Method and system for setting parameters of a processing cycle

Abstract

A method and system for setting parameters of a processing cycle in a home appliance are provided. First, a model is obtained that establishes a relationship between a set of configuration parameter values ​​of achievable processing performance. Next, a target processing performance for a processing cycle is obtained. Values ​​for a subset of configuration parameters are fixed, and the subset includes at most all configuration parameters, excluding the remaining configuration parameters. Next, values ​​for the remaining configuration parameters are determined. For these values, the difference between the target processing performance and the achievable processing performance predicted by the model using the values ​​is less than a predetermined threshold. Finally, parameters of a processing cycle, which may be setting parameters of a processing cycle in a home appliance, are output. The parameters include determined values ​​and fixed values.

Description

Technology Field

[0001] The present invention relates to the field of a method for parameterizing a processing cycle in a home appliance, for example, and to a system that enables the customization of configuration parameters set in a home appliance to perform a processing cycle. This may relate to the selection of an optimal combination of parameters, for example, to perform a washing cycle in a washing machine or dishwasher, to perform a drying cycle in a dryer, or to perform ironing of fabrics according to the target performance to be achieved by the user. Background Technology

[0002] Home appliances, such as washing machines, dishwashers, dryers, and irons, typically include several preset programs. Users can select the most suitable program to perform a processing cycle for items to be washed, dried, or ironed. Despite the increasing number of available programs or preset settings, users are provided with fixed values ​​for parameters based on assumptions made by the product manufacturer regarding the results of the processing cycle, or users are required to set all parameters individually without the technical expertise necessary to understand whether their selection is correct.

[0003] A processing cycle, such as a washing cycle in a washing machine or dishwasher, can be parameterized using a very complex combination of parameters, such as temperature, the length of the washing cycle, and the amount of detergent dispensed. Not all of these parameters, as well as other parameters that are not necessarily controllable, such as water hardness or drum rotation, are independent. Typically, changing one of these parameters can affect the effect of other parameters on the result of the processing cycle.

[0004] In 1959, Dr. Herbert Sinner noted that the mechanical properties of household appliances, the duration of the cleaning cycle, the maximum temperature reached during the cleaning cycle, and the characteristics of the chemicals distributed during the cleaning cycle are all related parameters. A variation in one will lead to a different effect on the cleaning performance of all other parameters.

[0005] To enable the user to achieve target performance, such as obtaining the cleanest items or performing the most ecological processing cycles while still providing an acceptable level of cleanliness, a special pre-recorded program is typically stored in the appliance's memory. This program sets the appliance's processing parameters according to standard settings, which do not adequately reflect the user's specific needs.

[0006] Today, the complexity of the interdependence of configuration parameters in processing cycles in home appliances is not fully understood. An attempt to model the behavior of a washing machine using mathematical functions was made in 2015. This is, for example, the paper [" Construction of Virtual Washing Machine It is explained in [Tenside Surf. Det. 52 (2015) page 193 by Emir Lasic, Rainer Stamminger, Christian Nitsch and Arnd Kessler].

[0007] It appears that the selection of parameters for the processing cycles performed on items is still largely based on inaccurate assumptions regarding processing performance achievable manually.

[0008] For the above reasons, a method and system for better setting of processing parameters of a processing cycle in home appliances are sought.

[0009] Summary of the Invention

[0010] To address the aforementioned need, the present invention provides a method for setting parameters of a processing cycle in a home appliance, and such a method is:

[0011] - A step of obtaining a model that establishes a relationship between reference configuration parameter values ​​of different sets of processing cycles and values ​​of achievable processing performance;

[0012] - A step of achieving target processing performance for a processing cycle;

[0013] - A step of obtaining a fixed value for a subset of at least one configuration parameter from a reference configuration parameter of a processing cycle — said subset of at least one configuration parameter includes at most all configuration parameters, excluding at least one remaining configuration parameter from the reference configuration parameter —;

[0014] - A step of determining a value for at least one remaining configuration parameter such that the difference between the target processing performance and the achievable processing performance predicted by the model using the value is less than a predetermined threshold;

[0015] - Step of outputting parameters of a processing cycle ― The parameters of the processing cycle can be used as setting parameters of a processing cycle in a home appliance, and the parameters include a determined value for at least one remaining configuration parameter and a fixed value for a subset of at least one configuration parameter ―

[0016] Includes

[0017] The present invention provides a method for setting parameters of a processing cycle of a home appliance in a customized manner and according to the goals that the user wishes to achieve through the processing cycle.

[0018] The term "processing cycle" refers to washing, cleaning, drying, or ironing performed on an item to be processed by a home appliance.

[0019] Among possible examples of household appliances to which the method of the present invention may be applied, washing machines, dishwashers, irons, and dryers are particularly suitable machines. However, the concept of household appliances should not be limited to these machines only. The method of the present invention may be applied to any processing cycle performed by any material means, including, for example, washing fabrics in a water bath without mechanical parts.

