Dehumidification control method and system based on humidity detection and intelligent dehumidifier

Abstract

The present application relates to the technical field of intelligent dehumidification, specifically a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, comprising the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, performing time synchronization and labeling, performing data normalization processing, and obtaining initial environmental humidity detection data. In the present application, the accuracy and foresight of dehumidification control are improved through humidity detection, advanced data processing, and trend prediction. The use of differential technology identifies subtle humidity changes, accurately reflects real-time fluctuations in environmental humidity, and uses a linear regression model to predict short-term humidity changes, improving the accuracy of the prediction results and reducing prediction errors. The use of the calculation results of the humidity difference in different areas enables accurate adjustment of the dehumidification equipment in different areas, continuous monitoring and dynamic adjustment of dehumidification parameters, ensures that the equipment always maintains an optimal operating state, significantly improves energy efficiency, and improves the comfort and safety of living and working environments.

Description

Technical Field

[0001] This invention relates to the field of intelligent dehumidification technology, and in particular to a dehumidification control method, system and intelligent dehumidifier based on humidity detection. Background Technology

[0002] The field of intelligent dehumidification technology involves using advanced sensors and control systems to automatically regulate humidity in the air, thereby providing a more comfortable and healthy living and working environment. It combines environmental monitoring, IoT technology, and automated control systems to achieve precise humidity control. Typically, it includes humidity sensors, microprocessors, and actuators (such as electronic valves or fans). These components work together to respond in real-time to environmental changes, automatically adjusting the equipment's operating status to achieve ideal humidity levels. It is widely used in homes, offices, industrial and warehouse environments, especially in places where strict humidity control is required to protect sensitive equipment or items.

[0003] Among them, humidity detection-based dehumidification control is a technology that uses environmental humidity data to dynamically adjust the operation of dehumidifiers. Its main purpose is to automatically control indoor humidity to prevent various problems caused by excessively high or low humidity, such as mold growth, wood expansion or contraction, and electronic equipment malfunctions. By monitoring indoor humidity in real time and comparing it with a set target humidity value, the dehumidifier can automatically turn on or off, effectively maintaining indoor humidity within an ideal range. This not only improves energy efficiency but also enhances the comfort and safety of living and working environments.

[0004] Current technologies primarily rely on basic humidity monitoring and equipment adjustment, which limits their effectiveness in handling complex or rapidly changing environmental conditions. Furthermore, they lack advanced data analysis and predictive capabilities, enabling only reactive regulation rather than predictive control, resulting in delayed responses to environmental changes in practice. This operational mode easily leads to frequent equipment start-ups and shutdowns, increasing energy consumption and equipment wear. For example, under rapidly changing humidity conditions, if the system fails to adjust in time, indoor environments may become susceptible to mold growth or structural material damage, affecting the safety and comfort of the space. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a dehumidification control method, system and intelligent dehumidifier based on humidity detection.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a dehumidification control method based on humidity detection, comprising the following steps:

[0007] S1: Collect outdoor and indoor humidity data through a humidity sensor, perform time synchronization and marking, and perform data normalization to obtain initial environmental humidity detection data;

[0008] S2: Based on the initial environmental humidity detection data, the differential technique is used to identify trends and seasonal fluctuations in the data, local analysis is performed on the data points, and the environmental humidity difference analysis results are output.

[0009] S3: Based on the environmental humidity difference analysis results, a linear regression model is used to predict the future short-term environmental humidity trend. The prediction error is analyzed by comparing with historical data to generate humidity trend prediction results.

[0010] S4: Using the humidity trend prediction results, calculate the humidity set difference in the different areas, and by controlling and adjusting the fan speed and operating frequency of the dehumidification equipment, match the needs of each area and output the area dehumidification plan;

[0011] S5: Apply the aforementioned regional dehumidification scheme, continuously monitor and record the real-time humidity data of the region, dynamically adjust the dehumidification parameters based on the real-time data, determine that the dehumidification equipment maintains the optimal operating state, and obtain a dynamic dehumidification control scheme.

[0012] As a further aspect of the present invention, the initial environmental humidity detection data includes a timestamp, normalized humidity value, and synchronization marker result; the environmental humidity difference analysis result includes trend offset value, fluctuation frequency record, and abnormal fluctuation point record; the humidity trend prediction result includes prediction confidence interval, prediction standard error, and prediction time range; the regional dehumidification scheme includes setting humidity difference, fan adjustment parameters, and frequency adjustment value; and the dynamic dehumidification control scheme includes real-time humidity monitoring data, equipment performance monitoring record, and environmental adaptability adjustment record.

