A tea garden soil quality dynamic evaluation and improvement control system

Abstract

This invention discloses a dynamic assessment and improvement regulation system for tea garden soil quality, relating to the fields of soil testing and agricultural planting technology. The proposed solution includes a soil monitoring module, an environmental monitoring module, a data preprocessing module, a dynamic soil quality assessment module, an improvement regulation module, and a management decision-making and visualization platform. These modules work collaboratively to form a complete closed loop for soil quality management. This invention constructs a closed loop for soil quality management through multi-module collaboration, achieving multi-dimensional, high-precision data collection to ensure data reliability. It uses a scientific model to dynamically assess and self-optimize soil quality, generating personalized improvement plans and forming a "policy implementation-feedback-optimization" closed loop. The visualization platform enables intuitive management and remote control, solving problems such as lagging data, blind improvement, and inefficient decision-making in traditional tea garden soil management. This improves management precision and decision-making efficiency, avoiding resource waste and environmental pollution.

Description

Technical Field

[0001] This invention relates to the fields of soil testing and agricultural planting technology, and in particular to a dynamic assessment and improvement control system for tea garden soil quality. Background Technology

[0002] Soil quality in tea gardens is a core factor determining the growth and development of tea trees, as well as the yield and quality of tea. High-quality tea garden soil must have a suitable pH value, sufficient organic matter reserves, a balanced nutrient ratio, and good aeration and permeability. Traditional tea garden soil management models have many prominent problems: First, they rely on manual sampling and testing, which has a long testing cycle, strong data lag, and limited sampling coverage, making it difficult to achieve real-time monitoring and dynamic tracking of soil quality across the entire area. Second, improvement measures are mostly based on growers' experience, lacking a systematic analysis of soil physicochemical properties, environmental conditions, and the growth needs of tea trees. This results in insufficient targeting and scientific rigor, often leading to over-fertilization and poor improvement effects. This not only wastes valuable resources such as fertilizer and water but may also trigger a chain of problems such as soil degradation, heavy metal accumulation, and pollution of the surrounding environment. Third, there is a lack of a unified visual management platform. Soil data, environmental data, and planting management data are stored in a scattered manner, making it difficult to achieve data integration, analysis, and efficient utilization. This leads to low decision-making efficiency and cannot meet the management needs of large-scale, refined tea gardens. Therefore, there is an urgent need to develop a tea garden soil quality management system that integrates specialized monitoring modules, precise assessment models, and an intelligent decision-making platform to completely solve the drawbacks of traditional management models. Summary of the Invention

[0003] The present invention proposes a dynamic assessment and improvement regulation system for tea garden soil quality, which solves the above-mentioned shortcomings of the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A dynamic assessment and improvement regulation system for tea garden soil quality includes a soil monitoring module, an environmental monitoring module, a data preprocessing module, a dynamic soil quality assessment module, an improvement regulation module, and a management decision-making and visualization platform. These modules work collaboratively to form a complete closed loop for soil quality control. The soil monitoring module is the core source of the system's data acquisition. It is positioned as a multi-dimensional, high-precision, full-cycle soil data capture unit. Through a distributed layout strategy of partitioning and layering, it comprehensively collects and reliably transmits various data information on the physical and chemical properties of tea garden soil and planting management information. The collected data information is then sent to the data preprocessing module to provide basic data support for subsequent evaluation and regulation. The environmental monitoring module is positioned as a comprehensive data capture and fusion center for microenvironment and regional meteorology. It collects environmental parameters related to the environment around the tea tree growth area and sends the collected data to the data preprocessing module. Through data fusion, a unified and complete environmental dataset is formed, providing environmental background support for soil quality assessment and improvement plan formulation, and solving the problem of fragmented environmental data in traditional management. The data preprocessing module is positioned as the central hub for ensuring data reliability. It cleans, standardizes, and verifies the quality of the collected multi-source data on soil, environment, and management, removes invalid data, unifies data formats, ensures data quality, and sends the processed data information to the dynamic soil quality assessment module, providing reliable and standardized input data for the dynamic soil quality assessment module and avoiding the impact of poor-quality data on the assessment results. The dynamic soil quality assessment module is the core decision analysis engine of the system. It receives multi-source data preprocessed by the data preprocessing module, and uses a scientific assessment model to determine the level of soil quality, identify the shortcomings of soil quality, and continuously improve the assessment accuracy through self-iterative optimization, providing precise direction for improvement and regulation, and sends the assessed data information to the improvement and regulation module. The improvement and regulation module is positioned as a precise policy implementation and effect closed-loop unit. It generates personalized improvement plans based on soil quality assessment results, tea tree growth needs and environmental conditions, and forms closed-loop regulation through execution feedback and parameter optimization to ensure that soil quality gradually meets the standards, thus solving the problem of blindness in traditional improvement measures. The management decision-making and visualization platform is positioned as a user interaction hub and decision support center, realizing data visualization, remote control, decision support and access management, providing users with an intuitive, convenient and secure system operation entry point, reducing the management threshold and improving decision-making efficiency.