[0020] The term "processing performance" includes goals that can be set by the user or programmed, for example, into the memory of the data processing unit. Examples of processing performance are provided below and typically include: maximum cleanliness of the items to be cleaned (dishes or fabrics), minimization of the number of stains or contaminants on the items to be cleaned, minimization of the amount of electricity or water used during the processing cycle, minimization of noise generated by the appliance during the processing cycle, and minimization of CO2 emissions generated during the processing cycle. Processing performance may typically be expressed in any unit indicating the degree to which the goals are achieved. In the case of cleanliness, this may be any number or grade, a grade provided from previous experiments performed on the items to be processed, or a value predicted based on an understanding of the processing cycle in the appliance.

[0021] The term "reference configuration parameter" refers to a parameter provided for or used to create a model that can predict a value for processing performance based on a combination or set of configuration parameters. The term "configuration parameter" refers to a physical or technical parameter that can be used to characterize a processing cycle. In the example of a washing machine, these may include parameters established, for example, by Herbert Sinner (temperature, the detergent used and its amount, the duration of the cycle, and the mechanical properties of the machine's drum). Additional parameters may be considered.

[0022] The "model" may typically include a list of distinct values ​​for different sets of configuration parameters and associated processing performance values. It may also include a function that includes a continuous extrapolation correspondence between the values ​​of the set of configuration parameters and the associated measurements, predictions, or extrapolated processing performance values.

[0023] The present invention can determine some values ​​of processing parameters from sensors or from general information about the environment in which the appliance is used. Such values ​​may include, for example, the water hardness of the water used in the processing cycle, the revolutions per minute of the appliance's drum, or at least values ​​technically achievable by the appliance. Others, such as the temperature or duration of the processing cycle, may be fixed by the user.

[0024] Next, the values ​​of the remaining non-fixed configuration parameters can be predicted from the model based on the goal selected by the user. These non-fixed configuration parameters may include, for example, the amount of cleaning agent to be applied.

[0025] In addition to providing suggestions for non-fixed configuration parameters, the model can also assist the user in changing some of the fixed parameters if it appears that one small variation of these will provide better achievement of the target processing performance.

[0026] According to one embodiment, the method is:

[0027] - Obtain the value of the processing performance achieved for a different set of reference configuration parameter values ​​of the previous processing cycle;

[0028] - Determining a function that fits the value of the acquired processing performance — said function establishes a relationship between different sets of configuration parameter values ​​and the value of the acquired processing performance, where the different sets of configuration parameters are variables of the function, and the value of the acquired processing performance is an approximation of the function's image —

[0029] - Obtaining a model in the form of a function by

[0030] It can additionally include.

[0031] According to one embodiment, the function is a multidimensional function including at least four variables.

[0032] Advantageously, at least four variables are the maximum temperature applied during the cleaning cycle, the number of rotations of the washing machine drum (e.g., per minute), the amount of detergent used, and the duration of the cleaning cycle. Additional parameters, such as the weight of the load to be cleaned, a value indicating the contamination of the load, and a value indicating the water hardness used may be added.

[0033] According to one embodiment, the function may be a polynomial function.

[0034] According to one embodiment, the model can be determined using an analysis of variance approach.

[0035] Alternatively, other methods may be used to obtain a model of the function based on a limited set of experimental datapoints. In particular, the present invention may use different linear regression approaches, machine learning approaches, or manual approximations. An automated approach is preferred because the complexity of the data to be fitted makes any manual estimation of the function, which can be used as a model to predict achievable processing performance values, difficult. Experimental design is a common approach used to determine the model. Preferred designs for the experimental approach include the I-optimization approach and response surface methodologies.

[0036] According to one embodiment, a different set of reference configuration parameters is:

[0037] - Load of items introduced inside the home appliance;

[0038] - Temperature inside the processing chamber of the home appliance;

[0039] - The number of rotations per second of a rotating element in a home appliance;

[0040] - The value of the water hardness of the water used by the appliance during the treatment cycle;

[0041] - Value of contamination of the contaminant on the item to be treated;

[0042] - Duration of the processing cycle;

[0043] - Amount of the type of cleaning agent

[0044] It may include at least two of them.

[0045] According to one embodiment, the achievable processing performance is:

[0046] - Cleanliness value of the processing cycle;

[0047] - Number of contaminants remaining on the item to be treated;

[0048] - A value indicating the amount of water used during the treatment cycle;

[0049] - A value indicating the amount of energy consumed during the processing cycle;

[0050] - A value representing greenhouse gas emissions generated during the treatment cycle;

[0051] - Duration of the processing cycle;

[0052] - A value representing noise generated by the appliance during the processing cycle;

[0053] - Temperature reached in the processing chamber of the appliance during the processing cycle;

[0054] - Revolutions per minute of the appliance drum during the processing cycle

[0055] It may include at least one of the following.

[0056] According to one embodiment, at least one remaining configuration parameter includes at least two remaining configuration parameters, and the method is:

[0057] - Based on the acquired model, determining one selected configuration parameter having a value from at least two remaining configuration parameters such that the difference between the target processing performance and the achievable processing performance predicted by the model using the value of the selected configuration parameter is minimized.