[0013] As a further aspect of the present invention, the specific steps for obtaining initial environmental humidity detection data by collecting outdoor and indoor humidity data through a humidity sensor, performing time synchronization and marking, and performing data normalization processing are as follows:

[0014] S101: Collects outdoor and indoor humidity data through a humidity sensor, synchronizes data timestamps, calibrates timestamp errors, and generates a time-synchronized humidity dataset.

[0015] S102: Based on the time-synchronized humidity dataset, mark the source of each data point, identifying it as an external or internal source, and uniquely encode each data point to generate a labeled enhanced humidity dataset;

[0016] S103: Based on the labeled enhanced humidity dataset, normalize the dataset, use the minimum-maximum scaling method to convert the data values ​​to the range of 0 to 1, verify the consistency and accuracy of the converted data, and generate initial environmental humidity detection data.

[0017] As a further aspect of the present invention, based on the initial environmental humidity detection data, the specific steps for identifying trends and seasonal fluctuations in the data using differential techniques, performing local analysis on the data points, and outputting environmental humidity difference analysis results are as follows:

[0018] S201: Based on the initial environmental humidity detection data, calculate the difference between consecutive data points, identify the humidity change trend by comparing the differences between the data points before and after, mark the significant change points, and generate trend-identified humidity data;

[0019] S202: Based on the trend identification humidity data, analyze the periodic changes in the data, identify the seasonal fluctuation pattern by comparing the difference results of data points in continuous periods, and calculate the average amplitude and periodic characteristics of the fluctuation to generate seasonal fluctuation humidity data.

[0020] S203: Based on the seasonal fluctuation humidity data, perform local analysis on each data point, highlight the key change areas within each cycle, summarize the humidity changes in the key change areas, and output the environmental humidity difference analysis results.

[0021] As a further aspect of the present invention, based on the environmental humidity difference analysis results, a linear regression model is used to predict the future short-term environmental humidity trend, and the prediction error is analyzed by comparing with historical data to generate a humidity trend prediction result. The specific steps are as follows:

[0022] S301: Based on the environmental humidity difference analysis results, perform data format conversion, arrange the humidity difference data according to the time series to ensure the continuity and time consistency of the data, and generate predictive analysis input data;

[0023] S302: Based on the predicted analysis input data, perform linear trend fitting on the humidity values ​​at continuous time points to predict the humidity changes in the future, record the predicted time range, and generate future humidity prediction data.

[0024] S303: Based on the predicted future humidity data, compare and analyze the predicted humidity value with the historical data at the corresponding time point, calculate the difference between the predicted value and the actual observed value, evaluate the error range and prediction accuracy of the model, and output the humidity trend prediction result.

[0025] As a further aspect of the present invention, the difference between the calculated predicted value and the actual observed value is determined according to the formula,

[0026]

[0027] Perform the calculation, where w i The time-weighted coefficient, For the predicted value, yi , where n is the actual observed value, n is the sample size, and MSE represents the difference value.

[0028] As a further aspect of the present invention, the specific steps for calculating the humidity setpoint difference in different areas using the humidity trend prediction results, and matching the needs of each area by controlling and adjusting the fan speed and operating frequency of the dehumidification equipment to output an area dehumidification solution are as follows:

[0029] S401: Based on the humidity trend prediction results, perform humidity demand analysis for each region, calculate the difference between the expected future humidity and the current actual humidity, set a new humidity target for each region, and generate regional humidity regulation demand data.

[0030] S402: Based on the humidity regulation demand data of the area, adjust the fan speed and operating frequency of the dehumidification equipment, gradually increase or decrease the fan speed to ensure accurate control of each area according to the humidity regulation demand, and generate equipment regulation parameter data;

[0031] S403: Based on the device adjustment parameter data, implement the dehumidification adjustment plan for each area, record the adjustment details of fan speed and operating frequency for each area, and output the area dehumidification plan.

[0032] As a further aspect of the present invention, the specific steps of applying the regional dehumidification scheme to continuously monitor and record the real-time humidity data of the region, dynamically adjust the dehumidification parameters based on the real-time data, and determine that the dehumidification equipment maintains the optimal operating state are as follows:

[0033] S501: Based on the aforementioned regional dehumidification scheme, monitor regional humidity data in real time, record data periodically, capture the periodic changes in humidity in each region, and generate real-time humidity monitoring data.

[0034] S502: Based on the real-time humidity monitoring data, analyze the data to determine the necessity of humidity adjustment, compare the target humidity value with the actual reading, calculate the difference and determine whether the operating parameters of the dehumidifier need to be adjusted, respond to changes in real-time data, and generate dynamically adjusted dehumidification parameters.

[0035] S503: Based on the dynamically adjusted dehumidification parameters, dynamically update the dehumidification equipment settings, optimize equipment operation to match real-time humidity requirements, continuously monitor the adjustment effect, verify the accuracy of humidity control, and output a dynamic dehumidification control scheme.

