Automatic control system and control method for sunshade

Abstract

The invention relates to the technical field of intelligent sunshade equipment control, in particular to an automatic control system and method for a sunshade, and aims to solve the problem that an existing automatic control system for the sunshade generally does not fully integrate multi-dimensional environment parameters and user intervention behaviors in the aspect of strategy generation, and the strategy generation efficiency is high. The problems that energy consumption, comfort and illumination uniformity are difficult to optimize synchronously under a dynamic meteorological condition, so that a control strategy is lack of individuation and foresight are solved; multi-dimensional environment semantic modeling, short-term illumination trend prediction and user preference learning are fused through a sun shading strategy dynamic generation module, a dynamic weighted multi-target optimization mechanism is constructed, energy consumption, comfort and illumination uniformity are synchronously considered under the condition that equipment constraints are met, a control target and an evaluation standard are updated in real time based on user intervention records, and the system is high in practicability. Accurate, prospective and highly personalized sunshade strategy generation is realized, and the intelligent level and comprehensive performance of the system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent sunshade equipment control technology, and more specifically, to an automated control system and control method for sunshades. Background Technology

[0002] With the development of smart cities, green buildings, and smart homes, building shading systems are becoming increasingly important in terms of energy conservation, comfort, and extending the lifespan of buildings. Traditional shading systems rely on manual operation or simple timer control, which is difficult to respond to real-time weather changes. They suffer from problems such as delayed response, high energy consumption, and poor user experience. In severe weather, they are prone to equipment damage or safety hazards. At the same time, they cannot adjust the shading status in time when there is insufficient light, thus reducing the efficiency of natural lighting.

[0003] Reference patent application CN121325611A discloses a multi-input multi-output fuzzy neural network control method and system for Panax notoginseng cultivation greenhouses. The method includes: acquiring raw parameters including greenhouse temperature, humidity, light intensity, water and fertilizer concentration, Panax notoginseng growth status, and water flow velocity, and standardizing them to obtain input parameters; inputting them into a three-layer backpropagation neural network pre-trained model, and outputting preliminary adjustment coefficients for six control quantities such as shading canopy opening; constructing a fuzzy rule base based on parameter coupling scenarios, inputting the preliminary adjustment coefficients to obtain fuzzy output quantities, and obtaining corrected adjustment coefficients by centroid method defuzzification; combining the equipment rated range to calculate control commands, dynamically adjusting to achieve closed-loop control, and the system automatically operates through modules such as multi-parameter acquisition and equipment control output. This application breaks through the limitations of traditional single-input single-output, reduces parameter fluctuation by more than 50%, adapts to the entire growth cycle of Panax notoginseng, improves water and fertilizer utilization and yield, reduces labor costs, and has significant economic benefits. However, existing automated control systems for sunshades typically do not fully integrate multi-dimensional environmental parameters and user intervention behaviors in strategy generation, making it difficult to simultaneously optimize energy consumption, comfort, and light uniformity under dynamic weather conditions. This results in a lack of personalization and foresight in control strategies. In the execution phase, they generally lack effective detection and autonomous recovery capabilities for anomalies such as motor jamming, positional deviation, and communication interruptions. When faced with conditions such as excessive wind speed, rainfall, or abnormal motion resistance, they cannot reliably trigger safety actions such as forced retraction, retraction, or locking, posing a risk of insufficient equipment protection. At the operation and maintenance level, they mostly adopt fixed-cycle maintenance or post-fault handling mechanisms, failing to quantitatively model the degradation process of key components such as motors, transmission mechanisms, and locking devices based on operating status and environmental stress. This makes it difficult to achieve dynamic assessment and graded early warning of remaining lifespan, thus restricting the improvement of system reliability and maintenance efficiency.

[0004] To address the aforementioned problems, this invention proposes an automated control system and method for sunshades. Summary of the Invention

[0005] The purpose of this invention is to provide an automated control system and method for sunshades, which solves the problems of existing automated control systems for sunshades, which often fail to fully integrate multi-dimensional environmental parameters and user intervention behaviors in strategy generation, making it difficult to simultaneously optimize energy consumption, comfort, and light uniformity under dynamic weather conditions, resulting in a lack of personalized and forward-looking control strategies; in the execution stage, they generally lack effective detection and autonomous recovery capabilities for anomalies such as motor jamming, position deviation, and communication interruption, and cannot reliably trigger safety actions such as forced retraction, retraction, or locking when faced with conditions such as excessive wind speed, rainfall, or abnormal motion resistance, posing a risk of insufficient equipment protection; at the operation and maintenance level, they mostly adopt fixed-period maintenance or post-fault handling mechanisms, failing to quantitatively model the degradation process of key components such as motors, transmission mechanisms, and locking devices based on operating status and environmental stress, making it difficult to achieve dynamic assessment and graded early warning of remaining life, thus restricting the improvement of system reliability and maintenance efficiency.

[0006] The objective of this invention is achieved through the following technical solution: An automated control system for sunshades, applied to a sunshade control and management platform, includes: The shading environment data processing module is used to collect shading environment parameters from the environment where the shading canopy is located, preprocess the collected shading environment parameters, and output standardized shading environment parameters. The shading strategy dynamic generation module performs multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction based on standardized shading environment parameters and user manual intervention records. It dynamically generates shading strategies through multi-objective optimization and updates personalized control strategies according to user preferences. The awning collaborative control module is used to decompose the shading strategy into collaborative control commands for each awning segment, and integrate manual commands, App control and external control platform linkage signals, and uniformly schedule and execute them according to priority; The fault-tolerant execution module of the sunshade is used to execute collaborative control commands. When motor jamming, position deviation or communication interruption is detected, a self-recovery mechanism is activated, and forced retraction, locking or retraction is performed when wind speed exceeds the limit, rainfall or abnormal motion resistance occurs. The awning maintenance early warning module is used to establish a prediction model of the remaining life of the motor, transmission mechanism and locking device based on standardized shading environment parameters and detection information of motor jamming and position deviation, and to trigger a maintenance early warning when the predicted life is lower than a set threshold.

