A method, system, medium, and apparatus for energy consumption optimization of a dual-rotor dehumidifier

Abstract

The application provides an energy consumption optimization method, system, medium and equipment of a double-rotor dehumidifier, relates to the field of industrial equipment control, and comprises the following steps: acquiring sensor data of a parameter sensor, respectively constructing a first energy consumption model of each cooling coil and a second energy consumption model of a regenerative heater; establishing front and rear rotor models based on a sparse nonlinear identification method; integrating and constructing an energy consumption calculation function; taking the temperature set value of the middle cooling coil and the mixed air dew point set value as adjustment parameters, and taking the energy consumption calculation function as an objective function to solve an optimal solution, so that precise regulation and control are realized. Through the fusion of the multi-level energy consumption model and the intelligent optimization algorithm, the application avoids energy waste caused by traditional fixed set values, eliminates excessive cooling and heating phenomena, and significantly reduces system energy consumption.

Description

Technical Field

[0001] This application relates to the field of industrial equipment control, and in particular to an energy consumption optimization method, system, medium, and device for a dual-rotor dehumidifier. Background Technology

[0002] The dual-rotor dehumidifier is a precision industrial-grade air handling device suitable for applications with strict humidity requirements. It consists of key components such as a front cooler, middle cooler, rear cooler, front rotor, rear rotor, front regeneration heater, and rear regeneration heater.

[0003] The dehumidifier features a honeycomb-shaped rotor divided into a dehumidification zone and a regeneration zone. The dehumidification zone uses dry, absorbent material to adsorb moisture from the air. The regeneration zone uses hot regeneration air to dry the moisture adsorbed on the absorbent material, restoring the rotor's dehumidification function. The air entering the dual-rotor dehumidifier is processed in the following order: front surface cooler, front rotor's dehumidification zone, and middle surface cooler. The front surface cooler first cools and dehumidifies the air (condensation occurs during air cooling), while the front rotor performs high-intensity dehumidification, significantly lowering the air's dew point. However, this process raises the air temperature, which is then cooled by the middle surface cooler. The cooled air is then divided into two streams: one stream passes through the rear rotor's dehumidification zone for deep dehumidification, and this deeply dehumidified air is then supplied to the workshop via the rear surface cooler. The other stream serves as regeneration air, entering the rear regeneration heater. The heated air then passes through the rear rotor's regeneration zone and then enters the front regeneration heater. After being heated by pre-regeneration, the air passes through the regeneration zone of the front rotor and is then exhausted from the dual-rotor dehumidification system.

[0004] Due to the complexity of the dual-rotor dehumidifier structure, effective energy consumption optimization is difficult. Existing control algorithms for rotary dehumidifiers are mostly localized controls aimed at achieving the desired supply air temperature and humidity. For example, many dual-rotor dehumidifier systems incorporate PID control, adjusting the cooling surface temperature setpoint if the supply air temperature does not meet requirements. However, relying solely on localized control is insufficient to effectively optimize the overall energy consumption of a dual-rotor dehumidifier. Therefore, effectively reducing the overall energy consumption of dual-rotor dehumidifiers is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide an energy consumption optimization method, system, computer-readable storage medium, electronic device, and computer program product for a dual-rotor dehumidifier, which effectively reduces the overall energy consumption of the dual-rotor dehumidifier.

[0006] To address the aforementioned technical problems, this application provides an energy consumption optimization method for a dual-rotor dehumidifier, the specific technical solution of which is as follows:

[0007] Acquire sensor data from several parameter sensors in the dual-rotor dehumidifier;

[0008] Based on the sensor data, a first energy consumption model corresponding to each surface cooler and a second energy consumption model corresponding to the regenerative heater are constructed respectively.

[0009] A front and rear wheel model was established based on sparse nonlinear identification.

[0010] An energy consumption calculation function is constructed based on the first energy consumption model, the second energy consumption model, and the front and rear wheel models;

[0011] The temperature setpoint of the middle surface cooler and the dew point setpoint of the mixed air in the dual-rotor dehumidifier are used as adjustment parameters, and the energy consumption calculation function is used as the objective function. The optimal solution of the objective function is calculated, and the operation of the dual-rotor dehumidifier is controlled based on the optimal solution. The optimal solution includes the optimal setpoint of the middle surface cooler temperature and the optimal setpoint of the mixed air dew point.

[0012] Optionally, constructing a first energy consumption model for each surface cooler based on the sensor data includes:

[0013] The front air temperature, fresh air humidity, fresh air volume, front surface cooling temperature and front surface cooling dew point are used as inputs, and the front surface cooling capacity is used as output to construct the front surface cooling model in the first energy consumption model.

[0014] Using the mixed air temperature, mixed air dew point, mixed air volume, middle surface cooling temperature, and middle surface cooling dew point as inputs, and the middle surface cooling capacity as output, the middle surface cooling model in the first energy consumption model is constructed.

[0015] Using the temperature behind the rear impeller, the dew point behind the rear impeller, the supply air temperature, the supply air dew point, and the supply air volume as inputs, and the cooling capacity of the rear surface cooling unit as output, the rear surface cooling unit model in the first energy consumption model is constructed.

[0016] Optionally, the calculation process for the cooling capacity corresponding to the front surface cooling, the middle surface cooling, and the rear surface cooling includes:

[0017] The cooling capacity is calculated based on the air mass flow rate, the first air enthalpy value when entering the surface cooler, the second air enthalpy value when exiting the surface cooler, the air density, and the air volumetric flow rate.

[0018] Optionally, establishing the front and rear wheel models based on sparse nonlinear identification includes:

[0019] For either the front wheel model or the rear wheel model, a corresponding feature library is established; the feature library contains the input variables of the front wheel model or the rear wheel model, the quadratic combination of the input variables, trigonometric function features, and mechanistic features; the mechanistic features are the mechanistic features of the front wheel model or the rear wheel model.