[0036] A dehumidification control system based on humidity detection includes:

[0037] The data synchronization and encoding module collects outdoor and indoor humidity data through humidity sensors, synchronizes data timestamps, marks the source of each data point, and uniquely encodes each data point to generate a labeled and enhanced humidity dataset.

[0038] The data normalization processing module normalizes the dataset based on the labeled enhanced humidity dataset, uses the minimum maximum scaling method to transform data values, calculates the difference between continuous data points, marks significant change points, and generates trend-identifying humidity data.

[0039] The periodic analysis module identifies humidity data based on the trend, analyzes periodic changes in the data, identifies seasonal fluctuation patterns, calculates the average amplitude and periodic characteristics of fluctuations, highlights key change areas within each cycle, and outputs environmental humidity difference analysis results.

[0040] The humidity prediction and analysis module converts the data format based on the environmental humidity difference analysis results, performs linear trend fitting on the humidity values ​​at continuous time points, predicts the humidity changes in the future, calculates the difference between the predicted value and the actual observed value, and outputs the humidity trend prediction result.

[0041] Based on the humidity trend prediction results, the dehumidification control and adjustment module calculates the difference between the expected future humidity and the current actual humidity, adjusts the fan speed and operating frequency of the dehumidification equipment, records the adjustment details of the fan speed and operating frequency for each area, and outputs the area dehumidification plan.

[0042] The dynamic dehumidification adjustment module, based on the regional dehumidification scheme, captures the periodic changes in humidity in each region, compares the target humidity value with the actual reading, calculates the difference, determines whether the operating parameters of the dehumidification equipment need to be adjusted, dynamically updates the dehumidification equipment settings, and outputs a dynamic dehumidification control scheme.

[0043] A humidity-detection-based intelligent dehumidifier includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the humidity-detection-based dehumidification control method described above.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In this invention, humidity detection, advanced data processing, and trend prediction are used to improve the accuracy and foresight of dehumidification control. Differential technology is used to identify subtle humidity changes, accurately reflecting real-time fluctuations in ambient humidity. A linear regression model is used to predict short-term humidity changes, improving the accuracy of prediction results and reducing prediction errors. The calculation results of humidity setpoint differences in different areas are used to achieve precise adjustment of dehumidification equipment in different areas. Dehumidification parameters are continuously monitored and dynamically adjusted to ensure that the equipment always maintains optimal operating conditions, significantly improving energy efficiency and significantly enhancing the comfort and safety of living and working environments. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0047] Figure 2 This is a flowchart illustrating the process of acquiring initial environmental humidity detection data in this invention.

[0048] Figure 3 This is a flowchart illustrating the process of obtaining the environmental humidity difference analysis results of this invention.

[0049] Figure 4 This is a flowchart illustrating the process of obtaining humidity trend prediction results according to the present invention.

[0050] Figure 5 This is a flowchart illustrating the process of obtaining the regional dehumidification solution of the present invention.

[0051] Figure 6 This is a flowchart illustrating the acquisition process of the dynamic dehumidification control scheme of the present invention.

[0052] Figure 7 This is a system flowchart of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0055] Please see Figure 1 A dehumidification control method based on humidity detection includes the following steps:

[0056] S1: Collect outdoor and indoor humidity data through a humidity sensor, perform time synchronization and marking, and perform data normalization to obtain initial environmental humidity detection data;

[0057] S2: Based on the initial environmental humidity detection data, the differential technique is used to identify trends and seasonal fluctuations in the data, perform local analysis on the data points, and output the environmental humidity difference analysis results;

[0058] S3: Based on the analysis results of environmental humidity differences, a linear regression model is used to predict the trend of environmental humidity changes in the near future. The prediction error is analyzed by comparing with historical data to generate humidity trend prediction results.

[0059] S4: Utilize humidity trend prediction results to calculate the humidity setpoint difference in different areas, and by controlling and adjusting the fan speed and operating frequency of the dehumidification equipment, match the needs of each area and output an area dehumidification solution;

[0060] S5: Apply a regional dehumidification solution, continuously monitor and record the real-time humidity data of the region, dynamically adjust the dehumidification parameters based on the real-time data, determine the optimal operating state of the dehumidification equipment, and obtain a dynamic dehumidification control solution.

[0061] Initial environmental humidity detection data includes timestamps, normalized humidity values, and synchronization marker results. Environmental humidity difference analysis results include trend offset values, fluctuation frequency records, and abnormal fluctuation point records. Humidity trend prediction results include prediction confidence intervals, prediction standard errors, and prediction time ranges. Regional dehumidification schemes include set humidity differences, fan adjustment parameters, and frequency adjustment values. Dynamic dehumidification control schemes include real-time humidity monitoring data, equipment performance monitoring records, and environmental adaptability adjustment records.