[0007] In a preferred embodiment of the present invention, the process of multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction based on standardized shading environment parameters and user manual intervention records in the shading strategy dynamic generation module includes: Obtain standardized shading environment parameters at the current moment. Generate thermal intensity from ambient temperature and relative humidity, humidity intensity from relative humidity and wind speed, and wind intensity from wind speed and wind direction to form a semantic description of the current environment. Obtain the semantic description of the environment at the previous moment and perform a weighted average of the two to generate the current multidimensional environmental scenario. Get the user manual intervention record with the time closest to the current moment and the multidimensional environmental scenario at the time of the occurrence. Calculate the maximum absolute difference of each component between this scenario and the current multidimensional environmental scenario as the scenario distance. If the scenario distance is less than the preset matching threshold, the corresponding target opening is used as the user preference label. Otherwise, no preference label is set. Combine the user preference label with the current multidimensional environmental scenario to generate a multidimensional environmental scenario containing the user preference. Obtain a recent fixed-length sequence of horizontal light intensity, calculate the rate of change of light intensity at adjacent moments from the end to the beginning, and assign contribution weights based on the sign and magnitude of the rate of change. Combine the light intensity trend representation with the light intensity at the most recent moment, extrapolate in the trend direction to obtain the predicted value of the horizontal light intensity at the next moment, and add the predicted value as a new component to the multidimensional environmental scenario that includes user preferences, and output the final multidimensional environmental semantic scenario.

[0008] In a preferred embodiment of the present invention, the process of dynamically generating a shading strategy through multi-objective optimization in the shading strategy dynamic generation module, and updating the personalized control strategy according to user preferences, includes: Obtain the actual values ​​of energy consumption, comfort, and light uniformity under the current control command of the shading device, and obtain the corresponding ideal values ​​for each. Calculate the absolute deviations of each item, and substitute the absolute deviations of energy consumption, comfort, and light uniformity into the dynamic weight calculation expression to obtain the energy consumption weight, comfort weight, and light uniformity weight. Obtain the minimum and maximum opening constraints of the shading device, construct a weighted objective function, and under the condition that the opening is not less than the minimum value and not greater than the maximum value, solve for the opening combination that minimizes the weighted objective function and output the initial shading strategy. The most recent user manual intervention record is obtained from the operation log. The maximum tolerable illuminance, the center value of the comfort temperature, and the energy consumption sensitivity coefficient are extracted. The maximum tolerable illuminance is used to set the upper limit threshold of the glare index. The center value of the comfort temperature is used as the reference temperature for the thermal discomfort index. The energy consumption sensitivity coefficient and the reference energy consumption value are used to calculate the adjusted ideal energy consumption value. Based on the adjusted ideal energy consumption value and the updated comfort calculation method, the actual and ideal values ​​of energy consumption, comfort, and light uniformity are obtained again, the corresponding absolute deviations are calculated, and these deviations are substituted into the dynamic weight calculation expression to obtain the optimization weights based on user preferences. An updated weighted objective function is constructed, and the optimal opening combination is solved under the same opening constraint to output a personalized shading strategy.

[0009] In a preferred embodiment of the present invention, the process of decomposing the shading strategy into collaborative control commands for each canopy segment in the shading canopy collaborative control module includes: Obtain the shading strategy and extract the target physical quantities. At the same time, obtain the solar azimuth angle, determine the solar region to which it belongs, obtain the greenhouse segment list, and determine the shading task type for each segment based on the correspondence between the solar region and the segment orientation. Based on the target physical quantity and the corresponding actual value inside the greenhouse, calculate the light deviation or temperature deviation, and generate the shading action intensity accordingly. For each segment, allocate the control amplitude according to its shading task type, and then map the control amplitude to the deployment ratio or blade angle according to the segment drive type to form the preliminary control quantity. The initial control quantities of each segment are sequentially coordinated in terms of time and space. After all segments are processed, the coordinated control commands for all canopy segments are output.

[0010] In a preferred embodiment of the present invention, the process of executing collaborative control commands in the sunshade fault-tolerant execution module and activating a self-recovery mechanism when motor jamming, positional deviation, or communication interruption is detected includes: Extract the target position, direction of movement, and positioning determination strategy identifier from the collaborative control command, start the motor to run in the specified direction, and perform positioning determination according to the strategy type. Stop the motor when the determination is met. During execution, three types of anomalies are monitored simultaneously: whether jamming occurs is determined by the motor drive enable signal and the position feedback update timestamp; whether position deviation occurs is determined by the validity of the original position feedback signal and the consistency between the actual movement direction and the command direction; and whether communication interruption occurs is determined by the heartbeat signal reception record. Once any abnormal event is triggered, all motor drive outputs are immediately cleared, and a recovery strategy is selected based on the event combination. The system executes the action sequence of the selected recovery strategy, while simultaneously collecting the current location of the awning, the motor status, and abnormal event markers. These data, along with the recovery strategy identifier, are packaged into a status report and sent to the monitoring terminal, completing the entire self-recovery mechanism process.

[0011] In a preferred embodiment of the present invention, the process of establishing a prediction model for the remaining life of the motor, transmission mechanism, and locking device in the awning maintenance early warning module based on standardized shading environment parameters and detection information on motor jamming and position deviation includes: Obtain the motor jamming coefficient, the normalized value of the position deviation and their corresponding weights. After verifying that the sum of the weights is equal to one, calculate the weighted fusion value and subtract the obtained fusion value from one to obtain the current comprehensive health index. At the same time, obtain the preset and fixed failure threshold, degradation nonlinear shape parameter and baseline degradation rate constant. If the degradation nonlinear shape parameter is equal to one, terminate the process; otherwise, continue execution. Obtain the environmental sensitivity coefficient vector and the standardized shading environmental parameter vector, multiply the two items one by one according to their corresponding relationship, sum them, and then take the natural index to obtain the environmental stress acceleration factor. Substitute the current comprehensive health index, failure threshold, degradation nonlinear shape parameter, baseline degradation rate constant and environmental stress acceleration factor into the formula to calculate the predicted remaining service life. Obtain a comprehensive health index sequence containing multiple time points and their corresponding timestamps. Use a recursive estimation algorithm to fine-tune the degradation nonlinear shape parameter, the baseline degradation rate constant, and the environmental sensitivity coefficient vector online, and output the updated degradation nonlinear shape parameter, the baseline degradation rate constant, and the environmental sensitivity coefficient vector.

