METHOD FOR OPERATING A HOUSEHOLD COOKING APPLIANCE AND HOUSEHOLD COOKING APPLIANCE

Description

[0001] The invention relates to a method for operating a household cooking appliance, comprising a cooking chamber, at least one food treatment device for treating food located in the cooking chamber with parameter configurations, wherein the food can be treated differently locally by at least two parameter configurations, and at least one sensor directed into the cooking chamber for determining measured value distributions <v>a surface property of the food being cooked, wherein, in the method, the at least one food treatment device is operated for a predetermined period of time with one of the parameter configurations in order to treat food located in the cooking chamber. Following the expiration of a period of time, a measured value distribution <V> of a surface property of the food being cooked is determined by means of the at least one sensor. The invention also relates to a household cooking appliance for carrying out the method. The invention is particularly advantageously applicable to microwave ovens.

[0002] US 2018 / 0098381 A1 and US 2017 / 0290095 A1 disclose a computer-implemented method for heating an object in a cooking chamber of an electronic oven toward a target state. The method comprises heating the object with a set of energy applications relative to the cooking chamber while the oven is in a particular configuration. The set of energy applications and the configuration define a respective set of variable energy distributions in the chamber. The method also comprises acquiring sensor data defining a respective set of responses of the object to the set of energy applications. The method also comprises generating a plan for heating the object in the chamber. The plan is generated by a control system of the oven and uses the sensor data.

[0003] WO 2012 / 109634 A1 discloses a device for treating objects with RF energy. The device may include a display for displaying an image of an object to be treated to a user, wherein the image comprises at least a first portion and a second portion of the object. The device may also include an input unit and at least one processor configured to: receive information based on an input provided to the input unit; and generate processing information for use in treating the object based on the received information to achieve a first processing result in the first portion of the object and a second processing result in the second portion of the object.

[0004] CN 107908144 A discloses a method for controlling an extractor hood.

[0005] DE 10 2007 048834 A1 is based on a household appliance whose control unit has a data connection for receiving image data from a camera of the household appliance. The household appliance can be designed as a cooking appliance in which a clearly recognizable browning and / or volume change of a cooked item is detectable.

[0006] EP 1 921 384 A1 discloses a device and a method for determining the temperature inside a food item. The device has at least one temperature sensor for detecting at least one surface temperature of the food item and / or an ambient temperature of the food item, in particular at a measuring location within a cooking chamber surrounding the food item, preferably with an ambient temperature sensor arranged at the measuring location. Furthermore, the device comprises at least one distance sensor for detecting one or a plurality of distances between the distance sensor, on the one hand, and one or a plurality of distance measuring points on the surface of the food item, on the other hand. In addition, the device comprises at least one time measuring device for detecting the time during preparation of the food item and at least one calculation device for calculating the temperature inside the food item.

[0007] 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 simple and effective way of automatically treating food to a desired surface property.

[0008] 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.

[0009] The object is achieved by a method for operating a household cooking appliance, having the features of claim 1.

[0010] This process has the advantage that it can treat the food effectively and in a short time so that it acquires a desired surface property, in particular a uniform surface property.

[0011] In particular, the method enables a targeted control of the distribution of properties on the surface of food when using food-treating radiation (e.g. microwave radiation, thermal radiation, etc.) with the aid of the data from the the quality value is determined from a difference between two different scalar variables which are calculated from the same at least one measured value distribution < V >, wherein the scalar variables (x arithm , x med ) are different mean values ​​and wherein the at least one food treatment device comprises at least one microwave device for introducing microwaves into the cooking chamber and / or at least one radiant heater for introducing heat radiation into the cooking chamber.

[0012] This process has the advantage that it can treat the food effectively and in a short time so that it acquires a desired surface property, in particular a uniform surface property.

[0013] In particular, the method enables targeted control of a property distribution on the surface of food being cooked when using food-treating radiation (e.g., microwave radiation, thermal radiation, etc.) with the aid of data from at least one sensor. This allows intelligent control of a cooking appliance to be implemented with little effort, which can achieve the best possible cooking result dynamically and only for a current period of time or a current moment. In particular, the associated computational effort is low, so that the method can be implemented particularly quickly. Furthermore, no memory is required to store large amounts of data. This means that even in conventional cooking appliances, targeted property patterns and property distributions can be set using only at least one simple sensor.

[0014] The surface property can be, for example, a temperature, humidity, or degree of browning measured on the surface of the food, but is not limited to this. The distribution < V > is also referred to below as the "measured value distribution" and represents a measured actual distribution of the food. Depending on the type of measured surface property, it can then be referred to as a temperature distribution, degree of browning distribution, etc. A desired target distribution of the surface property can be referred to as a target measured value distribution or simply target distribution < Z >. at least one sensor. In this way, an intelligent control of a cooking appliance can be implemented with little effort, which can achieve the best possible cooking result dynamically and only based on a current period of time or a current moment. In particular, the associated computational effort is low, so that the method can be implemented particularly quickly.Furthermore, no memory is required to store large amounts of data. This means that even conventional cooking appliances can be used to set specific property patterns and distributions, using only a simple sensor.

[0015] The surface property can be, for example, a temperature, humidity, or browning level measured on the surface of the food, but is not limited to these. The distribution < V > is also referred to below as the "measured value distribution" and represents a measured actual distribution of the food. Depending on the type of measured surface property, it can then be referred to as a temperature distribution, browning level distribution, etc. A desired target distribution of the surface property can be referred to as a target measured value distribution or simply target distribution < Z >.

[0016] The parameter configurations {S q} generally cover a specific value space, which is given by the corresponding setting or operating parameters. In other words, a parameter configuration S q corresponds to a specific qth set of setting or operating values ​​of the household cooking appliance. A parameter configuration S q comprises a setting value from at least two possible setting values ​​of at least one setting or operating parameter of the household cooking appliance. Each operating parameter can therefore assume at least two values ​​or states. In the simplest case, these two states can be "on" and "off." Because at least two parameter configurations treat the food differently locally, a different distribution of the surface property results from the corresponding influence on the food by the two parameter configurations.

[0017] The different mean values ​​can be calculated or determined from exactly one measured value distribution < V >.

[0018] The different means correspond to different types of means with different calculation rules. This allows the use of a practically usable or meaningful quality value with particularly low computing power. The means can include: a modal mean or mode as a measure of a characteristic with the highest frequency, a median value, an arithmetic mean, a geometric mean, a harmonic mean, a quadratic mean, a cubic mean. The means can also include, for example: a weighted mean (e.g., a weighted arithmetic mean, a weighted geometric mean, a weighted harmonic mean), a logarithmic mean, a Winsorized and trimmed mean, a quartile mean, a mid-shortest half, a Gastwirth-Cohen mean, moving averages, and other generalized means such as a Hölder mean, a Lehner mean, or a Stolarsky mean.