[0005] Furthermore, the soil monitoring module includes multiple distributed soil sensor groups, a planting management data entry unit, and a data transmission subunit; The distributed soil sensor array is arranged according to the tea garden plots and soil profile layers. It is used to collect and monitor the soil organic matter, nitrogen, phosphorus, potassium, water content and heavy metal content in the tea garden in real time. It includes a pH sensing subunit with a measurement range of 3.0-7.0 and an accuracy of ±0.01. The organic matter sensing subunit has a measurement range of 0.5%–10.0% and an accuracy of ±0.05%. The nitrogen, phosphorus, and potassium sensing subunit has a measurement range of 0-200 mg / kg for available nitrogen, 0-100 mg / kg for available phosphorus, and 0-300 mg / kg for available potassium; and a measurement range of 0-500 mg / kg for slow-release nitrogen, 0-200 mg / kg for slow-release phosphorus, and 0-500 mg / kg for slow-release potassium, with an accuracy of ±5%. The moisture sensing subunit has a measurement range of 0%–50% and an accuracy of ±1%. The bulk density sensing subunit has a measurement range of 1.0–1.8 g / cm³ and an accuracy of ±0.02 g / cm³. The porosity sensing subunit has a measurement range of 30%–60% and an accuracy of ±2%. The heavy metal sensing subunit has a lead content of 0-100 mg / kg, a cadmium content of 0-10 mg / kg, and a mercury content of 0-1 mg / kg, with an accuracy of ±10%. The planting management data entry unit includes a manual entry subunit and a system interface subunit, which are used to record and collect data on tea tree varieties, planting density, fertilization history, irrigation records, pest and disease control information, and pruning cycle data. The manual input subunit supports touch and keyboard input, and the system interface subunit has an API interface that is compatible with mainstream agricultural management systems. The fertilization history is divided into fertilizer type, application amount and application time; the irrigation record is divided into irrigation amount and irrigation time; the pest and disease control information is divided into pest and disease type, control agent and application dosage. The data transmission subunit adopts LoRa / Wi-Fi dual-mode transmission, with a transmission distance of ≥1km (LoRa mode), data transmission latency of ≤5s, and supports breakpoint resume function.

[0006] Furthermore, the environmental monitoring module includes multiple field environmental sensor groups, a meteorological data interface unit, and an environmental data fusion subunit; The field environment sensor group is used to collect and monitor the temperature, humidity, surface irradiance and wind speed of the surrounding environment in real time. It includes a canopy temperature sensing subunit, a near-ground humidity sensing subunit, a surface irradiance sensing subunit and a wind speed sensing subunit. The canopy temperature sensing subunit has a measurement range of -10℃ to 60℃ and an accuracy of ±0.2℃. The near-ground humidity sensing subunit has a measurement range of 0%–100%RH and an accuracy of ±3%RH. The surface illuminance sensing subunit has a measurement range of 0-200,000 lux and an accuracy of ±5%. The wind speed sensing subunit has a measurement range of 0-30 m / s and an accuracy of ±0.3 m / s; The meteorological data docking unit is used to access regional meteorological station data and obtain precipitation (accuracy ±0.1mm), extreme weather warnings and accumulated temperature (accuracy ±0.5℃・d) data. It includes an interface adaptation subunit (supporting HTTP / HTTPS / MQTT protocols) and a data filtering subunit (matching tea garden area data by timestamp). The extreme weather warnings include high temperature, rainstorm, frost and typhoon. The environmental data fusion subunit is used to align field microenvironment data and regional meteorological data according to the time dimension to generate an environmental dataset with a unified timestamp.

[0007] Furthermore, the data preprocessing unit includes an outlier removal module, a data standardization module, and a data quality verification subunit; The outlier removal module is used to identify and remove extreme outliers and fluctuation noise in sensor data. It includes an extreme value identification subunit (using the Raida criterion 3σ principle) and a noise filtering subunit. It uses the moving average method, with a window size of 5 acquisition cycles and weighting coefficients [0.1, 0.2, 0.4, 0.2, 0.1]. The formula for outlier removal (Raida criterion 3σ principle) is as follows: Suppose that the dataset collected by a certain sensor is... ; Sample mean: ; Sample standard deviation: ; Outlier detection: If ,but These are outliers and will be removed. in, n represents the data collected by the sensor in a single instance; Noise filtering (moving average method) calculation formula: Assume the sliding window size is 5 acquisition cycles, and the weighting coefficient is... The filtered data for the k-th period is: ; in, This is the filtered data for the k-th period. For the first The raw data collected in each cycle; The data standardization module adopts the Z-score standardization method, which includes a mean calculation subunit, a variance calculation subunit, and a normalization operation subunit, to convert soil, environmental, and management data of different dimensions into characteristic data of a unified standard. The formula for calculating data standardization (Z-score standardization) is as follows: ; in, This is the original data. The sample mean. The standard deviation is the sample standard deviation. After standardization, the mean of the data is 0 and the variance is 1. The data quality verification subunit is configured with data integrity verification (single-cycle data missing rate ≤5%) and data consistency verification (data fluctuation ≤20% over 3 consecutive cycles). Unqualified data will trigger a sensor re-acquisition command.

