Intelligent air conditioner closing method and system based on dynamic adjustment in cooling window period

Abstract

The invention discloses an air conditioner intelligent closing method and system based on cooling window period dynamic adjustment, and the method comprises the steps that in response to a received air conditioner closing instruction, current operation parameters and environment conditions of an air conditioner are obtained; dynamically calculating a cooling window period duration based on the operation parameters and the environmental conditions, and entering a cooling window period state according to the cooling window period duration; in the cooling window period state, air conditioner state judgment based on the temperature difference between the air outlet temperature and the reference temperature is suspended, and the cooling process of the air outlet of the air conditioner is monitored in real time; and when a preset cooling window period ending condition is met, the cooling window period state is quitted, and air conditioner state judgment based on the temperature difference is recovered. Compared with the prior art, by dynamically recognizing and adapting to the physical cooling process after the air conditioner is closed, temperature difference judgment is intelligently suspended, and the problem of repeated opening and closing caused by the temperature allowance of the air outlet is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent air conditioning control technology, and in particular to an intelligent air conditioning shutdown method and system based on dynamic adjustment of the cooling window period. Background Technology

[0002] In intelligent air conditioning control systems, when a shutdown command is issued, the air outlet temperature does not immediately drop to the ambient temperature. Instead, there is a continuous physical cooling process. During this cooling window, the outlet temperature may still be higher than the ambient temperature, causing the temperature difference-based control system to misjudge that the air conditioner is still running, thus triggering repeated shutdown commands. This not only causes the air conditioner to repeatedly turn on and off, accelerating equipment aging and wear, but also makes users feel abnormal switching behavior, reducing the user experience. Furthermore, unnecessary switching operations also increase energy consumption.

[0003] Currently, common solutions on the market include: timed shutdown technology, which only performs shutdown based on a preset time and cannot adapt to the cooling characteristics of different air conditioners; temperature threshold shutdown technology, which relies on a fixed temperature threshold for judgment and cannot handle the temperature margin within the cooling window; and simple delayed shutdown technology, which uses a fixed delay time and cannot adapt to the differences in cooling speed under different operating conditions.

[0004] A search revealed that Chinese Patent Publication No. CN116255714A discloses a method for controlling temperature rise and fall based on intelligent interconnection. This method acquires indoor ambient temperature, human body temperature, and wall temperature, and interconnects with devices such as smart windows and curtains. When the temperature difference exceeds a preset threshold, it automatically enters a rapid temperature rise and fall mode to quickly adjust the room temperature, reduce energy consumption, and improve comfort. However, this solution focuses on the active temperature control strategy and multi-device linkage during air conditioner operation, and does not address the problem of repeated switching on and off due to the residual temperature at the air outlet after the air conditioner is turned off.

[0005] Therefore, how to accurately identify and dynamically adapt to the physical cooling process after the air conditioner is turned off, so as to avoid misjudgment of temperature difference and repeated switching due to the temperature margin of the air outlet, is a technical problem that needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art by providing a method and system for intelligent air conditioning shutdown based on dynamic adjustment of the cooling window period.

[0007] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for intelligent shutdown of an air conditioner based on dynamic adjustment of the cooling window period is provided, comprising: in response to a received shutdown command of the air conditioner, acquiring the current operating parameters and environmental conditions of the air conditioner; Based on the operating parameters and environmental conditions, the duration of a cooling window period is dynamically calculated, and the cooling window period state is entered accordingly. During the cooling window period, the triggering of the air conditioner shutdown command based on the temperature difference between the air outlet temperature and the reference temperature is suspended, and the cooling process of the air outlet is monitored in real time. When the real-time monitoring results meet the preset cooling window period end conditions, the cooling window period state is exited, and the air conditioning status judgment based on the temperature difference is restored.

[0008] As a preferred technical solution, a basic window period duration is used as a benchmark. The data is obtained based on multiple influencing factors, including air conditioner type, air conditioner running time, ambient temperature, and ambient wind speed. The optimized cooling window duration is calculated based on the basic window duration and the influencing factors.

