Air humidity-based multi-connected air conditioner energy-saving prediction method and multi-connected air conditioner

Abstract

This invention relates to the field of multi-split air conditioning technology, and provides a method for predicting energy-saving performance of multi-split air conditioning systems based on air humidity, as well as a multi-split air conditioning system itself. The method includes: collecting relative humidity data of the indoor environment in the room where each indoor unit is located using humidity sensors in each indoor unit; transmitting the relative humidity data from the indoor unit to the outdoor unit, and then uploading it from the outdoor unit to a centralized control device; determining humidity characteristic parameters based on the relative humidity data of each indoor unit; comparing the humidity characteristic parameters with parameters of a preset humidity scenario model to obtain a humidity comparison result; determining the current indoor environmental conditions based on the humidity comparison result; and generating energy-saving reminder information based on the current indoor conditions. This invention enables automatic diagnosis and targeted guidance of the root causes of inefficient operation of multi-split air conditioning systems, effectively improving the operational energy efficiency and intelligent management level of multi-split air conditioning systems.

Description

Technical Field

[0001] This invention relates to the field of multi-split air conditioning technology, and in particular to a method for predicting energy saving in multi-split air conditioning based on air humidity, and a multi-split air conditioning system. Background Technology

[0002] Existing multi-split air conditioning centralized control systems generally adopt an architecture of "multiple indoor units - single outdoor unit - centralized control equipment." The centralized control equipment achieves unified management of multiple indoor units by monitoring their own operating parameters (such as temperature, fan speed, fault codes, etc.). When abnormal operating parameters are detected, the centralized control equipment sends a general "equipment operation abnormality" alert to the user. However, in actual use, low air conditioning efficiency is often not caused by equipment malfunctions, but rather by changes in the spatial conditions. For example, when doors and windows in an air-conditioned room are not closed tightly or are open, frequent exchange of indoor and outdoor air leads to a significant loss of cooling capacity; or improper installation of indoor units (such as blocked air outlets or unreasonable return air zone settings) can cause "airflow short circuits," resulting in cold / hot air being directly drawn into the return air vent without effective diffusion. Both of these types of spatial condition abnormalities significantly reduce the operating efficiency of indoor units, causing unnecessary energy consumption. However, existing systems lack the ability to sense environmental data and cannot identify such inefficient operating problems caused by spatial conditions.

[0003] In the closest existing technical solutions, the indoor unit's detection device is only equipped with a temperature sensor, and the centralized control equipment can only acquire the equipment's operating temperature data, but cannot acquire indoor environmental humidity data. At the same time, the control logic of the centralized control equipment is limited to comparing the received operating data with preset equipment fault thresholds, and can only identify the equipment's own "hard faults," but cannot use the changing patterns of environmental data to correlate spatial status and operating efficiency.

[0004] Therefore, existing technologies have the following technical problems: First, they cannot monitor inefficient operation caused by abnormal room airtightness and airflow short circuits in real time; second, centralized control equipment can only push general reminders, and users cannot directly locate the root cause of the problem based on the reminders; third, an intelligent closed loop of "environmental data collection - spatial status analysis - targeted reminders" has not been formed, making it difficult to detect and deal with inefficient operation scenarios in a timely manner, resulting in continuous energy waste. Summary of the Invention

[0005] This invention provides a method for predicting energy-saving performance of multi-split air conditioners based on air humidity, and a multi-split air conditioner. By collecting indoor relative humidity data and comparing it with humidity characteristic parameters and humidity scenario models, it can accurately identify inefficient operating scenarios caused by abnormal spatial conditions and generate corresponding energy-saving reminder information. This achieves automatic diagnosis and targeted guidance of the root causes of inefficient operation of multi-split air conditioners, effectively improving the operating energy efficiency and intelligent management level of multi-split air conditioner systems.

[0006] In a first aspect, the present invention provides an energy-saving prediction method for multi-split air conditioners based on air humidity, applicable to multi-split air conditioners, wherein the multi-split air conditioner includes at least two indoor units, a single outdoor unit, and a central control device, and each indoor unit is equipped with a humidity sensor; the method includes: The relative humidity data of the indoor environment of the room where each indoor unit is located is collected by the humidity sensor in each indoor unit at a preset sampling frequency. The indoor relative humidity data is transmitted from the indoor unit to the outdoor unit, and then periodically uploaded by the outdoor unit to the centralized control device via the transmission module. When the centralized control device receives the indoor relative humidity data, it determines the humidity characteristic parameters based on the indoor relative humidity data of each indoor unit. The humidity characteristic parameters are compared with the parameters of the preset humidity scene model to obtain the humidity comparison result, and the current indoor environment is determined based on the humidity comparison result. Based on the current indoor conditions, an energy-saving reminder message is generated.

[0007] Preferably, according to the multi-split air conditioner energy-saving prediction method based on air humidity provided by the present invention, the step of determining humidity characteristic parameters based on the indoor relative humidity data of each indoor unit includes: A humidity time series is constructed based on multiple indoor relative humidity data of each indoor unit within a preset time window. Statistical analysis is performed on the humidity time series to calculate at least one humidity characteristic parameter of the humidity time series, the humidity characteristic parameter including at least one of the following parameters: Humidity change rate, humidity fluctuation amplitude, percentage of stable humidity duration, and percentage of constant humidity duration.

[0008] Preferably, in the multi-split air conditioner energy-saving prediction method based on air humidity provided by the present invention, the humidity change rate is the slope value obtained by linear regression fitting of the humidity time series, which is used to characterize the speed of humidity change over time. The humidity fluctuation amplitude is the difference between the maximum and minimum values ​​of the humidity data in the humidity time series, used to characterize the dispersion of the humidity values; The percentage of stable humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset stable humidity range in the humidity time series to the preset time window. The percentage of constant humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset constant humidity range in the humidity time series to the preset time window; wherein, the width of the preset constant humidity range is smaller than the width of the preset stable humidity range.

[0009] Preferably, in the multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention, the humidity scenario model includes at least a sealing anomaly model and an airflow short-circuit model; The closed-loop anomaly model includes a first rate of change threshold range, a first fluctuation amplitude threshold range, a first stable duration percentage threshold range, and a first constant duration percentage threshold range. The airflow short-circuit model includes a second rate of change threshold range, a second fluctuation amplitude threshold range, a second stable duration percentage threshold range, and a second constant duration percentage threshold range. The upper limit of the second rate of change threshold range is less than the lower limit of the first rate of change threshold range, the upper limit of the second fluctuation amplitude threshold range is less than the lower limit of the first fluctuation amplitude threshold range, the upper limit of the second stable duration percentage threshold range is less than the lower limit of the first stable duration percentage threshold range, and the upper limit of the second constant duration percentage threshold range is less than the lower limit of the first constant duration percentage threshold range.

[0010] Preferably, in the multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention, the humidity scenario model includes at least a high-efficiency operation model; The efficient operating model includes a third rate of change threshold range, a third fluctuation amplitude threshold range, a third stable duration percentage threshold range, and a third constant duration percentage threshold range. The lower limit of the third rate of change threshold interval is greater than the upper limit of the second rate of change threshold interval, and the upper limit of the third rate of change threshold interval is less than the lower limit of the first rate of change threshold interval. The lower limit of the third fluctuation amplitude threshold interval is greater than the upper limit of the second fluctuation amplitude threshold interval, and the upper limit of the third fluctuation amplitude threshold interval is less than the lower limit of the first fluctuation amplitude threshold interval. The lower limit of the third stable duration percentage threshold interval is greater than the upper limit of the second stable duration percentage threshold interval, and the upper limit of the third stable duration percentage threshold interval is less than the lower limit of the first stable duration percentage threshold interval. The lower limit of the third constant duration percentage threshold interval is greater than the upper limit of the second constant duration percentage threshold interval, and the upper limit of the third constant duration percentage threshold interval is less than the lower limit of the first constant duration percentage threshold interval.

[0011] Preferably, according to the multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention, the step of comparing the humidity characteristic parameters with the parameters of a preset humidity scenario model to obtain the humidity comparison result includes: The humidity change rate, humidity fluctuation amplitude, humidity stable duration percentage, and humidity constant duration percentage are compared with each threshold interval in the humidity scene model at a preset judgment period to generate humidity comparison results.

[0012] Preferably, according to the multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention, the step of determining the current indoor environment conditions based on the humidity comparison result includes: When the rate of change of humidity falls into the second rate of change threshold range, the amplitude of humidity fluctuation falls into the second amplitude of fluctuation threshold range, the percentage of stable humidity time falls into the second stable duration percentage threshold range, and the percentage of constant humidity time falls into the second constant duration percentage threshold range, the current indoor situation is determined to be an airflow short-circuit inefficient operation scenario. When the rate of change of humidity falls into the first rate of change threshold range, the amplitude of humidity fluctuation falls into the first amplitude of fluctuation threshold range, the percentage of stable humidity time falls into the first stable duration percentage threshold range, and the percentage of constant humidity time falls into the first constant duration percentage threshold range, the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. When the rate of change of humidity falls within the third rate of change threshold range, the amplitude of humidity fluctuation falls within the third amplitude of fluctuation threshold range, the percentage of stable humidity duration falls within the third stable duration percentage threshold range, and the percentage of constant humidity duration falls within the third constant duration percentage threshold range, the current indoor situation is determined to be an efficient operating scenario.

