System for comprehensive management of tea garden

Abstract

The invention discloses a system for comprehensive management of a tea garden. The system comprises a bionic microenvironment regulation and control module, a self-adaptive learning core algorithm module, an ecological collaborative monitoring and intervention module, a multi-modal sensing and feedback module and a user interaction and visualization module. The bionic microenvironment regulation and control module adopts a bionic pipeline network to simulate an ant nest structure and is combined with a micro fan and a humidity regulator to provide irrigation volume W; the multi-mode sensing module calculates the leaf health degree H1 through the sound wave frequency V, the leaf temperature T and the soil humidity S, and the diagnosis precision reaches 90%. And the adaptive learning module optimizes W and the fertilization amount F based on Q to adapt to different tea tree requirements. The ecological module evaluates ecological balance through the E, the natural enemy release amount R is generated, and the use of chemical pesticides is reduced by 80% or above. And the user interaction module displays and supports parameter adjustment by using an AR (Augmented Reality) technology to form a perception-analysis-execution closed loop. The method has ecological benefits and economic values and is suitable for diversified tea garden management.

Description

Technical Field

[0001] The present invention relates to the technical field of tea garden management, in particular to a system for comprehensive management of tea gardens. Background Art

[0002] As an important cash crop and cultural symbol, tea cultivation and management directly impact yield, quality, and economic returns. With the advancement of agricultural modernization, tea garden management is gradually evolving from traditional manual experience to intelligent and automated processes. Currently, a variety of tea garden management technologies and equipment are available on the market, such as IoT-based soil moisture monitoring systems, automatic irrigation devices, and pest and disease monitoring cameras. While these technologies have improved tea garden management efficiency to a certain extent, significant challenges remain, limiting their widespread application in diverse tea garden environments.

[0003] Existing technologies mostly use traditional drip or sprinkler irrigation systems, which provide water and fertilizer through timed or manual methods. For example, common automatic irrigation equipment only performs simple on-off control based on soil moisture thresholds (such as starting when it falls below 50%), lacking the ability to comprehensively regulate the microenvironment of the tea tree root system. This method not only easily wastes water resources (irrigation efficiency is typically less than 70%), but also fails to simulate the dynamic infiltration process of soil moisture and air under natural conditions, resulting in damaged root health and reduced tea quality.

[0004] Current tea garden management systems mostly rely on preset rules or fixed models, such as setting the suitable humidity for a certain tea variety to 60%-70% and the light intensity to 4000-6000 lux. However, different tea varieties (such as Longjing, Tieguanyin, Pu'er) and growth stages (germination, maturity) have significantly different requirements for environmental parameters, and static rules cannot dynamically adapt to these changes. For example, during high temperatures in summer or sudden droughts, it is difficult for the system to adjust the irrigation volume W and fertilizer amount F in real time, resulting in suppressed tea tree growth or waste of resources.

[0005] Existing pest and disease monitoring methods mostly rely on cameras to capture leaf images and manually or through simple algorithms to identify pest and disease characteristics (such as leaf spots). This method often triggers an early warning only after the pests and diseases have caused significant damage, which lags behind in prevention and control. In addition, existing systems often directly recommend spraying chemical pesticides, ignoring the natural regulatory effects of natural enemy insects (such as ladybugs) or microorganisms in the tea garden ecosystem. For example, when aphids break out, the lack of a quantitative assessment of the ecological factor synergy index E leads to over-reliance on pesticides, affecting the ecological quality and market competitiveness of tea.

[0006] Current single environmental sensor (such as temperature and humidity sensor) or visual monitoring is difficult to fully reflect the health status of tea trees. For example, only judging irrigation demand by soil moisture S, ignoring key indicators such as leaf temperature t and vibration frequency f, leads to insufficient diagnostic accuracy (misjudgment rate can reach 20%-30%). The existing system lacks multi-modal data fusion (such as H1=w8V+w9T+w 10 S), and cannot timely discover potential problems such as nutrient deficiency or early disease and insect pests.

[0007] The user interface of the existing management system is mostly a simple mobile application, and the displayed data (such as humidity, light) is scattered and lacks intuitiveness, making it difficult for users to quickly grasp the overall state of the tea garden. For example, the manager needs to view the data of multiple devices respectively, and cannot intuitively present the distribution of H s 、H1、E through augmented reality (AR) technology. In addition, there is a lack of effective feedback mechanism, and users have difficulty verifying the effect in real time after adjusting W, F, R, resulting in low management efficiency.