[0062] Please see Figure 2 The specific steps of S1 are as follows:

[0063] S101: Collects outdoor and indoor humidity data through a humidity sensor, synchronizes data timestamps, calibrates timestamp errors, and generates a time-synchronized humidity dataset.

[0064] Outdoor and indoor humidity data are collected by humidity sensors, and the data timestamps are synchronized. The difference between the external and internal data timestamps is calculated and corrected using linear interpolation to ensure consistency. First, the time series and humidity values ​​from the external and internal humidity sensors are extracted, and the average and standard deviation of the timestamp deviations are calculated. The time deviations are adjusted based on the statistical results to ensure that the error is within an acceptable range, thereby generating a time-synchronized humidity dataset, which includes synchronized timestamps and corresponding humidity values.

[0065] S102: Based on the time-synchronized humidity dataset, each data point is marked with a source, identifying it as an external or internal source, and each data point is uniquely encoded to generate a labeled and enhanced humidity dataset;

[0066] Based on a time-synchronized humidity dataset, each data point is tagged with its source, indicating whether it originates from an external or internal source. A unique code is then generated for each data point. Specifically, a hash function is used to encode the timestamp and source, generating a unique identifier that is appended to each data point. During this process, data integrity is also checked to ensure that each data point is correctly tagged and encoded for subsequent data tracking and processing. The resulting tagged and enhanced humidity dataset will contain the timestamp, humidity value, source tag, and unique code for each data point.

[0067] S103: Based on the labeled enhanced humidity dataset, the dataset is normalized. The minimum-maximum scaling method is used to transform the data values ​​to the range of 0 to 1. The consistency and accuracy of the transformed data are verified, and the initial environmental humidity detection data is generated.

[0068] Based on the labeled enhanced humidity dataset, the dataset is normalized using a min-max scaling method to transform the data values ​​to the range of 0 to 1. Specifically, this involves calculating the maximum and minimum humidity values ​​in the dataset and then applying the formula: (humidity value - minimum value) / (maximum value - minimum value) to adjust the humidity values. This step also includes statistical analysis of the transformed data to verify its consistency and accuracy, ensuring that all data points are correctly normalized. After these steps, initial environmental humidity monitoring data is generated, providing a foundation for environmental monitoring and further data analysis.

[0069] Please see Figure 3 The specific steps of S2 are as follows:

[0070] S201: Based on the initial ambient humidity detection data, calculate the difference between consecutive data points, identify the humidity change trend by comparing the differences between the data points before and after, mark the significant change points, and generate trend-identified humidity data;

[0071] Based on initial environmental humidity measurement data, the difference between consecutive data points is calculated. This is done by subtracting the humidity value of the previous data point from the current one. This difference calculation reveals short-term humidity changes. The specific formula for the difference is Δh. i =h i -h i-1 , where h i and h i-1 Δh represents continuous humidity measurements. i This represents the difference value within each period. Next, by setting a change threshold, the differences between the data points before and after are compared to identify significant changes in humidity. Significant changes are those data points whose difference values ​​exceed the threshold, indicating rapid or substantial changes in humidity. In this way, significant changes are marked and given special identifiers in the dataset, generating trend-identifying humidity data that provides crucial information for subsequent detailed analysis.

[0072] S202: Based on trend identification of humidity data, analyze the periodic changes in the data, identify seasonal fluctuation patterns by comparing the difference results of data points in continuous periods, calculate the average amplitude of fluctuations and periodic characteristics, and generate seasonal fluctuation humidity data.

[0073] Based on trend identification of humidity data, time series analysis is first performed to identify periodic variations. Autocorrelation and partial autocorrelation function analysis techniques are used to detect and confirm periodic characteristics in the data. These techniques can identify recurring patterns in the data, such as seasonal fluctuations. Subsequently, the difference between data points within each period is calculated, and the average amplitude of the fluctuation is estimated using the difference values. The calculation formula is as follows: Where Δh i This represents the difference value within each period, where N is the number of data points within the period. Furthermore, by comparing the maximum and minimum values ​​within each period, the periodic characteristics are derived. By combining this information, seasonal fluctuation humidity data is generated, which can be used to further analyze the impact of seasonal changes on environmental monitoring.

[0074] S203: Based on seasonally fluctuating humidity data, perform local analysis on each data point, highlight the key change areas within each cycle, summarize the humidity changes in the key change areas, and output the results of environmental humidity difference analysis.