[0012] As a preferred embodiment of the present invention, an automated control method for a sunshade includes the following steps: Step 1: Collect shading environment parameters from the environment where the shading canopy is located, preprocess the collected shading environment parameters, and output standardized shading environment parameters; Step 2: Based on standardized shading environment parameters and user manual intervention records, perform multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction. Dynamically generate shading strategies through multi-objective optimization and update personalized control strategies according to user preferences. Step 3: Decompose the shading strategy into collaborative control commands for each canopy segment, and integrate manual commands, App control and external control platform linkage signals, and schedule and execute them uniformly according to priority; Step 4: Execute the coordinated control command. When motor jamming, position deviation or communication interruption is detected, the self-recovery mechanism is activated. When wind speed exceeds the limit, rainfall or abnormal motion resistance occurs, the forced retraction, locking or retraction is performed. Step 5: Based on standardized shading environment parameters and detection information on motor jamming and position deviation, establish a prediction model for the remaining life of the motor, transmission mechanism and locking device, and trigger a maintenance warning when the predicted life is lower than the set threshold.

[0013] Compared with the prior art, the advantages of this invention are: (1) In this invention, the shading strategy dynamic generation module integrates multi-dimensional environmental semantic modeling, short-term light trend prediction and user preference learning to construct a dynamic weighted multi-objective optimization mechanism. Under the constraints of equipment, it simultaneously considers energy consumption, comfort and light uniformity, and updates the control target and evaluation standard in real time based on user intervention records. This achieves accurate, forward-looking and highly personalized shading strategy generation, which significantly improves the intelligence level and comprehensive performance of the system. (2) In this invention, the fault-tolerant execution module of the sunshade can monitor faults such as motor jamming, position deviation and communication interruption in real time, and intelligently trigger self-recovery strategies such as reverse release, local position preservation or sensor degradation. When the wind speed exceeds the limit, or the rainfall or motion resistance is abnormal, it automatically performs safety actions such as forced retraction, retreat or locking. The dual mechanism of stable timing and manual reset ensures safe exit. Combined with status feedback and event log, it forms a closed-loop fault-tolerant control, which significantly improves the safety, robustness and autonomous recovery capability of the system under complex working conditions. (3) In this invention, the maintenance and early warning module of the sunshade canopy integrates standardized environmental parameters and status information such as motor jamming and position deviation to construct an online updatable remaining life prediction model. The degree of equipment degradation is dynamically quantified by the environmental stress acceleration factor, and early warning is triggered in stages when the predicted life is lower than the threshold. At the same time, the early warning log is fully recorded to achieve accurate and predictive maintenance of key components, effectively improving system reliability and maintenance efficiency. Attached Figure Description

[0014] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention; Figure 3 This is a flowchart of the steps in the automated control method for sunshades in this invention. Detailed Implementation

[0015] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0016] Example 1: As Figure 1 As shown, the present invention proposes an automated control system for sunshades, applied to a sunshade control and management platform, comprising: The shading environment data processing module is used to collect shading environment parameters from the environment where the shading canopy is located, including horizontal light intensity, instantaneous wind speed, wind direction, ambient temperature, relative humidity and rainfall status (0 indicates no rain, 1 indicates rain). The module preprocesses the collected shading environment parameters, including outlier removal, data smoothing, multi-source time alignment and format standardization, and outputs standardized shading environment parameters. The shading environment data processing module effectively improves the reliability and stability of data through preprocessing operations such as outlier removal, data smoothing, multi-source time alignment, and format standardization. It eliminates problems such as sensor noise, communication interference, and asynchronous sampling, and outputs unified, clear, and synchronized standardized environmental parameters. This not only enhances the system's adaptability and robustness to complex and changing outdoor environments, but also provides a high-quality and highly consistent input foundation for the intelligent control decisions of the shading canopy, thereby significantly improving the perception accuracy, response efficiency, and intelligence level of the entire shading system.