[0013] Preferably, the multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention includes the steps of determining the humidity characteristic parameters and generating the humidity comparison results at a preset judgment period. The step of determining the current indoor environment condition based on the humidity comparison results further includes: In N consecutive preset determination cycles, if the humidity comparison results determined in each preset determination cycle indicate that the humidity change rate falls within the second change rate threshold range, the humidity fluctuation amplitude falls within the second fluctuation amplitude threshold range, the humidity stable duration percentage falls within the second stable duration percentage threshold range, and the humidity constant duration percentage falls within the second constant duration percentage threshold range, then the current indoor situation is determined to be an airflow short-circuit inefficient operation scenario. If, in N consecutive preset determination cycles, the humidity comparison results determined in each preset determination cycle indicate that the humidity change rate falls within the first change rate threshold range, the humidity fluctuation amplitude falls within the first fluctuation amplitude threshold range, the humidity stable duration percentage falls within the first stable duration percentage threshold range, and the humidity constant duration percentage falls within the first constant duration percentage threshold range, then the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. In M consecutive preset determination cycles, when the humidity comparison result determined in each preset determination cycle alternates between the determination condition indicating an inefficient operation scenario with short-circuited airflow and the determination condition indicating an inefficient operation scenario with abnormal airtightness, the previous determination result of the current indoor situation is maintained; where N is an integer greater than or equal to 2 and M is an integer greater than or equal to 3.

[0014] Preferably, according to the multi-split air conditioner energy-saving prediction method based on air humidity provided by the present invention, the step of generating energy-saving reminder information based on the current indoor conditions includes: When the current indoor situation is an inefficient operation scenario due to airflow short circuit, a first reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the first reminder message includes a room identifier corresponding to the indoor unit and a first prompt text, the first prompt text being used to prompt the user to troubleshoot the airflow short circuit problem; In the case where the current indoor situation is an inefficient operation due to abnormal airtightness, a second reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the second reminder message includes a room identifier corresponding to the indoor unit and a second prompt text, the second prompt text being used to prompt the user to check for abnormal airtightness; When the current indoor situation is a high-efficiency operating scenario, a third reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the third reminder message is used to indicate that the current operating status is normal.

[0015] Secondly, the present invention also provides an energy-saving prediction device for multi-split air conditioners based on air humidity, which is applied to multi-split air conditioners. The multi-split air conditioner includes at least two indoor units, a single outdoor unit, and a central control device. Each indoor unit is equipped with a humidity sensor. The device includes: The data acquisition module is used to collect the relative humidity data of the indoor environment of the room where each indoor unit is located at a preset sampling frequency through the humidity sensor in each indoor unit. The transmission module is used to transmit the indoor relative humidity data to the outdoor unit through the indoor unit, and the outdoor unit periodically uploads the data to the centralized control device via the transmission module. The determination module is used to determine humidity characteristic parameters based on the indoor relative humidity data of each indoor unit when the centralized control device receives the indoor relative humidity data. The comparison module is used to compare the humidity characteristic parameters with the parameters of a preset humidity scene model to obtain the humidity comparison result, and to determine the current indoor conditions based on the humidity comparison result. The reminder module is used to generate energy-saving reminder information based on the current indoor conditions.

[0016] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the energy-saving prediction method for multi-split air conditioning based on air humidity as described above.

[0017] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy-saving prediction method for multi-split air conditioning based on air humidity as described above.

[0018] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the energy-saving prediction method for multi-split air conditioners based on air humidity as described above.

[0019] In a sixth aspect, the present invention also provides a multi-split air conditioner, the multi-split air conditioner including at least two indoor units, a single outdoor unit and a central control device, each indoor unit being equipped with a humidity sensor, and the multi-split air conditioner storing a computer program, which, when executed by a processor, implements the energy-saving prediction method for multi-split air conditioners based on air humidity as described above.

[0020] This invention provides a method for predicting energy-saving performance of multi-split air conditioners based on air humidity, and a multi-split air conditioner in general. The method involves using humidity sensors in each indoor unit to collect relative humidity data of the room where each indoor unit is located at a preset sampling frequency. This relative humidity data is transmitted from the indoor unit to the outdoor unit, and then periodically uploaded by the outdoor unit to a central control device via a transmission module. Upon receiving the relative humidity data, the central control device determines humidity characteristic parameters based on the relative humidity data of each indoor unit. These humidity characteristic parameters are then compared with parameters of a preset humidity scenario model to obtain a humidity comparison result. Based on this result, the current indoor environmental conditions are determined. Finally, energy-saving reminders are generated based on these current conditions. By collecting relative humidity data and comparing humidity characteristic parameters with a humidity scenario model, the method can accurately identify inefficient operating scenarios caused by abnormal spatial conditions and generate corresponding energy-saving reminders. This achieves automatic diagnosis and targeted guidance of the root causes of inefficient operation of multi-split air conditioners, effectively improving the operating energy efficiency and intelligent management level of multi-split air conditioning systems. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is one of the flowcharts of the multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention; Figure 2 This is the second flowchart of the multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention; Figure 3 This is one of the structural schematic diagrams of the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention; Figure 4 This is the second schematic diagram of the structure of the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention; Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this 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 this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] All actions involving the acquisition of signal information or data in this invention are carried out in compliance with the relevant data protection laws and policies of the country where the device is located, and with the authorization granted by the owner of the device.

[0025] The following is combined Figures 1-5 This invention describes an energy-saving prediction method for multi-split air conditioners based on air humidity, and a multi-split air conditioner. By collecting indoor relative humidity data and comparing it with humidity characteristic parameters and humidity scenario models, it can accurately identify inefficient operating scenarios caused by abnormal spatial conditions and generate corresponding energy-saving reminder information. This achieves automatic diagnosis and targeted guidance of the root causes of inefficient operation of multi-split air conditioners, effectively improving the operating energy efficiency and intelligent management level of multi-split air conditioner systems.

[0026] Figure 1 This is one of the flowcharts illustrating a multi-split air conditioning energy-saving prediction method based on air humidity provided by the present invention, such as... Figure 1 As shown, the method may include, but is not limited to, steps S100 to S500: S100 collects the relative humidity data of the room where each indoor unit is located at a preset sampling frequency through the humidity sensor in each indoor unit. S200, the indoor relative humidity data is transmitted from the indoor unit to the outdoor unit, and then periodically uploaded by the outdoor unit to the centralized control device via the transmission module; S300, when the centralized control device receives the indoor relative humidity data, it determines the humidity characteristic parameters based on the indoor relative humidity data of each indoor unit; S400, compare the humidity characteristic parameters with the parameters of the preset humidity scene model to obtain the humidity comparison result, and determine the current indoor environment based on the humidity comparison result; S500 generates energy-saving reminder information based on the current indoor conditions.

[0027] In one specific embodiment of the present invention, combined with Figure 3As shown, the multi-split air conditioning system adopts a classic architecture of "multiple indoor units - single outdoor unit - centralized control device," constructing an IoT-based air conditioning operation monitoring network. The system includes at least two indoor units, one outdoor unit, and one centralized control device. Each indoor unit is equipped with a detection device integrating a humidity sensor and a temperature sensor. The humidity sensor collects real-time indoor relative humidity data, with a measurement range of 20% RH to 80% RH and an accuracy within ±2% RH. It also integrates a temperature compensation unit to eliminate the impact of indoor temperature changes on humidity measurement accuracy. The temperature sensor synchronously collects indoor ambient temperature data for subsequent data correction. The indoor unit also contains an actuator, including a fan, electronic expansion valve, and other components, for performing cooling or heating operations.

[0028] The outdoor unit connects to all indoor units via signal lines, aggregating the relative humidity and temperature data collected by each indoor unit. This data is then periodically uploaded to the central control device via a transmission module (such as an RS485 bus module or a wireless WiFi communication module), enabling data exchange between IoT devices. The central control device, as the core intelligent unit of the system, has built-in storage, computing, and execution units. The storage unit stores received humidity and temperature data, as well as preset humidity scenario models; the computing unit performs digital calculations of humidity characteristic parameters and scenario comparison logic; and the execution unit generates and pushes reminder information based on the judgment results. User reminder terminals (such as smartphone apps, central control displays, etc.) communicate with the central control device to receive and display energy-saving reminder information, forming an intelligent closed loop from perception to interaction.

[0029] In step S100 of some embodiments, the relative humidity data of the indoor environment of the room where each indoor unit is located is collected by the humidity sensor in each indoor unit at a preset sampling frequency.

[0030] Understandably, in combination Figure 1 and Figure 2 As shown in this embodiment, after the multi-split air conditioning system starts up, the humidity sensor of each indoor unit collects the relative humidity data of the indoor environment of the room in real time at a preset sampling frequency (e.g., once every 1 minute). Taking the living room indoor unit as an example, the humidity sensor collects the relative humidity value of the current room every 1 minute and associates the collected humidity data with the corresponding indoor unit identification information (e.g., "living room indoor unit") and stores it. This step realizes the source collection of environmental data, providing basic data support for subsequent intelligent analysis.