[0019] In one embodiment, the two different mean values ​​comprise an arithmetic mean and a median value. Using these two types of mean values ​​is particularly easy to calculate and leads to a quality value that can approximate a desired target distribution of the surface property of the food being cooked particularly well and effectively. Using the comparison of arithmetic mean and median value is particularly advantageous if a uniform target distribution is to be achieved, since in this case the arithmetic mean corresponds to the median value. Thus, an approximation of the measured value distribution < V > to the target distribution < Z > can be estimated by a simple comparison between the arithmetic mean and the median value: the smaller the difference, the better the approximation. For a uniform distribution of the surface property, the underlying target values ​​Z i are the same, i.e. Z i = const.

[0020] The arithmetic mean x arithm can be calculated from the k measured values ​​V i with i = 1, ..., k underlying a measured value distribution < V >, for example according to x arithm = 1 k ∑ i = 1 k V i The median value x med can be calculated from the same distribution of measured values ​​< V > for the sorted values ​​V i , for example, according to x med = V k + 1 2 f ü r k ungerade 1 2 V k 2 + V k 2 + 1 f ü r k gerade be calculated.

[0021] One embodiment is that the quality value comprises the difference between the two different mean values. This allows for a particularly simple calculation and leads to a quality value that can particularly well and effectively approximate a desired target distribution of the surface properties of the food. The quality value Q can therefore be calculated in particular according to Q = x arithm − x med It has the advantageous property, particularly when a uniform target distribution of the surface property is desired, of representing a natural measure for the approximation of the measured value distribution < V > to a constant or uniform target distribution. When a uniform target distribution is achieved, Q = 0 applies. This significantly facilitates the use of the quality value as a criterion for device control.

[0022] In one embodiment, the quality criterion comprises reaching a predetermined threshold value (hereinafter referred to as the "quality threshold value" without loss of generality). This provides the advantage of providing an easily implementable measure for determining when a changeover or switching from one current parameter configuration to another should be made in order to more quickly achieve a desired target distribution of the surface properties of the product. If the quality value is determined as the difference in magnitude between two mean values, in particular the arithmetic mean and the median value, switching to the other parameter configuration can, for example, be predefined be taken, if the quality value is greater than the quality threshold or, in another variant, is at least as large as the quality threshold.

[0023] It is a further development that the quality criterion comprises reaching a predetermined quality threshold value, wherein the threshold value corresponds to the quality value determined immediately beforehand (for example at the end of the previous time period Δt), possibly plus or minus a predetermined offset factor. This can also be formulated such that the quality criterion comprises the currently determined quality value being smaller than a quality value determined immediately beforehand (for example at the end of the previous time period Δt), possibly plus or minus the offset factor. Compliance with the quality criterion thus corresponds in particular to the case that after the treatment of the food during its last time period Δt, an approximation to the target distribution < Z > was better than before.This refinement has the advantage of allowing a particularly quick and reliable response to a deterioration in the conformity of the surface properties of the food to a desired target distribution. This can be implemented particularly easily and effectively if the quality value is defined or calculated as the difference between two mean values, in particular the arithmetic mean and the median. It is a refinement that the quality threshold is a fixed, predefined value. However, it can also be a variable value that depends, for example, on at least one cooking parameter, at least one setting parameter, and / or the type of food, etc.

[0024] For example, if Q p is the current or last determined quality value and Q p-1 is the quality value determined before, the quality criterion for maintaining the parameter configuration in a further development can also be defined as Q p < a ⋅ Q p − 1 mit a ≤ 1 oder als Q p ≤ a ⋅ Q p − 1 mit a < 1 where a denotes an offset factor and a · Q p-1 corresponds to the quality threshold. The offset factor can be, for example, 1, 0.995, 0.99, 0.98, etc. The offset factor can be arbitrarily chosen, but then fixed, or it can be dynamically adjusted. This development can generally also be described as follows: if there is a sufficiently small deviation from the target value for the quality value Q p division < Z > results in the quality value Q p-1 , the procedure is continued while maintaining the current parameter configuration S q .

[0025] Instead of the offset factor, an additive offset can also be used. Using the offset factor can advantageously prevent quasi-static conditions in which only an infinitesimal cooking progress occurs.

[0026] In a further development, the at least one sensor comprises at least one infrared sensor and / or at least one optical sensor (sensitive in the visible spectrum). This allows a surface texture to be determined and evaluated particularly reliably. The optical sensor is particularly suitable for determining the degree of browning and / or the moisture content on the surface of the food being cooked, while the infrared sensor is particularly suitable for determining the temperature distribution on the surface of the food being cooked. The infrared sensor is particularly sensitive in the near infrared (NIR) range.

[0027] Thus, a further development provides a spatially resolved, in particular pixel-like, measured value distribution < V > of the surface texture of the food being cooked from the measured values ​​of at least one sensor, in particular as a two-dimensional image. For this purpose, at least one sensor can be a spatially resolved sensor. This advantageously enables particularly rapid implementation of the method.

[0028] In a further development, the at least one optical sensor comprises or is a camera that captures a pixel-like composite image of the food being cooked. The camera—in particular a digital camera—is advantageously a color camera, but can also be a black-and-white camera. Each of the pixels is assigned a corresponding measured value V i , e.g., a degree of browning.

[0029] In one embodiment, the at least one infrared sensor comprises at least one pixel-like IR camera for recording at least one pixel-like thermal image (also referred to as a "thermal imaging camera"). A corresponding measured value V i in the form of a temperature measurement is assigned to each of the pixels. The measured surface property of the food being cooked is then its surface temperature. Alternatively or additionally, at least one sensor can be moved relative to the food being cooked (e.g., by being mounted on a movable support) and perform measurements at different spatial positions, which are then combined to form an overall image. This provides the advantage that the surface, particularly of voluminous or non-flat food, can be more completely captured or measured.Alternatively or additionally, several sensors directed into the cooking chamber from different angles and / or at different positions can be used, the measurements of which can be combined, for example, to create an overall image. The at least one infrared sensor can then be designed, for example, as at least one so-called thermopile or "thermopile," etc. The at least one infrared sensor can also be designed as an IR spectroscope.

[0030] Additionally or alternatively, the food can be moved to measure its surface property(ies). For example, the food can be placed on a turntable. Additionally or alternatively, the food can be height-adjustable within the cooking chamber, e.g., by means of a height-adjustable holder for a food support (particularly motorized) or by means of a height-adjustable food support. The height adjustment of the food is, in particular, carried out automatically by the household cooking appliance.

