Energy autonomous agricultural internet of things discontinuous transmission control method and system

Abstract

The present application relates to the field of agricultural internet of things control, and provides an energy autonomous agricultural internet of things discontinuous transmission control method and system, the method comprising: obtaining real-time available energy based on battery sampling data, introducing an environmental geometric blocking coefficient and a historical light power curve to generate a predicted energy yield value, and normalizing to obtain an energy autonomy index; performing multi-stage hysteresis comparison to generate a network working parameter set, encapsulating as a collaborative scheduling instruction package and generating sensor aggregation data; monitoring a double-channel orthogonal pulse signal and a direct current bus voltage to generate an energy feedback identifier, generating a burst transmission instruction and outputting cloud reporting data flow; extracting the actual measured temperature in the shed and combining the target set temperature, calculating the theoretical film target stroke through incremental PID, and generating a motor drive PWM signal according to the energy safety coefficient. The present application combines multi-dimensional energy autonomous perception and mechanical-information orthogonal coupling strategy, and constructs an agricultural internet of things discontinuous transmission and adaptive environmental regulation closed-loop mechanism.

Description

Technical Field

[0001] This invention relates to the field of agricultural Internet of Things (IoT) control, and in particular to a method and system for controlling discontinuous transmission in agricultural IoT based on energy autonomy. Background Technology

[0002] With the deep integration of smart agriculture IoT and distributed photovoltaic technology, facility agriculture is gradually evolving from a traditional grid-dependent power supply architecture to an off-grid photovoltaic energy autonomous architecture. In complex greenhouse environments lacking grid support, there is a common problem of insufficient energy state coordination and intensified competition for communication resources between environmental sensing devices and actuators, easily leading to excessive communication power consumption, data link interruptions, and remote closed-loop control failures. How to achieve adaptive discontinuous transmission and multi-terminal collaborative control based on energy state under limited energy conditions has become a key technical challenge that urgently needs to be solved in the evolution of agricultural IoT systems towards low power consumption and high reliability.

[0003] Chinese patent CN220983745U discloses an IoT-based agricultural greenhouse environmental monitoring system. The system includes a data acquisition device, a terminal controller, a coordinator, a host computer, and a control system. The data acquisition device includes a gas concentration sensor, a temperature sensor, a soil moisture sensor, a light intensity sensor, and a pressure sensor. The coordinator is connected to a buzzer and an LED alarm light. This IoT-based agricultural greenhouse environmental monitoring system utilizes sensors to accurately collect environmental parameters such as soil moisture, temperature, air pressure, light intensity, and gas concentration in the agricultural greenhouse, achieving wireless and automated monitoring of crops.

[0004] However, current technology still faces many challenges. In the off-grid photovoltaic power supply architecture of smart agricultural greenhouses, environmental sensing terminals, such as temperature, humidity, and soil sensors, and actuators, such as solar film rolling devices, often operate in a discrete mode with uncoordinated energy states. To achieve real-time data transmission, environmental sensing terminals typically employ fixed high-frequency wireless communication strategies, ignoring their own battery storage limits and the fluctuations in current sunlight supply. When encountering continuous rainy days or low nighttime temperatures leading to reduced battery activity and energy scarcity, if the environmental sensing terminal continues to force high-frequency data transmission, the battery voltage may drop below the cutoff threshold due to excessive transient transmission power consumption, causing unexpected equipment shutdown. Once the environmental sensing terminal loses its environmental sensing capability due to energy overload, the greenhouse environmental control system will lose a critical feedback data source, unable to monitor abnormal temperature and humidity inside the greenhouse in real time. This will prevent the film rolling device from performing ventilation or insulation actions in a timely manner, potentially affecting crop growth. Summary of the Invention

[0005] To achieve the above objectives, this invention provides a discontinuous transmission control method for agricultural Internet of Things based on energy autonomy, the specific technical solution of which is as follows:

[0006] Extended Kalman filter iterative operation is performed on the collected battery terminal physical sampling data to obtain the real-time available energy value. An environmental geometric shading coefficient is introduced to perform time-domain integration operation on the historical illumination power curve to generate the predicted energy gain value. Based on the calculated mechanical rigidity task energy consumption, the real-time available energy value and the predicted energy gain value are normalized to generate the energy autonomy index.

[0007] Multi-level hysteresis comparison logic is performed on the energy autonomy index to generate a set of network operating parameters. The set of network operating parameters is mapped and encapsulated into a cooperative scheduling instruction packet and physical broadcast is performed. In response to the cooperative scheduling instruction packet, the hardware state is reconstructed to generate sensor aggregated data.

[0008] The system monitors dual-channel quadrature pulse signals and DC bus voltage to generate an energy feedback status indicator. It combines the energy autonomy index and the data buffer queue length based on the sensor aggregated data to quantify the real-time transmission cost. The real-time transmission cost is compared with the preset communication trigger threshold to generate a discontinuous burst transmission enable command. The system outputs a cloud-reported data stream based on the energy feedback status indicator.

[0009] The system analyzes the data stream reported from the cloud to extract the measured temperature inside the greenhouse. It then uses a discrete incremental PID algorithm to generate the theoretical target travel of the film winding process, combined with the preset target temperature. Finally, it outputs the actual film winding travel based on the energy safety factor established by the energy autonomy index. Based on the actual film winding travel, it generates a motor drive PWM signal.

[0010] Furthermore, the method for generating the energy autonomy index includes:

[0011] The battery terminal voltage and charging / discharging current are collected synchronously to obtain physical sampling data of the battery terminal. The extended Kalman filter algorithm is used to iteratively calculate the physical sampling data of the battery terminal to obtain a smooth state of charge ratio value. Based on the Coulomb integral principle, the smooth state of charge ratio value is mapped and converted into a real-time available energy value.

[0012] The current moment and historical illumination power curves are retrieved, and the ambient geometric shading coefficient is introduced to perform amplitude correction and weighted attenuation processing on the historical illumination power curves to obtain the effective power prediction value. Then, the effective power prediction value is integrated in the time domain to generate the predicted energy gain value.

[0013] The system retrieves preset mechanical task parameters to calculate the energy consumption of rigid mechanical tasks. It then combines the real-time available energy value, the predicted energy gain value, and the preset average working voltage of the battery pack to obtain the total available energy budget. A defined minimum survival power consumption is introduced to constrain and correct the total available energy budget in order to establish effective energy reserves. The effective energy reserves and the energy consumption of rigid mechanical tasks are normalized to generate an energy autonomy index.

[0014] Furthermore, the method for generating the aggregated sensor data includes:

[0015] Based on the energy autonomy index, a multi-level hysteresis comparison logic is used to determine the network cooperation mode identifier. Based on the network cooperation mode identifier, the reference sampling period is calculated through a nonlinear adaptive mapping function. The network cooperation mode identifier and the reference sampling period are encapsulated into a set of network working parameters.

[0016] The network operating parameter set is parsed, and the logical control parameters of the network operating parameter set are mapped to communication physical frames. The communication physical frames include multicast target identifiers, global synchronization wake-up time, acquisition frequency control words, differential compression ratio instructions, and mode status bits. The communication physical frames are encapsulated into cooperative scheduling instruction packets and physically broadcast through radio frequency units.

[0017] The system parses the collaborative scheduling instruction packet, uses the global synchronization wake-up time to calibrate the local clock, resets the trigger cycle of the hardware timer interrupt controller according to the acquisition frequency control word, and executes the local differential encoding algorithm in response to the differential compression ratio instruction to generate sensor aggregated data.

[0018] Furthermore, the step of using multi-level hysteresis comparison logic to determine the network cooperative mode identifier includes:

[0019] Read the preset saturation threshold and critical threshold, which respectively define the control state boundaries for the agricultural Internet of Things system to enter the unrestricted operation state and the minimum maintenance state;

[0020] Obtain the real-time calculated energy autonomy index and compare it numerically with the saturation threshold and critical threshold respectively;

[0021] If the energy autonomy index is greater than or equal to the saturation threshold, the solar film rolling device is determined to be in an energy-sufficient state, and the network collaboration mode identifier is set to full-load transparent transmission mode.

[0022] If the energy autonomy index is between the critical threshold and the saturation threshold, the solar film rolling device is determined to be in an energy balance adjustment state, and the network cooperative mode identifier is set to adaptive adjustment mode.

[0023] If the energy autonomy index is less than or equal to the critical threshold, the solar film rolling device is determined to be in an energy-deficient state, and the network collaboration mode identifier is set to sleep maintenance mode.

[0024] Furthermore, the method for outputting the cloud-reported data stream includes:

[0025] Based on the collected dual-channel quadrature pulse signals, the real-time rotation direction and real-time rotational angular velocity of the DC geared motor are analyzed, and the DC bus voltage is monitored synchronously. When the real-time rotation direction, real-time rotational angular velocity, and DC bus voltage simultaneously meet the preset feedback threshold conditions, an energy feedback status indicator is generated.

[0026] The feedback threshold conditions include simultaneously satisfying both kinematic state judgment conditions and electrical state judgment conditions; the kinematic state judgment condition is that the real-time rotation direction is consistent with the preset film unfolding operation direction, and the real-time rotation angular velocity exceeds the preset feedback speed threshold; the electrical state judgment condition is that the DC bus voltage is greater than the sum of the current open-circuit voltage of the lithium battery pack and the preset anti-interference hysteresis voltage threshold.

[0027] Retrieve the energy feedback status flag and energy autonomy index, analyze the data cache queue length based on the sensor aggregated data, construct a multi-dimensional transmission cost function for quantitative calculation to generate real-time transmission cost;

[0028] The system compares the real-time transmission cost with the preset communication trigger threshold to generate a discontinuous burst transmission enable command. It identifies the energy supply condition based on the energy feedback status identifier to execute differentiated power supply and transmission strategies. It also encapsulates the aggregated sensor data into a cloud-reported data stream.

[0029] Furthermore, the method for analyzing the real-time rotation direction and real-time rotational angular velocity of the DC geared motor includes:

[0030] The dual-channel quadrature pulse signal originating from the DC geared motor is acquired through the signal acquisition interface. The dual-channel quadrature pulse signal includes an A-phase pulse and a B-phase pulse with a phase difference of 90 degrees.

[0031] The timing phase relationship between the A-phase pulse and the B-phase pulse is analyzed by an orthogonal decoding algorithm to determine the real-time rotation direction of the DC geared motor.

[0032] The signal frequency of the dual-channel orthogonal pulse signal is calculated using the pulse frequency calculation method, and the real-time rotational angular velocity of the DC geared motor is calculated based on the preset number of magnetic pole pairs and reduction ratio.

[0033] The specific execution logic of the inversion solution includes: calculating the signal frequency and the circumference constant. The product of the number of magnetic pole pairs and the reduction ratio is used to obtain the electrical angular frequency, and the electrical angular frequency is divided by the product of the number of magnetic pole pairs and the reduction ratio to obtain the real-time rotational angular velocity.

[0034] Furthermore, the method for implementing the differentiated power supply and transmission strategy includes:

[0035] In response to discontinuous burst transmission enable commands, the logic state of the energy feedback status flag is detected to perform power supply loop switching and transmission control;

[0036] Before performing the power supply circuit switching, a direct power supply path from the DC bus to the power input terminal of the broadband wireless radio frequency transmission unit and a regulated power supply path from the lithium battery pack to the power input terminal of the broadband wireless radio frequency transmission unit are pre-constructed.