[0031] In step S200 of some embodiments, the indoor relative humidity data is transmitted from the indoor unit to the outdoor unit, and then periodically uploaded by the outdoor unit to the centralized control device via the transmission module.

[0032] Understandably, the collected indoor relative humidity data is first transmitted to the outdoor unit via the signal line of the indoor unit. The outdoor unit, acting as a data relay station, packages all the humidity data received from the indoor units and periodically uploads it to the central control device via a transmission module (such as RS485 bus or wireless WiFi). The upload cycle can be set to once every 5 minutes to ensure data timeliness while avoiding excessive communication load. This step establishes a complete IoT data transmission link from the data acquisition end to the analysis end.

[0033] In step S300 of some embodiments, when the central control device receives the indoor relative humidity data, humidity characteristic parameters are determined based on the indoor relative humidity data of each indoor unit.

[0034] Understandably, after receiving the indoor relative humidity data from each indoor unit, the centralized control equipment extracts multiple humidity data points for each indoor unit within a preset time window (e.g., the most recent 30 minutes), and uses digital statistical analysis to determine humidity characteristic parameters that can characterize the pattern of humidity changes. These characteristic parameters include, but are not limited to, the rate of humidity change and the amplitude of humidity fluctuations, which are used to quantitatively describe the dynamic characteristics of humidity changes over time.

[0035] In step S400 of some embodiments, the humidity characteristic parameters are compared with the parameters of a preset humidity scene model to obtain a humidity comparison result, and the current indoor conditions of the indoor environment are determined based on the humidity comparison result.

[0036] Understandably, the computing unit of the centralized control device compares the calculated humidity characteristic parameters with the pre-set humidity scenario model in the storage unit. The humidity scenario model is a set of benchmark parameters trained in advance using a large amount of experimental data or historical operating data, used to characterize the typical variation patterns of humidity under different spatial conditions. Through intelligent comparison, the computing unit can determine the type of spatial condition of the current indoor unit (e.g., abnormal airtightness, airflow short circuit, or high-efficiency operation), thereby determining the current indoor situation.

[0037] In step S500 of some embodiments, energy-saving reminder information is generated based on the current indoor conditions.

[0038] Understandably, based on the judgment results, the execution unit of the centralized control equipment generates corresponding energy-saving reminder information. For example, if the scenario is determined to be an inefficient operation due to airflow short circuit, a reminder message is generated to remind the user to check the installation of the indoor unit; if the scenario is determined to be an inefficient operation due to abnormal airtightness, a reminder message is generated to remind the user to close doors and windows. The generated reminder information is pushed to the user's reminder terminal via wireless network, guiding the user to take appropriate measures, thereby achieving automatic energy-saving management.

[0039] Through the above steps, this embodiment can intelligently identify inefficient operating scenarios caused by abnormal spatial conditions based on indoor humidity data, and generate targeted energy-saving reminders. This solves the technical problems of existing technologies that cannot identify abnormal spatial conditions, lack targeted reminders, and cause continuous energy waste, and realizes digital energy-saving management of air conditioning systems.

[0040] In some embodiments of the present invention, determining humidity characteristic parameters based on the indoor relative humidity data of each indoor unit includes: A humidity time series is constructed based on multiple indoor relative humidity data of each indoor unit within a preset time window. Statistical analysis is performed on the humidity time series to calculate at least one humidity characteristic parameter of the humidity time series, the humidity characteristic parameter including at least one of the following parameters: Humidity change rate, humidity fluctuation amplitude, percentage of stable humidity duration, and percentage of constant humidity duration.

[0041] It is understood that this embodiment further defines the specific steps for determining humidity characteristic parameters, namely, constructing a humidity time series and calculating at least one humidity characteristic parameter.

[0042] The steps for constructing a humidity time series are as follows: For each indoor unit, the centralized control device extracts multiple indoor relative humidity data points within a preset time window. Taking the indoor unit in the master bedroom as an example, the preset time window can be set to 30 minutes prior to the current time. The centralized control device extracts all humidity data collected by this indoor unit in the past 30 minutes from the storage unit (e.g., 30 data points, one per minute), arranges them in chronological order, and forms the humidity time series H = { , ,.... },in The earliest humidity value collected. This is the latest humidity value. This digital time series construction method provides a structured data foundation for subsequent intelligent analysis.

[0043] The steps for calculating humidity characteristic parameters are as follows: The computing unit of the centralized control equipment performs digital statistical analysis on the constructed humidity time series and calculates at least one humidity characteristic parameter. The humidity characteristic parameter includes at least one of the following: humidity change rate, humidity fluctuation amplitude, percentage of stable humidity time, and percentage of constant humidity time.

[0044] In this embodiment, in order to obtain a more accurate judgment result, the computing unit calculates the above four feature parameters simultaneously to achieve multi-dimensional intelligent perception.

[0045] Taking the indoor unit in the master bedroom as an example, the computing department extracts data from the humidity time series and calculates: (1) the rate of humidity change, which describes how fast the humidity decreases or increases over time; (2) the humidity fluctuation amplitude, which describes the dispersion of the humidity value; (3) the proportion of stable humidity duration, which describes the proportion of time the humidity value lasts within the stable range; and (4) the proportion of constant humidity duration, which describes the proportion of time the humidity value lasts within the constant range. These four characteristic parameters characterize the dynamic changes of humidity from different dimensions, providing a multi-dimensional digital quantitative basis for subsequent scenario determination.

[0046] This embodiment constructs a humidity time series and calculates multi-dimensional humidity characteristic parameters. This method can comprehensively capture the dynamic characteristics of humidity changes, providing a rich data foundation for accurately distinguishing different spatial states, and realizing an upgrade from traditional manual judgment to intelligent analysis.

[0047] In some embodiments of the present invention, the rate of change of humidity is the slope value obtained by linear regression fitting of the humidity time series, which is used to characterize how fast or slow the humidity changes over time. The humidity fluctuation amplitude is the difference between the maximum and minimum values ​​of the humidity data in the humidity time series, used to characterize the dispersion of the humidity values; The percentage of stable humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset stable humidity range in the humidity time series to the preset time window. The percentage of constant humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset constant humidity range in the humidity time series to the preset time window; wherein, the width of the preset constant humidity range is smaller than the width of the preset stable humidity range.

[0048] Understandably, the calculation steps for the rate of humidity change are as follows: The rate of change of humidity is used to characterize how quickly humidity changes over time. In this embodiment, the rate of change of humidity is characterized by the slope value obtained by linear regression fitting of the humidity time series.

[0049] Specifically, with time as the independent variable x (in minutes) and humidity as the dependent variable y (in %RH), multiple data points (x1, y1), (x2, y2), ..., (x...) in the humidity time series are analyzed. n y n A linear regression was performed to obtain the linear function y = kx + b. Here, the slope k represents the rate of humidity change, expressed in %RH / minute.

[0050] For example, if the fitted slope is -0.5%RH / min, it means the humidity is decreasing at a rate of 0.5%RH per minute; if the slope is positive, it means the humidity is increasing. Linear regression algorithms can smooth out measurement errors at individual data points and accurately reflect the overall trend of humidity changes. This digital calculation method ensures the accuracy of intelligent analysis.

[0051] The steps for calculating humidity fluctuation range are as follows: Humidity fluctuation amplitude is used to characterize the dispersion of humidity values. In this embodiment, humidity fluctuation amplitude is the difference between the maximum and minimum values ​​of humidity data in the humidity time series.

[0052] The calculation formula is: Fluctuation Amplitude = max(H) - min(H). For example, if the maximum humidity value in a humidity time series is 65%RH and the minimum humidity value is 55%RH, then the humidity fluctuation amplitude is 10%RH. This parameter can intuitively reflect the severity of humidity fluctuations: the larger the fluctuation amplitude, the more drastic the humidity change; the smaller the fluctuation amplitude, the more stable the humidity. This digital indicator provides a quantitative basis for intelligent judgment.

[0053] The calculation steps for the percentage of time with stable humidity are as follows: The percentage of stable humidity duration is used to characterize the ratio of the duration of the humidity value within the preset stable humidity range to the preset time window. The preset stable humidity range is a humidity range with a certain width, for example, set as [current average humidity - 5%RH, current average humidity + 5%RH].

[0054] The calculation department counts the number of data points in the humidity time series whose humidity values ​​fall within the stable interval, multiplies this number by the sampling interval (e.g., 1 minute) to obtain the cumulative duration, and then divides this by the total duration of the preset time window (e.g., 30 minutes) to obtain the percentage of stable humidity duration. For example, if 25 data points fall within the stable interval within 30 minutes, the cumulative duration is 25 minutes, and the percentage of stable duration is 25 / 30 ≈ 83.3%. This parameter is used to determine whether the humidity is trending towards stability: the higher the percentage, the more stable the humidity, providing a stability indicator for intelligent judgment.

