DE102017217585A1 - Method for operating a household appliance and household appliance - Google Patents

Abstract

A method (S1-S5) is for operating a household appliance (K), which domestic appliance (K) has a treatment room for treating therein housed loading at least one type of loading, comprising the steps of: (a) correlating a plurality of state variables determined in different ways (S1, Z) for the same operation and determining an error case based on the error conditions associated with the state variables (S2); (b) plausibility checking of an error case (S3) determined in step a) and (c) adaptation of at least one operating procedure (S5, B) of the household appliance (K) based on the steps (a) and (b) as well as on the basis of predefined prioritization with regard to contradictory ones Operating requirements (S4). A household appliance (K) has a treatment room for treating therein loaded at least one loading type and a control device (C) for operating the household appliance, wherein the control device (C) is adapted to run the process. The invention is particularly advantageous applicable to refrigerators, especially refrigerators and / or freezers.

Description

The invention relates to a method for operating a household appliance and a corresponding household appliance. The invention is particularly advantageous applicable to refrigerators, especially refrigerators and / or freezers.

WO 2015/189009 A1 discloses a refrigerator having a compressor and a controller for controlling the compressor, wherein the controller detects statistical data about a user behavior and controls the speed of the compressor in dependence on the detected statistical data. This ensures that in phases in which a user is less likely to operate the opening door of the refrigeration device, it is assumed that a reduced cooling capacity is used to maintain a setpoint temperature. As a consequence, a lower compressor speed can be assumed in these phases, which results in a reduced noise load for a user and additionally saves energy.

WO 2015/189013 A1 discloses a home appliance having a controllable operation, comprising: a detector for detecting a current use of the household appliance by a user; provision means for providing a likelihood profile, the likelihood profile indicating probabilities of future uses of the appliance as a function of current uses of the appliance, the probabilities of future uses being associated with predictive operating parameters for the controllable operation of the appliance; a processor configured to determine a likelihood of future use of the home appliance based on a likelihood profile and the current usage of the home appliance and to select a predictive operating parameter associated with the determined likelihood; and a controller for setting the selected predictive operating parameter to control the home appliance.

It is the object of the present invention to at least partially overcome the disadvantages of the prior art and, in particular, to provide a particularly adaptable way of adapting operating sequences of a domestic appliance to changing conditions.

This object is achieved according to the features of the independent claims. Advantageous embodiments are the subject of the dependent claims, the description and the drawings.

The object is achieved by a method for operating a domestic appliance, which household appliance has a treatment room for treating therein at least one loading method, comprising the steps of: (a) correlating a plurality of state variables determined in different ways for a same operation and determining one If so, based on the error conditions associated with the state variables; (b) plausibility checking of a fault detected in step a); and (c) adjusting at least one operation of the home appliance based on steps (a) and (b) and predefined prioritization of conflicting operational requirements.

In contrast to conventional fuzzy logic, this method is very clear with very opposite states. For example, while a home appliance with conventional fuzzy logic needs to be told extreme state changes to be reasonably taken into account, the above method provides layered "artificial intelligence" (KI) with multiple instances that is faster and substantially more adaptive than conventional fuzzy logic. The method thus advantageously makes it possible to use an adaptive artificial intelligence for condition detection on at least three independent processing levels (corresponding to steps a) to c), thereby rapid error handling and detection of exceptional conditions as well as short, medium and long-term independent optimization of operating procedures , The procedure is comparable to the short-, medium-, long-term memory of a person with regard to occurring error cases.

A household appliance is a device that is used for household management. This may be a large household appliance, such as a washing machine, a tumble dryer, a dishwasher, a cooking appliance, etc. The household appliance may also be a small household appliance, such as a water heater, a coffee machine, a food processor or a vacuum cleaner.

It is an embodiment that the household appliance is a refrigerator with a treatment space in the form of a refrigerated space for cooling refrigerated goods (e.g., food). The type of loading can be a food type. Under a refrigeration device is in particular a household refrigeration appliance understood, ie a refrigerator, which is used for household management in restaurants or catering, and in particular serves to store food at certain temperatures, such as a refrigerator, a freezer, a Kühlgefrierkombination, a freezer or a wine fridge.

A first possible way in step a) for determining a state variable may be a determination of one or more state variables from direct and indirect measurements, eg calculated from the measured values of different sensors, eg an air flow, a dew point etc.

