Big - tent environment multi - parameter coordination and regulation system based on internet of things

Abstract

The application discloses a greenhouse environment multi-parameter collaborative regulation system based on the Internet of Things and belongs to the technical field of agricultural Internet. The system comprises an intention package generation module, a data generation module, a collaborative arbitration module and a protocol execution module. The intention package generation module is used for generating an environment regulation intention package. The data generation module is used for generating control commitment data containing an action plan, an effective time window and a predicted environment impact. The collaborative arbitration module is used for generating an arbitration resolution containing a coordination time sequence. The protocol execution module is used for receiving the arbitration resolution. The trajectory comparison module is used for determining whether to re-trigger the regulation process according to a comparison result. The application introduces an interaction mechanism of the environment regulation intention package and the control commitment data, and utilizes the collaborative arbitration and the trajectory comparison module to perform global optimization and process monitoring, thereby solving the problems of regulation process disorder, temporary environmental imbalance and system secondary oscillation caused by the response delay difference of devices in the prior art.

Description

Technical Field

[0001] This invention relates to the field of agricultural internet technology, and in particular to a multi-parameter collaborative control system for greenhouse environment based on the Internet of Things. Background Technology

[0002] As facility agriculture develops towards precision and intelligence, utilizing Internet of Things (IoT) technology to automatically regulate multiple parameters such as temperature, humidity, and light within greenhouses has become a key means of improving crop yield and quality. Existing typical greenhouse environmental control systems usually collect data through sensor networks, and the central controller calculates based on preset rules or models before sending control commands to actuators such as fans, shading curtains, and wet curtains, thereby achieving automated management of environmental parameters.

[0003] In existing technologies, this centralized, synchronized control mode has significant drawbacks. When making decisions, the system typically issues "simultaneous" or simply sequential commands to multiple actuators, ignoring the inherent response delays of different devices in terms of physical startup, reaching rated power, and generating actual environmental effects. This mismatch between command timing and device dynamic response leads to asynchronous activation of devices during control, easily causing temporary imbalances in environmental parameters. More seriously, the system often misinterprets transient fluctuations in sensor feedback as control failures, triggering a new round of more aggressive compensation commands, ultimately causing frequent start-stop cycles and power oscillations – a "secondary oscillation" problem. This not only wastes energy and accelerates equipment wear and tear but also keeps the greenhouse environment constantly fluctuating, failing to provide a consistently stable and ideal environment for crop growth. Summary of the Invention

[0004] This application provides an IoT-based multi-parameter collaborative control system for greenhouse environments, which solves the problems in the prior art where the collaborative control process is disordered due to differences in equipment response delays, and is prone to causing temporary imbalances in environmental parameters and system oscillations. It achieves orderly collaboration and smooth control of multiple actuators in the time dimension, ensuring a smooth transition of environmental parameters and stable system operation.

[0005] This application provides an IoT-based multi-parameter collaborative control system for greenhouse environment, including: an intent packet generation module: used to acquire environmental parameters collected by sensors, generate an environmental control intent packet based on the difference between the environmental parameters and a preset target value, and send the environmental control intent packet to the IoT gateway; Data generation module: used to receive environmental control intent packets broadcast by IoT gateways, calculate expected contribution, and generate control commitment data including action plans, effective time windows, and predicted environmental impact. Collaborative Arbitration Module: This module performs conflict detection on all received control commitment data in both time and space dimensions, selects the optimal subset of commitments based on the conflict detection results, and generates an arbitration resolution that includes a coordinated timeline. Protocol execution module: Used to receive arbitration decisions and autonomously initiate commitment execution based on the corresponding triggering conditions in the arbitration decisions; Trajectory Comparison Module: This module compares the real-time environmental parameter change trajectory with the comprehensive prediction trajectory generated by superimposing the predicted environmental impact amount in the arbitration decision, and determines whether to re-trigger the control process based on the comparison results.