[0008] Current technology is mostly independent modules (such as irrigation module, monitoring module) running, lacking systematic integration. For example, soil moisture monitoring results are not directly linked with disease and insect pest early warning or irrigation execution, and the concept of management strategy comprehensive score Q is not proposed, making it difficult to form a closed loop from perception to execution. This dispersion limits the overall optimization ability of tea garden management, especially in large-scale tea gardens (such as water and fertilizer waste rate can reach 30%-40%).

[0009] In view of the above problems, there is an urgent need for a comprehensive and innovative tea garden management system. SUMMARY

[0010] The purpose of the present application is to solve the technical problems raised in the background art, and to provide a tea garden comprehensive management system.

[0011] The technical solution of the present application to achieve the above purpose is:

[0012] A tea garden comprehensive management system, comprising a bionic microenvironment regulation module, an adaptive learning core algorithm module, an ecological coordination monitoring and intervention module, a multi-modal perception and feedback module, and a user interaction and visualization module;

[0013] The bionic microenvironment regulation module is used to regulate the microenvironment of tea tree roots, and provides irrigation amount W and microenvironment air circulation;

[0014] The adaptive learning core algorithm module dynamically optimizes management parameters based on management strategy comprehensive score Q, which is calculated by weighting soil moisture suitability H s 、leaf health degree H1、light intensity suitability L and ecological factor coordination index E, and is used to adjust irrigation amount W and fertilizer amount F;

[0015] The ecological coordination monitoring and intervention module monitors and maintains the ecological balance of the tea garden through an ecological factor coordination index E, which is calculated from the number of beneficial insects N, soil microbial activity M, and organic matter content O, and generates a natural enemy release amount R;

[0016] The multi-modal perception and feedback module calculates a leaf health degree H1 from a sound wave vibration frequency standardization value V, a leaf temperature suitability T, and a soil humidity suitability S, for analyzing the health status of the tea tree;

[0017] The user interaction and visualization module is used to show real-time data H s , H1, E to the user and support manual adjustment of parameters W, F, R;

[0018] Each module is interconnected through a data communication network to form a closed-loop system for tea garden management.

[0019] As a preferred technical solution of the present application, the bionic microenvironment regulation module includes a bionic pipeline network, a micro fan, and a humidity regulator; the bionic pipeline network is composed of porous bionic materials, simulates the structure of an ant nest in nature, is arranged in the soil of the tea garden, and is used for water and air penetration and delivery; the micro fan and the humidity regulator are connected with the pipeline network and automatically adjust the microenvironment of the tea tree root system according to the soil humidity and air flow demand.

[0020] As a preferred technical solution of the present application, the porous bionic material of the bionic pipeline network has a porous structure with a pore size range of 10-50 microns, and a hydrophilic coating is coated on the surface of the material to enhance the water penetration efficiency and reduce water resource waste; the air volume adjustment range of the micro fan is 0.1-0.5 cubic meters per minute, and the humidity control accuracy of the humidity regulator is ±5%.

[0021] As a preferred technical solution of the present application, the self-adaptive learning core algorithm module adopts an algorithm based on reinforcement learning, dynamically adjusts the irrigation amount W, the fertilization amount F, and the light adjustment parameter through the tea garden environment data and tea tree growth feedback data collected by the sensor; and the calculation formula of the management strategy comprehensive score Q is as follows:

[0022] Q=w1H s +w2H I +w3L+w4E

[0023] Q: management strategy comprehensive score (dimensionless, range 0-1); Hs: soil humidity suitability (dimensionless, range 0-1), calculated by H s =1-|S-S0| / S0, wherein S is the actual soil humidity (%) and S0 is the suitable humidity (%); H I: Leaf health (dimensionless, range 0-1), provided by the multimodal perception module; L: Light intensity suitability (dimensionless, range 0-1), calculated by L=1-|l-l0| / l0, where l is the actual light intensity (lux) and l0 is the suitable light intensity (lux); E: Ecological factor synergy index (dimensionless, range 0-1), provided by the ecological synergy monitoring module; w1, w2, w3, w4: Weight coefficients (dimensionless, 0.3, 0.3, 0.2, 0.2 respectively, summing to 1); The system is iteratively optimized through Q value, and the learning cycle is 7-30 days.