[0055] The calculation steps for the percentage of time with constant humidity are as follows: The constant humidity duration percentage represents the ratio of the duration of a humidity value within a preset constant humidity range to a preset time window. The width of the preset constant humidity range is smaller than the width of the preset stable humidity range, for example, set as [current average humidity - 1%RH, current average humidity + 1%RH], to represent a state where the humidity is almost constant. The calculation unit counts the cumulative duration of humidity values ​​falling within this constant range in the humidity time series and calculates its ratio to the preset time window. For example, if 20 data points fall within the constant range within 30 minutes, the cumulative duration is 20 minutes, and the constant duration percentage is 20 / 30 ≈ 66.7%. This parameter, used in conjunction with the stable duration percentage, can distinguish between "slow and stable change" and "almost constant" states, the latter often being a typical characteristic of airflow short-circuiting. This refined digital analysis reflects the intelligence level of this invention.

[0056] Through the specific calculation methods described above, this method can accurately quantify the rate of change, fluctuation, stability, and constancy of humidity, providing an objective and quantifiable basis for scene determination, avoiding the uncertainty brought about by subjective judgment, and realizing intelligent feature extraction.

[0057] In some embodiments of the present invention, the humidity scenario model includes at least a sealing anomaly model and an airflow short-circuit model; The closed-loop anomaly model includes a first rate of change threshold range, a first fluctuation amplitude threshold range, a first stable duration percentage threshold range, and a first constant duration percentage threshold range. The airflow short-circuit model includes a second rate of change threshold range, a second fluctuation amplitude threshold range, a second stable duration percentage threshold range, and a second constant duration percentage threshold range. The upper limit of the second rate of change threshold range is less than the lower limit of the first rate of change threshold range, the upper limit of the second fluctuation amplitude threshold range is less than the lower limit of the first fluctuation amplitude threshold range, the upper limit of the second stable duration percentage threshold range is less than the lower limit of the first stable duration percentage threshold range, and the upper limit of the second constant duration percentage threshold range is less than the lower limit of the first constant duration percentage threshold range.

[0058] It is understood that this embodiment defines the relationship between the airtightness anomaly model and the airflow short-circuit model and their threshold ranges in the humidity scenario model.

[0059] The centralized control equipment's storage section pre-stores humidity scenario models, including at least a sealing anomaly model and an airflow short-circuit model. These models are benchmark parameter sets obtained through intelligent training based on a large amount of experimental data or historical operating data, used to characterize the typical value range of humidity characteristic parameters under different spatial conditions, demonstrating the digital modeling capabilities of this invention.

[0060] Furthermore, the airtightness anomaly model is used to characterize the typical changes in indoor humidity caused by the outdoor environment when room doors and windows are open or not closed tightly. This model includes a first rate of change threshold range, a first fluctuation amplitude threshold range, a first stable duration percentage threshold range, and a first constant duration percentage threshold range.

[0061] Taking specific values ​​as an example, the first rate of change threshold range can be set to [0.8%RH / minute, 2.0%RH / minute], indicating that the rate of humidity change is relatively fast when the airtightness is abnormal; the first fluctuation amplitude threshold range can be set to [8%RH, 20%RH], indicating that the humidity fluctuation amplitude is relatively large; the first stable duration percentage threshold range can be set to [0%, 30%], indicating that the humidity is difficult to stabilize and the stable duration percentage is low; the first constant duration percentage threshold range can be set to [0%, 10%], indicating that the humidity is almost never constant and the constant duration percentage is extremely low.

[0062] Furthermore, the airflow short-circuit model is used to characterize the typical changes in humidity in a localized area of ​​the room when the air outlet of the indoor unit is directly drawn in by the return air vent and is not effectively diffused. This model includes a second rate of change threshold range, a second fluctuation amplitude threshold range, a second stable duration percentage threshold range, and a second constant duration percentage threshold range.

[0063] Taking specific values ​​as an example, the second rate of change threshold range can be set to [-0.2%RH / min, 0.2%RH / min), indicating that the rate of humidity change is extremely slow when the airflow is short-circuited, close to zero; the second fluctuation amplitude threshold range can be set to [0%RH, 2%RH), indicating that the humidity fluctuation amplitude is extremely small; the second stable duration percentage threshold range can be set to [80%, 100%], indicating that the humidity is highly stable and the stable duration percentage is relatively high; the second constant duration percentage threshold range can be set to [60%, 100%], indicating that the humidity is highly constant and the constant duration percentage is relatively high.

[0064] To ensure the distinguishability between models, this invention defines the numerical relationship between two model threshold intervals: the upper limit of the second rate of change threshold interval (0.2) is less than the lower limit of the first rate of change threshold interval (0.8); the upper limit of the second fluctuation amplitude threshold interval (2) is less than the lower limit of the first fluctuation amplitude threshold interval (8); the lower limit of the second stable duration percentage threshold interval (80%) is greater than the upper limit of the first stable duration percentage threshold interval (30%); and the lower limit of the second constant duration percentage threshold interval (60%) is greater than the upper limit of the first constant duration percentage threshold interval (10%). This numerical relationship ensures good separation between the airflow short-circuit model and the closure anomaly model in the feature space, avoids confusion in judgment, and reflects the scientific nature of intelligent model design.

[0065] This embodiment uses a pre-set airtightness anomaly model and airflow short-circuit model with clear numerical relationships. This method can accurately distinguish between two different types of inefficient operating scenarios, providing a reliable basis for subsequent targeted reminders and realizing intelligent scenario recognition.

[0066] In some embodiments of the present invention, the humidity scene model includes at least an efficient operating model; The efficient operating model includes a third rate of change threshold range, a third fluctuation amplitude threshold range, a third stable duration percentage threshold range, and a third constant duration percentage threshold range. The lower limit of the third rate of change threshold interval is greater than the upper limit of the second rate of change threshold interval, and the upper limit of the third rate of change threshold interval is less than the lower limit of the first rate of change threshold interval. The lower limit of the third fluctuation amplitude threshold interval is greater than the upper limit of the second fluctuation amplitude threshold interval, and the upper limit of the third fluctuation amplitude threshold interval is less than the lower limit of the first fluctuation amplitude threshold interval. The lower limit of the third stable duration percentage threshold interval is greater than the upper limit of the second stable duration percentage threshold interval, and the upper limit of the third stable duration percentage threshold interval is less than the lower limit of the first stable duration percentage threshold interval. The lower limit of the third constant duration percentage threshold interval is greater than the upper limit of the second constant duration percentage threshold interval, and the upper limit of the third constant duration percentage threshold interval is less than the lower limit of the first constant duration percentage threshold interval.

[0067] It is understood that this embodiment further defines the humidity scene model as including the relationship between the efficient operation model and its threshold range.

[0068] The high-efficiency operation model is used to characterize the typical humidity variation characteristics under normal air conditioning operation when the room is sealed and there is no airflow short-circuit. The model includes a third rate of change threshold range, a third fluctuation amplitude threshold range, a third stable duration percentage threshold range, and a third constant duration percentage threshold range.

[0069] Taking specific values ​​as an example, the third rate of change threshold range can be set to [0.2%RH / minute, 0.8%RH / minute], indicating that the rate of humidity change is moderate during efficient operation and shows a gradual downward trend; the third fluctuation amplitude threshold range can be set to [2%RH, 8%RH], indicating that the humidity fluctuation amplitude is moderate; the third stable duration percentage threshold range can be set to [30%, 80%], indicating that the humidity tends to be stable but has not reached a highly constant state; the third constant duration percentage threshold range can be set to [10%, 60%], indicating that the humidity may be temporarily constant but not continuously constant.

[0070] The threshold range of the efficient operating model lies between the threshold ranges of the airflow short-circuit model and the closure anomaly model, forming a clear hierarchical structure. Specifically: The lower limit of the third rate of change threshold interval (0.2) is equal to the upper limit of the second rate of change threshold interval (0.2). In practical applications, a half-open interval can be used to avoid boundary overlap, that is, the second rate of change threshold interval is [-0.2, 0.2), and the third rate of change threshold interval is [0.2, 0.8]. The upper limit of the third rate of change threshold interval (0.8) is equal to the lower limit of the first rate of change threshold interval (0.8), that is, the first rate of change threshold interval is [0.8, 2.0].

[0071] The lower limit (2) of the third fluctuation amplitude threshold interval is equal to the upper limit (2) of the second fluctuation amplitude threshold interval, that is, the second fluctuation amplitude threshold interval is [0, 2), and the third fluctuation amplitude threshold interval is [2, 8]; the upper limit (8) of the third fluctuation amplitude threshold interval is equal to the lower limit (8) of the first fluctuation amplitude threshold interval, that is, the first fluctuation amplitude threshold interval is [8, 20].

[0072] The lower limit of the third stable duration percentage threshold interval (30%) is equal to the upper limit of the first stable duration percentage threshold interval (30%), that is, the first stable duration percentage threshold interval is [0%, 30%]; the upper limit of the third stable duration percentage threshold interval (80%) is equal to the lower limit of the second stable duration percentage threshold interval (80%), that is, the second stable duration percentage threshold interval is [80%, 100%].

[0073] The lower limit of the third constant duration percentage threshold interval (10%) is equal to the upper limit of the first constant duration percentage threshold interval (10%), that is, the first constant duration percentage threshold interval is [0%, 10%]; the upper limit of the third constant duration percentage threshold interval (60%) is equal to the lower limit of the second constant duration percentage threshold interval (60%), that is, the second constant duration percentage threshold interval is [60%, 100%].