[0006] Furthermore, the step of generating an environmental control intent package based on the difference between environmental parameters and preset target values ​​includes: Acquire multidimensional real-time environmental data including temperature, humidity, light intensity, and carbon dioxide concentration, and calculate the absolute value of the deviation between the real-time environmental data of each dimension and the preset target value of the corresponding dimension. The absolute value of the deviation is input into the preset crop growth requirement model to determine the control priority of each dimension of environmental parameters and the total allowable time range for control. Based on the control priority and the total time range, determine the maximum allowable fluctuation threshold during the parameter change process, and set coupling constraints between environmental parameters; The aforementioned target values, total time range, maximum permissible fluctuation threshold, and coupling constraints are encapsulated into an environmental control intent package in a structured data format.

[0007] Furthermore, the steps for generating control commitment data that includes action plans, effective time windows, and predicted environmental impacts include: Retrieve its own stored capability model, which records the rated output power and physical range of motion of the actuator under different environmental benchmarks; Based on current mechanical status data and historical execution records, extract the response delay time from receiving the command to producing the physical effect and the rising edge slope to reach the peak effect from the dynamic response feature library. The target values ​​in the environmental control intention package are matched with the self-capability model to calculate the amount of parameter changes that can be completed within the total time range, which is used as the expected contribution. The start time of the action plan is determined based on the response delay time, and the estimated effective time window is calculated by combining the rising edge slope. The expected contribution, action plan, effective time window, and predicted energy consumption for implementing the plan are encapsulated into control commitment data, and this data is uploaded to the collaborative arbitration module.

[0008] Furthermore, the steps for generating an arbitration decision that includes the coordination timeline include: After receiving the control commitment data, a multi-dimensional spatial coordinate system is established with time as the horizontal axis and environmental impact as the vertical axis, and all control commitment data are projected onto the multi-dimensional spatial coordinate system. By traversing the projection results, control commitment data that have opposite effects on the same environmental parameter within the same time segment are identified as logical conflicts. Control commitment combinations that, although acting in the same direction, have an environmental impact exceeding the maximum permissible fluctuation threshold in the intent packet within the same time segment are identified as overshoot conflicts. Control commitment data with logical conflicts and overshoot conflicts are removed, and the remaining control commitment subset is scored according to the preset energy efficiency evaluation function. The optimal commitment subset with the highest score is selected, and the start-up trigger time of each commitment under the global unified clock is calculated in reverse according to the response delay time of each implementing agency to form an arbitration decision.

[0009] Furthermore, the steps for autonomously initiating the execution of commitments include: Synchronize with the global reference clock in the IoT system via a built-in timer; The controlled terminal unit continuously parses the received arbitration resolution and extracts specific control commitment entries that match its own identifier; Before the start-up trigger time is reached, the controlled terminal unit remains silent or maintains its current operating power; When the global reference clock reaches the start-up trigger time, or when the real-time data returned by the sensor meets the trigger conditions in the arbitration decision, the logic controller inside the controlled terminal unit drives the actuator to perform actions according to the action plan in the control commitment data, and monitors the mechanical stroke position data in real time during the action.

[0010] Further steps to determine whether to re-trigger the control process include: Extract the predicted environmental impact quantities committed by each controlled terminal unit in the arbitration decision, and superimpose the quantities linearly or nonlinearly according to the time series to construct a comprehensive prediction trajectory; Using the comprehensive predicted trajectory as the center line, a dynamic envelope interval is established based on the maximum permissible fluctuation threshold in the intent packet; During the execution cycle, real-time environmental parameter data fed back by the sensor is acquired at a preset sampling frequency, and the real-time environmental parameter data points are plotted as actual change trajectories. The system compares the positional relationship between the actual trajectory and the dynamic envelope interval in real time. If the continuous sampling points in the actual trajectory are outside the dynamic envelope interval, or if a mechanical fault signal of the actuator is received from the controlled terminal unit, an anomaly report is generated, the execution of the current arbitration decision is forcibly suspended, and each actuator is controlled to return to the safe preset state.

[0011] Furthermore, the coupling constraint conditions also include: During the execution of temperature control measures, an upper limit is set for the correlation ratio between the rate of change in air humidity and the rate of change in temperature. During the execution of the light control intention, set the environmental vibration parameters generated by the opening and closing of the shading curtain; During carbon dioxide concentration control, a constraint relationship is set between the start / stop of the ventilation pump and indoor air pressure fluctuations; The above constraints are converted into Boolean logic or threshold ranges and encapsulated into the constraint fields of the environmental control intent package.

