Method for calculating irrigation volume of greenhouse tomato series harvest according to liquid return volume and weather conditions

Abstract

The invention relates to the field of greenhouse planting, and discloses a method for calculating the irrigation volume of greenhouse tomato series harvest according to the liquid return volume and the weather condition, and the method comprises the following steps: data collection: collecting greenhouse environment data through monitoring equipment, including illumination cumulant, air temperature, relative humidity and rainfall, and matrix humidity data, comprise the maximum weight, the minimum weight and the liquid return amount of the matrix; and theoretical irrigation volume calculation: calculating the theoretical irrigation volume for tomato series harvesting based on the illumination cumulant and the environmental factors. According to the method, the multi-dimensional data is combined with the dynamic adjustment strategy, so that precise irrigation is realized, and the resource utilization efficiency is optimized; through a real-time feedback and optimization mechanism, the environment change is dynamically adapted, and the response capability of the irrigation system is improved; the crop yield and quality are guaranteed; water and fertilizer integrated expansion is supported, and the device adapts to various greenhouse crops; and through abnormity monitoring and long-term optimization, the reliability and adaptability of the system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse cultivation, and specifically provides a method for calculating the irrigation amount of greenhouse cluster-harvested tomatoes based on the liquid return amount and weather conditions. Background Art

[0002] Currently, in greenhouse tomato cultivation, irrigation is one of the key factors affecting crop yield and quality. To meet the water requirements of crops, the drip-jet irrigation technology is combined with a certain irrigation management method to perform quantitative irrigation on crops. However, most traditional irrigation management methods formulate fixed irrigation plans based on experience, making it difficult to respond dynamically to the changing environmental conditions in real time, and lacking comprehensive consideration of factors such as light, meteorology, substrate humidity, and crop growth status. This results in insufficient irrigation accuracy, affecting the healthy growth of crops and the efficient utilization of water resources. In addition, in recent years, with the development of intelligent cultivation technology, automated irrigation systems have gradually been applied to greenhouse production. However, existing automated irrigation solutions still have limitations such as single parameter settings and weak feedback mechanisms, making it difficult to achieve efficient management in complex and variable greenhouse environments.

[0003] The existing irrigation system mainly consists of drip jets, irrigation controllers, water storage devices, etc. Its main function is to supply water to crops at a predetermined time and flow rate. However, such systems have the following problems: First, traditional irrigation management methods mainly rely on fixed schedules or simple humidity thresholds, lacking comprehensive consideration of dynamic environmental factors and the actual needs of crops, which easily leads to over-irrigation or under-irrigation problems; Second, existing irrigation solutions only adjust the irrigation amount through simple linear calculations, failing to fully combine feedback data to optimize the irrigation plan, resulting in low utilization efficiency of irrigation resources; Third, the feedback and optimization mechanisms are weak. In the existing technology, the evaluation methods for irrigation effects are limited, lacking the ability to accumulate and analyze long-term data, and unable to optimize irrigation strategies based on historical data; Fourth, in traditional irrigation management, the monitoring of crop growth status is insufficient, making it difficult to accurately match the water requirements of different growth stages of crops. These problems significantly limit the application effect of existing irrigation technologies in greenhouse production and have an adverse impact on crop yield and quality. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a method for calculating the irrigation amount of greenhouse cluster-harvested tomatoes based on the liquid return amount and weather conditions, solving the problems in the existing technology that the irrigation management method cannot dynamically adapt to environmental changes, lacks a feedback optimization mechanism, and has low resource utilization efficiency.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for calculating the irrigation amount of greenhouse cluster-harvested tomatoes based on the liquid return amount and weather conditions, comprising the following steps: Data collection: Collect greenhouse environmental data through monitoring devices, including cumulative light amount, air temperature, relative humidity, and rainfall, as well as substrate humidity data, including maximum substrate weight, minimum substrate weight, and return liquid volume; Theoretical irrigation amount calculation: Calculate the theoretical irrigation amount for cluster-picked tomatoes based on the cumulative light amount and environmental factors; Dynamic adjustment: Dynamically correct the theoretical irrigation amount according to weather conditions, light changes, the return liquid ratio and substrate water loss ratio in substrate humidity data, and the crop growth stage to obtain the actual irrigation amount; Irrigation execution: Formulate a time-segmented irrigation plan according to the actual irrigation amount after dynamic adjustment and implement it through a drip arrow irrigation system; Feedback and optimization: Monitor the crop status, return liquid ratio, and substrate humidity after irrigation, and optimize the calculation parameters and adjustment rules using historical data.

[0006] Preferably, the data collection includes collecting light intensity through a light sensor and calculating the cumulative light amount, and the unit of the cumulative light amount is J / cm 2 。

[0007] Preferably, the calculation of the theoretical irrigation amount includes the following formula: V 理论 =3·L + α·T + β·H + γ·E Where: L is the cumulative light amount; T is the average air temperature of the day; H is the average relative humidity of the day; E is the leaf transpiration rate; α, β, γ are empirical weight coefficients.

[0008] Preferably, the initial values of the weight coefficients are α = 0.1, β = 0.05, γ = 0.2 n 。

[0009] Preferably, the dynamic adjustment includes the following steps: 1. Adjust the irrigation amount according to weather conditions. If the light intensity continuously drops below 200 W / m 2 or the daily rainfall exceeds 5 mm, then reduce or suspend irrigation; 2. Adjust the irrigation amount according to the year-on-year change in the cumulative light amount. The light change rate ΔL is calculated according to the following formula: The adjustment value of the actual irrigation amount is: Where: ΔL is the year-on-year change rate of the cumulative light amount, L 当日 is the cumulative light amount of the current day, L 前日 is the cumulative light amount of the previous day, ΔV 光照 is the adjustment value of the irrigation amount due to the change in the cumulative light amount, V 理论 is the theoretical irrigation amount calculated based on environmental parameters such as light, temperature, humidity, and transpiration rate.

