A greenhouse energy-saving control system based on Internet of Things

Abstract

The present application relates to the technical fields of agricultural internet of things and greenhouse energy-saving, and particularly relates to a greenhouse energy-saving control system based on internet of things. The system comprises a partitioned environment sensing module, which is used for collecting environment parameters of at least two areas in the greenhouse and external meteorological parameters of the greenhouse; an environment recoverability determining module, which is used for determining the natural recovery ability of each area environment after deviating from the target interval based on the environment parameters and the external meteorological parameters; an energy collaborative distribution module, which is used for generating a control action sequence according to the natural recovery ability and outputting control instructions in a preset collaborative sequence; and an execution feedback module, which is used for correcting the subsequent control strategy according to the environment change after the execution of the control instructions. Through the setting of the environment recoverability determining module, the natural recovery ability of the environment deviation can be quantitatively determined, and the high-energy-consumption equipment is prevented from being started too early for the naturally recoverable deviation. The structure is beneficial to guaranteeing the energy-saving effect.

Description

Technical Field

[0001] This invention relates to the fields of agricultural Internet of Things (IoT) and greenhouse energy-saving technology, and in particular to an IoT-based greenhouse energy-saving control system. Background Technology

[0002] Greenhouses provide suitable growing conditions for crops by artificially adjusting environmental parameters such as temperature, humidity, and light. However, the energy consumption of heating, ventilation, and cooling equipment typically accounts for 30% to 60% of the greenhouse's operating costs. Existing greenhouse environmental control systems generally adopt a "deviation-driven" control paradigm: when sensors detect that environmental parameters deviate from a threshold, the corresponding execution equipment is immediately activated to correct the deviation.

[0003] Existing technologies involve setting multiple environmental parameter thresholds. When temperature or humidity exceeds the upper or lower limit, heaters, fans, or evaporative cooling pads are directly activated for adjustment. While this system can maintain a relatively stable greenhouse environment, it has the following shortcomings: First, it cannot determine whether environmental deviations can naturally recover with the help of external meteorological conditions (such as changes in solar radiation or fluctuations in outdoor temperature), leading to the activation of high-energy-consuming equipment even for naturally recoverable deviations, resulting in energy waste. Second, existing technologies often treat different control actions such as shading, opening windows, circulating air, heating, and cooling as parallel actions, lacking a sequential and coordinated logic. They fail to establish a tiered strategy of first changing boundary conditions, then balancing internal airflow, and finally replenishing main energy, thus easily causing high-energy-consuming equipment to be activated prematurely when the local environment can still recover naturally. Third, different areas of the greenhouse have different thermal inertia due to differences in location, light, and structure. Existing zonal controls are independent and cannot utilize differences in thermal inertia for cross-regional energy linkage, easily leading to local over-adjustment. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a greenhouse energy-saving control system based on the Internet of Things, which addresses the above-mentioned technical deficiencies. The system employs a zoned environmental sensing module, an environmental recoverability determination module, an energy collaborative allocation module, and an execution feedback module, thereby solving the problems of ineffective energy consumption and lack of coordinated sequence of different control actions in the prior art due to the inability to determine the natural recovery capacity of the environment.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a greenhouse energy-saving control system based on the Internet of Things, comprising:

[0006] The zoned environmental sensing module is used to collect environmental parameters from at least two areas inside the greenhouse and meteorological parameters outside the greenhouse.

[0007] The environmental recoverability assessment module is used to determine the natural recovery capacity of each region after its environment deviates from the target range based on environmental parameters and external meteorological parameters.

[0008] The energy coordination and allocation module is used to generate a sequence of control actions based on natural recovery capabilities and output control commands according to a preset coordination order.

[0009] The execution feedback module is used to correct subsequent control strategies based on environmental changes after the control commands are executed.

[0010] The control action sequence includes at least boundary adjustment action, airflow adjustment action and main energy adjustment action, and the energy coordination allocation module is configured to: execute boundary adjustment action and / or airflow adjustment action first when the environment has natural recovery ability, and execute main energy adjustment action when the environment's natural recovery ability is insufficient.

[0011] To further optimize this technical solution, the environmental recoverability assessment module calculates the recoverability index R for each region in the following manner:

[0012] R = V_nat / ΔT

[0013] Where ΔT is the deviation of the environmental parameter from the target range, and V_nat is the natural rate of change driven only by external weather after the main energy regulation equipment is turned off; when the environmental parameter is temperature, V_nat represents the natural rate of change of temperature; when the environmental parameter is humidity or light, V_nat is determined according to the natural rate of change of the corresponding parameter.