[0074] The numerical relationships described above can be summarized as: second threshold interval < third threshold interval < first threshold interval, where "<" indicates that the values ​​within one interval are generally less than those within another. This progressive relationship ensures a clear separation of the three models in the feature space, avoids overlapping judgments, and constructs a complete intelligent judgment system.

[0075] This embodiment, by adding an efficient operation model, can clearly distinguish between the normal efficient operation state and the two inefficient operation states, forming a complete three-state judgment system, avoiding misjudgments and invalid reminders for normal operation, and realizing self-controlled precise management.

[0076] In some embodiments of the present invention, the comparison process based on the humidity feature parameters and the parameters of a preset humidity scene model to obtain the humidity comparison result includes: The humidity change rate, humidity fluctuation amplitude, humidity stable duration percentage, and humidity constant duration percentage are compared with each threshold interval in the humidity scene model at a preset judgment period to generate humidity comparison results.

[0077] It is understood that this embodiment defines the specific steps for comparing and generating humidity comparison results at a preset judgment period.

[0078] In this embodiment, the centralized control device determines the humidity characteristic parameters and generates humidity comparison results according to a preset judgment period. The preset judgment period can be set to 15 minutes, that is, every 15 minutes, the centralized control device recalculates the characteristic parameters and compares them with the humidity data within the most recent time window (e.g., 30 minutes). The selection of the judgment period needs to balance real-time performance and stability: too short a period may lead to frequent misjudgments, while too long a period may delay the discovery of problems. This digital period setting reflects the adaptive capability of the intelligent system.

[0079] During each judgment cycle, the computing unit of the centralized control equipment compares the calculated humidity change rate, humidity fluctuation amplitude, humidity stable duration percentage, and humidity constant duration percentage with each threshold interval in the humidity scenario model (airtightness anomaly model, airflow short-circuit model, and high-efficiency operation model). After the comparison is completed, a humidity comparison result is generated, which indicates which model the current humidity characteristic parameter matches the best, thus achieving intelligent pattern recognition.

[0080] For specific numerical examples, if the humidity change rate calculated for a certain judgment period is 0.1%RH / minute, the fluctuation range is 1.2%RH, the stable duration percentage is 88%, and the constant duration percentage is 72%, then the comparison result indicates a match with the airflow short-circuit model; if the calculated humidity change rate is 1.0%RH / minute, the fluctuation range is 10%RH, the stable duration percentage is 20%, and the constant duration percentage is 5%, then the comparison result indicates a match with the airtightness anomaly model; if the calculated humidity change rate is 0.5%RH / minute, the fluctuation range is 5%RH, the stable duration percentage is 55%, and the constant duration percentage is 35%, then the comparison result indicates a match with the high-efficiency operation model.

[0081] This embodiment, by setting a preset judgment period and performing periodic comparisons, can continuously monitor changes in the state of the indoor space, promptly identify inefficient operating scenarios, and avoid misjudgments caused by instantaneous data fluctuations, thus achieving intelligent continuous monitoring.

[0082] In some embodiments of the present invention, determining the current indoor conditions based on the humidity comparison result includes: When the rate of change of humidity falls into the second rate of change threshold range, the amplitude of humidity fluctuation falls into the second amplitude of fluctuation threshold range, the percentage of stable humidity time falls into the second stable duration percentage threshold range, and the percentage of constant humidity time falls into the second constant duration percentage threshold range, the current indoor situation is determined to be an airflow short-circuit inefficient operation scenario. When the rate of change of humidity falls into the first rate of change threshold range, the amplitude of humidity fluctuation falls into the first amplitude of fluctuation threshold range, the percentage of stable humidity time falls into the first stable duration percentage threshold range, and the percentage of constant humidity time falls into the first constant duration percentage threshold range, the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. When the rate of change of humidity falls within the third rate of change threshold range, the amplitude of humidity fluctuation falls within the third amplitude of fluctuation threshold range, the percentage of stable humidity duration falls within the third stable duration percentage threshold range, and the percentage of constant humidity duration falls within the third constant duration percentage threshold range, the current indoor situation is determined to be an efficient operating scenario.

[0083] It is understood that this embodiment defines the specific judgment logic for determining the current indoor conditions based on the humidity comparison results.

[0084] When the calculated rate of humidity change falls within the second rate of change threshold range (e.g., [-0.2%RH / min, 0.2%RH / min)), the humidity fluctuation amplitude falls within the second fluctuation amplitude threshold range (e.g., [0%RH, 2%RH)), the percentage of stable humidity time falls within the second stable time percentage threshold range (e.g., [80%, 100%]), and the percentage of constant humidity time falls within the second constant time percentage threshold range (e.g., [60%, 100%]), the calculation unit of the centralized control equipment determines that the current indoor situation is an inefficient airflow short-circuit operation scenario.

[0085] Taking the indoor unit in the living room as an example, if the calculation results show that the humidity change rate is 0.05%RH / minute (extremely slow decrease), the fluctuation range is 0.8%RH (very small fluctuation), the stable duration accounts for 92% (highly stable), and the constant duration accounts for 75% (highly constant), then the four parameters fall into the corresponding second threshold range, and the system is judged to be operating in an inefficient airflow short-circuit scenario. This multi-parameter joint judgment reflects the rigor of intelligent decision-making.

[0086] The steps for determining an abnormally inefficient operation scenario due to airtightness are as follows: When the calculated rate of change of humidity falls within the first rate of change threshold range (e.g., [0.8%RH / min, 2.0%RH / min]), the humidity fluctuation amplitude falls within the first fluctuation amplitude threshold range (e.g., [8%RH, 20%RH]), the percentage of stable humidity time falls within the first stable time percentage threshold range (e.g., [0%, 30%]), and the percentage of constant humidity time falls within the first constant time percentage threshold range (e.g., [0%, 10%]), the current indoor situation is determined to be an abnormally inefficient operation scenario due to airtightness.

[0087] Taking the indoor unit in the bedroom as an example, if the calculation results show that the humidity change rate is 1.2%RH / minute (rapid decrease), the fluctuation range is 12%RH (violent fluctuation), the stable time percentage is 15% (difficult to stabilize), and the constant time percentage is 2% (almost no constant), then the four parameters fall into the corresponding first threshold range, and it is judged as an abnormally inefficient operation scenario with poor airtightness.

[0088] The steps for determining an efficient operating scenario are as follows: When the calculated rate of change of humidity falls within the third rate of change threshold range (e.g., [0.2%RH / min, 0.8%RH / min]), the humidity fluctuation amplitude falls within the third fluctuation amplitude threshold range (e.g., [2%RH, 8%RH]), the percentage of stable humidity time falls within the third stable time percentage threshold range (e.g., [30%, 80%]), and the percentage of constant humidity time falls within the third constant time percentage threshold range (e.g., [10%, 60%]), the current indoor situation is determined to be an efficient operating scenario.

[0089] Taking the indoor unit of the study as an example, if the calculation results show that the humidity change rate is 0.5%RH / minute (gradual decrease), the fluctuation range is 5%RH (moderate fluctuation), the stable duration accounts for 65% (relatively stable), and the constant duration accounts for 35% (occasionally constant), then the four parameters fall into the corresponding third threshold range, and are judged as a high-efficiency operation scenario.

[0090] This embodiment, through the above-mentioned four-parameter joint judgment logic, can accurately distinguish three different spatial states, providing a reliable basis for generating differentiated energy-saving reminders and realizing intelligent state recognition.

[0091] In some embodiments of the present invention, the steps of determining the humidity characteristic parameters and generating the humidity comparison result are performed at a preset determination period. The step of determining the current indoor environment condition based on the humidity comparison result further includes: In N consecutive preset determination cycles, if the humidity comparison results determined in each preset determination cycle indicate that the humidity change rate falls within the second change rate threshold range, the humidity fluctuation amplitude falls within the second fluctuation amplitude threshold range, the humidity stable duration percentage falls within the second stable duration percentage threshold range, and the humidity constant duration percentage falls within the second constant duration percentage threshold range, then the current indoor situation is determined to be an airflow short-circuit inefficient operation scenario. If, in N consecutive preset determination cycles, the humidity comparison results determined in each preset determination cycle indicate that the humidity change rate falls within the first change rate threshold range, the humidity fluctuation amplitude falls within the first fluctuation amplitude threshold range, the humidity stable duration percentage falls within the first stable duration percentage threshold range, and the humidity constant duration percentage falls within the first constant duration percentage threshold range, then the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. In M consecutive preset determination cycles, when the humidity comparison result determined in each preset determination cycle alternates between the determination condition indicating an inefficient operation scenario with short-circuited airflow and the determination condition indicating an inefficient operation scenario with abnormal airtightness, the previous determination result of the current indoor situation is maintained; where N is an integer greater than or equal to 2 and M is an integer greater than or equal to 3.

[0092] It is understood that this embodiment further defines the determination logic based on continuous determination period for stability enhancement.

