operation notes include:

[0113] Heat map annotation: Mark the low fertility high-risk area (nutrient content <1.5g / kg) on ​​the tillage depth heat map and mark it "Add organic matter";

[0114] Mark "Shallow Tillage Protection" in the dense root layer (20-35cm) (triggered when rotary tillage depth offset > ±2cm);

[0115] Time series chart annotation: The period with a rainfall probability > 60% in the fertilization time series chart is marked as the "rain avoidance window" (highlighted in red);

[0116] For periods when the accumulated temperature of frozen soil thawing is less than 5℃d, mark "slow-release fertilizer priority" (blue label);

[0117] Improvement suggestion labeling: In the soil improvement suggestions, areas with a gap of >30% should be labeled with "deep tillage cycle ≥2 years";

[0118] In areas with pH < 6.5, mark "Ammonium nitrogen disabled".

[0119] Specifically, the content of the promotion service interface unit in the instruction execution module is as follows:

[0120] F1. Extract the tillage depth heat map and fertilization time series map from the visual agricultural technology operation standard package, and integrate the mechanical operation time series to generate dynamic instruction metadata;

[0121] The steps for generating dynamic instruction metadata in step F1 are as follows:

[0122] F11. Based on the spatial distribution coordinates of the tillage depth heatmap, extract polygons of tillage depth anomalies with fertility markers and simultaneously associate them with the rainfall risk warning period boundaries in the fertilization time series map.

[0123] F12. When the conversion rate of ammonium nitrogen in the area with soil pH < 6.5 within the polygon of abnormal tillage depth decreases by ≥ 40%, a forced switching command for nitrate nitrogen is generated and superimposed on the boundary of the rainfall risk warning period.

[0124] F13. When the ammonia volatilization loss rate of ammonium nitrogen in the pH>7.0 region is >25%, embed the ammonium nitrogen ratio limit instruction during the rainfall risk warning period and construct dynamic instruction metadata containing correction parameters for fertility anomaly areas.

[0125] F2. Based on the type of the preset farmer terminal, call the preset device resource profile and decompose the dynamic instruction metadata into text operation summary, heat map compressed slice and risk time axis sequence;

[0126] F3. Based on the agricultural operation cycle in the planting characteristics of the black soil region, the peak period of the rainfall probability distribution curve is superimposed to generate a rain shelter scenario snapshot and bind it to the risk time axis sequence.

[0127] F4. Embed the first verification interface of soil improvement suggestions in the text operation summary, and embed the second verification interface of fertilization time series diagram in the heat map compressed slice. Integrate the text operation summary, heat map compressed slice, risk time axis sequence and rain shelter scenario snapshot into a dynamic operation guide adapted to the preset farmer terminal.

[0128] The beneficial effects of this example are as follows: pH and nutrient content are accurately collected using soil moisture sensors; crop stem height, leaf nitrogen accumulation, and root vertical distribution density are obtained using a UAV multispectral imager; and rainfall probability distribution curves and frozen soil thawing accumulated temperature values ​​are predicted using weather radar. The generation module dynamically calculates the baseline amounts of quick-acting fertilizer and slow-release fertilizer based on the target nutrient requirement range (including a slow-release nitrogen fertilizer enhancement strategy) and phased fertilization strategies (quick-acting fertilizer window period / slow-release fertilizer safety period). The collaborative calculation module reduces the fertilization baseline amount by 20%–40%. For areas with rainfall >60% or accumulated temperature <5℃d, the rotary tillage depth value is reduced by 20%–30% (accumulated temperature <5℃d). The instruction execution module uses an FPGA chip to decode pulse sequences to drive the fertilizer discharge motor and hydraulic actuator, simultaneously generating a tillage depth heat map with fertility markings, a rainfall risk warning fertilization time sequence map, and soil improvement suggestions. The extension service interface unit integrates dynamic instruction metadata to generate dynamic operation guidelines adapted to the terminal (including rain shelter scenario snapshots and verification interfaces), realizing precise control of fertilization and tillage coordination and closed-loop optimization of agricultural technology extension in cold and humid black soil areas.