[0017] The shading strategy dynamic generation module performs multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction based on standardized shading environment parameters and user manual intervention records. It dynamically generates shading strategies through multi-objective optimization and updates personalized control strategies according to user preferences. The process of multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction based on standardized shading environment parameters and user manual intervention records in the dynamic generation module of shading strategy includes: Obtain standardized shading environment parameters at the current moment, including horizontal light intensity, instantaneous wind speed, wind direction, ambient temperature, relative humidity, and rainfall status. Generate thermal intensity from ambient temperature and relative humidity, moisture intensity from relative humidity and wind speed, and wind intensity from wind speed and wind direction to form the current environmental semantic description. Obtain the environmental semantic description from the previous moment and perform a weighted average between the two (the current one has a higher weight) to generate the current multidimensional environmental scenario. Get the user manual intervention record with the time closest to the current moment and the multidimensional environmental scenario at the time of the occurrence. Calculate the maximum absolute difference of each component between this scenario and the current multidimensional environmental scenario as the scenario distance. If the scenario distance is less than the preset matching threshold, the corresponding target opening is used as the user preference label. Otherwise, no preference label is set. Combine the user preference label with the current multidimensional environmental scenario to generate a multidimensional environmental scenario containing the user preference. A recent fixed-length sequence of horizontal surface illumination intensity is obtained. The rate of change of illumination at adjacent moments is calculated from the end to the beginning. Contribution weights are assigned according to the sign and magnitude of the rate of change (the greater the recent change, the higher the weight). The sequence is weighted and summed to generate a representation of the illumination change trend. Combining the illumination change trend representation with the illumination intensity at the most recent moment, the prediction value of the horizontal surface illumination intensity at the next moment is extrapolated in the trend direction. The prediction value is added as a new component to the multidimensional environmental scenario containing user preferences. The final multidimensional environmental semantic scenario is output, which includes thermal intensity, humidity intensity, wind intensity, user preference tags, and the predicted value of illumination intensity. The process of dynamically generating shading strategies through multi-objective optimization in the shading strategy dynamic generation module, and updating personalized control strategies based on user preferences, includes: Obtain the actual energy consumption (determined by motor power consumption and associated air conditioning load), actual comfort (determined by glare index and thermal discomfort index), and actual light uniformity (determined by the illuminance variance of the working surface) under the current shading device control command, and obtain the corresponding ideal values ​​for each. Calculate the absolute deviations for each item, and substitute the absolute deviations of energy consumption, comfort, and light uniformity into the dynamic weight calculation expression: ,in It is the optimization weight of the i-th objective at time t. In the current control command The actual value of the i-th target. It is the ideal value for the i-th target. It is the absolute deviation between the current actual value and the ideal value of the i-th target. It is a positive real number hyperparameter. The denominator ensures that the sum of the three weights is equal to one, so as to obtain the energy consumption weight, comfort weight, and illumination uniformity weight. Obtain the minimum and maximum opening constraints of the shading device, construct a weighted objective function (which is the sum of the products of each weight and its corresponding absolute deviation), and under the condition that the opening is not less than the minimum value and not greater than the maximum value, solve for the opening combination that minimizes the weighted objective function and output the initial shading strategy. The most recent user manual intervention record is obtained from the operation log. The maximum tolerable illuminance, comfort temperature center value, and energy consumption sensitivity coefficient are extracted from it. The maximum tolerable illuminance is used to set the upper limit threshold of the glare index. If the glare index exceeds the upper limit threshold, the actual comfort value is increased by a penalty item proportional to the amount of excess. The comfort temperature center value is used as the reference temperature for the thermal discomfort index. The thermal discomfort index is calculated based on the difference between the ambient temperature and the reference temperature. The energy consumption sensitivity coefficient and the reference energy consumption value are used to calculate the adjusted ideal energy consumption value. Based on the adjusted ideal energy consumption value and the updated comfort calculation method, the actual and ideal values ​​of energy consumption, comfort, and light uniformity are obtained again, the corresponding absolute deviations are calculated, and these deviations are substituted into the dynamic weight calculation expression to obtain the optimization weights based on user preferences. The updated weighted objective function is constructed, and the optimal opening combination is solved under the same opening constraint to output a personalized shading strategy. The advantage of the dynamic shading strategy generation module lies in its ability to construct a multi-dimensional environmental semantic scenario based on standardized environmental parameters and user-manually recorded data, integrating heat, humidity, wind intensity, and light change trends. It identifies user preferences through scenario matching and then uses a dynamic weighting mechanism to optimize energy consumption, comfort, and light uniformity in real time. Within the constraints of shading device opening, it solves for the optimal control strategy. Simultaneously, it dynamically adjusts evaluation indicators and ideal targets by combining user-defined maximum tolerable illuminance, comfort temperature center value, and energy sensitivity coefficient, iteratively generating personalized shading solutions that better meet individual needs. This significantly improves environmental adaptability, control foresight, and user experience satisfaction while ensuring energy-efficient operation.

[0018] The awning collaborative control module is used to decompose the shading strategy into collaborative control commands for each awning segment, and integrate manual commands, App control and external control platform linkage signals, and uniformly schedule and execute them according to priority; The process of decomposing the shading strategy into collaborative control commands for each canopy segment in the shading canopy collaborative control module includes: Obtain the shading strategy and extract the target physical quantities. At the same time, obtain the solar azimuth angle and determine the solar region to which it belongs (eastern region, zenith region, western region, or southern region). Obtain the greenhouse segment list, which contains the orientation identifier and drive type of each segment. Based on the correspondence between the solar region and the segment orientation, determine the shading task type of each segment. If the segment orientation corresponds to the current solar region, it is set as the main shading. If it is a north-facing facade, it is set as no shading. The rest are set as auxiliary shading. Based on the target physical quantities (light intensity, temperature, or shading ratio) and the corresponding actual values ​​inside the greenhouse, the light deviation or temperature deviation is calculated, and the shading action intensity is generated accordingly. For each segment, the control amplitude is assigned according to its shading task type: the control amplitude of the main shading segment is equal to the shading action intensity, the control amplitude of the auxiliary shading segment is equal to the shading action intensity multiplied by the preset auxiliary ratio, and the control amplitude of the non-shading segment is set to the minimum allowable value. Then, according to the segment drive type (shading net or louver), the control amplitude is mapped to the unfolding ratio or blade angle to form the initial control quantity. The initial control quantities of each segment are sequentially coordinated in terms of time and space: first, the control quantities of the previous control cycle are combined to limit their rate of change to no more than the maximum allowable rate of change; then, they are compared with the control quantities of adjacent segments to ensure that the difference does not exceed the allowable spatial coordination threshold, and necessary adjustments are made. After all segments are processed, the coordinated control command for all canopy segments is output. The process of integrating manual commands, App control, and external control platform linkage signals in the awning collaborative control module, and scheduling their execution according to priority, includes: The system retrieves the latest arriving control commands and the current queue of commands to be executed. If the two commands originate from different sources, they are decided according to a fixed priority order: commands issued by the on-site emergency button take precedence over commands issued by the external control platform, commands issued by the external control platform take precedence over commands issued by the mobile application, commands issued by the mobile application take precedence over commands generated by the shading strategy, commands generated by the shading strategy take precedence over commands issued by the on-site control panel. If the sources are the same, the latest command takes precedence. If the mandatory safety flag is valid, the queue of commands to be executed is cleared and the mandatory safety command is set as the only command to be executed. The system analyzes the operation object and operation type of the arbitration command. If the operation object is global, corresponding control commands are generated for all canopy segments. If the operation object is a specific segment, control commands are generated only for the corresponding segment. The target state of each segment is determined according to the operation type, including fully closed, fully extended, or specified ratio and angle. During the generation process, it checks whether the target state of each segment and its adjacent segments meets the mechanical motion continuity constraint. If not, the target state of the current segment is adjusted to the adjacent value direction to the allowable deviation range, forming a coordinated issued command. The coordinated command is sent to each segment execution mechanism, and the actual status returned by the execution mechanism is obtained. If the deviation between the actual status and the target status exceeds the allowable range, the fault segment identifier is recorded, and a compensation target status is generated for the adjacent segments of the fault segment, so that the adjacent segments enhance the shading action to make up for the missing coverage. At the same time, the command source, timestamp, arbitration result, execution status and feedback result are collected and written to the operation log. After all segments are processed, a multi-source command fusion scheduling execution completion signal is output. The awning collaborative control module can intelligently decompose the global strategy into coordinated instructions for each segment, dynamically allocate shading tasks based on the sun's position and segment orientation, and ensure smooth operation and structural safety through time rate limits and spatial consistency constraints. At the same time, it integrates multi-source control instructions, arbitrates and schedules them in real time according to priority, and verifies and adjusts mechanical continuity while ensuring priority for emergency operations. The module also supports execution feedback and fault neighbor area compensation to maintain the integrity of system functions and record complete logs, achieving highly reliable, robust, and multi-level collaborative unified control.

