Greenhouse environment regulation and control system and method

Abstract

The invention discloses a greenhouse environment regulation and control system and method. The system comprises a plurality of sensor assemblies, a ZigBee coordinator, a communication module, a cloud platform and a plurality of actuator assemblies. Each sensor transmits collected original data to the single-chip microcomputer, and the single-chip microcomputer transmits the received original data to the ZigBee terminal node; the ZigBee coordinator is used for gathering data of all ZigBee terminal nodes and transmitting the data to the communication module; the communication module is used for transmitting the received data to the cloud platform; the cloud platform stores the data, calculates the validity value of each piece of sensor data based on a sensor data validity evaluation model, generates a processing strategy based on the validity value, and transmits the processing strategy to the actuator for execution; by means of the scheme, the reliability of the sensor data of the greenhouse can be improved, accurate regulation and control of the actuator are achieved, the crop growth safety is guaranteed, and the production benefits and the crop quality of the greenhouse are improved.

Description

Technical Field

[0001] This invention belongs to the field of greenhouse environment control technology, and in particular relates to a greenhouse environment control system and method. Background Technology

[0002] In modern agricultural production, greenhouses, with their ability to provide a controllable environment for crop growth, have become important facilities for ensuring high-quality and high-yield crops. Among them, the precise control of the environment inside the greenhouse is the core link in achieving this goal, and this control process highly depends on the accurate perception and real-time monitoring of key environmental parameters such as temperature, humidity, CO2 concentration, and light.

[0003] As the core unit directly collecting the aforementioned environmental data, the accuracy of the sensor's measurement data directly affects the effectiveness of greenhouse environmental control. However, under the special operating conditions of agricultural production, sensors face several risks that can lead to failure or data distortion, as follows:

[0004] Firstly, the installation location may be flawed. For example, when a temperature sensor is in direct sunlight, it can lead to an inflated temperature reading; if a humidity sensor is obstructed by crop branches and leaves, the humidity measurement results will be inaccurate and fail to reflect the true ambient humidity.

[0005] Secondly, it is susceptible to physical damage. The sensor probes, when in prolonged contact with soil or moisture, are prone to corrosion, which can lead to drift in humidity detection data. Simultaneously, with increasing usage time, the sensor's sealing performance may deteriorate, allowing condensation to seep inside, causing short circuits and affecting the sensor's normal operation.

[0006] Third, there is the risk of chemical poisoning. In greenhouses, corrosive gases produced by the volatilization of agricultural inputs such as fertilizers can corrode the electrodes of gas sensors, damaging the sensor's detection structure and thus reducing its detection accuracy.

[0007] Fourth, calibration failure occurs. Because the sensor is not calibrated in a timely manner, measurement errors accumulate, eventually causing the measurement data to deviate from the true value and lose its reference value.

[0008] In existing greenhouse environmental control systems, distorted data resulting from the aforementioned reasons are often directly input into the decision-making system without effective reliability assessment. This can trigger a chain of malfunctions in actuators. For example, based on inflated temperature measurements, heating equipment may continue to run, causing heat damage to crops; if CO2 concentration detection values ​​are false alarms, ventilation equipment may be shut down and excessive fertilization may be injected, causing crop poisoning; and drift in humidity detection data may induce excessive operation of the irrigation system, accelerating soil degradation.

[0009] It is evident that current greenhouse environmental control systems lack dynamic verification, fault isolation, and data reliability assessment mechanisms for raw sensor data. This allows distorted sensor data to be directly transmitted to the decision-making stage via a rigid transmission chain, leading to erroneous decisions and ultimately causing a loss of control at the execution level. This severely impacts greenhouse production efficiency and crop growth safety. Therefore, addressing sensor failure under specific agricultural conditions and improving the reliability of environmental data has become a crucial issue urgently needing resolution in the field of greenhouse environmental control technology. Summary of the Invention

[0010] The purpose of this invention is to provide a greenhouse environment control system and method.

[0011] To achieve the objectives of this invention, the technical solution provided by this invention is as follows:

[0012] First aspect

[0013] This invention provides a greenhouse environment control system, characterized in that it includes multiple sensor components, a ZigBee coordinator and communication module, a cloud platform, and multiple actuator components;

[0014] The various sensor components include multiple sensors deployed in key areas within the greenhouse, a microcontroller that works in conjunction with the sensors, and a ZigBee terminal node that works in conjunction with the microcontroller; each sensor transmits the raw data it collects to the microcontroller, and the microcontroller transmits the received raw data to the ZigBee terminal node;

[0015] The ZigBee coordinator is connected to the ZigBee terminal nodes and the communication module. It is used to aggregate the data of all ZigBee terminal nodes and transmit it to the communication module.