[0008] Furthermore, the dynamic soil quality assessment module includes an index weight determination subunit, a fuzzy comprehensive evaluation subunit, a grade determination subunit, and a model self-iterative optimization subunit; The sub-unit for determining the index weights adopts the analytic hierarchy process, which includes a target layer definition sub-unit, a criterion layer division sub-unit, an index layer refinement sub-unit, and a weight calculation sub-unit, and outputs a weight system. The formula for calculating indicator weights is as follows: Determine the normalization of matrix columns: ; in, To determine matrix elements, The number of indicators; Summation: ; Weight normalization: ; Consistency check: Maximum eigenvalue: ; Consistency Indicators: ; Consistency ratio: ,Require ; The criteria layer division sub-units include soil physicochemical indicators, environmental parameters, and planting management data; Its soil physicochemical indicators account for 60%: pH value 0.2, organic matter 0.25, nitrogen, phosphorus and potassium 0.3, water content 0.1, bulk density 0.05, heavy metals 0.1; Environmental parameters account for 25%: canopy temperature 0.08, near-surface humidity 0.07, precipitation 0.05, and sunlight 0.05; Planting management data accounted for 15%: fertilization rationality 0.06, irrigation frequency 0.04, and frequency of pests and diseases 0.05; The fuzzy comprehensive evaluation subunit includes a fuzzy matrix construction subunit, a membership degree calculation subunit (using a trapezoidal membership function), and a matrix operation subunit, which perform fuzzy operations on standardized feature data; The grade determination subunit is equipped with a grade threshold subunit, which is used to output the soil quality grade and the shortcoming index. The grades are divided into: Excellent: ≥0.8; Good: 0.6-0.8; Medium: 0.4-0.6; Poor: 0.2-0.4; Severe: 0.2; The model self-iterative optimization subunit is used to perform production dynamic optimization and evaluation model, and it includes a data accumulation subunit, a weight adjustment subunit, and a threshold update subunit. The data accumulation subunit is used to collect monitoring data for six consecutive months. The weight adjustment subunit is based on indicator contribution analysis; The threshold update subunit is based on industry standards and actual planting results.

[0009] Furthermore, the improved control module includes a scheme generation unit, an execution feedback unit, a parameter optimization subunit, and a scheme storage subunit; The scheme generation unit includes a demand analysis subunit (tea tree growth cycle: juvenile stage, mature stage, dormant stage), a stress factor identification subunit (soil weakness index, environmental stress), a measure matching subunit, and a dosage calculation subunit, used to output targeted improvement measures. The specific scheme is shown below: Soil pH adjustment plan: including selection of quicklime / sulfur powder, calculation of application rate and planning of application frequency; The formula for calculating soil pH adjustment (quicklime application rate) is as follows: ; in, The amount of quicklime applied (kg / hm²) is used. The thickness of the topsoil layer (cm) Soil bulk density (g / cm³) and CEC are cation exchange capacities (cmol / kg). For the target pH value, The current pH value. Adjustment coefficients (0.8 for sand, 1.0 for loam, and 1.2 for clay); Nutrient supplementation plan: including the ratio of organic to inorganic fertilizers, selection and dosage of microbial agents, and determination of application time window; The formula for calculating nutrient supplementation (nitrogen, phosphorus, and potassium application rates) is as follows: ; in, This refers to the amount of fertilizer applied (kg / hm²). The target yield is (kg / hm²). Nutrient absorption coefficient per unit yield (kg / kg) This represents the current nutrient content of the soil (mg / kg). Soil nutrient utilization coefficient, This refers to the nutrient content (%) of the fertilizer. Fertilizer utilization rate (0.3 for organic fertilizer, 0.6 for inorganic fertilizer); Water management plan: including recommendations for irrigation volume, irrigation cycle and irrigation method; The formula for calculating water regulation (irrigation volume) is as follows: Net irrigation amount: ; in, Net irrigation volume (m³). The irrigated area is (hm²). The thickness of the topsoil layer (cm) Soil bulk density (g / cm³) , Suitable moisture content range (%) Current moisture content (%); Actual irrigation amount: ; in, The irrigation water utilization coefficient is used (0.9 for drip irrigation, 0.7 for sprinkler irrigation, and 0.5 for flood irrigation). Soil structure improvement plan: including crop rotation selection, straw return parameters and soil conditioner dosage; Green pest and disease control program: including selection of biological control agents, proportion of natural enemy insects to be released, and physical control measures; The improvement measures are precisely matched. A mapping library of "soil problems - improvement technologies" is established through the measure matching sub-unit. Some core mapping relationships are shown in Table 1: Table 1

[0010] At the same time, measures should be taken to optimize environmental conditions: for example, avoid applying chemical fertilizers at noon during hot weather, choose to irrigate in the evening, reduce the application of organic fertilizers in the open during the rainy season, and adopt the method of applying fertilizers in strips and covering them with soil.

[0011] Furthermore, the execution feedback unit includes a data acquisition subunit (data acquisition every 3 days after improvement), an effect comparison subunit (comparison with preset target value), and a deviation judgment subunit (deviation threshold of 10%). The parameter optimization subunit is used to adjust the improvement parameters according to the degree of deviation, including application rate, application frequency, and irrigation amount, thereby generating a secondary control scheme. The degree of deviation is divided into: The formula for calculating the deviation of the improvement effect is as follows: ; in, The deviation rate, The measured value is the improved value. The target value; Slight deviation; Moderate deviation; , severe deviation; The solution storage subunit stores improvement solutions categorized by soil type, tea tree variety, and problem type, and supports the retrieval of historical solutions.

[0012] Furthermore, the management decision-making and visualization platform includes a data visualization interface, a remote control unit, a decision support database, and an access control subunit; The data visualization interface includes: The map display sub-unit is used to display the tea garden zoning map, monitoring point location, and quality level color markings. The quality level color markings are divided into: excellent green, good blue, medium yellow, poor orange, and terrible red. The trend analysis sub-unit is used to display the 7-day / 30-day / 90-day indicator change curves and year-on-year / month-on-month analysis; The radar chart display sub-unit is used to show comparisons of performance across multiple dimensions. The solution presentation sub-unit is used to display the list of improvement measures, application volume, implementation progress, and expected results; The effect comparison sub-unit is used to display the indicator bar charts and level change labels before and after the improvement.

[0013] Furthermore, the remote control unit includes: The data viewing sub-unit is used for querying and exporting real-time and historical data. The instruction issuing subunit is used to issue instructions for implementing improvement measures and controlling irrigation equipment; The parameter configuration subunit is used to arbitrarily adjust the monitoring frequency according to actual needs, and at the same time modify the evaluation standard parameters. The early warning sub-unit is used to issue early warnings for indicators exceeding standards and for warnings and alerts when improvement effects do not meet expectations.