[0019] The fault-tolerant execution module of the sunshade is used to execute collaborative control commands. When motor jamming, position deviation or communication interruption is detected, a self-recovery mechanism is activated, and forced retraction, locking or retraction is performed when wind speed exceeds the limit, rainfall or abnormal motion resistance occurs. The process by which the sunshade fault-tolerant execution module executes collaborative control commands and initiates a self-recovery mechanism when motor jamming, positional deviation, or communication interruption is detected includes: Extract the target position, direction of motion, and positioning determination strategy identifier from the collaborative control command, start the motor to run in the specified direction, and perform positioning determination according to the strategy type: if it is a position threshold type, determine whether the deviation between the current position feedback and the target position is less than the allowable error; if it is a time timeout type, determine whether the running time exceeds the maximum allowable time; if it is a current characteristic type, determine whether the drive current has entered the steady state range, and stop the motor if the determination is met. During execution, three types of anomalies are monitored simultaneously: whether jamming has occurred is determined by the motor drive enable signal and the position feedback update timestamp; whether position deviation has occurred is determined by the validity of the original position feedback signal and the consistency between the actual movement direction and the command direction; and whether communication interruption has occurred is determined by the heartbeat signal reception record. Once any abnormal event is triggered, all motor drive outputs are immediately cleared, and a recovery strategy is selected according to the event combination: when jamming and position deviation exist simultaneously, a reverse release recovery strategy is selected; when only communication is interrupted, a local position preservation recovery strategy is selected; and when there is only jamming or a single position deviation, a sensor degradation recovery strategy is selected. The system executes the action sequence of the selected recovery strategy, while simultaneously collecting the current position of the awning, the motor status, and abnormal event markers. These data are packaged together with the recovery strategy identifier into a status report and sent to the monitoring terminal, completing the entire process of the self-recovery mechanism. The process of forcibly retracting, locking, or retracting the awning in the fault-tolerant execution module when wind speed exceeds the limit, rainfall occurs, or motion resistance is abnormal includes: Acquire wind speed over-limit event flags, rainfall event flags, and motion resistance abnormal event flags. When any event flag is valid, clear all motion commands. If the wind speed over-limit event flag or the rainfall event flag is valid, execute a forced retraction action. If only the motion resistance abnormal event flag is valid, execute a brief retreat action and obtain position feedback before and after the retreat. Compare the two to see if they are the same. If they are the same, execute a forced retraction action. Obtain the current position feedback and the reference value of the fully retracted position of the sunshade. When the two are consistent, execute the locking action and enter the safe standby state. At the same time, block all unfolding control commands. Then obtain the status of all current event markers. When all are invalid, start the stabilization timer and obtain the stabilization count value and the preset stabilization period threshold. When the stabilization count value reaches or exceeds the preset stabilization period threshold, generate an automatic exit permission signal. Obtain the trigger event type for this safety standby. If it is triggered by the abnormal motion resistance event marker, wait for and obtain the manual reset confirmation signal. After the automatic exit permission signal is valid or the manual reset confirmation signal arrives, exit the safety standby state. Finally, obtain the trigger event type, execution strategy identifier and the final position status of the sunshade, generate a safety event log and send it to the monitoring terminal. The advantages of the awning fault-tolerant execution module lie in its comprehensive anomaly perception and multi-level self-recovery capabilities. It can monitor faults such as motor jamming, position deviation, and communication interruption in real time during the execution of collaborative control commands. Based on the anomaly type, it intelligently selects recovery strategies such as reverse release, local position maintenance, or sensor degradation, quickly generating status reports and maintaining system controllability. Simultaneously, the module has proactive protection mechanisms against external or mechanical risks such as excessive wind speed, rainfall, and abnormal motion resistance. It can automatically trigger forced retraction, retraction, or locking actions to prevent secondary damage while ensuring equipment safety. Furthermore, it ensures the reliability of exiting the safe standby state through a dual mechanism of stable timing and manual reset. The entire process integrates status feedback, event discrimination, strategy execution, and log recording to form a closed-loop fault-tolerant control, significantly improving the awning's operational robustness, safety, and autonomous recovery capabilities under complex operating conditions.

