Humidity control method and system combined with condensed water recovery and electronic equipment

Abstract

The invention discloses a humidity control method and system combined with condensate water recovery and electronic equipment, and relates to the technical field of air conditioning. The method comprises the following steps: when the water level of a collecting tank reaches a preset height, activating a water quality monitoring array to automatically detect water quality; processing and analyzing according to the real-time water quality information to obtain real-time purified water and transferring the real-time purified water to a water storage tank; presetting an environment information acquisition window, acquiring historical data and performing environment change prediction; humidity control optimization is carried out according to the prediction data, and PID parameter adjustment amount is obtained; the water pump pumps pure water into the nano water ion generator at preset pressure, and ionizes water molecules to adjust humidity based on PID adjustment amount and prediction time window constraint. The technical problems that the condensate water recycling efficiency is low and the environment humidity adjustment is inaccurate are solved, and the technical effects of improving the water quality treatment efficiency of condensate water recycling and the environment humidity adjustment precision are achieved by integrating water quality real-time analysis, dynamic humidity control and PID parameter optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning, and in particular to a humidity control method, system and electronic equipment combined with condensed water recovery. Background Art

[0002] With the increasing requirements for air quality and indoor comfort in modern society, humidity regulation has gradually become an important part of environmental control technology. Existing humidity control methods usually rely on humidifiers or dehumidifiers to operate alone, but these devices have high energy consumption and cannot effectively utilize existing resources in the environment, resulting in energy waste and low equipment efficiency. At the same time, condensed water, as an inevitable by-product of the operation of air conditioning systems, is often directly discharged, which not only wastes potential water resources, but also may cause environmental problems.

[0003] During the operation of the air conditioning or air conditioning system, water vapor in the air will condense into water droplets on the surface of the evaporator to form condensed water. These condensed water are discharged directly without treatment, which not only wastes water resources, but also may have a negative impact on the environment due to improper discharge, such as increased humidity and mold growth. In addition, since the condensed water may contain pollutants such as dust and microorganisms, strict water quality treatment is required even if it is recycled, which increases the complexity of the system.

[0004] Many existing systems use nano water ion, an emerging air purification and humidity control technology, which ionizes water molecules through a high-voltage electric field to generate charged nano-scale water ions. These water ions have strong redox ability and can effectively kill bacteria and viruses in the air and decompose harmful gases. At the same time, the released water ions can also increase the humidity of the air and improve the comfort of the indoor environment. However, there are also some problems with nano water ion generators in practical applications. First, nano water ion generators require continuous water supply. The traditional method is to connect tap water or add water manually, which is cumbersome to operate and increases water consumption. Second, if the water quality is poor, it may cause scaling inside the generator, affecting the life of the equipment and the efficiency of ion generation. Third, nano water ion generators usually exist as independent devices and cannot be effectively integrated with other equipment such as air conditioning systems, resulting in low energy efficiency. Summary of the invention

[0005] The present application provides a humidity control method, system and electronic equipment combined with condensed water recovery, which solves the technical problems of low condensed water recovery efficiency and inaccurate environmental humidity regulation.

[0006] In view of the above problems, the present application provides a humidity control method, system and electronic device combined with condensed water recovery.

[0007] In a first aspect of the present application, a humidity control method in combination with condensed water recovery is provided, the method comprising:

[0008] When the water level of the condensate collection tank reaches a preset water level, the water quality monitoring array is activated to automatically detect the water quality and obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate collection tank; in the process of performing water quality processing and analysis according to the real-time water quality information, and performing condensate processing based on the analysis results to obtain real-time pure water, the real-time pure water is dynamically transferred to the water storage tank; a preset environmental information collection window is set, and historical environmental data is collected with the environmental information collection window as a constraint to obtain a historical environmental data sequence; environmental change prediction is performed according to the historical environmental data sequence to obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a prediction time window identifier, wherein the prediction time window is 1 / K of the environmental information collection window; humidity control optimization is performed according to the predicted environmental data sequence to obtain a PID parameter adjustment amount; after the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the real-time pure water in the nano water ion generator is ionized into nano-scale water ions with the PID parameter adjustment amount as the parameter adjustment control constraint and the prediction time window as the parameter adjustment time constraint to adjust the humidity of the target environment.

[0009] A second aspect of the present application provides a humidity control system combined with condensed water recovery, the system comprising:

[0010] Automatic water quality detection module: when the water level in the condensate collection tank reaches a preset water level, the water quality monitoring array is activated to automatically detect the water quality and obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate collection tank; Condensate treatment module: in the process of performing water quality treatment analysis according to the real-time water quality information, and performing condensate treatment based on the analysis results, and obtaining real-time pure water, the real-time pure water is dynamically transferred to the water storage tank; Historical environment data acquisition module: preset an environment information acquisition window, and perform historical environment data acquisition with the environment information acquisition window as a constraint to obtain a historical environment data sequence; Environmental change prediction module: based on the historical environment data According to the sequence, environmental changes are predicted to obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a prediction time window identifier, wherein the prediction time window is 1 / K of the environmental information acquisition window; a humidity control optimization module: humidity control optimization is performed according to the predicted environmental data sequence to obtain a PID parameter adjustment amount; a humidity adjustment module: after the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the PID parameter adjustment amount is used as a parameter adjustment control constraint, and the prediction time window is used as a parameter adjustment time constraint, the real-time pure water in the nano water ion generator is ionized into nano-scale water ions to adjust the humidity of the target environment.

[0011] According to a third aspect of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0012] When the water level of the condensate collection tank reaches a preset water level, the water quality monitoring array is activated to automatically detect the water quality and obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate collection tank; in the process of performing water quality processing and analysis according to the real-time water quality information, and performing condensate processing based on the analysis results to obtain real-time pure water, the real-time pure water is dynamically transferred to the water storage tank; a preset environmental information collection window is set, and historical environmental data is collected with the environmental information collection window as a constraint to obtain a historical environmental data sequence; environmental change prediction is performed according to the historical environmental data sequence to obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a prediction time window identifier, wherein the prediction time window is 1 / K of the environmental information collection window; humidity control optimization is performed according to the predicted environmental data sequence to obtain a PID parameter adjustment amount; after the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the real-time pure water in the nano water ion generator is ionized into nano-scale water ions with the PID parameter adjustment amount as the parameter adjustment control constraint and the prediction time window as the parameter adjustment time constraint to adjust the humidity of the target environment.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0014] First, when the water level in the condensate collection tank reaches a preset water level, the water quality monitoring array is activated to automatically detect the water quality and obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate collection tank; then, water quality treatment and analysis is performed according to the real-time water quality information, and condensate treatment is performed based on the analysis results to obtain real-time pure water, in which the real-time pure water is dynamically transferred to a water storage tank, and then an environmental information collection window is preset, and historical environmental data is collected with the environmental information collection window as a constraint to obtain a historical environmental data sequence; further, environmental Change prediction, obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a predicted time window identifier, wherein the predicted time window is 1 / K of the environmental information acquisition window, and then perform humidity control optimization according to the predicted environmental data sequence to obtain a PID parameter adjustment amount; finally, after the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the real-time pure water in the nano water ion generator is ionized into nano-scale water ions to adjust the humidity of the target environment with the PID parameter adjustment amount as the parameter control constraint and the predicted time window as the parameter adjustment time constraint. The technical problems of low condensate recycling efficiency and inaccurate environmental humidity adjustment are solved, and the technical effect of improving the water quality treatment efficiency of condensate recycling and the adjustment accuracy of environmental humidity is achieved by integrating real-time water quality analysis, dynamic humidity control and PID parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic flow chart of a humidity control method combined with condensed water recovery provided in an embodiment of the present application;