[0014] Furthermore, the decision support database includes: Historical database is used to store monitoring data of tea gardens for more than three years; A case study database is used to store case studies and effect data of improvement solutions for different soil problems. The variety parameter library is used to store the growth requirements and suitable soil conditions of common tea tree varieties. A standard database for storing parameters of national / industry soil quality standards and tea planting technical specifications; The permission management subunit sets up three levels of permissions: administrator, operator, and viewer, which correspond to system configuration, command execution, and data viewing functions, respectively.

[0015] Compared with existing technologies, the beneficial effects of this invention are: 1. This invention clarifies the function and technical parameters of each sub-unit through the unit-level detailed design of each module, improves the feasibility and stability of the system, solves the problems of vague module functions and inconvenient operation in traditional systems, and realizes multi-dimensional and high-precision data acquisition through the sub-unit refinement of the soil and environmental monitoring module. Dual-mode transmission and data fusion technology ensure the real-time performance and integrity of the data. 2. This invention ensures the reliability of input data through a multi-level verification mechanism in the data preprocessing unit, laying the foundation for accurate assessment. Through the hierarchical design and self-iterative optimization function of the dynamic soil quality assessment module, the scientificity and adaptability of the assessment are improved, and it can dynamically reflect changes in soil quality. 3. This invention achieves a closed loop of "precise policy implementation - effect feedback - parameter adjustment" by improving the refined scheme generation and dynamic optimization mechanism of the control module, ensuring that the improvement effect meets the target and avoiding resource waste. Through the integration of management decision-making and multiple sub-units of the visualization platform, it provides an intuitive display effect. In summary, this system constructs a closed loop for soil quality management through multi-module collaboration, achieving multi-dimensional and high-precision data collection to ensure data reliability. It dynamically evaluates soil quality using scientific models and self-optimizes, generating personalized improvement plans and forming a "policy implementation-feedback-optimization" closed loop. With the help of a visualization platform, it enables intuitive management and remote control, solving problems such as lagging data, blind improvement, and inefficient decision-making in traditional tea garden soil management. This improves the precision of management and the efficiency of decision-making, and avoids resource waste and environmental pollution. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of a dynamic assessment and improvement control system for tea garden soil quality proposed in this invention; Figure 2 This is a block diagram of the soil monitoring module of a dynamic assessment and improvement regulation system for tea garden soil quality proposed in this invention; Figure 3 This is a block diagram of the environmental monitoring module of a dynamic assessment and improvement regulation system for tea garden soil quality proposed in this invention; Figure 4 This is a block diagram of the data preprocessing module of a dynamic assessment and improvement regulation system for tea garden soil quality proposed in this invention; Figure 5 This is a block diagram of the soil quality dynamic assessment module of a tea garden soil quality dynamic assessment and improvement regulation system proposed in this invention; Figure 6 This is a block diagram of the improvement and control module of a dynamic assessment and improvement control system for tea garden soil quality proposed in this invention; Figure 7This is a structural diagram of a management decision-making and visualization platform for a dynamic assessment and improvement regulation system for tea garden soil quality proposed in this invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0019] Example, refer to Figure 1-7 A dynamic assessment and improvement control system for tea garden soil quality includes a soil monitoring module, an environmental monitoring module, a data preprocessing module, a dynamic soil quality assessment module, an improvement control module, and a management decision-making and visualization platform. These modules work collaboratively to form a complete closed loop for soil quality control. The soil monitoring module is the core source of the system's data acquisition. It is positioned as a multi-dimensional, high-precision, full-cycle soil data capture unit. Through a distributed layout strategy of partitioning and layering, it comprehensively collects and reliably transmits various data information on the physical and chemical properties of tea garden soil and planting management information. The collected data information is then sent to the data preprocessing module to provide basic data support for subsequent assessment and regulation. The environmental monitoring module is positioned as a comprehensive data capture and fusion center for microenvironment and regional meteorology. It collects environmental parameters related to the environment around the tea tree growth area and sends the collected data to the data preprocessing module. Through data fusion, a unified and complete environmental dataset is formed, providing environmental background support for soil quality assessment and improvement plan formulation, and solving the problem of fragmented environmental data in traditional management. The data preprocessing module is positioned as the central hub for ensuring data reliability. It cleans, standardizes, and verifies the quality of collected multi-source data on soil, environment, and management, removes invalid data, unifies data formats, ensures data quality, and sends the processed data to the dynamic soil quality assessment module. This provides reliable and standardized input data for the dynamic soil quality assessment module and avoids the impact of poor-quality data on the assessment results. The dynamic soil quality assessment module is the core decision analysis engine of the system. It receives multi-source data preprocessed by the data preprocessing module, and uses a scientific assessment model to determine the level of soil quality, identify the shortcomings of soil quality, and continuously improve the assessment accuracy through self-iterative optimization, providing precise direction for improvement and regulation, and sending the assessed data information to the improvement and regulation module. The improvement and regulation module is positioned as a precise policy implementation and effect closed-loop unit. It generates personalized improvement plans based on soil quality assessment results, tea tree growth needs and environmental conditions, and forms closed-loop regulation through execution feedback and parameter optimization to ensure that soil quality gradually meets the standards, thus solving the problem of blindness in traditional improvement measures. The management decision-making and visualization platform is positioned as a user interaction hub and decision support center, realizing data visualization, remote control, decision support and access management. It provides users with an intuitive, convenient and secure system operation entry point, reduces the management threshold and improves decision-making efficiency.