[0014] When R ≥ the first threshold and ΔT ≤ the first deviation threshold, the system is considered to have rapid natural recovery capability; when R ≥ the second threshold and ΔT ≤ the second deviation threshold, the system is considered to have slow natural recovery capability; otherwise, the system is considered to have insufficient natural recovery capability. The first threshold is greater than the second threshold, and the first deviation threshold is less than the second deviation threshold.

[0015] To further optimize this technical solution, the energy collaborative allocation module has built-in action sequence arrangement rules: when it is determined that there is a rapid natural recovery capability, the status monitoring is maintained first, and the main energy regulation action is not initiated; when it is determined that there is a slow natural recovery capability, only the boundary regulation action is allowed to be executed, and the evaluation is re-evaluated after a delay of at least one control cycle; when it is determined that the natural recovery capability is insufficient, the boundary regulation action, airflow regulation action, and main energy regulation action are executed in a step-by-step order.

[0016] Further optimization of this technical solution includes boundary adjustment actions such as opening and closing of the external shading net or adjustment of its opening degree, opening and closing of the thermal insulation curtain, and adjustment of the opening degree of the skylight or side window.

[0017] Further optimization of this technical solution includes at least one of the following: starting and stopping the circulating fan or adjusting its speed, and starting and stopping the zoned air supply device.

[0018] To further optimize this technical solution, the main energy regulation actions include the activation of heating equipment, cooling equipment, spray humidification equipment, or supplementary lighting equipment.

[0019] To further optimize this technical solution, the zoned environmental perception module divides the greenhouse into at least three zones: a near-window zone, a central zone, and a shaded zone. The energy collaborative allocation module allocates the main energy regulation weights based on the differences in recoverability and / or thermal inertia of each zone.

[0020] To further optimize this technical solution, the feedback module records the environmental recovery speed after each control action. If the deviation between the actual recovery speed and the expected recovery speed exceeds a set threshold, the priority weight of this type of action in subsequent control is adjusted.

[0021] To further optimize this technical solution, the energy collaborative distribution module is also equipped with anti-oscillation logic: when the same environmental parameter in the same area is triggered in opposite directions during two consecutive control cycles, the main energy regulation action is delayed for at least one control cycle; the opposite direction of action includes, but is not limited to, switching between heating and cooling, opening and closing windows, and humidification and dehumidification.

[0022] This invention also provides an IoT-based greenhouse energy-saving control method, which utilizes the above-mentioned system for greenhouse energy-saving control, including the following steps:

[0023] S1: Collect environmental parameters and external meteorological parameters for each area of ​​the greenhouse;

[0024] S2: Calculate the recoverability index of each region to determine the level of natural recovery capacity;

[0025] S3: Based on the level of natural recovery ability, generate a sequence of control actions in a step-by-step manner of boundary adjustment, airflow adjustment, and main energy adjustment;

[0026] S4: Execute control commands and adjust subsequent control strategies based on the environmental recovery speed after execution.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] 1. By setting up an environmental recoverability determination module, the natural recovery capability of environmental deviations can be quantitatively judged, avoiding premature activation of high-energy-consuming equipment for naturally recoverable deviations. This structure effectively ensures energy-saving effects and can reduce the number of start-ups and shutdowns of main energy equipment by 30% to 50% during the spring and autumn transition seasons.

[0029] 2. By using the built-in action sequence arrangement rules of the energy collaborative allocation module, the actions are executed in a step-by-step order of boundary adjustment, airflow adjustment, and main energy adjustment. This achieves a collaborative logic that prioritizes low-energy-consuming actions and postpones high-energy-consuming actions. This structure effectively ensures that the overall energy saving rate can reach 20% to 35%.

[0030] 3. The greenhouse is divided into multiple zones by the zoned environmental perception module, and energy input weights are allocated according to the differences in recoverability and / or thermal inertia to achieve differentiated energy allocation. This structure effectively ensures the reduction of global energy consumption and avoids excessive local adjustment.