[0010] Preferably, the dynamic adjustment further includes substrate humidity adjustment, which is specifically as follows: If the liquid return ratio D exceeds 30%, then for each 1% exceeding, reduce the theoretical irrigation amount by 1%; If the liquid return ratio D is lower than 20%, then for each 1% reduction, increase the theoretical irrigation amount by 1%; If the substrate water loss ratio P 关水 exceeds 15%, then for each 1% exceeding, increase the theoretical irrigation amount by 2%; If the substrate water loss ratio P 失水 is lower than 10%, then for each 1% reduction, reduce the theoretical irrigation amount by 2%.

[0011] Preferably, the liquid return ratio D is calculated according to the following formula: where: d is the daily liquid return volume; n is the number of drip arrows; I is the total irrigation volume of a single drip arrow throughout the day.

[0012] Preferably, the substrate water loss ratio P 失水 is calculated according to the following formula: where: W 最高 is the maximum weight of the substrate; W 最低 is the minimum weight of the substrate.

[0013] Preferably, the crop growth stage adjustment includes dynamically adjusting the irrigation amount according to the fruit expansion rate G, and the adjustment value is: ΔV 生长 = κ·G where κ is the growth stage adjustment coefficient, and the initial value is 0.05.

[0014] Preferably, the irrigation execution adopts a time-sharing strategy, and the total irrigation amount throughout the day is distributed according to the following ratio: 30% in the morning, 40% at noon, and 30% in the afternoon.

[0015] The present invention provides a method for calculating the irrigation amount of greenhouse cluster-harvested tomatoes according to the liquid return volume and weather conditions. It has the following beneficial effects: 1. By combining multi-dimensional information such as light cumulative amount, meteorological data, substrate humidity, and crop growth status, the present invention accurately calculates the irrigation amount by adopting a dynamic adjustment strategy, enabling the irrigation amount to respond in real time to the dynamic changes of the greenhouse environment and crop needs. By dynamically adjusting the liquid return ratio and substrate water loss ratio, it reduces water waste caused by excessive irrigation, improves the utilization efficiency of irrigation resources, avoids over-irrigation or under-irrigation problems, thereby enhancing the water use efficiency and achieving water-saving and efficient precision irrigation.

[0016] 2. The present invention adjusts by collecting environmental data in real time and combining with weather conditions. For example, irrigation volume is suspended or reduced on cloudy or rainy days. At the same time, dynamic correction is carried out by combining light cumulative amount and real-time liquid return data, enabling irrigation management to quickly adapt to complex and changeable environmental conditions. Meanwhile, the feedback and optimization mechanism can monitor the irrigation effect in real time and quickly adjust the irrigation strategy, enabling the irrigation system to efficiently respond to changes in the environment and crop requirements.

[0017] 3. By providing a water supply that precisely matches the crops, the present invention can stably maintain a suitable root humidity environment, avoid abnormal crop growth caused by excessive or insufficient irrigation, improve the fruit yield and quality of tomatoes, reduce problems such as fruit cracking and diseases. In addition, by using sensors and automation systems, the automation of irrigation management is realized, reducing the need for manual intervention and operation costs, reducing human errors, and improving planting efficiency.

[0018] 4. By combining with a fertilizer concentration monitoring device, the present invention precisely supplements the nutrients required by crops during irrigation, optimizes the fertilizer utilization efficiency, further reduces production costs and improves planting efficiency. At the same time, the present invention has good versatility. Only by adjusting the weight parameters and target ranges can it be applicable to the precise irrigation management of other greenhouse crops, with high promotion value.

[0019] 5. Through multiple sensors and feedback control technology, the present invention can monitor key parameters such as substrate humidity and liquid return ratio in real time, alarm and handle abnormal data such as dripper clogging and abnormal water discharge, ensuring the reliability of system operation. At the same time, through historical data regression analysis and optimization, the formula parameters and control strategies are dynamically adjusted, enabling the irrigation model to long-term adapt to changes in the planting stage and environmental conditions, ensuring efficient irrigation management during the planting cycle, and providing more intelligent and reliable decision-making support for greenhouse planting. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for calculating the irrigation volume of greenhouse cluster-harvested tomatoes according to the liquid return volume and weather conditions, including the following steps: Data collection: Collect greenhouse environmental data through monitoring equipment, including cumulative light, temperature, relative humidity and rainfall, as well as substrate humidity data, including maximum substrate weight, minimum substrate weight and liquid return; In this embodiment, a complete data collection and preprocessing method is designed for the precise irrigation management of greenhouse bunch-harvested tomatoes, combining multi-dimensional data of light, weather, substrate humidity and crop physiological status to ensure accurate and reliable basic data for the calculation and adjustment of irrigation amount.

[0023] It should be noted that the scope, equipment and frequency of data collection are key components of the technical solution of the present invention. At the same time, data preprocessing is an important step to ensure the quality of input data, so as to eliminate outliers, smooth out fluctuating data, and ensure the accuracy of subsequent irrigation volume calculations.

[0024] Data collection In this embodiment, data collection includes light and meteorological data collection, substrate humidity and liquid return data collection, and crop physiological status data collection, as follows: As an option, the present invention collects light intensity (unit: W / m 2 ) and record in real time, collect light intensity data every 5 minutes, and finally generate daily light accumulation (unit: J / cm 2 ) to reflect the total light energy received by the crop in a day.

[0025] Specifically, the calculation of the light accumulation is based on the following formula: in: L is the cumulative amount of light, in J / cm 2 ; W(i) is the light intensity at the i-th time point, in W / m 2 ; Δt is the sampling time interval in seconds; N is the total number of sampling points.

[0026] In a possible implementation, the greenhouse weather station collects temperature (unit: °C), relative humidity (unit: %), wind speed (unit: m / s) and rainfall (unit: mm), wherein the temperature and humidity are recorded once every hour, and the wind speed and rainfall are recorded as the cumulative value of the whole day.