[0037] If the energy feedback status flag is detected as a valid high-level state 1, it is determined that the solar film rolling device is in dynamic potential energy feedback mode, and a bypass switching command is issued to conduct the direct power supply energy path. The back electromotive force generated by the DC geared motor drives the broadband wireless radio frequency transmission unit to perform full-speed burst transmission of the sensor aggregated data.

[0038] If the energy feedback status flag is detected as invalid low level 0, it is determined that the solar film roll device is in a static energy storage consumption state. A battery power supply command is issued to conduct the regulated power supply path, consume the chemical energy storage of the lithium battery pack to activate the broadband wireless radio frequency transmission unit to send sensor aggregated data and reset the data buffer queue length.

[0039] Furthermore, the method for generating the motor drive PWM signal includes:

[0040] The data stream reported from the cloud is analyzed to extract the measured temperature inside the greenhouse. The temperature control error between the measured temperature inside the greenhouse and the preset target temperature is calculated. The theoretical film rolling adjustment increment is solved using a discretized incremental PID algorithm. The theoretical film rolling target stroke stored in the local control register at the previous sampling time is combined to generate the theoretical film rolling target stroke at the current sampling time.

[0041] The energy safety factor is calculated based on the energy autonomy index using the energy safety factor function. The energy safety factor is then used to correct the theoretical film winding target stroke at the current sampling time to generate the actual film winding execution stroke.

[0042] The target pulse count value is determined based on the actual film winding stroke. Algebraic difference calculation is performed in combination with the current cumulative pulse value collected in real time to generate the position control deviation. The position control deviation is used to generate the motor drive PWM signal, and the real-time phase current is collected synchronously to estimate the real-time load torque.

[0043] Furthermore, the method for calculating the theoretical film adjustment increment includes:

[0044] Obtain the temperature control error at the current sampling time, the temperature control error at the previous sampling time, and the temperature control error at the sampling time before that.

[0045] Calculate the difference between the temperature control error at the current sampling time and the temperature control error at the previous sampling time, and multiply the difference by a preset proportional gain coefficient to generate a proportional term;

[0046] Multiply the temperature control error at the current sampling time by the preset integral time constant to generate the integral term;

[0047] Calculate the second-order difference consisting of the temperature control error at the current sampling time, the temperature control error at the previous sampling time, and the temperature control error at the time before that sampling time, and multiply the second-order difference by a preset differential time constant to generate a differential term;

[0048] The proportional, integral, and differential terms are algebraically summed to generate the theoretical film adjustment increment that drives the DC geared motor to perform relative displacement.

[0049] An agricultural IoT discontinuous transmission control system based on energy autonomy, the system comprising an energy autonomy module, a collaborative scheduling module, a transmission control module, and an environmental regulation module;

[0050] The energy autonomy module is used to perform extended Kalman filter iterative operation on the collected battery terminal physical sampling data to obtain the real-time available energy value, introduce the environmental geometric shading coefficient to perform time-domain integration operation on the historical light power curve to generate the predicted energy gain value, and perform normalization processing on the real-time available energy value and the predicted energy gain value based on the solved mechanical rigidity task energy consumption to generate the energy autonomy index.

[0051] The collaborative scheduling module is used to perform multi-level hysteresis comparison logic on the energy autonomy index to generate a network working parameter set, map and encapsulate the network working parameter set into a collaborative scheduling instruction package and perform physical broadcast, and reconstruct the hardware state in response to the collaborative scheduling instruction package to generate sensor aggregated data.

[0052] The transmission control module is used to monitor dual-channel quadrature pulse signals and DC bus voltage to generate an energy feedback status indicator, combine the energy autonomy index and the data buffer queue length based on the sensor aggregated data to quantify the real-time transmission cost, compare the real-time transmission cost with the preset communication trigger threshold to generate a discontinuous burst transmission enable command, and output the cloud reporting data stream based on the energy feedback status indicator.

[0053] The environmental control module is used to parse the data stream reported from the cloud to extract the measured temperature inside the shed, combine the preset target temperature with the discrete incremental PID algorithm to generate the theoretical film rolling target stroke, combine the energy safety factor established based on the energy autonomy index to output the actual film rolling execution stroke, and generate the motor drive PWM signal according to the actual film rolling execution stroke.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] This invention corrects the amplitude by introducing an environmental geometric shading coefficient and normalizes the real-time available energy value and predicted energy gain value of the battery based on the energy consumption of mechanical rigid tasks. This avoids the problem of the distortion in the evaluation of mechanical driving capability caused by a single voltage or remaining power percentage index under variable load conditions, and achieves quantitative alignment between the physical state of the energy supply side and the rigid tasks of the mechanical demand side.

[0056] This invention maps the energy autonomy index into a coordinated scheduling instruction package containing global synchronization timing and acquisition control parameters. This package drives environmental sensing devices to perform hardware clock calibration and physical modulation of the acquisition frequency in response to the instruction package. This achieves strict coordination between distributed sensing nodes and edge aggregation gateways in the time domain and energy status, eliminating idle listening energy consumption caused by clock drift and energy islanding effects caused by non-coordination of communication and energy status.

[0057] This invention identifies the gravitational potential energy feedback interval of a DC geared motor as a strong excitation factor and introduces a multidimensional transmission cost function to achieve time-domain orthogonal coupling between mechanical transient energy flow and information bit flow. It utilizes regenerated electrical energy to directly drive the wireless communication module to perform discontinuous burst transmission, effectively eliminating the impact and dependence of communication transient peak power consumption on the limited battery chemical energy storage.

[0058] This invention utilizes an energy autonomy index to construct an energy safety factor function, and applies nonlinear constraints to truncate the theoretical film rolling target stroke generated based on the PID algorithm. This achieves adaptive decoupling between greenhouse environment control requirements and equipment energy survivability, effectively avoiding the risks of battery over-discharge and actuator stagnation caused by blindly performing large-scale mechanical movements under energy-deficient conditions. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1This is a flowchart illustrating the principle of the non-continuous transmission control method for agricultural Internet of Things based on energy autonomy according to the present invention.

[0061] Figure 2 This is a schematic diagram of the lighting principle based on environmental geometric occlusion of the present invention;

[0062] Figure 3 This is a flowchart of the energy feedback state determination process based on the fusion of mechanical motion vectors and electrical characteristics of the present invention;

[0063] Figure 4 This is a functional block diagram of the non-continuous transmission control system for agricultural Internet of Things based on energy autonomy of the present invention. Detailed Implementation

[0064] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Example 1:

[0066] Please see Figure 1 As shown, this embodiment provides a discontinuous transmission control method for agricultural IoT based on energy autonomy, including:

[0067] Step S1000: Collect physical sampling data from the battery terminal. Perform extended Kalman filter iterative operations to obtain the real-time available energy value. Introducing the environmental geometric shading coefficient to influence historical illumination power curves Perform time-domain integration to generate predicted energy gain values. And based on the calculated mechanical rigidity task energy consumption Real-time available energy value and predicted energy gain value Perform normalization to generate the energy autonomy index. .

[0068] Specifically, this step aims to use the solar roll-up device as a dynamic observation object for energy autonomous nodes, and to collect physical sampling data from its battery terminals. and historical illumination power curves in environmental dimensions As a multidimensional state input, the battery terminal physical sampling data Includes battery terminal voltage and charging / discharging current By employing extended Kalman filter recursive computation and a sliding time window prediction algorithm, the nonlinear electrochemical energy storage state, i.e., the real-time available energy value, is determined. And dynamic photovoltaic energy input, i.e., predicted energy return value. This is mapped to a dimensionless control parameter for sustainable task execution, namely the energy autonomy index. This enables the quantitative alignment of the physical state on the energy supply side and the rigid tasks on the mechanical demand side at the source of control, providing real-time decision variables for subsequent discontinuous transmission control.

[0069] Further, step S1000 includes:

[0070] Step S1100: Synchronously acquire battery terminal voltage and charging and discharging current Obtain physical sampling data at the battery end The extended Kalman filter algorithm is used to sample the physical data at the battery terminal. Iterative calculations are performed to obtain the smoothed state-of-charge ratio. And based on the Coulomb integral principle, the smoothed state-of-charge ratio value is... Mapping to real-time available energy values .

[0071] Specifically, this step aims to collect physical sampling data from the battery terminals of solar film roll-up devices in the physical world, which exhibit nonlinear fluctuations and are accompanied by strong electromagnetic interference under mechanical loads. As a dynamic observation object, the physical sampling data at the battery terminal... Specifically, this includes the battery terminal voltage. and charging / discharging current By utilizing the recursive state estimation logic of time update prediction and measurement update correction in the Extended Kalman Filter (EKF) algorithm, the transient voltage drop caused by the surge current and internal resistance voltage division effect due to the sudden change in mechanical load at the moment of motor start-up is mapped to a real-time available energy value characterizing the actual chemical energy storage inside the battery. This achieves orthogonal decoupling between electrochemical energy state signals and mechanical motion noise signals at the data acquisition source.

[0072] In the specific implementation process, this step establishes a physical data acquisition channel through the battery management unit built into the solar film rolling device, and defines the raw signal acquired by this channel as the physical sampling data at the battery end. In response to the physical phenomenon of non-chemical transient drops in voltage readings caused by the drastic fluctuations in load current generated by the film-rolling motor during startup and operation due to the work done to overcome the resistance of the greenhouse film rolling, this step does not directly use the original voltage reading as the decision basis, but instead executes continuous signal processing logic.

[0073] The specific execution logic of the continuous signal processing logic is as follows: First, at a preset sampling frequency... The battery management unit synchronously collects the battery terminal voltage of the controlled lithium battery pack. and charging / discharging current This allows for the acquisition of discrete physical sampling data at the battery terminal. Subsequently, a system state-space equation based on the second-order RC equivalent circuit of the battery is constructed, and the collected charging and discharging currents are used to... The battery terminal voltage is used as the excitation input variable driving the state-space equations of the system. This serves as the observation vector used to calibrate the system's state-space equations. Based on this, an extended Kalman filter algorithm is used for iterative computation. In the time update prediction stage, the prior state at the current time is predicted based on the state estimate from the previous time step, and the theoretical predicted terminal voltage is calculated using the system's state-space equations. In the measurement update correction stage, the actually acquired observation voltage, i.e., the battery terminal voltage, is calculated. The numerical residual between the predicted terminal voltage and the estimated terminal voltage is used to dynamically calculate the Kalman gain by combining the minimization criterion of the estimation error covariance matrix. This Kalman gain is then used to weight and correct the prior state, thereby eliminating measurement noise and motor operation interference, and estimating in real time the smoothed state-of-charge ratio that converges to the true chemical state inside the battery. Finally, based on the Coulomb integral principle, the dimensionless smooth state-of-charge ratio is... Mapping into real-time usable energy values ​​with specific physical dimensions .