[0020] The sparse nonlinear recognition result matrix is ​​used to perform sparse recognition on the features contained in the feature library to obtain the front wheel model or the rear wheel model; the front wheel model or the rear wheel model contains historical data matrices of different output variables.

[0021] Optionally, before calling the result matrix of sparse nonlinear recognition to perform sparse recognition on the features contained in the feature library, the method further includes:

[0022] Set a sparsity threshold based on the prediction target;

[0023] The sequential least squares regression method is used to iteratively calculate the coefficients of each prediction target. If the sparsity is less than the sparsity threshold, the corresponding prediction target will not participate in the next round of calculation until the result converges to obtain the sparse coefficient matrix.

[0024] Accordingly, the result matrix of sparse nonlinear identification is called to perform sparse identification on the features contained in the feature library, resulting in historical data matrices of different output variables, including:

[0025] The sparse coefficient matrix is ​​used to perform sparse identification on the features contained in the feature library, and the predicted target at each time step is calculated.

[0026] Optionally, if the first energy consumption model includes a front surface cooling model, a middle surface cooling model, and a rear surface cooling model, the second energy consumption model includes a front regeneration model and a rear regeneration model, and the front and rear impeller models include a front impeller model and a rear impeller model, constructing an energy consumption calculation function based on the first energy consumption model, the second energy consumption model, and the front and rear impeller models includes:

[0027] The fresh air temperature, fresh air humidity, and fresh air volume are used as inputs to the front surface cooling model, and the front surface cooling temperature, front surface cooling dew point, and front surface cooling capacity are output through the front surface cooling model.

[0028] The fresh air volume, the front surface cooling temperature and the front surface cooling dew point output by the front surface cooling model are used as inputs to the front impeller model, and the front impeller rear temperature and front impeller rear dew point are output through the front impeller model.

[0029] The mixed air temperature, mixed air dew point, and mixed air volume are calculated based on the fresh air volume, the temperature behind the front impeller, the dew point behind the front impeller, the return air temperature, the return air dew point, and the return air volume.

[0030] The mixed air temperature, the mixed air dew point, and the mixed air volume are used as inputs to the middle surface cooling model, and the middle surface cooling temperature, the middle surface cooling dew point, and the cooling capacity of the middle surface cooling are output through the middle surface cooling model.

[0031] The middle surface cooling temperature, the middle surface cooling dew point, and the regeneration air volume are used as inputs to the post-regeneration model, and the post-regeneration energy consumption, post-regeneration temperature, and post-regeneration dew point are output through the post-regeneration model.

[0032] The middle surface cooling temperature, the middle surface cooling dew point, and the air supply volume are used as inputs to the rear impeller model, and the rear impeller dew point and rear impeller temperature are output by the rear impeller model.

[0033] The dew point behind the rear impeller, the temperature behind the rear impeller, and the air volume are used as inputs to the rear surface cooling model, and the air supply temperature, air supply dew point, and cooling capacity of the rear surface cooling model are output.

[0034] The post-regeneration temperature and the post-regeneration dew point are used as inputs to the post-rotor model, and the post-exhaust air temperature and the post-exhaust air dew point are output by the post-rotor model.

[0035] The rear exhaust air temperature, the rear exhaust air dew point, and the regeneration air volume are used as inputs to the front regeneration model, and the energy consumption of the front regeneration heater, the front regeneration temperature, and the front regeneration dew point are output by the rear rotor model.

[0036] The front regeneration temperature and the front regeneration dew point are used as inputs to the front impeller model, and the front exhaust air temperature and the front exhaust air dew point are output by the front impeller model.

[0037] The total energy consumption of the energy consumption calculation function is the sum of the energy consumption of the front surface cooling model, the middle surface cooling model, the rear surface cooling model, the front regeneration model, and the rear regeneration model; the independent variables of the energy consumption calculation function are the middle surface cooling temperature and the mixed wind dew point.

[0038] This application also provides an energy consumption optimization system for a dual-rotor dehumidifier, including:

[0039] The data acquisition module is used to acquire sensor data from several parameter sensors in the dual-rotor dehumidifier;

[0040] The first model building module is used to build a first energy consumption model for each surface cooler and a second energy consumption model for the regenerative heater based on the sensor data.

[0041] The second model building module is used to establish front and rear wheel models based on sparse nonlinear identification.

[0042] The energy consumption simulation module is used to construct an energy consumption calculation function based on the first energy consumption model, the second energy consumption model, and the front and rear wheel models;

[0043] The control module is used to take the temperature setpoint of the middle surface cooler and the dew point setpoint of the mixed air in the dual-rotor dehumidifier as adjustment parameters, take the energy consumption calculation function as the objective function, calculate the optimal solution of the objective function, and control the operation of the dual-rotor dehumidifier based on the optimal solution; the optimal solution includes the optimal setpoint of the middle surface cooler temperature and the optimal setpoint of the mixed air dew point.

[0044] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0045] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when it invokes the computer program in the memory.

[0046] This application also provides a computer program product, including a computer program that, when executed, implements the steps of the method described above.

[0047] This application provides an energy consumption optimization method for a dual-rotor dehumidifier, comprising: acquiring sensor data from several parameter sensors in the dual-rotor dehumidifier; constructing a first energy consumption model corresponding to each surface cooler and a second energy consumption model corresponding to the regenerative heater based on the sensor data; establishing front and rear rotor models based on sparse nonlinear identification; constructing an energy consumption calculation function based on the first energy consumption model, the second energy consumption model, and the front and rear rotor models; using the temperature setpoint and the mixed air dew point setpoint of the middle surface cooler in the dual-rotor dehumidifier as adjustment parameters, using the energy consumption calculation function as the objective function, calculating the optimal solution of the objective function, and controlling the operation of the dual-rotor dehumidifier based on the optimal solution; the optimal solution includes the optimal setpoint of the middle surface cooler temperature and the optimal setpoint of the mixed air dew point.