[0031] In a further development, the at least one sensor comprises at least one sensor directed into the cooking chamber for determining pixel-like measured value distributions < V > on the food to be cooked, and the scalar variables in the form of the different measured values ​​are calculated from the k individual pixels of the at least one, in particular exactly one, measured value distribution < V >. The value of each pixel corresponds to a measured value V i . This has the advantage that the measured values ​​can be determined particularly easily from the measured value distribution < V >. A further advantage is that a measured value distribution < V > typically consists of many pixels and thus many measured values ​​V i , and mean values ​​calculated therefrom are therefore particularly robust.

[0032] The pixels can be used at their original resolution to implement the method, making it particularly robust. However, the original resolution can also be reduced to reduce computational effort.

[0033] One possible embodiment is that the at least one sensor comprises at least one infrared sensor directed into the cooking chamber for determining pixel-like measured value distributions < V > in the form of temperature distributions on the food being cooked, and the scalar variables are calculated from the individual pixels V i of the temperature distributions. However, the scalar variables can also be determined analogously from browning distributions, moisture distributions, etc.

[0034] One embodiment provides that the method is terminated if the quality value Q reaches a predefined termination criterion and / or the product being cooked or its measured value distribution < V > reaches a predefined target value V Target . This advantageously allows a particularly reliable approximation of the finished product to a desired final state. The termination criterion can be dependent, in particular, on the last recorded measured value distribution < V >.

[0035] If the criterion includes the food reaching a specified target value V Target, this target value can be compared with the measured value distribution < V >, but this is not required. For example, the criterion can also include reaching a user- or program-specified cooking time, core temperature, etc.

[0036] In one embodiment, the food to be cooked has reached the specified target value V target when max (< V >) ≥ V target or min (< V >) ≥ V target is met. In this way, various desired end states of the food to be cooked can be achieved particularly reliably. The criterion max (< V >) ≥ V target, for example, indicates that the process should be terminated if even just one pixel has reached the target value V target. In this way, over-treatment of the food to be cooked can be advantageously prevented. The criterion min (< V p >) ≥ V target indicates that the process should be terminated when all pixels have reached the target value V target. In this way, incomplete treatment of the food to be cooked can be advantageously prevented.

[0037] One embodiment is that the termination criterion includes the achievement—in particular, the achievement or undershooting—of a target quality value Q target. Assuming that the smaller Q is, the better a measured value distribution < V > approximates the target distribution < Z >, the termination criterion can be met, for example, if Q p ≤ Q target.

[0038] It is an embodiment that the at least one food treatment device comprises at least one microwave device for introducing microwaves into the cooking chamber, wherein different field distributions of the microwaves in the cooking chamber can be generated by at least two parameter configurations S q of the microwave device.

[0039] The household cooking appliance can thus be a microwave appliance, with the food processing device then comprising at least one microwave device for introducing microwaves into the cooking chamber. The microwave device comprises, in particular, at least one microwave generator (e.g., a magnetron, an inverter-controlled microwave generator, a solid-state microwave generator, etc.). Setting or operating parameters of the microwave generator that change a field distribution in the cooking chamber (particularly in the case of semiconductor-based generation of microwave power) can include, for example, the operating frequency, or, in the case of multiple microwave generators and / or feed points, their relative phase, etc.

[0040] The microwave device may further comprise a microwave guide for guiding the microwaves generated by the microwave generator into the cooking chamber. The microwave guide may be or comprise, for example, a waveguide or an RF cable.

[0041] The microwave device can further comprise at least one adjustable field-modifying component, meaning that the field distribution of the microwaves in the cooking chamber varies depending on the position of the field-modifying component. Depending on the setting of the adjustment or operating parameters of these field-modifying components, a specific field distribution and thus a specific heating or modification pattern in the food will be established.

[0042] The at least one field-modifying component can, for example, comprise or be at least one rotatable antenna that couples microwave energy into the cooking chamber, e.g., from the microwave guide. These rotating antennas are typically not rotationally symmetrical, so that an angular position can be specified as a setting or operating parameter, which can be specifically adjusted, e.g., via a stepper motor. In a further development, the at least one rotatable antenna can also be adjustable in terms of its height.

[0043] The at least one field-modifying component can additionally or alternatively comprise at least one microwave reflector whose spatial position can be adjusted. The microwave reflector can be rotatable and / or displaceable. A rotatable microwave reflector can be designed as a mode stirrer ("wobbler"). A displaceable microwave reflector can be designed as a spatially displaceable dielectric (e.g., made of Teflon).

[0044] In the event that the at least one food treatment device has or comprises a microwave device, the at least one setting or operating parameter can be at least one operating parameter from the group respective rotation angle of at least one rotatable antenna; respective height position of at least one rotatable antenna; spatial position of at least one microwave reflector; microwave frequency; relative phases between different microwave generators; This does not exclude the possibility of setting additional operating parameters of the microwave device that could alter the field distribution.

[0045] However, the household cooking appliance can also be an oven, in which case the food treatment device then has at least one - in particular electrically operated - radiant heater for introducing heat radiation into the cooking chamber, e.g. at least one bottom heat heater, at least one top heat heater and / or at least one grill heater.

[0046] It is a further development that in the case of an oven, the at least one food treatment unit comprises at least one food treatment unit from the group comprising at least one electric radiant heater, at least one induction coil, at least one jet-directed cooling air fan, at least a jet-directed hot air device and / or at least one jet-directed water feed device This provides the advantage that the surface property can be standardized using multiple devices (if present in the household cooking appliance), individually or in any combination, or adjusted to a different target distribution of the surface property. This, in turn, increases the effectiveness of the process. A jet-directed device can be understood, in particular, as a substance introduction unit designed to introduce at least one locally limited, directed stream of substance into the cooking chamber for the local treatment of the food being cooked.

[0047] The at least one electric radiant heater serves to heat the cooking chamber or the food being cooked therein by emitting heat radiation. It can be a tubular heater, alternatively or additionally, e.g., a printed circuit board, a resistance surface heating element, etc. If the household cooking appliance is equipped with at least one electric radiant heater, the cooking chamber can also be referred to as the oven chamber.

[0048] The at least one radiant heater can, for example, comprise at least one bottom heat heater for generating a bottom heat or bottom heat function, at least one top heat heater for generating a top heat or top heat function, at least one grill heater for generating a grill function (optionally together with the at least one top heat heater), a ring heater for generating a hot air or hot air function, etc. The setting or operating parameters of a radiant heater can, in particular, comprise different electrical outputs or output levels, e.g. < 0 W, 200 W, ..., 800 W >.

[0049] In one embodiment, the at least one electric radiant heater comprises at least two radiant heaters, and the parameter configuration includes setting values ​​for at least two of the radiant heaters. In other words, different power distributions corresponding to different sets of setting parameters of at least two radiant heaters can be used to implement the method.