[0020] In this invention, the soil monitoring module includes multiple distributed soil sensor groups, a planting management data entry unit, and a data transmission subunit; The distributed soil sensor array is arranged according to the tea garden plots and soil profile layers. It is used to collect and monitor the soil organic matter, nitrogen, phosphorus and potassium, water content and heavy metal content in the tea garden in real time. It includes a pH sensing subunit with a measurement range of 3.0-7.0 and an accuracy of ±0.01. The organic matter sensing subunit has a measurement range of 0.5%–10.0% and an accuracy of ±0.05%. The nitrogen, phosphorus, and potassium sensing subunit has a measurement range of 0-200 mg / kg for available nitrogen, 0-100 mg / kg for available phosphorus, and 0-300 mg / kg for available potassium; and a measurement range of 0-500 mg / kg for slow-release nitrogen, 0-200 mg / kg for slow-release phosphorus, and 0-500 mg / kg for slow-release potassium, with an accuracy of ±5%. The moisture sensing subunit has a measurement range of 0%–50% and an accuracy of ±1%. The bulk density sensing subunit has a measurement range of 1.0–1.8 g / cm³ and an accuracy of ±0.02 g / cm³. The porosity sensing subunit has a measurement range of 30%–60% and an accuracy of ±2%. The heavy metal sensing subunit has a lead content of 0-100 mg / kg, a cadmium content of 0-10 mg / kg, and a mercury content of 0-1 mg / kg, with an accuracy of ±10%. The planting management data entry unit includes a manual entry subunit and a system interface subunit, which are used to record and collect data on tea tree varieties, planting density, fertilization history, irrigation records, pest and disease control information, and pruning cycle data. The manual input sub-unit supports touch and keyboard input, and the system interface sub-unit has an API interface, which is compatible with mainstream agricultural management systems. Fertilization history is divided into fertilizer type, application amount and application time; irrigation records are divided into irrigation amount and irrigation time; pest and disease control information is divided into pest and disease type, control agent and application dosage. The data transmission subunit adopts LoRa / Wi-Fi dual-mode transmission, with a transmission distance of ≥1km (LoRa mode), data transmission latency of ≤5s, and supports breakpoint resume function.

[0021] In this invention, the environmental monitoring module includes multiple field environmental sensor groups, a meteorological data interface unit, and an environmental data fusion subunit; The field environment sensor group is used to collect and monitor the temperature, humidity, surface irradiance and wind speed of the surrounding environment in real time. It includes a canopy temperature sensing subunit, a near-ground humidity sensing subunit, a surface irradiance sensing subunit and a wind speed sensing subunit. The canopy temperature sensing subunit has a measurement range of -10℃ to 60℃ and an accuracy of ±0.2℃. The near-ground humidity sensing subunit has a measurement range of 0%–100%RH and an accuracy of ±3%RH. The surface illuminance sensing subunit has a measurement range of 0-200,000 lux and an accuracy of ±5%. The wind speed sensing subunit has a measurement range of 0-30 m / s and an accuracy of ±0.3 m / s; The meteorological data docking unit is used to access regional meteorological station data and obtain precipitation (accuracy ±0.1mm), extreme weather warnings and accumulated temperature (accuracy ±0.5℃・d) data. It includes an interface adaptation subunit (supporting HTTP / HTTPS / MQTT protocols) and a data filtering subunit (matching tea garden area data by timestamp). Extreme weather warnings include high temperature, rainstorm, frost and typhoon. The environmental data fusion subunit is used to align field microenvironment data with regional meteorological data along the time dimension to generate an environmental dataset with a unified timestamp.

[0022] In this invention, the data preprocessing unit includes an outlier removal module, a data standardization module, and a data quality verification subunit; The outlier removal module is used to identify and remove extreme outliers and fluctuation noise in sensor data. It includes an extreme value identification subunit (using the Raida criterion 3σ principle) and a noise filtering subunit. It uses the moving average method, with a window size of 5 acquisition cycles and weighting coefficients [0.1, 0.2, 0.4, 0.2, 0.1]. The formula for outlier removal (Raida criterion 3σ principle) is as follows: Suppose that the dataset collected by a certain sensor is... ; Sample mean: ; Sample standard deviation: ; Outlier detection: If ,but These are outliers and will be removed. in, n represents the data collected by the sensor in a single instance; Noise filtering (moving average method) calculation formula: Assume the sliding window size is 5 acquisition cycles, and the weighting coefficient is... The filtered data for the k-th period is: ; in, This is the filtered data for the k-th period. For the first The raw data collected in each cycle; The data standardization module adopts the Z-score standardization method, which includes a mean calculation sub-unit, a variance calculation sub-unit, and a normalization operation sub-unit, to convert soil, environmental, and management data of different dimensions into characteristic data of a unified standard. The formula for calculating data standardization (Z-score standardization) is as follows: ; in, This is the original data. The sample mean. The standard deviation is the sample standard deviation. After standardization, the mean of the data is 0 and the variance is 1. The data quality verification subunit is configured with data integrity verification (single-cycle data missing rate ≤5%) and data consistency verification (data fluctuation ≤20% for 3 consecutive cycles). Unqualified data will trigger the sensor to re-acquire data.