[0129] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A precision fertilization and tillage co-management system for cold and humid black soil regions, characterized in that: It includes an acquisition module, a generation module, a collaborative computing module, and an instruction execution module; The acquisition module is used to acquire basic soil parameters, crop growth parameters, and future weather parameters in the cold and humid black soil region. The generation module generates the required fertilization control parameters and tillage control parameters based on the basic soil parameters, crop growth parameters, and future weather parameters, combined with preset historical tillage operations. The method for obtaining the historical tillage amount in the generation module is as follows: the generation module calls the preset black soil area tillage database, extracts the average rotary tillage depth and fertilizer applicator travel speed benchmark value of the same plot in the cold and humid black soil area in the past three years, and uses the extracted results as the historical tillage amount. The steps for generating the fertilization control parameters in the generation module are as follows: S1. Based on the pH value and nutrient content in the basic soil parameters, and combined with the crop stem height and leaf nitrogen accumulation in the crop growth parameters, a target nutrient requirement range is generated. S2. Load the rainfall probability distribution curve and the frozen soil ablation temperature value from the future weather parameters. When the rainfall probability in the rainfall probability distribution curve exceeds the preset first threshold or the frozen soil ablation temperature value does not reach the preset critical value, generate a phased fertilization strategy based on the target nutrient demand range. S3. Convert the phased fertilization strategy into fertilization control parameters, which include fertilizer type ratio and fertilizer applicator travel speed reference value. The generation steps of the tillage control parameters in the generation module are as follows: the vertical distribution density of crop roots in the crop growth parameters is compared with a preset second threshold, and a rotary tillage depth adjustment command is generated according to the comparison result. The rotary tillage depth adjustment command is corrected by combining the average rotary tillage depth in the historical tillage operation volume. The corrected rotary tillage depth adjustment command is bound to the fertilizer applicator travel speed reference value to generate tillage control parameters. The collaborative computing module converts the fertilization control parameters into fertilization operation volume benchmark values ​​and simultaneously converts the tillage control parameters into tillage operation volume benchmark values. Based on the fertilization operation volume benchmark values ​​and the tillage operation volume benchmark values, it generates the final fertilization instruction and the final tillage instruction. The instruction execution module includes an execution unit, a promotion data generation unit, and a promotion service interface unit. The execution unit is used to execute the final fertilization instruction and the final tillage instruction. The promotion data generation unit is used to parse the execution results of the execution unit and generate a visual agricultural technology operation standard package based on the preset planting characteristics of the cold and humid black soil area. The promotion service interface unit is used to convert the visual agricultural technology operation standard package into dynamic operation guidance and transmit it to the preset farmer terminal.

2. The precision fertilization and tillage co-management system for cold and humid black soil areas according to claim 1, characterized in that: The basic soil parameters in the acquisition module are collected by a soil moisture sensor, and the basic soil parameters include pH value and nutrient content. The crop growth parameters are obtained by a combination of near-ground temperature and humidity sensors and a multispectral imager. The crop growth parameters include crop stem height, leaf nitrogen accumulation, and crop root vertical distribution density. The future weather parameters are based on weather radar and satellite cloud imagery predictions, and include precipitation probability distribution curves, predicted snowfall thickness, and accumulated temperature values ​​for permafrost ablation.

3. The precision fertilization and tillage co-management system for cold and humid black soil areas according to claim 2, characterized in that: The method for generating the final fertilization instruction in the collaborative computing module is as follows: Q1. Extract the fertilizer type ratio from the fertilizer control parameters as a reference parameter, and extract the reference value of the fertilizer applicator's travel speed as an operating parameter. Input the reference parameter, the operating parameter, and the pre-applied fertilizer reference amount into a preset prediction model. Dynamically simulate the scenario in the prediction model that matches the future weather parameters to generate the first simulation result. Q2. Load the rainfall probability distribution curve and the accumulated temperature of frozen soil thawing in the future weather parameters. When the future weather parameters meet the weather conditions in the phased fertilization strategy, reduce the fertilization benchmark amount according to the first preset ratio, take the reduced fertilization benchmark amount as the actual application amount, and output the final fertilization command after binding it with the operation parameters.