[0050] It is a The development allows the radiant heaters to be operated individually or individually, regardless of whether several radiant heaters are operated together when a specific operating mode (e.g., grill mode) is selected. This offers the advantage of being able to provide power distributions that are particularly well-coordinated to achieve the desired distribution of surface properties.

[0051] A further development is that the radiant heaters can be activated (in particular only) as functional "operating mode" groups or heating types that are assigned to specific operating modes. In one variant, exactly one radiant heater can be activated in at least one operating mode, or exactly one radiant heater can be assigned to this operating mode. In at least one other operating mode, at least two radiant heaters are activated, or at least two radiant heaters are assigned to this other operating mode. The specified local power distributions can then result from the power inputs of radiant heaters assigned to different operating modes.

[0052] The household cooking appliance can also be a combination of an oven and a microwave oven, e.g. an oven with additional microwave functionality or a microwave oven with additional oven function, wherein the combination appliance then has at least one microwave device and at least one radiant heater.

[0053] In one embodiment, in order to determine the measured value distribution < V > of the food to be cooked, its measured value distribution < V > is isolated in an image, in particular a thermal image, recorded from the cooking chamber by means of the at least one sensor, i.e. only the measured value distribution of the food to be cooked is considered for the method, while the surface property of the surroundings of the food to be cooked (e.g. a food carrier, cooking chamber walls, etc.) is ignored or removed. In other words, measured values ​​of the surface of the food to be cooked are separated from measured values ​​of other surfaces or image areas. To achieve this, an image recorded by the sensor can, for example, be subjected to image analysis, in particular object recognition. This enables particularly precise, automatic determination of the position of the food to be cooked in the cooking chamber.

[0054] The surface The temperature of the food in the cooking chamber can be determined alternatively or additionally by evaluating thermal changes at the beginning of the cooking process. For example, the surface of the food will generally heat up more slowly than a typically metallic food carrier, which can be detected and evaluated, for example, in a thermal image sequence. Alternatively or additionally, temporal changes in wavelength-dependent reflection can be evaluated.

[0055] Alternatively, the position of the food in the cooking chamber can be determined in another way, e.g. by the user. For example, in a further development, an optical image of the cooking chamber can be recorded and made available to a user for viewing, e.g. on a touch-sensitive screen, for example of the household cooking appliance and / or a user terminal such as a smartphone or tablet PC. The user can then determine the image area that corresponds to the food. This can be done, for example, by moving a finger or stylus along the contour of the food recognized by the user on the touch-sensitive screen. Alternatively, the recorded image can be visually divided into sub-areas, and a user can select those sub-areas on which the food is shown, in particular on which the food is predominantly shown, in particular on which only the food is shown.The household cooking appliance can then only use the parts selected by the user to carry out the process.

[0056] It is a design that the process runs iteratively by the at least one food treatment device is operated in a p-th iteration step with p ≥ 1 for the predetermined time period Δt with a q-th parameter configuration S q with q ≤ p in order to treat food located in the cooking chamber, following the expiration of the time period Δt, a p-th measured value distribution < V p > of the surface property of the food is determined by means of the at least one sensor, the quality value Q p is calculated for the p-th measured value distribution < V p >, and if the predetermined quality criterion is met for the quality value Q p, the at least one food treatment device is operated in a subsequent (p+1)-th iteration step with the same q-th parameter configuration S q, and if the predetermined quality criterion is not met for the quality value Q p,a different parameter configuration S q+1 is set and the at least one food treatment device is then operated in a subsequent (p+1)-th iteration step with the different parameter configuration S q+1. ,

[0057] It is a design that a) the at least one food treatment device is operated in a p-th iteration step p with p ≥ 1 for the predetermined time period Δt with a q-th parameter configuration S q with q ≤ p in order to treat food G located in the cooking chamber 2, b) following the expiration of the time period Δt, a p-th measured value distribution < V p > of the surface property of the food G is determined by means of the at least one sensor 9, c) from a comparison of the p-th measured value distribution < V p > with a p-1-th measured value distribution recorded before step a<V p-1 > a pattern of change<ES q > is calculated and stored, d for all change patterns {< ES q >} stored so far in the course of this procedure, a respective evaluation value B q is calculated, which represents a difference between a deviation of a target distribution < Z > from the measured value distribution < V p > and a deviation of the target distribution < Z > from a prediction pattern < V' p >,where the prediction pattern < V' p > represents an overlay of the measured value distribution < V p > with the associated change pattern < ES q >, e) the parameter configuration S q is set whose evaluation value B q satisfies at least one predetermined criterion, f) the quality value Q p is calculated for the p-th measured value distribution < V p >, g) if the predetermined quality criterion is satisfied for the quality value Q p, branching iteratively to step a) while maintaining the current parameter configuration S q, and h) if the predetermined quality criterion is not satisfied for the quality value Q p, the other parameter configuration S q+1 is set and then branching iteratively to step a).

[0058] By introducing a prediction pattern and an evaluation value, the advantage is achieved that the desired target distribution can be approximated particularly effectively.

[0059] Step g) is carried out in particular for the case that the p-th measured value distribution < V p > is better adapted to the target distribution < Z > than the previous, (p-1)-th measured value distribution < V p-1 >, i.e., has resulted in an improvement of the actual distribution < V > towards achieving the target distribution < Z >. Step h) is then carried out in particular for the case that the p-th measured value distribution < V p > has not resulted in any improvement compared to the previous measured value distribution < V p-1 >.

[0060] This method can therefore mean that the measured value distribution < V p > is possibly even worse (or at least not sufficiently better) adapted to the target distribution < Z > than the previous measured value distribution < V p-1 >, even though the underlying parameter configuration S q, according to its evaluation value B q, was probably the best result of all previously set parameter configurations S q. Consequently, a new parameter configuration S q+1 can now be selected and set which has not been used before. The pool of parameter configurations {S q} for carrying out the method is therefore expanded successively and as needed. Whether the new parameter configuration S q+1 actually results in a better measured value distribution < V p+1 > than the previous measured value distribution < V p > is not known, however.

[0061] It is a further development that the other parameter configuration S q+1 is specifically specified or is selected randomly or pseudorandomly.

[0062] In particular, for homogeneous target distributions < Z > = const. or Z i = const. ∀i can hold.

[0063] The change pattern < E(S q ) > is a function of the measured value distribution < V p > recorded in the p-th iteration step and the measured value distribution < V p-1 > recorded in the previous, (p-1)-th iteration step, which can also be expressed as < E > = f (< V p >, < V p-1 >). The measured value distributions < V p > and < V p-1 > are in turn based on the respective parameter configurations S q, which can be the same or different. In particular, the comparison can be a general difference.

[0064] In case the surface property is a temperature, the change pattern < E(S q ) > represents the temperature variation resulting from a specific parameter configuration S q and can be determined by comparing the temperature distributions at the iteration steps (p-1) and p.