[0012] Furthermore, the steps for determining the effective time window in the control commitment data include: Obtain the mechanical travel time required for the actuator to perform a specific action, and sum the response delay time with the mechanical travel time to obtain the total time consumed by the actuator to produce an action; Based on the current wind speed and volume environmental parameters inside the greenhouse, calculate the physical lag time required for the environmental parameters to reach dynamic equilibrium; By shifting and superimposing the total time and physical lag time on the timeline, the starting point and ending point of the action plan from physical initiation to substantial change in environmental parameters are determined, thereby defining the effective time window. The effective time window is written into the control commitment data in the form of a timestamp interval, which serves as the logical basis for time alignment by the collaborative arbitration module.

[0013] Furthermore, the steps for calculating the effective time window in the control commitment data include: First, calculate the total mechanical time required for the equipment to function. This includes the response delay time from receiving the instruction to starting the action. And the mechanical travel time required from the start of the action to the completion of the predetermined physical travel. ,Right now: ; Considering environmental response lag time That is, when the equipment acts on the environment, it takes time for the environmental parameters to reach a new equilibrium, which is estimated by a simplified lumped parameter model; Define the effective time window: the starting point of the window. The time when the environmental effects begin, not earlier than the start of the mechanical action, can be counted as... ; End point of window This is the moment when environmental effects basically reach a stable state, which can be counted as... .

[0014] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By introducing an interaction mechanism between intent packets and commitment data, each device can proactively report its action commitments based on its own response characteristics. This allows the system to take into account the differences in the speed at which different devices take effect during the planning stage, thereby avoiding the disconnect between device actions caused by synchronous commands and solving the problem of mismatch between command timing and response latency.

[0015] Furthermore, by conducting conflict detection and time scheduling of all commitments through collaborative arbitration, the environmental effects of each device are smoothly connected on the timeline, preventing temporary environmental imbalances caused by asynchronous effects and making the control process more stable and orderly.

[0016] Furthermore, by comparing actual environmental data with the expected channel based on the commitment, the control is only deemed a failure and the process is restarted when the data deviates continuously. This gives the system tolerance to process fluctuations, preventing frequent equipment start-ups and shutdowns and power oscillations caused by accidental triggering of new instructions due to short-term fluctuations, thus saving energy and extending equipment life. Attached Figure Description

[0017] Figure 1 A schematic diagram of the structure of a multi-parameter collaborative control system for greenhouse environment based on the Internet of Things provided in this application embodiment. Detailed Implementation

[0018] This application provides an IoT-based multi-parameter collaborative control system for greenhouse environments, which solves the problems of disordered control processes, temporary environmental imbalances, and secondary system oscillations caused by the mismatch in dynamic response delays of actuators in the prior art. By introducing an interaction mechanism between environmental control intent packages and control commitment data, and through a collaborative arbitration module for global conflict resolution and time orchestration, it achieves orderly and smooth collaborative control of multiple actuators in the spatiotemporal dimension, ensuring a smooth transition of environmental parameters and stable system operation.

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] like Figure 1 The diagram shown is a schematic of the structure of a multi-parameter collaborative control system for greenhouse environment based on the Internet of Things provided in this application embodiment. It includes: an intent packet generation module: used to acquire environmental parameters collected by sensors, generate an environmental control intent packet based on the difference between the environmental parameters and the preset target value, and send the environmental control intent packet to the Internet of Things gateway; Data generation module: It is used to receive environmental control intention packets broadcast by IoT gateways, calculate the expected contribution based on the internally stored capability model and dynamic response feature library, generate control commitment data including action plan, effective time window and predicted environmental impact, and send the control commitment data to the collaborative arbitration module; Collaborative Arbitration Module: This module performs conflict detection on all received control commitment data in both time and space dimensions, filters the optimal commitment subset based on the conflict detection results, assigns trigger conditions to each control commitment in the optimal commitment subset, and generates an arbitration resolution containing the coordination sequence. Protocol execution module: Used to receive arbitration decisions and autonomously initiate commitment execution based on the corresponding triggering conditions in the arbitration decisions; Trajectory Comparison Module: This module compares the real-time environmental parameter change trajectory with the comprehensive prediction trajectory generated by superimposing the predicted environmental impact amount in the arbitration decision, and determines whether to re-trigger the control process based on the comparison results.