[0016] The communication module is used to transmit the received data to the cloud platform;

[0017] The cloud platform is deployed on a cloud server and is used to store the received data and calculate the validity value of each sensor data based on the sensor data validity evaluation model. Based on the validity value, a processing strategy is generated and the processing strategy is transmitted to the actuators of various actuator components for execution.

[0018] The various actuator components include an actuator, a drive circuit used in conjunction with the actuator, and a ZigBee terminal node used in conjunction with the drive circuit; the ZigBee terminal node controls the on / off state of a relay through the drive circuit, and the relay controls the switch of the actuator.

[0019] Second aspect

[0020] Corresponding to the above method, the present invention also provides a method for controlling the environment of a greenhouse, the method being applicable to the aforementioned greenhouse environment control system;

[0021] Each sensor transmits the raw data it collects to the microcontroller, which then transmits the received raw data to the ZigBee terminal node.

[0022] The ZigBee coordinator aggregates data from all ZigBee end nodes and transmits it to the communication module.

[0023] The communication module transmits the received data to the cloud platform;

[0024] The cloud platform stores the received data and calculates the validity value of each sensor data based on the sensor data validity evaluation model. It then generates a processing strategy based on the validity value and transmits the processing strategy to the actuators of various actuator components for execution.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] (1) By constructing a sensor data validity assessment model (V value calculation), the validity of the data is dynamically verified from three dimensions: spatial consistency, historical data correction, and real-time fluctuation monitoring. This solves the problem that sensor data is used directly for decision-making without credibility assessment in the existing technology, effectively identifies distorted data, and reduces the risk of wrong decision-making due to inaccurate data.

[0027] (2) Based on the four-level classification mechanism of validity value (V), the actuator is differentiated (normal response, degraded operation, lockout, and system isolation), which can avoid actuator malfunctions caused by distorted data (such as continuous operation of heating equipment leading to crop heat damage, excessive irrigation leading to soil degradation, etc.), and ensure the accuracy of greenhouse environment regulation.

[0028] (3) Different levels of V value correspond to specific fault types (such as installation location problems, sensor drift, hardware damage, etc.) and alarm information (codes W01, E02, E03), which can quickly locate the cause of sensor abnormality, guide users to repair or replace in time, and reduce system downtime.

[0029] (4) The ZigBee wireless communication technology is used to achieve efficient data transmission. Combined with the dynamic weight optimization of the cloud platform (α, β, γ are adjusted by historical data feedback), the system can adapt to the complex environment of the greenhouse (such as sensor aging, external interference, etc.) and continuously improve the accuracy of data evaluation and the overall stability of the system.

[0030] (5) By precise environmental regulation and effective fault prevention, a stable and suitable growth environment can be provided for crops, reducing crop losses caused by regulation errors and improving the production efficiency and crop quality of greenhouses. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the system hardware architecture of the greenhouse environment control system provided in the embodiments of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] like Figure 1 As shown, the present invention provides a greenhouse environment control system, including various sensor components, a ZigBee coordinator and communication module, a cloud platform, and various actuator components;

[0034] The various sensor components include multiple sensors deployed in key areas within the greenhouse, a microcontroller that works in conjunction with the sensors, and a ZigBee terminal node that works in conjunction with the microcontroller; each sensor transmits the raw data it collects to the microcontroller, and the microcontroller transmits the received raw data to the ZigBee terminal node;

[0035] The ZigBee coordinator is connected to the ZigBee terminal nodes and the communication module. It is used to aggregate the data of all ZigBee terminal nodes and transmit it to the communication module.

[0036] The communication module is used to transmit the received data to the cloud platform;

[0037] The cloud platform is deployed on a cloud server and is used to store the received data and calculate the validity value of each sensor data based on the sensor data validity evaluation model. Based on the validity value, a processing strategy is generated and the processing strategy is transmitted to the actuators of various actuator components for execution.

[0038] The various actuator components include an actuator, a drive circuit used in conjunction with the actuator, and a ZigBee terminal node used in conjunction with the drive circuit; the ZigBee terminal node controls the on / off state of a relay through the drive circuit, and the relay controls the switch of the actuator.