[0065] In addition, for all change patterns {< E(S q ) >} stored so far in the course of this process, a respective evaluation value B q is calculated which represents a difference between a deviation of a target distribution < Z > from the measured value distribution < V p > and a deviation of the target distribution < Z > from a prediction pattern < V' p >, whereby the prediction pattern < V' p > represents an overlay of the measured value distribution < V p > with the associated change pattern < E(S q ) >. The prediction pattern < V' p > corresponds to the measured value distribution that would arise if the change pattern < E(S q ) > were applied to < V p >.

[0066] The evaluation value B q , in turn, indicates how closely an application of the corresponding change pattern < E(S q ) > to the current measured value distribution < V p > is likely to approximate this measured value distribution < V p > to the target distribution < Z >. This provides the advantage of easily estimating the effect of a setting of the available parameter configurations S q on the next iteration step.

[0067] The fact that the parameter configuration S q is set whose evaluation value B q satisfies at least one given criterion means that exactly such an evaluation value B q results, namely the evaluation value B q whose application in the next iteration step is likely to achieve the best approximation to the target distribution < Z >.

[0068] In case the household cooking appliance has a microwave function, it is a further development that the at least one food treatment device comprises a microwave device (6) for introducing microwaves into the cooking chamber (G), wherein different field distributions of the microwaves in the cooking chamber (2) can be generated by at least two parameter configurations (S q ) of the microwave device (6), the surface property is a surface temperature of the food (G) and the at least one sensor (9) comprises at least one infrared sensor (9) directed into the cooking chamber (2) for determining temperature distributions < V > on the food (G), whereby in the process a) the microwave device (6) is operated in a p-th iteration step with p ≥ 1 for a predetermined time period (Δt) with a q-th parameter configuration (S q ) with q ≤ p in order to treat food (G) located in the cooking chamber (2) with microwaves, b) following the expiration of the time period (Δt) by means of the at least one infrared sensor (9) a p-th temperature distribution < V p > of the food (G) is determined, c) from a comparison of the p-th temperature distribution<V p > with a (p-1)-th temperature distribution recorded before step a)<V p-1 > a change pattern (E(S q )> is calculated and stored, d) for all change patterns stored so far in the course of this procedure {<E(S q )>} a respective evaluation value B q is calculated, which represents a difference between a deviation of a target temperature distribution < Z > from the temperature distribution < V p > and a deviation of the target temperature distribution < Z > from a prediction pattern < V' p >,where the prediction pattern < V' p > is a superposition of the temperature distribution<V p > with the associated change pattern < E(S q ) >, e) the parameter configuration (S q ) is set whose evaluation value B q satisfies at least one predetermined criterion, f) the quality value (Q p ) is calculated for the p-th temperature distribution < V p >, g) if the predetermined quality criterion is satisfied for the quality value Q p, branching iteratively to step a) while maintaining the current parameter configuration (S q ), and h) if the predetermined quality criterion is not satisfied for the quality value Q p, the other parameter configuration (S q+1 ) is set and then branching iteratively to step a).

[0069] It is a configuration that the change pattern (E(S q )> is calculated pixel by pixel as the difference between the p-th measured value distribution<V p > and the (p-1)th distribution<V p-1 > is calculated, in particular according to < E S q > = < V p > − < V p − 1 > or with respect to an i-th pixel according to E S q i = V p , i − V p − 1 , i is calculated. The change pattern < E(S q ) > represents the effect of a treatment of the food when setting the parameter configuration S q. The change pattern < E(S q ) > can also be referred to as the change distribution.

[0070] It is a configuration that the evaluation value B q = B(S q ) according to B q = ∑ < Z ∗ > − < V p > d − < Z ∗ > − < V ′ p > d or for i = 1, ..., k pixels according to B q = ∑ i = 1 k Z ∗ i − V p , i d − Z ∗ i − V ′ p , i d is calculated, where the prediction pattern < V' p > is calculated, for example, according to < V ′ p > = < V p > + < E S q > can be calculated and the exponential factor d is given. < E(S q ) >, < V' p > and < V p > can have absolute temperatures as components in the following and are then in particular not - e.g. normalized - relative distributions.

[0071] < Z* > denotes the target distribution, which, based on the current measured value distribution < V p > and the arithmetic mean x arithm of the k components of < V p > derived therefrom, is aimed for as the current target state under consideration of temperature values. x arithm is, in particular, a temperature specification in °C. While the target distribution < Z > is dimensionless, < Z* > is maintained in °C. Thus, the target distribution < Z* > can be component-wise for all Z* i according to Z ∗ i = x arithm ∗ Z i which can also be written as < Z* > = x arithm · < Z >. The exponential factor d specifies the extent to which deviations from the target distribution < Z > should be taken into account. For d > 1, the evaluation value B q favors heating patterns < E(S q ) > that compensate for large differences between the actual measured value distribution < V p > and the target distribution < Z >.

[0072] Likewise, a standardized quality value Q p,norm can be introduced. This has the particular advantage of being independent of absolute temperatures and always lying in the value range from 0 to 1. For this purpose, all k components V i of < V p > are standardized to the maximum value V p,max = max {V p,i}, whereby < V p_norm > is determined component by component according to: V p _ norm , i = V p , i V max

[0073] Analogously, Q p_norm can be calculated according to: Q p _ norm = 1 V p _ max 1 k ∑ i = 1 k V p , i be defined. In the following, standardized and non-standardized values ​​such as Q p_norm and Q p can be used interchangeably. In general, the procedure can be performed interchangeably with standardized (especially unitless) values ​​or quantities and with non-standardized values ​​or quantities.

[0074] Depending on the food being cooked, an individual selection of d may be advantageous. In particular, this allows a distinction to be made between quickly heated food with a low heat capacity (e.g., popcorn) and food with a higher heat capacity and correspondingly slower response (e.g., a larger roast).

[0075] The prediction pattern However, < V' p > can also be calculated in another way, e.g. by weighted addition of the change pattern < E(S q ) > with the measured value distribution < V p >.

[0076] The object is also achieved by a household cooking appliance designed to carry out the method as described above. The household cooking appliance can be designed analogously to the method and has the same advantages.

[0077] It is an embodiment that has at least one food treatment device for treating food located in the cooking chamber with a plurality of parameter configurations, wherein the food can be treated differently locally by at least two parameter configurations, and at least one sensor directed into the cooking chamber for determining distributions < V > of a surface property of the food, and a data processing device for carrying out the method.

[0078] The above-described properties, 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 is explained in more detail in connection with the drawings. Fig. 1 shows a simplified sketch of a household cooking appliance configured to carry out the method described above; and Fig. 2 shows various steps of the method described above.