[0017] Figure 2 A schematic diagram of the structure of a humidity control system combined with condensate recovery provided in an embodiment of the present application.

[0018] Explanation of the reference numerals: automatic water quality detection module 11 , condensed water treatment module 12 , historical environmental data collection module 13 , environmental change prediction module 14 , humidity control optimization module 15 , humidity adjustment module 16 . DETAILED DESCRIPTION

[0019] The present application solves the technical problems of low condensate water recovery efficiency and inaccurate environmental humidity regulation by providing a humidity control method, system and electronic equipment combined with condensate water recovery.

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0021] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or electronic devices.

[0022] Embodiment 1, as Figure 1 As shown, the present application provides a humidity control method combined with condensed water recovery, wherein the method comprises:

[0023] When the water level of the condensate collection tank reaches a preset water level, a water quality monitoring array is activated to automatically detect water quality and obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate collection tank.

[0024] In one embodiment, when the water level in the condensate collection tank reaches a preset height, the water level sensor installed in the condensate collection tank first detects the water level change and transmits the detection signal to the control system in real time. After receiving the signal, the control system immediately activates the water quality monitoring array to perform real-time water quality detection on the condensate in the collection tank, wherein the condensate collection tank is located below the evaporator and is used to collect water droplets condensed on the surface of the evaporator. The water quality monitoring array is composed of a plurality of water quality monitors distributed at different positions, each of which includes a turbidity meter, a conductivity meter, a residual chlorine detector and a pH meter, which are used to comprehensively analyze multiple quality indicators of the condensate. These monitors are connected to the control system via signal lines to transmit the monitored water quality signals. information; during the water quality detection process, the control system combines the feedback data of multiple monitors, screens out reliable ones, and generates real-time water quality information of the condensed water in the condensed water collection tank. These real-time water quality information will be used for subsequent water quality treatment and analysis to achieve the purification of the condensed water in the condensed water collection tank; when the water level sensor detects that the water level of the condensed water in the condensed water collection tank reaches the preset upper limit, the control system will automatically open the drain valve to discharge excess water. When the water level is lower than the preset lower limit, the control system will instruct to increase the collection of condensed water to ensure the continuity of water supply; through this design, the immediacy of monitoring can be guaranteed, which not only effectively improves the efficiency of condensed water recovery, but also ensures the purity of water quality and the stable operation of the system.

[0025] Furthermore, when the water level of the condensate collection tank reaches a preset water level, the water quality monitoring array is activated to automatically detect the water quality to obtain real-time water quality information. The method includes:

[0026] The water level of the condensate collection tank is dynamically monitored by configuring a water level sensor; when the water level sensor detects that the water level of the condensate collection tank reaches a preset water level, the water quality monitoring array is activated, wherein the water quality monitoring array includes K water quality monitors, each of which is composed of a turbidity meter, a conductivity meter, a residual chlorine detector and a pH meter; equipment failure is judged based on K local water quality information returned by the K water quality monitors, and M reliable water quality information is obtained from the K local water quality information based on the judgment result, wherein M is a positive integer less than or equal to K; the real-time water quality information is output by performing information fusion on the M reliable water quality information.

[0027] Preferably, when the water level in the condensate collection tank changes, the control system dynamically monitors the water level through the configured water level sensor. The water level sensor monitors the height of the water level in the tank in real time and transmits the detected data to the control system. When the water level reaches the preset water level, the control system receives a signal, activates the water quality monitoring array, and starts automatic water quality detection. The water quality monitoring array is composed of multiple water quality monitors (K in total), which are installed in different positions of the condensate collection tank. Each water quality monitor is composed of a turbidity meter, a conductivity meter, a residual chlorine detector and a pH meter, which are used to measure the turbidity, conductivity, residual chlorine concentration and pH value in the water, respectively, so as to comprehensively evaluate the quality of the condensate. The monitor transmits these local water quality information (K data points in total) back to the control system for further analysis; in the control system, by The K local water quality information is used to judge the equipment failure and evaluate whether the status of each monitor is normal. If a monitor fails or its data fluctuates abnormally, the control system will automatically identify and mark it as unreliable data. According to the fault judgment result, M reliable water quality information is screened out from the K water quality data, where M is a positive integer less than or equal to K; then, information fusion processing is performed on the screened M reliable water quality information, and combined with the data provided by each monitor to obtain a comprehensive real-time water quality information. This processing process ensures that the obtained water quality data has high accuracy and reliability, and avoids the influence of a single equipment failure or data error on the water quality assessment result; finally, real-time water quality information is output, and the condensed water is further processed and adjusted based on this information to ensure that the quality of the condensed water meets the requirements and enters the next link of use or storage.

[0028] Further, according to the K local water quality information returned by the K water quality monitors, equipment failure judgment is performed, and according to the judgment result, M reliable water quality information is obtained by screening from the K local water quality information. The method includes:

[0029] Interactively obtain a sensor data range set, a data change rate set, a device rated temperature set, a device rated current set and a communication interval scale set; construct a first fault judgment branch, a second fault judgment branch, a third fault judgment branch, a fourth fault judgment branch and a fifth fault judgment branch based on the sensor data range set, the data change rate set, the device rated temperature set, the device rated current set and the communication interval scale set; complete the construction of the equipment fault judgment model by connecting the first fault judgment branch, the second fault judgment branch, the third fault judgment branch, the fourth fault judgment branch and the fifth fault judgment branch in parallel; collect historical information of the K water quality monitoring instruments to obtain K groups of fault association judgment information; screen and obtain M credible monitoring instruments by mapping the K groups of fault association judgment information and K local water quality information and loading them into the equipment fault judgment model; call the M local water quality information of the M credible monitoring instruments as the M credible water quality information.