[0074] The real-time available energy value The specific mapping and conversion logic is as follows: First, read the nominal rated capacity pre-stored in the non-linear storage unit built into the solar film rolling device. As a full-range reference, the smoothed state-of-charge ratio value output by the extended Kalman filter algorithm at the start of the integration in this continuous integration cycle is obtained. This is established as the starting anchor point for the evolution of the current state within this continuous integration cycle. Subsequently, integration of the physical sampled data is initiated, and within the dynamic time window formed by the start of integration and the current calculation time, the collected charging and discharging currents are processed. The instantaneous value of the integral variable at each moment, i.e., the real-time current sample value, is continuously integrated over time to calculate the cumulative charge consumption flowing through the battery circuit within the dynamic time window. During the integration process, a coulomb efficiency coefficient is introduced as a correction factor; the real-time current sample value is multiplied by the coulomb efficiency coefficient and then divided by the nominal rated capacity. This process transforms the physical current fluctuation into a circuit consumption rate relative to the total battery capacity, eliminating energy measurement errors caused by battery internal resistance heating or chemical side reactions. Finally, the corrected cumulative circuit consumption is subtracted from the initial anchor point to obtain the current remaining capacity percentage, which is then remapped back to physical dimensions by multiplying it by the nominal rated capacity. This allows us to calculate the control reference variable that quantifies the solar film winding equipment's ability to perform film winding and data transmission tasks at the current moment, namely, the real-time available energy value. The nominal rated capacity It is the factory calibration constant stored in the control board of the solar film rolling equipment; the coulombic efficiency coefficient is a dimensionless environmental adaptive correction parameter, whose value is obtained by dynamically looking up a table based on the current ambient temperature, and is used to compensate for the influence of the difference in electrochemical reaction efficiency under low temperature or high temperature environment on the integration accuracy.

[0075] Step S1200: Retrieve the current time and historical illumination power curves. Introducing the environmental geometric shading coefficient to influence historical illumination power curves Amplitude correction and weighted attenuation are performed to obtain the effective power prediction value, and time-domain integration is performed on the effective power prediction value to generate the predicted energy gain value. .

[0076] Specifically, this step aims to identify the volatility and uncontrollability of external light resources in agricultural greenhouses as key environmental variables affecting the system's energy balance. A sliding time window prediction algorithm based on geometric shading constraints of the greenhouse environment is then used to predict these variables, using the main control unit's real-time clock and historical light power curves. The calculated time-varying trajectory of the baseline irradiance, which changes dynamically over time, is mapped to the length of the prediction time window. The predicted energy gain value can be locked in advance and incorporated into the planning. This process constructs an energy prediction and control mechanism based on environmental forecasting in the control loop, transforming future photovoltaic power generation potential into a virtual energy budget that can be called upon at the present moment. This overcomes the lag caused by traditional feedback control relying solely on the current battery capacity, and provides a safety boundary for overdraft transmission under low power conditions.

[0077] In the specific implementation process, in order to achieve dynamic matching between the predicted state of energy supply and the load planning of mechanical demand, this step does not monitor the current instantaneous charging power in isolation, but executes an energy prediction and control mechanism based on environmental prediction.

[0078] The specific execution logic of the energy prediction and control mechanism is as follows: First, read the real-time clock of the main control unit of the solar film rolling device, i.e., the current time, and retrieve the historical solar power curve from the local memory of the main control unit. The historical illumination power curve is mentioned above. The standard benchmark trend of photovoltaic power generation modules of solar roll film equipment under ideal unshaded conditions as the sunshine duration progresses.

[0079] Subsequently, a prediction time window length covering the next critical action cycle is set. The key action cycle encompasses either the sensor wake-up acquisition cycle or the film winding mechanical action cycle, establishing a dynamic time interval extending from the current moment to the predicted cutoff moment. The predicted cutoff moment is defined by the current moment and the length of the predicted time window. The superposition time. Within this dynamic time interval, based on the current weather trend weight, such as the cloud cover factor, on the historical solar power curve. Amplitude correction is performed, and then the corrected power value is divided by the effective light-receiving area of ​​the photovoltaic module. This allows for the calculation of the dynamically changing light power density per unit area over time, i.e., the reference irradiance. The effective light-receiving area of ​​the photovoltaic power generation module is... It is a system constant of the physical dimension of photovoltaic power generation module. Its value is the net light-sensitive area after deducting the frame and busbar areas from the total surface area of ​​the photovoltaic power generation module. It is used to map the reference irradiance per unit area to the total radiative flux acting on the entire surface of the photovoltaic power generation module.

[0080] Considering that the unique steel frame structure of greenhouses can cause periodic physical shading of the photovoltaic power generation modules fixed to the greenhouse roof, which varies with the angle of sunlight, this step does not simply add up the baseline irradiance. Instead, it introduces an environmental geometric shading coefficient for correction. Based on the seasonal variation of the solar altitude angle and the geometric parameters of the photovoltaic power generation module installation location, the baseline irradiance is weighted and attenuated using the environmental geometric shading coefficient. This eliminates the shadow loss caused by the greenhouse steel frame or roll-up film rods projecting onto the photovoltaic power generation modules at specific incident angles, thereby obtaining an effective power prediction value that reflects the actual light reception.

[0081] Further, please refer to Figure 2 As shown, Figure 2 This is a schematic diagram of the lighting principle based on environmental geometric occlusion of the present invention. Figure 2 The aim is to reveal the interaction mechanism between the static physical structure of a greenhouse and the dynamically changing angle of sunlight incidence. For example... Figure 2 As shown, the steel frame structure of the greenhouse, such as the roll-up film rod illustrated, constitutes a fixed physical shading source around the photovoltaic power generation modules. With the dynamic changes in the solar altitude angle during the current season, the angle of incidence of sunlight relative to the photovoltaic power generation modules changes over time. The different colored light paths in the figure visually illustrate the dynamic shading effect caused by the dynamic changes in the solar altitude angle: purple light represents that under higher solar incidence angle conditions, direct sunlight can avoid obstacles, allowing the photovoltaic power generation module surface to be fully illuminated without obstruction; conversely, red light represents that under lower solar incidence angle conditions, the roll-up film rod projects a shadow area onto the photovoltaic power generation module surface, resulting in shading loss.

[0082] Finally, a time-domain integration operation is performed on the effective power prediction value. The specific execution logic of the time-domain integration operation is as follows: for each moment within the dynamic time interval, the environmental geometric shading coefficient and the effective light-receiving area of ​​the photovoltaic power generation module are calculated. Reference irradiance and overall conversion efficiency coefficient The product of these four factors is then accumulated over time to calculate the length of the prediction time window. The total energy that photovoltaic power generation modules can capture from the environment and convert into chemical energy within a given time period represents the predicted energy gain value. The overall conversion efficiency coefficient is mentioned above. It is a dimensionless constant characterizing the energy loss characteristics of photovoltaic power generation links, used to map physical light energy into chemical energy actually stored in the battery.

[0083] Step S1300: Retrieve preset mechanical task parameters to calculate the mechanical rigidity task energy consumption. Combined with real-time available energy values Predicted energy gain value and the preset average operating voltage of the battery pack To obtain the total available energy budget, a defined minimum survival power consumption is introduced. Constraints are applied to the total available energy budget to establish effective energy reserves, and to assess effective energy reserves and energy consumption for mechanically rigid tasks. Perform normalization to generate the energy autonomy index. .

[0084] Specifically, this step aims to use the real-time available energy value from step S1100. and the predicted energy gain value from step S1200 The positive energy potential on the power supply side of the solar film rolling device is established, and the average operating voltage of the battery pack is introduced. This aims to establish a homogeneous metric benchmark between electrochemical energy storage state and mechanical work demand, while also inverting the mechanical rigidity task energy consumption based on mechanical task parameters. and minimum survival power consumption Establishing a rigid energy constraint on the energy consumption side for solar roll-up film equipment, a normalized exponent synthesis strategy is used to map static electrochemical parameters characterizing absolute stock into dimensionless dynamic state variables characterizing the task execution potential under specific greenhouse physical load conditions, namely, the energy autonomy index. This eliminates the problem of inconsistent power consumption evaluation standards caused by load differences at the data source.

[0085] In the specific implementation process, in order to quantify the ability of the solar film winding equipment to support future film winding or unwinding mechanical actions under the current state, this step does not directly use voltage or remaining power percentage as the control basis, but instead implements a normalized index synthesis strategy based on mechanical rigidity constraints.

[0086] The specific execution logic of the normalized index synthesis strategy is as follows: First, the preset mechanical task parameters are called to calculate the electrical energy consumption corresponding to the mechanical work required for the DC geared motor of the solar film rolling device to complete one uninterrupted full physical stroke, i.e., the mechanical rigid task energy consumption. The mechanical task parameters are a set of constants pre-stored in the main control unit, characterizing the inherent physical structure of the greenhouse, specifically including the film-rolling load torque. , full stroke radius Transmission efficiency and reduction ratio The film load torque This is a mechanical load constant determined based on the greenhouse length, film weight, and film-rolling rod friction coefficient, expressed in Newton-meters (N·m); the full-stroke radian... The transmission efficiency is the total angle that the motor shaft of the film winding unit needs to rotate from fully closed to fully open, expressed in radians (rad); This includes the motor's internal resistance loss and hysteresis loss, and its value range is... The reduction ratio It is the ratio of the input speed to the output speed of the mechanical gearbox. The energy consumption of the mechanical rigid task... The calculation process follows the principles of energy conservation and mechanical transmission, as follows: The main control unit first calculates the film roll load torque. With full stroke radian The product of these factors yields the mechanical work done at the output; subsequently, this mechanical work is divided by the transmission efficiency. Reduction ratio The product of these three factors, along with the unit conversion factor of 3600, maps the mechanical work into electrical energy consumption in milliwatt-hours (mWh), i.e., the energy consumption of rigid mechanical tasks. .

[0087] Secondly, define the minimum survival power consumption. This parameter characterizes the prediction time window length determined in step S1200. Internally, the energy baseline required to maintain the wireless data transmission unit in a minimum power listening state to receive emergency interrupt or reset commands is established, thus defining the standby power consumption boundary for the solar roll film device to maintain the minimum responsiveness of the communication link.

[0088] Finally, the energy autonomy index is implemented. The synthesis operation, specifically the synthesis operation logic, is as follows: The real-time available energy value... Multiply by the average operating voltage of the battery pack To convert to energy dimensions and compare with the predicted energy gain value Adding them together, we obtain the predicted time window length for the solar film rolling device. The total available energy budget is then calculated; subsequently, the minimum survival power consumption is deducted from this total available energy budget. This yields the effective energy reserve for performing active tasks; finally, this effective energy reserve is divided by the energy consumption of mechanically rigid tasks. That is, through normalization, the dimensionless control variable, namely the energy autonomy index, is calculated. The average operating voltage of the battery pack is... It is the nominal voltage constant during the discharge plateau of a lithium battery pack, used to achieve the physical conversion of the charge dimension (mAh) to the energy dimension (mWh).