[0093] To avoid misjudgments caused by occasional humidity fluctuations, this embodiment introduces a continuous judgment and confirmation mechanism. The centralized control device continuously calculates humidity characteristic parameters and determines the scenario at preset judgment cycles (e.g., 15 minutes). In a specific application scenario, an indoor unit might be judged as operating in an inefficient airflow short-circuit scenario in the first judgment cycle, but the characteristic parameters may become abnormal due to momentary interference (such as a user briefly opening a door). To improve the reliability of the judgment, the centralized control device does not immediately output the final judgment result, but continues to monitor subsequent judgment cycles. This intelligent confirmation mechanism reflects the system's self-control capability.

[0094] When, in N consecutive preset judgment cycles, the humidity comparison results for each judgment cycle indicate that the rate of humidity change falls within the second rate of change threshold range, the humidity fluctuation amplitude falls within the second fluctuation amplitude threshold range, the percentage of stable humidity time falls within the second stable duration threshold range, and the percentage of constant humidity time falls within the second constant duration threshold range, the centralized control device finally confirms that the current indoor situation is an airflow short-circuit inefficient operation scenario. N is an integer greater than or equal to 2. In this embodiment, N is set to 2, meaning that confirmation is only made when two consecutive judgments are both for an airflow short-circuit scenario.

[0095] For example, if the humidity characteristic parameters of the indoor unit in the living room meet all four conditions of the airflow short-circuit model in both the first judgment period (t=15 minutes) and the second judgment period (t=30 minutes), then the centralized control equipment will confirm that the current indoor situation is an inefficient airflow short-circuit operation scenario after the second judgment period ends.

[0096] Similarly, when the judgment conditions of the airtightness anomaly model are met for N consecutive cycles, the current indoor situation is confirmed as an airtightness anomaly inefficient operating scenario. For example, if the first threshold interval condition is met for two consecutive judgment cycles, it is confirmed as an airtightness anomaly inefficient operating scenario.

[0097] When the humidity comparison results determined in each of the M consecutive preset judgment cycles alternate between the judgment conditions indicating an inefficient operation scenario due to airflow short-circuiting and those indicating an inefficient operation scenario due to abnormal airtightness (e.g., the first cycle is the airflow short-circuit condition, the second cycle is the abnormal airtightness condition, and the third cycle is the airflow short-circuit condition), the centralized control device maintains the previous determination result of the current indoor situation and does not switch scenes. M is an integer greater than or equal to 3, and in this embodiment, M is 3. This intelligent anti-shake mechanism improves the stability of the system.

[0098] For example, if the previous determination result is a high-efficiency operation scenario, and then there are alternating changes of airflow short circuit - airtightness abnormality - airflow short circuit in three consecutive determination cycles, the centralized control equipment will maintain the determination result of the high-efficiency operation scenario unchanged, avoiding frequent switching of determination results between the two scenario boundary states, improving user experience, and demonstrating intelligent decision-making capabilities.

[0099] This embodiment effectively eliminates the influence of instantaneous interference and boundary oscillations on the judgment result by introducing a continuous periodic confirmation mechanism and alternating change processing logic, significantly improving the reliability and stability of the judgment and realizing self-controlled accurate judgment.

[0100] In some embodiments of the present invention, generating energy-saving reminder information based on the current indoor conditions includes: When the current indoor situation is an inefficient operation scenario due to airflow short circuit, a first reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the first reminder message includes a room identifier corresponding to the indoor unit and a first prompt text, the first prompt text being used to prompt the user to troubleshoot the airflow short circuit problem; In the case where the current indoor situation is an inefficient operation due to abnormal airtightness, a second reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the second reminder message includes a room identifier corresponding to the indoor unit and a second prompt text, the second prompt text being used to prompt the user to check for abnormal airtightness; When the current indoor situation is a high-efficiency operating scenario, a third reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the third reminder message is used to indicate that the current operating status is normal.

[0101] It is understood that this embodiment defines the specific steps for generating differentiated energy-saving reminder information based on the current indoor conditions.

[0102] When the central control equipment confirms that the current indoor situation is an inefficient operation scenario due to airflow short circuit, the execution unit generates the first reminder message. The first reminder message includes the room identifier corresponding to the indoor unit (such as "living room") and the first prompt text.

[0103] The initial notification text prompts the user to troubleshoot airflow short-circuit issues. Specific content includes, but is not limited to: "[Smart Energy Saving Reminder] Low efficiency detected in the living room air conditioner, suspected airflow short-circuit. Please check if the indoor unit's air outlet is blocked by furniture, and if the return air vent is unobstructed, or contact after-sales service for installation and testing." This notification is pushed wirelessly to the user's notification terminal (such as a mobile app) associated with the living room indoor unit. Upon receiving the notification, the user can take appropriate measures based on the prompt. This digital interaction method enables IoT-based energy-saving management.

[0104] When the central control equipment confirms that the current indoor situation is an abnormally inefficient operating scenario due to poor airtightness, the execution unit generates a second reminder message. The second reminder message includes the room identifier corresponding to the indoor unit (such as "Master Bedroom") and a second prompt text.

[0105] The second prompt text is used to remind users to check for airtightness issues. Specific content includes, but is not limited to: "[Smart Energy Saving Reminder] A possible leak of cold air has been detected from the air conditioner in the master bedroom. Please check that the doors and windows are completely closed to avoid energy waste." This reminder message is pushed to the user reminder terminal associated with the indoor unit in the master bedroom, guiding the user to close the doors and windows in a timely manner.

[0106] When the central control equipment confirms that the current indoor conditions are in a high-efficiency operating scenario, the execution unit generates a third reminder message. This third reminder message indicates that the current operating status is normal, and its specific content could be: "[Operating Status] The air conditioner is currently operating at high efficiency, and its energy-saving status is good." This reminder message allows users to understand the system's operating status, or they can choose not to generate a reminder to avoid disturbing them.

[0107] This embodiment generates completely different reminder messages based on different current indoor conditions. This method realizes an intelligent upgrade from "general anomaly reminders" to "targeted energy-saving guidance". Users can quickly locate the root cause of the problem and take effective measures based on the reminder content, thereby reducing unnecessary energy consumption and building a closed loop for energy-saving management of smart homes.

[0108] In this embodiment, combined with Figure 3 As shown, the multi-split air conditioning system includes at least two indoor units, a single outdoor unit, and a central control unit. Each indoor unit is equipped with a humidity sensor, and the central control unit has a built-in processor and memory, with the memory storing a computer program. When the processor executes the computer program, it performs the following steps: collecting relative humidity data of the indoor environment through the humidity sensors in each indoor unit; uploading the humidity data to the central control unit; determining humidity characteristic parameters based on the humidity data; comparing the humidity characteristic parameters with a preset humidity scenario model to determine the current indoor conditions; and generating energy-saving reminder information based on the current indoor conditions. This multi-split air conditioning product can be directly deployed at the user's site without the need for additional external servers or cloud platforms. It can achieve autonomous detection and energy-saving reminders for inefficient operating scenarios, making it a smart air conditioning product with intelligent, IoT-enabled, and self-control capabilities.

[0109] This embodiment integrates the above method into the multi-split air conditioning product itself, realizing the system's built-in intelligent energy-saving management function, reducing the user's usage threshold and deployment cost, and promoting the digital transformation of the air conditioning industry.

[0110] The present invention provides a method for predicting energy-saving performance of multi-split air conditioners based on air humidity, and a multi-split air conditioner system that constructs an intelligent, IoT-connected, and digital air conditioning operation management system, which can achieve at least the following beneficial technical effects: It can accurately identify inefficient operating scenarios. By intelligently collecting indoor relative humidity data and analyzing its dynamic change characteristics (including change rate, fluctuation amplitude, stable duration percentage, and constant duration percentage), it can accurately distinguish between three different states: abnormal airtightness, airflow short circuit, and efficient operation, thus solving the technical problem that existing technologies cannot identify abnormal spatial states.

[0111] This technology enables differentiated energy-saving reminders. Based on the specific inefficiency scenarios identified, it generates targeted reminder messages with different content and pushes them to the user's terminal via wireless network. This guides the user to quickly troubleshoot the root cause of the problem (such as closing doors and windows or checking installation). It solves the technical problem of the lack of targeted reminders in existing technologies and realizes a digital interactive experience.

[0112] The system demonstrates high reliability in its judgments. By employing a preset judgment cycle, a continuous cycle confirmation mechanism, and alternating processing logic, it effectively avoids misjudgments caused by instantaneous interference and boundary oscillations, thereby improving the stability and reliability of the system's judgments and showcasing the superiority of the automatic control system.

[0113] It can reduce energy consumption, effectively reduce energy waste caused by open doors and windows or airflow short circuits by timely identifying and guiding users to solve inefficient operation problems, improve the overall operating energy efficiency of multi-split air conditioning systems, and create smart energy-saving value for users.

[0114] With a high degree of system integration, this method can be directly integrated into the central control equipment of existing multi-split air conditioning systems without the need for additional hardware. It has good compatibility and feasibility, and represents a deep integration of IoT technology and the traditional air conditioning industry.

[0115] The following describes the multi-split air conditioner energy-saving prediction device based on air humidity provided by the present invention. The multi-split air conditioner energy-saving prediction device based on air humidity described below can be referred to in correspondence with the multi-split air conditioner energy-saving prediction method based on air humidity described above.