[0023] In this invention, the dynamic soil quality assessment module includes an index weight determination subunit, a fuzzy comprehensive evaluation subunit, a grade determination subunit, and a model self-iterative optimization subunit. The sub-unit for determining indicator weights adopts the analytic hierarchy process, which includes a target layer definition sub-unit, a criterion layer division sub-unit, an indicator layer refinement sub-unit, and a weight calculation sub-unit, outputting a weight system. The formula for calculating indicator weights is as follows; Determine the normalization of matrix columns: ; in, To determine matrix elements, The number of indicators; Summation: ; Weight normalization: ; Consistency check: Maximum eigenvalue: ; Consistency Indicators: ; Consistency ratio: ,Require ; The criteria layer is divided into sub-units that include soil physicochemical indicators, environmental parameters, and planting management data; Its soil physicochemical indicators account for 60%: pH value 0.2, organic matter 0.25, nitrogen, phosphorus and potassium 0.3, water content 0.1, bulk density 0.05, heavy metals 0.1; Environmental parameters account for 25%: canopy temperature 0.08, near-surface humidity 0.07, precipitation 0.05, and sunlight 0.05; Planting management data accounted for 15%: fertilization rationality 0.06, irrigation frequency 0.04, and frequency of pests and diseases 0.05; The fuzzy comprehensive evaluation subunit includes a fuzzy matrix construction subunit, a membership degree calculation subunit (using trapezoidal membership function), and a matrix operation subunit, which perform fuzzy operations on standardized feature data; The grade determination subunit sets the grade threshold subunit, which is used to output the soil quality grade and the weak link index; The grades are divided into: Excellent: ≥0.8; Good: 0.6-0.8; Medium: 0.4-0.6; Poor: 0.2-0.4; Severe: 0.2; The model self-iterative optimization subunit is used to evaluate the production dynamic optimization model. It includes a data accumulation subunit, a weight adjustment subunit, and a threshold update subunit. The data accumulation sub-unit is used to statistically analyze monitoring data for six consecutive months; The weight adjustment sub-unit is based on indicator contribution analysis; The threshold update subunit is based on industry standards and actual planting results.

[0024] In this invention, the improved control module includes a scheme generation unit, an execution feedback unit, a parameter optimization subunit, and a scheme storage subunit; The scheme generation unit includes a demand analysis subunit (tea tree growth cycle: juvenile stage, mature stage, dormant stage), a stress factor identification subunit (soil weakness index, environmental stress), a measure matching subunit, and a dosage calculation subunit, which are used to output targeted improvement measures. The specific scheme is shown below: Soil pH adjustment plan: including selection of quicklime / sulfur powder, calculation of application rate and planning of application frequency; The formula for calculating soil pH adjustment (quicklime application rate) is as follows: ; in, The amount of quicklime applied (kg / hm²) is used. The thickness of the topsoil layer (cm) Soil bulk density (g / cm³) and CEC are cation exchange capacities (cmol / kg). For the target pH value, The current pH value. Adjustment coefficients (0.8 for sand, 1.0 for loam, and 1.2 for clay); Nutrient supplementation plan: including the ratio of organic to inorganic fertilizers, selection and dosage of microbial agents, and determination of application time window; The formula for calculating nutrient supplementation (nitrogen, phosphorus, and potassium application rates) is as follows: ; in, This refers to the amount of fertilizer applied (kg / hm²). The target yield is (kg / hm²). Nutrient absorption coefficient per unit yield (kg / kg) This represents the current nutrient content of the soil (mg / kg). Soil nutrient utilization coefficient, This refers to the nutrient content (%) of the fertilizer. Fertilizer utilization rate (0.3 for organic fertilizer, 0.6 for inorganic fertilizer); Water management plan: including recommendations for irrigation volume, irrigation cycle and irrigation method; The formula for calculating water regulation (irrigation volume) is as follows: Net irrigation amount: ; in, Net irrigation volume (m³). The irrigated area is (hm²). The thickness of the topsoil layer (cm) Soil bulk density (g / cm³) , For the suitable moisture content range (%), Current moisture content (%) Actual irrigation amount: ; in, The irrigation water utilization coefficient is used (0.9 for drip irrigation, 0.7 for sprinkler irrigation, and 0.5 for flood irrigation). Soil structure improvement plan: including crop rotation selection, straw return parameters and soil conditioner dosage; Green pest and disease control program: including selection of biological control agents, proportion of natural enemy insects to be released, and physical control measures; The improvement measures are precisely matched. A mapping library of "soil problems - improvement technologies" is established through the measure matching sub-unit. Some core mapping relationships are shown in Table 1: Table 1

[0025] At the same time, measures should be taken to optimize environmental conditions: for example, avoid applying chemical fertilizers at noon during hot weather, choose to irrigate in the evening, reduce the application of organic fertilizers in the open during the rainy season, and adopt the method of applying fertilizers in strips and covering them with soil.

[0026] In this invention, the execution feedback unit includes a data acquisition subunit (data is collected once every 3 days after improvement), an effect comparison subunit (compared with the preset target value), and a deviation judgment subunit (deviation threshold of 10%). The parameter optimization subunit is used to adjust the improvement parameters, including application rate, application frequency, and irrigation volume, based on the degree of deviation, thereby generating a secondary control scheme. The degree of deviation is divided into: The formula for calculating the deviation of the improvement effect is as follows: ; in, The deviation rate, The measured value is the improved value. The target value; Slight deviation; Moderate deviation; , severe deviation; The solution storage sub-unit stores improvement solutions categorized by soil type, tea tree variety, and problem type, and supports the retrieval of historical solutions.