[0048] This application deeply integrates a multi-level energy consumption model with intelligent optimization algorithms. First, it utilizes a parameter sensor network deployed at key nodes of the dual-rotor dehumidifier to collect real-time sensor data from multi-dimensional parameters, including temperature, humidity, airflow, and pressure. Based on this, for different energy-consuming units in the system, such as the pre-cooler, middle cooler, post-cooler, and regenerative heater, a first energy consumption model and a second energy consumption model are established, combining physical mechanisms and data-driven approaches. These models fully consider the different thermodynamic characteristics of each cooler due to variations in the air state point it processes, as well as the unique energy conversion patterns of the regenerative heater during the rotor regeneration process, thus ensuring the accuracy and relevance of energy consumption calculations. By introducing a sparse nonlinear identification method to establish front and rear impeller models, the strong nonlinear characteristics of adsorption and desorption dynamics during impeller dehumidification are effectively captured. Simultaneously, sparsity constraints prevent overfitting, significantly reducing computational complexity while maintaining model accuracy. Furthermore, the functional division of labor in the dual-impeller series structure—with the front impeller bearing the main dehumidification load and the rear impeller performing deep dehumidification—is fully considered, accurately reflecting the mass and heat transfer characteristics of the two impellers in different operating ranges. Subsequently, by organically integrating the first and second energy consumption models with the front and rear impeller models, an energy consumption calculation function covering the entire dehumidifier system is formed, comprehensively characterizing the energy flow relationship from fresh air pretreatment, impeller dehumidification to regeneration heating. The optimization phase uses the temperature setpoint of the intermediate surface cooler and the dew point setpoint of the mixed air as core adjustment parameters, and uses the energy consumption calculation function as the objective function for optimization. Essentially, it seeks the optimal operating point that minimizes the total system energy consumption while satisfying the supply air parameter constraints. The optimal solution, which incorporates the synergistic effect of the optimal setpoints for the surface cooling temperature and the mixed air dew point, enables precise control of the dual-rotor dehumidifier's operating status, avoiding energy waste caused by traditional fixed setpoint control methods. This application achieves adaptive adjustment to complex and variable operating conditions through closed-loop control of model prediction and real-time optimization, ensuring the system always operates close to its theoretical optimal operating range. By precisely matching the dehumidification load and energy input, it effectively eliminates the over-cooling and over-heating phenomena common in traditional control strategies, significantly reducing the energy consumption of the refrigeration unit and regenerative heater.

[0049] This application also provides an energy consumption optimization system for a dual-rotor dehumidifier, a computer-readable storage medium, an electronic device, and a computer program product, which have the aforementioned beneficial effects, and will not be elaborated here. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0051] Figure 1 A flowchart illustrating an energy consumption optimization method for a dual-rotor dehumidifier provided in an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of the data structure of the front surface cooling model provided in the embodiments of this application;

[0053] Figure 3 This is a schematic diagram of the data structure of the surface cooling model provided in the embodiments of this application;

[0054] Figure 4 This is a schematic diagram of the data structure of the post-cooling model provided in the embodiments of this application;

[0055] Figure 5 This is a schematic diagram of the data structure of the pre-regeneration model provided in the embodiments of this application;

[0056] Figure 6 This is a schematic diagram of the data structure of the post-regeneration model provided in the embodiments of this application;

[0057] Figure 7 This is a schematic diagram of the data structure of the front wheel model provided in the embodiments of this application;

[0058] Figure 8 This is a schematic diagram of the data structure of the rear wheel model provided in the embodiments of this application;

[0059] Figure 9 This is a schematic diagram of model integration and connection provided in the embodiments of this application;

[0060] Figure 10 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] See Figure 1, Figure 1 A flowchart illustrating an energy consumption optimization method for a dual-rotor dehumidifier provided in this application embodiment, the method comprising:

[0063] S101: Obtain sensor data from several parameter sensors in the dual-rotor dehumidifier;

[0064] S102: Construct a first energy consumption model for each surface cooler and a second energy consumption model for the regenerative heater based on the sensor data.

[0065] S103: Establishing a front and rear wheel model based on sparse nonlinear identification;

[0066] S104: Construct an energy consumption calculation function based on the first energy consumption model, the second energy consumption model, and the front and rear wheel models;

[0067] S105: Using the temperature setpoint of the middle surface cooler and the dew point setpoint of the mixed air in the dual-rotor dehumidifier as adjustment parameters, and using the energy consumption calculation function as the objective function, calculate the optimal solution of the objective function, and control the operation of the dual-rotor dehumidifier based on the optimal solution; the optimal solution includes the optimal setpoint of the middle surface cooler temperature and the optimal setpoint of the mixed air dew point.

[0068] Parameter sensors are used to monitor air temperature and humidity parameters in real time. Corresponding parameter sensors are installed for key structural components of the dual-rotor dehumidifier, including the front, middle, and rear surface coolers, the front and rear rotors, the front regeneration heater, and the rear regeneration heater.

[0069] In one feasible implementation, temperature sensors can be distributed at locations such as the fresh air inlet, the inlet and outlet of the front surface cooler, the inlet and outlet of the middle surface cooler, the inlet and outlet of the rear surface cooler, the inlet and outlet of the regenerator, the processing side and regeneration side of the front and rear impellers, and the air outlet, to monitor the thermal status of each node.

[0070] Humidity sensors or dew point sensors are placed at the fresh air inlet, before and after the impeller, in the mixing duct, and at the air outlet to characterize the changes in air humidity.

[0071] Air volume sensors are installed in the fresh air duct, return air duct, regenerated air duct, and supply air duct to obtain the volumetric flow rate of each branch.

[0072] Pressure sensors are placed before and after the filter, before and after the surface cooler, and on both sides of the impeller to monitor airflow resistance and equipment operating status.

[0073] In addition, energy metering devices are installed at the chilled water inlet and outlet of the surface cooler and the steam or hot water inlet and outlet of the regenerator to directly obtain energy consumption data.