A second possible way takes into account the medium- and long-term user behavior and corresponds to a "learned" determination of this size. Here, learned behavior of the user can be transferred to a first storage instance (comparable to the short-term memory). If the user repeats certain patterns more frequently, there is a transfer to a higher storage instance (comparable to the middle-time memory). If at the level of the medium-term memory there is also a more frequent repetition of certain patterns, e.g. comparable to human imprinting, the behavior migrates to a highest memory instance (comparable to long-term memory).

A third approach involves the consideration of input behavior parameters by the user, e.g. a mode of operation optimized for a specific type of loading.

A fourth possible way involves knowing the physical relationships or physical design of the household appliance, including the interaction of the actuators and sensors. Thus, for example, from the measured external humidity and a certain temperature in the outer region of a refrigerator via a mathematical formula, the dew point within the device after opening the door over a defined period.

A fifth possible way involves knowledge of the probable peculiarities of goods placed in the treatment room, e.g. by a user entering these goods via a user interface.

Through its interaction with the household appliance, the user influences the household appliance by all means, and thus the state variables or functional parameters of the household appliance. Step a) can use at least two of these ways to calculate values for the same state variables.

For correlating in step a), correlation relationships can be set up which relate to possible contradictions between the state variables calculated in different ways and assign deviations in the values of the same state variables weighted to possible errors ("error relationships"). The error contexts may be e.g. be formulated in the form of an "error matrix". In the following, the invention will be described on the basis of the error matrix, but it may also be understood as meaning other formulations of the error contexts.

In one example, a humidity derived from a refrigerator load entered by a user does not correspond to the measured humidity. As weighted errors can then: a faulty user input ( 1 ) - an open household appliance door ( 2 ) - a faulty flap position ( 3 ) - a faulty blower control ( 4 ) - a faulty sensor ( 5 ) - a compressor error ( 6 ) are set up. The errors are escalating and affect the operating conditions of the household appliance. The refrigerator defines in step b) thus a faulty user input regarding the refrigerator load as the first possible cause of the error.

The error matrix may be graded in structure, eg with respect to different deviation values such as: negative humidity difference 5% / negative humidity difference 10% / negative humidity difference 20% / negative humidity difference 30% etc., because different error graduations vary in the values of the state variables allow the cause of the error. The first defined in the error matrix negative humidity difference thus corresponds to the basis of when the home appliance detects a deviation as an error or treated.

In step b) the household appliance checks for plausibility of whether it has made the correct error case. In the above example, the refrigerator can make the user-specific refrigerator load plausible via the calculated air humidity, the measured air humidity and the heat capacity of the refrigerator contents. The plausibility relationships may be e.g. be formulated in the form of a "plausibility matrix". In the following, the invention will be described on the basis of the plausibility matrix, but it may also be understood as meaning other formulations of the plausibility context.

The refrigerator subsequently adapts in step c) an operating procedure - eg a cooling and / or a defrosting process - according to the plausibility error, for example in the present example to a cooling process with low humidity and high heat capacity (drinks and convenience food). The adjustment is made by additionally considering pre-defined prioritization of conflicting operational requirements, that is, using prioritization rules that address conflicting operational requirements. The contradictory operating requirements, which often occur in real environmental conditions, can be solved by the Prioritization rules are lifted. The prioritization rules can build on a variety of considerations, such as safety, quality, comfort, energy efficiency, external impact, corporate identity, etc.

One possible method for checking the effectiveness of the error correction used according to prioritization according to step c) may be conventional fuzzy logic. In particular, the most likely results of re-measurements are used to test the success of the prioritization rules.

The prioritization rules may be formulated in the form of a prioritization matrix. In the following, the invention is described on the basis of the prioritization matrix, but it may also be understood as meaning other formulations of the prioritization rules. In particular, the prioritization matrix can be a developer-defined two-dimensional matrix describing all possible conflicting operational requirements or all prioritization rules.

In the above example, the refrigerator has e.g. from the medium- and long-term user behavior that the user very rarely opens the refrigerator doors at certain times of the day and could therefore cool and defrost very slowly and with high energy efficiency. At the same time, the refrigerator's user-specific refrigerator load and optimized air humidity mean that the user primarily cools fish and meat. Based on the prioritization rules, proper cooling of fish and meat prior to energy-efficient long-term refrigeration is prioritized.