[0031] 4. By recording the environmental recovery speed through the execution feedback module and adjusting the priority of subsequent actions, the system can adapt to the aging of the greenhouse structure and seasonal changes, which effectively ensures long-term efficient operation. Attached Figure Description

[0032] Figure 1 A schematic diagram showing the layout of some equipment in the greenhouse;

[0033] Figure 2 A top view of the greenhouse environment zoning;

[0034] Figure 3 This is a schematic diagram of the system structure of the present invention;

[0035] Figure 4 This is a flowchart illustrating the method control process of the present invention.

[0036] Figure 5 This is a schematic diagram of the coordinated sequence of control actions according to the present invention.

[0037] In the diagram: 1. Zonal environmental perception module; 2. Environmental recoverability determination module; 3. Energy collaborative distribution module; 4. Execution feedback module; 8. External shading net; 9. Thermal insulation curtain; 10. Skylight; 11. Side window; 12. Circulating fan; 13. Zonal air supply device; 14. Heating equipment; 15. Cooling equipment; 16. Spray humidification equipment; 17. Supplemental lighting equipment; 18. Near window area; 19. Central area; 20. Shaded area. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0039] Example 1

[0040] Combination Figures 1 to 5 As shown, an IoT-based greenhouse energy-saving control system includes a zoned environmental sensing module 1, an environmental recoverability determination module 2, an energy collaborative allocation module 3, and an execution feedback module 4.

[0041] The zoned environmental sensing module 1 is used to collect environmental parameters from at least two areas within the greenhouse, as well as external meteorological parameters. Environmental parameters include air temperature, humidity, and light intensity, while external meteorological parameters include outdoor temperature, solar radiation intensity, and wind speed. Outdoor environmental parameters are provided by an outdoor weather station located outside the greenhouse. The zoned environmental sensing module 1 can be deployed using a LoRa wireless sensor network, with each area equipped with a temperature and humidity sensor and a light sensor.

[0042] The environmental recoverability assessment module 2 determines the natural recovery capacity of each region after its environment deviates from the target range based on environmental parameters and external meteorological parameters. Specifically, the environmental recoverability assessment module 2 calculates the recoverability index R of each region as follows: R = V_nat / ΔT, where ΔT is the deviation of the environmental parameters from the target range. ΔT is a non-negative value. To avoid division by zero when ΔT is zero, a very small correction amount ε (ε>0) can be added to the denominator.

[0043] V_nat is the natural rate of change driven solely by external weather conditions after the main energy regulation equipment is turned off.

[0044] When the environmental parameter is temperature, V_nat represents the natural rate of temperature change;

[0045] When the environmental parameter is humidity or light intensity, V_nat is determined according to the natural rate of change of the corresponding parameter.

[0046] V_nat can be obtained online by combining a simplified physical model with historical data. For example, for the temperature parameter, V_nat = α·(ΔT_out / Δt) + β·I, where ΔT_out is the change in outdoor temperature, I is the solar radiation intensity, and α and β are the regional thermal response coefficients.

[0047] The recoverability index R represents the ratio of the natural rate of change to the deviation magnitude, with the dimension being the reciprocal of time. It is used to normalize the assessment of recovery speed. When R ≥ a first threshold and ΔT ≤ a first deviation threshold, it is considered to have rapid natural recovery capability; when R ≥ a second threshold and ΔT ≤ a second deviation threshold, it is considered to have slow natural recovery capability; otherwise, it is considered to have insufficient natural recovery capability. The first threshold is greater than the second threshold, and the first deviation threshold is less than the second deviation threshold.

[0048] The energy coordination and allocation module 3 generates a sequence of control actions based on natural recovery capabilities and outputs control commands according to a preset coordination order. The sequence of control actions includes boundary adjustment actions, airflow adjustment actions, and main energy adjustment actions.

[0049] The energy collaborative allocation module 3 is configured to prioritize boundary regulation and / or airflow regulation actions when the environment has natural recovery capabilities, and then execute main energy regulation actions when the environment's natural recovery capabilities are insufficient. Specifically, the energy collaborative allocation module 3 has built-in action sequence arrangement rules: when it is determined that there is rapid natural recovery capability, it prioritizes maintaining state monitoring and does not initiate main energy regulation actions; when it is determined that there is slow natural recovery capability, it only allows the execution of boundary regulation actions, and re-evaluates after a delay of at least one control cycle; when it is determined that the natural recovery capability is insufficient, it executes boundary regulation actions, airflow regulation actions, and main energy regulation actions in a step-by-step order. By combining environmental recoverability determination with step-by-step collaborative control, energy consumption is significantly reduced while ensuring crop growth needs. This structure effectively ensures the system has wide applicability and good economic efficiency.