[0074] This document does not specify the exact steps for data acquisition, preprocessing, and storage for parameter sensors. Sensor data can be acquired using periodic polling or event-triggered methods. Preprocessing may include outlier removal, missing value imputation, data smoothing, and unit standardization, among other processes, and is not specifically limited here. Preprocessed sensor data can be stored, timestamped, in a local database or cloud server, with a storage format supporting structured queries for easy subsequent processing.

[0075] In steps S102 and S103, several models need to be constructed respectively. Based on the law of conservation of energy, the energy consumption of the surface cooler can be expressed as a function of energy exchange between the air side and the chilled water side. Specifically, by measuring the temperature and humidity of the air at the inlet and outlet of the surface cooler, the change in air enthalpy is calculated, thereby determining the cooling load of the surface cooler; simultaneously, combined with the chilled water flow rate and the inlet and outlet temperature difference, the heat carried away by the chilled water side is calculated. By establishing the energy balance relationship between the air side and the chilled water side, an energy consumption calculation model for the surface cooler is constructed, thus reflecting the performance changes of the surface cooler under different operating conditions.

[0076] It should be noted that there is no predetermined construction order between the first energy consumption model and the second energy consumption model in step S102. The corresponding models can be established after the corresponding data acquisition requirements are met. For details, see [link to relevant documentation]. Figure 2 , Figure 3 and Figure 4 Taking the first energy consumption model, which includes the front surface cooling model, the middle surface cooling model, and the rear surface cooling model, as an example, Figure 2 This is a schematic diagram of the data structure of the front surface cooling model provided in the embodiments of this application. Figure 3 This is a schematic diagram of the data structure of the surface cooling model provided in the embodiments of this application. Figure 4 This is a schematic diagram of the data structure of the post-cooling model provided in the embodiments of this application.

[0077] The inputs to the front cooling model include fresh air temperature, fresh air dew point, fresh air volume, front cooling temperature, and front cooling dew point; the output is the cooling capacity required for the front cooling system.

[0078] The inputs to the cooling capacity model for the middle surface cooling system are the mixed air temperature, mixed air dew point, mixed air volume, middle surface cooling temperature, and middle surface cooling dew point; the output is the cooling capacity used by the middle surface cooling system.

[0079] The inputs to the cooling capacity model for the rear surface cooling system are the temperature behind the rear impeller, the dew point behind the rear impeller, the supply air volume, the supply air temperature, and the supply air dew point; the output is the cooling capacity of the rear surface cooling system.

[0080] The front surface cooler's chilled water valve is typically opened to its maximum by default to maximize the cooling effect of the front surface cooler, thereby removing condensate from the hot and humid air and lowering the temperature of the air entering the front impeller's dehumidification zone, thus improving the dehumidification efficiency of the front impeller. Therefore, the front surface cooler's temperature setpoint is generally fixed and is not listed as an adjustable variable in this application. The rear surface cooler utilizes existing PID control logic to automatically adjust the opening of the rear surface cooler's chilled water valve based on the temperature after the rear impeller and the workshop temperature setpoint. Therefore, the rear surface cooler's temperature is also not listed as an adjustable variable in this application.

[0081] Whether it's a front surface cooler, a middle front surface cooler, or a rear front surface cooler, the cooling capacity of the surface cooler is calculated based on the enthalpy difference between the air entering and exiting the surface cooler.

[0082] ;

[0083] Where Q is the cooling capacity (kW), Air mass flow rate (kg / s) The enthalpy (kJ / kg) of the air entering the surface cooler. V represents the enthalpy of the air exiting the surface cooler (kJ / kg), and V represents the air volumetric flow rate (m³). 3 / h, where ρ is the air density.

[0084] The formula for calculating the enthalpy of air is:

[0085] ;

[0086] Where T is the dry-bulb temperature of the air (°C), This refers to the absolute moisture content (kg water / kg dry air).

[0087] The formula for calculating absolute moisture content is:

[0088] ;

[0089] in, is the saturated water vapor pressure (Pa) corresponding to the dew point temperature, and P is the atmospheric pressure (Pa).

[0090] The formula for calculating saturated water vapor pressure is:

[0091] ;

[0092] The formula for calculating relative moisture content is:

[0093] ;

[0094] The energy consumption model for regenerative heaters is similar to that for surface coolers, and can further consider the differences in the characteristics of the heating medium (steam, hot water, or electric heating). For steam heating, the energy consumption model for regenerative heaters can be built based on parameters such as steam flow rate, steam pressure, steam temperature, and condensate temperature, and the heating amount is determined by calculating the latent heat and sensible heat released by the steam. The energy consumption model needs to consider the impact of steam quality (dryness) on heat exchange efficiency, and the impact of condensate discharge temperature on steam trap performance. For hot water heating, the energy consumption model for regenerative heaters is established based on hot water flow rate and inlet / outlet temperature difference, and also needs to consider the energy consumption of the hot water pump.

[0095] The energy consumption models for both the pre-regenerative heater and the post-regenerative heater in the second energy consumption model are shown in the following equations:

[0096] ;

[0097] in, Energy consumption of the regenerative heater (kW), Air mass flow rate (kg / s) The enthalpy (kJ / kg) of the air entering the regeneration heater. V is the enthalpy of the air discharged from the regenerator (kJ / kg), and V is the air volumetric flow rate (m³). 3 / h, This refers to air density.

[0098] The enthalpy of the air entering and exiting the regeneration heater can be calculated based on the temperature and dew point of the air entering and exiting the regeneration heater.

[0099] See Figure 5 and Figure 6 , Figure 5 This is a schematic diagram of the data structure of the pre-regeneration model provided in the embodiments of this application. Figure 6 This is a schematic diagram of the data structure of the post-regeneration model provided in an embodiment of this application. The input variables of the pre-regeneration model are the exhaust air temperature, exhaust air dew point, regeneration air volume, pre-regeneration temperature, and pre-regeneration dew point; the output variable is the pre-regeneration power. The input variables of the post-regeneration heater are the mixed air temperature, mixed air dew point, regeneration air volume, post-regeneration temperature, and post-regeneration dew point; the output variable is the post-regeneration power.