[0021] Furthermore, the step of generating an environmental control intent package based on the difference between environmental parameters and preset target values ​​includes: Acquire multidimensional real-time environmental data including temperature, humidity, light intensity, and carbon dioxide concentration, and calculate the absolute value of the deviation between the real-time environmental data of each dimension and the preset target value of the corresponding dimension. The absolute value of the deviation is input into the preset crop growth requirement model to determine the control priority of each dimension of environmental parameters and the total allowable time range for control. Based on the control priority and the total time range, the maximum allowable fluctuation threshold during the parameter change process is determined, and coupling constraints between environmental parameters are set, wherein the coupling constraints include the tolerance range of the second environmental parameter during the adjustment of the first environmental parameter; The aforementioned target values, total time range, maximum permissible fluctuation threshold, and coupling constraints are encapsulated into an environmental control intent package in a structured data format.

[0022] Furthermore, the steps for generating control commitment data that includes action plans, effective time windows, and predicted environmental impacts include: The controlled terminal unit of the data generation module retrieves its own stored capability model, which records the rated output power and physical action range of the actuator under different environmental benchmarks. Based on the current mechanical state data and historical execution records, the controlled terminal unit extracts from the dynamic response feature library the response delay time of the actuator from receiving the command to producing the physical effect, as well as the rising edge slope of reaching the peak effect; The target values ​​in the environmental control intention package are matched with the self-capability model to calculate the amount of parameter changes that can be completed within the total time range, which is used as the expected contribution. The start time of the action plan is determined based on the response delay time, and the estimated effective time window is calculated by combining the rising edge slope. The expected contribution, action plan, effective time window, and predicted energy consumption for implementing the plan are encapsulated into control commitment data, and this data is uploaded to the collaborative arbitration module.

[0023] Each controlled terminal unit (such as a fan controller) stores a capability model of its driven equipment. This capability model records the equipment's performance parameters under standard operating conditions; for example, for a fan, the model records its nominal airflow at various speed settings. For the sunshade curtain motor, the model recorded the nominal time required to complete the full stroke. .

[0024] Upon receiving an environmental control intent packet, the unit first retrieves its stored dynamic response feature library. This feature library describes the device's dynamic characteristics, with key parameters including response latency. (The time from when the unit receives the instruction to when the device begins physical action), and the effect rise time constant. (Describes the time required for the device output to reach 63.2% of its stable value from the start).

[0025] Next, the unit matches the target in the intent packet with its own capabilities and calculates the expected contribution. For example, a wind turbine unit needs to estimate its contribution to... How much cooling can it contribute in a short period of time? This can be estimated based on a simplified ventilation and cooling model. This model originates from the fundamental principles of fluid mechanics and heat exchange, and the formula can be expressed as: .in, This is the planned operating time of the wind turbine. It refers to the greenhouse volume. It's the outdoor temperature. It is the heat exchange efficiency coefficient. This is the expected contribution.

[0026] Then, the unit determines the effective time window. Window start time. Response delay and effect ramp-up need to be considered, and can be estimated as follows: ( (This is the current time). The window end time is related to the operation duration and total time constraints.