[0075] Based on seasonally fluctuating humidity data, local analysis is performed on each data point, focusing on key areas of change within each cycle. By analyzing the points of maximum increase and decrease within each cycle, possible causes of these changes are identified, such as abrupt changes in environmental factors or periodic natural variations. Furthermore, data points within the key change areas are analyzed in depth to summarize the characteristics and trends of humidity changes in each region. Finally, these local analysis results are summarized to output the environmental humidity difference analysis results, where the humidity change in each key change area can be expressed by the formula Var. k =max(Δh) k )-min(Δh k To describe, Var k This represents the maximum humidity variation range in the k-th period, where Δh k This represents the humidity difference value within the k-th period. The results not only provide in-depth insights into environmental changes but also offer data support for developing countermeasures.

[0076] Please see Figure 4 The specific steps of S3 are as follows:

[0077] S301: Based on the results of the environmental humidity difference analysis, the data format is converted, and the humidity difference data is arranged according to the time series to ensure the continuity and time consistency of the data, and to generate predictive analysis input data.

[0078] Based on the environmental humidity difference analysis results, data format conversion was performed, arranging the humidity difference data according to time series to ensure data continuity and temporal consistency. First, the timestamp and corresponding humidity difference value for each data point were extracted from the difference analysis results. Then, the data was sorted according to the timestamps, ensuring all data were arranged in chronological order, thereby eliminating the impact of time misalignment during data collection or processing. This formatting process generates structured predictive analysis input data, providing a clear and consistent data foundation for subsequent data analysis and trend prediction.

[0079] S302: Based on the predictive analysis input data, perform linear trend fitting on the humidity values ​​at continuous time points to predict the humidity changes in the future, record the predicted time range, and generate future humidity prediction data.

[0080] Based on the predictive analysis input data, a linear trend fitting is performed on the humidity values ​​at continuous time points to predict future humidity changes and record the predicted time range. Specifically, a linear regression model is applied, and the relationship between humidity values ​​and time is estimated using the least squares method. The fitting formula is H(t)=β0+

[0081] Let β1·t be the slope, where β0 is the intercept, β1 is the slope representing the trend over time, t represents the time variable, and H(t) represents the predicted humidity value at any given time t. This model calculates the optimal β0 and β1 based on existing data points, thereby predicting the humidity value at any given time. This method generates future humidity prediction data, providing a basis for further decision-making and planning.

[0082] S303: Based on future humidity prediction data, compare and analyze the predicted humidity values ​​with historical data at the corresponding time points, calculate the difference between the predicted values ​​and the actual observed values, evaluate the error range and prediction accuracy of the model, and output humidity trend prediction results.

[0083] Calculate the difference between the predicted value and the actual observed value according to the formula,

[0084]

[0085] Perform the calculation, where w i The time-weighted coefficient, For the predicted value, y i , where n is the actual observed value, n is the sample size, and MSE represents the difference value.

[0086] The execution process is as follows:

[0087] First, assign a weight w to each data point based on their time proximity. i More recent data is given higher weight to reflect its greater impact on prediction accuracy. Next, the squared difference between the predicted and actual values ​​at each time point is calculated, and this squared difference is multiplied by the corresponding weight. Finally, all weighted squared differences are summed and divided by the sample size n to obtain the mean squared error (MSE) between the predicted and observed values. By introducing a time-weighted coefficient, the impact of prediction errors at different time points on the overall model performance can be more accurately reflected.

[0088] Confirm the weighting coefficient w i The steps are as follows:

[0089] To determine the weight allocation strategy, one can choose linearly decreasing weights, where the most recent observation is given the highest weight;

[0090] The weights are calculated based on the time distance from the observation point to the prediction start point;

[0091] Normalize all weights to ensure that the sum is 1, ensuring a reasonable allocation of weight coefficients and effectively reflecting the importance of data at different time points.

[0092] Please see Figure 5 The specific steps of S4 are as follows:

[0093] S401: Based on the humidity trend prediction results, perform humidity demand analysis for each region, calculate the difference between the expected future humidity and the current actual humidity, set a new humidity target for each region, and generate regional humidity regulation demand data.

[0094] Based on humidity trend forecasts, a humidity demand analysis is performed for each region, calculating the difference between the expected future humidity and the current actual humidity. The formula ΔH1 = H is used. 预期 -H 实际 H 预期 It is the expected humidity predicted based on environmental conditions and seasonal demand, while H 实际 The current humidity is obtained from recent environmental monitoring data, and ΔH1 is the humidity difference value. By analyzing this difference value, new humidity targets are set for each region. These targets will be adjusted according to the specific environmental needs and usage of each region. This generates regional humidity regulation requirement data, which will directly influence subsequent equipment regulation strategies to ensure that each region achieves its optimal environmental conditions.