[0089] The energy autonomy index The physical meaning of is the number of complete standard mechanical actions that the current energy reserves of a solar film roll-up device can theoretically support, after deducting the standby energy consumption boundary for maintaining the minimum response capability of the system communication link. If the energy autonomy index... If the value is less than 1, it indicates that the solar film rolling equipment is in a state of energy deficiency and cannot support a complete mechanical action, requiring the subsequent step S4200 to be triggered; if the energy autonomy index is less than 1, it indicates that the solar film rolling equipment is in a state of energy deficiency and cannot support a complete mechanical action, requiring the subsequent step S4200 to be triggered. A value greater than or equal to 1 indicates that the solar film rolling device is in a state of sufficient energy, which can be used as a criterion for increasing the sensor sampling frequency in the subsequent S2000 network scheduling step.

[0090] Step S2000, for the energy autonomy index Perform multi-level hysteresis comparison logic to generate the network operating parameter set. Set network operating parameters The mapping is encapsulated into a cooperative scheduling instruction package. It also performs physical broadcasts in response to coordinated scheduling instruction packets. Reconstruct hardware state to generate aggregated sensor data .

[0091] Specifically, this step aims to improve the energy autonomy index output by step S1300. As a state feedback variable, a nonlinear mapping relationship between physical layer energy constraints and information layer discrete sampling frequency is established using multi-level hysteresis comparison logic to generate cooperative scheduling instruction packets. This enables adaptive reverse control of the operating status of distributed environmental sensing devices.

[0092] Further, step S2000 includes:

[0093] Step S2100, based on the energy autonomy index Using multi-level hysteresis comparison logic to determine network cooperation mode identifiers Based on network collaboration mode identifier The reference sampling period is calculated using a nonlinear adaptive mapping function. Identify the network collaboration mode and reference sampling period Encapsulated as a network operating parameter set .

[0094] Specifically, this step aims to incorporate the energy autonomy index from step S1300. As a reference input characterizing the current energy autonomy level of the solar film rolling device, a nonlinear adaptive mapping function and multi-level hysteresis comparison logic are used to determine the dynamic energy autonomy index of the solar film rolling device under the coupling effect of light disturbance and mechanical work. This is mapped to the network collaboration mode identifier of environmental sensing devices when performing environmental monitoring tasks. and the quantization reference sampling period This enables the regulation of sensor network behavior based on energy supply status at the control source of the agricultural IoT system, generating a network operating parameter set containing the two parameters mentioned above. .

[0095] In the specific implementation process, to prevent state switching jitter in the control loop near the energy critical point, this step establishes a threshold including a saturation threshold. and critical threshold The segmented hysteresis control logic is executed as follows:

[0096] First, define the saturation threshold. With critical threshold These two threshold parameters define the control state boundaries for the agricultural IoT system to enter unrestricted operation and minimum maintenance states, respectively. Secondly, multi-level hysteresis comparison logic is used to determine the current network cooperation mode identifier. The specific execution logic of the multi-level hysteresis comparison logic is as follows: Read the real-time input energy autonomy index. And compare it with two threshold parameters; if the energy autonomy index Greater than or equal to the saturation threshold If the solar film rolling device is found to be in a state of sufficient energy, the network collaboration mode will be identified. Set to full load pass-through mode; if energy autonomy index Located at the critical threshold With saturation threshold If the solar film rolling device is in a state of energy balance regulation, the network collaboration mode will be identified. Set to adaptive adjustment mode; if the energy autonomy index Less than or equal to the critical threshold If the solar film rolling device is found to be in an energy-deficient state, the network collaboration mode will be identified. Set to hibernation maintenance mode.

[0097] Subsequently, based on the determined network cooperation mode identifier The reference sampling period is calculated using a nonlinear adaptive mapping function. The specific execution logic of the nonlinear adaptive mapping function is as follows: when the network cooperative mode identifier... When determined to be in full-load pass-through mode, the reference sampling period will be... Directly locked to the minimum sampling interval To meet the preset environmental feature capture time resolution requirements; when the network cooperative mode is identified. When the system is determined to be in adaptive adjustment mode, negative feedback linear adjustment calculation is performed to calculate the saturation threshold. With the energy autonomy index The difference is calculated, multiplied by the adaptive damping coefficient, and then incremented by 1. This difference is then compared with the base sampling interval constant. Multiply them to calculate the reference sampling period. This achieves an inverse dynamic mapping between the sampling period and energy self-consistency capability; when the network cooperative mode is identified... When determined to be in sleep maintenance mode, the reference sampling period will be adjusted. Forced to lock to the maximum sampling interval This is to ensure the effectiveness of network connectivity while maintaining the lowest possible energy consumption. The reference sampling period is... It is used to directly define the time interval between two consecutive environmental data acquisition actions performed by the sensor analog-to-digital converter; the adaptive damping coefficient is a dimensionless feedback gain constant used to adjust the response sensitivity of the sampling period relative to energy changes; the basic sampling interval constant... It is a linear operating reference point set in adaptive adjustment mode, used to characterize the default sampling frequency under standard energy conditions.

[0098] Finally, the reference sampling period calculated above is used... Network Collaboration Mode Identifier They are collectively encapsulated into a set of network operating parameters. .

[0099] Step S2200: parse the network operating parameter set. Set network operating parameters The logical control parameters are mapped to communication physical frames, which contain multicast target identifiers. Global synchronization wake-up time Acquisition frequency control word Differential compression ratio command and mode status bit The communication physical frame is encapsulated into a cooperative scheduling instruction packet. It is then physically broadcast via a radio frequency unit.

[0100] Specifically, this step aims to integrate the network operating parameter set from step S2100. The main control unit's real-time clock of the solar film rolling equipment is used as the decision mapping object. Communication physical frames are used to construct and synchronize broadcast logic, and the network operating parameter set is... The discretized logic control parameters contained therein are mapped to communication physical frames conforming to low-power wireless transmission protocol specifications, namely, cooperative scheduling instruction packets. By forcing the distributed deployment of environmental sensing devices and the solar roll-up devices acting as edge gateways to maintain strict global synchronization wake-up time in the time domain. The unification eliminates idle listening energy consumption caused by clock drift or asynchronous communication, and realizes the physical closed-loop implementation of the energy state-based adaptive sampling and adjustment strategy from the decision center to the end execution node.

[0101] In the specific implementation process, in order to ensure that the distributed environmental sensing devices can fully parse and execute the adaptive sampling strategy based on energy state, this step performs communication physical frame construction and synchronous broadcast logic. The specific execution logic is as follows:

[0102] First, the main control unit parses the input network operating parameter set. Construct a collaborative scheduling instruction package The physical frame structure of the communication. This construction process will be included in the network operating parameter set. The logical control parameters in the multicast target identifier are mapped to specific fields of the communication physical frame, which specifically includes the following five control fields: multicast target identifier. Global synchronization wake-up time Acquisition frequency control word Differential compression ratio command and mode status bit .

[0103] Secondly, perform parameter calculation and filling for each control domain, specifically as follows: for the multicast target identifier The main control unit of the solar film rolling equipment identifies a specific set of environmental sensors based on the current control objectives. This could be a cluster of sensors located within the same greenhouse zone or belonging to the same functional category, enabling precise control of specific agricultural operation areas. (Regarding global synchronization wake-up time...) By using the control domain parameters to calibrate the sleep timers of all environmental sensing devices, the wake-up sequence of all devices in the agricultural IoT system is unified. First, the real-time clock of the main control unit is read, i.e., the current time, and compared with the network operating parameter set. Reference sampling period in Adding them together gives the next unified wake-up time across the entire network, i.e., the globally synchronized wake-up time. This forces all environmental sensors to wake up simultaneously for data exchange, eliminating idle listening power consumption. (Regarding the acquisition frequency control word...) The startup interval of the analog-to-digital converter built into the environmental sensing device is directly controlled using this control domain parameter, and its value is taken from the network operating parameter set. Reference sampling period in The reciprocal of the value. (For differential compression ratio instructions) Detection network operating parameter set Network collaboration mode identifier If it is in adaptive adjustment mode, then the differential compression ratio command will be applied. Setting it to the active state forces the environmental sensing device to use differential coding algorithms to process the raw environmental data and only transmit the values ​​of changes, thereby reducing the byte length of the RF data packet and lowering transmission power consumption. (Regarding the mode status bit) This is directly mapped to a network cooperation mode identifier. The hexadecimal code is used to indicate that the environmental sensing device switches to the corresponding communication operating mode sequence, such as switching from full load pass-through mode to sleep maintenance mode.

[0104] Finally, the radio frequency physical broadcast of the control commands and closed-loop feedback verification are performed. The solar roll-up device broadcasts the cooperative scheduling command packet at maximum transmit power on a dedicated control channel via its integrated low-power radio frequency unit. After the broadcast is completed, the main control unit opens for a duration equal to the confirmation reception window length. The receiving window waits for response confirmation frames from representative environmental sensing devices to verify whether the adaptive sampling strategy has been successfully distributed to the distributed environmental sensing terminals.

[0105] Step S2300: Parse the cooperative scheduling instruction packet Utilizing global synchronization wake-up time Calibrate the local clock according to the frequency control word. Resets the trigger cycle of the hardware timer interrupt controller and responds to differential compression ratio instructions. Execute local differential coding algorithms to generate aggregated sensor data. .

[0106] Specifically, this step aims to integrate the collaborative scheduling instruction package from the solar film rolling device in step S2200. As an external control stimulus, the hardware reconfigurability of environmental sensing devices is utilized to directly parse the instruction packets contained in the cooperative scheduling. The acquisition frequency control word and differential compression ratio instructions This is mapped to the trigger cycle of the hardware timer interrupt controller and the on / off state of the peripheral power supply circuit. A local differential coding algorithm is then used to map the raw environmental data directly acquired by the analog-to-digital converter in the environmental sensing device into aggregated sensor data containing only changing characteristics. This enables closed-loop control of passive response and active power consumption suppression of solar film rolling equipment at distributed environmental sensing nodes.

[0107] In the specific implementation process, the environmental sensing device receives the broadcast coordinated scheduling instruction packet through the radio frequency unit. Then, immediately execute the following hardware state reconstruction and source data processing logic, the specific execution logic is as follows:

[0108] First, clock calibration and sleep planning are performed. Environmental sensing devices extract coordinated scheduling instruction packets. Global synchronization wake-up time This parameter is used to correct the accumulated drift error of the local real-time clock of the environmental sensing device. Subsequently, the global synchronization wake-up time is calculated. The time difference between the current local operating time and the current time difference is written into the hardware sleep timer. The countdown for automatic wake-up after the microcontroller of the environmental sensing device enters the deep hardware sleep state is set to ensure radio silence before the next global synchronization communication time arrives.

[0109] Secondly, physical modulation of the acquisition frequency and reconfiguration of peripheral power supplies are performed. Environmental sensing devices extract collaborative scheduling instruction packets. Acquisition frequency control word Reset the trigger cycle of the hardware timer interrupt controller. If the frequency control word is being sampled... Instruction package for coordinated scheduling Network Collaboration Mode Identifier In sleep maintenance mode, i.e., the reference sampling period Maximum sampling interval Environmental sensing devices perform peripheral power gating operations, physically cutting off the power supply circuits of peripherals whose power consumption exceeds a preset energy consumption grading cutoff threshold, such as soil conductivity sensors. For concentration sensors, only the core temperature sensor is powered to maintain a minimum level of environmental sensing capability; if the frequency control word is collected... Instruction package for coordinated scheduling Network Collaboration Mode Identifier In full-load transparent transmission mode or adaptive adjustment mode, the conduction state of all peripheral power supply circuits is restored, and the control word of the acquisition frequency is followed. The set frequency is used to start the analog-to-digital converter for data acquisition. The preset energy consumption tier cutoff threshold is a power judgment limit based on the rated power consumption characteristics of the peripheral hardware components of the environmental sensing device. Its value is determined according to the operating power consumption parameters provided in the specifications of each hardware component in the environmental sensing device. Specifically, it is set at the upper limit of the static operating power consumption of core maintenance sensors, such as the core temperature sensor, and relative to the power consumption of sensors such as the soil conductivity sensor, etc. The lower limit of the startup or operating power consumption of high-energy-consuming sensors, such as concentration sensors, serves as a quantitative physical boundary for power gating of peripheral devices.