[0058] It can additionally include.

[0059] According to one embodiment, the method is:

[0060] - A group of values ​​for at least one remaining configuration parameter is determined such that the difference between the target processing performance and the achievable processing performance predicted by the model using the group of values ​​is less than a predetermined threshold;

[0061] - Outputting a list of proposed configuration parameters of a processing cycle — said processing cycle parameters are adapted to set processing parameters of a home appliance using a group of determined values ​​for at least one remaining configuration parameter and fixed values ​​for a subset of at least one configuration parameter —

[0062] It can additionally include.

[0063] The present invention can be further utilized by using a method to set configuration parameters of a home appliance.

[0064] In particular, the use of this method is:

[0065] - Executing a processing cycle with configuration parameters set for the home appliance

[0066] It may include.

[0067] The present invention also relates to a system for setting parameters of a processing cycle in a home appliance, and such a system is:

[0068] - Can communicate with storage media and:

[0069] - Acquire and save a model that establishes the relationship between reference configuration parameter values ​​of different sets of processing cycles and values ​​of achievable processing performance;

[0070] - Achieve target processing performance for the processing cycle;

[0071] - Obtain a fixed value for a subset of at least one configuration parameter from a reference configuration parameter of a processing cycle — said subset of at least one configuration parameter includes at most all configuration parameters, excluding at least one remaining configuration parameter from the reference configuration parameter —;

[0072] - Determine the value for at least one remaining configuration parameter such that the difference between the target processing performance and the achievable processing performance predicted by the model using the said value is less than a predetermined threshold.

[0073] A data processing device configured;

[0074] - A home appliance capable of receiving instructions from a data processing unit and receiving parameters of a processing cycle in the form of a determined value for at least one remaining configuration parameter and a fixed value for a subset of at least one configuration parameter.

[0075] Includes

[0076] The aforementioned system is typically suitable for implementing the aforementioned method.

[0077] According to one embodiment, the system is:

[0078] - At least one sensor for determining at least one value from a subset of configuration parameters

[0079] It may additionally include.

[0080] More than one sensor may be used, some of which are arranged on the appliance, while others may also be standalone sensors or sensors that are part of another device interacting with the appliance, and such devices distribute the cleaning agent within the appliance.

[0081] According to one embodiment, the system is:

[0082] - A dispensing device disposed inside the processing chamber of a home appliance and configured to dispense at least one cleaning agent into the processing chamber

[0083] It may additionally include.

[0084] According to one embodiment, the dispensing device may be configured to dispense an adjustable amount of at least one cleaning agent at an adjustable time of the treatment cycle, and the adjustable amount and the adjustable time are additional configuration parameters of the treatment cycle.

[0085] The method of the present invention may be further implemented by a computer. Consequently, the present invention also includes a computer program product suitable for implementing the steps of the aforementioned method.

[0086] The present invention also relates to a non-transient computer-readable storage medium storing a computer program comprising instructions for executing a method for setting parameters of a processing cycle in a home appliance, said instructions being:

[0087] - Access a model that establishes a relationship between reference configuration parameter values ​​of different sets of processing cycles and values ​​of achievable processing performance;

[0088] - Receive target processing performance for the processing cycle;

[0089] - Obtain a fixed value for a subset of at least one configuration parameter from a reference configuration parameter of a processing cycle — said subset of at least one configuration parameter includes at most all configuration parameters, excluding at least one remaining configuration parameter from the reference configuration parameter —;

[0090] - Determine the value for at least one remaining configuration parameter such that the difference between the target processing performance and the achievable processing performance predicted by the model using the said value is minimized;

[0091] - Outputting parameters of a processing cycle — said parameters are configured to control the processing cycle of a home appliance using a determined value for at least one remaining configuration parameter and a fixed value for a subset of at least one configuration parameter —

[0092] Includes Brief explanation of the drawing

[0093] Hereinafter, the present disclosure will be described together with the following drawings, in which similar numbers indicate similar elements. Figure 1 is a schematic diagram of a Sinner diagram illustrating the interdependence between seven configuration parameters in the washing cycle of a washing machine. FIG. 2 is a simplified flowchart of a method according to an exemplary embodiment. FIG. 3 is a schematic diagram of one possible group of elements forming part of a system usable to implement the method of the present invention. Figure 4 is a contour plot showing achievable performance parameter values ​​for different loads of fabric and different amounts of detergent distributed during washing cycles in a washing machine. Figure 5 is another contour plot illustrating achievable performance parameter values ​​for different loads of fabric and different amounts of detergent distributed during washing cycles in a washing machine. FIG. 6 is a third contour plot illustrating achievable performance parameter values ​​for different loads of fabric and different amounts of detergent distributed during washing cycles in a washing machine according to another exemplary description of the present invention. FIG. 7 is a fourth contour plot illustrating achievable performance parameter values ​​for different loads of fabric and different amounts of detergent distributed during washing cycles in a washing machine according to the exemplary description of the present invention shown in FIG. 6. Figure 8 is a two-dimensional graph showing the energy consumption of a washing machine as a function of temperature during the processing cycle of the machine under fixed conditions for water hardness, load, amount of detergent used, contamination of the introduced item, and rotation of the drum per minute. Specific details for implementing the invention

[0094] details

[0095] The present invention provides a method for determining the optimal combination of parameters for executing a processing cycle, particularly in household appliances. Additionally, the method of the present invention may be considered for local application in any device capable of accommodating an article to be processed. A bowl containing detergent, which has no mechanical parts filled with water and is used for hand washing or to leave fabric within the bowl without applying mechanical effort, may also be considered as a household appliance within the sense of the present invention. However, the simplest application of this method is determining the accurate and most appropriate settings in a machine, such as a washing machine, dishwasher, dryer, or iron.