[0030] Optionally, when making a device fault judgment, first obtain relevant information of various sensor data in an interactive manner, including a sensor data range set (the measurement range of each sensor), a data change rate set (the rate at which each sensor data changes), a device rated temperature set (the operating temperature range of the device), a device rated current set (the current range for normal operation of the device), and a communication interval scale set (the time interval and frequency of data transmission). This information is used to define the normal parameter range and expected working state of the device operation; based on the above data set, construct multiple fault judgment branches, including a first fault judgment branch, a second fault judgment branch, a third fault judgment branch, a fourth fault judgment branch, and a fifth fault judgment branch, wherein the first fault judgment branch is used to monitor whether the sensor data exceeds the normal measurement range, the second fault judgment branch is used to monitor whether the sensor data change rate is abnormal, the third fault judgment branch is used to judge whether the device is within the abnormal temperature range, the fourth fault judgment branch is used to monitor whether the device exceeds the rated current range, and the fifth fault judgment branch is used to judge whether a communication interval problem occurs during data transmission. These branches are all constructed in the same way, such as a support vector machine (SVM), a decision tree, etc.; then, the judgment branches constructed in the same way are connected in parallel to construct a comprehensive The equipment fault judgment model can monitor the equipment status in real time when the system is running, ensure that the equipment works as expected, and avoid system efficiency reduction or damage due to faults; then, historical information is collected for each water quality monitor to obtain K groups of fault-related judgment information, which includes historical data changes of water quality monitors under different working conditions and fault conditions of water quality monitors. These historical data are then used to map and train each judgment branch in the constructed equipment fault judgment model. Taking the judgment branch constructed by the support vector machine as an example, by inputting historical data into these branches, these branches are constructed in a high-dimensional feature space through the principle of maximizing the classification interval. The optimal decision plane in the training process is completed by solving a constrained optimization problem. The objective function of this problem is to minimize the weighted sum of classification error and branch complexity. The original problem is transformed into a dual problem by using the Lagrange multiplier method. The branches find the key points in the historical data in the form of support vectors. These support vectors define the classification boundaries. During the training process, by adjusting the hyperparameter C (controlling the penalty for misclassification) and the smoothing parameter γ (the parameter of the kernel function), the classification performance on the historical data is optimized and analyzed. After the training is completed, these can classify and judge the real-time input sensor data, and output whether the device is in a fault according to the boundary rules of the support vector;Then, K local water quality information (real-time water quality data from each water quality monitor) is mapped and loaded into the equipment fault judgment model. The model can screen out M credible monitors, where M represents the monitors in good working condition screened out from the K water quality monitors, that is, the results output by the five judgment branches are all normal monitors, and the value of M is less than or equal to K; finally, the M local water quality information of the M credible water quality monitors is called and used as the final credible water quality information for subsequent water quality analysis, processing and adjustment. Through this process, not only efficient water quality monitoring is achieved, but also the accuracy of the data and the long-term stable operation of the equipment are ensured, avoiding unnecessary interference caused by faults. ;

[0031] In the process of performing water quality treatment analysis according to the real-time water quality information and performing condensed water treatment based on the analysis results to obtain real-time pure water, the real-time pure water is dynamically transferred to a water storage tank.

[0032] In one embodiment, after obtaining real-time water quality information, the control system will detect and analyze the condensed water based on this information, and process the condensed water through a water quality treatment unit based on the analysis results, wherein the detection and analysis is performed based on a pre-constructed water quality treatment analysis model, and this water quality treatment analysis model will generate a corresponding treatment strategy based on the real-time water quality information, which is used to control the water quality treatment unit to purify the condensed water. The inlet of the water quality treatment unit is connected to the condensed water collection tank through a diversion pipe to ensure that the condensed water can smoothly flow into the treatment unit for treatment, and the outlet of the water quality treatment unit is connected to the condensed water collection tank through the diversion pipe to ensure that the condensed water can flow into the treatment unit smoothly for treatment. The inlet of the water storage tank is connected to ensure that the treated pure water can be stored smoothly and provide guarantee for subsequent use. The water quality treatment unit includes multiple treatment stages in the purification process of condensed water, namely coarse filtration, fine filtration, ultrafiltration, reverse osmosis, ultraviolet disinfection and capacitive deionization. In the initial coarse filtration stage, large particles of impurities such as dust and sand in the water are removed by stainless steel filter (pore size 50 microns), and fine filtration and ultrafiltration are used to further remove fine suspended matter, colloids, bacteria, viruses and large molecular organic matter. Then, the soluble salts and heavy metal ions in the water are removed through the reverse osmosis membrane (RO membrane), ensuring that the conductivity of the outlet water is less than 1. 0μS / cm, and then sterilized by ultraviolet lamp with a wavelength of 254 nanometers. If necessary, it can also be disinfected by ozone to ensure that the treated water is sterile. Then, the dissolved ions in the water are removed by capacitor deionization equipment to further improve the purity of the water and ensure that the water quality supplied to the nano water ion generator meets high standards, prevent equipment scaling and corrosion, and extend the service life of the system, thereby significantly improving the stability and service life of the equipment. All these treatment processes are monitored in real time by water quality monitoring instruments, including turbidity meters, conductivity meters, residual chlorine detectors and pH meters, which collect water quality parameters in real time and feed the data back to the control system. The control system adjusts the operating status of the water treatment unit according to the water quality information, such as increasing the backwash frequency or replacing the reverse osmosis membrane. Once the water treatment unit successfully treats the condensed water into pure water that meets the standards, the water is dynamically transferred to the water storage tank through the pipeline. The water level sensor in the water storage tank also monitors the water level in the water storage tank. When the water level reaches the upper limit, the control system will automatically open the drain valve to avoid overflow. If the water level is too low, the collection and treatment of condensed water will be increased to ensure that the water volume in the water storage tank is maintained within a stable range. The pure water in this water storage tank will continuously supply water to the nano water ion generator to achieve the recycling of water resources.

[0033] Furthermore, in the process of performing water quality processing analysis according to the real-time water quality information and performing condensed water processing based on the analysis results to obtain real-time pure water, the real-time pure water is dynamically transferred to a water storage tank, and the method includes:

[0034] Interactively obtain multiple sample water quality treatment information, wherein each sample water quality treatment information includes sample water quality information and sample water quality treatment strategy; use the multiple sample water quality treatment information as training data to train and obtain a water quality treatment analysis model; input the real-time water quality information into the water quality treatment analysis model to perform water quality treatment analysis and obtain a real-time water quality treatment strategy; in the process of adopting the real-time water quality treatment strategy to operate the water quality treatment unit to perform multi-stage water quality treatment of condensed water to obtain real-time pure water, the real-time pure water is dynamically transferred to the water storage tank through a diversion pipe.