[0110] Finally, source data compression and caching are performed. Environmental sensor device detection and coordination scheduling instruction package. Differential compression ratio command in The state is then entered, and the following source-side data processing logic is executed: if a differential compression ratio instruction is detected... In the effective state, corresponding to the adaptive adjustment mode, the local differential coding algorithm is activated: the microcontroller reads the raw environmental data collected at the current moment and performs a differential operation with the raw environmental data cached at the previous moment to generate sensor aggregated data containing only time-varying features. And aggregate the data from the sensor. Stored in a circular buffer in local memory. If a differential compression ratio command is detected. If the state is invalid, then the sensor aggregated data will be... This is directly equivalent to the raw environmental data. The raw environmental data refers to the physical environmental indicators directly collected by the analog-to-digital converter in the environmental sensing device.

[0111] Step S3000: Monitor dual-channel quadrature pulse signals and DC bus voltage Generate energy feedback status identifier Combined with the energy autonomy index and based on sensor aggregated data Length of parsed data cache queue Quantifying the cost of real-time transmission The cost of real-time transmission The value is compared with a preset communication trigger threshold to generate a discontinuous burst transmission enable command. And based on the energy feedback status indicator Output cloud-reported data stream .

[0112] Specifically, this step aims to convert the dual-channel orthogonal pulse signals of a solar film roll-up device in the physical world during the film-laying process. and DC bus voltage As the object of physical state observation, the gravitational potential energy feedback range for the conversion of gravitational potential energy into electrical energy is identified using the principle of electromechanical energy conversion, and this range is used as a strong excitation factor to couple the energy autonomy index from step S1300. Aggregate data with the sensor data to be sent from step S2300 Construct a multidimensional transmission cost function to identify the energy feedback state generated by mechanical actions. Mapped to the real-time transmission cost of information transmission When the communication trigger threshold is met, a discontinuous burst transmission is triggered, and a cloud-reported data stream is output. This enables time-domain orthogonal coupling of mechanical energy flow and information bit flow, resolving the resource competition problem between communication power consumption and limited energy storage.

[0113] Further, step S3000 includes:

[0114] Step S3100: Based on the acquired dual-channel quadrature pulse signals Analysis of the real-time rotation direction of a DC geared motor and real-time rotational angular velocity Synchronous monitoring of DC bus voltage In real-time rotation direction Real-time rotational angular velocity and DC bus voltage When the preset feedback threshold conditions are met simultaneously, an energy feedback status indicator is generated. .

[0115] Specifically, this step aims to use the mechanical motion state of the solar film winding equipment during the film unwinding action as a physical observation object, utilizing dual-channel quadrature pulse signals from the Hall element built into the solar film winding equipment. Analyze the rotational characteristics and combine them with the DC bus voltage from the motor drive circuit. Based on the electrical characteristics, a multimodal state observer is constructed to capture the physical moment when the film roll enters the power generation mode under gravity traction, i.e., the gravitational potential energy feedback range, and this is mapped to a binary control variable at the logical level, i.e., the energy feedback state identifier. This provides a real-time physical trigger source for subsequent steps that utilize mechanical feedback energy to drive information transmission.

[0116] In the specific implementation process, this step serves as the physical state perception link of the control strategy, executing a state estimation logic based on the fusion of multi-source heterogeneous signals. The specific execution logic is as follows:

[0117] First, the mechanical motion vectors are calculated. The main control unit of the solar film winding equipment acquires dual-channel quadrature pulse signals from the Hall element built into the DC geared motor in real time through its high-speed signal acquisition interface. This signal typically consists of two phase pulses, A-phase and B-phase, with a 90-degree phase difference. The main control unit uses an orthogonal decoding algorithm to analyze and calculate the timing phase relationship between the A-phase and B-phase pulses, determining the real-time rotation direction of the DC geared motor. Simultaneously, dual-channel orthogonal pulse signals are obtained using the pulse frequency calculation method. signal frequency And combined with the number of pole pairs of the DC geared motor With reduction ratio The real-time rotational angular velocity of the DC geared motor was calculated by inversion. The real-time rotational angular velocity The specific calculation logic is as follows: The main control unit first calculates the signal frequency. and constant The product of these factors yields the electrical angular frequency of the DC geared motor rotor during the electromagnetic field variation period; subsequently, this electrical angular frequency is divided by the number of pole pairs. With reduction ratio The product of these two discrete, electrically characteristic, dual-channel orthogonal pulse signals represents the electrical characteristics of the signal. Mapped to the real-time rotational angular velocity of a DC geared motor, which is a continuous, mechanically characteristic measurement. This enables the calculation of macroscopic mechanical motion vectors from microscopic pulse characteristics. When the real-time rotation direction is detected... It is aligned with the preset film unwinding direction and rotates at a real-time angular velocity. Exceeding the preset feedback speed threshold When this occurs, the film winding transmission mechanism is determined to be in a passive acceleration state under gravity traction, possessing the kinematic basis for generating back electromotive force. The preset feedback speed threshold value... This is the minimum mechanical angular velocity characteristic value required for the DC geared motor used in solar film winding equipment to enter an effective power generation condition. Its value is usually determined based on the back electromotive force constant and reduction ratio of the DC geared motor. and the current open-circuit voltage of the lithium battery pack Experimental measurements were conducted to determine that the induced electromotive force generated when the rotor of the DC geared motor cuts magnetic field lines during passive rotation is sufficient in magnitude to overcome the DC bus voltage. The potential is used to achieve reverse energy feedback to the lithium battery pack; the back electromotive force constant is the induced electromotive force generated by the rotor of the DC geared motor at a unit speed, and its value is usually determined by the manufacturer's technical specifications; the current open-circuit voltage of the lithium battery pack. It is a dynamic reference value for determining whether a non-source voltage rise has occurred.

[0118] Secondly, the electrical energy flow is identified. The main control unit of the solar film roll-up device synchronously monitors the DC bus voltage in the motor drive circuit via an analog-to-digital converter. In non-operating or motorized states, due to the internal resistance of the lithium battery pack and the voltage drop across the circuit, the DC bus voltage... The numerical characteristic is that it is less than or equal to the current open-circuit voltage of the lithium battery pack. In generator mode, the back electromotive force generated by the DC geared motor rotor cutting magnetic field lines will be higher than the battery terminal voltage. Current is injected into the DC bus through the freewheeling diode in the motor drive circuit, causing the DC bus voltage to... Lifting. The main control unit of the solar film rolling equipment compares the DC bus voltage in real time. With the current open-circuit voltage of the lithium battery pack When the DC bus voltage is detected Greater than the current open-circuit voltage of the lithium battery pack and anti-interference hysteresis voltage threshold When the sum of these values ​​is reached, it is determined that there is a reverse energy flow in the motor drive circuit from the DC geared motor end to the lithium battery pack end, indicating that the solar film rolling device has entered the gravitational potential energy feedback range. The current open-circuit voltage of the lithium battery pack is then considered. It is a dynamic reference value for determining whether a non-source voltage rise has occurred; the anti-interference hysteresis voltage threshold. It is a voltage tolerance dead zone value set to prevent misjudgment of status due to power supply ripple, and its value is usually set to 0.3V to 0.5V.

[0119] Finally, the energy feedback status flag is executed. The main control unit of the solar film rolling device performs a logical AND operation on the kinematic determination results from the mechanical side and the energy flow direction determination results from the electrical side: if and only if the mechanical side satisfies the real-time rotation direction... The rotational angular velocity is consistent with the direction of film unfolding and is in real time. Exceeding the feedback speed threshold And the electrical side meets the DC bus voltage requirements. Greater than the current open-circuit voltage of the lithium battery pack With anti-interference hysteresis voltage threshold When the sum is reached, the main control unit will indicate the energy feedback status. Set to active high level 1; otherwise, if any single condition is not met, the energy feedback status flag will be reset. Reset and maintain the invalid low-level state 0. The energy feedback state indicator is mentioned above. It is a binary state variable used to characterize whether a solar film rolling device has the ability to use mechanical potential energy to offset communication power consumption, and its value is 0 or 1.

[0120] Further, please refer to Figure 3 As shown, Figure 3 This is a flowchart of the energy feedback state determination based on the fusion of mechanical motion vectors and electrical characteristics of the present invention.

[0121] Step S3200: Retrieve the energy feedback status indicator and Energy Autonomy Index Based on sensor aggregated data Parse data cache queue length A multidimensional transmission cost function is constructed and quantitatively calculated to generate the real-time transmission cost. .

[0122] Specifically, this step aims to identify the energy feedback status from the physical layer of step S3100. Length of data cache queue at the logical level And the energy autonomy index from the system level of step S1300 This is mapped to a unified decision metric, namely, real-time transmission cost. By constructing a multidimensional transmission cost function that includes an energy constraint penalty term, a data backlog incentive term, and a mechanical energy flow compensation term, the comprehensive execution cost of performing data transmission operations at the current moment is numerically quantified, thereby providing a quantitative optimal control criterion for subsequent steps to dynamically switch between static energy storage consumption conditions and dynamic potential energy feedback conditions.

[0123] In the specific implementation process, the main control unit of the solar film rolling device executes multi-dimensional parameter fusion and cost calculation logic, as follows:

[0124] First, synchronous acquisition of multi-source state parameters is performed. The main control unit of the solar film roll-up device accesses the local memory in real time and reads the aggregated sensor data used to store data in step S2300. The write and read pointer addresses of the circular buffer are specified. The main control unit uses address difference operation logic—that is, the address difference between the write and read pointer addresses—to calculate the length of the data buffer queue for the total amount of data to be uploaded. Simultaneously, the main control unit retrieves the energy autonomy index stored in memory. and energy feedback status indicator .