4. The precision fertilization and tillage co-management system for cold and humid black soil areas according to claim 3, characterized in that: The steps for generating the final tillage instruction in the collaborative computing module are as follows: W1. Generate an initial rotary tillage depth value based on the modified rotary tillage depth adjustment command in the tillage control parameters; W2. Load the accumulated temperature value of frozen soil layer thawing in the future weather parameters. When the accumulated temperature value of frozen soil layer thawing does not reach the preset critical value, compress the initial rotary tillage depth value according to the second preset ratio. W3. Call the rainfall probability distribution curve in the future weather parameters. When the rainfall probability in the rainfall probability distribution curve exceeds the preset first threshold, link the fertilizer machine travel speed benchmark value in the fertilizer control parameters to generate the tillage speed optimization value. Bind the corrected rotary tillage depth value with the tillage speed optimization value and output the final tillage command.

5. The precision fertilization and tillage co-management system for cold and humid black soil areas according to claim 4, characterized in that: The execution unit in the instruction execution module includes an instruction parsing subunit, a multi-channel control subunit, and a device driver subunit; The instruction parsing subunit is used to receive the final fertilization instruction and the final tillage instruction sent by the collaborative computing module, and decode them into fertilization control pulse sequence and tillage control pulse sequence, respectively. The multi-channel control subunit is connected to the fertilizer type ratio regulator, travel speed controller and rotary tillage depth hydraulic valve of the pre-fertilizing implement, and generates the equipment analog voltage control signal of the fertilizing implement according to the fertilization control pulse sequence and the tillage control pulse sequence. The device drive subunit converts the device analog voltage control signal into drive current through a power amplifier circuit, thereby controlling the operation of the fertilizer discharge motor of the fertilizer applicator and the hydraulic actuator of the tillage implement.

6. The precision fertilization and tillage co-management system for cold and humid black soil areas according to claim 5, characterized in that: The steps for generating the visual agricultural technology operation standard package in the instruction execution module are as follows: H1. Based on the current waveform of the fertilizer discharge motor of the execution unit and the stroke trajectory of the hydraulic actuator, extract the actual fertilizer application deviation value, rotary tillage depth offset and mechanical operation time sequence. H2. Based on the offset of the rotary tillage depth, the spatial distribution of nutrient content in the basic soil parameters is associated with the offset of the rotary tillage depth. A layered color model is generated by combining the vertical distribution density of crop roots. The actual fertilizer application deviation value is superimposed to construct a tillage depth heat map with fertility markers. H3. Based on the time series of mechanical operations, calculate the discrete points of the fertilizer applicator's travel speed, integrate the rainfall probability distribution curve in the future weather parameters, and generate a fertilizer application time series diagram, which contains a rainfall risk warning. H4. Based on the areas marked as low fertility in the tillage depth heat map, associate the gaps in the target nutrient requirement range and generate soil improvement suggestions, which include the organic matter replenishment ratio and deep tillage cycle. H5 spatially aligns the tillage depth heat map, the fertilization time sequence map, and the soil improvement suggestions, adds agronomic operation annotations based on the planting characteristics of the cold and humid black soil area, and generates a visual agricultural technology operation standard package.

7. The precision fertilization and tillage co-management system for cold and humid black soil areas according to claim 6, characterized in that: The specific content of the promotion service interface unit in the instruction execution module is as follows: F1. Extract the tillage depth heat map and the fertilization time sequence map from the visual agricultural technology operation standard package, and integrate the mechanical operation time sequence to generate dynamic instruction metadata; F2. Based on the type of the preset farmer terminal, call the preset device resource profile and decompose the dynamic instruction metadata into text operation summary, heat map compressed slice and risk time axis sequence; F3. Based on the agricultural operation cycle in the planting characteristics of the black soil area, the peak period of the rainfall probability distribution curve is superimposed to generate a rain shelter scenario snapshot and bind it to the risk time axis sequence. F4. Embed the first verification interface of the soil improvement suggestion in the text operation summary, and embed the second verification interface of the fertilization time series diagram in the heat map compressed slice, and integrate the text operation summary, the heat map compressed slice, the risk time axis sequence and the rain shelter scenario snapshot into a dynamic operation guide adapted to the preset farmer terminal.

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