[0096] The method relies on a model capable of predicting the outcome of a processing cycle when a specific set of configuration parameters is selected to execute the processing cycle. This ability to predict the outcome enables finding suitable values ​​for the configuration parameters of the processing cycle to achieve the desired processing performance. In particular, the present invention may utilize historical measurements of such processing cycles and generate a function that processes configuration parameters as input and provides a value for processing performance as output. This function is a multidimensional function that typically has at least two, and sometimes more than seven, variables as inputs.

[0097] As described in FIG. 1, processing performance (110), such as obtaining a clean fabric, can be set as a goal or objective to be achieved. The parameters of the washing cycle, particularly those that can be selected or programmed in the washing machine, appear to be interdependent. A change in one parameter may lead to the need to modify another parameter to maintain the same value of the fabric's cleanliness at the end of the washing cycle.

[0098] The processing parameters are also referred to as configuration parameters (101-108) and may include, typically, the load of the fabric inserted into the washing machine, the amount of added detergent, the duration of the washing cycle, the temperature setting for the washing process, mechanical properties of the washing machine such as the number of revolutions per minute of the washing machine, water hardness, the degree of soiling of the fabric to be cleaned, and the type of fabric.

[0099] Similar interdependence graphs can be generated for other processing cycles for dishwashers, irons, or dryers. For clarity, the following example will be provided in relation to the determination of the correct combination of configuration parameters (101-108) in a washing machine.

[0100] For the purpose of explanation, FIG. 2 provides a flowchart of the steps occurring in the method (210) of the present invention to set parameters of the processing cycle in a home appliance.

[0101] First, a model (201) is obtained that establishes a relationship between a set of configuration parameters considered as input to the model and a value of achievable processing performance considered as output to the model.

[0102] The model (201) can be obtained in different ways. It can be generated based on linear interpolation of different data points. The data points are formed by a list of values ​​for a set of configuration parameters, for example, from past knowledge of a cleaning cycle or past measurements taken for a similar cleaning cycle. Each set of configuration parameters is associated with a processing performance value.

[0103] Configuration parameters (101-108) typically include: the load of the article introduced into the appliance, the temperature inside the appliance's processing chamber, the number of rotations per second of the appliance's rotating element, the value of the water hardness of the water used by the appliance during the processing cycle, the value of the contamination of the contaminant on the article to be processed, the duration of the processing cycle, and the amount of the type of cleaning agent. Fewer or more of these parameters may be considered. For example, it is possible to include only two of the parameters listed above in a washing machine. The model may also be generated based on more sophisticated regression of data points, for example, using the least squares method.

[0104] Manual interpolation of the graphical representation of data points in coordinate space can also be performed.

[0105] Other analytical methods may be used to fit a set of data points to a mathematical function. In particular, an "experimental design" approach can be used to fit a set of distinct data points to a mathematical function. This experimental design approach can be assimilated into an analysis of variance approach. Both approaches are widely known from the prior art.

[0106] For example, methods including randomized experiments, optimal design, and response surface methodologies may be used.

[0107] Desirable approaches include I-optimal design and response surface methods. Bayesian optimization of the data set can be additionally used in the dynamic design of the experimental approach.

[0108] The model generated by this approach can advantageously be a polynomial function involving multiple variables or any other function capable of accurately reproducing the dependency of multiple variables.

[0109] A polynomial function of multiple variables can be used advantageously as follows:

[0110]

[0111] Here, f is a polynomial function of M variables x1 to xM, N is the degree of the polynomial function, and a(k s ) is a coefficient confirmed by the design of the experimental approach.

[0112] Examples of such functions are further provided below in one exemplary embodiment of the present invention.

[0113] This function, in particular, the coefficient a(k s Once the value of ) is determined, the interdependencies of all configuration parameters for the processing cycle are known.

[0114] It should be noted that a function f can be defined for combinations of different contaminant types to be cleaned, or a single function can be defined individually for each contaminant type. If a general function f for all combinations of contaminant types is known, a reduced function can be extracted for each contaminant type by removing a portion of the experimental data points corresponding to the contribution of unrelated contaminant types.

[0115] The function f may include interdependence of constituent parameters regardless of the type of appliance used. In fact, the appliance itself is not even a relevant machine for the application of the teachings of the present invention. As mentioned above, in any processing context, even washing a fabric by hand in a bowl of water containing detergent can be considered assimilated into the "appliance."