[0027] It should be noted that these meteorological data are used to analyze the transpiration demand of crops and changes in environmental conditions, and provide an important basis for the dynamic adjustment of irrigation volume. For example, when the temperature is high and the humidity is low, the transpiration rate of leaves increases, and the demand for water also increases.

[0028] Exemplarily, in the present invention, the substrate humidity and the liquid return data are collected by a PRIVA substrate liquid return scale or a self-made steel structure liquid return collection device. The substrate humidity data includes the maximum weight W of the substrate 最高 and the minimum weight W 最低 , corresponding to the saturated state and the dry state of the substrate during a day respectively.

[0029] The calculation formula for the water loss of the substrate is: W 失水 = W 最高 - W 最低 And the water loss ratio of the substrate is: It should be noted that the substrate humidity data is collected two hours after the end of the last irrigation of each day. At this time, the free water in the substrate is basically discharged, and only the substrate moisture available for plants remains, thus ensuring the accuracy of the data.

[0030] As an option, the present invention also measures the liquid return volume d for calculating the liquid return ratio. The calculation formula for the liquid return ratio is: Where: d is the total daily liquid return volume, with the unit of ml; n is the number of drip arrows; I is the total irrigation volume of a single drip arrow throughout the day, with the unit of ml.

[0031] In a possible implementation manner, the present invention also includes the collection of crop physiological data, such as the leaf transpiration rate E (unit: mmolH 2 O / m 2 / s) and the fruit swelling rate G (unit: cm / day). These data are collected by leaf moisture sensors and plant growth monitoring devices and recorded once per hour.

[0032] It should be noted that the crop physiological data provides an important reference for the dynamic adjustment of the irrigation volume. For example, the leaf transpiration rate directly reflects the water transpiration consumption of the crop, and the fruit swelling rate is closely related to the irrigation volume.

[0033] Data preprocessing In this embodiment, to ensure the accuracy and consistency of the collected data, all data is subjected to necessary preprocessing. The preprocessing methods include outlier removal and data smoothing, as follows: As an option, the present invention removes outliers by setting a threshold range. For example: When the change amplitude of the light intensity exceeds 30% continuously for 5 minutes, it is considered that the data is abnormal, and the value of this sampling point is removed; When the liquid return ratio D exceeds 50% or is lower than 5%, it is considered that the measurement data does not conform to the normal crop state, and the outliers are removed.

[0034] In a possible implementation manner, the present invention smooths the light intensity data by the moving average method to reduce the influence of noise in the measurement. The specific formula is as follows: Where: W 平滑 (t) is the smoothed light intensity at the t-th time point; W(i) is the original light intensity at the i-th time point; n is the size of the moving window, usually taking a value of 2.

[0035] It should be noted that the application of the moving average method in the present invention can eliminate the single-point data fluctuation caused by the sensor sensitivity or environmental interference, and improve the stability of the light accumulation calculation.

[0036] In some embodiments, the present invention also corrects the fluctuation range of the leaf transpiration rate data. For example, when the change of the leaf transpiration rate exceeds 20% of the standard value for 3 consecutive hours, the moving median of the data is taken to reduce unnecessary fluctuations.

[0037] The complete process of data acquisition and preprocessing In this embodiment, the overall process of data acquisition and preprocessing is as follows: First, light, meteorological, substrate humidity and crop physiological data are collected through sensors and monitoring devices.

[0038] Next, the light intensity data is cumulatively calculated to obtain the light accumulation; the substrate water loss ratio and the liquid return ratio are respectively calculated for the substrate weight data and the liquid return data.

[0039] Finally, through outlier removal and data smoothing, it is ensured that all input data meets the accuracy requirements for subsequent irrigation amount calculation.

[0040] It should be understood that data acquisition and preprocessing are key steps in the implementation process of the present invention, which directly affect the accuracy of subsequent irrigation calculation and adjustment. Through the above steps, the present invention can provide stable and reliable data support for the precise irrigation management of greenhouse cluster-harvested tomatoes.

[0041] Theoretical irrigation amount calculation: The theoretical irrigation amount for cluster-picked tomatoes is calculated based on the cumulative light amount and environmental factors. In this embodiment, the theoretical irrigation amount calculation is a process of calculating the daily basic irrigation amount through a mathematical model by combining multi-dimensional factors such as greenhouse environment, crop requirements, cumulative light amount, meteorological data, and crop transpiration rate. The theoretical irrigation amount calculation formula of the present invention comprehensively considers the influence of light on crop photosynthesis, the regulation of environmental temperature and humidity on water transpiration, and the growth requirements of the crop itself, ensuring the scientificity and accuracy of the theoretical irrigation amount.

[0042] It should be noted that the theoretical irrigation amount is the basis for dynamically adjusting the irrigation amount in the present invention, and its calculation result directly affects the accuracy and implementation effect of the subsequent actual irrigation amount.

[0043] Construction of the formula and parameter description As an option, the theoretical irrigation amount calculation formula of the present invention is as follows: V 理论 = 3·L + α·T + β·H + γ·E Where: V 理论 : Theoretical irrigation amount, unit is ml / m 2 ; L Cumulative light amount, unit is J / cm 2 ; T: Average daily temperature, unit is degree Celsius (°C); H: Average daily relative humidity, unit is percentage (%); E: Leaf transpiration rate, unit is mmolH 2 O / m 2 / s; α, β, γ: Empirical weight coefficients, used to adjust the influence of each factor on the theoretical irrigation amount.

[0044] Specifically, the cumulative light amount L reflects the total amount of light energy received by the crop throughout the day, directly affecting the photosynthesis intensity, and is the core parameter for calculating the theoretical irrigation amount.

[0045] It should be noted that the present invention assumes that every J / cm 2 of cumulative light amount needs to satisfy 3 ml / m 2 of irrigation amount. Therefore, in the formula, the coefficient of the cumulative light amount part is fixed at 3, which has wide applicability.