[0009] As a preferred technical solution, dynamically calculating the duration of the cooling window further includes: Obtain the historical cooling data of the air conditioner; A historical data learning factor is calculated based on the historical cooling data, and the optimized cooling window duration is adjusted using the historical data learning factor.

[0010] As a preferred technical solution, real-time monitoring of the cooling process at the air conditioner outlet specifically includes: The current temperature difference between the outlet temperature and the reference temperature is periodically collected; Based on the trend of the current temperature difference collected for a preset number of consecutive times, it is determined whether the cooling process is in a stable state.

[0011] As a preferred technical solution, the preset window period end conditions include: The time elapsed since entering the cooling window period has reached or exceeded the duration of the cooling window period. Real-time monitoring determined that the temperature at the air outlet had stabilized.

[0012] According to a second aspect of the present invention, an intelligent air conditioning shutdown system based on dynamic adjustment of the cooling window period is provided, the system comprising: The window period calculation module dynamically calculates the duration of a cooling window period based on the air conditioner's operating parameters and environmental conditions. The status control module, in response to the air conditioner shutdown command, controls the system to enter the cooling window period state, and in this state, it suspends the triggering of the air conditioner shutdown command based on the temperature difference between the air outlet temperature and the reference temperature. The cooling monitoring module monitors the cooling process of the air conditioner outlet in real time during the cooling window period and provides the monitoring results to the status control module to determine whether the end condition of the window period is met. The state control module is further configured to control the system to exit the cooling window state when the monitoring results of the cooling monitoring module meet the preset window end conditions.

[0013] As a preferred technical solution, the system further includes: The parameter acquisition module collects the operating parameters of the air conditioner and the environmental conditions, and provides them to the window period calculation module.

[0014] As a preferred technical solution, the window period calculation module is specifically configured as follows: By querying a predefined impact factor mapping table, the impact factors corresponding to air conditioner type, running time, ambient temperature, and ambient wind speed can be obtained. The basic window duration is multiplied by multiple influencing factors to obtain an initial dynamic window period; The initial dynamic window period is limited to a preset range, and the cooling window period duration is output.

[0015] As a preferred technical solution, the window period calculation module also acquires the historical cooling data of the air conditioner and calculates a historical data learning factor based on this data to adjust the duration of the cooling window period.

[0016] As a preferred technical solution, the status control module provides a status query interface during the cooling window period. When the temperature difference judgment logic calls the status query interface, it returns a status indication for pausing the temperature difference check.

[0017] Compared with the prior art, the present invention has the following advantages: 1. This invention dynamically calculates the cooling window period based on air conditioner operating parameters and environmental conditions, and pauses the air conditioner status judgment based on temperature difference during this state while monitoring the cooling process in real time. It can accurately identify and adapt to the physical cooling process after the air conditioner is turned off, avoiding misjudgment of temperature difference and repeated switching problems caused by the temperature margin of the air outlet.

[0018] 2. This invention can adaptively adjust the window period length according to multiple parameters such as air conditioner type, running time, and environmental conditions, thereby improving adaptability and control accuracy under different operating conditions.

[0019] 3. This invention introduces a historical data learning mechanism, enabling the system to continuously optimize window period calculations and possess the ability to learn and continuously improve itself.

[0020] 4. This invention adopts a combination of real-time monitoring and trend analysis, which can prevent false triggering and end the window period in advance after the temperature stabilizes, thus balancing reliability and efficiency.

[0021] 5. This invention effectively reduces unnecessary switching operations while ensuring that the air conditioner can be shut down normally, thereby reducing equipment wear and tear, extending the service life of the air conditioner, and achieving energy conservation.