[0079] Fig.1 shows a sectional side view of a sketch of a household cooking appliance in the form of a microwave oven 1, which is used to carry out the Fig.2 The method is described in more detail. The microwave oven 1 has a cooking chamber 2 with a front loading opening 3, which can be closed by a door 4. In the cooking chamber 2, food G is arranged on a food support 5.

[0080] The household cooking appliance 1 further comprises at least one food treatment unit in the form of a microwave generating device 6. The microwave generating device 6 can comprise, for example, an inverter-controlled microwave generator, a rotatable and / or height-adjustable rotating antenna 7, and / or a rotatable and / or height-adjustable wobbler (not shown). Additionally, the microwave appliance 1 can comprise infrared radiant heaters (not shown), e.g., a bottom heat heater, a top heat heater, and / or a grill heater.

[0081] The microwave generating device 6 is controlled by a control unit 8. In particular, the microwave generating device 6 can be set to at least two parameter configurations S q , S q+1 with different field distributions in the cooking chamber 2. Different parameter configurations S q , S q+1 can, for example, correspond to different angles of rotation of the rotating antenna 7. The angle of rotation thus corresponds to a field-varying setting or operating parameter of the microwave device 1 with at least two setting values ​​in the form of angle of rotation values.

[0082] The control unit 8 is also connected to an optical sensor in the form of a thermal imaging camera 9. The thermal imaging camera 9 is arranged such that it is directed into the cooking chamber 2 and can record a pixel-like thermal image of the food G. Thus, the thermal imaging camera 9 can be used to record or determine a temperature distribution < V > on the surface of the food G.

[0083] The control unit 8 can also be configured to perform the method described above and can also serve as an evaluation device for this purpose. Alternatively, the evaluation can run on an external device, such as a network computer or the so-called "cloud" (not shown).

[0084] Fig.2 shows various steps of the process described above, which are shown, for example, in the Fig.1 described microwave device 1. This method is designed as an iteration method, wherein the number of iterations is specified by the step or iteration index p.

[0085] After the food G is introduced into the cooking chamber 2, the process is started, and an initial step S0 is first performed. This initial step S0 can be assigned an iteration index p = 0.

[0086] In a first sub-step S0-1 of the initial step S0, a target temperature T target is set for the food G.

[0087] Subsequently, in a sub-step S0-2, a first parameter configuration S q = S 1 is set for the rotating antenna 7, and then the food G is treated for a predetermined period of time Δt (e.g., between 2 s and 15 s) using microwaves emitted by the microwave generating device 6. The number of parameter configurations S q set so far within the scope of the method is denoted by the index q. Initially, q = 1. The first parameter configuration S 1 can be predetermined or selected randomly or pseudorandomly.

[0088] After the time period Δt has elapsed, an initial temperature distribution < V p=0 > of the food G is determined in a third sub-step S0-3 using the thermal camera.

[0089] The temperature distribution < V p > of the food G is a segmented temperature distribution in that it has different sub-areas with respective uniform temperature values. For example, the image recorded by the thermal imaging camera can be divided into image segments with a specific edge length or a specific number of pixels. The value represented by a segment is a constant temperature value for this segment and can be determined, for example, by averaging the pixel values ​​contained in the respective segment. In an extreme case, the segments correspond to individual pixels, i.e. the temperature distribution of the food used to carry out the method is a pixel-by-pixel temperature distribution. In the following, it is assumed by way of example that the temperature distribution < V p > of the food G is divided into k segments V p;i with i = 1, ..., k, i.e. that < V p > = < V p;1 ; ...; V p;k >.

[0090] In a method step S1, the microwave device is operated for the predetermined time period Δt with a q-th parameter configuration S q with q ≤ p in order to treat food G located in the cooking chamber with microwaves. If step S1 is run for the first time after the initial step S0 or if step S1 immediately follows the initial step S0, p = q = 1 applies. Since the parameter configuration S q can be selected from a group of a maximum of p parameter configurations, when step S1 is run for the first time, initially only the parameter configuration S 1 set in step S0-2 is available.

[0091] In a step S2, after the time period Δt has elapsed, a p-th temperature distribution < V p > of the food G is determined using the thermal camera. The determination of the temperature distribution can comprise averaging the temperature measurement values ​​of individual pixels assigned to the respective segment V p;i if the segments V p;i comprise more than one pixel.

[0092] In a simplified example with k = 4 segments, the temperature distribution < V p > in iteration step p can look like this: < V p > = 45 48 46 45 where the individual temperature values ​​V p,i are given in degrees Celsius.

[0093] In step S3, a query is made as to whether the temperature distribution < V p > measured in step S2 has reached or exceeded the target temperature value T Target. If yes ("Y"), the method is terminated in step S4. The condition or query in step S3 can generally be written as < V p > ≥ T Target and, in an example, as max V p , i ≥ T ziel , ie, the method is terminated when at least one segment V p,i of the temperature distribution < V p > has exceeded the target temperature. Alternatively, the method can be terminated, for example, when a certain number of segments V p,i , a certain percentage of the segments V p,i , or all segments V p,i have reached or exceeded the target temperature value T target . The latter condition can also be referred to as min {V p,i} ≥ T target .

[0094] If the condition is not met in the query performed in step S3 ("N"), the system branches to step S5.

[0095] In step S5, the previously measured p-th temperature distribution < V p > is compared or linked with the previously measured temperature distribution < V p-1 >, and from this, a change pattern < E(S q ) > specific to the currently set parameter configuration S q is calculated, and this change pattern < E(S q ) > is then saved. This can be done in particular by comparing the temperature distributions < V p-1 > and < V p > segment by segment, i.e., by linking corresponding segments of the two temperature distributions < V p-1 > and < V p > with the same index i.

[0096] Specifically, the change pattern < E(S q ) > can be calculated as the difference between the two temperature distributions < V p-1 > and < V p >, i.e., < E(S q ) > = < V p > - < V p-1 > is determined. The change pattern < E(S q ) > is therefore also divided into k segments E i (S q ). In particular, segments V p;i and V p-1;i with the same index i are subtracted from each other, i.e., for all segments E i (S q ) the connection E i S q = V p − V p − 1 is calculated. The change pattern < E(S q ) > corresponds to a segmented distribution of the temperature differences between the two temporally successive temperature distributions < V p-1 > and < V p > and thus in terms of content to an effect on the food G caused by this set parameter configuration S q.

[0097] Referring to the example above, for example, if < V p − 1 > = 44 42 44 43 applies, results then a change pattern E q ≡ < E(S q ) > according to E q = 45 48 46 45 − 44 42 44 43 = 1 6 2 2

[0098] The change pattern < E(S q ) > can be expressed not only as a temperature difference but also, for example, as a temperature increase per unit of time. The physical unit in this case can be expressed as °C / s.

[0099] In step S6, a respective evaluation value B(S q ) is calculated for all previously stored change patterns < E(S) > = {< E(S q ) >}. During the first run through step S5, only the change pattern < E(S 1 ) > is present, so only one evaluation value B(S 1 ) is calculated.