[0024] As a preferred technical solution of the present invention, the adaptive learning core algorithm module generates the irrigation amount W and the fertilization amount F based on the Q value, and the calculation formula is as follows:

[0025] W=W0×(1-H s )×k1

[0026] F=F0×(1-H1)×k2

[0027] Where: W: irrigation amount (unit: liter / square meter); W0: basic irrigation amount (unit: liter / square meter, preset to 5 liters / square meter); F: fertilizer amount (unit: g / square meter); F0: basic fertilizer amount (unit: g / square meter, preset to 10 g / square meter); k1, k2: adjustment coefficients (dimensionless, value range 0.5-1.5), determined by Q value segmented mapping; when Q < 0.6, k1 and k2 increase; when Q ≥ 0.8, k1 and k2 decrease.

[0028] As a preferred technical solution of the present invention, the ecological synergistic monitoring and intervention module includes an ecological sensor group, a natural enemy release device and a data analysis unit; the calculation formula of the ecological factor synergy index E is as follows: E=w5N+w6M+w7O, wherein: E: ecological factor synergy index (dimensionless, range 0-1); N: standardized number of beneficial insects (dimensionless, range 0-1), calculated by N=n / n0, wherein n is the actual number of insects (individuals / square meter), n0 is a reference value (preset to 10 / square meter), the natural enemy release device is an electronic cage equipped with Natural enemy insects corresponding to pests; M: soil microbial activity level (dimensionless, range 0-1), calculated by M=m / m0, where m is the actual activity (enzyme activity unit, U / g) and m0 is the reference value (preset to 50U / g); O: organic matter content percentage (dimensionless, range 0-1), calculated by O=o / o0, where o is the actual content (%) and o0 is the reference value (preset to 5%); w5, w6, w7: weight coefficients (dimensionless, 0.4, 0.3, 0.3 respectively, and the total is 1); when E<0.6, the release of natural enemies is triggered.

[0029] As a preferred technical scheme of the present application, the release amount R of the natural enemy release device is calculated by the following formula: R = R0 x (0.6 - E) x A, wherein: R: natural enemy release amount (unit: pieces); R0: basic release amount (unit: pieces per square meter, preset as 10 pieces per square meter); E: ecological factor synergy index (dimensionless, range 0-1); A: tea garden area (unit: square meters); when E is greater than or equal to 0.6, R = 0.

[0030] As a preferred technical scheme of the present application, the multi-modal perception and feedback module includes a sound wave sensor, an infrared thermal imager, and a temperature and humidity sensor; the calculation formula of the leaf health degree H1 is as follows: H1 = w8V + w9T + w 10 S wherein: H1: leaf health degree (dimensionless, range 0-1); V: sound wave vibration frequency standardized value (dimensionless, range 0-1), calculated by V = 1 - |f - fo| / fo, wherein f is the actual frequency (Hz), and fo is the healthy leaf frequency (preset as 100 Hz); T: leaf temperature suitability (dimensionless, range 0-1), calculated by T = 1 - |t - to| / to, wherein t is the actual temperature (℃), and to is the suitable temperature (preset as 25 ℃); S: soil humidity suitability (dimensionless, range 0-1), consistent with H s ; w8, w9, w 10 : weight coefficients (dimensionless, 0.3, 0.4, and 0.3 respectively, and the sum is 1); when H1 is less than 0.7, a warning is generated.

[0031] As a preferred technical scheme of the present application, the multi-modal perception and feedback module feeds back H1 to the adaptive learning core algorithm module as an input parameter of the Q value; when H1 continuously falls below 0.7 for more than 3 days, the system automatically adjusts the values of W and F, and records the adjustment effect to optimize subsequent strategies.

[0032] As a preferred technical scheme of the present application, the user interaction and visualization module includes a mobile terminal and an AR display device, the mobile terminal receives real-time data and suggestions such as H, H, and E through a wireless network, and the AR display device generates a three-dimensional state view of the tea garden based on augmented reality technology, with soil humidity distribution, leaf health degree, and intervention suggestions marked in the view, and the user can adjust W, F, or R through gestures or voice commands.

[0033] Compared with the prior art, the present application has the following beneficial effects:

[0034] This invention significantly improves the accuracy of tea garden management and resource utilization efficiency by introducing a bionic microenvironment control module and a multimodal sensing and feedback module. The bionic pipe network mimics the structure of an ant nest and, combined with micro fans and humidity regulators, can dynamically adjust the microenvironment of the tea tree roots, increasing irrigation efficiency by 15% to 35% and reducing water waste by more than 30%. Multimodal sensing technology integrates the acoustic vibration frequency V, leaf temperature T, and soil moisture S to calculate leaf health H1, increasing diagnostic accuracy to over 90%. This reduces the misjudgment rate by 20% to 30% compared to traditional single monitoring methods, effectively ensuring tea quality.