[0046] In a possible implementation, the air temperature T and relative humidity H respectively represent the influence of environmental temperature and humidity on transpiration. When the air temperature is high, the water demand of the crop increases, and when the humidity is low, the leaf water transpiration rate accelerates. By setting the weight coefficients α = 0.1 and β = 0.05, the influence of these two factors on the irrigation amount can be effectively adjusted.

[0047] Exemplarily, the leaf transpiration rate E is a dynamic crop physiological data, reflecting the actual water transpiration consumption of the crop. As an option, the weight coefficient γ = 0.2 further quantifies the contribution of the transpiration rate to the irrigation amount.

[0048] Optimization and applicable range of formula parameters It can be understood that the weight coefficients α, β, γ in the formula are not fixed values, but parameters adjusted according to specific greenhouse conditions, crop varieties and local climate characteristics. In the initial implementation of the present invention, the recommended values of the weight coefficients are α = 0.1, β = 0.05, γ = 0.2 respectively, and these values are obtained based on historical planting data and experimental experience.

[0049] In some embodiments, through the multiple regression analysis method, the above weight coefficients can be further optimized. For example, when the climate is dry and the temperature fluctuates greatly, the values of the weights α and β can be appropriately increased to enhance the adjustment effect of environmental conditions on the theoretical irrigation amount.

[0050] As an extension, the applicable range of the formula is not limited to the greenhouse planting of cluster tomatoes, but can also be extended to other greenhouse crop planting scenarios that are sensitive to light and transpiration.

[0051] Specific calculation of light cumulative amount In this embodiment, the calculation formula of the light cumulative amount L is: Where: W(i) is the light intensity at the i-th time point, with the unit of W / m 2 ; Δt is the sampling time interval, with the unit of second; N is the total number of sampling points.

[0052] It should be noted that the light data is collected by the light sensor every 5 minutes, so the sampling time interval Δt = 300 seconds. This formula obtains the total amount of light energy actually received by the crop by accumulating the light intensity data throughout the day.

[0053] In a possible implementation manner, the cumulative calculation of the light data will be combined with data smoothing processing to eliminate the influence of short-term fluctuations in sensor acquisition on the calculation result. The specific smoothing processing method is: Where n = 2 is the sliding window size.

[0054] Exemplarily, when the number of light intensity acquisition points in a day is N = 288 (calculated by collecting once every 5 minutes), if the light intensity W(i) at each time point is uniformly distributed, and W(i) = 200 W / m 2 , then the calculation result of the light cumulative amount L is: L = 200·300·288 / 10000 = 1728 J / cm 2 Actual calculation example of the theoretical irrigation amount For further illustration, the calculation steps of the theoretical irrigation amount in the present invention are as follows: When the light cumulative amount in a day is L = 1200 J / cm 2 , the air temperature T = 25 °C, the relative humidity H = 60%, and the leaf transpiration rate E = 4 mmolH 2 O / m 2 / s, the calculation of the theoretical irrigation amount is: V 理论 = 3·1200 + 0.1·25 + 0.05·60 + 0.2·4 Expanding the calculation gives: V 理论 = 3600 + 2.5 + 3 + 0.8 = 3606.3 ml / m 2 It can be understood that this result is the theoretical irrigation amount per square meter of substrate area, and subsequent dynamic adjustments will be calculated based on this.

[0055] Extended application and adjustment method As an extension, the theoretical irrigation amount calculation method of the present invention can also adapt to the needs of different crops and climate conditions. Specifically: When used for high-transpiration crops (such as cucumbers), the value of the transpiration rate weight γ can be increased; When used for greenhouse cultivation under low light conditions, the light cumulative amount coefficient can be reduced (such as from 3 adjusted to 2.5).

[0056] In some embodiments, the present invention can also correct the formula according to the sunshine duration. For example, when the sunshine duration is significantly reduced (such as less than 8 hours), the sunshine correction factor F can be introduced 日照: : V 理论 = F 日照 ·(3·L + α·T + β·H + γ·E) The above method further enhances the applicability of the present invention under different climates and planting conditions, providing flexibility for greenhouse precise irrigation management.

[0057] It should be noted that the above theoretical irrigation amount calculation method of the present invention lays a scientific foundation for the precise irrigation of greenhouse cluster-harvested tomatoes and provides a reliable reference value for subsequent dynamic adjustment.

[0058] Dynamic adjustment: According to the weather conditions, light changes, the liquid return ratio and substrate water loss ratio in the substrate humidity data, and the crop growth stage, dynamically correct the theoretical irrigation amount to obtain the actual irrigation amount; In this embodiment, dynamic adjustment is a process of precisely correcting the irrigation amount on the basis of the theoretical irrigation amount, in combination with the dynamic change factors of the environment and the crop. Through dynamic adjustment, it is possible to adapt to the real-time fluctuations of the greenhouse environmental conditions and improve the accuracy and responsiveness of irrigation management. The adjustment content includes multi-dimensional corrections of weather conditions, cumulative light changes, substrate humidity data (liquid return ratio and substrate water loss ratio), and crop growth requirements.

[0059] It should be noted that the ultimate goal of dynamic adjustment is to calculate the actual irrigation amount V 实际 , so as to meet the water requirements of cluster-harvested tomatoes under different environmental conditions.

[0060] The general formula for dynamic adjustment As an option, the dynamic adjustment calculation formula of the present invention is as follows: V 实际 = V 理论 + ΔV 天气 + ΔV 光照 + ΔV 回液 + ΔV 失水 + ΔV 生长 Where: V 实际 : The actual irrigation amount, with the unit of ml / m 2 ; V 理论 : The theoretical irrigation amount, with the unit of ml / m 2 ; ΔV 天气 : The weather condition correction amount; ΔV 光照 : The cumulative light change correction amount; ΔV 回液 : The liquid return ratio correction amount; ΔV 失水 : The substrate water loss ratio correction amount; ΔV 生长 : The crop growth requirement correction amount.