In the prioritization matrix, a prioritization entry can be located at a corresponding location, which can assume one of the following priorities (prioritization values): Cooling Meat ( 1 ) - Refrigeration Convenience Food ( 2 ) - cooling vegetables ( 3 ) - Energy efficiency ( 4 ) - hysteresis ( 5 ) - Comfort feature noise development ( 6 ). The importance decreases from 1 to 6.

If the plausibility check fails in step b) or if no stable operating state arises after the adaptation in step c), the next error cause from the error matrix is used.

In contrast to the prioritization matrix, which is fixed and e.g. has been determined during development, the home appliance adaptively handles the error matrix. If a certain error occurs again or frequently, the domestic appliance "learns" this and adjusts the error position of this error in the error matrix. If, for example, a user repeatedly leaves the refrigerator door open or if a sensor is defective, this error is continuously / more importantly classified from repetition to repetition.

The plausibility matrix serves to check the plausibility of error and normal states while the error matrix weights error states. The household appliance is either in an error state or in a standard state.

Example: The refrigerator has a dew point of 2.8 ° C at a room temperature of 21 ° C and a humidity of 30%. At the same time the refrigerator measures a dew point of 7 ° C during a ten-minute calming phase after closing the refrigerator door. Furthermore, the user has defined in user queries as user-specific loading that he stores a lot of fruit and vegetables in the refrigerator. The refrigerator correlates / compares the calculated humidity with the measured humidity and the user-specific refrigerator load and determines the cooling processes and defrost processes according to the plausibility matrix, which contains all cooperating mechanisms.

The plausibility matrix may be handled adaptively, in particular similar to the error matrix, for example, when values due to refrigerator aging drift.

The prioritization matrix is thus particularly static, not plausible, and defines the operating states in the case of contradictory requirements. In particular, the error matrix is adaptive, plausible, and defines and weights the fault conditions of the household appliance. In particular, the plausibility matrix is adaptive, plausible, and defines and weights the standard states of the household appliance.

The above method can therefore use in particular three instances (corresponding to a correlation, plausibility check and prioritization) in order to adapt at least one operating sequence of the household appliance. While a single instance can make a wrong decision in case of a decision (eg a party, which is not actually a mistake, but a "temporary" normal state, to classify as an error), this decision in combination with the other two instances is likely to be correct Decision corrected. This method is faster and much more adaptable to state changes than conventional fuzzy logic.

The interaction of the method in relation to plausibility, correlation and prioritization works or monitors in particular continuously or quasi-continuously. In particular, can with every change of state, however small, the process will be run through, for example, every 100 ms in a refrigerator of today's design.

In a further example, if a user is a vegetarian who eats virtually only fruits and vegetables and goes to bed early, a refrigerator will adjust to his habits by the above method, in particular through step c), which records the plausibility matrix entries changes / optimized if the system is in the standard state. If the user remains within his habits, the operations are - after an initial adaptation - by step c) barely or only slightly changed. However, if this user celebrates a party where a lot of beer is stored for one evening, due to the different loading type (fruit and vegetables <-> beer bottles, the type of loading is prioritized in the prioritization matrix over other state variables) the error matrix will make a "mistake" so that in step c) the operation of "Fruits and vegetables - slow, flat cooling curve with medium to high humidity in the refrigerator" is briefly changed to a "drinks, closed convenience food - moderate cooling curve at medium humidity in the refrigerator" operating mode. In this case, it is used in step c) that the prioritization matrix, in conjunction with the plausibility matrix and the error matrix, prioritizes the type of loading for setting the operating sequences. The refrigerator would first classify the strong drift of the temperatures and the constant opening of the doors as a mistake. The error matrix would change the cooling process as a quick reaction. The prioritization matrix would prioritize the converted cooling process for a period x and make it the "normal case". The plausibility matrix would return the system to its original state after a period of time x, unless the user celebrates a permanent state from the party. If the user clears the beer on the day after the party and fills in fruit and vegetables, this is recognized again by means of the error matrix, and it is switched back to a short course "fruit and vegetables". However, the party is still impressed with the medium- and long-term user behavior, so that for a certain period of time, the operation "fruits and vegetables" is still affected.

If the user frequently repeats a certain type of error from the error matrix, for example, if he allows the refrigerator door to be opened more frequently, the error case in the error matrix and, consequently, in the plausibility matrix receive a higher weighting. The error case is first plausibilized in the error matrix before the possible error of a wrong user input, which significantly increases the reaction speed of the system.