[0100] In step S103, a sparse nonlinear identification method combining mechanism and data is needed to establish the front and rear wheel models. This method can flexibly combine mechanistic and data information, and the sparse identification method avoids overfitting, thus enhancing the robustness of the model. Secondly, compared with other data-driven models, the front and rear wheel models have relatively low data requirements and lower computational costs.

[0101] Sparse nonlinear identification is a systematic identification method that automatically selects a small number of key terms from a large number of candidate basis functions to construct a concise and accurate nonlinear model.

[0102] Specifically, this may include the following two steps:

[0103] The first step is to establish a corresponding feature library for either the front wheel model or the rear wheel model. The feature library contains the input variables of the front wheel model or the rear wheel model, the quadratic combination of the input variables, trigonometric function features, and mechanistic features. The mechanistic features are the mechanistic features of the front wheel model or the rear wheel model.

[0104] The second step is to call the result matrix of sparse nonlinear recognition to perform sparse recognition on the features contained in the feature library to obtain the front wheel model or the rear wheel model; the front wheel model or the rear wheel model contains historical data matrices of different output variables.

[0105] The first step involves determining the input and output variables. After collecting and organizing the air temperature and humidity parameters gathered by the sensors within the dual-rotor dehumidifier, a feature library is established. Based on this, and according to the physical mechanism and engineering experience of rotary dehumidification, a candidate basis function set can be constructed, including polynomial functions, trigonometric functions, exponential functions, logarithmic functions, and cross terms composed of input variables. The basis function library can be large enough to cover possible nonlinear relationships, but computational feasibility must also be considered.

[0106] ;

[0107] in, The feature prepared for modeling is a symbolic set, where x represents the set of all input variables; , represents the quadratic combination of all input variables, such as pre-regeneration temperature × pre-regeneration dew point, pre-surface cooling temperature × pre-regeneration temperature; It represents trigonometric function features, such as tanh (front surface cooling temperature) and tanh (front surface cooling dew point); other trigonometric function features, such as sin(x) and cos(x), can also be included in the feature library as candidate features; FP represents the set of features obtained based on the mechanism.

[0108] For the front impeller model, x = [front surface cooling temperature, front surface cooling dew point, fresh air volume, front regenerator temperature, front regenerator dew point, regenerator air volume].

[0109] For the rear rotor model, x = [middle surface cooling temperature, middle surface cooling dew point, mixing air volume, rear regeneration temperature, rear regeneration dew point, regeneration air volume].

[0110] The mechanism described in this application stems from the fact that when humid air passes through the dehumidification zone of the rotor, water vapor is adsorbed, generating heat of adsorption.

[0111] ;

[0112] in, The heat of adsorption represents the heat released when water vapor molecules are captured by the adsorbent; its specific value is determined by the adsorption material of the rotor. Specific heat capacity of moist air (kJ / kgK); This represents the difference in absolute moisture content. Based on the mechanism characteristics, it is entered into the feature library for sparse nonlinear recognition.

[0113] In the second step, sparse identification is performed on the predicted target and features in the feature library:

[0114] ;

[0115] Here, X (uppercase) represents the historical data matrix with different input variables, and Y represents the historical data matrix with different output variables (prediction target). This is the result matrix of sparse nonlinear identification. ; To predict the number of targets.

[0116] See Figure 7 and Figure 8 , Figure 7 This is a schematic diagram of the data structure of the front wheel model provided in the embodiments of this application. Figure 8 This is a schematic diagram of the data structure of the rear impeller model provided in the embodiments of this application. The output variables of the front impeller model are the temperature behind the front impeller, the dew point behind the front impeller, the front exhaust air temperature, and the front exhaust air dew point.

[0117] The output variables of the rear impeller model are the rear impeller temperature, the rear impeller dew point, the rear exhaust air temperature, and the rear exhaust air dew point.

[0118] Specific calculation result matrix When using sequential least squares regression, different sparsity thresholds λ can be set for different prediction targets.

[0119] After the first least squares regression, each feature will obtain a coefficient for each prediction target. If the absolute value of the coefficient is less than the corresponding threshold, the coefficient is forced to 0, indicating that the feature will not participate in the next round of least squares regression identification. This process is repeated several times until the results converge, yielding the final sparse coefficient matrix.

[0120] After obtaining the sparse coefficient matrix, the predicted target at each time step can be calculated using this matrix:

[0121] ;

[0122] in, The data set for each time step of the model's prediction target. This is the data set of the feature library at each time step.

[0123] In S104, after establishing the energy consumption models of each part inside the dual-rotor dehumidifier, as well as the front and rear rotor models, this application integrates and connects the models of each part as follows: Figure 9 As shown, Figure 9 This is a schematic diagram of the model integration and connection provided in the embodiments of this application.

[0124] Figure 9 In this model, the fresh air temperature, fresh air humidity, and fresh air volume are used as inputs to the front surface cooling model, and the front surface cooling temperature, front surface cooling dew point, and front surface cooling capacity are output through the front surface cooling model.

[0125] The fresh air volume, the front surface cooling temperature and the front surface cooling dew point output by the front surface cooling model are used as inputs to the front impeller model, and the front impeller rear temperature and front impeller rear dew point are output through the front impeller model.

[0126] The mixed air temperature, mixed air dew point, and mixed air volume are calculated based on the fresh air volume, the temperature behind the front impeller, the dew point behind the front impeller, the return air temperature, the return air dew point, and the return air volume.

[0127] The mixed air temperature, the mixed air dew point, and the mixed air volume are used as inputs to the middle surface cooling model, and the middle surface cooling temperature, the middle surface cooling dew point, and the cooling capacity of the middle surface cooling are output through the middle surface cooling model.