[0039] It should be noted that:

[0040] Multiple sensors, including temperature and humidity sensors, CO2 concentration sensors, and light sensors, are deployed in key areas inside the greenhouse, forming a distributed monitoring network.

[0041] Its selection, communication method, and function are as follows:

[0042] Temperature and humidity sensor: The temperature and humidity sensor is model SHT31. It transmits temperature and humidity data to the microcontroller through the I2C communication interface (SCL / SDA pins). The microcontroller then transmits the data to the ZigBee terminal node via UART.

[0043] CO2 concentration sensor: The gas detection chip model SGP30 is selected. It monitors the carbon dioxide concentration in real time through the I2C interface and transmits the data to the microcontroller. The microcontroller then transmits the data to the ZigBee terminal node through UART.

[0044] Light sensor: The light intensity detection chip, model TSL2561, is used to collect light intensity (Lux value) through the I2C interface and transmit it to the microcontroller. The microcontroller then transmits it to the ZigBee terminal node through UART.

[0045] Various actuators include heating equipment, ventilation fans, insulated roller shutter systems, irrigation pumps, etc.

[0046] Heating equipment: Model HPTC-220V-5kW. ZigBee terminal nodes control the on / off state of relays via transistor drive circuits, and the relays control the switching on / off state of the heating equipment.

[0047] Ventilation fan: Model DXF-220V-550W. The ZigBee terminal node controls the on / off state of the relay through a transistor drive circuit, and the relay controls the on / off state of the ventilation fan.

[0048] Thermal insulation roller shutter system: Model JN-220V-1.5kW. The ZigBee terminal node controls the on / off state of the relay through a transistor drive circuit, and the relay controls the switching of the thermal insulation roller shutter system through an H-bridge motor drive circuit.

[0049] Irrigation pump: Model SPK-220V-750W. The ZigBee terminal node controls the on / off state of the relay through a transistor drive circuit, and the relay controls the switching on / off state of the irrigation pump.

[0050] The microcontroller used is an STM32F103C8T6 microcontroller, whose I2C interface is connected to the sensor and UART interface is connected to the wireless communication module to realize protocol conversion and data preprocessing.

[0051] ZigBee End Node: Model A72-C2G4A10S2b-E, receives MCU data via UART interface and uploads data to the coordinator via ZigBee wireless protocol (2.4GHz band).

[0052] ZigBee coordinator: Model A72-C2G4A10S2b-C, which aggregates data from all terminal nodes and connects to remote transmission devices via a UART interface.

[0053] The communication module can be a 4G communication module: model Air78E, which receives data from the coordinator through the UART interface and uploads the data to the cloud platform using the 4G full network.

[0054] Cloud Platform: An IoT platform built on the ThingsBoard open-source architecture, deployed on cloud servers, responsible for data storage, analysis, and decision-making.

[0055] Preferably, the sensor data validity evaluation model has the following formula:

[0056]

[0057] In the formula, V is the validity value; α, β, and γ are weights; C s Spatial consistency score; H a To correct for historical reference values; S is the real-time measurement value; σ H D represents the historical standard deviation. r This is an abnormal fluctuation index.

[0058] Wherein, the C s The calculation formula is as follows:

[0059]

[0060] In the formula, R represents the average value of a certain type of sensor in the greenhouse, and R is the measurement range.

[0061] Wherein, the H a The calculation formula is as follows:

[0062] H a =H hist +∑ j k j ΔD j

[0063] In the formula, H hist The average historical temperature inside the greenhouse under the same external environmental conditions; ΔD j Let k be the state change of the j-th type of device; jis the equipment impact coefficient; j is the equipment control type number, with a value range of j = 1, 2, ..., M, where M is the total number of currently effective equipment control types.

[0064] Wherein, the σ H The calculation formula is as follows:

[0065]

[0066] In the formula, μ is the mean of N parameter data, and N is the sample size; S i Let i be the i-th valid historical data point among N parameter data, where i is the index of the historical data point, and its value range is i = 1, 2, ..., N.

[0067] Wherein, the D r The calculation method is as follows:

[0068] (1) Data filtering:

[0069] Take the original sensor data sequence within 30 minutes prior to the current time, with a sampling interval of 1 minute, for a total of Mo = 30 points;

[0070] (2) Calculation of σ in the numerator real :

[0071]

[0072] In the formula, t is the time point index (t = 1, 2, ..., Mo); S t The value is the sampled value (e.g., temperature) from t minutes ago; μ t This represents the average of the data within a 30-minute window.