[0022] 6. This invention can be integrated into a smart home controller or deployed on a cloud platform, and has good industrialization prospects and application value. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the dynamic window period calculation algorithm of the present invention; Figure 3 This is a flowchart of the real-time monitoring algorithm for the cooling process of the present invention; Figure 4 This is a system architecture diagram of the present invention; Figure 4 As indicated by the standard number: 1. Parameter acquisition module; 2. Window period calculation module; 3. Cooling monitoring module; 4. Status control module; 5. Historical learning module; 6. User interface module. Detailed Implementation

[0024] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] Example 1: like Figure 1 As shown, this invention provides a method for intelligent air conditioner shutdown based on dynamic adjustment of the cooling window period. The method specifically includes: Step S1: In response to the received air conditioner shutdown command (which may be from user manual operation, scheduled task or smart scene trigger), obtain the current operating parameters and environmental conditions through the sensor network and air conditioner status interface; the operating parameters include at least the air conditioner type and the duration of continuous operation; the environmental conditions include at least the ambient temperature and ambient wind speed.

[0026] Step S2: Based on the parameters obtained in step S1, execute the dynamic window period calculation algorithm to determine an optimized cooling window period duration. The specific calculation process is as follows: Figure 2As shown, it includes: Set a base window period, for example, 300 seconds; Based on multiple influencing factors such as air conditioner type, operating time, ambient temperature, and ambient wind speed, the corresponding adjustment coefficients are obtained by querying a predefined factor mapping table; Multiply the base window period by all adjustment coefficients to obtain an initial dynamic window period; Limit the dynamic window period to a preset minimum (e.g., 120 seconds) and maximum (e.g., 900 seconds), and output the final optimized cooling window period duration; Once the calculations are complete, the system immediately enters the cooling window period.

[0027] Step S3: During the cooling window, the system's core control logic suspends the air conditioning status judgment based on the temperature difference between the outlet temperature and the reference temperature (usually the ambient temperature). Simultaneously, the system activates a real-time monitoring algorithm for the cooling process, the specific process of which is as follows: Figure 3 As shown, it includes: The current temperature difference between the outlet temperature and the reference temperature is periodically collected; Record time-series data containing timestamps and temperature difference values; Analyze the temperature difference change trend of multiple consecutive data points and calculate the temperature difference change rate; Determine whether the cooling process is in a steady state based on the rate of change of temperature difference; The monitoring and judgment logic is as follows: If the calculated average temperature difference change rate is greater than 0.1°C / cycle, it is determined that the temperature difference is still increasing; If the absolute value of the average temperature difference change rate is less than 0.05°C / cycle, the temperature difference is considered to have stabilized. If the average temperature difference change rate is negative and the absolute value is greater than 0.05°C / cycle, it is determined that the temperature difference is decreasing.

[0028] Step S4: The system continuously determines whether the preset cooling window period end condition is met, including two parallel determination paths: 1. Check whether the time elapsed since entering the cooling window period has reached or exceeded the optimized cooling window period duration calculated in step S22; 2. Based on the monitoring results of step S3, determine whether the air outlet temperature has reached a stable state in advance, and check that the temperature difference fluctuation of multiple consecutive data points is less than 0.1°C.

[0029] Step S105: When any termination condition in step S4 is met, the system immediately exits the cooling window state, resumes normal operation mode, and re-enables the air conditioning status judgment logic based on temperature difference.

[0030] Upon receiving a shutdown command, the method of this invention dynamically calculates the duration of a cooling window based on the air conditioner's operating parameters and environmental conditions, and pauses temperature difference judgments that may be falsely triggered during this period. By monitoring the temperature trend of the air outlet in real time, the system can intelligently end the window period after the actual cooling is completed, thereby effectively avoiding the problem of repeated opening and closing caused by the residual temperature of the air outlet.

[0031] The specific implementation process of this invention is described below using a specific scenario: Scenario 1: Smart shut-off of home split air conditioner Scenario description: After running continuously for 2 hours in the summer, the split air conditioner in the family living room issues a shutdown command through the smart home system.