[0020] Example 2: The technical solution of this embodiment of the invention differs from that of Example 1 in that: like Figure 2 As shown, the awning maintenance early warning module is used to establish a prediction model of the remaining life of the motor, transmission mechanism and locking device based on standardized shading environment parameters and detection information of motor jamming and position deviation, and to trigger a maintenance early warning when the predicted life is lower than a set threshold. The process of establishing a prediction model for the remaining life of the motor, transmission mechanism, and locking device in the awning operation and maintenance early warning module, based on standardized shading environment parameters and detection information on motor jamming and position deviation, includes: Obtain the motor jamming coefficient, the normalized value of the position deviation and their corresponding weights. After verifying that the sum of the weights is equal to one, calculate the weighted fusion value and subtract the obtained fusion value from one to obtain the current comprehensive health index. At the same time, obtain the preset and fixed failure threshold, degradation nonlinear shape parameter and baseline degradation rate constant. If the degradation nonlinear shape parameter is equal to one, terminate the process; otherwise, continue execution. Obtain the environmental sensitivity coefficient vector and the standardized shading environmental parameter vector, multiply them item by item according to their corresponding relationships, sum them, and then take the natural index to obtain the environmental stress acceleration factor. Substitute the current comprehensive health index, failure threshold, degradation nonlinear shape parameter, baseline degradation rate constant, and environmental stress acceleration factor into the formula to calculate the predicted remaining service life: ,in This indicates the projected remaining useful life, which is the length of time the equipment can continue to operate reliably from the current moment. The comprehensive health index at the current moment is obtained by fusing the motor jamming coefficient and the normalized position deviation, with a value ranging from 0 to 1. This represents the preset failure threshold, indicating the critical point at which the motor, transmission mechanism, or locking device becomes unreliable. This represents the nonlinear shape parameter of degradation, used to describe how the degradation rate changes as health indicators decrease. Represents the baseline degradation rate constant, and represents the natural aging rate under standard conditions. This represents the vector of environmental sensitivity coefficients, corresponding to the degree to which environmental parameters contribute to degradation. Represents a standardized shading environment parameter vector. This represents the comprehensive environmental stress acceleration factor, which is always greater than zero and is used to quantify the overall accelerating effect of the current environment on degradation. Obtain a comprehensive health index sequence containing multiple time points and their corresponding timestamps. Use a recursive estimation algorithm to fine-tune the degradation nonlinear shape parameter, the baseline degradation rate constant, and the environmental sensitivity coefficient vector online. Output the updated degradation nonlinear shape parameter, the baseline degradation rate constant, and the environmental sensitivity coefficient vector. Replace the original corresponding parameters with the updated parameters for the next prediction. Output a signal indicating that the model parameter update is complete. The maintenance early warning module for awnings triggers a maintenance warning when the predicted lifespan is below a set threshold, including the following process: Obtain the predicted remaining service life and the warning time threshold, and calculate twice the warning threshold. Determine whether the predicted remaining service life is less than or equal to the warning time threshold to obtain the secondary warning condition establishment mark. Determine whether the predicted remaining service life is greater than the warning time threshold and less than or equal to twice the warning threshold to obtain the primary warning condition establishment mark. If the Level 2 warning condition is met and the flag is valid, an audible and visual alarm is triggered. The device identifier, the predicted remaining service life for the work order, the deterioration cause identifier, the current jamming coefficient, and the current position deviation status are obtained. The preset responsible person is queried based on the device identifier, and a maintenance work order is generated. The work order content includes the device identifier, the predicted remaining service life, the deterioration cause identifier, the current jamming coefficient, and the current position deviation status. The maintenance work order is pushed to the preset responsible person. If the Level 1 warning condition is met and the flag is valid, a maintenance prompt is recorded on the operation and maintenance platform. Obtain the warning trigger time, prediction basis, associated status data, and associated environmental data; determine the warning level; reserve fields for handling records; and write the warning trigger time, prediction basis, associated status data, associated environmental data, warning level, and handling record fields into the log. The advantages of the awning maintenance early warning module lie in its ability to establish an accurate remaining life prediction model based on standardized environmental parameters and detection information such as motor jamming and position deviation. It dynamically assesses the degradation of motors, transmission mechanisms, and locking devices by calculating comprehensive health indicators and environmental stress acceleration factors, and uses a recursive estimation algorithm to fine-tune model parameters online to improve prediction accuracy. When the predicted life falls below a set threshold, it triggers different levels of maintenance early warnings, from audible and visual alarms to generating maintenance work orders and notifying responsible personnel, ensuring timely measures are taken to prevent equipment failure. Furthermore, the module records detailed early warning logs, including early warning trigger time, basis, level, and related status and environmental data, providing crucial support for subsequent analysis. This enables predictive maintenance of key awning components, effectively extending equipment life, reducing the risk of unexpected downtime, and improving system reliability and maintenance efficiency.

[0021] Example 3: The technical solution of this embodiment of the invention differs from that of Example 1 and Example 2 in that: like Figure 3 As shown, an automated control method for a sunshade includes the following steps: Step 1: Collect shading environment parameters from the environment where the shading canopy is located, preprocess the collected shading environment parameters, and output standardized shading environment parameters; Step 2: Based on standardized shading environment parameters and user manual intervention records, perform multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction. Dynamically generate shading strategies through multi-objective optimization and update personalized control strategies according to user preferences. Step 3: Decompose the shading strategy into collaborative control commands for each canopy segment, and integrate manual commands, App control and external control platform linkage signals, and schedule and execute them uniformly according to priority; Step 4: Execute the coordinated control command. When motor jamming, position deviation or communication interruption is detected, the self-recovery mechanism is activated. When wind speed exceeds the limit, rainfall or abnormal motion resistance occurs, the forced retraction, locking or retraction is performed. Step 5: Based on standardized shading environment parameters and detection information on motor jamming and position deviation, establish a prediction model for the remaining life of the motor, transmission mechanism and locking device, and trigger a maintenance warning when the predicted life is lower than the set threshold. The advantages of this automated control method for sunshades lie in its integration of environmental perception and user preferences, dynamic generation of personalized sunshade strategies, and precise coordination of execution across all canopy segments. It supports multi-source command priority scheduling, fault self-recovery, and emergency protection in extreme weather. Furthermore, it predicts the lifespan of key components online based on equipment status and environmental stress, enabling graded early warning and predictive maintenance, thereby comprehensively improving the system's safety, reliability, and intelligence.