[0035] The present invention adopts the adaptive learning core algorithm module and the ecological collaborative monitoring and intervention module to achieve dynamic optimization and ecological friendliness of the management strategy. The adaptive algorithm is achieved through Q=w1H s +w2H I The module calculates a comprehensive score using the formula (E=w5N+w6M+w7O) and combines it with reinforcement learning to optimize irrigation volume (W) and fertilizer application (F) within 7-30 days to meet the needs of different tea varieties and growth stages. This results in a 5%-12% increase in tea yield and a 25%-28% reduction in fertilizer use. The ecological module assesses ecological balance using the formula (E=w5N+w6M+w7O), prioritizing the release of natural enemies (for example, releasing 400 ladybugs can reduce aphids by 85%), and reducing chemical pesticide use by over 80%, thereby improving the green quality and market competitiveness of tea.

[0036] The present invention enhances the convenience of management and the closed loop of the system through user interaction and visualization modules. AR devices and mobile terminals display H s , H1, and E. Users can adjust W, F, and R through gestures or voice commands, improving operational efficiency by over 50%. The system forms a closed loop from perception (H1 and E), analysis (Q), and execution (W, F, and R). Long-term operation can save 20% to 30% in management costs. It is particularly suitable for tea gardens ranging from small and medium-sized (such as a 500-square-meter Longjing tea garden) to large tea gardens (such as a 5,000-square-meter Pu'er tea garden), and has broad application prospects and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 The present invention is a system block diagram of a tea garden comprehensive management system. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0040] Referring to Figure 1 As shown in the drawings, a system for comprehensive management of a tea garden includes a bionic microenvironment regulation module, a self-adaptive learning core algorithm module, an ecological coordination monitoring and intervention module, a multi-modal sensing and feedback module, and a user interaction and visualization module.

[0041] The bionic microenvironment regulation module is used for adjusting the root system microenvironment of tea trees, and providing irrigation amount W and microenvironment air flow; the bionic microenvironment regulation module includes a bionic pipeline network, a micro fan and a humidity regulator; the bionic pipeline network is composed of porous bionic materials, simulates the structure of an ant nest in nature, is arranged in the soil of the tea garden, and is used for permeating and transporting water and air; the micro fan and the humidity regulator are connected with the pipeline network, automatically adjust the root system microenvironment of tea trees according to the soil humidity and air flow demand, the porous bionic material of the bionic pipeline network has a porous structure with a pore size range of 10-50 microns, and a hydrophilic coating is coated on the surface of the material to enhance the water permeation efficiency and reduce water resource waste; the air volume adjustment range of the micro fan is 0.1-0.5 cubic meters per minute, and the humidity control accuracy of the humidity regulator is ± 5%.

[0042] The self-adaptive learning core algorithm module dynamically optimizes management parameters based on a management strategy comprehensive score Q, the Q is weightedly calculated by soil humidity suitability H s , leaf health H1, light intensity suitability L and ecological factor coordination index E, and is used for adjusting irrigation amount W and fertilizer amount F; the self-adaptive learning core algorithm module adopts an algorithm based on reinforcement learning, dynamically adjusts irrigation amount W, fertilizer amount F and light adjustment parameters through tea garden environment data and tea tree growth feedback data collected by a sensor; and a calculation formula of the management strategy comprehensive score Q is as follows:

[0043] Q=w1H s +w2H I +w3L+w4E

[0044] Wherein, Q is the management strategy comprehensive score (dimensionless, range 0-1); Hs is the soil humidity suitability (dimensionless, range 0-1), is calculated by H s =1-|S-S0| / S0, wherein S is actual soil humidity (%) and S0 is suitable humidity (%); H1 is the leaf health (dimensionless, range 0-1), is calculated by H I: Leaf health (dimensionless, range 0-1), provided by the multimodal perception module; L: Light intensity suitability (dimensionless, range 0-1), calculated by L = 1-|l-l0| / l0, where l is the actual light intensity (lux) and l0 is the suitable light intensity (lux); E: Ecological factor synergy index (dimensionless, range 0-1), provided by the ecological synergy monitoring module; w1, w2, w3, w4: Weight coefficients (dimensionless, 0.3, 0.3, 0.2, 0.2 respectively, summing to 1); The system is iteratively optimized through Q value, with a learning cycle of 7-30 days. The adaptive learning core algorithm module generates irrigation amount W and fertilizer amount F based on the Q value. The calculation formula is as follows:

[0045] W=W0×(1-H s )×k1

[0046] F=F0×(1-H1)×k2

[0047] Where: W: irrigation amount (unit: liter / square meter); W0: basic irrigation amount (unit: liter / square meter, preset to 5 liters / square meter); F: fertilizer amount (unit: g / square meter); F0: basic fertilizer amount (unit: g / square meter, preset to 10 g / square meter); k1, k2: adjustment coefficients (dimensionless, value range 0.5-1.5), determined by Q value segmented mapping; when Q < 0.6, k1 and k2 increase; when Q ≥ 0.8, k1 and k2 decrease.

[0048] The ecological synergy monitoring and intervention module monitors and maintains the ecological balance of the tea garden through the ecological factor synergy index E. The E is calculated by the number of beneficial insects N, the activity of soil microorganisms M and the organic matter content O, and generates the natural enemy release amount R. The ecological synergy monitoring and intervention module includes an ecological sensor group, a natural enemy release device and a data analysis unit. The calculation formula of the ecological factor synergy index E is as follows: E=w5N+w6M+w7O, where: E: ecological factor synergy index (dimensionless, range 0-1); N: standardized number of beneficial insects (dimensionless, range 0-1), which is calculated by N=n / n0 calculation, where n is the actual number of insects (individuals / square meter) and n0 is a reference value (preset to 10 / square meter). The natural enemy release device is an electronic cage filled with natural enemy insects corresponding to the pests; M: soil microbial activity level (dimensionless, range 0-1), calculated by M=m / m0, where m is the actual activity (enzyme activity unit, U / g) and m0 is a reference value (preset to 50 U / g); O: organic matter content percentage (dimensionless, range 0-1), calculated by O=o / o0, where o is the actual content (%) and o0 is a reference value (preset to 5%);

[0049] w5, w6, w7: weight coefficients (dimensionless, 0.4, 0.3, 0.3 respectively, and the total is 1); when E<0.6, the release of natural enemies is triggered, and the release amount R of the natural enemy release device is calculated by the following formula: R=R0×(0.6-E)×A, where: R: natural enemy release amount (unit: piece); R0: basic release amount (unit: piece / square meter, preset as 10 pieces / square meter); E: ecological factor synergy index (dimensionless, range 0-1); A: tea garden area (unit: square meter); when E≥0.6, R=0.

[0050] The multimodal perception and feedback module calculates the leaf health H through the normalized value of the acoustic vibration frequency V, the leaf temperature suitability T and the soil moisture suitability S. l , used to analyze the health status of tea trees; the multimodal perception and feedback module includes an acoustic sensor, an infrared thermal imager, and a temperature and humidity sensor; the calculation formula of the leaf health H is as follows: H1=w8V+w9T+w 10 S Where: H1: leaf health (dimensionless, range 0-1); V: normalized value of acoustic vibration frequency (dimensionless, range 0-1), calculated by V = 1-|f-fo| / fo, where f is the actual frequency (Hz) and fo is the frequency of healthy leaves (preset to 100 Hz); T: leaf temperature suitability (dimensionless, range 0-1), calculated by T = 1-|t-to| / to, where t is the actual temperature (℃) and to is the suitable temperature (preset to 25℃); s: soil moisture suitability (dimensionless, range 0-1), and H s Consistent; w8, w9, w 10 : Weight coefficient (dimensionless, 0.3, 0.4, 0.3 respectively, summing to 1); when H1<0.7, an early warning is generated. The multimodal perception and feedback module converts H l Feedback to the adaptive learning core algorithm module as the input parameter of Q value; when H l If it remains below 0.7 for more than 3 days, the system will automatically adjust the values ​​of W and F and record the adjustment effects to optimize subsequent strategies.

[0051] The user interaction and visualization module is used to display real-time data to users. s 、H l , E and supports manual adjustment of parameters W, F, and R; the user interaction and visualization module includes a mobile terminal and an AR display device. The mobile terminal receives real-time data and suggestions such as H, H, E through a wireless network. The AR display device generates a three-dimensional status view of the tea garden based on augmented reality technology, and the view is marked with soil moisture distribution, leaf health and intervention suggestions. Users can adjust W, F or R through gestures or voice commands.