[0095] S402: Based on regional humidity control demand data, adjust the fan speed and operating frequency of the dehumidifier, gradually increase or decrease the fan speed to ensure accurate control of each area according to humidity control needs, and generate equipment adjustment parameter data;

[0096] Based on regional humidity control demand data, the fan speed and operating frequency of the dehumidifier are adjusted. First, the required fan speed adjustment is calculated based on the humidity control demand data for each region, using formula V. 新 =V 旧 +k·ΔH1, where V 新 and V 旧 These represent the adjusted and current fan speeds, respectively. ΔH1 is the humidity difference value, and k is a calibration coefficient used to determine the relationship between speed change and humidity difference. By gradually increasing or decreasing the fan speed, accurate control is ensured for each zone according to its humidity regulation requirements. This method generates equipment regulation parameter data, which provides precise control parameters for the actual operation of the equipment.

[0097] S403: Based on the equipment adjustment parameter data, implement the dehumidification adjustment plan for each area, record the adjustment details of fan speed and operating frequency for each area, and output the area dehumidification plan;

[0098] Based on the equipment adjustment parameter data, a dehumidification adjustment plan is implemented for each area. In this step, the fan speed and operating frequency of the dehumidifier are adjusted according to the new humidity target and equipment adjustment parameters set for each area. The adjustment details of the fan speed and operating frequency for each area are recorded, including the fan speed values ​​before and after the adjustment, the operating frequency, and the specific time points of the adjustment. Finally, the dehumidification plan for each area is output, detailing how adjusting the fan speed and frequency meets the specific humidity requirements of each area, ensuring optimal environmental comfort and equipment efficiency.

[0099] Please see Figure 6 The specific steps of S5 are as follows:

[0100] S501: Based on the regional dehumidification solution, it monitors regional humidity data in real time, records data periodically, captures the periodic changes in humidity in each region, and generates real-time humidity monitoring data.

[0101] Based on a zoned dehumidification solution, humidity data is monitored in real time and recorded periodically. First, humidity sensors are deployed to continuously collect humidity data from each zone. The sensors send data periodically, with each data point timestamped to ensure real-time accuracy. Next, time-series analysis is used to capture the periodic changes in humidity in each zone, using formula H... t =αH t-1 +(1-α)H 新测量 To smooth the data, where α is the smoothing coefficient and H... t This is the estimated humidity for the current cycle, H. t-1 It is the humidity of the previous cycle, H. 新测量 These are the latest measurements, which are processed to generate real-time humidity monitoring data, providing a basis for subsequent analysis and adjustments.

[0102] S502: Based on real-time humidity monitoring data, analyze the data to determine the necessity of humidity adjustment, compare the target humidity value with the actual reading, calculate the difference and determine whether the operating parameters of the dehumidifier need to be adjusted, respond to changes in real-time data, and generate dynamically adjusted dehumidification parameters.

[0103] Based on real-time humidity monitoring data, the necessity of humidity adjustment is determined through data analysis. The target humidity value is compared with the actual reading, and the difference ΔH2 = H is calculated using the formula. 目标 -H 实测 , where H 目标 Indicates the target humidity value, H 实测This represents the measured humidity value. If the difference ΔH2 exceeds the preset tolerance range, it indicates that the operating parameters of the dehumidifier need to be adjusted. Based on these analysis results, dynamically adjusted dehumidification parameters are generated, including adjusting the fan speed or operating frequency to respond to changes in real-time data and ensure that humidity is controlled within the ideal range.

[0104] S503: Based on dynamically adjusted dehumidification parameters, dynamically update dehumidification equipment settings, optimize equipment operation to match real-time humidity requirements, continuously monitor adjustment effects, verify the accuracy of humidity control, and output dynamic dehumidification control scheme.

[0105] Based on dynamically adjusted dehumidification parameters, the dehumidification equipment settings are dynamically updated. The equipment operation is optimized according to the adjusted parameters, matching real-time humidity requirements and ensuring equipment operating efficiency and humidity control accuracy. The adjustment effect is continuously monitored, with real-time recording of equipment operating status and humidity control data to verify the accuracy of humidity control. The adjustment strategy is further optimized based on the monitoring results. A dynamic dehumidification control scheme is output, providing detailed operation and maintenance guidelines for equipment management, ensuring that the area humidity is maintained at an optimal level.

[0106] Please see Figure 7 A dehumidification control system based on humidity detection includes:

[0107] The data synchronization and encoding module collects outdoor and indoor humidity data through humidity sensors, synchronizes data timestamps, marks the source of each data point, and uniquely encodes each data point to generate a labeled and enhanced humidity dataset.

[0108] The data normalization processing module is based on the labeled enhanced humidity dataset. It normalizes the dataset, uses the minimum maximum scaling method to transform data values, calculates the difference between continuous data points, marks points of significant change, and generates trend-identifying humidity data.

[0109] The periodic analysis module identifies humidity data based on trend recognition, analyzes the periodic changes in the data, identifies seasonal fluctuation patterns, calculates the average amplitude and periodic characteristics of fluctuations, highlights key change areas within each cycle, and outputs environmental humidity difference analysis results.