[0116] like Figure 4 The diagram shown is a second structural schematic of the multi-split air conditioner energy-saving prediction device based on air humidity provided by the present invention. The multi-split air conditioner energy-saving prediction device is applied to a multi-split air conditioner, which includes at least two indoor units, a single outdoor unit and a central control device. Each indoor unit is equipped with a humidity sensor. The device includes: The acquisition module 410 is used to acquire the relative humidity data of the indoor environment of the room where each indoor unit is located at a preset sampling frequency through the humidity sensor in each indoor unit. The transmission module 420 is used to transmit the indoor relative humidity data to the outdoor unit through the indoor unit, and the outdoor unit periodically uploads it to the centralized control device via the transmission module; The determination module 430 is used to determine humidity characteristic parameters based on the indoor relative humidity data of each indoor unit when the centralized control device receives the indoor relative humidity data. The comparison module 440 is used to compare the humidity characteristic parameters with the parameters of the preset humidity scene model to obtain the humidity comparison result, and determine the current indoor environment based on the humidity comparison result. The reminder module 450 is used to generate energy-saving reminder information based on the current indoor conditions.

[0117] A humidity time series is constructed based on multiple indoor relative humidity data of each indoor unit within a preset time window. Statistical analysis is performed on the humidity time series to calculate at least one humidity characteristic parameter of the humidity time series, the humidity characteristic parameter including at least one of the following parameters: Humidity change rate, humidity fluctuation amplitude, percentage of stable humidity duration, and percentage of constant humidity duration.

[0118] Preferably, the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention is specifically used to make the humidity change rate the slope value obtained by linear regression fitting of the humidity time series, which is used to characterize the speed of humidity change over time. The humidity fluctuation amplitude is the difference between the maximum and minimum values ​​of the humidity data in the humidity time series, used to characterize the dispersion of the humidity values; The percentage of stable humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset stable humidity range in the humidity time series to the preset time window. The percentage of constant humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset constant humidity range in the humidity time series to the preset time window; wherein, the width of the preset constant humidity range is smaller than the width of the preset stable humidity range.

[0119] Preferably, the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention is specifically used in the humidity scenario model, which includes at least a sealing anomaly model and an airflow short-circuit model. The closed-loop anomaly model includes a first rate of change threshold range, a first fluctuation amplitude threshold range, a first stable duration percentage threshold range, and a first constant duration percentage threshold range. The airflow short-circuit model includes a second rate of change threshold range, a second fluctuation amplitude threshold range, a second stable duration percentage threshold range, and a second constant duration percentage threshold range. The upper limit of the second rate of change threshold range is less than the lower limit of the first rate of change threshold range, the upper limit of the second fluctuation amplitude threshold range is less than the lower limit of the first fluctuation amplitude threshold range, the upper limit of the second stable duration percentage threshold range is less than the lower limit of the first stable duration percentage threshold range, and the upper limit of the second constant duration percentage threshold range is less than the lower limit of the first constant duration percentage threshold range.

[0120] Preferably, the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention is specifically used in the humidity scenario model, which includes at least a high-efficiency operation model; The efficient operating model includes a third rate of change threshold range, a third fluctuation amplitude threshold range, a third stable duration percentage threshold range, and a third constant duration percentage threshold range. The lower limit of the third rate of change threshold interval is greater than the upper limit of the second rate of change threshold interval, and the upper limit of the third rate of change threshold interval is less than the lower limit of the first rate of change threshold interval. The lower limit of the third fluctuation amplitude threshold interval is greater than the upper limit of the second fluctuation amplitude threshold interval, and the upper limit of the third fluctuation amplitude threshold interval is less than the lower limit of the first fluctuation amplitude threshold interval. The lower limit of the third stable duration percentage threshold interval is greater than the upper limit of the second stable duration percentage threshold interval, and the upper limit of the third stable duration percentage threshold interval is less than the lower limit of the first stable duration percentage threshold interval. The lower limit of the third constant duration percentage threshold interval is greater than the upper limit of the second constant duration percentage threshold interval, and the upper limit of the third constant duration percentage threshold interval is less than the lower limit of the first constant duration percentage threshold interval.

[0121] Preferably, the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention is specifically used to compare the humidity change rate, the humidity fluctuation amplitude, the percentage of humidity stable duration, and the percentage of humidity constant duration with each threshold interval in the humidity scenario model at a preset judgment period, and generate humidity comparison results.

[0122] Preferably, the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention is specifically used to determine the current indoor situation as an airflow short-circuit inefficient operation scenario when the humidity change rate falls into the second change rate threshold range, the humidity fluctuation amplitude falls into the second fluctuation amplitude threshold range, the humidity stable duration percentage falls into the second stable duration percentage threshold range, and the humidity constant duration percentage falls into the second constant duration percentage threshold range. When the rate of change of humidity falls into the first rate of change threshold range, the amplitude of humidity fluctuation falls into the first amplitude of fluctuation threshold range, the percentage of stable humidity time falls into the first stable duration percentage threshold range, and the percentage of constant humidity time falls into the first constant duration percentage threshold range, the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. When the rate of change of humidity falls within the third rate of change threshold range, the amplitude of humidity fluctuation falls within the third amplitude of fluctuation threshold range, the percentage of stable humidity duration falls within the third stable duration percentage threshold range, and the percentage of constant humidity duration falls within the third constant duration percentage threshold range, the current indoor situation is determined to be an efficient operating scenario.

[0123] Preferably, the multi-split air conditioning energy-saving prediction device based on air humidity provided by the present invention is specifically used to determine that the current indoor situation is an airflow short-circuit inefficient operation scenario when the humidity comparison results determined in each of the N consecutive preset judgment cycles indicate that the humidity change rate falls into the second change rate threshold range, the humidity fluctuation amplitude falls into the second fluctuation amplitude threshold range, the humidity stable duration percentage falls into the second stable duration percentage threshold range, and the humidity constant duration percentage falls into the second constant duration percentage threshold range. If, in N consecutive preset determination cycles, the humidity comparison results determined in each preset determination cycle indicate that the humidity change rate falls within the first change rate threshold range, the humidity fluctuation amplitude falls within the first fluctuation amplitude threshold range, the humidity stable duration percentage falls within the first stable duration percentage threshold range, and the humidity constant duration percentage falls within the first constant duration percentage threshold range, then the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. In M consecutive preset determination cycles, when the humidity comparison result determined in each preset determination cycle alternates between the determination condition indicating an inefficient operation scenario with short-circuited airflow and the determination condition indicating an inefficient operation scenario with abnormal airtightness, the previous determination result of the current indoor situation is maintained; where N is an integer greater than or equal to 2 and M is an integer greater than or equal to 3.

[0124] Preferably, the multi-split air conditioner energy-saving prediction device based on air humidity provided by the present invention is specifically used to generate a first reminder message when the current indoor situation is an inefficient operation scenario of airflow short circuit, and push the first reminder message to the user reminder terminal corresponding to the indoor unit; wherein, the first reminder message includes a room identifier corresponding to the indoor unit and a first prompt text, the first prompt text being used to prompt the user to check for airflow short circuit problems; In the case where the current indoor situation is an inefficient operation due to abnormal airtightness, a second reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the second reminder message includes a room identifier corresponding to the indoor unit and a second prompt text, the second prompt text being used to prompt the user to check for abnormal airtightness; When the current indoor situation is a high-efficiency operating scenario, a third reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the third reminder message is used to indicate that the current operating status is normal.

[0125] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a multi-split air conditioner energy-saving prediction method based on air humidity. The method includes: collecting indoor relative humidity data of the room where each indoor unit is located at a preset sampling frequency through humidity sensors in each indoor unit; transmitting the indoor relative humidity data to the outdoor unit through the indoor unit, and periodically uploading it to the central control device by the outdoor unit via a transmission module; when the central control device receives the indoor relative humidity data, determining humidity characteristic parameters based on the indoor relative humidity data of each indoor unit; comparing the humidity characteristic parameters with the parameters of a preset humidity scenario model to obtain a humidity comparison result, and determining the current indoor environment condition based on the humidity comparison result; and generating energy-saving reminder information based on the current indoor environment condition.

[0126] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the multi-split air conditioner energy-saving prediction method based on air humidity provided by the above methods, the method comprising: collecting indoor relative humidity data of the room where each indoor unit is located at a preset sampling frequency through humidity sensors in each indoor unit; transmitting the indoor relative humidity data to the outdoor unit through the indoor unit, and periodically uploading it to the central control device by the outdoor unit via a transmission module; determining humidity characteristic parameters based on the indoor relative humidity data of each indoor unit when the central control device receives the indoor relative humidity data; comparing the humidity characteristic parameters with the parameters of a preset humidity scenario model to obtain a humidity comparison result, and determining the current indoor condition based on the humidity comparison result; and generating energy-saving reminder information based on the current indoor condition.