[0035] Preferably, the control system obtains multiple sample water quality treatment information in an interactive manner, each sample water quality treatment information includes sample water quality information and sample water quality treatment strategy, the sample water quality information includes water quality data collected at different time points and environmental conditions, such as turbidity, conductivity, residual chlorine content, pH value, etc., and the sample water quality treatment strategy includes the water quality treatment scheme used under these conditions, for example, the parameters and operating conditions set for each stage of the water quality treatment unit (coarse filtration, fine filtration, ultrafiltration, reverse osmosis, ultraviolet disinfection, capacitive deionization, etc.); these multiple sample water quality treatment information are used as training data, and the water quality treatment analysis model is trained through machine learning methods (such as support vector machines, decision trees or neural networks). During the training process, taking the long short-term memory network (LSTM) in the neural network as an example, by inputting sample water quality information, learn how to generate corresponding treatment strategies according to different water quality conditions. The training process of the model includes forward propagation, loss calculation, back propagation, parameter update and other steps. Through these steps, the control system can establish a system from water quality information to water quality treatment. The mapping relationship between strategies can automatically generate a reasonable treatment plan when facing new data; when the control system obtains real-time water quality information during operation, the information will be input into the trained water quality treatment analysis model. The model will process and analyze the real-time water quality information to generate a real-time water quality treatment strategy. The strategy will determine the operating parameters and operation methods of each treatment stage in the water quality treatment unit, such as the CDI operating parameters during capacitor deionization to remove soluble ions in water, prevent equipment scaling, and ensure that the water quality meets the use standards of the nano water ion generator. Through these CDI operating parameters, the purity of water can be further improved, providing high-quality water source for subsequent nano water ion generation; when the water quality treatment unit starts to operate according to the real-time water quality treatment strategy, the condensed water undergoes multi-stage filtration, reverse osmosis, capacitor deionization and ultraviolet disinfection, and finally obtains pure water. At this time, the treated real-time pure water is dynamically transferred to the water storage tank through the diversion pipe for storage. The water storage tank provides sufficient and standard water source for subsequent use, thereby improving the overall performance and energy efficiency of the system.

[0036] Furthermore, the sample water quality treatment strategy includes sample condensed water flow, sample ultraviolet intensity, sample irradiation time and sample CDI operating parameters.

[0037] Optionally, the sample water quality treatment strategy includes sample condensate flow rate, sample ultraviolet intensity, sample irradiation time and sample CDI operating parameters, which are used to describe and optimize the water quality treatment process of condensate water. The sample condensate flow rate refers to the amount of condensate flowing through the water quality treatment unit during the water quality treatment process. This parameter determines the speed at which water flows through each treatment stage (such as coarse filtration, fine filtration, ultrafiltration, reverse osmosis, etc.), thereby affecting the water treatment efficiency and the load of each filtration stage. Reasonable control of the condensate flow rate helps to ensure the optimal working state of each water quality treatment stage, thereby improving the treatment effect. The sample ultraviolet intensity refers to the radiation energy released by the ultraviolet lamp, which directly affects the effect of ultraviolet disinfection. The higher the intensity of the ultraviolet light, the better the disinfection. The better the effect, in the water quality treatment strategy, the sterilization efficiency is optimized by adjusting the ultraviolet intensity to ensure that the treated water meets high safety standards; the sample irradiation time refers to the time that water is in contact with the ultraviolet irradiation lamp. This time determines the exposure degree of microorganisms in the water, which in turn affects the disinfection effect. Too short an irradiation time may cause some microorganisms to be unable to be effectively killed, and too long an irradiation time may waste energy. Therefore, it is necessary to adjust the irradiation time according to the actual water quality conditions; the sample CDI operating parameters include voltage, current, charging and discharging cycles, etc. Adjusting these parameters can change the deionization efficiency of the electrode plate, thereby affecting the water purification effect. Optimizing these parameters can improve the deionization efficiency and ensure the maximum removal of ions during the condensate treatment process. These sample water quality treatment strategies can achieve more efficient and stable water quality treatment and ensure that the water quality meets the predetermined standards by dynamically adjusting multiple factors such as condensate flow, ultraviolet intensity, irradiation time and CDI operating parameters.

[0038] An environmental information collection window is preset, and historical environmental data is collected with the environmental information collection window as a constraint to obtain a historical environmental data sequence.

[0039] In one embodiment, the system terminal presets an environmental information collection window. This window is a time range used to limit and define the environmental data collected within a specific time period. For example, this collection window can be set to a day, a week or a shorter time period, depending on the frequency and accuracy requirements of the environmental changes that need to be monitored; then, based on this environmental information collection window, the control system will collect historical environmental data within the set time range. The historical environmental data includes environmental parameters such as temperature, humidity, and air quality. These data help to understand the changes in the environment within a certain period of time in the past. Through this process, a historical environmental data sequence can be collected, that is, a collection of environmental data arranged in chronological order within the preset collection window. These data will be used for subsequent analysis and prediction to help the control system determine the trend of environmental changes and make corresponding control decisions.

[0040] Environmental change prediction is performed based on the historical environmental data sequence to obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a prediction time window identifier, wherein the prediction time window is 1 / K of the environmental information collection window.

[0041] In one embodiment, the previously collected historical environmental data sequence is used to provide basic data for prediction. By analyzing these data, the control system can identify the laws and trends of environmental changes based on the environmental change prediction model. The model generates future environmental parameter prediction values ​​based on the change pattern in the historical data. These prediction values ​​are organized into a new data set, namely the predicted environmental data sequence. This predicted environmental data sequence is marked by a specific time period, which is defined as a prediction time window. The length of this prediction time window is 1 / K of the environmental information collection window. For example, if the environmental information collection window is 10 hours and K=2, the prediction time window is 5 hours. This proportional relationship enables the prediction result to match the time structure of the collected data more accurately. In this way, not only can the future environmental change trend be predicted, but also the applicable time range of the prediction result can be clarified, providing a key basis for subsequent control and decision-making.

[0042] Furthermore, environmental change prediction is performed based on the historical environmental data sequence to obtain a predicted environmental data sequence, and the method includes:

[0043] The natural state data of the target environment is collected to obtain natural environment time series data, wherein the natural environment time series data includes natural temperature time series data and natural humidity time series data; the sum of the duration of the prediction time window and the environmental information collection window is used as a data division scale to divide the natural environment time series data into multiple stages of environmental time series data; the prediction time window and the environmental information collection window are used to divide the multiple stages of environmental time series data into multiple groups of front environment time series data and back environment time series data; the multiple groups of front environment time series data and back environment time series data are used as training data to train an environmental change prediction model pre-constructed using an LSTM model; the historical environmental data sequence is input into the environmental change prediction model to obtain the predicted environmental data sequence.