[0032] Implementation process: The system detected a shutdown command and obtained the following parameters: air conditioner type is split air conditioner, running time is 120 minutes, ambient temperature is 28°C, and ambient wind speed is 0.5m / s (no wind). Dynamic calculation of cooling window period: Base window period 300 seconds × split air conditioner factor 1.0 × long-term operation factor 1.1 × ambient 28°C factor 1.0 × windless factor 1.1 = 363 seconds (6.05 minutes). Entering the 6.05-minute cooling window, temperature difference assessment is paused; Real-time monitoring showed that the temperature stabilized after approximately 4.5 minutes; The system ended the window period early and resumed normal monitoring.

[0033] Result: Successfully avoided a potential accidental shutdown due to residual heat from the air vent; the user did not perceive any abnormal switching behavior.

[0034] Scenario 2: Central Air Conditioning Management in Offices Scenario description: After the office staff leaves work, the central air conditioning management system triggers a batch shutdown.

[0035] Implementation process: The system received a batch shutdown command and obtained parameters for each air conditioner: air conditioner type is central air conditioning, average running time is 360 minutes, ambient temperature is 26°C, and ambient wind speed is 1.2m / s (light breeze). Personalized calculation window period: Basic window period 300 seconds × Central air conditioning factor 1.3 × Ultra-long operation factor 1.3 × Ambient 26°C factor 1.0 × Light wind factor 1.0 = 507 seconds (8.45 minutes); The system enters batch management mode, and all air conditioners simultaneously enter the cooling window period, pausing their individual temperature difference judgments. Monitor the cooling progress of each air conditioner in real time, and restore monitoring one by one according to the actual cooling completion time; All air conditioners completed cooling within the calculated window period, and none of them experienced repeated switching on and off.

[0036] Effect: Enables intelligent shutdown management of large-scale air conditioning systems, reducing energy consumption and equipment wear.