[0128] The middle surface cooling temperature, the middle surface cooling dew point, and the regeneration air volume are used as inputs to the post-regeneration model, and the post-regeneration energy consumption, post-regeneration temperature, and post-regeneration dew point are output through the post-regeneration model.

[0129] The middle surface cooling temperature, the middle surface cooling dew point, and the air supply volume are used as inputs to the rear impeller model, and the rear impeller dew point and rear impeller temperature are output by the rear impeller model.

[0130] The dew point behind the rear impeller, the temperature behind the rear impeller, and the air volume are used as inputs to the rear surface cooling model, and the air supply temperature, air supply dew point, and cooling capacity of the rear surface cooling model are output.

[0131] The post-regeneration temperature and the post-regeneration dew point are used as inputs to the post-rotor model, and the post-exhaust air temperature and the post-exhaust air dew point are output by the post-rotor model.

[0132] The rear exhaust air temperature, the rear exhaust air dew point, and the regeneration air volume are used as inputs to the front regeneration model, and the energy consumption of the front regeneration heater, the front regeneration temperature, and the front regeneration dew point are output by the rear rotor model.

[0133] The front regeneration temperature and the front regeneration dew point are used as inputs to the front impeller model, and the front exhaust air temperature and the front exhaust air dew point are output by the front impeller model.

[0134] The total energy consumption of the energy consumption calculation function is the sum of the energy consumption of the front surface cooling model, the middle surface cooling model, the rear surface cooling model, the front regeneration model, and the rear regeneration model; the independent variables of the energy consumption calculation function are the middle surface cooling temperature and the mixed wind dew point.

[0135] This application connects the models of the various parts of the dual-rotor dehumidifier described above. When establishing the overall energy consumption model, the energy consumption of each part is expressed as an expression for relevant temperature and humidity variables. This enables simulation of the system's interconnected changes after modifications to the set values ​​of the surface cooling temperature and the mixed air dew point. This lays the foundation for overall energy consumption optimization.

[0136] Total energy consumption formula:

[0137] ;

[0138] In the above formula, each letter Q on the right side of the equal sign represents the energy consumption corresponding to the structure indicated by the subscript on the right, such as... The value represents the energy consumption of the pre-regeneration stage, and the left side of the equals sign represents the total energy consumption. The objective of this application is to model the main energy-consuming components and the front and rear rotors of the dual-rotor dehumidifier separately, and to reduce the total energy consumption of the dual-rotor dehumidifier while ensuring the temperature and humidity requirements of the workshop by adjusting the set values ​​of the middle surface cooling temperature and the mixed air dew point.

[0139] In the process of optimizing total energy consumption, the set values ​​of the intermediate surface cooling temperature and the mixed dew point are used as adjustable optimization parameters.

[0140] Adjusting the middle surface cooling temperature directly affects the energy consumption of the middle surface cooling system and the dehumidification efficiency of the rear rotor, and indirectly affects the energy consumption of the rear surface cooling system and the energy consumption of both pre- and post-regeneration processes. Therefore, the middle surface cooling temperature is considered an adjustable and optimized parameter. This application calculates the corresponding temperature after adjusting the middle surface cooling temperature based on the rear rotor model, and also calculates the corresponding cooling load required for the rear surface cooling system. Furthermore, changing the middle surface cooling temperature alters the temperature of the air entering the post-regeneration heater, thus affecting the energy consumption of post-regeneration heating. This application calculates the post-regeneration heating energy consumption after changing the middle surface cooling temperature setpoint using a total energy consumption model.

[0141] Based on the PID control logic of the operating dual-rotor dehumidifier, adjusting the mixed dew point setpoint alters the energy consumption of the pre-regeneration heater. For example, increasing the mixed air dew point setpoint reduces the valve opening of the pre-regeneration heater, thus lowering the pre-regeneration temperature and reducing its energy consumption. Similarly, decreasing the mixed air dew point setpoint increases the valve opening of the pre-regeneration heater, thereby increasing its energy consumption. Simultaneously, changes in the mixed air dew point setpoint, combined with changes in the intermediate surface cooling temperature setpoint, alter the cooling capacity of the intermediate surface cooling unit. These interconnected changes are difficult to capture in previous dual-rotor dehumidifier control systems. This application utilizes the interconnectedness of various model components to calculate the interconnected temperature, humidity, and energy consumption changes within the dual-rotor dehumidifier after changes in the intermediate surface cooling temperature setpoint and the mixed air dew point setpoint, thereby more accurately calculating the optimal setpoints. As can be seen, this step models the key components of the dual-rotor dehumidifier, simulates the linkage of internal variables, and establishes a total energy consumption model. It simulates the linkage changes that occur in the system after the setpoints of the middle surface cooling temperature and the mixed air dew point are changed, thereby solving the limited nonlinear optimization problem. Under the condition of meeting the temperature and humidity requirements of the workshop terminal, it provides the optimal setpoints of the middle surface cooling temperature and the mixed air dew point in real time to reduce the energy consumption of the dual-rotor dehumidifier.