[0027] Finally, the unit will calculate the action plan (such as "fan speed 3") and the effective time window. Predicting environmental impacts (describing the contribution as a function or numerical value, such as...) And the predicted energy consumption is packaged into control commitment data and uploaded. Furthermore, the steps for generating an arbitration decision that includes the coordination timeline include: After receiving the control commitment data, a multi-dimensional spatial coordinate system is established with time as the horizontal axis and environmental impact as the vertical axis, and all control commitment data are projected onto the multi-dimensional spatial coordinate system. By traversing the projection results, control commitment data that have opposite effects on the same environmental parameter within the same time segment are identified as logical conflicts. Control commitment combinations that, although acting in the same direction, have an environmental impact exceeding the maximum permissible fluctuation threshold in the intent packet within the same time segment are identified as overshoot conflicts. Control commitment data with logical conflicts and overshoot conflicts are removed, and the remaining control commitment subset is scored according to the preset energy efficiency evaluation function. The optimal commitment subset with the highest score is selected, and the start-up trigger time of each commitment under the global unified clock is calculated in reverse according to the response delay time of each implementing agency to form an arbitration decision.

[0028] Furthermore, the steps for autonomously initiating the execution of commitments include: The timer built into the controlled terminal unit is synchronized with the global reference clock in the IoT system to ensure that the time base deviation of each controlled terminal unit is less than a preset millisecond threshold. The controlled terminal unit continuously parses the received arbitration resolution, extracts the specific control commitment entry that matches its own identifier, and determines the start-up trigger time or sensor trigger condition specified in the entry. Before the start-up trigger time is reached, the controlled terminal unit remains silent or maintains its current operating power; When the global reference clock reaches the start-up trigger time, or when the real-time data returned by the sensor meets the trigger conditions in the arbitration decision, the logic controller inside the controlled terminal unit drives the actuator to perform actions according to the action plan in the control commitment data, and monitors the mechanical stroke position data in real time during the action.

[0029] Further steps to determine whether to re-trigger the control process include: Extract the predicted environmental impacts committed by each controlled terminal unit in the arbitration decision, and superimpose the impacts linearly or nonlinearly according to the time series to construct a comprehensive prediction trajectory that reflects the expected changes in environmental parameters within the regulation cycle. Using the comprehensive predicted trajectory as the center line, a dynamic envelope interval is established based on the maximum permissible fluctuation threshold in the intent packet; During the execution cycle, real-time environmental parameter data fed back by the sensor is acquired at a preset sampling frequency, and the real-time environmental parameter data points are plotted as actual change trajectories. The system compares the positional relationship between the actual trajectory and the dynamic envelope interval in real time. If the continuous sampling points in the actual trajectory are outside the dynamic envelope interval, or if a mechanical fault signal of the actuator is received from the controlled terminal unit, an anomaly report is generated, the execution of the current arbitration decision is forcibly suspended, and each actuator is controlled to return to the safe preset state.

[0030] Furthermore, the coupling constraint conditions also include: During the execution of temperature control, an upper limit is set for the correlation ratio between the rate of change of air humidity and the rate of change of temperature to prevent the formation of condensation due to excessively rapid cooling. During the execution of the light control intention, the environmental vibration parameters generated by the opening and closing of the sunshade curtain are set to prevent mechanical vibration from interfering with the acquisition frequency of the precision sensor. During carbon dioxide concentration regulation, a constraint relationship between the start / stop of the ventilation pump and indoor air pressure fluctuations is set to ensure that air pressure changes are within the physiologically safe range defined by the crop growth model. The above constraints are converted into Boolean logic or threshold ranges and encapsulated into the constraint fields of the environmental control intent package for subsequent compliance verification by the arbitration module.

[0031] Furthermore, the steps for determining the effective time window in the control commitment data include: Obtain the mechanical travel time required for the actuator to perform a specific action, and sum the response delay time with the mechanical travel time to obtain the total time consumed by the actuator to produce an action; Based on the current wind speed and volume environmental parameters inside the greenhouse, calculate the physical lag time required for the environmental parameters to reach dynamic equilibrium; By shifting and superimposing the total time and physical lag time on the timeline, the starting point and ending point of the action plan from physical initiation to substantial change in environmental parameters are determined, thereby defining the effective time window. The effective time window is written into the control commitment data in the form of a timestamp interval, which serves as the logical basis for time alignment by the collaborative arbitration module.