[0037] To more clearly illustrate the technical solution of this invention, the implementation logic of some core algorithms is shown below through code examples: 1. Dynamic adjustment algorithm for cooling window period: class CoolingWindowOptimizer: def __init__(self): self.base_window = 300 Base window duration: 5 minutes self.optimization_factors = {} def calculate_dynamic_window(self, ac_params, env_conditions, historical_data): """ Calculate the dynamic cooling window period :param ac_params: Air conditioner parameters (type, power, running time, etc.) :param env_conditions: Environmental conditions (temperature, humidity, wind speed, etc.) :param historical_data: Historical cooling data :return: Optimized window length (seconds) """ 1. Air Conditioning Type Factor type_factor = self.get_ac_type_factor(ac_params['type']) 2. Runtime factor runtime_factor = self.get_runtime_factor(ac_params['runtime_minutes']) 3. Ambient temperature factor temp_factor = self.get_temperature_factor(env_conditions['temperature']) 4. Wind speed influencing factors wind_factor = self.get_wind_factor(env_conditions['wind_speed']) 5. Historical data learning factors learning_factor = self.get_learning_factor(historical_data) Dynamic window period calculation dynamic_window = (self.base_window type_factor runtime_factor temp_factor wind_factor learning_factor) Window range restrictions (2-15 minutes) return max(120, min(900, dynamic_window)) def get_ac_type_factor(self, ac_type): "Air Conditioner Type Influence Factor" factors = { 'central_ac': 1.3, Central air conditioning cools down slowly. 'split_ac': 1.0, Split Air Conditioner Standard 'window_ac': 0.8, window unit cools down quickly. 'portable_ac': 0.7, Portable air conditioner cools down the fastest. 'multi_split': 1.2, Multi-split systems cool down more slowly. 'ceiling_cassette': 1.1 Ceiling machine standard } return factors.get(ac_type, 1.0) def get_runtime_factor(self, runtime_minutes): "Runtime Impact Factor" if runtime_minutes < 30: Return 0.7 Short-term operation, rapid cooling elif runtime_minutes < 120: Return 0.9 Medium running time elif runtime_minutes < 240: 1.1 Slow cooling during prolonged operation else: Return 1.3 Extremely long operating time, slowest cooling def get_temperature_factor(self, ambient_temp): """Ambient temperature influencing factors""" if ambient_temp < 20: 1.2 Slow cooling in low-temperature environments elif ambient_temp < 28: Return 1.0 Comfort Temperature Standard elif ambient_temp < 35: Return 0.9 Rapid cooling in high-temperature environments else: Return 0.8 Fastest cooling in ultra-high temperature environments def get_wind_factor(self, wind_speed): "Wind speed influencing factors" if wind_speed < 1.0: 1.1 Cooling is slower in windless environments elif wind_speed < 3.0: Return 1.0 Breeze Standard elif wind_speed < 5.0: Return 0.9 Rapid cooling after stroke else: Return 0.8 Strong winds bring the fastest temperature drop def get_learning_factor(self, historical_data): "Historical Data Learning Factor" if not historical_data or len(historical_data) < 5: Return 1.0 Insufficient data, using default value. Calculate the historical average cooling time avg_cooling_time = sum(historical_data) / len(historical_data) Learning factor: The ratio of historical data to the baseline window period learning_ratio = avg_cooling_time / self.base_window Limit the learning factor range (0.7-1.3) return max(0.7, min(1.3, learning_ratio)) 2. Real-time monitoring algorithm for the cooling process: class CoolingProcessMonitor: def __init__(self): self.cooling_data = [] `self.temperature_threshold = 0.5` is the temperature stability threshold. def monitor_cooling_process(self, outlet_temp, reference_temp, time_elapsed): """ Real-time monitoring of the cooling process :param outlet_temp: Outlet temperature :param reference_temp: Reference temperature :param time_elapsed: The elapsed time :return: Cooling status (active - cooling down, stable - stable) """ Calculate the current temperature difference current_diff = abs(outlet_temp - reference_temp) Record cooling data self.cooling_data.append({ 'time': time_elapsed, 'temp_diff': current_diff, 'outlet_temp': outlet_temp }) Analyzing the cooling trend if len(self.cooling_data) >= 3: return self.analyze_cooling_trend() else: Return 'active' Insufficient data, cooling is in progress by default. def analyze_cooling_trend(self): "Analyzing the cooling trend" Get the 3 most recent data points recent_data = self.cooling_data[-3:] Calculate the rate of temperature change temp_changes = [] for i in range(1, len(recent_data)): change = recent_data[i]['temp_diff'] - recent_data[i-1]['temp_diff'] temp_changes.append(change) Judging the cooling trend avg_change = sum(temp_changes) / len(temp_changes) If avg_change > 0.1: The temperature difference is still increasing. return 'active' elif abs(avg_change) < 0.05: Temperature difference is stable. return 'stable' else: The temperature difference is decreasing but not yet stable. return 'active' def should_end_cooling_window(self): "Determining whether the cooling window should end" if len(self.cooling_data) < 5: return False Check the stability of the most recent data points recent_stable = all( abs(self.cooling_data[i]['temp_diff'] - self.cooling_data[i-1]['temp_diff']) < 0.1 for i in range(-4, 0) ) return recent_stable Example 2: like Figure 4 As shown, the present invention provides an intelligent air conditioning shutdown system based on dynamic adjustment of the cooling window period, the system comprising: Parameter acquisition module 1: Responsible for real-time data acquisition from various sensors and air conditioning equipment interfaces, including: Air conditioner operating parameters: Equipment type, current operating mode, continuous operating time, compressor status, etc. are obtained through the air conditioner communication protocol; Environmental condition data: Parameters such as temperature, humidity, and wind speed are acquired through an environmental sensor network; User commands: Receive shutdown commands from the user interface, timers, or smart scenes.

[0038] Window period calculation module 2: This is the core algorithm module, which realizes the dynamic optimization calculation of the cooling window period. Its specific functions include: Query the predefined mapping tables of various influencing factors to obtain the adjustment coefficients corresponding to air conditioner type, running time, ambient temperature, and ambient wind speed; Access the historical database to obtain historical cooling data of the air conditioner, and calculate the historical data learning factor based on the ratio of the historical average cooling time to the basic window period; Based on the basic window period duration, it is multiplied by all the acquired influence factors and learning factors to calculate the initial dynamic window period; The calculated dynamic window period is limited to between the minimum and maximum values ​​preset by the system, and the final optimized cooling window period duration is output.