[0044] Optionally, first collect natural state data of the target environment. The purpose of this step is to collect natural environmental conditions that are not affected by human intervention, such as changes in temperature and humidity, so as to obtain natural temperature time series data and natural humidity time series data. These data are arranged in chronological order to form a continuous data sequence, reflecting the natural change law of the target environment over a period of time; then, the sum of the duration of the prediction time window and the environmental information collection window is used as the data division scale to ensure that the data division structure meets the requirements of the prediction model. Through this division scale, the natural environment time series data is divided into multiple stages, each stage represents the changes in environmental data within a certain period of time, and these stages represent the evolution process of environmental conditions in different time periods; after dividing the time series data into multiple stages, according to the length of the prediction time window and the environmental information collection window, the sliding window method is used to divide the natural environment time series data of multiple stages into multiple front environment time series data and post environment time series data. The front environment time series data The previous environmental time series data refers to the historical data used to input into the prediction model, while the subsequent environmental time series data is the actual environmental change result, which is used to compare with the prediction result and evaluate the accuracy of the prediction model; then, all the previous environmental time series data-post-environmental time series data pairs are used as training data sets to train an LSTM model (including input layer, LSTM layer, fully connected layer, output layer). During the training process, through forward propagation, loss calculation, back propagation, parameter update and other steps, the LSTM model learns how to predict the subsequent environmental time series data based on the previous environmental time series data. Through multiple iterations, an environmental change prediction model is constructed; then, the previously collected historical environmental data sequence is input into the trained environmental change prediction model. The model will generate the corresponding predicted environmental data sequence based on these input data, that is, the prediction results of future environmental changes. These prediction results will be used for subsequent environmental control and regulation strategies to ensure that they can be dynamically adjusted under the forward-looking guidance of environmental changes.

[0045] Humidity control optimization is performed according to the predicted environmental data sequence to obtain PID parameter adjustment amounts.

[0046] In one embodiment, after obtaining the predicted environmental data sequence, the change of humidity of the target environment in the next period of time is understood to obtain the predicted environmental humidity sequence; then, the PID (proportional-integral-differential) control algorithm is used to adjust the humidity. The PID controller is a widely used control method that can optimize the system response by continuously adjusting the proportional parameter (P), integral parameter (I) and differential parameter (D), wherein the proportional parameter represents the magnitude of the current error of the response. The larger the proportional parameter, the more sensitive the response. The integral parameter represents the accumulated historical error of the response. The larger the integral parameter, the stronger the response to the long-term error. The differential parameter represents the rate of change of the response error. The larger the differential parameter, the more attention is paid to the speed of error change. In the optimization process, the control error is calculated according to the difference between the current environmental humidity and the target humidity. This error represents the deviation between the current humidity and the target humidity. The PID controller minimizes the humidity control error by adjusting the proportional, integral and differential parameters, and ensures that the control system can respond to the change of environmental humidity quickly and smoothly. Through the analysis of the predicted data and the adjustment of the PID controller parameters, the PID parameter adjustment amount is finally obtained. This adjustment amount represents the correction that needs to be made to the current PID parameters to make the humidity adjustment more accurate and efficient.

[0047] Furthermore, the humidity control optimization is performed according to the predicted environmental data sequence to obtain the PID parameter adjustment amount, and the method includes:

[0048] Extract a predicted environmental humidity sequence from the predicted environmental data sequence; interactively obtain a target environmental humidity setting, and use the target environmental humidity setting to perform error calculation on the predicted environmental humidity sequence to obtain a predicted error sequence; predefine an initial PID parameter combination; preset a parameter adjustment scale and a parameter adjustment evaluation function; update the initial PID parameter combination based on the parameter adjustment scale, and use the parameter adjustment evaluation function to evaluate and screen the update results until the PID parameter adjustment amount with the smallest calculation result of the parameter adjustment evaluation function is obtained.

[0049] Preferably, humidity-related information is extracted from the predicted environmental data sequence to form a predicted environmental humidity sequence, which reflects the changing trend of the future environmental humidity and provides basic data for control optimization; the target environmental humidity setting value is obtained in an interactive manner, and then the prediction error is calculated point by point according to the target humidity and the predicted environmental humidity sequence to obtain a prediction error sequence, and at the same time, the difference between the current point and the previous point is calculated based on the prediction error sequence to obtain a prediction error change rate sequence; then, the initial PID parameter combination (K p , K i , K d), and set the parameter adjustment scale (such as the maximum step size of adjustment) and the parameter adjustment evaluation function, where the parameter adjustment evaluation function is the weighted sum formula of humidity error and energy consumption, and the weight of humidity error and the weight of energy consumption are adjusted according to environmental requirements; then, the fuzzy adaptive PID control algorithm is adopted to fuzzify the error and the error change rate through fuzzy logic, and convert the error and the error change rate into fuzzy set variables, such as negative large (such as humidity error above -10%), negative medium (such as humidity error above -5%), zero, positive medium (such as humidity error above +5%), etc., and then dynamically match K according to the fuzzy rule table p , K i , K d For example, when the error is positive and the error change rate is medium, increase K p , keep K i , K d When the error is small and the error change rate is medium, reduce K p , while increasing K d , keep K i After determining the direction of change, the fuzzy reasoning result is defuzzified by combining the parameter adjustment scale to calculate the new K p , K i , K d ; Then, multiple PID parameter combinations are generated according to the parameter adjustment scale, and humidity control simulation is performed on each group of parameters to obtain the corresponding humidity error and control energy consumption, and then the parameter adjustment evaluation function is combined to perform minimization optimization, and the PID parameter combination is continuously updated until the result of the parameter adjustment evaluation function tends to be minimized or reaches the set accuracy requirement, thereby obtaining the optimized PID parameter adjustment amount corresponding to the ambient humidity; in addition, the changes in temperature and air quality are obtained from the predicted environmental data sequence to form a predicted ambient temperature sequence and a predicted ambient air quality sequence, and similar operations are performed on the predicted ambient temperature sequence and the predicted ambient air quality sequence, and combined with the corresponding target ambient temperature and target ambient air quality, the optimized PID parameter adjustment amount corresponding to the ambient temperature and the optimized PID parameter adjustment amount corresponding to the ambient air quality are obtained through matching of the corresponding fuzzy rule table, defuzzification and evaluation screening; finally, these optimized PID parameter adjustment amounts are weighted summed to obtain the final PID parameter adjustment amount, and it is applied to the PID controller to adjust the output power of the nano water ion generator to achieve precise control of humidity regulation.