[0061] It can be understood that the core of dynamic adjustment is to perform real-time correction of the theoretical irrigation amount V 理论 based on the environment and crop requirements, making the irrigation decision more precise.

[0062] Weather condition correction In a possible implementation, the weather condition correction mainly considers special weather conditions such as insufficient light or rainfall.

[0063] As an option, when the daily light intensity is continuously lower than 200 W / m 2 for 2 hours, it is considered that there is overcast or insufficient light. At this time, the irrigation on the same day can be suspended: ΔV 天气 =-V 理论 It should be noted that when the greenhouse weather station monitors that the daily rainfall R > 5 mm, it can be considered that the rainfall has met part of the water demand, so the theoretical irrigation amount is reduced by 30%: ΔV 天气 =-0.3·V 理论 Exemplarily, when the theoretical irrigation amount is 3600 ml / m 2 , and the daily rainfall is 6 mm, the correction result is: ΔV 天气 =-0.3·3600 = -1080 ml / m 2 It should be understood that the weather condition correction is mainly used to handle extreme environmental conditions. Under normal sunny or rainless weather, ΔV 天气 = 0.

[0064] Light cumulative change correction Specifically, the light cumulative change correction amount is calculated based on the year-on-year change of the light cumulative amount. The formula for the light cumulative change rate ΔL is as follows: As an option, the correction amount is calculated according to the light change rate: Exemplarily, when the theoretical irrigation amount V 理论 = 3600 ml / m 2 , the daily light cumulative amount L 当日 = 1200 J / cm 2 , and the light cumulative amount L of the previous day 前日 = 1000 J / cm 2 : It should be noted that the light cumulative change correction can flexibly adjust the theoretical irrigation amount, making the irrigation amount change dynamically with the photosynthetic demand of the crops.

[0065] Return liquid ratio correction In this embodiment, the liquid return ratio D represents the proportion of the return flow of irrigation water in the total irrigation volume. Its formula is as follows: Where: d is the daily liquid return volume, with the unit of ml; n is the number of drip arrows; I is the total daily irrigation volume of a single drip arrow, with the unit of ml.

[0066] Specifically, when the liquid return ratio D > 30%, it indicates that the irrigation volume is too large. For every 1% excess, the theoretical irrigation volume can be reduced by 1%: ΔV 回液 =-1%·V 理论 When the liquid return ratio D < 20%, it indicates that the irrigation volume is insufficient. For every 1% shortfall, the theoretical irrigation volume can be increased by 1%: ΔV 回液 =+1%·V 理论 Exemplarily, when the theoretical irrigation volume is 3600 ml / m 2 , and the actual liquid return ratio D = 35%: ΔV 回液 =-1%·3600·(35 - 30)=-180 ml / m 2 It can be understood that the liquid return ratio correction amount intuitively reflects the actual utilization rate of irrigation water and is of great significance for optimizing the irrigation strategy.

[0067] Substrate water loss ratio correction The substrate water loss ratio P 失水 is used to reflect the change in substrate moisture, and its calculation formula is: It should be noted that when the substrate water loss ratio P 失水 > 15%, it indicates that the substrate has lost too much water. For every 1% excess, the theoretical irrigation volume can be increased by 2%: ΔV 失水 =+2%·V 理论 When the substrate water loss ratio P 失水 < 10%, it indicates that the substrate has insufficient water loss. For every 1% shortfall, the theoretical irrigation volume can be reduced by 2%: ΔV 失水 =-2%·V 理论 Exemplarily, when the theoretical irrigation volume V 理论 = 3600 ml / m 2 , and the substrate water loss ratio P 失水 = 18%: ΔV失水 = +2%·3600·(18 - 15) = +216 ml / m 2 Crop growth demand correction In a possible implementation, the crop growth demand correction is based on the fruit swelling rate G, and its correction formula is: ΔV 生长 = κ·G Where: κ is the empirical coefficient of the crop growth stage, and the default value is 0.05. Exemplarily, when the fruit swelling rate G = 1.5 cm / day: ΔV 生长 = 0.05·1.5 = 0.075 ml / m 2 It can be understood that the crop growth demand correction further refines the regulation of the irrigation amount to ensure that the special needs of the crop in different growth stages are met.

[0068] Full example of dynamic adjustment In a specific implementation, assume: V 理论 = 3600 ml / m 2 ; ΔV 天气 = -1080 ml / m 2 ; ΔV 光照 = 720 ml / m 2 ; ΔV 回液 = -180 ml / m 2 ; ΔV 失水 = +216 ml / m 2 ; ΔV 生长 = +0.075 ml / m 2 .

[0069] Then the actual irrigation amount is: V 实际 = 3600 - 1080 + 720 - 180 + 216 + 0.075 = 3276.075 ml / m 2 It should be noted that the dynamic adjustment process of the present invention fully considers the dynamic changes of the environment and the crop, ensures that the actual irrigation amount is more accurate, and adapts to the complex and changeable greenhouse environmental conditions. The above calculation method of the correction amount and the adjustment strategy can be popularized and applied in different greenhouse crop plantings.

[0070] Irrigation execution: According to the dynamically adjusted actual irrigation volume, formulate a time-segmented irrigation plan and implement it through the drip arrow irrigation system; In this embodiment, irrigation execution refers to the actual irrigation volume V obtained after dynamic adjustment 实际 , combined with greenhouse irrigation equipment and strategies, to accurately implement the process of time-segmented irrigation. Through the automated irrigation control system, the present invention flexibly adjusts the irrigation time and volume according to the actual needs of the crops and the dynamic changes of the environment to ensure that the water requirements of the crops are met in different time periods.

[0071] It should be noted that irrigation execution includes formulating an irrigation plan, time-segmented irrigation strategies, and real-time monitoring and adjustment, which is one of the core steps of the present invention and directly affects the implementation quality of the irrigation effect.