[0110] The humidity prediction and analysis module converts the data format based on the results of the environmental humidity difference analysis, performs linear trend fitting on the humidity values ​​at continuous time points, predicts the humidity changes in the future, calculates the difference between the predicted value and the actual observed value, and outputs the humidity trend prediction results.

[0111] Based on humidity trend prediction results, the dehumidification control and adjustment module calculates the difference between the expected future humidity and the current actual humidity, adjusts the fan speed and operating frequency of the dehumidification equipment, records the adjustment details of the fan speed and operating frequency for each area, and outputs the area dehumidification plan.

[0112] The dynamic dehumidification adjustment module is based on the regional dehumidification scheme. It captures the periodic changes in humidity in each region, compares the target humidity value with the actual reading, calculates the difference and determines whether the operating parameters of the dehumidification equipment need to be adjusted, dynamically updates the dehumidification equipment settings, and outputs a dynamic dehumidification control scheme.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dehumidification control method based on humidity detection, characterized by, The method comprises the following steps: Collecting outdoor humidity data and indoor humidity data through humidity sensors, performing time synchronization and labeling, performing data normalization processing, and obtaining initial environmental humidity detection data; Based on the initial environmental humidity detection data, using differential technology to identify trends and seasonal fluctuations in the data, performing local analysis on the data points, and outputting environmental humidity difference analysis results; According to the environmental humidity difference analysis results, using a linear regression model to predict the trend of environmental humidity in the future short term, comparing with historical data to analyze prediction error, and generating humidity trend prediction results; Using the humidity trend prediction results, calculating the humidity setting difference of the difference area, matching the demand of each area by controlling the fan speed and operating frequency of the dehumidification equipment, and outputting the regional dehumidification scheme; Applying the regional dehumidification scheme, continuously monitoring and recording real-time humidity data of the area, dynamically adjusting dehumidification parameters according to real-time data, determining the optimal operating state of the dehumidification equipment, and obtaining a dynamic dehumidification control scheme.

2. The dehumidification control method based on humidity detection according to claim 1, wherein, The initial environmental humidity detection data includes timestamp, normalized humidity value and synchronization label result, the environmental humidity difference analysis result includes trend offset value, fluctuation frequency record and abnormal fluctuation point record, the humidity trend prediction result includes prediction confidence interval, prediction standard error and prediction time range, the regional dehumidification scheme includes set humidity difference, fan adjustment parameter and frequency adjustment value, and the dynamic dehumidification control scheme includes real-time humidity monitoring data, equipment efficiency monitoring record and environmental adaptability adjustment record.

3. The dehumidification control method based on humidity detection according to claim 1, wherein, The specific steps of collecting outdoor humidity data and indoor humidity data through humidity sensors, performing time synchronization and labeling, and performing data normalization processing to obtain initial environmental humidity detection data are as follows: Collecting outdoor humidity data and indoor humidity data through humidity sensors, synchronizing data timestamps, and calibrating timestamp errors to generate a time-synchronized humidity data set; Based on the time-synchronized humidity data set, each data point is labeled as external or internal source, and each data is uniquely encoded to generate a labeled enhanced humidity data set; Based on the labeled enhanced humidity data set, the data set is normalized by using the min-max scaling method to convert the data value to the range of 0 to 1, verifying the consistency and accuracy of the converted data, and generating initial environmental humidity detection data.

4. The dehumidification control method based on humidity detection according to claim 1, wherein, Based on the initial environmental humidity detection data, using differential technology to identify trends and seasonal fluctuations in the data, performing local analysis on the data points, and outputting environmental humidity difference analysis results, the specific steps are as follows: Based on the initial environmental humidity detection data, calculating the difference value between consecutive data points, identifying the humidity change trend by comparing the difference between the data points, marking the significant change points, and generating trend identification humidity data; Based on the trend identification humidity data, analyzing the periodic changes in the data, comparing the difference results of the data points in the continuous period, identifying the seasonal fluctuation mode, and calculating the average amplitude and periodic characteristics of the fluctuation, and generating seasonal fluctuation humidity data; Based on the seasonal fluctuation humidity data, local analysis is performed on each data point to highlight the key change area in each period, and the humidity change in the key change area is summarized to output the environmental humidity difference analysis result.