[0022] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. An automated control system for sunshades, applied to a sunshade control and management platform, characterized in that, include: The shading environment data processing module is used to collect shading environment parameters from the environment where the shading canopy is located, preprocess the collected shading environment parameters, and output standardized shading environment parameters. The shading strategy dynamic generation module performs multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction based on standardized shading environment parameters and user manual intervention records. It dynamically generates shading strategies through multi-objective optimization and updates personalized control strategies according to user preferences. The awning collaborative control module is used to decompose the shading strategy into collaborative control commands for each awning segment, and integrate manual commands, App control and external control platform linkage signals, and uniformly schedule and execute them according to priority; The fault-tolerant execution module of the sunshade is used to execute collaborative control commands. When motor jamming, position deviation or communication interruption is detected, a self-recovery mechanism is activated, and forced retraction, locking or retraction is performed when wind speed exceeds the limit, rainfall or abnormal motion resistance occurs. The awning maintenance early warning module is used to establish a prediction model of the remaining life of the motor, transmission mechanism and locking device based on standardized shading environment parameters and detection information of motor jamming and position deviation, and to trigger a maintenance early warning when the predicted life is lower than a set threshold.

2. The automated control system for a sunshade awning according to claim 1, characterized in that, The process of multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction based on standardized shading environment parameters and user manual intervention records in the dynamic generation module of the shading strategy includes: Obtain standardized shading environment parameters at the current moment. Generate thermal intensity from ambient temperature and relative humidity, humidity intensity from relative humidity and wind speed, and wind intensity from wind speed and wind direction to form a semantic description of the current environment. Obtain the semantic description of the environment at the previous moment and perform a weighted average of the two to generate the current multidimensional environmental scenario. Get the user manual intervention record with the time closest to the current moment and the multidimensional environmental scenario at the time of the occurrence. Calculate the maximum absolute difference of each component between this scenario and the current multidimensional environmental scenario as the scenario distance. If the scenario distance is less than the preset matching threshold, the corresponding target opening is used as the user preference label. Otherwise, no preference label is set. Combine the user preference label with the current multidimensional environmental scenario to generate a multidimensional environmental scenario containing the user preference. Obtain a recent fixed-length sequence of horizontal light intensity, calculate the rate of change of light intensity at adjacent moments from the end to the beginning, and assign contribution weights based on the sign and magnitude of the rate of change. Combine the light intensity trend representation with the light intensity at the most recent moment, extrapolate in the trend direction to obtain the predicted value of the horizontal light intensity at the next moment, and add the predicted value as a new component to the multidimensional environmental scenario that includes user preferences, and output the final multidimensional environmental semantic scenario.

3. The automated control system for a sunshade awning according to claim 2, characterized in that, The process of dynamically generating shading strategies through multi-objective optimization in the shading strategy dynamic generation module, and updating personalized control strategies based on user preferences, includes: Obtain the actual values ​​of energy consumption, comfort, and light uniformity under the current control command of the shading device, and obtain the corresponding ideal values ​​for each. Calculate the absolute deviations of each item, and substitute the absolute deviations of energy consumption, comfort, and light uniformity into the dynamic weight calculation expression to obtain the energy consumption weight, comfort weight, and light uniformity weight. Obtain the minimum and maximum opening constraints of the shading device, construct a weighted objective function, and under the condition that the opening is not less than the minimum value and not greater than the maximum value, solve for the opening combination that minimizes the weighted objective function and output the initial shading strategy. The most recent user manual intervention record is obtained from the operation log. The maximum tolerable illuminance, the center value of the comfort temperature, and the energy consumption sensitivity coefficient are extracted. The maximum tolerable illuminance is used to set the upper limit threshold of the glare index. The center value of the comfort temperature is used as the reference temperature for the thermal discomfort index. The energy consumption sensitivity coefficient and the reference energy consumption value are used to calculate the adjusted ideal energy consumption value. Based on the adjusted ideal energy consumption value and the updated comfort calculation method, the actual and ideal values ​​of energy consumption, comfort, and light uniformity are obtained again, the corresponding absolute deviations are calculated, and these deviations are substituted into the dynamic weight calculation expression to obtain the optimization weights based on user preferences. An updated weighted objective function is constructed, and the optimal opening combination is solved under the same opening constraint to output a personalized shading strategy.

4. The automated control system for a sunshade awning according to claim 1, characterized in that, The process of decomposing the shading strategy into collaborative control commands for each canopy segment in the shading canopy collaborative control module includes: Obtain the shading strategy and extract the target physical quantities. At the same time, obtain the solar azimuth angle, determine the solar region to which it belongs, obtain the greenhouse segment list, and determine the shading task type for each segment based on the correspondence between the solar region and the segment orientation. Based on the target physical quantity and the corresponding actual value inside the greenhouse, calculate the light deviation or temperature deviation, and generate the shading action intensity accordingly. For each segment, allocate the control amplitude according to its shading task type, and then map the control amplitude to the deployment ratio or blade angle according to the segment drive type to form the preliminary control quantity. The initial control quantities of each segment are sequentially coordinated in terms of time and space. After all segments are processed, the coordinated control commands for all canopy segments are output.

5. The automated control system for a sunshade awning according to claim 4, characterized in that, The process of integrating manual commands, App control, and external control platform linkage signals in the sunshade collaborative control module, and scheduling and executing them in a unified manner according to priority, includes: Get the latest arriving control command and the current queue of commands to be executed. If the two commands come from different sources, they are decided according to a fixed priority order. If they come from the same source, the latest command takes precedence. If the mandatory safety flag is valid, the queue of commands to be executed is cleared and the mandatory safety command is set as the only command to be executed. The operation objects and operation types of the arbitration command are analyzed. During the generation process, it is checked whether the target state of each segment and its adjacent segments meets the mechanical motion continuity constraint. If not, the target state of the current segment is adjusted to the adjacent value direction to the allowable deviation range, forming a coordinated command to be issued. The coordinated commands are sent to the execution mechanisms of each segment, and the actual status returned by the execution mechanisms is obtained. At the same time, the source of the command, timestamp, arbitration result, execution status and feedback result are collected and written into the operation log. After all segments are processed, a multi-source command fusion scheduling execution completion signal is output.