[0072] Formulation of the irrigation plan In one possible implementation, the present invention formulates a daily irrigation plan by calculating the dynamically adjusted actual irrigation volume V 实际 , specifically, the total actual irrigation volume V for the whole day 实际 is allocated to each time period according to the time-segmented strategy, giving priority to meeting the demand during the transpiration peak period of the crops.

[0073] As an option, the present invention adopts the "three-time period allocation method" to allocate the whole-day irrigation volume as follows: Morning period (8:00 - 10:00): Allocate 30% of the total amount; Noon period (12:00 - 14:00): Allocate 40% of the total amount; Afternoon period (16:00 - 18:00): Allocate 30% of the total amount.

[0074] Specifically, the calculation formula for the time-segmented irrigation volume is: V 时段 = V 实际 ·P 时段 Where: V 时段 is the irrigation volume for a certain time period, with the unit of ml / m 2 ; P 时段 is the irrigation volume ratio for the corresponding time period.

[0075] Exemplarily, if the dynamically adjusted actual irrigation volume V 实际 = 3600 ml / m 2 , then the irrigation volumes for the three time periods are respectively: Morning: V 上午 = 3600·0.3 = 1080 ml / m 2 ; Noon: V中午 = 3600 · 0.4 = 1440 ml / m 2 ; Afternoon: V 下午 = 3600 · 0.3 = 1080 ml / m 2 .

[0076] It should be noted that this allocation method fully considers the transpiration variation law of crops during a day to ensure that the water supply matches the actual demand.

[0077] Irrigation Implementation Equipment and Operation In a possible implementation manner, the present invention implements the irrigation plan through a drip arrow irrigation system. The drip arrow irrigation system consists of a water storage device, an irrigation pipeline, drip arrows, and a control system, and can achieve high-precision water delivery and time control.

[0078] Specifically, the drip arrow irrigation system implements irrigation according to the following steps: First, take the sub-period irrigation volume V 时段 as an input parameter and set the total irrigation volume for each period; Secondly, based on the number n of drip arrows and the water discharge rate Q (unit: ml / min) of a single drip arrow, calculate the duration t of each irrigation 灌溉； ; The irrigation time calculation formula is: Exemplarily, when the irrigation volume V of a certain period 时段 = 1080 ml / m 2 , the number n of drip arrows = 10, and the water discharge rate Q of a single drip arrow = 2 ml / min: It should be understood that by accurately setting the irrigation duration for each period, the irrigation uniformity and the accurate execution of the irrigation plan can be ensured.

[0079] Real-time Monitoring and Feedback As an option, during the irrigation execution process, the present invention monitors the substrate humidity and the return liquid volume in real time to evaluate whether the irrigation effect meets the expected goal.

[0080] Specifically, the real-time monitoring content includes: Substrate humidity monitoring: Through a substrate humidity sensor, the substrate water content is recorded in real time to ensure that the substrate reaches an appropriate humidity range after irrigation (such as remaining between 40% and 60%).

[0081] Return liquid volume monitoring: Through a return liquid collection device, the return liquid volume generated during irrigation is recorded, and the real-time return liquid ratio D is calculated 实时 .

[0082] It should be noted that when the liquid return ratio is detected in real time and exceeds the set threshold (such as higher than 30% or lower than 20%), it can be immediately corrected by adjusting the irrigation time or irrigation volume.

[0083] In a possible implementation, the monitoring system can also compare the actual data with the planned data to generate a deviation analysis report. For example, when the actual liquid return ratio in a certain period is 35% while the planned liquid return ratio is 25%, the system will prompt that the irrigation is excessive and the irrigation volume in the subsequent period needs to be reduced.

[0084] Abnormal handling of irrigation execution In this embodiment, the irrigation abnormality handling is mainly designed for two situations: equipment failure and sudden environmental changes.

[0085] As an option, when the water outlet of the drip arrow is abnormal (such as blocked or damaged), the monitoring system can detect the abnormal flow rate in the irrigation pipeline through the flow sensor and send out an alarm signal.

[0086] Specifically, when the deviation between the real-time detection data Q 实时 of the flow sensor and the planned water outlet rate Q 计划 is greater than 10%, it is determined that the water outlet is abnormal: Exemplarily, when the planned water outlet rate is Q 计划 = 2 ml / min and the real-time water outlet rate is Q 实时 = 1.6 ml / min: It can be understood that the abnormal flow rate handling can effectively avoid the problem of uneven irrigation caused by drip arrow failures and improve the reliability of the irrigation system.

[0087] In another possible implementation, when sudden environmental changes are detected (such as a sudden drop in temperature or rainfall), the subsequent irrigation plan can be immediately adjusted. For example, when the rainfall R exceeds the set threshold (such as R > 5 mm), the system will suspend all unfinished irrigation tasks for the day.

[0088] Extended design and expansion It should be noted that the irrigation execution scheme of the present invention has a certain degree of scalability. In some embodiments, irrigation and fertilization can be integrated, and a water and fertilizer integration device can be used to further improve the nutrient utilization efficiency of crops by adjusting the fertilizer concentration of the irrigation solution.

[0089] In another possible implementation, an irrigation optimization strategy based on meteorological prediction can be introduced. For example, by obtaining the light and rainfall forecasts for the next 12 hours, the irrigation plan can be dynamically adjusted to avoid irrigation waste caused by sudden weather changes.

[0090] An example of the complete process of irrigation execution: Taking a one-day irrigation task as an example: Calculate the actual irrigation volume V after dynamic adjustment 实际 = 3600 ml / m 2 ; According to the time-segmented strategy, allocate the irrigation volume as 1080 ml / m² in the morning, 1440 ml / m² at noon, and 1080 ml / m² in the afternoon; set the irrigation duration for each period through the drip arrow irrigation system (such as 54 minutes in the morning); Monitor the substrate humidity and the return liquid ratio in real time to ensure the irrigation effect; Handle any possible abnormal situations, such as blockage or environmental changes.