[0116] Despite the high level of generalization provided by function f and its ability to predict the results of a processing process regardless of the appliance used, function f can provide much more accurate results if established based on data related to the type of appliance. For example, the inclusion of the drum's revolutions per minute in a washing machine is a parameter that can influence processing performance values ​​and is more appliance-specific. Therefore, function f can also be reduced or determined as a function specific to the appliance.

[0117] Model (201), for example, when the aforementioned polynomial function is obtained or determined, a target processing performance (202) is set. This target is advantageously set qualitatively or quantitatively. A qualitative target value may be set as a general goal, for example, to obtain the cleanest fabric. A qualitative goal may include more than one additional criterion, for example, a limit on some other parameter, such as a method to reach maximum cleanliness while consuming less than X grams of detergent, or a method to obtain cleaning performance exceeding a value Y while minimizing energy consumption. To determine the combination of constituent parameters that enable the achievement of such qualitative target processing performance, a mathematical analysis of the variation of the function f and the image may be introduced.

[0118] Alternatively, the target processing performance (202) may be defined as a quantitative value. This may be, for example, the amount of energy that is not overcome, the noise level that is not reached during the processing cycle, the maximum temperature at which the processing cycle must occur, the maximum amount of CO2 generated during the processing cycle, or the maximum number of contaminants allowed on the article to be processed.

[0119] Examples of achievable treatment performance include: a cleanliness value of the treatment cycle; the number of contaminants remaining on the item to be treated; a value indicating the amount of water used during the treatment cycle; a value indicating the amount of energy consumed during the treatment cycle; a value indicating greenhouse gas emissions generated during the treatment cycle; the duration of the treatment cycle; a value indicating the noise generated by the appliance during the treatment cycle; the temperature reached within the appliance's treatment chamber during the treatment cycle; and the number of revolutions per minute of the appliance's drum during the treatment cycle.

[0120] According to a basic embodiment of the present invention, the configuration parameters (101-108) of the model are not fixed, and the values ​​of each configuration parameter (101-108) are determined by studying the variation of the model.

[0121] A set of values ​​for configuration parameters (101-108) is considered to satisfy the target processing performance (202) when the difference between the value of the prediction output by the model using these values ​​and the value of the target processing performance (202) is less than a predetermined threshold, indicated by epsilon in FIG. 2.

[0122] This predetermined threshold can be set, for example, as a value within 25% of the best achievable processing performance value according to the model's prediction. The threshold can also be set by a user who can determine an allowable margin within the target performance defined by him / her. A simple approach may consist of determining the extrema of the function f corresponding to the best achievable processing performance. The corresponding values ​​for the configuration parameters are then defined as settings for the processing cycles.

[0123] According to different and more frequent embodiments, a subset (203) of the configuration parameters (101-108) has fixed values. These values ​​are fixed by the user, by the technical capabilities of the appliance, or by other external factors. This may be, for example, the case of water hardness, which may have a fixed value depending on the location where the processing cycle occurs. Other remaining configuration parameters, such as the revolutions per minute of the appliance's drum, may be limited to a number of values ​​technically accessible by the appliance, or may be limited to a finite number of distinct values. This will subsequently reduce the space of values ​​that need to be analyzed for the function f to determine the values ​​of the remaining configuration parameters.

[0124] Some of the values ​​from the subset (203) may be measured by a sensor that is part of the appliance or by a standalone sensor. An example of a sensor includes a water hardness measuring device. Water hardness may be measured, for example, by a calibrated electrode that measures the conductivity of water, or by an optical sensor that measures the turbidity of water which may be related to its hardness. Other sensors are used to measure the temperature inside a drum or housing in which the article to be processed is placed inside the appliance, or the rotational speed per minute of the product's drum.

[0125] The values ​​of the remaining configuration parameters (204) are determined using a model. As illustrated in FIG. 2, the function f is used by trying different values ​​of the remaining configuration parameters (204) until a value is found where the difference in the output of the achievable processing performance predicted by the function is sufficiently close to the target processing performance value (202). The allowable difference must be lower than the previously mentioned predetermined threshold.

[0126] Finally, a setting parameter (205) to be used for a processing cycle may be output and, for example, set on a home appliance to execute a processing cycle. The setting parameter (205) consists of a fixed value of a subset (203) of the configuration parameters and the value of the remaining configuration parameters (204) determined using a prediction from a model.

[0127] In the example described in FIG. 2, the remaining configuration parameter (204) consists of the load of the item to be cleaned and the amount of detergent to be used in the appliance. However, it should be noted that other remaining configuration parameters may be used. For example, the timing of the release of detergent into the housing of the appliance may be the remaining configuration parameter (204), or the temperature of the water in the housing, or the duration of the treatment cycle.

[0128] In addition to the embodiments presented for the purpose of the above description, the present invention may further utilize the mathematical properties of the acquired model to determine the most suitable processing setting (205). For example, the extrema of the function f may be determined by calculating the partial derivatives of this function.