6. The automated control system for a sunshade awning according to claim 1, characterized in that, The process by which the fault-tolerant execution module of the sunshade executes collaborative control commands and initiates a self-recovery mechanism when motor jamming, positional deviation, or communication interruption is detected includes: Extract the target position, direction of movement, and positioning determination strategy identifier from the collaborative control command, start the motor to run in the specified direction, and perform positioning determination according to the strategy type. Stop the motor when the determination is met. During execution, three types of anomalies are monitored simultaneously: whether jamming occurs is determined by the motor drive enable signal and the position feedback update timestamp; whether position deviation occurs is determined by the validity of the original position feedback signal and the consistency between the actual movement direction and the command direction; and whether communication interruption occurs is determined by the heartbeat signal reception record. Once any abnormal event is triggered, all motor drive outputs are immediately cleared, and a recovery strategy is selected based on the event combination. The system executes the action sequence of the selected recovery strategy, while simultaneously collecting the current location of the awning, the motor status, and abnormal event markers. These data, along with the recovery strategy identifier, are packaged into a status report and sent to the monitoring terminal, completing the entire self-recovery mechanism process.

7. The automated control system for a sunshade awning according to claim 6, characterized in that, The process of forcibly retracting, locking, or retracting the awning fault-tolerant execution module when wind speed exceeds limits, rainfall occurs, or motion resistance is abnormal includes: Acquire wind speed over-limit event flags, rainfall event flags, and motion resistance abnormal event flags. When any event flag is valid, clear all motion commands. If the wind speed over-limit event flag or the rainfall event flag is valid, execute a forced convergence action. If only the motion resistance abnormal event flag is valid, execute a brief regress action. Obtain the current position feedback of the sunshade and the reference value of the fully retracted position. When the two are consistent, perform the locking action. Then obtain the status of all current event markers. When all are invalid, start the stabilization timer. The system retrieves the trigger event type for this safety standby. If it is triggered by an abnormal motion resistance event, it waits for and retrieves a manual reset confirmation signal. After the automatic exit permission signal is valid or the manual reset confirmation signal arrives, it exits the safety standby state. Finally, it retrieves the trigger event type, execution strategy identifier, and the final position status of the sunshade, generates a safety event log, and sends it to the monitoring terminal.

8. The automated control system for a sunshade awning according to claim 1, characterized in that, The process of establishing a prediction model for the remaining life of the motor, transmission mechanism, and locking device in the awning maintenance early warning module, based on standardized shading environment parameters and detection information on motor jamming and position deviation, includes: Obtain the motor jamming coefficient, the normalized value of the position deviation and their corresponding weights. After verifying that the sum of the weights is equal to one, calculate the weighted fusion value and subtract the obtained fusion value from one to obtain the current comprehensive health index. At the same time, obtain the preset and fixed failure threshold, degradation nonlinear shape parameter and baseline degradation rate constant. If the degradation nonlinear shape parameter is equal to one, terminate the process; otherwise, continue execution. Obtain the environmental sensitivity coefficient vector and the standardized shading environmental parameter vector, multiply the two items one by one according to their corresponding relationship, sum them, and then take the natural index to obtain the environmental stress acceleration factor. Substitute the current comprehensive health index, failure threshold, degradation nonlinear shape parameter, baseline degradation rate constant and environmental stress acceleration factor into the formula to calculate the predicted remaining service life. Obtain a comprehensive health index sequence containing multiple time points and their corresponding timestamps. Use a recursive estimation algorithm to fine-tune the degradation nonlinear shape parameter, the baseline degradation rate constant, and the environmental sensitivity coefficient vector online, and output the updated degradation nonlinear shape parameter, the baseline degradation rate constant, and the environmental sensitivity coefficient vector.

9. The automated control system for a sunshade awning according to claim 8, characterized in that, The process of triggering a maintenance warning when the predicted lifespan of the awning maintenance early warning module is lower than a set threshold includes: Obtain the predicted remaining service life and the warning time threshold, and calculate twice the warning threshold. Determine whether the predicted remaining service life is less than or equal to the warning time threshold to obtain the secondary warning condition establishment mark. Determine whether the predicted remaining service life is greater than the warning time threshold and less than or equal to twice the warning threshold to obtain the primary warning condition establishment mark. If the Level 2 warning condition is met and the flag is valid, an audible and visual alarm is triggered. The device identifier, the predicted remaining service life for the work order, the deterioration cause identifier, the current jamming coefficient, and the current position deviation status are obtained. The preset responsible person is queried based on the device identifier, a maintenance work order is generated, and the maintenance work order is pushed to the preset responsible person. If the Level 1 warning condition is met and the flag is valid, a maintenance prompt is recorded on the operation and maintenance platform. Obtain the warning trigger time, prediction basis, associated status data, and associated environmental data; determine the warning level; reserve fields for handling records; and write the warning trigger time, prediction basis, associated status data, associated environmental data, warning level, and handling record fields into the log.

10. An automated control method for a sunshade awning, applied to an automated control system for a sunshade awning as described in any one of claims 1-9, characterized in that, Includes the following steps: Step 1: Collect shading environment parameters from the environment where the shading canopy is located, preprocess the collected shading environment parameters, and output standardized shading environment parameters; Step 2: Based on standardized shading environment parameters and user manual intervention records, perform multi-dimensional environmental semantic scenario modeling and short-term weather trend prediction. Dynamically generate shading strategies through multi-objective optimization and update personalized control strategies according to user preferences. Step 3: Decompose the shading strategy into collaborative control commands for each canopy segment, and integrate manual commands, App control and external control platform linkage signals, and schedule and execute them uniformly according to priority; Step 4: Execute the coordinated control command. When motor jamming, position deviation or communication interruption is detected, the self-recovery mechanism is activated. When wind speed exceeds the limit, rainfall or abnormal motion resistance occurs, the forced retraction, locking or retraction is performed. Step 5: Based on standardized shading environment parameters and detection information on motor jamming and position deviation, establish a prediction model for the remaining life of the motor, transmission mechanism and locking device, and trigger a maintenance warning when the predicted life is lower than the set threshold.

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