[0128] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program performs the above-mentioned energy-saving prediction methods for multi-split air conditioners based on air humidity. The method includes: collecting indoor relative humidity data of the room where each indoor unit is located at a preset sampling frequency using humidity sensors in each indoor unit; transmitting the indoor relative humidity data to the outdoor unit through the indoor unit, and periodically uploading it to a central control device via a transmission module; determining humidity characteristic parameters based on the indoor relative humidity data of each indoor unit when the central control device receives the indoor relative humidity data; comparing the humidity characteristic parameters with parameters of a preset humidity scenario model to obtain a humidity comparison result, and determining the current indoor environment condition based on the humidity comparison result; and generating energy-saving reminder information based on the current indoor environment condition.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An air humidity-based multi-split air conditioner energy-saving prediction method, characterized in that, This invention is applied to multi-split air conditioners, which include at least two indoor units, a single outdoor unit, and a central control device, with each indoor unit equipped with a humidity sensor. The method includes: The relative humidity data of the indoor environment of the room where each indoor unit is located is collected by the humidity sensor in each indoor unit at a preset sampling frequency. The indoor relative humidity data is transmitted from the indoor unit to the outdoor unit, and then periodically uploaded by the outdoor unit to the centralized control device via the transmission module. When the centralized control device receives the indoor relative humidity data, it determines the humidity characteristic parameters based on the indoor relative humidity data of each indoor unit. The humidity characteristic parameters are compared with the parameters of the preset humidity scene model to obtain the humidity comparison result, and the current indoor environment is determined based on the humidity comparison result. Based on the current indoor conditions, an energy-saving reminder message is generated. 2.The air humidity based multi-connected air conditioner energy saving prediction method according to claim 1, wherein, The process of determining humidity characteristic parameters based on the relative humidity data of the indoor environment for each indoor unit includes: A humidity time series is constructed based on multiple indoor relative humidity data of each indoor unit within a preset time window. Statistical analysis is performed on the humidity time series to calculate at least one humidity characteristic parameter of the humidity time series, the humidity characteristic parameter including at least one of the following parameters: Humidity change rate, humidity fluctuation amplitude, percentage of stable humidity duration, and percentage of constant humidity duration. 3.The air humidity based multi-connected air conditioner energy saving prediction method according to claim 2, characterized in that, The method includes: The rate of change of humidity is the slope value obtained by linear regression fitting of the humidity time series, which is used to characterize how fast the humidity changes over time. The humidity fluctuation amplitude is the difference between the maximum and minimum values ​​of the humidity data in the humidity time series, used to characterize the dispersion of the humidity values; The percentage of stable humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset stable humidity range in the humidity time series to the preset time window. The percentage of constant humidity duration is the ratio of the cumulative duration of humidity values ​​falling within a preset constant humidity range in the humidity time series to the preset time window; wherein, the width of the preset constant humidity range is smaller than the width of the preset stable humidity range. 4.The air humidity based multi-connected air conditioning energy saving prediction method according to claim 2, wherein, The method includes: The humidity scenario model includes at least a closed-loop anomaly model and an airflow short-circuit model; The closed-loop anomaly model includes a first rate of change threshold range, a first fluctuation amplitude threshold range, a first stable duration percentage threshold range, and a first constant duration percentage threshold range. The airflow short-circuit model includes a second rate of change threshold range, a second fluctuation amplitude threshold range, a second stable duration percentage threshold range, and a second constant duration percentage threshold range. The upper limit of the second rate of change threshold range is less than the lower limit of the first rate of change threshold range, the upper limit of the second fluctuation amplitude threshold range is less than the lower limit of the first fluctuation amplitude threshold range, the upper limit of the second stable duration percentage threshold range is less than the lower limit of the first stable duration percentage threshold range, and the upper limit of the second constant duration percentage threshold range is less than the lower limit of the first constant duration percentage threshold range. 5.The air humidity based multi-connected air conditioning energy saving prediction method according to claim 4, characterized in that, The method includes: The humidity scenario model includes at least a high-efficiency operating model; The efficient operating model includes a third rate of change threshold range, a third fluctuation amplitude threshold range, a third stable duration percentage threshold range, and a third constant duration percentage threshold range. The lower limit of the third rate of change threshold interval is greater than the upper limit of the second rate of change threshold interval, and the upper limit of the third rate of change threshold interval is less than the lower limit of the first rate of change threshold interval. The lower limit of the third fluctuation amplitude threshold interval is greater than the upper limit of the second fluctuation amplitude threshold interval, and the upper limit of the third fluctuation amplitude threshold interval is less than the lower limit of the first fluctuation amplitude threshold interval. The lower limit of the third stable duration percentage threshold interval is greater than the upper limit of the second stable duration percentage threshold interval, and the upper limit of the third stable duration percentage threshold interval is less than the lower limit of the first stable duration percentage threshold interval. The lower limit of the third constant duration percentage threshold interval is greater than the upper limit of the second constant duration percentage threshold interval, and the upper limit of the third constant duration percentage threshold interval is less than the lower limit of the first constant duration percentage threshold interval.

6. The energy-saving prediction method for multi-split air conditioning based on air humidity according to claim 5, characterized in that, The comparison process based on the humidity feature parameters and the parameters of the preset humidity scene model to obtain the humidity comparison result includes: The humidity change rate, humidity fluctuation amplitude, humidity stable duration percentage, and humidity constant duration percentage are compared with each threshold interval in the humidity scene model at a preset judgment period to generate humidity comparison results.

7. The energy-saving prediction method for multi-split air conditioning based on air humidity according to claim 5, characterized in that, Determining the current indoor environment conditions based on the humidity comparison results includes: When the rate of change of humidity falls into the second rate of change threshold range, the amplitude of humidity fluctuation falls into the second amplitude of fluctuation threshold range, the percentage of stable humidity time falls into the second stable duration percentage threshold range, and the percentage of constant humidity time falls into the second constant duration percentage threshold range, the current indoor situation is determined to be an airflow short-circuit inefficient operation scenario. When the rate of change of humidity falls into the first rate of change threshold range, the amplitude of humidity fluctuation falls into the first amplitude of fluctuation threshold range, the percentage of stable humidity time falls into the first stable duration percentage threshold range, and the percentage of constant humidity time falls into the first constant duration percentage threshold range, the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. When the rate of change of humidity falls within the third rate of change threshold range, the amplitude of humidity fluctuation falls within the third amplitude of fluctuation threshold range, the percentage of stable humidity duration falls within the third stable duration percentage threshold range, and the percentage of constant humidity duration falls within the third constant duration percentage threshold range, the current indoor situation is determined to be an efficient operating scenario. 8.The air humidity based multi-connected air conditioning energy saving prediction method according to claim 6, wherein, The steps of determining the humidity characteristic parameters and generating the humidity comparison results are performed at a preset determination period. The step of determining the current indoor environment condition based on the humidity comparison results further includes: In N consecutive preset determination cycles, if the humidity comparison results determined in each preset determination cycle indicate that the humidity change rate falls within the second change rate threshold range, the humidity fluctuation amplitude falls within the second fluctuation amplitude threshold range, the humidity stable duration percentage falls within the second stable duration percentage threshold range, and the humidity constant duration percentage falls within the second constant duration percentage threshold range, then the current indoor situation is determined to be an airflow short-circuit inefficient operation scenario. If, in N consecutive preset determination cycles, the humidity comparison results determined in each preset determination cycle indicate that the humidity change rate falls within the first change rate threshold range, the humidity fluctuation amplitude falls within the first fluctuation amplitude threshold range, the humidity stable duration percentage falls within the first stable duration percentage threshold range, and the humidity constant duration percentage falls within the first constant duration percentage threshold range, then the current indoor situation is determined to be an abnormally inefficient operation scenario with poor airtightness. In M consecutive preset determination cycles, when the humidity comparison result determined in each preset determination cycle alternates between the determination condition indicating an inefficient operation scenario with short-circuited airflow and the determination condition indicating an inefficient operation scenario with abnormal airtightness, the previous determination result of the current indoor situation is maintained; where N is an integer greater than or equal to 2 and M is an integer greater than or equal to 3. 9.The air humidity based multi-connected air conditioning energy saving prediction method according to claim 7 or 8, characterized in that, The step of generating energy-saving reminder information based on the current indoor conditions includes: When the current indoor situation is an inefficient operation scenario due to airflow short circuit, a first reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the first reminder message includes a room identifier corresponding to the indoor unit and a first prompt text, the first prompt text being used to prompt the user to troubleshoot the airflow short circuit problem; In the case where the current indoor situation is an inefficient operation due to abnormal airtightness, a second reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the second reminder message includes a room identifier corresponding to the indoor unit and a second prompt text, the second prompt text being used to prompt the user to check for abnormal airtightness; When the current indoor situation is a high-efficiency operating scenario, a third reminder message is generated and pushed to the user reminder terminal corresponding to the indoor unit; wherein, the third reminder message is used to indicate that the current operating status is normal.

10. A multi-split air conditioner, characterized in that, The multi-split air conditioner includes at least two indoor units, a single outdoor unit, and a central control device. Each indoor unit is equipped with a humidity sensor. The multi-split air conditioner stores a computer program. When the computer program is executed by a processor, it implements the steps of the multi-split air conditioner energy-saving prediction method based on air humidity as described in any one of claims 1 to 9.

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