[0052] System structure overview: Bionic microenvironment control module: contains bionic pipe network (pore size 10-50 microns, imitating ant nest), micro fan (0.1-0.5 cubic meters / minute), humidity regulator (±5%), adjusts W.

[0053] Adaptive learning core algorithm module: Q = 0.3H s +0.3H l +0.2L+0.2E, generates W and F.

[0054] Ecological collaborative monitoring and intervention module: E = 0.4N + 0.3M + 0.3O, generating R.

[0055] Multimodal perception and feedback module: H1=0.3V+0.4T+0.3S.

[0056] User interaction and visualization module: Display H s , H1, E, adjust W, F, R.

[0057] Example 1: Spring management of a small Longjing tea garden;

[0058] Location: Hangzhou, Zhejiang, area A = 500 m2, Longjing No. 43, spring bud break period, S0 = 60%, I0 = 5000 lux, t0 = 25°C, f0 = 100 Hz.

[0059] Implementation process, bionic microenvironment regulation module: bionic pipe network (pore size 20 μm, hydrophilic coating), micro fan 0.2 cubic meters / minute, humidity regulator ±5%.

[0060] W0=5 liters / square meter, F0=10 grams / square meter.

[0061] Multimodal perception and feedback module:

[0062] f=90Hz(20-200Hz), V=1-|90-100| / 100=0.9;

[0063] t=26℃(±0.5℃), T=1-|26-25| / 25=0.96;

[0064] S=50%,H s =1-|50-60| / 60=0.833;

[0065] H1=0.3×0.9+0.4×0.96+0.3×0.833=0.904.

[0066] Adaptive learning core algorithm module:

[0067] I=4800lux, L=1-|4800-5000| / 5000=0.96;

[0068] Q=0.3×0.833+0.3×0.904+0.2×0.96+0.2×0.8=0.873;

[0069] Q≥0.8, k1=k2=0.8, W=5×(1-0.833)×0.8=0.668 liters / square meter, F=10×(1-0.904)×0.8=0.768 grams / square meter.

[0070] Ecological collaborative monitoring and intervention module: n = 8 individuals / m2, N = 8 / 10 = 0.8; m = 40 U / g, M = 40 / 50 = 0.8; o = 4%, O = 4 / 5 = 0.8. E = 0.4 × 0.8 + 0.3 × 0.8 + 0.3 × 0.8 = 0.8, R = 10 × (0.6 - 0.8) × 500 = 0.

[0071] User interaction and visualization module:

[0072] AR glasses display H s =0.833, H1=0.904, E=0.8, the user did not adjust W, F, R.

[0073] Implementation effect:

[0074] After 7 days, H1=0.95, and the germination rate increased by 8%.

[0075] Example 2: Pest and disease control in a medium-sized Tieguanyin tea garden;

[0076] Location: Anxi, Fujian, area A = 2000 m2, Tieguanyin tea, summer, aphid problem, S0 = 65%, I0 = 6000 lux, t0 = 28°C, f0 = 100 Hz.

[0077] Implementation process:

[0078] Multimodal perception and feedback module:

[0079] f=60Hz, V=1-|60-100| / 100=0.6; t=30℃, T=1-|30-28| / 28=0.929;

[0080] S=60%,H s =1-|60-65| / 65=0.923.

[0081] H1=0.3×0.6+0.4×0.929+0.3×0.923=0.829 (>0.7, no warning).

[0082] Ecological collaborative monitoring and intervention module: n = 4 / m2, N = 4 / 10 = 0.4; m = 35 U / g, M = 35 / 50 = 0.7;

[0083] O=3.5%, O=3.5 / 5=0.7.

[0084] E=0.4×0.4+0.3×0.7+0.3×0.7=0.58, E<0.6, R=10×(0.6-0.58)×2000=400.

[0085] Adaptive learning core algorithm module:

[0086] I=6200lux,L=1-|6200-6000| / 6000=0.967.

[0087] Q=0.3×0.923+0.3×0.829+0.2×0.967+0.2×0.58=0.835.

[0088] Q≥0.8, k1=k2=0.9, W=5×(1-0.923)×0.9=0.347 liters / square meter, F=10×(1-0.829)×0.9=1.539 grams / square meter.

[0089] Bionic microenvironment regulation module:

[0090] Fan 0.3 cubic meters / minute, duct execution W = 0.347 liters / square meters, F = 1.539 grams / square meters.

[0091] User interaction and visualization module:

[0092] Mobile terminal displays H s =0.923, H1=0.829, E=0.58, the user adjusts F to 2 g / m2.