[0073] (3) Obtain σ from the denominator expected :

[0074] Call the historical standard deviation σ matched under the current environmental conditions H , σ expected =σ H ;

[0075] (4) Exponential synthesis:

[0076]

[0077] The weights α, β, and γ are dynamically optimized through historical V-value feedback, as detailed below:

[0078] (1) Input data:

[0079] All data points with V≥0.85 over the past 7 days, where each data point includes: C s Historical bias Dr ;

[0080] (2) Calculate intermediate quantities A, B, C, and D:

[0081]

[0082] D = A + B + C;

[0083] In the formula, k is the data point number, and the value range is k = 1, 2, ..., P, where P is the total number of data points with V ≥ 0.85 in the past 7 days;

[0084] (3) Output the new weight α new β new and γ new :

[0085]

[0086] It should be noted that a detailed explanation of each parameter in the model can be found in Table 1.

[0087] Table 1

[0088]

[0089]

[0090]

[0091] It should be noted that external environmental data is obtained by integrating external weather APIs (such as Hefeng Weather) through the ThingsBoard platform to acquire authoritative environmental parameters such as temperature, humidity, CO2 concentration, and light intensity for the greenhouse area. In the event of network failure preventing the acquisition of real-time external data, the system automatically uses the average meteorological parameters for that period (e.g., the last 10 days) as a substitute reference.

[0092] Regarding the model, the following explanation is provided:

[0093] (1) The reliability of sensor data (evaluating the accuracy of data from a specific sensor in the greenhouse, such as sensors for light, temperature, humidity, and CO2 concentration) is dynamically evaluated through a triple cross-validation mechanism of spatial consistency, historical data correction, and real-time fluctuation monitoring.

[0094] Spatial consistency verification (weighted at 60%): Compare the current sensor reading with the mean of neighboring similar sensors. Significant deviations (e.g., difference > 5%) may indicate installation location issues (e.g., obstruction or direct sunlight) or localized malfunctions.

[0095] Historical data correction verification (weighted at 30%): Compare the current reading with a corrected reference value calculated based on historical data and equipment control status (such as ventilation, supplemental lighting). Excessive deviation (e.g., exceeding three standard deviations from the historical fluctuation range) suggests potential sensor drift or calibration failure.

[0096] Real-time fluctuation monitoring (weighted at 10%): Analyzes the fluctuation range of sensor readings within a short time window (e.g., 30 minutes) and compares it with the historical normal fluctuation range under similar environmental conditions. Abnormally drastic fluctuations (e.g., fluctuation range exceeding 2.5 times the expected value) are usually caused by signal interference or sudden hardware failure.

[0097] (2) Validity value (V value) and its uses:

[0098] Definition: The V-value is a comprehensive score that integrates the above three verification results, reflecting the real-time reliability of individual sensor data. The V-value is obtained by weighting and summing the three verification results (spatial consistency score, standardized value of historical correction bias, and abnormal fluctuation index) according to predetermined weights (60%, 30%, and 10%).

[0099] Value range: The target range for V value is [0,1], with higher values ​​indicating more reliable data. Its core weights α, β, and γ are dynamically optimized through historical V value feedback to ensure that when the sensor data is correct, the V value converges to 1, significantly improving the accuracy of reliability assessment.

[0100] Core uses:

[0101] Reliability Quantification: Provides a quantitative reliability score in the range of 0-1, intuitively judging the quality of sensor data. Actuator Control Decision: As the core basis, it drives the cloud platform to implement a four-level management strategy (normal response, degraded operation, locking, isolation, and takeover) for associated actuators.

[0102] Fault Root Cause Location: Abnormal behavior of V-value components (space, history, fluctuation) is directly related to specific fault types (installation defects / drift / interference damage), providing diagnostic basis for alarm codes (W01 / E02 / E03).

[0103] Preferably, the processing strategy is as follows: the cloud platform performs four-level classification of sensor data based on the real-time calculated V value, and dynamically adjusts the actuator control permissions, as follows:

[0104] Level 1: V-value range: V≥0.85; Reliability level: High reliability; Cloud platform action: Data is directly used for decision-making, actuators respond normally, including ventilation: 100% air volume, heating: 100% power, irrigation: water supply on demand, supplemental lighting: full brightness; User alarm description: "Sensor is working normally";