[0129] A graphical representation of a function in a contour plot can also be used to display to the user, for example, the full range of possible values ​​to reach a target processing performance. To obtain a complete graphical representation of the variation of the function f, several contour plots can be plotted in which all constituent parameters except two are fixed.

[0130] The present invention also relates to a system capable of implementing the method described above. An embodiment of an element of such a system is schematically illustrated in FIG. 3. The system (3) may include a home appliance (100) configured to receive the output of the method of the present invention, i.e., to ultimately perform a processing cycle, i.e., a processing setting (205). A sensor (310) may be located within the home appliance or elsewhere on a standalone dispensing unit (300), or may be provided separately. The sensor retains any feature of the system (3). A data processing unit (311) is configured to acquire or establish a model based on measurements provided to the data processing unit (311). A self-learning approach may be used in which each processing cycle supplies data points containing all used values ​​for configuration parameters during the processing cycle to a memory (312) and provides values ​​for the processing performance thus acquired. Some of the values ​​may be input into the memory (312) by a user. The data processing unit (311) may access the data stored in the memory (312) to perform model estimation.

[0131] In the example described in FIG. 3, the data processing unit (311) is part of the home appliance (100). However, any computing device, for example, a mobile device (200) in which all relevant information required to establish a model based on data as well as acquired or measured data is available, may be used instead. A computer or server may also be used.

[0132] The data processing unit (311) can typically exchange information with the home appliance (100), and accordingly, the data processing unit can transmit a processing setting (205) to the home appliance (100) to parameterize the next processing cycle.

[0133] To display some information to the user, the display on the home appliance may allow, for example, the manual selection of values ​​for configuration parameters. The proposed settings may be displayed, for example, when target processing performance is selected by the user.

[0134] Alternatively, all displays and selections may occur on a remote device, such as a mobile device (200), which can subsequently establish communication with the home appliance.

[0135] Optionally, the system may include a dispensing device (300). The dispensing device is configured to be placed inside the housing of the appliance, for example, in the drum in the case of a washing machine. Then, a cleaning agent can be dispensed at any programmable time of the processing cycle. Then, the amount of the dispensed cleaning agent as well as the timing of such dispensing can serve as additional configuration parameters for the processing cycle to further improve the achievable processing performance.

[0136] Examples

[0137] The present invention will be further described using some of the embodiments provided below.

[0138] In this first embodiment, a model for a processing cycle on a washing machine having seven configuration parameters provided in Table 1 below is obtained:

[0139]

[0140] Table 1

[0141] As a processing performance value, the degree of cleanliness when washing a mixture of 20 different types of stains is selected. The performance rating is provided as a score in arbitrary units: the higher the score, the better the cleaning performance for the mixture of stains.

[0142] The design of the aforementioned experimental approach leads to the determination of the following polynomial function that best describes the relationship between the seven parameters and achievable processing performance:

[0143]

[0144]

[0145] Here, term a k The values ​​for the coefficients are provided in Table 2 below.

[0146]

[0147]

[0148]

[0149] Table 2

[0150] The following configuration parameters were fixed by the user or determined via sensors.

[0151] x3=40℃; x4=14 dH; x5=50 revolutions / min; x6=3.25; x7=90 min

[0152] Consequently, a contour plot is shown in FIG. 4 displaying the achievable processing parameter value (43) as a function of the amount of detergent on the x-axis (20) and the load of the item on the y-axis (10). This contour plot illustrates that a small amount of detergent is not efficient for cleaning a large amount of items in a washing machine. In fact, the value of the achievable processing parameter is low at the upper left corner (41) of the contour plot in FIG. 4. Therefore, since the highest value for the achievable processing parameter is found at the lower right corner (42) of the contour plot in FIG. 4, it is recommended to administer a larger amount of detergent and reduce the amount of items to be cleaned.

[0153] These contour plots are particularly useful for finding the optimal combination of the amount of detergent used and the load of the introduced article. The dependence of these two factors is not linear, as illustrated by the curvature of the straight contour lines for the contour plot in Fig. 4. One interesting lesson may be that for 4 kg of fabric to be washed, at least 15 grams of detergent must be dispensed to achieve an acceptablely high level of cleanliness of the article after the washing cycle.

[0154] FIG. 5 illustrates how the contour plot of FIG. 4 changes when the values ​​of some of the fixed parameters change. In the contour plot of FIG. 5, the value of the achievable cleanliness (53) of the fabric is shown as a function of the amount of cleaning agent administered on the x-axis (20) and the load of the article on the y-axis (10). Other configuration parameters are fixed as follows:

[0155] x3=40℃; x4=14 dH; x5=39 revolutions / min; x6=3.25; x7=120 min.

[0156] In other words, the duration of the cleaning cycle increases, and the drum's rotational speed per minute decreases.

[0157] As illustrated by the contour lines of the contour plot in FIG. 5, this leads to different dependencies of x1 and x2. In particular, 4 kg of fabric now requires only 10 grams of the same detergent to reach a sufficiently high cleanliness value. For a 5 kg load, a change in the dosage of detergent from 15 g to 24 g leads to an increase in the cleanliness value of about 3%. The upper left corner of the contour plot in FIG. 5 contains the lowest achievable cleanliness value (51), and the lower right corner of the contour plot in FIG. 5 contains the highest achievable cleanliness value (53).