[0125] Secondly, the multidimensional transmission cost function is calculated. Based on the minimum cost principle in optimal control theory, the main control unit of the solar film rolling device calculates the real-time transmission cost using a pre-set weighted coupling model. The computational logic aims to balance the conflicting control objectives of static energy storage and dynamic information throughput, utilizing gravitational potential energy feedback as an external disturbance variable to disrupt this balance. The construction logic of the multidimensional transmission cost function is as follows: the main control unit calculates the energy autonomy index. The reciprocal of the given value is multiplied by the energy sensitivity coefficient to construct an energy constraint penalty term, which characterizes the static energy storage loss impedance caused by initiating communication when the battery power is insufficient; the main control unit calculates the data buffer queue length. The product of the delay sensitivity coefficient and the negative value is used to construct a data backlog incentive term, which characterizes the transmission necessity weight introduced by the increased risk of communication delay as the amount of data backlog increases; the main control unit calculates the energy feedback status flag. The mechanical energy flow compensation term is constructed by multiplying the mechanical coupling weight coefficient by the mechanical coupling weight coefficient and taking its negative value. This term characterizes the cost compensation effect of using feedback electrical energy for communication when gravitational potential energy feedback exists. Specifically, the energy sensitivity coefficient is a positive real-number weighted factor used to adjust the sensitivity of the solar film winding device to the remaining battery power. A larger value indicates a higher weight assigned to the energy constraint penalty term by the main control unit, causing the solar film winding device to be more inclined to suppress communication and implement a conservative energy storage strategy. The delay sensitivity coefficient is a positive real-number weighted factor used to adjust the solar film winding device to the real-time requirements of the data. A larger value indicates a higher weight assigned to the data backlog incentive term by the main control unit, causing the solar film winding device to be more inclined to reduce data transmission delay by sending data frequently. The mechanical coupling weight coefficient is a positive real-number weighted factor used to characterize the strong intervention capability of mechanical potential energy in communication decisions. The value of the mechanical coupling weight coefficient is set to be much larger than the values ​​of the energy sensitivity coefficient and the delay sensitivity coefficient to ensure that once the dynamic potential energy feedback condition occurs (i.e., the energy feedback status indicator is displayed), the system is ready to respond. When set to a valid high level (state 1), the mechanical energy flow compensation term, as the dominant control variable, will force real-time transmission of the cost. A step drop occurs, thus establishing the highest priority for communication triggering at the algorithm level.

[0126] Finally, the quantitative decision result is output. The main control unit algebraically sums the energy constraint penalty term, data backlog incentive term, and mechanical energy flow compensation term to obtain the dimensionless real-time transmission cost. The main control unit will calculate the real-time transmission cost. The output is used as the trigger for discontinuous burst transmissions in subsequent steps.

[0127] Step S3300, real-time transmission cost The value is compared with a preset communication trigger threshold to generate a discontinuous burst transmission enable command. Based on the energy feedback status indicator Identify energy supply conditions to implement differentiated power supply and transmission strategies, and aggregate sensor data. Encapsulated as a cloud-reported data stream .

[0128] Specifically, this step aims to reduce the real-time transmission cost from step S3200. Energy feedback status indicator from step S3100 And the sensor aggregated data from the circular buffer stored in local memory in step S2300. As input, and based on the cost of real-time transmission Energy feedback status indicator that has experienced a step decrease The logic state dynamically identifies the current energy supply condition and controls the broadband wireless radio frequency transmission unit to complete the aggregation of sensor data under static energy consumption or dynamic potential energy feedback conditions. Physical layer burst transmission and protocol encapsulation to output cloud-reported data streams. This resolves the resource competition between transient power consumption and limited battery capacity at the physical link layer.

[0129] In the specific implementation process, the main control unit of the solar film rolling device executes dual-modal control decisions and physical layer burst transmission logic. The specific execution logic is as follows:

[0130] First, the cost of performing real-time transmission. Threshold decision. The main control unit will transmit the cost in real time. The value is compared with a preset communication trigger threshold. This communication trigger threshold is a dimensionless real constant used to define the critical boundary for the solar film roll-up device to initiate wireless data transmission. When the computational relationship and real-time transmission cost are satisfied... When the threshold is less than the communication trigger threshold, the main control unit of the solar film roll-up device determines that transmission is feasible at the current moment and generates a valid discontinuous burst transmission enable command. If the value is set to 1, the main control unit of the solar film rolling device will implement transmit lockout control on the broadband wireless radio frequency transmission unit, forcing it to remain in deep sleep state, and will send the generated discontinuous burst transmission enable command to the main control unit. Set to 0.

[0131] Secondly, the power supply circuit switching and transmission control based on operating condition identification are executed. The main control unit responds to the discontinuous burst transmission enable command. And based on the current energy feedback status. Identify energy supply conditions to execute differentiated power supply and transmission strategies. The execution logic of the differentiated power supply and transmission strategies is as follows:

[0132] If an energy feedback status indicator is detected When the system is in a valid high-level state (1), the main control unit determines that the solar film winding device is in dynamic potential energy feedback mode. At this time, the main control unit sends a bypass switching command to the power management unit, establishing a direct power supply path from the DC bus to the power input of the broadband wireless RF transmission unit. The back electromotive force generated by the DC geared motor under gravity is directly supplied to the broadband wireless RF transmission unit. The main control unit detects transient non-active voltage rises on the DC bus and, within the effective energy utilization window maintained by the back electromotive force, simultaneously activates the broadband wireless RF transmission unit and retrieves sensor aggregation data from the circular buffer. It performs full-speed burst transmission. The direct power supply path is a physical transmission path that crosses the lithium battery pack charging and discharging circuit, directly coupled from the DC bus to the broadband wireless radio frequency transmission unit via a power bypass switch. Its value varies with the DC bus voltage. Real-time fluctuations are used to avoid ohmic losses caused by the internal resistance of the lithium battery pack and the charge / discharge management circuit, enabling the transient back electromotive force generated by the DC geared motor to be directly converted into radio frequency transmission power with the highest energy utilization rate. The full-speed burst transmission refers to the broadband wireless radio frequency transmission unit transmitting asynchronous data in a short period of time at the maximum communication rate supported by the hardware physical link within the effective energy utilization window. Specifically, it does not perform cross-cycle flow shaping or rate limiting, aiming to utilize the transient high-power supply period before the back electromotive force energy disappears to clear as much data load as possible in the ring buffer, thereby achieving an optimal match between energy utilization efficiency and data throughput.

[0133] If an energy feedback status indicator is detected If the signal is an invalid low-level state (0), the main control unit determines that the solar film winding device is in a static energy storage consumption state. At this time, the main control unit sends a battery power supply command to the power management unit, establishing a regulated power supply path from the lithium battery pack to the power input of the broadband wireless radio frequency transmission unit. The main control unit consumes the chemical energy stored in the lithium battery pack to wake up the broadband wireless radio frequency transmission unit, aggregating the sensor data from the circular buffer. Pack and send, and clear the circular buffer to reset the data buffer queue length. The regulated power supply path is an electrical connection path that converts the electrical energy of the lithium battery pack into a constant voltage output. Its value is determined based on the rated operating voltage in the hardware specifications of the broadband wireless radio frequency transmission unit and is preset in the power management unit. It is used to provide high stability and low ripple power support for the radio frequency unit and ensure the anti-interference capability of the communication link in the static energy storage power supply mode.

[0134] Finally, the cloud-based data reporting stream is executed. The protocol encapsulation and physical transmission are handled accordingly. Regardless of whether the operation is triggered by dynamic potential energy feedback or static energy storage consumption, the main control unit will extract the aggregated sensor data. Encapsulated as a cloud-based reporting data stream conforming to a preset IoT transmission protocol The data is modulated by a broadband wireless radio frequency transmission unit and then transmitted to a remote management platform to complete the discontinuous data interaction at the physical layer.

[0135] Step S4000: Analyze the cloud-reported data stream. Extracting the measured temperature inside the greenhouse Combined with preset target temperature Theoretical film roll target stroke is generated using a discretized incremental PID algorithm. Combined with the energy autonomy index Established energy safety factor Output the actual roll film execution stroke According to the actual film roll execution stroke Generate motor drive PWM signal .

[0136] Specifically, this step aims to process the cloud-reported data stream from step S3300. Includes measured temperature inside the greenhouse and the energy autonomy index from step S1300 The system's energy health, as a dual decision variable, is used to map the theoretical control requirements for eliminating environmental temperature deviations into the theoretical roll-up target stroke using a discretized incremental PID algorithm and constraint adjudication logic based on energy safety boundaries. Furthermore, the survival and maintenance boundary under energy-scarce conditions is mapped to an energy safety factor. By analyzing the theoretical target stroke of the film winding Dynamic correction generates actual film roll execution stroke Ultimately, it is converted into a motor drive PWM signal. This achieves orthogonal decoupling and adaptive closed-loop control between the precision of greenhouse environment regulation and the energy survivability of equipment.

[0137] Further, step S4000 includes:

[0138] Step S4100: Analyze the cloud-reported data stream. Extracting the measured temperature inside the greenhouse Calculate the measured temperature inside the greenhouse. With the preset target temperature To address the temperature control error, a discretized incremental PID algorithm is used to calculate the theoretical film adjustment increment. This increment is then combined with the theoretical film target travel stored in the local control register from the previous sampling time to generate the theoretical film target travel for the current sampling time. .

[0139] Specifically, this step aims to convert the measured temperature inside the greenhouse, determined by the thermodynamic properties of the greenhouse, into the physical world. The target temperature set in accordance with agricultural planting techniques is pre-defined. As the input variable of the temperature feedback control loop, the measured temperature inside the shed... Parsing the cloud-reported data stream from step S3300 The target set temperature Stored in the local control register, the nonlinear real-time temperature control error is mapped to the theoretical film winding target stroke at the control level using a discretized incremental PID algorithm. This achieves orthogonal decoupling of environmental regulation needs and energy supply status at the control algorithm level.

[0140] In the specific implementation process, the main control unit of the solar film rolling equipment executes control law calculation logic based on real-time temperature control error. The specific execution logic is as follows:

[0141] First, the environmental state deviation is quantitatively extracted. The main control unit of the solar film roll-up device processes the received data stream from the cloud. Unpack the data and extract the measured temperature inside the greenhouse at the current sampling time. Subsequently, the main control unit reads the preset target temperature from the local control register. The temperature control error at the current sampling moment is calculated using a subtraction operator. This temperature control error characterizes the deviation of the current thermal environment inside the greenhouse from the target set temperature. The numerical amplitude and directional characteristics.

[0142] Secondly, the algorithm for calculating the theoretical adjustment increment is executed. The main control unit of the solar film winding equipment calls a preset discretized incremental PID algorithm, using the temperature control error at the current sampling moment, the temperature control error at the previous sampling moment, and the temperature control error at the sampling moment before that as input variables, to calculate the theoretical film winding adjustment increment required to eliminate the temperature control error at the current sampling moment. The specific execution logic of the discretized incremental PID algorithm is as follows: the main control unit of the solar film winding equipment calculates the difference between the temperature control error at the current sampling moment and the temperature control error at the previous sampling moment, and multiplies it by the proportional gain coefficient. This yields a proportional term, used to respond to the instantaneous rate of change of the temperature control error; simultaneously, the main control unit of the solar film rolling device extracts the temperature control error at the current sampling moment and multiplies it by the integral time constant. The integral term is obtained and used to eliminate the steady-state error of the temperature feedback control loop. Further, the main control unit of the solar film winding device calculates the second-order differences of the temperature control error at the current sampling time, the temperature control error at the previous sampling time, and the temperature control error at the time before that, and multiplies them by the differential time constant. The differential term is obtained and used to predict the changing trend of temperature control error. Finally, the main control unit algebraically sums the proportional, integral, and differential terms to generate the theoretical film-winding adjustment increment, representing the relative displacement that the DC geared motor of the solar film-winding device should execute at the current sampling moment. The proportional gain coefficient is... It is a positive real-valued weighting factor used to adjust the sensitivity of the control system to the rate of change of temperature control error, and is used to determine the dominant correction strength for eliminating transient deviations; the integral time constant These are weighting coefficients used to adjust the control system's ability to eliminate steady-state errors; their values ​​determine the strength of the correction for accumulated residual errors under steady-state conditions. The differential time constant... It is a damping coefficient used to adjust the sensitivity of the control system to the rate of change of error. Its value determines the strength of suppression of the second-order rate of change of temperature control error.