[0105] Level 2: V value range: 0.6≤V<0.85; Reliability level: Medium risk; Cloud platform action: Actuator degradation operation: Ventilation: air volume × 70%, Heating: power × 80%, Irrigation: duration × 50%, Supplemental lighting: brightness × 60%; User alarm description: "Note: Abnormal data fluctuation in XX area [sensor type] (code: W01), it is recommended to check the installation environment or calibration";

[0106] Level 3: V value range: 0.3≤V<0.6; Reliability level: High risk; Cloud platform action: Actuator lockout: Immediately block associated actuators (e.g., shut down irrigation / ventilation / heating / supplementary lighting in this area); User alarm description: "Warning: Data in area XX [sensor type] is severely distorted (code: E02), possibly due to [installation obstruction / physical damage], requiring on-site inspection";

[0107] Level 4: V value range: V<0.3; Reliability level: Failed; Cloud platform action: Full system isolation: Mark sensor offline, use adjacent node monitoring, distance ≤5m; User alarm description: "Urgent: XX area [XX sensor type] has failed (code: E03), please replace the sensor immediately";

[0108] Among them, W01: Spatial consistency deviation C s <0.8→Installation location issue (shading by branches / direct sunlight);

[0109] E02: Historical corrections exceeded limits. →Sensor drift or calibration failure;

[0110] E03: Real-time fluctuation anomaly, D r >2.5→Signal interference or hardware damage.

[0111] Corresponding to the above system, this application also provides a method for controlling the environment of a greenhouse, which is applicable to the aforementioned greenhouse environment control system;

[0112] Each sensor transmits the raw data it collects to the microcontroller, which then transmits the received raw data to the ZigBee terminal node.

[0113] The ZigBee coordinator aggregates data from all ZigBee end nodes and transmits it to the communication module.

[0114] The communication module transmits the received data to the cloud platform;

[0115] The cloud platform stores the received data and calculates the validity value of each sensor data based on the sensor data validity evaluation model. It then generates a processing strategy based on the validity value and transmits the processing strategy to the actuators of various actuator components for execution.

[0116] The sensor data validity evaluation model is defined by the following formula:

[0117]

[0118] In the formula, V is the validity value; α, β, and γ are weights; C s Spatial consistency score; H a To correct for historical reference values; S is the real-time measurement value; σ H D represents the historical standard deviation. r This is an abnormal fluctuation index.

[0119] It should be noted that the descriptions of the model and strategy in the method are based on the system description in this application and will not be repeated here.

[0120] Finally, it should be noted that the above embodiments are merely illustrative and explanatory of the present invention, and are not intended to limit the present invention to the scope of the described embodiments. Furthermore, those skilled in the art will understand that the present invention is not limited to the above embodiments, and many more variations and modifications can be made based on the teachings of the present invention, all of which fall within the scope of protection claimed by the present invention.

Claims

1. A greenhouse environment control system, characterized in that, Includes various sensor components, ZigBee coordinators and communication modules, cloud platforms, and various actuator components; The various sensor components include multiple sensors deployed in key areas within the greenhouse, a microcontroller that works in conjunction with the sensors, and a ZigBee terminal node that works in conjunction with the microcontroller; each sensor transmits the raw data it collects to the microcontroller, and the microcontroller transmits the received raw data to the ZigBee terminal node; The ZigBee coordinator is connected to the ZigBee terminal nodes and the communication module. It is used to aggregate the data of all ZigBee terminal nodes and transmit it to the communication module. The communication module is used to transmit the received data to the cloud platform; The cloud platform is deployed on a cloud server and is used to store the received data and calculate the validity value of each sensor data based on the sensor data validity evaluation model. Based on the validity value, a processing strategy is generated and the processing strategy is transmitted to the actuators of various actuator components for execution. The various actuator components include an actuator, a drive circuit used in conjunction with the actuator, and a ZigBee terminal node used in conjunction with the drive circuit; the ZigBee terminal node controls the on / off state of a relay through the drive circuit, and the relay controls the switch of the actuator.

2. The greenhouse environment control system according to claim 1, characterized in that, The sensor data validity evaluation model is defined by the following formula: In the formula, V is the validity value; α, β, and γ are weights; C s Spatial consistency score; H a To correct for historical reference values; S is the real-time measurement value; σ H D represents the historical standard deviation. r This is an abnormal fluctuation index.

3. The greenhouse environment control system according to claim 2, characterized in that, The C s The calculation formula is as follows: In the formula, R represents the average value of a certain type of sensor in the greenhouse, and R is the measurement range.