In particular, for a refrigeration appliance, especially for a refrigerator, the state variables may in particular state parameters (such as a temperature inside and outside the treatment room, a humidity, a condensing point inside and outside the refrigerator, etc.), a type of loading of the household appliance and a e.g. include short, medium and long term user behavior. The user behavior may e.g. be determined by a user input at a user interface and / or by a detection of an interaction of the user with the household appliance (door opening times and door opening duration, etc.).

It is still an embodiment that the state variables comprise a plurality of operating parameters and, in correlation thereto, at least one type of loading and at least one type of user behavior derived therefrom.

The state variables can be measured or calculated. The state variables can therefore also be derived from each other. The state variables may also include factory defaults, e.g. the capacity of a refrigerator.

The loading type may be determined by means of an input by a user and / or by object recognition (e.g., "Camera in Fridge"; CiF).

• - Fisch;
• - Fleisch;
• - Obst;
• - Gemüse;
• - Verpackte Lebensmittel;

umfasst.It is still an embodiment that the at least one type of loading at least one member from the group comprising:

• - Fish;
• - meat;
• - fruit;
• - Vegetables;
• - Packaged food;

includes.

• - eine relative Luftfeuchte in einem Außenbereich;
• - eine Temperatur in einem Außenbereich;
• - eine Temperatur in einem Kühlraum;
• - eine absolute Feuchte im Kühlraum;
• - eine Taupunkttemperatur;
• - Öffnungszeiten der Kühlschranktür;
• - eine Wärmekapazität der Beladung;
• - eine Wassermenge in dem Kühlraum;

umfassen.It is still an embodiment that the state parameters include at least one member of the group:

• - a relative humidity in an outdoor area;
• a temperature in an outdoor area;
• a temperature in a cold room;
• - an absolute humidity in the cold room;
• a dew point temperature;
• - opening hours of the refrigerator door;
• a heat capacity of the load;
• an amount of water in the cold room;

include.

• - eine Kühlungsleistung;
• - eine Luftfeuchte in dem Kühlraum;
• - eine Luftzirkulation (z.B. deren Stärke, Ein/ Aus); und / oder
• - eine Strömungsrichtung von Kühlluft in dem Kühlraum.

It is still another embodiment that the operations in step c) are adjusted with respect to

• - a cooling performance;
• - a humidity in the refrigerator;
• - an air circulation (eg their strength, on / off); and or
• a flow direction of cooling air in the cooling space.

• - zum Korrelieren in Schritt a) eine Fehlermatrix aufgestellt wird, deren Einträge gewichtete Fehlerzusammenhänge von auf jeweils unterschiedlichen Wegen bestimmten Zustandsgrößen entsprechen;
• - zum Plausibilisieren in Schritt b) eine Plausibilisierungsmatrix aufgestellt wird, deren Einträge Plausibilisierungszusammenhängen zum Plausibilisieren eines in Schritt a) festgestellten Fehlers entsprechen; und
• - zum Anpassen in Schritt c) eine Priorisierungsmatrix bereitgestellt wird, deren Einträge vordefinierten Priorisierungen bezüglich widersprüchlicher Betriebsanforderungen entspricht.

It is still an embodiment that

• - to correlate in step a) an error matrix is set up whose entries correspond to weighted error relationships of state variables determined in different ways;
• - for plausibility checking in step b) a plausibility matrix is set up whose entries correspond to plausibility contexts for plausibility of an error ascertained in step a); and
• for adaptation in step c), a prioritization matrix is provided whose entries correspond to predefined priorities with regard to contradictory operating requirements.

The object is also achieved by a household appliance, comprising a treatment room for treating therein accommodated loading of at least one type of loading and a control device for operating the household appliance, wherein the control device is adapted to run the method as described above. The controller may then also be referred to as an inertial measurement unit (IMU) with respect to the method, which enables the interaction of plausibility, correlation and prioritization.

The above-described characteristics, features and advantages of this invention, as well as the manner in which they are achieved, will become clearer and more clearly understood in connection with the following schematic description of an embodiment which will be described in detail in conjunction with the drawings.

1 shows a sketch of the inventive method for a household appliance in the form of a refrigerator K as well as the fridge K , The refrigerator K has a refrigerator R for treating foods of at least one food type housed therein and a controller C to operate the refrigerator K on, the control device C is also set up to run the procedure as described below.