[0158] Figure 6 is another contour plot obtained for a difference model simulating the achievable cleanliness performance when removing lipstick stains on fabric.

[0159] Similar to FIGS. 4 and 5, a contour plot showing the cleanliness result (63) for removing lipstick stains is plotted as a function of the amount of cleaning agent introduced on the x-axis (20) and the load of the fabric on the y-axis (10). Other configuration parameters are fixed as follows:

[0160] x3=40℃; x4=14 dH; x5=50 revolutions / min; x6=3.25; x7=90 min

[0161] The contour plot of FIG. 6 illustrates that the overall achievable processing performance is somewhat poor. The upper left corner (61) of the contour plot corresponds to a particularly low achievable processing performance value. The lower right corner (62) of the contour plot of FIG. 6, which includes the highest achievable processing performance value under these conditions, is still hardly acceptable.

[0162] To resolve this situation, the duration of the cleaning cycle is increased from 90 minutes to 120 minutes, as well as the temperature in the machine drum is increased from 40°C to 48°C. The stain contamination is reduced from 3.25 to 1.

[0163] These variations lead to the contour plot of Fig. 7, which illustrates a much more satisfactory achievable cleanliness result (73) compared to the case of Fig. 6. The upper left corner (71) of this contour plot contains a lower achievable treatment performance value, similar to the highest value in Fig. 5. The contour lines of Fig. 7 indicate greater flexibility in increasing the value of the cleanliness result by increasing the amount of detergent used. The value obtained at the lower right corner (72) of this contour plot is 20% higher than the value at the upper left corner (71).

[0164] The above embodiment specifically focuses on the cleanliness value as the processing performance value, but other processing performances may be selected. In particular, the goal may also be to minimize the energy used to perform the processing cycle.

[0165] Figure 8 illustrates a two-dimensional graphical representation of the function f obtained when six of the configuration parameters are fixed.

[0166] x1=15g; x2= 5 kg; x4=14 dH; x5=50 revolutions / min; x6=3.25; x7=90 min

[0167] Accordingly, the graph of FIG. 8 shows the effect of temperature x3 shown on the horizontal axis (82) on energy consumption shown on the vertical axis (81). To consume less than 900 Wh of energy, the graph of FIG. 8 teaches that the temperature must be set to less than 37℃.

[0168] The steps of the above-described embodiments and modes of embodiment may be implemented by a processor, such as a computer. A computer program product comprising the steps of the above-described method may be used to implement the method on a computer.

[0169] It is possible to store a computer program containing instructions for implementing the method of the present invention on different non-transient computer-readable storage media. These may include, for example, a processor or chip, an FPGA (field programmable gate array), an electronic circuit comprising several processors or chips, a hard drive, a flash or SD card, a USB stick, a CD-ROM or DVD-ROM or Blu-ray disc, or a floppy disk.

[0170] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that a vast number of variations exist. Furthermore, it should be understood that the exemplary embodiments or exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of the various embodiments in any manner. Rather, the foregoing detailed description will provide a convenient roadmap for implementing the exemplary embodiments considered herein to those skilled in the art. It is understood that various changes may be made to the function and arrangement of the elements described in the exemplary embodiments without departing from the scope of the various embodiments described in the appended claims.