5. The dehumidification control method based on humidity detection according to claim 1, wherein, According to the environmental humidity difference analysis result, a linear regression model is used to predict the change trend of the environmental humidity in the future short term, and the prediction error is analyzed by comparing with the historical data to generate the specific steps of the humidity trend prediction result: Based on the environmental humidity difference analysis result, data format conversion is performed to arrange the humidity difference data in time sequence to ensure the continuity and time consistency of the data, and to generate the prediction analysis input data; Based on the prediction analysis input data, linear trend fitting is performed on the humidity values of the continuous time points to predict the humidity change in the future time, and the predicted time range is recorded to generate the future humidity prediction data; Based on the future humidity prediction data, the predicted humidity value is compared with the historical data of the corresponding time point to calculate the difference between the predicted value and the actual observation value, evaluate the error range and prediction accuracy of the model, and output the humidity trend prediction result.

6. The humidity detection-based dehumidification control method according to claim 5, wherein, The difference between the predicted value and the actual observation value is calculated according to the formula, A calculation is made in which, is a time weighting factor, is a predicted value, is an actual observed value, is a number of samples, denotes a difference value.

7. The humidity detection-based dehumidification control method according to claim 1, wherein, Using the humidity trend prediction result, the humidity setting difference of the difference area is calculated, and by controlling the fan speed and operating frequency of the dehumidification equipment, the demand of each area is matched, and the specific steps of the regional dehumidification scheme are output: Based on the humidity trend prediction result, humidity demand analysis is performed on each area to calculate the difference value between the future expected humidity and the current actual humidity, set a new humidity target for each area, and generate regional humidity regulation demand data; Based on the regional humidity regulation demand data, the fan speed and operating frequency of the dehumidification equipment are adjusted, and the fan speed is increased or decreased step by step to ensure accurate control of each area according to the humidity regulation demand, and device adjustment parameter data is generated; Based on the device adjustment parameter data, the dehumidification adjustment scheme of each area is implemented, and the adjustment details of the fan speed and operating frequency of each area are recorded, and the regional dehumidification scheme is output.

8. The humidity detection-based dehumidification control method according to claim 1, wherein, Applying the regional dehumidification scheme, the real-time humidity data of the area is continuously monitored and recorded, and the dehumidification parameters are dynamically adjusted according to the real-time data to determine the optimal operating state of the dehumidification equipment, and the specific steps of the dynamic dehumidification control scheme are as follows: Based on the regional dehumidification scheme, real-time monitoring of regional humidity data is performed, and data recording is performed periodically to capture the periodic change of humidity in each area to generate real-time humidity monitoring data; Based on the real-time humidity monitoring data, the necessity of humidity adjustment is analyzed by comparing the target humidity value with the actual reading, calculating the difference and determining whether the operating parameters of the dehumidification equipment need to be adjusted, and responding to the changes in real-time data to generate dynamically adjusted dehumidification parameters; Based on the dynamically adjusted dehumidification parameters, the dehumidification equipment settings are dynamically updated, the equipment operation is optimized to match the real-time humidity demand, the adjustment effect is continuously monitored, the accuracy of humidity control is verified, and the dynamic dehumidification control scheme is output.

9. A dehumidification control system based on humidity detection, characterized by, The humidity detection-based dehumidification control system is used to execute the humidity detection-based dehumidification control method of any one of claims 1-8, and the system comprises: The data synchronization and coding module collects outdoor humidity data and indoor humidity data through a humidity sensor, synchronizes data timestamps, marks the source of each data point, and uniquely encodes each data, generating a labeled enhanced humidity dataset; The data normalization processing module performs normalization processing on the dataset based on the labeled enhanced humidity dataset, performs data value conversion using the min-max scaling method, calculates the difference value between consecutive data points, marks significant change points, and generates trend-identified humidity data; The periodicity analysis module analyzes the periodic changes in the data based on the trend-identified humidity data, identifies seasonal fluctuation patterns, and calculates the average amplitude and periodicity characteristics of the fluctuations, highlighting key change areas within each cycle, and outputs environmental humidity difference analysis results; The humidity prediction analysis module performs data format conversion based on the environmental humidity difference analysis results, performs linear trend fitting on the humidity values at consecutive time points, predicts the humidity changes in the future, calculates the difference between the predicted value and the actual observed value, and outputs the humidity trend prediction results; The dehumidification control adjustment module calculates the difference between the expected humidity in the future and the current actual humidity based on the humidity trend prediction results, adjusts the fan speed and operating frequency of the dehumidification equipment, records the adjustment details of the fan speed and operating frequency in each area, and outputs the regional dehumidification scheme; The dynamic dehumidification adjustment module captures the periodic changes in humidity in each area based on the regional dehumidification scheme, compares the target humidity value with the actual reading, calculates the difference and determines whether to adjust the operating parameters of the dehumidification equipment, dynamically updates the dehumidification equipment settings, and outputs the dynamic dehumidification control scheme.

10. A dehumidification control intelligent dehumidifier based on humidity detection, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor executes the computer program to implement the steps of the humidity detection-based dehumidification control method of any one of claims 1-8.

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