[0100] The evaluation value B(S q ) is based here on a respective linkage of the temperature distribution < V p > and a prediction pattern < V' p > with a target pattern < Z > for the food G. The prediction pattern < V' p > corresponds to a segment-like temperature distribution, which corresponds to an approximated or approximated temperature distribution for the next iteration step if the parameter configuration S q were applied.

[0101] The prediction pattern < V' p > can be used for a specific change pattern < E(S q ) > e.g. segment-wise according to < V ′ p > = < V p > + < E S q > In the example above, this would < V ′ p > = 46 54 48 47 result.

[0102] The evaluation value B(S q ) represents a quality or a measure of a probable deviation of the prediction pattern < V' p > from a target pattern < Z > for the food G. The "best" calculation value B(S q ) indicates that, when the microwave device is set to the corresponding parameter configuration S q , the target pattern < Z > is likely to be approximated better than with other previously set or tested parameter configurations S q . The evaluation value B q = B(S q ) can also be referred to as the "prediction quality."

[0103] Specifically, the evaluation value B(S q ) can be calculated according to B q = ∑ < Z ∗ > − < V p > d − < Z ∗ > − < V ′ p > d be calculated, which in segment-related representation of the calculation B q = ∑ i = 1 k Z ∗ i − V p , i d − Z ∗ i − V ′ p , i d where k is the number of segments i. In this case, the larger the value of B q , the better the target distribution < Z > is approximated.

[0104] The value of the exponent d is a preset value that determines the extent to which deviations from the target distribution < Z > are taken into account. For d > 1, the evaluation value B favors those change patterns < E(S q ) > that compensate for large differences between the current temperature distribution < V p > and the target distribution < Z >.

[0105] In the above example, if the (normalized) target distribution < Z > is a uniform temperature distribution with T target = 80 °C, ie < Z > = 1 1 1 1 holds, so that for d = 1 and an arithmetic mean x arithm (< V p >) with x arithm = 45 + 48 + 46 + 45 4 ° C = 46 ° C < Z ∗ > = 46 46 46 46 ° C follows and a valuation value is derived B S q = 1 ∗ 46 − 45 − 1 ∗ 46 − 46 + 1 ∗ 46 − 48 − 1 ∗ 46 − 54 + 1 ∗ 46 − 46 − 1 ∗ 46 − 48 + 1 ∗ 46 − 45 − 1 ∗ 46 − 47 = 1 − 0 + 2 − 8 + 0 − 2 + 1 − 1 = 1 − 6 − 2 + 0 = − 7 results.

[0106] For comparison, the evaluation value B j of another, older warming pattern < E j > with j < q is now determined: E j = 3 1 1 2 B S j = 1 ∗ 46 − 45 − 1 ∗ 46 − 48 + 1 ∗ 46 − 48 − 1 ∗ 46 − 49 + 1 ∗ 46 − 46 − 1 ∗ 46 − 47 + 1 ∗ 46 − 45 − 1 ∗ 46 − 47 = 1 − 2 + 2 − 3 + 0 − 1 + 1 − 1 = = 1 − 1 − 1 + 0 = − 3

[0107] As a result, the change pattern < Ej > ≡ < E (S j ) > would be selected, since B(S j ) > B(S q ). Comparing the pattern < V' p (E q ) >, which results from applying < E q > ≡ < E(S q ) >, and < V' p (E j ) >, which results from applying < E(S j ) >, shows that the result < V' p (E j ) > is more uniform: < V ′ p E q > = 46 54 48 47 < V ′ p E j > = 48 49 48 47

[0108] In a variant of the procedure, instead of x arithm < V p > = 1 k ∑ i = 1 k V p , i an average value x' arithm can be used, which already takes into account the expected warming when applying a change pattern < E(S q ) >, which is expressed in the form x ′ arithm < V p > + < E S q > = 1 k ∑ i = 1 k V p , i + E i S q x arithm and x' arithm can be expressed in °C.

[0109] In another variant, the average warming of a change pattern < E(S q ) > can also be taken into account, especially in comparison to the average warming of the entire set of all change patterns.

[0110] It is a further development to exclude change patterns that do not have a certain minimum threshold in their average warming. This can prevent incorrect control of the process, since in the limiting case < E(S q ) > = < 0 > with V p , i = V ′ p , i und für B q = ∑ i = 1 k Z ∗ i − V p , i d − Z ∗ i − V ′ p , i d somit B q = 0 . applies.

[0111] In step S7, the parameter configuration S q from the available group of parameter configurations {S q} that have already been set at least once is selected that is expected to best approximate the target distribution < Z >. This can, in particular, be the parameter configuration S q that corresponds to the largest evaluation value B(S q ).

[0112] In a step S8, for the p-th temperature distribution < V p >, an associated (p-th) scalar quality value Q p (< V p >, < Z >) is calculated, which measures a deviation of the currently measured, p-th temperature distribution < V p > from the target distribution < Z > or represents a measure of the similarity of the currently measured, p-th temperature distribution < V p > with the target distribution < Z >. In the present case, a uniform or homogeneous target distribution < Z > with < Z > = const. has been selected, and the quality value Q p is a difference between the two scalar quantities, arithmetic mean x arithm and median x med , in particular an absolute value of the difference. This enables a particularly simple calculation and leads to a quality value that can approximate a desired target distribution of the surface property of the food particularly well and effectively. The quality value Q can therefore be determined in particular according to Q p = x arithm < V p > − x med < V p > with x arithm < V p > = 1 k ∑ i = 1 k V p , i and with x med < V p > = V p , k + 1 2 f ü r k ungerade 1 2 V p , k 2 + V p , k 2 + 1 f ü r k gerade be calculated. In general, the smaller Q p is, the closer x arithm is to x med and thus < V p > to < Z >. Analogously, the normalized quality value Q p,norm can also be used instead of Q p.

[0113] In this calculation step, in one variant, the temperature distribution < V* p > normalized to the maximum temperature value V p,max of the segments V p,i with its segments V* p,i = e.g. V p,i / V p,max is advantageously used instead of the temperature distribution < V p >.

[0114] In step S9, which may also be optional, a check is made to determine whether Q p ≤ Q target, i.e., whether the quality value Q p has reached a specified target value Q target, i.e., whether the target distribution < Z > or < Z* > has been achieved with sufficient accuracy. If yes ("Y"), the process branches back to step S1.

[0115] If the quality value Q p has not reached the quality value Q target ("N"), the process branches to step S10.