Claims

Claim 1 - A step of obtaining a model (201) that establishes a relationship between the values ​​of reference configuration parameters (101-108) of different sets of processing cycles and the values ​​of achievable processing performance (110) - said model (201) includes a multidimensional function that includes at least four configuration parameters as input -; - A step of obtaining a target processing performance (202) for a processing cycle; - A step of obtaining a fixed value for a subset (203) of at least one configuration parameter from the reference configuration parameters of a processing cycle ― said subset of at least one configuration parameter includes up to all configuration parameters except at least one remaining configuration parameter (204) from the reference configuration parameters ―; - A step of determining a value for at least one remaining configuration parameter such that the difference between the target processing performance and the achievable processing performance predicted by the model using said value is less than a predetermined threshold - said at least one remaining configuration parameter (204) is the amount of the type of cleaning agent or the timing of the release of said cleaning agent into the housing of the appliance -; - A step of outputting the parameters of the processing cycle A method (210) for setting parameters of a processing cycle in a home appliance (100), comprising: ― The parameters of the processing cycle can be used as setting parameters (205) of the processing cycle in the home appliance, and the parameters include a determined value for at least one remaining configuration parameter and a fixed value for a subset of at least one configuration parameter. Claim 2 A method according to claim 1, further comprising: obtaining a value of processing performance achieved for a different set of reference configuration parameter values ​​of a previous processing cycle; and determining a function that fits the obtained value of processing performance—said that the function establishes a relationship between the value of a different set of configuration parameter values ​​and the value of the obtained processing performance, wherein the value of the obtained processing performance is an approximation of the image of the function and the value of the configuration parameter of a different set is a variable of the function. Claim 3 In paragraph 2, the method in which the function is a polynomial function. Claim 4 A method in which, in any one of paragraphs 1 to 3, the model is determined using an analysis of variance approach. Claim 5 A method according to any one of claims 1 to 3, wherein a different set of reference configuration parameters comprises: - a load of an article introduced into the appliance; - a temperature inside the processing chamber of the appliance; - a number of rotations per second of a rotating element of the appliance; - a value of the water hardness of the water used by the appliance during the processing cycle; - a value of the contamination of a contaminant on the article to be processed; - a duration of the processing cycle; and - a quantity of the type of cleaning agent. Claim 6 A method according to any one of claims 1 to 3, wherein the achievable treatment performance comprises: - a cleanliness value of the treatment cycle; - the number of contaminants remaining on the article to be treated; - a value indicating the amount of water used during the treatment cycle; - a value indicating the amount of energy consumed during the treatment cycle; - a value indicating greenhouse gas emissions generated during the treatment cycle; - the duration of the treatment cycle; - a value indicating the noise generated by the appliance during the treatment cycle; - the temperature reached in the treatment chamber of the appliance during the treatment cycle; - the number of revolutions per minute of the drum of the appliance during the treatment cycle. Claim 7 A method according to any one of claims 1 to 3, wherein at least one remaining configuration parameter comprises at least two remaining configuration parameters, and the method further comprises: determining, based on the acquired model, from at least two remaining configuration parameters, one selected configuration parameter having a value such that the difference between the target processing performance and the achievable processing performance predicted by the model using the value of the selected configuration parameter is minimized. Claim 8 A method according to any one of claims 1 to 3, further comprising: - determining a group of values ​​for at least one remaining configuration parameter such that the difference between the target processing performance and the achievable processing performance predicted by a model using said group of values ​​is less than a predetermined threshold; and - outputting a list of proposed configuration parameters of a processing cycle — wherein the parameters of the processing cycle are adapted to set the processing parameters of a home appliance using a determined group of values ​​for at least one remaining configuration parameter and a fixed value for at least one subset of current configuration parameters. Claim 9 Use of the method according to claim 1 for setting configuration parameters of a home appliance. Claim 10 In paragraph 9, - an additional use comprising executing a processing cycle with configuration parameters set for the home appliance. Claim 11 A system (3) for setting parameters of a processing cycle in a home appliance, and: - can communicate with a storage medium (312); - obtain and store a model (201) that establishes a relationship between values ​​of reference configuration parameters (101-108) of different sets of processing cycles and values ​​of achievable processing performance; - said model (201) includes a multidimensional function that includes at least four configuration parameters as inputs; - obtain a target processing performance (202) for a processing cycle; - obtain a fixed value for a subset (203) of at least one configuration parameter from the reference configuration parameter of the processing cycle, ― said subset of at least one configuration parameter includes at most all configuration parameters except for at least one remaining configuration parameter (204) from the reference configuration parameter ―; - A data processing device (311) configured to determine the value for at least one remaining configuration parameter such that the difference between the target processing performance and the achievable processing performance predicted by the model using said value is less than a predetermined threshold - said at least one remaining configuration parameter (204) is the amount of the type of cleaning agent or the timing of the release of said cleaning agent into the housing of the appliance; - A system comprising an appliance (100) capable of receiving commands from the data processing device and receiving parameters of a processing cycle in the form of a determined value for at least one remaining configuration parameter and a fixed value for a subset of at least one configuration parameter. Claim 12 In claim 11, a system further comprising at least one sensor (310) for determining at least one value from a subset of configuration parameters. Claim 13 A system according to claim 11 or 12, further comprising a dispensing device (300) disposed inside the processing chamber of a home appliance and configured to dispense at least one cleaning agent into the processing chamber. Claim 14 In paragraph 13, the dispensing device is configured to dispense an adjustable amount of at least one cleaning agent at an adjustable time of a treatment cycle, and said adjustable amount and adjustable time are additional configuration parameters of the treatment cycle. Claim 15 A non-transient computer-readable storage medium storing a computer program comprising instructions for executing a method for setting parameters of a processing cycle in a home appliance, wherein the instructions: - access a model that establishes a relationship between reference configuration parameter values ​​of different sets of processing cycles and values ​​of achievable processing performance - said model comprises a multidimensional function that includes at least four configuration parameters as input -; - receive a target processing performance for a processing cycle; - obtain a fixed value for a subset of at least one configuration parameter from the reference configuration parameter of the processing cycle - said subset of at least one configuration parameter includes up to all configuration parameters except at least one remaining configuration parameter from the reference configuration parameter -; - determine a value for at least one remaining configuration parameter such that the difference between the target processing performance and the achievable processing performance predicted by the model using said value is minimized - said at least one remaining configuration parameter is the amount of a type of cleaning agent or the timing of the release of said cleaning agent into the housing of the home appliance -; and - output parameters of the processing cycle - said parameters are determined for at least one remaining configuration parameter A storage medium comprising — configured to control the processing cycle of a home appliance using a value and a fixed value for a subset of at least one configuration parameter.

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