[0142] This application embodiment deeply integrates a multi-level energy consumption model with intelligent optimization algorithms. First, it utilizes a parameter sensor network deployed at key nodes of the dual-rotor dehumidifier to collect real-time sensor data from multi-dimensional parameter sensors, including temperature, humidity, airflow, and pressure. Based on this, for different energy-consuming units in the system, such as the front, middle, and rear surface coolers and the regenerative heater, a first energy consumption model and a second energy consumption model are established, combining physical mechanisms and data-driven approaches. These models fully consider the different thermodynamic characteristics of each surface cooler due to variations in the air state point it processes, as well as the unique energy conversion laws of the regenerative heater during the rotor regeneration process, thereby ensuring the accuracy and relevance of energy consumption calculations. By introducing a sparse nonlinear identification method to establish front and rear impeller models, the strong nonlinear characteristics of adsorption and desorption dynamics during impeller dehumidification are effectively captured. Simultaneously, sparsity constraints prevent overfitting, significantly reducing computational complexity while maintaining model accuracy. Furthermore, the functional division of labor in the dual-impeller series structure—with the front impeller bearing the main dehumidification load and the rear impeller performing deep dehumidification—is fully considered, accurately reflecting the mass and heat transfer characteristics of the two impellers in different operating ranges. Subsequently, by organically integrating the first and second energy consumption models with the front and rear impeller models, an energy consumption calculation function covering the entire dehumidifier system is formed, comprehensively characterizing the energy flow relationship from fresh air pretreatment, impeller dehumidification to regeneration heating. The optimization phase uses the temperature setpoint of the intermediate surface cooler and the dew point setpoint of the mixed air as core adjustment parameters, and uses the energy consumption calculation function as the objective function for optimization. Essentially, it seeks the optimal operating point that minimizes the total system energy consumption while satisfying the supply air parameter constraints. The optimal solution, which incorporates the synergistic effect of the optimal setpoints for the surface cooling temperature and the mixed air dew point, enables precise control of the dual-rotor dehumidifier's operating status, avoiding energy waste caused by traditional fixed setpoint control methods. This application achieves adaptive adjustment to complex and variable operating conditions through closed-loop control of model prediction and real-time optimization, ensuring the system always operates close to its theoretical optimal operating range. By precisely matching the dehumidification load and energy input, it effectively eliminates the over-cooling and over-heating phenomena common in traditional control strategies, significantly reducing the energy consumption of the refrigeration unit and regenerative heater.

[0143] This application also provides an energy consumption optimization system for a dual-rotor dehumidifier, including:

[0144] The data acquisition module is used to acquire sensor data from several parameter sensors in the dual-rotor dehumidifier;

[0145] The first model building module is used to build a first energy consumption model for each surface cooler and a second energy consumption model for the regenerative heater based on the sensor data.

[0146] The second model building module is used to establish front and rear wheel models based on sparse nonlinear identification.

[0147] The energy consumption simulation module is used to construct an energy consumption calculation function based on the first energy consumption model, the second energy consumption model, and the front and rear wheel models;

[0148] The control module is used to take the temperature setpoint of the middle surface cooler and the dew point setpoint of the mixed air in the dual-rotor dehumidifier as adjustment parameters, take the energy consumption calculation function as the objective function, calculate the optimal solution of the objective function, and control the operation of the dual-rotor dehumidifier based on the optimal solution; the optimal solution includes the optimal setpoint of the middle surface cooler temperature and the optimal setpoint of the mixed air dew point.

[0149] This application also provides an embodiment of a computer-readable storage medium and a computer program product. Both the computer-readable storage medium and the computer program product may store a computer program that, when executed by a processor, implements the steps of the method described in the above method embodiments.

[0150] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0151] The computer-readable storage medium provided in this embodiment includes the method mentioned above, and has the same effect.

[0152] This application also provides an electronic device, see [link to document]. Figure 10 The present application provides a structural diagram of an electronic device, such as... Figure 10 As shown, it may include a processor 1410 and a memory 1420.

[0153] The processor 1410 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 1410 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 1410 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 1410 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 1410 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0154] The memory 1420 may include one or more computer-readable storage media, which may be non-transitory. The memory 1420 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 1420 is used to store at least the following computer program 1421, which, after being loaded and executed by the processor 1410, is capable of implementing the relevant steps in the methods executed by the electronic device side as disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 1420 may also include an operating system 1422 and data 1423, etc., and the storage method may be temporary storage or permanent storage. The operating system 1422 may include Windows, Linux, Android, etc.

[0155] In some embodiments, the electronic device may further include a display screen 1430, an input / output interface 1440, a communication interface 1450, a sensor 1460, a power supply 1470, and a communication bus 1480.

[0156] certainly, Figure 10 The structure of the electronic device shown does not constitute a limitation on the electronic device in the embodiments of this application. In practical applications, the electronic device may include more than [other components]. Figure 10 More or fewer components as shown, or combinations of certain components.

[0157] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. As the system provided in the embodiments corresponds to the method provided in the embodiments, the description is relatively simple; relevant parts can be found in the method section.

[0158] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

[0159] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for optimizing energy consumption in a dual-rotor dehumidifier, characterized in that, include: Acquire sensor data from several parameter sensors in the dual-rotor dehumidifier; Based on the sensor data, a first energy consumption model corresponding to each surface cooler and a second energy consumption model corresponding to the regenerative heater are constructed respectively. A front and rear wheel model was established based on sparse nonlinear identification. An energy consumption calculation function is constructed based on the first energy consumption model, the second energy consumption model, and the front and rear wheel models; The temperature setpoint of the middle surface cooler and the dew point setpoint of the mixed air in the dual-rotor dehumidifier are used as adjustment parameters, the energy consumption calculation function is used as the objective function, the optimal solution of the objective function is calculated, and the dual-rotor dehumidifier is controlled to run based on the optimal solution. The optimal solution includes the optimal setpoint for the surface cooling temperature and the optimal setpoint for the mixed air dew point; Among them, the front and rear wheel models established based on sparse nonlinear identification include: For either the front wheel model or the rear wheel model, a corresponding feature library is established; the feature library contains the input variables of the front wheel model or the rear wheel model, the quadratic combination of the input variables, trigonometric function features, and mechanistic features; the mechanistic features are the mechanistic features of the front wheel model or the rear wheel model. The sparse nonlinear recognition result matrix is ​​used to perform sparse recognition on the features contained in the feature library to obtain the front wheel model or the rear wheel model; the front wheel model or the rear wheel model contains historical data matrices of different output variables.