[0143] Finally, the theoretical roll-up target stroke without energy constraints is generated. The main control unit of the solar film winding device reads the theoretical target winding distance from the previous sampling moment stored in the local control register, and performs an algebraic superposition operation with the theoretical winding adjustment increment calculated at the current sampling moment to generate the theoretical target winding distance at the current sampling moment. The theoretical target stroke of the film winding. Characterizes the temperature feedback regulation loop without introducing the energy autonomy index. The ideal position control command, when used as a constraint variable, serves as the original reference input signal for the subsequent step S4200 to perform energy safety boundary truncation.

[0144] Step S4200, based on the energy autonomy index Calculate the energy security factor using the energy security factor function. Utilizing the energy safety factor The theoretical roll-up target distance at the current sampling time Perform correction calculations to generate the actual roll film execution stroke. .

[0145] Specifically, this step aims to transfer the theoretical roll-up target stroke from step S4100. With the energy autonomy index from step S1300 As the input vector for constraint control, a constraint adjudication logic based on the energy safety boundary is established using a preset energy safety factor function. Under energy-scarce conditions, the ideal environmental control requirements are nonlinearly truncated to generate the actual film winding execution stroke. This allows for the provision of a minimum energy threshold sufficient to perform mechanical reset protection actions, such as closing the film winding mechanism, preventing the risk of actuator stagnation or greenhouse environment runaway caused by blindly performing large-scale film winding actions that deplete the battery.

[0146] In the specific implementation process, the main control unit of the solar film roll-up device executes constraint adjudication logic based on energy safety boundaries. The specific execution logic is as follows:

[0147] First, the energy security boundary mapping is calculated. The main control unit of the solar roll-up device retrieves the real-time updated energy autonomy index from the local control register. The value is then substituted into a preset energy safety factor function for mapping calculation. This energy safety factor function defines a nonlinear transformation rule from energy autonomy quantification index to mechanical action amplitude limit coefficient, used to calculate the energy safety factor that the solar film rolling device is allowed to perform at the current moment. The specific mapping logic is as follows: when the energy autonomy index Above the preset energy sufficiency threshold When the time is right, it indicates that the solar film rolling equipment has the rated load operating capacity, and the main control unit will adjust the energy safety factor. Setting it to 1 indicates an unconstrained operating state; when the energy autonomy index... Located at the preset energy sufficiency threshold and preset energy survival threshold When the value is between these values, it indicates that the solar film rolling device is in the energy self-constraint range, and the main control unit performs monotonic decay calculation to ensure the energy safety factor. With the energy autonomy index The energy autonomous index decreases and decays monotonically; when the energy autonomous index decreases... Below the preset energy survival threshold When this occurs, it indicates that the solar film rolling equipment has entered a critical state of energy shortage, and the main control unit forcibly increases the energy safety factor. Setting it to 0 triggers the energy consumption action interlocking protection mechanism. Among these, the energy safety factor... The range of values ​​is within The dimensionless cutoff factor is used to characterize the permissible degree of mechanical action amplitude of the DC geared motor under the current energy state; the energy adequacy threshold is... It is the lower limit boundary value of energy that defines the full-power operation capability of a solar film roll-up device; the energy survival threshold. This is the minimum safety threshold that defines the minimum energy reserve required for the solar film roll-up equipment to maintain its mechanical reset action. When this value is below, the solar film roll-up equipment is prohibited from any energy-consuming opening action. The specific execution logic of the monotonic decay calculation is as follows: Calculate the energy autonomy index. Energy survival threshold The difference is then divided by the energy adequacy threshold. Energy survival threshold The difference is used to obtain the energy safety factor for linear decay. .

[0148] Finally, the constraints of the theoretical travel are truncated. The main control unit uses the calculated energy safety factor. As a multiplicative weighting factor, the theoretical roll-up target stroke... A correction calculation is performed. By multiplying the ideal stroke by a safety factor, the main control unit physically truncates any movement exceeding the energy tolerance range, thereby generating the final actual film winding stroke used to drive the motor. .

[0149] Step S4300: Perform the stroke according to the actual film roll. Establish target pulse count value Combined with the current cumulative pulse value collected in real time Perform algebraic interpolation to generate position control deviations. Using position control deviation Generate motor drive PWM signal Synchronously collect real-time phase current To estimate real-time load torque .

[0150] Specifically, this step aims to transmit the actual film roll execution stroke from step S4200. As the input reference quantity of the servo control system, it is converted into a target pulse count value at the physical level using kinematic mapping relationships. By constructing a system that includes position control deviation and real-time load torque The dual closed-loop control topology generates a motor drive PWM signal with an adjustable duty cycle. The DC geared motor is driven to perform the film winding action. During the process of safely correcting the stroke in response to the DC geared motor's energy, transient current suppression algorithms and mechanical overload fuses are used to eliminate surge impacts on the power supply bus caused by mechanical actions and the risk of continuous overcurrent due to mechanical failure, ensuring the actual film winding stroke is guaranteed. Physical-level reliable execution.

[0151] In the specific implementation process, the main control unit of the solar film rolling device executes closed-loop servo control logic from instruction parsing to physical drive. The specific execution logic is as follows:

[0152] First, the discretization mapping of control commands is executed. The main control unit of the solar film winding device reads the preset total number of full-scale pulses from its local memory. Compare it with the actual film winding stroke. Perform multiplication to calculate the target pulse count value corresponding to the target mechanical position state. The total number of full-scale pulses. The total number of position feedback pulses corresponding to the full physical stroke required for the film winding unit to move from the zero-position closed state to the full-scale unfolded state.

[0153] Subsequently, the main control unit acquires the current cumulative pulse value fed back by the motor Hall sensor in real time. and compare it with the target pulse count value. Perform algebraic interpolation to calculate the position control deviation, which represents whether the current actual mechanical position lags behind or leads the target position, using the quantized difference of the pulse to be executed. The main control unit controls the deviation based on the position. The polarity of the numerical sign is used to generate the direction control level signal used to define the turn-on sequence of the power transistors in the full-bridge drive circuit. This locks the mechanical motion vector direction of the DC geared motor, driving the DC geared motor of the solar film winding equipment to execute position control deviations. The forward winding of the film is opened or the reverse unwinding of the film is closed, with the convergence direction consistent.

[0154] Finally, torque-based monitoring and fault diagnosis are implemented. The DC geared motor in the solar film winding device responds to the motor drive PWM signal. During the operation of performing mechanical motion, the main control unit collects the real-time phase current in real time through a sampling resistor connected in series in the drive circuit. And combined with the preset motor torque constant and gearbox transmission efficiency By multiplying the three factors, the real-time load torque applied to the mechanical output shaft of the DC geared motor can be estimated. The preset motor torque constant... This refers to the inherent physical characteristic quantity of the DC geared motor used in solar film rolling equipment, which is capable of generating electromagnetic torque under unit current. Its value is typically determined and set in the main control unit based on the manufacturer's technical specifications. The gearbox transmission efficiency... This is the quantization coefficient of the integrated gearbox for the DC geared motor. It characterizes the degree of energy retention during the transmission of motor power to the output shaft via its internal reduction gear set. Its value is preset as an empirical constant less than 1 based on the gearbox's mechanical structure, number of gear stages, and friction losses. The main control unit will then display the real-time load torque. Compared with the preset stall safety threshold Perform real-time comparison: If real-time load torque is detected Exceeding the preset stall safety threshold If the main control unit determines that the mechanical transmission link of the solar film winding device is rigidly stuck, it will immediately adjust the PWM signal of the motor drive. The output cutoff operation resets the effective duty cycle to zero and enters the overload cutoff protection state to avoid irreversible thermal damage and ineffective energy loss caused by the motor windings being in a locked-rotor high-current state for a long time; if a position control deviation is detected... The absolute value is less than the preset positioning dead zone threshold. The main control unit's DC geared motor and its driven film load have converged to the target mechanical position, and the motor drive PWM signal has been stopped. The modulation output is activated and electronic braking logic is enabled to complete this adaptive execution cycle. The stall safety threshold is mentioned above. It is a preset torque critical upper limit value in the main control unit, used to define the physical boundary between normal load operation and abnormal mechanical stall in the mechanical transmission link of the solar film winding equipment; the positioning dead zone threshold It is a range of permissible error pulses used to eliminate the back-and-forth oscillation phenomenon in the position closed-loop servo control loop of solar film winding equipment caused by mechanical backlash or signal jitter near the target position. When the position control deviation... When entering this interval, the main control unit considers it as a single adaptive execution cycle completed.

[0155] Example 2:

[0156] This embodiment, based on Embodiment 1, provides an agricultural IoT discontinuous transmission control system based on energy autonomy, such as... Figure 4 As shown, the system includes an energy autonomy module, a collaborative scheduling module, a transmission control module, and an environmental regulation module;

[0157] The energy autonomy module is used to collect physical sampling data from the battery terminals. Perform extended Kalman filter iterative operations to obtain the real-time available energy value. Introducing the environmental geometric shading coefficient to influence historical illumination power curves Perform time-domain integration to generate predicted energy gain values. And based on the calculated mechanical rigidity task energy consumption Real-time available energy value and predicted energy gain value Perform normalization to generate the energy autonomy index. .

[0158] The coordinated scheduling module is used for energy autonomy index. Perform multi-level hysteresis comparison logic to generate the network operating parameter set. Set network operating parameters The mapping is encapsulated into a cooperative scheduling instruction package. It also performs physical broadcasts in response to coordinated scheduling instruction packets. Reconstruct hardware state to generate aggregated sensor data .

[0159] The transmission control module is used to monitor dual-channel quadrature pulse signals. and DC bus voltage Generate energy feedback status identifier Combined with the energy autonomy index and based on sensor aggregated data Length of parsed data cache queue Quantifying the cost of real-time transmission The cost of real-time transmission The value is compared with a preset communication trigger threshold to generate a discontinuous burst transmission enable command. And based on the energy feedback status indicator Output cloud-reported data stream .

[0160] The environmental control module is used to parse the data stream reported from the cloud. Extracting the measured temperature inside the greenhouse Combined with preset target temperature Theoretical film roll target stroke is generated using a discretized incremental PID algorithm. Combined with the energy autonomy index Established energy safety factor Output the actual roll film execution stroke According to the actual film roll execution stroke Generate motor drive PWM signal .