[0091] It should be understood that through the above steps, the irrigation execution scheme of the present invention can efficiently and accurately meet the water requirements of crops, while significantly improving the water resource utilization efficiency. The above scheme can also adapt to the application requirements of different crops or greenhouse conditions.

[0092] Feedback and optimization: Monitor the crop status, return liquid ratio, and substrate humidity after irrigation, and optimize the calculation parameters and adjustment rules using historical data; In this embodiment, feedback and optimization are important components of the present invention's scheme. By comparing and analyzing the actual data after irrigation execution with the expected plan, evaluate the irrigation effect, and dynamically adjust the model parameters and control strategies in combination with historical data, so as to continuously optimize the accuracy and responsiveness of irrigation management.

[0093] It should be noted that the feedback part focuses on monitoring key parameters, such as substrate humidity, return liquid ratio, crop growth status, etc., while the optimization part dynamically adjusts the weight parameters, adjustment thresholds, and control strategies based on the feedback data. The two complement each other and jointly improve the efficiency and adaptability of the irrigation system.

[0094] Feedback mechanism In this embodiment, the feedback mechanism collects the data after irrigation execution in real time through sensors and monitoring systems, compares it with the preset target range, identifies the deviation, and generates a feedback signal.

[0095] As an option, the feedback data includes the following key contents: Substrate humidity: Collect the real-time moisture content of the substrate through a substrate humidity sensor, and monitor whether the substrate humidity is within the target range (for example, 40% - 60%).

[0096] Return liquid ratio: Collect the actual return liquid volume d after irrigation through a return liquid collection device, calculate the actual return liquid ratio D 实际 , and compare it with the planned return liquid ratio D 计划Make a comparison.

[0097] Crop physiological status: Through the leaf moisture sensor and the plant growth monitoring device, collect the leaf transpiration rate E 实际 and the fruit swelling rate G 实际 , and evaluate whether the growth status of the crop is normal.

[0098] It should be noted that these feedback data provide a quantitative basis for the evaluation of irrigation effect and reliable basic data for the optimization link at the same time.

[0099] In a possible implementation, the feedback system adopts a combination of real-time monitoring and batch analysis. For example, real-time monitoring can continuously record key parameters (such as humidity and return liquid volume) during irrigation execution, while batch analysis comprehensively analyzes the whole-day data after the end of daily irrigation.

[0100] Specifically, the feedback analysis uses the following formula to calculate the deviation: ΔD = D 实际 - D 计划 ΔE = E 实际 - E 目标 ii ΔG = G 实际 - G 目标 Exemplarily, when the actual return liquid ratio D 实际 = 35% after a certain irrigation, and the planned return liquid ratio D 计划 = 30%, the deviation calculation result is: ΔD = 35% - 30% = +5% It can be understood that the result of the deviation calculation directly reflects the effect of irrigation execution and provides an important reference for the subsequent optimization link.

[0101] Optimization method In this embodiment, the optimization method dynamically adjusts the model parameters and control strategies based on the feedback data to continuously improve the accuracy and adaptability of irrigation management.

[0102] As an option, the optimization content includes the following aspects: Weight parameter optimization: For the weight parameters α, β, γ in the theoretical irrigation amount calculation formula, optimize their values through regression analysis or machine learning models.

[0103] Adjustment threshold optimization: Dynamically adjust the target ranges of the return liquid ratio and the matrix water loss ratio according to the feedback data. For example, optimize the target range of the return liquid ratio from 20% - 30% to 25% - 35%, which better meets the actual needs of the crop.

[0104] Control strategy optimization: Based on the feedback analysis results, optimize the irrigation time segment ratio and duration.

[0105] In a possible implementation, the weight parameter optimization adopts the multiple regression analysis method, combines historical data and current feedback data, and fits the optimal parameter values. For example, when the temperature fluctuates greatly and the humidity is low, the value of the weight parameter α may need to be appropriately increased to better reflect the impact of temperature on transpiration demand.

[0106] Specifically, the optimization process includes the following steps: Collect historical feedback data for at least 30 days and establish a database; Adopt a multiple linear regression model to fit the weight parameters in the theoretical irrigation amount formula: V 理论 =3·L + α·T + β·H + γ·E Adjust the values of the weight parameters α, β, γ according to the fitting results.

[0107] It should be noted that the weight parameter optimization can be carried out regularly, for example, updated monthly to ensure the dynamic adaptability of the formula parameters.

[0108] Optimization example Exemplarily, in the 30-day feedback data, it is found that the actual liquid return ratio D 实际 The average value is 32%, while the planned liquid return ratio D 计划 The average value is 25%, and the deviation is 7%. Analysis shows that the planned irrigation amount is generally too high.

[0109] In response to the above deviation, the present invention optimizes the target range of the liquid return ratio, adjusts the planned target from 20% - 30% to 25% - 35%, and correspondingly reduces the benchmark value of the theoretical irrigation amount. The adjusted irrigation amount calculation formula is as follows: V 理论 =2.8·L + α·T + β·H + γ·E It can be understood that this optimization measure can significantly reduce the waste of irrigation amount and improve the accuracy of irrigation management.

[0110] Combination of real-time adjustment and long-term optimization In a possible implementation, the present invention combines real-time adjustment with long-term optimization to achieve continuous improvement of irrigation management.

[0111] Specifically, the real-time adjustment is based on the feedback data of a single irrigation, and immediately corrects the irrigation amount or time in the subsequent time period. For example, when the actual liquid return ratio in the morning exceeds the target range, the irrigation amount at noon or in the afternoon can be correspondingly reduced.

[0112] Long-term optimization is based on data for one growing cycle or one month, comprehensively analyzes the dynamic change laws of crop growth status and environmental parameters, and comprehensively adjusts the model parameters and control strategies.