[0027] In this invention, the management decision and visualization platform includes a data visualization interface, a remote control unit, a decision support database, and an access control subunit; The data visualization interface includes: The map display sub-unit is used to display the tea garden zoning map, monitoring point location, and quality level color markings. The quality level color markings are divided into: excellent green, good blue, medium yellow, poor orange, and terrible red. The trend analysis sub-unit is used to display the 7-day / 30-day / 90-day indicator change curves and year-on-year / month-on-month analysis; The radar chart display sub-unit is used to show comparisons of performance across multiple dimensions. The solution presentation sub-unit is used to display the list of improvement measures, application volume, implementation progress, and expected results; The effect comparison sub-unit is used to display the indicator bar charts and level change labels before and after the improvement.

[0028] In this invention, the remote control unit includes: The data viewing sub-unit is used for querying and exporting real-time and historical data. The instruction issuing subunit is used to issue instructions for implementing improvement measures and controlling irrigation equipment; The parameter configuration subunit is used to arbitrarily adjust the monitoring frequency according to actual needs, and at the same time modify the evaluation standard parameters. The early warning sub-unit is used to issue early warnings for indicators exceeding standards and for warnings and alerts when improvement effects do not meet expectations.

[0029] In this invention, the decision support database includes: Historical database is used to store monitoring data of tea gardens for more than three years; A case study database is used to store case studies and effect data of improvement solutions for different soil problems. The variety parameter library is used to store the growth requirements and suitable soil conditions of common tea tree varieties. A standard database for storing parameters of national / industry soil quality standards and tea planting technical specifications; The permission management sub-unit sets up three levels of permissions: administrator, operator, and viewer, corresponding to system configuration, command execution, and data viewing functions, respectively.

[0030] The working principle, specific implementation method and process are as follows: Data acquisition phase: The soil monitoring module collects soil physicochemical parameters in real time through a distributed sensor group, records relevant management information through the planting management data entry unit, and transmits the data to the data preprocessing module via LoRa / Wi-Fi dual mode. The environmental monitoring module collects microenvironmental parameters through a field environmental sensor group, obtains regional meteorological data through the meteorological data docking unit, and transmits the data to the data preprocessing module after data fusion. Data preprocessing stage: The data preprocessing module uses the Raida criterion 3σ principle to remove outliers, filters noise using the moving average method, and uses the Z-score standardization method to unify the data format. After integrity and consistency verification, the qualified data is transmitted to the soil quality dynamic assessment module. Quality assessment phase: The dynamic soil quality assessment module uses the analytic hierarchy process to determine the weights of the indicators, performs calculations on the standardized data through fuzzy comprehensive evaluation, outputs the soil quality grade and the weak point indicators based on the grade threshold, and continuously optimizes the assessment model through the model self-iterative optimization sub-unit. Improvement and regulation phase: Based on the evaluation results, tea tree growth needs and environmental conditions, the improvement and regulation module generates personalized improvement plans. Users can view the plans and issue execution instructions through the management decision and visualization platform. After execution, data is collected every 3 days to compare the measured values ​​with the target values. The improvement parameters are adjusted according to the degree of deviation to form a closed-loop regulation. Management Decision-Making Phase: Users can view data visualization results through the management decision-making and visualization platform, perform remote control, parameter configuration, and other operations. The system issues early warning prompts based on data anomalies, the decision support database provides users with data support and solution references, and the access control subunit ensures operational security.

[0031] In practical applications, based on the actual conditions such as the size of the tea garden, soil type, and tea variety, the monitoring frequency, evaluation standards, and other parameters can be adjusted through the parameter configuration sub-unit of the management decision-making and visualization platform to adapt to the usage needs of different scenarios.

[0032] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic assessment and improvement regulation system for tea garden soil quality, comprising a soil monitoring module, an environmental monitoring module, a data preprocessing module, a dynamic assessment module for soil quality, an improvement regulation module, and a management decision-making and visualization platform, characterized in that: The soil monitoring module is used to comprehensively collect and reliably transmit various data information on the physical and chemical properties of tea garden soil and planting management information, and send the collected data information to the data preprocessing module. The environmental monitoring module is used to collect environmental parameters related to the growth of tea trees, and at the same time, send the collected data to the data preprocessing module. The data preprocessing module is used to clean, standardize and verify the quality of the collected multi-source data on soil, environment and management, and send the processed data information to the dynamic soil quality assessment module. The dynamic soil quality assessment module is used to receive multi-source data preprocessed by the data preprocessing module, determine the soil quality level through a scientific assessment model, and send the assessed data information to the improvement and regulation module. The improvement and control module is used to generate personalized improvement plans based on soil quality assessment results, tea tree growth needs and environmental conditions. The management decision and visualization platform is used to realize data visualization, remote control, decision support and access control, and provides users with an intuitive, convenient and secure system operation interface.

2. The tea garden soil quality dynamic assessment and improvement regulation system according to claim 1, characterized in that, The soil monitoring module includes multiple distributed soil sensor groups, a planting management data entry unit, and a data transmission subunit; The distributed soil sensor group is arranged according to the tea garden plot zoning and soil profile stratification. It is used to collect and monitor the soil organic matter, nitrogen, phosphorus and potassium, water content and heavy metal content in the tea garden in real time. It includes pH sensing subunit, organic matter sensing subunit, nitrogen, phosphorus and potassium sensing subunit, water sensing subunit, bulk density sensing subunit, porosity sensing subunit and heavy metal sensing subunit. The planting management data entry unit includes a manual entry subunit and a system interface subunit, which are used to record and collect data on tea tree varieties, planting density, fertilization history, irrigation records, pest and disease control information, and pruning cycle data. The data transmission subunit adopts LoRa / Wi-Fi dual-mode transmission, with a transmission distance of ≥1km, a data transmission delay of ≤5s, and supports breakpoint resume function.