[0093] Effect: After 10 days, H1=0.91, E=0.76, and the number of aphids was reduced by 85%.

[0094] Example 3: Long-term optimization and H l Exception handling

[0095] Location: Pu'er, Yunnan, area A = 5000 m2, Pu'er tea, autumn, S0 = 70%, I0 = 4500 lux, t0 = 22°C, f0 = 100 Hz.

[0096] Implementation process:

[0097] Multimodal perception and feedback module:

[0098] Initial: f = 110 Hz, V = 1-|110-100| / 100 = 0.9; t = 21°C, T = 1-|21-22| / 22 = 0.955; S = 68%, H s =1-|68-70| / 70=0.971.

[0099] H1=0.3×0.9+0.4×0.955+0.3×0.971=0.943.

[0100] On the 5th day, due to drought, f = 50 Hz, V = 0.5; t = 24 ° C, T = 0.909; S = 55%,

[0101] H s =0.786, H1=0.3×0.5+0.4×0.909+0.3×0.786=0.65, H1<0.7 for 3 days.

[0102] Adaptive learning core algorithm module:

[0103] Initial: I = 4600 lux, L = 0.978, E = 0.9, Q = 0.3 × 0.971 + 0.3 × 0.943 + 0.2 × 0.978 + 0.2 × 0.9 = 0.95.

[0104] W = 5 × (1-0.971) × 0.7 = 0.102 liters / square meter, F = 10 × (1-0.943) × 0.7 = 0.399 grams / square meter.

[0105] After H1<0.7: Q=0.3×0.786+0.3×0.65+0.2×0.978+0.2×0.9=0.807, k1=k2=1.2, W=5×(1-0.786)×1.2=1.284 liters / square meter, F=10×(1-0.65)×1.2=4.2 grams / square meter, record the adjustment effect.

[0106] Ecological collaborative monitoring and intervention module: n = 9 / m2, N = 0.9; m = 45 U / g, M = 0.9; o = 4.5%, O = 0.9.

[0107] E=0.4×0.9+0.3×0.9+0.3×0.9=0.9, R=0.

[0108] Bionic microenvironment control module: fan 0.15 cubic meters / minute, after adjustment W = 1.284 liters / square meter, F = 4.2 grams / square meter.

[0109] User interaction and visualization module: AR display H s =0.786, H1=0.65, E=0.9, the user confirms the adjustment.

[0110] Effect: After 3 months, H1 returned to 0.95, water was saved by 35%, and quality was improved by 12%. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for comprehensive management of tea gardens, characterized in that: It includes a bionic microenvironment regulation module, an adaptive learning core algorithm module, an ecological collaborative monitoring and intervention module, a multimodal perception and feedback module, and a user interaction and visualization module; The bionic microenvironment control module is used to regulate the microenvironment of the tea tree root system, providing irrigation volume W and microenvironment air circulation; The adaptive learning core algorithm module dynamically optimizes management parameters based on the comprehensive score Q of the management strategy, where Q is determined by the soil moisture suitability H. s , leaf health H1, light intensity suitability L and ecological factor synergy index E are weighted and calculated to adjust irrigation amount W and fertilizer amount F; The ecological synergy monitoring and intervention module monitors and maintains the ecological balance of the tea garden through the ecological factor synergy index E, which is calculated by the number of beneficial insects N, soil microbial activity M and organic matter content O, and generates the natural enemy release rate R; The multimodal perception and feedback module calculates the leaf health H1 through the normalized value V of the acoustic vibration frequency, the leaf temperature suitability T and the soil moisture suitability S, for analyzing the health status of the tea tree; The user interaction and visualization module is used to display real-time data H to the user s , H1, E and supports manual adjustment of parameters W, F, R; Each module is interconnected through a data communication network to form a closed-loop system for tea garden management.

2. A tea garden comprehensive management system according to claim 1, characterized in that: The bionic microenvironment control module includes a bionic pipe network, a micro fan and a humidity regulator; the bionic pipe network is made of porous bionic materials, mimics the structure of ant nests in nature, and is arranged in the soil of the tea garden for the infiltration and transportation of water and air; the micro fan and humidity regulator are connected to the pipe network to automatically adjust the microenvironment of the tea tree roots according to soil moisture and air circulation requirements.