[0161] The parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0162] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling discontinuous transmission in agricultural IoT based on energy autonomy, characterized in that, include: Extended Kalman filter iterative operation is performed on the collected battery terminal physical sampling data to obtain the real-time available energy value. An environmental geometric shading coefficient is introduced to perform time-domain integration operation on the historical illumination power curve to generate the predicted energy gain value. Based on the calculated mechanical rigidity task energy consumption, the real-time available energy value and the predicted energy gain value are normalized to generate the energy autonomy index. Multi-level hysteresis comparison logic is performed on the energy autonomy index to generate a set of network operating parameters. The set of network operating parameters is mapped and encapsulated into a cooperative scheduling instruction packet and physical broadcast is performed. In response to the cooperative scheduling instruction packet, the hardware state is reconstructed to generate sensor aggregated data. The system monitors dual-channel quadrature pulse signals and DC bus voltage to generate an energy feedback status indicator. It combines the energy autonomy index and the data buffer queue length based on the sensor aggregated data to quantify the real-time transmission cost. The real-time transmission cost is compared with the preset communication trigger threshold to generate a discontinuous burst transmission enable command. The system outputs a cloud-reported data stream based on the energy feedback status indicator. The system analyzes the data stream reported from the cloud to extract the measured temperature inside the greenhouse. It then uses a discrete incremental PID algorithm to generate the theoretical target travel of the film winding process, combined with the preset target temperature. Finally, it outputs the actual film winding travel based on the energy safety factor established by the energy autonomy index. Based on the actual film winding travel, it generates a motor drive PWM signal.

2. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 1, characterized in that, The method for generating the energy autonomy index includes: The battery terminal voltage and charging / discharging current are collected synchronously to obtain physical sampling data of the battery terminal. The extended Kalman filter algorithm is used to iteratively calculate the physical sampling data of the battery terminal to obtain a smooth state of charge ratio value. Based on the Coulomb integral principle, the smooth state of charge ratio value is mapped and converted into a real-time available energy value. The current moment and historical illumination power curves are retrieved, and the ambient geometric shading coefficient is introduced to perform amplitude correction and weighted attenuation processing on the historical illumination power curves to obtain the effective power prediction value. Then, the effective power prediction value is integrated in the time domain to generate the predicted energy gain value. The system retrieves preset mechanical task parameters to calculate the energy consumption of rigid mechanical tasks. It then combines the real-time available energy value, the predicted energy gain value, and the preset average working voltage of the battery pack to obtain the total available energy budget. A defined minimum survival power consumption is introduced to constrain and correct the total available energy budget in order to establish effective energy reserves. The effective energy reserves and the energy consumption of rigid mechanical tasks are normalized to generate an energy autonomy index.

3. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 1, characterized in that, The method for generating the aggregated sensor data includes: Based on the energy autonomy index, a multi-level hysteresis comparison logic is used to determine the network cooperation mode identifier. Based on the network cooperation mode identifier, the reference sampling period is calculated through a nonlinear adaptive mapping function. The network cooperation mode identifier and the reference sampling period are encapsulated into a set of network working parameters. The network operating parameter set is parsed, and the logical control parameters of the network operating parameter set are mapped to communication physical frames. The communication physical frames include multicast target identifiers, global synchronization wake-up time, acquisition frequency control words, differential compression ratio instructions, and mode status bits. The communication physical frames are encapsulated into cooperative scheduling instruction packets and physically broadcast through radio frequency units. The system parses the collaborative scheduling instruction packet, uses the global synchronization wake-up time to calibrate the local clock, resets the trigger cycle of the hardware timer interrupt controller according to the acquisition frequency control word, and executes the local differential encoding algorithm in response to the differential compression ratio instruction to generate sensor aggregated data.

4. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 3, characterized in that, The step of using multi-level hysteresis comparison logic to determine the network cooperative mode identifier includes: Read the preset saturation threshold and critical threshold, which respectively define the control state boundaries for the agricultural Internet of Things system to enter the unrestricted operation state and the minimum maintenance state; Obtain the real-time calculated energy autonomy index and compare it numerically with the saturation threshold and critical threshold respectively; If the energy autonomy index is greater than or equal to the saturation threshold, the solar film rolling device is determined to be in an energy-sufficient state, and the network collaboration mode identifier is set to full-load transparent transmission mode. If the energy autonomy index is between the critical threshold and the saturation threshold, the solar film rolling device is determined to be in an energy balance adjustment state, and the network cooperative mode identifier is set to adaptive adjustment mode. If the energy autonomy index is less than or equal to the critical threshold, the solar film rolling device is determined to be in an energy-deficient state, and the network collaboration mode identifier is set to sleep maintenance mode.

5. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 1, characterized in that, The method for outputting the cloud-reported data stream includes: Based on the collected dual-channel quadrature pulse signals, the real-time rotation direction and real-time rotational angular velocity of the DC geared motor are analyzed, and the DC bus voltage is monitored synchronously. When the real-time rotation direction, real-time rotational angular velocity, and DC bus voltage simultaneously meet the preset feedback threshold conditions, an energy feedback status indicator is generated. The feedback threshold conditions include simultaneously satisfying both kinematic state judgment conditions and electrical state judgment conditions; the kinematic state judgment condition is that the real-time rotation direction is consistent with the preset film unfolding operation direction, and the real-time rotation angular velocity exceeds the preset feedback speed threshold; the electrical state judgment condition is that the DC bus voltage is greater than the sum of the current open-circuit voltage of the lithium battery pack and the preset anti-interference hysteresis voltage threshold. Retrieve the energy feedback status flag and energy autonomy index, analyze the data cache queue length based on the sensor aggregated data, construct a multi-dimensional transmission cost function for quantitative calculation to generate real-time transmission cost; The system compares the real-time transmission cost with the preset communication trigger threshold to generate a discontinuous burst transmission enable command. It identifies the energy supply condition based on the energy feedback status identifier to execute differentiated power supply and transmission strategies. It also encapsulates the aggregated sensor data into a cloud-reported data stream.

6. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 5, characterized in that, The method for analyzing the real-time rotation direction and real-time rotational angular velocity of the DC geared motor includes: The dual-channel quadrature pulse signal originating from the DC geared motor is acquired through the signal acquisition interface. The dual-channel quadrature pulse signal includes an A-phase pulse and a B-phase pulse with a phase difference of 90 degrees. The timing phase relationship between the A-phase pulse and the B-phase pulse is analyzed by an orthogonal decoding algorithm to determine the real-time rotation direction of the DC geared motor. The signal frequency of the dual-channel orthogonal pulse signal is calculated using the pulse frequency calculation method, and the real-time rotational angular velocity of the DC geared motor is calculated based on the preset number of magnetic pole pairs and reduction ratio. The specific execution logic of the inversion solution includes: calculating the signal frequency and the circumference constant. The product of the number of magnetic pole pairs and the reduction ratio is used to obtain the electrical angular frequency, and the electrical angular frequency is divided by the product of the number of magnetic pole pairs and the reduction ratio to obtain the real-time rotational angular velocity.

7. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 5, characterized in that, The implementation method of the differentiated power supply and transmission strategy includes: In response to discontinuous burst transmission enable commands, the logic state of the energy feedback status flag is detected to perform power supply loop switching and transmission control; Before performing the power supply circuit switching, a direct power supply path from the DC bus to the power input terminal of the broadband wireless radio frequency transmission unit and a regulated power supply path from the lithium battery pack to the power input terminal of the broadband wireless radio frequency transmission unit are pre-constructed. If the energy feedback status flag is detected as a valid high-level state 1, it is determined that the solar film rolling device is in dynamic potential energy feedback mode, and a bypass switching command is issued to conduct the direct power supply energy path. The back electromotive force generated by the DC geared motor drives the broadband wireless radio frequency transmission unit to perform full-speed burst transmission of the sensor aggregated data. If the energy feedback status flag is detected as invalid low level 0, it is determined that the solar film roll device is in a static energy storage consumption state. A battery power supply command is issued to conduct the regulated power supply path, consume the chemical energy storage of the lithium battery pack to activate the broadband wireless radio frequency transmission unit to send sensor aggregated data and reset the data buffer queue length.

8. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 1, characterized in that, The method for generating the motor drive PWM signal includes: The data stream reported from the cloud is analyzed to extract the measured temperature inside the greenhouse. The temperature control error between the measured temperature inside the greenhouse and the preset target temperature is calculated. The theoretical film rolling adjustment increment is solved using a discretized incremental PID algorithm. The theoretical film rolling target stroke stored in the local control register at the previous sampling time is combined to generate the theoretical film rolling target stroke at the current sampling time. The energy safety factor is calculated based on the energy autonomy index using the energy safety factor function. The energy safety factor is then used to correct the theoretical film winding target stroke at the current sampling time to generate the actual film winding execution stroke. The target pulse count value is determined based on the actual film winding stroke. Algebraic difference calculation is performed in combination with the current cumulative pulse value collected in real time to generate the position control deviation. The position control deviation is used to generate the motor drive PWM signal, and the real-time phase current is collected synchronously to estimate the real-time load torque.

9. The agricultural IoT discontinuous transmission control method based on energy autonomy according to claim 8, characterized in that, The method for calculating the theoretical film adjustment increment includes: Obtain the temperature control error at the current sampling time, the temperature control error at the previous sampling time, and the temperature control error at the sampling time before that. Calculate the difference between the temperature control error at the current sampling time and the temperature control error at the previous sampling time, and multiply the difference by a preset proportional gain coefficient to generate a proportional term; Multiply the temperature control error at the current sampling time by the preset integral time constant to generate the integral term; Calculate the second-order difference consisting of the temperature control error at the current sampling time, the temperature control error at the previous sampling time, and the temperature control error at the time before that sampling time, and multiply the second-order difference by a preset differential time constant to generate a differential term; The proportional, integral, and differential terms are algebraically summed to generate the theoretical film adjustment increment that drives the DC geared motor to perform relative displacement.

10. An agricultural IoT discontinuous transmission control system based on energy autonomy, used to implement the agricultural IoT discontinuous transmission control method based on energy autonomy as described in any one of claims 1-9, characterized in that, The system includes an energy autonomy module, a collaborative scheduling module, a transmission control module, and an environmental regulation module; The energy autonomy module is used to perform extended Kalman filter iterative operation on the collected battery terminal physical sampling data to obtain the real-time available energy value, introduce the environmental geometric shading coefficient to perform time-domain integration operation on the historical light power curve to generate the predicted energy gain value, and perform normalization processing on the real-time available energy value and the predicted energy gain value based on the solved mechanical rigidity task energy consumption to generate the energy autonomy index. The collaborative scheduling module is used to perform multi-level hysteresis comparison logic on the energy autonomy index to generate a network working parameter set, map and encapsulate the network working parameter set into a collaborative scheduling instruction package and perform physical broadcast, and reconstruct the hardware state in response to the collaborative scheduling instruction package to generate sensor aggregated data. The transmission control module is used to monitor dual-channel quadrature pulse signals and DC bus voltage to generate an energy feedback status indicator, combine the energy autonomy index and the data buffer queue length based on the sensor aggregated data to quantify the real-time transmission cost, compare the real-time transmission cost with the preset communication trigger threshold to generate a discontinuous burst transmission enable command, and output the cloud reporting data stream based on the energy feedback status indicator. The environmental control module is used to parse the data stream reported from the cloud to extract the measured temperature inside the shed, combine the preset target temperature with the discrete incremental PID algorithm to generate the theoretical film rolling target stroke, combine the energy safety factor established based on the energy autonomy index to output the actual film rolling execution stroke, and generate the motor drive PWM signal according to the actual film rolling execution stroke.

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