3. The tea garden soil quality dynamic assessment and improvement regulation system according to claim 1, characterized in that, The environmental monitoring module includes multiple field environmental sensor groups, a meteorological data docking unit, and an environmental data fusion subunit. The field environment sensor group is used to collect and monitor the temperature, humidity, surface irradiance and wind speed of the surrounding environment in real time. It includes a canopy temperature sensing subunit, a near-ground humidity sensing subunit, a surface irradiance sensing subunit and a wind speed sensing subunit. The meteorological data docking unit is used to access regional meteorological station data and obtain precipitation, extreme weather warnings and accumulated temperature data, including an interface adaptation subunit and a data filtering subunit. The environmental data fusion subunit is used to align field microenvironment data and regional meteorological data according to the time dimension to generate an environmental dataset with a unified timestamp.

4. The tea garden soil quality dynamic assessment and improvement regulation system according to claim 1, characterized in that, The data preprocessing unit includes an outlier removal module, a data standardization module, and a data quality verification subunit. The outlier removal module is used to identify and remove extreme outliers and fluctuation noise in sensor data, and it includes an extreme value identification subunit and a noise filtering subunit. The data standardization module adopts the Z-score standardization method, which includes a mean calculation subunit, a variance calculation subunit, and a normalization operation subunit, to convert soil, environmental, and management data of different dimensions into characteristic data of a unified standard. The data quality verification subunit is configured with data integrity verification and data consistency verification. Unqualified data will trigger a sensor re-acquisition command.

5. The tea garden soil quality dynamic assessment and improvement regulation system according to claim 1, characterized in that, The dynamic soil quality assessment module includes an index weight determination subunit, a fuzzy comprehensive evaluation subunit, a grade determination subunit, and a model self-iterative optimization subunit. The sub-unit for determining the index weights adopts the analytic hierarchy process, which includes a target layer definition sub-unit, a criterion layer division sub-unit, an index layer refinement sub-unit, and a weight calculation sub-unit, and outputs a weight system. The fuzzy comprehensive evaluation subunit includes a fuzzy matrix construction subunit, a membership degree calculation subunit, and a matrix operation subunit, which performs fuzzy operations on standardized feature data. The grade determination subunit is equipped with a grade threshold subunit, which is used to output the soil quality grade and the shortcoming index. The model self-iterative optimization subunit is used to perform dynamic optimization and evaluation of the production model, and it includes a data accumulation subunit, a weight adjustment subunit, and a threshold update subunit.

6. The tea garden soil quality dynamic assessment and improvement control system according to claim 1, characterized in that, The improved control module includes a scheme generation unit, an execution feedback unit, a parameter optimization subunit, and a scheme storage subunit; The scheme generation unit includes a demand analysis subunit, a stress factor identification subunit, a measure matching subunit, and a dose calculation subunit, used to output targeted improvement measures. Specific schemes are shown below: Soil pH adjustment plan: including selection of quicklime / sulfur powder, calculation of application rate and planning of application frequency; Nutrient supplementation plan: including the ratio of organic to inorganic fertilizers, selection and dosage of microbial agents, and determination of application time window; Water management plan: including recommendations for irrigation volume, irrigation cycle and irrigation method; Soil structure improvement plan: including crop rotation selection, straw return parameters and soil conditioner dosage; Green pest and disease control program: including selection of biological control agents, proportion of natural enemy insects to be released and physical control measures.

7. The tea garden soil quality dynamic assessment and improvement control system according to claim 6, characterized in that, The execution feedback unit includes a data acquisition subunit, an effect comparison subunit, and a deviation determination subunit; The parameter optimization subunit is used to adjust the improved parameters according to the degree of deviation, thereby generating a secondary control scheme; The solution storage subunit stores improvement solutions categorized by soil type, tea tree variety, and problem type, and supports the retrieval of historical solutions.

8. The tea garden soil quality dynamic assessment and improvement control system according to claim 1, characterized in that, The management decision and visualization platform includes a data visualization interface, a remote control unit, a decision support database, and an access management subunit. The data visualization interface includes: The map display sub-unit is used to display tea garden zoning maps, monitoring point locations, and quality level color markings; The trend analysis sub-unit is used to display the 7-day / 30-day / 90-day indicator change curves and year-on-year / month-on-month analysis; The radar chart display sub-unit is used to show comparisons of performance across multiple dimensions. The solution presentation sub-unit is used to display the list of improvement measures, application volume, implementation progress, and expected results; The effect comparison sub-unit is used to display the indicator bar charts and level change labels before and after the improvement.

9. A dynamic assessment and improvement control system for tea garden soil quality according to claim 8, characterized in that, The remote control unit includes: The data viewing sub-unit is used for querying and exporting real-time and historical data. The instruction issuing subunit is used to issue instructions for implementing improvement measures and controlling irrigation equipment; The parameter configuration subunit is used to arbitrarily adjust the monitoring frequency according to actual needs, and at the same time modify the evaluation standard parameters. The early warning sub-unit is used to issue early warnings for indicators exceeding standards and for warnings and alerts when improvement effects do not meet expectations.

10. A dynamic assessment and improvement control system for tea garden soil quality according to claim 8, characterized in that, The decision support database includes: Historical database is used to store monitoring data of tea gardens for more than three years; A case study database is used to store case studies and effect data of improvement solutions for different soil problems. The variety parameter library is used to store the growth requirements and suitable soil conditions of common tea tree varieties. A standard database for storing parameters of national / industry soil quality standards and tea planting technical specifications; The permission management subunit sets up three levels of permissions: administrator, operator, and viewer, which correspond to system configuration, command execution, and data viewing functions, respectively.

Images