In the process, in one step S1 ("Measuring step") for a same operation several state variables Z determined in different ways, for example, measured and derived from other parameters. Such state variables may include, for example: a dew point, a user-specific refrigerator load (eg determinable from user information), a short, medium, and long-term user behavior (eg determinable from opening times of the refrigerator door), and / or a heat capacity of the refrigerator contents.

The following will be in one step S2 ("Correlation step") several state variables determined in different ways Z for a same operation correlated and possibly determined at least one error case based on the state variables associated error relationships.

In one step S3 ("Plausibilisierungsschritt"), the at least one error case is plausibility.

In one step S4 ("Prioritizing step") will be at least one operation of the home appliance based on the steps S2 and S3 and adjusted based on predefined prioritization of conflicting operational requirements.

This can be done in one step S5 ("Adaptation step") certain operating parameters B the refrigerator, for example, in terms of a cooling performance, a humidity in the refrigerator, an air circulation (strength, on / off) and / or a flow direction of cooling air in the refrigerator.

The steps S2 to S4 respectively. S1 to S5 can be executed in a loop, as for the steps S2 to S4 indicated.

The procedure can be performed periodically, eg every 100 ms. The method can alternatively or additionally be triggered by an event, eg by changing a state variable Z ,

Of course, the present invention is not limited to the embodiment shown.

Generally, "on", "an", etc. may be taken to mean a singular or a plurality, in particular in the sense of "at least one" or "one or more" etc., unless this is explicitly excluded, e.g. by the expression "exactly one", etc.

Also, a number may include exactly the specified number as well as a usual tolerance range, as long as this is not explicitly excluded.

LIST OF REFERENCE NUMBERS

C

control device

K

fridge

R

refrigerator

S1

measuring step

S2

correlation step

S3

Plausibilisierungsschritt

S4

prioritization step

S5

matching step

Z

state variable

B

operating parameters

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited patent literature

• WO 2015/189009 A1 [0002]
• WO 2015/189013 A1 [0003]

Claims (8)

A method (S1-S5) for operating a domestic appliance (K), which domestic appliance (K) has a treatment room for treating therein at least one loading mode, comprising the steps: a) correlating a plurality of state variables (S1, Z) determined in different ways for a similar operational sequence and determining an error case on the basis of error relationships (S2) assigned to the state variables; b) Plausibilisieren a detected in step a) error case (S3) and c) adjusting at least one operating procedure (S5, B) of the household appliance (K) based on the steps (a) and (b) as well as on the basis of predefined priorities with regard to contradictory operating requirements (S4). Method (S1-S5) after Claim 1 in which the household appliance (K) is a refrigerator with a refrigerated space (R) as the treatment room for refrigerating food and the type of loading is a food type. Method (S1-S5) after Claim 2 in which the state variables (Z) comprise a plurality of operating parameters and, in correlation thereto, at least one type of loading and at least one type of user behavior derived therefrom. Method (S1-S5) after Claim 2 and 3 in which the at least one type of loading is at least one member of the group: - fish; - meat; - fruit; - Vegetables; - Packaged food; includes. Method according to one of Claims 3 or 4 in which the condition parameters (Z) are at least one member of the group: - a relative humidity in an outdoor area; a temperature in an outdoor area; a temperature in a cold room; - an absolute humidity in the cold room; a dew point temperature; - opening hours of the refrigerator door; a heat capacity of the load; an amount of water in the cold room; include. Method (S1-S5) according to one of Claims 2 to 5 in which the operations in step c) are adjusted (S4, S5) with respect to - a cooling capacity; - a humidity in the refrigerator; - air circulation (strength, on / off); and / or a flow direction of cooling air in the cooling space. Method (S1-S5) according to one of the preceding claims, in which - for correlating (S2) in step a), an error matrix is set up, the entries of which correspond to weighted error relationships of state variables (Z) determined on different paths; - for plausibility checking (S3) in step b) a plausibility matrix is set up, the entries of which correspond to plausibility contexts for plausibility of an error ascertained in step a); and for adapting (S4) in step c), a prioritization matrix is provided whose entries correspond to predefined priorities with regard to contradictory operating requirements. Domestic appliance (K), comprising a treatment room for treating therein housed loading at least one type of loading and a control device (C) for operating the household appliance, wherein the control device (C) is adapted to run the method according to one of the preceding claims.

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