3. A tea garden comprehensive management system according to claim 2, characterized in that: The porous bionic material of the bionic pipe network has a porous structure with a pore size range of 10-50 microns, and the surface of the material is coated with a hydrophilic coating to enhance water penetration efficiency and reduce water waste; the air volume adjustment range of the micro fan is 0.1-0.5 cubic meters / minute, and the humidity control accuracy of the humidity regulator is ±5%.

4. A tea garden comprehensive management system according to claim 1, characterized in that: The adaptive learning core algorithm module uses a reinforcement learning-based algorithm to dynamically adjust the irrigation amount W, fertilizer amount F, and light regulation parameters through tea garden environmental data and tea tree growth feedback data collected by sensors. The calculation formula of the management strategy comprehensive score Q is as follows: Q / w1H s +w2H I +w3L+w4E Where: Q: comprehensive score of management strategy; Hs: soil moisture suitability, which is determined by H s = 1-|S-S0| / S0, where S is the actual soil moisture and S0 is the appropriate humidity; H I : Leaf health, provided by the multimodal perception module; L: Light intensity suitability; calculated by L = 1-|l-l0| / l0, where l is the actual light intensity and l0 is the suitable light intensity (lux); E: Ecological factor synergy index, provided by the ecological synergy monitoring module; w1, w2, w3, w4: Weight coefficients; The system is iteratively optimized through Q value, and the learning cycle is 7-30 days.

5. A tea garden comprehensive management system according to claim 4, characterized in that: The adaptive learning core algorithm module generates the irrigation amount W and fertilization amount F based on the Q value, and the calculation formula is as follows: W=W0×(1-H s )×k1 F=F0×(1-H1)×k2 Where: W: irrigation amount; W0: basic irrigation amount; F: fertilizer amount; F0: basic fertilizer amount; k1, k2: adjustment coefficients, determined by Q value segmented mapping; when Q < 0.6, k1 and k2 increase; when Q ≥ 0.8, k1 and k2 decrease.

6. A tea garden comprehensive management system according to claim 1, characterized in that: The ecological synergistic monitoring and intervention module includes an ecological sensor group, a natural enemy release device and a data analysis unit; the calculation formula of the ecological factor synergy index E is as follows: E=w5N+w6M+w7O, where: E: ecological factor synergy index; N: standardized number of beneficial insects, calculated by N=n / n0, where n is the actual number of insects and n0 is a reference value; the natural enemy release device is an electronic cage, which contains natural enemy insects corresponding to the pests; M: soil microbial activity level, calculated by M=m / m0, where m is the actual activity and m0 is the reference value; O: organic matter content percentage, calculated by O=o / o0, where o is the actual content and o0 is the reference value; w5, w6, w7: weight coefficients; when E<0.6, the natural enemy release is triggered.

7. A tea garden comprehensive management system according to claim 6, characterized in that: The release amount R of the natural enemy release device is calculated by the following formula: R=R0×(0.6-E)×A, wherein: R: natural enemy release amount; R0: basic release amount; E: ecological factor synergy index (dimensionless, range 0-1); A: tea garden area; when E≥0.6, R=0.

8. The tea garden comprehensive management system according to claim 1, characterized in that: The multimodal perception and feedback module includes an acoustic sensor, an infrared thermal imager, and a temperature and humidity sensor; the leaf health H is calculated as follows: H1 = w8V + w9T + w 10 S Where: H1: leaf health; V: normalized value of acoustic vibration frequency, V = 1-|f-fo| / fo, where f is the actual frequency and fo is the frequency of healthy leaves (preset to 100 Hz); T: leaf temperature suitability, calculated by T = 1-|t-to| / to, where t is the actual temperature and to is the suitable temperature; s: soil moisture suitability, which is related to H s Consistent; w8, w9, w 10 : Weight coefficient; when H1<0.7, an early warning is generated.

9. A tea garden comprehensive management system according to claim 8, characterized in that: The multimodal perception and feedback module feeds H1 back to the adaptive learning core algorithm module as the input parameter of the Q value; when H1 remains below 0.7 for more than 3 days, the system automatically adjusts the values ​​of W and F and records the adjustment effects to optimize subsequent strategies.

10. The tea garden comprehensive management system according to claim 1, characterized in that: The user interaction and visualization module includes a mobile terminal and an AR display device. The mobile terminal receives real-time H, H, and E data and suggestions via a wireless network. The AR display device generates a three-dimensional status view of the tea garden based on augmented reality technology, with soil moisture distribution, leaf health, and intervention suggestions marked in the view. Users can adjust W, F, or R through gestures or voice commands.

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