[0050] Further, the initial PID parameter combination is updated based on the parameter adjustment scale, and the parameter adjustment evaluation function is used to evaluate and screen the update results until the PID parameter adjustment amount with the minimum calculation result of the parameter adjustment evaluation function is obtained. The method includes:

[0051] Based on the parameter adjustment scale, an initial PID parameter combination is updated to obtain a plurality of updated PID parameter combinations; humidity control simulation is performed on the plurality of updated PID parameter combinations to obtain a plurality of updated humidity errors and a plurality of updated control energy consumptions; the plurality of updated humidity errors and the plurality of updated control energy consumptions are input into the parameter adjustment evaluation function to obtain a plurality of updated evaluation values; based on the plurality of updated evaluation values, a second update starting point is obtained by screening the plurality of updated PID parameter combinations; and so on, the PID parameter combination is updated according to the parameter adjustment scale and the parameter adjustment evaluation function until the PID parameter adjustment amount with the minimum calculation result of the parameter adjustment evaluation function is obtained.

[0052] Optionally, based on the parameter adjustment scale, the control system will perform multiple parameter updates on the initial PID parameter combination through the aforementioned update method to obtain multiple updated PID parameter combinations; then, the target ambient humidity, the current ambient humidity and multiple updated PID parameter combinations are input into the simulation environment to simulate the control effect of each PID parameter combination, and the humidity error (deviation from the target humidity) and control energy consumption (nano water ion generator operating power) corresponding to each set of parameters are recorded, thereby obtaining multiple updated humidity errors and multiple updated control energy consumptions; then, each set of recorded updated humidity errors and updated control energy consumption is input into the parameter adjustment evaluation function, and the updated evaluation of each updated PID parameter combination is calculated by combining the humidity error weight and the control energy consumption weight in the function. value, and then arrange these updated evaluation values ​​in ascending order to extract the updated PID parameter combination corresponding to the smallest updated evaluation value, and use the updated PID parameter combination as the second update starting point; then, repeat the above process, based on the second update starting point, generate a new round of multiple updated PID parameter combinations, and perform humidity control simulation and evaluation function calculation on the new multiple updated PID parameter combinations, and screen the optimal combination again until the evaluation function result no longer decreases significantly (converges) after multiple iterations or meets the set accuracy requirements. At this time, the currently screened updated PID parameter combination will be used as the optimal PID parameter adjustment amount, which can minimize the humidity error, minimize the energy consumption, and achieve the optimal humidity control effect.

[0053] After the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the real-time pure water in the nano water ion generator is ionized into nano-scale water ions to adjust the humidity of the target environment using the PID parameter adjustment amount as the parameter control constraint and the prediction time window as the parameter adjustment time constraint.

[0054] In one embodiment, a water pump extracts real-time pure water that has undergone multi-stage treatment from a water storage tank and transports it to a nano water ion generator through a connecting pipe. During this process, a pressure regulating valve and a check valve are installed in the pipe in sequence to ensure the stability of the water supply and the safety of the system. The pressure regulating valve is responsible for adjusting the water flow pressure to keep the water supply pressure within a set range, while the check valve prevents the water from flowing back when the water pump stops working, thereby protecting the equipment from potential damage. When the pure water reaches the nano water ion generator, the water is first introduced into the discharge electrode assembly, which is composed of a charging core and a discharge shell. A strong electric field is formed under the action of the high-voltage electrode. At this time, the control circuit adjusts the voltage, current and frequency in real time to optimize the electric field strength and uniformity to ensure that the ionization efficiency reaches the optimal state. The high-voltage electric field ionizes the water molecules to generate nano In this process, the electrode of the nano water ion generator adopts an optimized design. For example, an anodized Ti-6Al-4V titanium alloy electrode is used, and the electrode surface area and ionization efficiency are greatly improved through nano coating and porous structure. The current and voltage are monitored in real time on the electrode surface. Combined with the PID control algorithm, the concentration and distribution of the generated water ions are dynamically adjusted according to the humidity data fed back by the humidity sensor to meet the humidity requirements of the target environment; the generated nano water ions are released into the air from the air outlet, which not only plays a role in air purification, but also realizes humidity regulation. In the whole process, the control system uses the predicted time window as the time constraint in the adjustment process, and uses the PID parameter adjustment amount as the parameter constraint to ensure the accuracy of humidity regulation and the optimization of energy consumption, so as to make the whole humidity control process efficient and stable.

[0055] In summary, the embodiments of the present application have at least the following technical effects:

[0056] First, when the water level in the condensate collection tank reaches a preset water level, the water quality monitoring array is activated to automatically detect the water quality and obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate collection tank; then, water quality treatment and analysis is performed according to the real-time water quality information, and condensate treatment is performed based on the analysis results to obtain real-time pure water, in which the real-time pure water is dynamically transferred to a water storage tank, and then an environmental information collection window is preset, and historical environmental data is collected with the environmental information collection window as a constraint to obtain a historical environmental data sequence; further, environmental Change prediction, obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a predicted time window identifier, wherein the predicted time window is 1 / K of the environmental information acquisition window, and then perform humidity control optimization according to the predicted environmental data sequence to obtain a PID parameter adjustment amount; finally, after the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the real-time pure water in the nano water ion generator is ionized into nano-scale water ions to adjust the humidity of the target environment with the PID parameter adjustment amount as the parameter control constraint and the predicted time window as the parameter adjustment time constraint. The technical problems of low condensate recycling efficiency and inaccurate environmental humidity adjustment are solved, and the technical effect of improving the water quality treatment efficiency of condensate recycling and the adjustment accuracy of environmental humidity is achieved by integrating real-time water quality analysis, dynamic humidity control and PID parameter optimization.

[0057] Embodiment 2 is based on the same inventive concept as the humidity control method combined with condensed water recovery in the previous embodiment. Figure 2 As shown, the present application provides a humidity control system combined with condensed water recovery, wherein the system includes:

[0058] Automatic water quality detection module 11: When the water level of the condensate water collection tank reaches a preset water level, the water quality monitoring array is activated to perform automatic water quality detection to obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate water collection tank; Condensate water treatment module 12: In the process of performing water quality treatment analysis according to the real-time water quality information, and performing condensate water treatment based on the analysis results to obtain real-time pure water, the real-time pure water is dynamically transferred to the water storage tank; Historical environment data acquisition module 13: Preset an environment information acquisition window, and perform historical environment data acquisition with the environment information acquisition window as a constraint to obtain a historical environment data sequence; Environmental change prediction module 14: Based on the historical environment The method comprises the following steps: a first step of predicting environmental changes based on an environmental data sequence to predict environmental changes and obtaining a predicted environmental data sequence, wherein the predicted environmental data sequence has a prediction time window identifier, wherein the prediction time window is 1 / K of the environmental information collection window; a humidity control optimization module 15: performing humidity control optimization according to the predicted environmental data sequence to obtain a PID parameter adjustment amount; a humidity adjustment module 16: after a water pump pumps the real-time pure water into the nano-water ion generator at a preset pressure, using the PID parameter adjustment amount as a parameter adjustment control constraint and the prediction time window as a parameter adjustment time constraint, ionizing the real-time pure water in the nano-water ion generator into nano-scale water ions to adjust the humidity of the target environment.