[0032] Furthermore, the steps for calculating the effective time window in the control commitment data include: First, calculate the total mechanical time required for the equipment to function. This includes the response delay time from receiving the instruction to starting the action. And the mechanical travel time required from the start of the action to the completion of the predetermined physical journey (such as the curtain moving from point A to point B). ,Right now: ; Considering environmental response lag time This refers to the time required for environmental parameters (such as temperature) to reach a new equilibrium after equipment acts on the environment (e.g., a fan agitates the air), which is estimated using a simplified lumped parameter model. For example, for ventilation, this can be estimated based on the greenhouse volume. (m) 3 ) and fan air volume (m) 3 / s), using the concept of air exchange rate to estimate mixing time: This formula is based on the fundamental principles of fluid mixing, and the factor 2 is an empirical coefficient representing the time required to achieve basic homogeneity.

[0033] Define the effective time window: the starting point of the window. The time when the environmental effects begin, not earlier than the start of the mechanical action, can be counted as... ; End point of window This is the moment when the environmental effects basically reach stability (i.e., the mechanical actions are completed and environmental mixing is basically complete), which can be counted as... ; This is by The defined intervals, precisely written into the control commitment data, are the key basis for the arbitration module to perform time alignment and conflict judgment.

[0034] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0036] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0037] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0038] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0039] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-parameter collaborative control system for greenhouse environment based on the Internet of Things, characterized in that: include: Intent packet generation module: used to acquire environmental parameters collected by sensors, generate environmental control intent packets based on the difference between environmental parameters and preset target values, and send the environmental control intent packets to the IoT gateway; Data generation module: used to receive environmental control intent packets broadcast by IoT gateways, calculate expected contribution, and generate control commitment data including action plans, effective time windows, and predicted environmental impact. Collaborative Arbitration Module: This module performs conflict detection on all received control commitment data in both time and space dimensions, selects the optimal subset of commitments based on the conflict detection results, and generates an arbitration resolution that includes a coordinated timeline. Protocol execution module: Used to receive arbitration decisions and autonomously initiate commitment execution based on the corresponding triggering conditions in the arbitration decisions; Trajectory Comparison Module: This module compares the real-time environmental parameter change trajectory with the comprehensive prediction trajectory generated by superimposing the predicted environmental impact amount in the arbitration decision, and determines whether to re-trigger the control process based on the comparison results.

2. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 1, characterized in that, The steps for generating an environmental control intent package based on the difference between environmental parameters and preset target values ​​include: Acquire multidimensional real-time environmental data including temperature, humidity, light intensity, and carbon dioxide concentration, and calculate the absolute value of the deviation between the real-time environmental data of each dimension and the preset target value of the corresponding dimension. The absolute value of the deviation is input into the preset crop growth requirement model to determine the control priority of each dimension of environmental parameters and the total allowable time range for control. Based on the control priority and the total time range, determine the maximum allowable fluctuation threshold during the parameter change process, and set coupling constraints between environmental parameters; The aforementioned target values, total time range, maximum permissible fluctuation threshold, and coupling constraints are encapsulated into an environmental control intent package in a structured data format.

3. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 1, characterized in that, The steps for generating control commitment data that includes action plans, effective time windows, and predicted environmental impacts include: Retrieve its own stored capability model, which records the rated output power and physical range of motion of the actuator under different environmental benchmarks; Based on current mechanical status data and historical execution records, extract the response delay time from receiving the command to producing the physical effect and the rising edge slope to reach the peak effect from the dynamic response feature library. The target values ​​in the environmental control intention package are matched with the self-capability model to calculate the amount of parameter changes that can be completed within the total time range, which is used as the expected contribution. The start time of the action plan is determined based on the response delay time, and the estimated effective time window is calculated by combining the rising edge slope. The expected contribution, action plan, effective time window, and predicted energy consumption for implementing the plan are encapsulated into control commitment data, and this data is uploaded to the collaborative arbitration module.

4. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 1, characterized in that, The steps for generating an arbitration decision that includes the coordination timeline include: After receiving the control commitment data, a multi-dimensional spatial coordinate system is established with time as the horizontal axis and environmental impact as the vertical axis, and all control commitment data are projected onto the multi-dimensional spatial coordinate system. By traversing the projection results, control commitment data that have opposite effects on the same environmental parameter within the same time segment are identified as logical conflicts. Control commitment combinations that, although acting in the same direction, have an environmental impact exceeding the maximum permissible fluctuation threshold in the intent packet within the same time segment are identified as overshoot conflicts. Control commitment data with logical conflicts and overshoot conflicts are removed, and the remaining control commitment subset is scored according to the preset energy efficiency evaluation function. The optimal commitment subset with the highest score is selected, and the start-up trigger time of each commitment under the global unified clock is calculated in reverse according to the response delay time of each implementing agency to form an arbitration decision.

5. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 1, characterized in that, The steps to autonomously initiate the execution of a commitment include: Synchronize with the global reference clock in the IoT system via a built-in timer; The controlled terminal unit continuously parses the received arbitration resolution and extracts specific control commitment entries that match its own identifier; Before the start-up trigger time is reached, the controlled terminal unit remains silent or maintains its current operating power; When the global reference clock reaches the start-up trigger time, or when the real-time data returned by the sensor meets the trigger conditions in the arbitration decision, the logic controller inside the controlled terminal unit drives the actuator to perform actions according to the action plan in the control commitment data, and monitors the mechanical stroke position data in real time during the action.

6. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 1, characterized in that, The steps to determine whether to re-trigger the control process include: Extract the predicted environmental impact quantities committed by each controlled terminal unit in the arbitration decision, and superimpose the quantities linearly or nonlinearly according to the time series to construct a comprehensive prediction trajectory; Using the comprehensive predicted trajectory as the center line, a dynamic envelope interval is established based on the maximum permissible fluctuation threshold in the intent packet; During the execution cycle, real-time environmental parameter data fed back by the sensor is acquired at a preset sampling frequency, and the real-time environmental parameter data points are plotted as actual change trajectories. The system compares the positional relationship between the actual trajectory and the dynamic envelope interval in real time. If the continuous sampling points in the actual trajectory are outside the dynamic envelope interval, or if a mechanical fault signal of the actuator is received from the controlled terminal unit, an anomaly report is generated, the execution of the current arbitration decision is forcibly suspended, and each actuator is controlled to return to the safe preset state.

7. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 2, characterized in that, The coupling constraint conditions also include: During the execution of temperature control measures, an upper limit is set for the correlation ratio between the rate of change in air humidity and the rate of change in temperature. During the execution of the light control intention, set the environmental vibration parameters generated by the opening and closing of the shading curtain; During carbon dioxide concentration control, a constraint relationship is set between the start / stop of the ventilation pump and indoor air pressure fluctuations; The above constraints are converted into Boolean logic or threshold ranges and encapsulated into the constraint fields of the environmental control intent package.

8. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 1, characterized in that, The steps for determining the effective time window in the control commitment data include: Obtain the mechanical travel time required for the actuator to perform a specific action, and sum the response delay time with the mechanical travel time to obtain the total time consumed by the actuator to produce an action; Based on the current wind speed and volume environmental parameters inside the greenhouse, calculate the physical lag time required for the environmental parameters to reach dynamic equilibrium; By shifting and superimposing the total time and physical lag time on the timeline, the starting point and ending point of the action plan from physical initiation to substantial change in environmental parameters are determined, thereby defining the effective time window. The effective time window is written into the control commitment data in the form of a timestamp interval, which serves as the logical basis for time alignment by the collaborative arbitration module.

9. The IoT-based multi-parameter collaborative control system for greenhouse environment as described in claim 8, characterized in that, The steps for calculating the effective time window in the control commitment data include: First, calculate the total mechanical time required for the equipment to function. This includes the response delay time from receiving the instruction to starting the action. And the mechanical travel time required from the start of the action to the completion of the predetermined physical travel. ,Right now: ； Considering environmental response lag time That is, when the equipment acts on the environment, it takes time for the environmental parameters to reach a new equilibrium, which is estimated by a simplified lumped parameter model; Define the effective time window: the starting point of the window. The time when the environmental effects begin, not earlier than the start of the mechanical action, can be counted as... ; End point of window This is the moment when environmental effects basically reach a stable state, which can be counted as... .

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