[0039] Cooling monitoring module 3: Responsible for real-time tracking of temperature changes at the air conditioning outlet during the cooling window period. Its specific functions include: The system periodically reads the outlet temperature and reference temperature from the temperature sensor and calculates the current temperature difference. Records contain time-series data including timestamps and corresponding temperature differences; Analyze the temperature difference change trend of multiple consecutive data points, and determine whether the cooling process is in a stable state by calculating the temperature difference change rate; Based on the temperature fluctuation range of multiple consecutive data points, it is determined whether the temperature has reached a stable condition, so that the state control module can decide whether to end the window period early.

[0040] State Control Module 4: Manages the state transitions and decision-making logic of the entire system. Specific functions include: In response to the shutdown command, the window period calculation process is triggered, and the system enters the cooling window period state. During the cooling window, the shutdown judgment logic based on the temperature difference between the air outlet temperature and the reference temperature is suspended in the system. Send a shutdown command to the air conditioning unit; Based on the feedback from the cooling monitoring module and the elapsed time, determine whether the conditions for the end of the window period are met. When the termination condition is met, the control system exits the cooling window state and resumes normal state monitoring logic. Provide a status query interface that returns whether the current state is within a cooling window when called by external logic, in order to determine whether to perform a temperature difference check.

[0041] Historical Learning Module 5: Supports long-term system optimization and self-adaptation, and its specific functions include: Store complete cooling process data after each air conditioner is turned off, including environmental conditions, calculation window period, actual cooling time, etc. Statistical analysis of historical data is performed to calculate key indicators such as average cooling time; Based on the statistical analysis results, data support is provided for the window period calculation module to optimize the calculation of historical data learning factors, enabling the system to continuously optimize itself as it is used.

[0042] User Interface Module 6: Provides an interaction channel between the system and the user. Its specific functions include: It provides a configuration interface that allows users to view and adjust system parameters, such as the base window period and temperature threshold. The system displays real-time information to users, including the current system status, cooling progress, and estimated remaining time. Provides query, statistics, and visualization functions for historical operation records; When the system detects an anomaly, it sends an alarm or notification to the user.

[0043] The workflow of this system is as follows: The parameter acquisition module 1 continuously monitors the system status, and when a shutdown command is detected, it immediately acquires all relevant parameters. The window period calculation module 2 receives parameters, executes a dynamic calculation algorithm, and outputs the optimized cooling window period duration. Upon receiving the calculation results, the state control module 4 switches the system state to the cooling window period and pauses the temperature difference judgment logic; simultaneously, the state control module 4 sends a shutdown command to the air conditioning equipment. The cooling monitoring module 3 starts working, monitoring the temperature change of the air outlet in real time, and feeding back the monitoring results to the status control module 4; Based on monitoring results and timeout judgment, the status control module 4 determines when to exit the cooling window period status; When the termination condition is met, the state control module 4 will restore the system state to normal and re-enable the temperature difference judgment logic. The historical learning module 5 records complete data of this cooling process for subsequent algorithm optimization; User interface module 6 provides users with status feedback and system interaction capabilities throughout the process.

[0044] This system can be implemented through the following hardware platforms: Smart home controller: integrates temperature sensor and air conditioning control interface; IoT gateway: Supports centralized management of multiple air conditioning devices; Cloud service platform: Provides algorithm computing and data storage services Mobile terminal application: Enables remote monitoring and configuration functions.

[0045] The core of the system of this invention includes a parameter acquisition module for collecting data, a calculation module for dynamically calculating the optimal duration window, a cooling monitoring module for real-time monitoring of temperature trends, and a state control module responsible for intelligent state switching. Through the collaborative work of each module, the system autonomously manages the cooling process, achieving precise and adaptive control of the air conditioner shutdown process from the system architecture level.