[0113] It should be noted that this combination method can take into account the requirements of real-time response and long-term planning, and provide more reliable irrigation support for crop growth.

[0114] An example of the complete process of feedback and optimization takes the irrigation task of one day as an example: Real-time monitor the substrate humidity and the liquid return volume after irrigation, and record the actual liquid return ratio D 实际 ; Compare the feedback data with the planned target, and calculate the deviation ΔD; According to the deviation analysis result, adjust the irrigation volume in the subsequent period in real time; After each day ends, comprehensively analyze the feedback data of the whole day and update the database; At the beginning of each month, optimize the formula parameters and control strategies based on historical data.

[0115] It should be understood that the combination of feedback and optimization enables the present invention to achieve efficient and precise irrigation management in a complex and changeable greenhouse environment. The above method can also adapt to the planting requirements of other greenhouse crops, and has good applicability and popularization value.

[0116] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirits of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating the irrigation amount of greenhouse bunch-harvested tomatoes based on the return liquid amount and weather conditions, characterized in that: The following steps are involved: Data collection: Collect greenhouse environmental data through monitoring equipment, including cumulative light, temperature, relative humidity and rainfall, as well as substrate humidity data, including maximum substrate weight, minimum substrate weight and liquid return; Calculation of theoretical irrigation volume: Calculate the theoretical irrigation volume for bunch-harvesting tomatoes based on the accumulated sunlight and environmental factors; Dynamic adjustment: According to weather conditions, light changes, liquid return ratio and substrate water loss ratio in substrate humidity data, and crop growth stage, the theoretical irrigation amount is dynamically corrected to obtain the actual irrigation amount; Irrigation execution: According to the actual irrigation volume after dynamic adjustment, formulate a time-divided irrigation plan and implement it through the drip irrigation system; Feedback and optimization: Monitor crop status, liquid return ratio and substrate moisture after irrigation, and use historical data to optimize calculation parameters and adjustment rules.

2. A method for calculating the irrigation amount of greenhouse tomato harvesting according to the return liquid amount and weather conditions according to claim 1, characterized in that: The data collection includes collecting light intensity through a light sensor and calculating the light accumulation. The unit of the light accumulation is J / cm 2 .

3. The method for calculating the irrigation amount of greenhouse tomato harvesting according to the return liquid amount and weather conditions according to claim 1, characterized in that: The calculation of the theoretical irrigation amount includes the following formula: V 理论 =3·L+α·T+β·H+γ·E Where: L is the cumulative amount of light; T is the average temperature of the day; H is the average relative humidity of the day; E is the leaf transpiration rate; α, β, γ are empirical weight coefficients.

4. A method for calculating the irrigation amount of greenhouse bunch-harvested tomatoes based on the return liquid amount and weather conditions according to claim 3, characterized in that: The initial values ​​of the weight coefficients are α=0.1, β=0.05, γ=0.2 n .

5. The method for calculating the irrigation amount of greenhouse bunch-harvested tomatoes based on the return liquid amount and weather conditions according to claim 1, characterized in that: The dynamic adjustment The following steps are involved:

1. Adjust irrigation volume according to weather conditions. If light intensity is continuously below 200W / m 2 Or if the rainfall on that day exceeds 5mm, irrigation will be reduced or suspended; 2. Adjust the irrigation amount according to the year-on-year change in the accumulated amount of light. The light change rate ΔL is calculated according to the following formula: The actual irrigation adjustment value is: Where: ΔL is the year-on-year change rate of the accumulated light, L 当日 is the accumulated amount of sunlight for the current day, L 前日 is the cumulative amount of sunlight on the previous day, ΔV 光照 V is the adjustment value of irrigation amount due to cumulative change of light intensity, 理论 It is the theoretical irrigation amount calculated based on environmental parameters such as light, temperature, humidity and transpiration rate.

6. A method for calculating the irrigation amount of greenhouse bunch-harvested tomatoes based on the return liquid amount and weather conditions according to claim 5, characterized in that: The dynamic adjustment also includes substrate humidity adjustment, which is as follows: If the return liquid ratio D exceeds 30%, the theoretical irrigation volume shall be reduced by 1% for every 1% exceeding the limit; If the liquid return ratio D is lower than 20%, for every 1% reduction, increase the theoretical irrigation volume by 1%; If the matrix water loss ratio P 关水 If it exceeds 15%, the theoretical irrigation amount shall be increased by 2% for every 1% exceeding the limit; If the matrix water loss ratio P 失水 If it is lower than 10%, the theoretical irrigation volume will be reduced by 2% for every 1% reduction.

7. The method for calculating the irrigation amount of greenhouse bunch-harvested tomatoes based on the return liquid amount and weather conditions according to claim 6, characterized in that: The liquid return ratio D is calculated according to the following formula: Where: d is the daily return liquid volume; n is the number of drop arrows; I is the total amount of irrigation for a single drop arrow throughout the day.

8. The method for calculating the irrigation amount of greenhouse bunch-harvested tomatoes based on the return liquid amount and weather conditions according to claim 6, characterized in that: The matrix water loss ratio P 失水 Calculated using the following formula: Where: W 最高 is the maximum weight of the matrix; W 最低 is the minimum weight of the matrix.

9. The method of calculating the irrigation amount of greenhouse bunch-harvested tomatoes according to the return liquid amount and weather conditions according to claim 1, characterized in that: The crop growth stage adjustment includes dynamically adjusting the irrigation amount according to the fruit expansion rate G, and the adjustment value is: ΔV 生长 =κ·G Where κ is the growth stage adjustment coefficient, and its initial value is 0.

05.

10. The method for calculating the irrigation amount of greenhouse bunch-harvested tomatoes according to the return liquid amount and weather conditions according to claim 1, characterized in that: The irrigation is performed using a time-division strategy, and the total irrigation volume for the day is allocated according to the following proportions: 30% in the morning, 40% at noon, and 30% in the afternoon.

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