2. The energy consumption optimization method according to claim 1, characterized in that, Based on the sensor data, the first energy consumption model corresponding to each surface cooler is constructed as follows: The front air temperature, fresh air humidity, fresh air volume, front surface cooling temperature and front surface cooling dew point are used as inputs, and the front surface cooling capacity is used as output to construct the front surface cooling model in the first energy consumption model. Using the mixed air temperature, mixed air dew point, mixed air volume, middle surface cooling temperature and middle surface cooling dew point as inputs, and the middle surface cooling capacity as output, the middle surface cooling model in the first energy consumption model is constructed. Using the rear rotor temperature, rear rotor dew point, supply air temperature, supply air dew point, and supply air volume as inputs, and the rear surface cooling capacity as output, the rear surface cooling model in the first energy consumption model is constructed.

3. The energy consumption optimization method according to claim 2, characterized in that, The calculation process for the cooling capacity corresponding to the front surface cooling, the middle surface cooling, and the rear surface cooling includes: The cooling capacity is calculated based on the air mass flow rate, the first air enthalpy value when entering the surface cooler, the second air enthalpy value when exiting the surface cooler, the air density, and the air volumetric flow rate.

4. The energy consumption optimization method according to claim 1, characterized in that, Before calling the result matrix of sparse nonlinear recognition to perform sparse recognition on the features contained in the feature library, the method further includes: Set a sparsity threshold based on the prediction target; The sequential least squares regression method is used to iteratively calculate the coefficients of each prediction target. If the absolute value of the coefficient is less than the sparsity threshold, the corresponding prediction target will not participate in the next round of calculation until the result converges to obtain the sparse coefficient matrix. Accordingly, the result matrix of sparse nonlinear identification is called to perform sparse identification on the features contained in the feature library, resulting in historical data matrices of different output variables, including: The sparse coefficient matrix is ​​used to perform sparse identification on the features contained in the feature library, and the predicted target at each time step is calculated.

5. The energy consumption optimization method according to claim 1, characterized in that, If the first energy consumption model includes a front surface cooling model, a middle surface cooling model, and a rear surface cooling model, the second energy consumption model includes a front regeneration model and a rear regeneration model, and the front and rear impeller models include a front impeller model and a rear impeller model, then constructing an energy consumption calculation function based on the first energy consumption model, the second energy consumption model, and the front and rear impeller models includes: The fresh air temperature, fresh air humidity, and fresh air volume are used as inputs to the front surface cooling model, and the front surface cooling temperature, front surface cooling dew point, and front surface cooling capacity are output through the front surface cooling model. The fresh air volume, the front surface cooling temperature and the front surface cooling dew point output by the front surface cooling model are used as inputs to the front impeller model, and the front impeller rear temperature and front impeller rear dew point are output through the front impeller model. The mixed air temperature, mixed air dew point, and mixed air volume are calculated based on the fresh air volume, the temperature behind the front impeller, the dew point behind the front impeller, the return air temperature, the return air dew point, and the return air volume. The mixed air temperature, the mixed air dew point, and the mixed air volume are used as inputs to the middle surface cooling model, and the middle surface cooling temperature, the middle surface cooling dew point, and the cooling capacity of the middle surface cooling are output through the middle surface cooling model. The mixed air temperature, the mixed air dew point, and the regenerated air volume are used as inputs to the post-regeneration model, and the post-regeneration energy consumption, post-regeneration temperature, and post-regeneration dew point are output through the post-regeneration model. The middle surface cooling temperature, the middle surface cooling dew point, and the air supply volume are used as inputs to the rear impeller model, and the rear impeller dew point and rear impeller temperature are output by the rear impeller model. The dew point behind the rear impeller, the temperature behind the rear impeller, and the air supply volume are used as inputs to the rear surface cooling model, and the air supply temperature, air supply dew point, and cooling capacity of the rear surface cooling model are output. The post-regeneration temperature and the post-regeneration dew point are used as inputs to the post-rotor model, and the post-exhaust air temperature and the post-exhaust air dew point are output by the post-rotor model. The rear exhaust air temperature, the rear exhaust air dew point, and the regeneration air volume are used as inputs to the front regeneration model, and the front regeneration model outputs the front regeneration energy consumption, the front regeneration temperature, and the front regeneration dew point. The front regeneration temperature and the front regeneration dew point are used as inputs to the front impeller model, and the front exhaust air temperature and the front exhaust air dew point are output by the front impeller model. The total energy consumption of the energy consumption calculation function is the sum of the energy consumption of the front surface cooling model, the middle surface cooling model, the rear surface cooling model, the front regeneration model, and the rear regeneration model; the independent variables of the energy consumption calculation function are the middle surface cooling temperature and the mixed wind dew point.

6. An energy consumption optimization system for a dual-rotor dehumidifier, characterized in that, include: The data acquisition module is used to acquire sensor data from several parameter sensors in the dual-rotor dehumidifier; The first model building module is used to build a first energy consumption model for each surface cooler and a second energy consumption model for the regenerative heater based on the sensor data. The second model building module is used to establish front and rear wheel models based on sparse nonlinear identification. The energy consumption simulation module is used to construct an energy consumption calculation function based on the first energy consumption model, the second energy consumption model, and the front and rear wheel models; The control module is used to take the temperature setpoint of the middle surface cooler and the dew point setpoint of the mixed air in the dual-rotor dehumidifier as adjustment parameters, take the energy consumption calculation function as the objective function, calculate the optimal solution of the objective function, and control the operation of the dual-rotor dehumidifier based on the optimal solution; the optimal solution includes the optimal setpoint of the middle surface cooler temperature and the optimal setpoint of the mixed air dew point. The second model building module is a module used to perform the following steps: For either the front wheel model or the rear wheel model, a corresponding feature library is established; the feature library contains the input variables of the front wheel model or the rear wheel model, the quadratic combination of the input variables, trigonometric function features, and mechanistic features; the mechanistic features are the mechanistic features of the front wheel model or the rear wheel model. The sparse nonlinear recognition result matrix is ​​used to perform sparse recognition on the features contained in the feature library to obtain the front wheel model or the rear wheel model; the front wheel model or the rear wheel model contains historical data matrices of different output variables.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the steps of the method as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, Includes a computer program, which, when executed, implements the steps of the method as described in any one of claims 1 to 5.

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