[0046] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent shutdown of an air conditioner based on dynamic adjustment of the cooling window period, characterized in that, include: In response to the received command to shut down the air conditioner, obtain the current operating parameters and environmental conditions of the air conditioner; Based on the operating parameters and environmental conditions, the duration of a cooling window period is dynamically calculated, and the cooling window period state is entered accordingly. During the cooling window period, the triggering of the air conditioner shutdown command based on the temperature difference between the air outlet temperature and the reference temperature is suspended, and the cooling process of the air outlet is monitored in real time. When the real-time monitoring results meet the preset cooling window period end conditions, the cooling window period state is exited, and the air conditioning status judgment based on the temperature difference is restored.

2. The intelligent air conditioning shutdown method based on dynamic adjustment of cooling window period according to claim 1, characterized in that, The duration of the cooling window is dynamically calculated, including: Based on a basic window period duration; The data is obtained based on multiple influencing factors, including air conditioner type, air conditioner running time, ambient temperature, and ambient wind speed. The optimized cooling window duration is calculated based on the basic window duration and the influencing factors.

3. The intelligent air conditioning shutdown method based on dynamic adjustment of the cooling window period according to claim 2, characterized in that, The dynamic calculation of the cooling window duration also includes: Obtain the historical cooling data of the air conditioner; A historical data learning factor is calculated based on the historical cooling data, and the optimized cooling window duration is adjusted using the historical data learning factor.

4. The intelligent air conditioning shutdown method based on dynamic adjustment of the cooling window period according to claim 1, characterized in that, Real-time monitoring of the cooling process at the air conditioner outlet specifically includes: The current temperature difference between the outlet temperature and the reference temperature is periodically collected; Based on the trend of the current temperature difference collected for a preset number of consecutive times, it is determined whether the cooling process is in a stable state.

5. The intelligent air conditioning shutdown method based on dynamic adjustment of the cooling window period according to claim 1, characterized in that, The preset window period end conditions include: The time elapsed since entering the cooling window period has reached or exceeded the duration of the cooling window period. Real-time monitoring determined that the temperature at the air outlet had stabilized.

6. An intelligent air conditioning shut-off system for implementing the method as described in any one of claims 1-5, characterized in that, include: The window period calculation module dynamically calculates the duration of a cooling window period based on the air conditioner's operating parameters and environmental conditions. The status control module, in response to the air conditioner shutdown command, controls the system to enter the cooling window period state, and in this state, it suspends the triggering of the air conditioner shutdown command based on the temperature difference between the air outlet temperature and the reference temperature. The cooling monitoring module monitors the cooling process of the air conditioner outlet in real time during the cooling window period and provides the monitoring results to the status control module to determine whether the end condition of the window period is met. The state control module is further configured to control the system to exit the cooling window state when the monitoring results of the cooling monitoring module meet the preset window end conditions.

7. The intelligent air conditioning shutdown system according to claim 6, characterized in that, The system also includes: The parameter acquisition module collects the operating parameters of the air conditioner and the environmental conditions, and provides them to the window period calculation module.

8. The intelligent air conditioning shutdown system according to claim 6, characterized in that, The specific configuration of the window period calculation module is as follows: By querying a predefined impact factor mapping table, the impact factors corresponding to air conditioner type, running time, ambient temperature, and ambient wind speed can be obtained. The basic window duration is multiplied by multiple influencing factors to obtain an initial dynamic window period; The initial dynamic window period is limited to a preset range, and the cooling window period duration is output.

9. The intelligent air conditioning shutdown system according to claim 8, characterized in that, The window period calculation module also acquires the historical cooling data of the air conditioner and calculates a historical data learning factor based on this data to adjust the duration of the cooling window period.

10. The intelligent air conditioning shutdown system according to claim 6, characterized in that, The status control module provides a status query interface during the cooling window period. When the temperature difference judgment logic calls the status query interface, it returns a status indication for pausing the temperature difference check.

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