[0059] Furthermore, the water quality automatic detection module 11 is also used to perform the following method:

[0060] The water level of the condensate collection tank is dynamically monitored by configuring a water level sensor; when the water level sensor detects that the water level of the condensate collection tank reaches a preset water level, the water quality monitoring array is activated, wherein the water quality monitoring array includes K water quality monitors, each of which is composed of a turbidity meter, a conductivity meter, a residual chlorine detector and a pH meter; equipment failure is judged based on K local water quality information returned by the K water quality monitors, and M reliable water quality information is obtained from the K local water quality information based on the judgment result, wherein M is a positive integer less than or equal to K; the real-time water quality information is output by performing information fusion on the M reliable water quality information.

[0061] Furthermore, the water quality automatic detection module 11 is also used to perform the following method:

[0062] Interactively obtain a sensor data range set, a data change rate set, a device rated temperature set, a device rated current set and a communication interval scale set; construct a first fault judgment branch, a second fault judgment branch, a third fault judgment branch, a fourth fault judgment branch and a fifth fault judgment branch based on the sensor data range set, the data change rate set, the device rated temperature set, the device rated current set and the communication interval scale set; complete the construction of the equipment fault judgment model by connecting the first fault judgment branch, the second fault judgment branch, the third fault judgment branch, the fourth fault judgment branch and the fifth fault judgment branch in parallel; collect historical information of the K water quality monitoring instruments to obtain K groups of fault association judgment information; screen and obtain M credible monitoring instruments by mapping the K groups of fault association judgment information and K local water quality information and loading them into the equipment fault judgment model; call the M local water quality information of the M credible monitoring instruments as the M credible water quality information.

[0063] Furthermore, the condensate treatment module 12 is also used to perform the following method:

[0064] Interactively obtain multiple sample water quality treatment information, wherein each sample water quality treatment information includes sample water quality information and sample water quality treatment strategy; use the multiple sample water quality treatment information as training data to train and obtain a water quality treatment analysis model; input the real-time water quality information into the water quality treatment analysis model to perform water quality treatment analysis and obtain a real-time water quality treatment strategy; in the process of adopting the real-time water quality treatment strategy to operate the water quality treatment unit to perform multi-stage water quality treatment of condensed water to obtain real-time pure water, the real-time pure water is dynamically transferred to the water storage tank through a diversion pipe.

[0065] Furthermore, the condensate treatment module 12 is also used to perform the following method:

[0066] The sample water quality treatment strategy includes sample condensed water flow, sample ultraviolet intensity, sample irradiation time and sample CDI operation parameters.

[0067] Furthermore, the environmental change prediction module 14 is also used to perform the following method:

[0068] The natural state data of the target environment is collected to obtain natural environment time series data, wherein the natural environment time series data includes natural temperature time series data and natural humidity time series data; the sum of the duration of the prediction time window and the environmental information collection window is used as a data division scale to divide the natural environment time series data into multiple stages of environmental time series data; the prediction time window and the environmental information collection window are used to divide the multiple stages of environmental time series data into multiple groups of front environment time series data and back environment time series data; the multiple groups of front environment time series data and back environment time series data are used as training data to train an environmental change prediction model pre-constructed using an LSTM model; the historical environmental data sequence is input into the environmental change prediction model to obtain the predicted environmental data sequence.

[0069] Furthermore, the humidity control optimization module 15 is also used to execute the following method:

[0070] Extract a predicted environmental humidity sequence from the predicted environmental data sequence; interactively obtain a target environmental humidity setting, and use the target environmental humidity setting to perform error calculation on the predicted environmental humidity sequence to obtain a predicted error sequence; predefine an initial PID parameter combination; preset a parameter adjustment scale and a parameter adjustment evaluation function; update the initial PID parameter combination based on the parameter adjustment scale, and use the parameter adjustment evaluation function to evaluate and screen the update results until the PID parameter adjustment amount with the smallest calculation result of the parameter adjustment evaluation function is obtained.

[0071] Furthermore, the humidity control optimization module 15 is also used to execute the following method:

[0072] Based on the parameter adjustment scale, an initial PID parameter combination is updated to obtain a plurality of updated PID parameter combinations; humidity control simulation is performed on the plurality of updated PID parameter combinations to obtain a plurality of updated humidity errors and a plurality of updated control energy consumptions; the plurality of updated humidity errors and the plurality of updated control energy consumptions are input into the parameter adjustment evaluation function to obtain a plurality of updated evaluation values; based on the plurality of updated evaluation values, a second update starting point is obtained by screening the plurality of updated PID parameter combinations; and so on, the PID parameter combination is updated according to the parameter adjustment scale and the parameter adjustment evaluation function until the PID parameter adjustment amount with the minimum calculation result of the parameter adjustment evaluation function is obtained.

[0073] Embodiment 3, based on the same inventive concept as the humidity control method combined with condensate recovery in the aforementioned embodiment 1, the present application provides an electronic device, which may be a server, including a processor, a memory and a network interface connected through a system bus, wherein the processor of the electronic device is used to provide computing and control capabilities, the memory of the electronic device includes a non-volatile storage medium and an internal memory, the non-volatile storage medium stores an operating system, a computer program and a database, the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium, the database of the electronic device is used to store data, the network interface of the electronic device is used to communicate with an external terminal through a network connection, and the computer program is executed by the processor to implement the humidity control method combined with condensate recovery.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0076] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A humidity control method in combination with condensed water recovery, characterized in that: The method comprises: When the water level of the condensate collection tank reaches a preset water level, a water quality monitoring array is activated to automatically detect water quality and obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensate collection tank; In the process of performing water quality processing analysis according to the real-time water quality information and performing condensed water processing based on the analysis results to obtain real-time pure water, the real-time pure water is dynamically transferred to a water storage tank; Preset an environmental information collection window, and collect historical environmental data with the environmental information collection window as a constraint to obtain a historical environmental data sequence; Performing environmental change prediction according to the historical environmental data sequence to obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a prediction time window identifier, wherein the prediction time window is 1 / K of the environmental information collection window; Perform humidity control optimization according to the predicted environmental data sequence to obtain PID parameter adjustment; After the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the real-time pure water in the nano water ion generator is ionized into nano-scale water ions to adjust the humidity of the target environment using the PID parameter adjustment amount as the parameter control constraint and the prediction time window as the parameter adjustment time constraint.

2. The humidity control method combined with condensed water recovery according to claim 1, characterized in that: The humidity control optimization is performed according to the predicted environmental data sequence to obtain the PID parameter adjustment amount, and the method includes: Extracting a predicted environmental humidity sequence from the predicted environmental data sequence; interactively obtaining a target ambient humidity setting, and using the target ambient humidity setting to perform error calculation on a predicted ambient humidity sequence to obtain a predicted error sequence; Predefine initial PID parameter combination; Preset parameter adjustment scale and parameter adjustment evaluation function; The initial PID parameter combination is updated based on the parameter adjustment scale, and the parameter adjustment evaluation function is used to evaluate and screen the update results until the PID parameter adjustment amount with the minimum calculation result of the parameter adjustment evaluation function is obtained.