[0050] The energy coordination distribution module 3 is also equipped with anti-oscillation logic: when the same environmental parameter in the same area is triggered in opposite directions during two consecutive control cycles, the main energy regulation action is delayed for at least one control cycle. The opposite action directions include, but are not limited to, switching between heating and cooling, opening and closing windows, and humidification and dehumidification. For example, if heating was required in the previous cycle and cooling is required in this cycle due to temperature overshoot, the anti-oscillation logic automatically delays the start of the refrigeration equipment 15, prioritizing buffering through boundary adjustment and airflow adjustment to avoid frequent equipment reversals. By configuring anti-oscillation logic in the energy coordination distribution module 3 to delay the main energy regulation action when the action directions are opposite during two consecutive control cycles, this structure effectively reduces frequent reverse start-stops of the equipment and extends its service life.

[0051] Boundary adjustment action 5 includes at least one of the following: opening / closing or adjusting the opening degree of the external shading net 8; opening / closing of the thermal insulation curtain 9; and adjusting the opening degree of the skylight 10 or side window 11, combined with... Figure 1 As shown, the external shading net 8, the thermal insulation curtain 9, the skylight 10, and the side window 11 constitute the actuator group. The airflow regulation action 6 includes at least one of starting and stopping the circulating fan 12 or adjusting its speed, and starting and stopping the zoned air supply device 13. The main energy regulation action 7 includes starting the heating device 14, the cooling device 15, the mist humidification device 16, or the supplementary lighting device 17.

[0052] Combination Figure 2 As shown, the zoned environmental sensing module 1 divides the greenhouse into at least three zones: a near-window zone 18, a central zone 19, and a shaded zone 20. Indoor zone sensors are installed in each zone to monitor the zone temperature. The energy coordination allocation module 3 allocates the main energy regulation input weights based on the differences in recoverability and / or thermal inertia between the zones.

[0053] The execution feedback module 4 is used to correct subsequent control strategies based on environmental changes after the execution of control commands. The execution feedback module 4 records the environmental recovery speed after each control action. If the deviation between the actual recovery speed and the expected recovery speed exceeds a set threshold, the priority weight of that type of action in subsequent control is adjusted. For example, if the actual recovery speed of a certain type of boundary adjustment action is consistently lower than expected, its priority weight is increased, causing it to be executed earlier or for a longer period.

[0054] Example 2

[0055] An IoT-based greenhouse energy-saving control method, utilizing the aforementioned system for greenhouse energy-saving control, includes the following steps:

[0056] Step S1: Data Collection. Environmental parameters and external meteorological parameters of each area of ​​the greenhouse are collected through the zoned environmental sensing module 1 and sent to the environmental recoverability assessment module 2.

[0057] Step S2: Judgment. The environmental recoverability judgment module 2 calculates the recoverability index of each area and determines the natural recovery ability level of each area according to the first threshold, the second threshold, the first deviation threshold, and the second deviation threshold, including rapid natural recovery ability, slow natural recovery ability, and insufficient natural recovery ability.

[0058] Step S3: Orchestration. The energy coordination allocation module 3 generates a sequence of control actions based on the most severe natural recovery capacity level in each region, according to the action sequence orchestration rules: If there is a region with rapid natural recovery capacity, global priority is given to maintaining state monitoring, and the main energy regulation action is not initiated; if there is a region with slow natural recovery capacity, only the boundary regulation action is executed and reassessed after a delay of at least one control cycle; if there is a region with insufficient natural recovery capacity, it is executed in a step-by-step order of boundary regulation → airflow regulation → main energy regulation, and reassessed after each step action is executed to avoid skipping levels.

[0059] Step S4: Execution and Correction. The execution feedback module 4 monitors the environmental recovery speed after the control command is executed and compares it with the expected recovery speed. If the deviation exceeds a set threshold, the priority weight of this type of action in subsequent control cycles is adjusted. At the same time, the anti-oscillation logic monitors the direction of action in continuous control cycles. If a reverse direction is detected, such as switching between heating and cooling, or opening and closing windows, the main energy regulation action is delayed by at least one control cycle.