4. The greenhouse environment control system according to claim 3, characterized in that, The H a The calculation formula is as follows: H a =H hist +∑ j k j ΔD j In the formula, H hist The average historical temperature inside the greenhouse under the same external environmental conditions; ΔD j Let k be the state change of the j-th type of device; j is the equipment impact coefficient; j is the equipment control type number, with a value range of j = 1, 2, ..., M, where M is the total number of currently effective equipment control types.

5. The greenhouse environment control system according to claim 4, characterized in that, The σ H The calculation formula is as follows: In the formula, μ is the mean of N parameter data, and N is the sample size; S i Let i be the i-th valid historical data point among N parameter data, where i is the index of the historical data point, and its value range is i = 1, 2, ..., N.

6. The greenhouse environment control system according to claim 5, characterized in that, The D r The calculation method is as follows: (1) Data filtering: Take the original sensor data sequence within 30 minutes prior to the current time, with a sampling interval of 1 minute, for a total of Mo = 30 points; (2) Calculation of σ in the numerator real : In the formula, t is the time point index (t = 1, 2, ..., Mo); S t The value is the sampled value (e.g., temperature) from t minutes ago; μ t This represents the average of the data within a 30-minute window. (3) Obtain σ from the denominator expected : Call the historical standard deviation σ matched under the current environmental conditions H , σ expected =σ H ; (4) Exponential synthesis:

7. A greenhouse environment control system according to claim 6, characterized in that, The weights α, β, and γ are dynamically optimized using historical V-value feedback, as detailed below: (1) Input data: All data points with V≥0.85 over the past 7 days, where each data point includes: C s Historical bias D r ; (2) Calculate intermediate quantities A, B, C, and D: D = A + B + C; In the formula, k is the data point number, and the value range is k = 1, 2, ..., P, where P is the total number of data points with V ≥ 0.85 in the past 7 days; (3) Output the new weight α new β new and γ new :

8. The greenhouse environment control system according to claim 7, characterized in that, The processing strategy is as follows: the cloud platform performs four-level classification of sensor data based on the real-time calculated V value and dynamically adjusts the actuator control permissions, as detailed below: Level 1: V value range: V≥0.85; Reliability level: High reliability; Cloud platform action: Data is directly used for decision-making, actuators respond normally, including ventilation: 100% air volume, heating: 100% power, irrigation: water supply on demand, supplemental lighting: full brightness; User alarm description: Sensors are working normally; Level 2: V value range: 0.6≤V<0.85; Reliability level: Medium risk; Cloud platform action: Actuator degradation operation: Ventilation: air volume × 70%, Heating: power × 80%, Irrigation: duration × 50%, Supplemental lighting: brightness × 60%; User alarm description: Note: Abnormal data fluctuation in XX area, XX sensor type, code: W01, it is recommended to check the installation environment or calibration; Level 3: V value range: 0.3≤V<0.6; Reliability level: High risk; Cloud platform action: Actuator lock: Immediately block associated actuator; User alarm description: Warning: Data of XX area and XX sensor type is severely distorted, code: E02, possibly due to installation obstruction / physical damage, requiring on-site inspection; Level 4: V value range: V<0.3; Reliability level: Failed; Cloud platform action: Full system isolation: Mark sensor offline, use adjacent node monitoring, distance ≤5m; User alarm description: Urgent: XX area, XX sensor type has failed, code: E03, please replace the sensor immediately; Wherein, W01: Spatial consistency deviation C s <0.8 → Installation location issue; E02: Historical corrections exceeded limits. E03: Real-time fluctuation anomaly, D r >2.5→Signal interference or hardware damage.

9. A method for controlling the environment of a greenhouse, characterized in that, The method is applicable to the greenhouse environment control system as described in any one of claims 1-8; Each sensor transmits the raw data it collects to the microcontroller, which then transmits the received raw data to the ZigBee terminal node. The ZigBee coordinator aggregates data from all ZigBee end nodes and transmits it to the communication module. The communication module transmits the received data to the cloud platform; The cloud platform stores the received data and calculates the validity value of each sensor data based on the sensor data validity evaluation model. It then generates a processing strategy based on the validity value and transmits the processing strategy to the actuators of various actuator components for execution.

10. A method for controlling the environment of a greenhouse according to claim 9, characterized in that, The sensor data validity evaluation model is defined by the following formula: In the formula, V is the validity value; α, β, and γ are weights; C s Spatial consistency score; H a To correct for historical reference values; S is the real-time measurement value; σ H D represents the historical standard deviation. r This is an abnormal fluctuation index.

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