3. The humidity control method combined with condensed water recovery according to claim 2, characterized in that: Based on the parameter adjustment scale, an initial PID parameter combination is updated, and the parameter adjustment evaluation function is used to evaluate and screen the update results until the PID parameter adjustment amount with the minimum calculation result of the parameter adjustment evaluation function is obtained, the method comprising: Based on the parameter adjustment scale, an initial PID parameter combination is updated to obtain multiple updated PID parameter combinations; Performing humidity control simulation on the multiple updated PID parameter combinations to obtain multiple updated humidity errors and multiple updated control energy consumptions; Inputting the multiple updated humidity errors and the multiple updated control energy consumptions into the parameter adjustment evaluation function to obtain multiple updated evaluation values; Filtering the plurality of updated PID parameter combinations based on the plurality of updated evaluation values ​​to obtain a second update starting point; By analogy, the PID parameter combination is updated according to the parameter adjustment scale and the parameter adjustment evaluation function until the PID parameter adjustment amount with the minimum calculation result of the parameter adjustment evaluation function is obtained.

4. The humidity control method combined with condensed water recovery according to claim 1, characterized in that: When the water level in the condensate collection tank reaches a preset water level, the water quality monitoring array is activated to automatically detect the water quality and obtain real-time water quality information. The method includes: Dynamically monitor the water level of the condensate collection tank by configuring a water level sensor; When the water level sensor detects that the water level of the condensate collection tank reaches a preset water level, the water quality monitoring array is activated, wherein the water quality monitoring array includes K water quality monitors, each of which is composed of a turbidity meter, a conductivity meter, a residual chlorine detector, and a pH meter; Performing equipment failure judgment based on the K local water quality information returned by the K water quality monitors, and obtaining M credible water quality information from the K local water quality information based on the judgment result, where M is a positive integer less than or equal to K; The real-time water quality information is output by performing information fusion on the M credible water quality information.

5. The humidity control method combined with condensed water recovery according to claim 1, characterized in that: In the process of performing water quality processing and analysis according to the real-time water quality information and performing condensed water processing based on the analysis results to obtain real-time pure water, the real-time pure water is dynamically transferred to a water storage tank, the method comprising: Interactively obtain a plurality of sample water quality processing information, wherein each sample water quality processing information includes sample water quality information and a sample water quality processing strategy; Using the water quality treatment information of the plurality of samples as training data to obtain a water quality treatment analysis model through training; Inputting the real-time water quality information into the water quality treatment analysis model to perform water quality treatment analysis to obtain a real-time water quality treatment strategy; In the process of adopting the real-time water quality treatment strategy to operate the water quality treatment unit to perform multi-stage water quality treatment of condensed water to obtain real-time pure water, the real-time pure water is dynamically transferred to the water storage tank through the diversion pipeline.

6. The humidity control method combined with condensed water recovery according to claim 5, characterized in that: The sample water quality treatment strategy includes sample condensed water flow, sample ultraviolet intensity, sample irradiation time and sample CDI operation parameters.

7. The humidity control method combined with condensed water recovery according to claim 1, characterized in that: Performing environmental change prediction based on the historical environmental data sequence to obtain a predicted environmental data sequence, the method comprises: Collecting natural state data of the target environment to obtain natural environment time series data, wherein the natural environment time series data includes natural temperature time series data and natural humidity time series data; The sum of the duration of the prediction time window and the environmental information collection window is used as a data division scale to divide the natural environment time series data into multiple stages of environmental time series data; The prediction time window and the environment information collection window are used to divide the multiple stage environment time series data into multiple groups of pre-environment time series data-post-environment time series data; Using the multiple groups of pre-environment time series data and post-environment time series data as training data, training an environmental change prediction model pre-built using an LSTM model; The historical environmental data sequence is input into the environmental change prediction model to obtain the predicted environmental data sequence.

8. The humidity control method combined with condensed water recovery according to claim 4, characterized in that: According to the K local water quality information returned by the K water quality monitors, equipment failure judgment is performed, and according to the judgment result, M reliable water quality information is obtained by screening from the K local water quality information. The method includes: Interactively obtain a sensing data range set, a data change rate set, a device rated temperature set, a device rated current set, and a communication interval scale set; Constructing a first fault judgment branch, a second fault judgment branch, a third fault judgment branch, a fourth fault judgment branch and a fifth fault judgment branch based on the sensing data range set, the data change rate set, the device rated temperature set, the device rated current set and the communication interval scale set; By connecting the first fault judgment branch, the second fault judgment branch, the third fault judgment branch, the fourth fault judgment branch and the fifth fault judgment branch in parallel, the construction of the equipment fault judgment model is completed; Collecting historical information of the K water quality monitors to obtain K groups of fault association judgment information; By loading the K groups of fault association judgment information and K local water quality information mappings into the equipment fault judgment model, M credible monitoring instruments are screened and obtained; The M local water quality information of the M trusted monitoring instruments are called as the M trusted water quality information.

9. A humidity control system combined with condensate recovery, characterized in that: A humidity control method combined with condensed water recovery for implementing any one of claims 1 to 8, comprising: Automatic water quality detection module: when the water level of the condensed water collection tank reaches a preset water level, the water quality monitoring array is activated to perform automatic water quality detection to obtain real-time water quality information, wherein the water quality monitoring array is installed in the condensed water collection tank; Condensate treatment module: in the process of performing water quality treatment analysis according to the real-time water quality information and performing condensate treatment based on the analysis result to obtain real-time pure water, the real-time pure water is dynamically transferred to the water storage tank; Historical environment data collection module: preset an environment information collection window, and collect historical environment data with the environment information collection window as a constraint to obtain a historical environment data sequence; Environmental change prediction module: predicting environmental changes according to the historical environmental data sequence to obtain a predicted environmental data sequence, wherein the predicted environmental data sequence has a prediction time window identifier, wherein the prediction time window is 1 / K of the environmental information collection window; Humidity control optimization module: performs humidity control optimization according to the predicted environmental data sequence to obtain PID parameter adjustment amount; Humidity adjustment module: After the water pump pumps the real-time pure water into the nano water ion generator at a preset pressure, the PID parameter adjustment amount is used as the parameter adjustment control constraint, and the predicted time window is used as the parameter adjustment time constraint, so as to ionize the real-time pure water in the nano water ion generator into nano-scale water ions to adjust the humidity of the target environment.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the humidity control method combined with condensed water recovery described in any one of claims 1 to 8 are implemented.

Images