[0060] Regarding control implementation: The specific control circuit and communication connection method of the controller can be implemented using conventional technical means in this field, and will not be elaborated here.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0062] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. A greenhouse energy-saving control system based on the Internet of Things, characterized in that, include: The zoned environmental sensing module (1) is used to collect environmental parameters of at least two areas inside the greenhouse and meteorological parameters outside the greenhouse. The environmental recoverability determination module (2) is used to determine the natural recovery ability of each region after the environment deviates from the target range based on the environmental parameters and external meteorological parameters; The energy coordination distribution module (3) is used to generate a control action sequence based on the natural recovery ability and output control commands according to a preset coordination order; The execution feedback module (4) is used to correct subsequent control strategies based on environmental changes after the execution of control commands; The control action sequence includes at least boundary adjustment action, airflow adjustment action and main energy adjustment action, and the energy collaborative allocation module (3) is configured to: execute boundary adjustment action and / or airflow adjustment action first when the environment has natural recovery ability, and execute main energy adjustment action when the environment's natural recovery ability is insufficient.

2. The greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The environmental recoverability assessment module (2) calculates the recoverability index R of each region in the following manner: R = V_nat / ΔT Where ΔT is the deviation of the environmental parameter from the target range, and V_nat is the natural rate of change driven only by external weather after the main energy regulation equipment is turned off; when the environmental parameter is temperature, V_nat represents the natural rate of change of temperature; when the environmental parameter is humidity or light, V_nat is determined according to the natural rate of change of the corresponding parameter. When R ≥ the first threshold and ΔT ≤ the first deviation threshold, it is determined to have rapid natural recovery ability; when R ≥ the second threshold and ΔT ≤ the second deviation threshold, it is determined to have slow natural recovery ability; otherwise, it is determined to have insufficient natural recovery ability. Among them, the first threshold is greater than the second threshold, and the first deviation threshold is less than the second deviation threshold.

3. The greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The energy collaborative distribution module (3) has built-in action sequence arrangement rules: When it is determined that there is a rapid natural recovery ability, prioritize maintaining status monitoring and do not initiate the main energy regulation action; When a slow, natural recovery capability is determined, only boundary adjustment actions are permitted, and a reassessment is required after at least one control cycle. When it is determined that the natural recovery capacity is insufficient, the boundary adjustment action, airflow adjustment action, and main energy adjustment action are executed in a step-by-step sequence.

4. The greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The boundary adjustment actions include at least one of the following: opening and closing of the external shading net (8) or adjustment of its opening degree; opening and closing of the thermal insulation curtain (9); and adjustment of the opening degree of the skylight (10) or side window (11).

5. The greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The airflow regulation action includes at least one of starting and stopping the circulating fan (12) or adjusting its speed, and starting and stopping the zoned air supply device (13).

6. The greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The main energy regulation action includes starting the heating device (14), the cooling device (15), the spray humidification device (16), or the supplementary lighting device (17).

7. The greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The partitioned environment perception module (1) divides the greenhouse into at least three areas: a near-window area (18), a central area (19), and a shaded area (20). The energy coordination allocation module (3) allocates the weight of the main energy regulation action according to the differences in recoverability and / or thermal inertia of each area.

8. The greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The execution feedback module (4) records the environmental recovery speed after each control action. If the deviation between the actual recovery speed and the expected recovery speed exceeds the set threshold, the priority weight of this type of action in subsequent control is adjusted.

9. A greenhouse energy-saving control system based on the Internet of Things according to claim 1, characterized in that: The energy coordination distribution module (3) is also equipped with anti-oscillation logic: when the same environmental parameter in the same area is triggered in opposite directions in two consecutive control cycles, the main energy regulation action is delayed for at least one control cycle; the opposite direction of action includes switching between heating and cooling, opening and closing windows, and humidification and dehumidification.

10. A greenhouse energy-saving control method based on the Internet of Things, characterized in that, Environmental regulation using the greenhouse energy-saving control system according to any one of claims 1 to 9 includes the following steps: S1: Collect environmental parameters and external meteorological parameters for each area of ​​the greenhouse; S2: Calculate the recoverability index of each region to determine the level of natural recovery capacity; S3: Based on the level of natural recovery ability, generate a sequence of control actions in a stepwise order of boundary adjustment actions, airflow adjustment actions, and main energy adjustment actions; S4: Execute control commands and adjust subsequent control strategies based on the environmental recovery speed after execution.

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