[0116] In step S10, it is queried whether the quality value Q p is better or worse than the quality value Q p-1 calculated for the previous (p-1)th step, which is symbolized by the expression "Q p ↕ Q p-1 ?" In particular, when using the calculation rule Q p = x arithm < V p > − x med < V p > with x arithm < V p > = 1 k ∑ i = 1 k V p , i and x med < V p > = V p , k + 1 2 f ü r k ungerade 1 2 V p , k 2 + V p , k 2 + 1 f ü r k gerade the expression "Q p ↕ Q p-1 ?" by Q p < a ⋅ Q p − 1 ? be replaced, where a · Q p-1 corresponds to a quality threshold and a ≤ 1. In particular, this ensures that the improvement of the quality value Q p compared to the quality value Q p-1 of the previous iteration must reach or exceed a certain minimum value, especially if a < 1, e.g., if a = 0.995. This advantageously prevents quasi-static states from occurring in which only an infinitesimal cooking progress occurs. The minimum value a can be arbitrarily chosen, but then fixed, or it can be adjusted dynamically. If the quality value Q p is better than the quality value Q p-1 ("J"), i.e. in particular if the condition Q p < a · Q p-1 is met, the process branches back to step S1 while maintaining the current parameter configuration S q. The iteration index p is incremented by the value one according to p := p+1.However, if the condition is not fulfilled ("N"), i.e. the quality value Q p is not better or even worse than the quality value Q p-1 , the process branches to step S11.

[0117] If in step S10 ("N") (i.e., in particular, Q p ≥ a · Q p-1), a new parameter configuration S q+1 is set in step S11, and then the process branches back to step S1. The iteration index p is incremented by one according to p := p+1 ("iterative back branching"). The new parameter configuration S q+1 has not yet been set within the process. It can be predefined or chosen randomly or pseudorandomly. This increases the number of group members of the group {S q} of parameter configurations S q by one.

[0118] The method described above enables targeted control of the heating distribution of food using microwave or RF radiation with the aid of data from a thermal imaging camera. This allows for the intelligent control of a microwave cooking appliance with minimal effort, which can achieve the best possible cooking result dynamically and based only on the current moment.

[0119] This means that even in conventional microwave ovens, specific temperature patterns and distributions can be set, something that was previously considered almost impossible - using only a simple thermal camera and a stepper motor for the rotating antenna.

[0120] Of course, the present invention is not limited to the embodiment shown.

[0121] The above method steps can also be performed in a different order or, if necessary, even in parallel. For example, the order of steps S5 to S7 and S8 to S10 can be reversed, steps S3 and S4 can be performed immediately before or after step S8, etc.

[0122] Steps S7 and S8 can also be carried out for step p = 1 if a quality value Q 0 is present, e.g. because it was calculated in the initial step S0.

[0123] In a further, also generally applicable, modification, step S10 can be performed directly after step S7 (i.e., steps S8 and S9 can be omitted). The quality assessment can then be performed predictively in the form Q p = Q p (< V p > + < E(S q ) >, < Z >), even before the parameter configuration S q is actually set.

[0124] It can also be taken into account that, due to the variability of the food being cooked and the overall system, it is possible that certain change patterns < E(S q ) > that were used in the past are no longer valid. It can then generally be advantageous if change patterns < E(S q ) > that have not been used for a long time (e.g. after one minute) are dynamically updated or sporadically checked for their validity. This can be done, for example, by an intermediate step in which the microwave oven 1 is set to the associated parameter configuration S q and then, after the food being cooked has been treated with this parameter configuration S q, the associated change pattern < E(S q ) > is calculated and saved instead of the old change pattern < E(S q ) >.

[0125] Furthermore, the step sequence S3, S4 can be swapped with the step sequence S1, S2. Then, instead of step S1, the program branches back to step S3.

[0126] In addition, standardized or non-standardized values ​​and sizes can be used.

[0127] In general, the procedure can be performed with standardized or non-standardized values ​​and distributions.

[0128] In general, "a", "an", etc., can be understood as a singular or a plural, 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.

[0129] A numerical value may also include the exact number stated as well as a usual tolerance range, as long as this is not explicitly excluded. List of reference symbols

[0130] 1Microwave oven 2Cooking chamber 3Loading opening 4Door 5Food carrier 6Microwave generating device 7Rotating antenna 8Control unit 9Thermal imaging camera B(S q )Evaluation value < E(S q ) >Change pattern GFood pIteration step Q p Quality value of the p-th iteration Q Target Target quality value S q Parameter configuration S1-S11Process steps T Target Target temperature ΔtDuration < V >Temperature distribution on the surface of the food < V p >Temperature distribution in the p-th iteration x arithm Arithmetic mean x med Median value< / v>

Claims

1. Method (S1-S11) for operating a household cooking appliance (1), having - a cooking chamber (2), - at least one food treatment apparatus (6) having multiple parameter configurations (Sq + Sq+1) for treating food (G) located in the cooking chamber (2), wherein the food (G) can be treated locally differently by means of at least two parameter configurations (Sq, Sq+1), - wherein the at least one food treatment apparatus (6) comprises at least one microwave apparatus (6) for introducing microwaves into the cooking chamber (G) and / or at least one radiant heating element for introducing thermal radiation into the cooking chamber (G) and - at least one sensor (9) directed into the cooking chamber (2) to determine measured-value distributions < V > of a surface property of the food (G), wherein, in the method, - the at least one food treatment apparatus (6) is operated for a predetermined time period (Δt) with one of the parameter configurations (Sq) in order to treat food (G) located in the cooking chamber (2), - following expiration of a time period (Δt), a measured-value distribution < V > of a surface property of the food (G) is determined by means of the at least one sensor (9), - a quality value (Q) is determined from the measured-value distribution < V > and, - if the quality value (Q) does not meet a predetermined quality criterion, the food treatment apparatus (6) is subsequently operated with another of the parameter configurations (Sq+1), characterised in that - the quality value (Q) is determined from a difference between two different scalar variables, in the form of different average values (xarith, xmed), calculated from the same at least one measured-value distribution < V >, wherein - the different average values (xarith, xmed) originate from the group comprising a modal mean, a median value, an arithmetic mean, a weighted arithmetic mean, a geometric mean, a weighted geometric mean, a harmonic mean, a weighted harmonic mean, a quadratic mean, a cubic mean, a logarithmic mean, a winsorised mean, a quartile mean, a mean of the shortest half, a Gastwirth-Cohen mean, a moving average, a Hölder mean, a Lehner mean, a Stolarsky mean.

2. Method (S1-S11) according to claim 1, wherein the two average values (xarith, xmed) comprise an arithmetic mean (xarith) and a median value (xmed).

3. Method (S1-S11) according to one of the preceding claims, in which the quality value comprises an amount of the difference of the two average values (xarith, xmed).

4. Method (S1-S11) according to claim 3, in which the quality criterion comprises reaching or falling below a predetermined quality threshold value.

5. Method (S1-S11) according to one of the preceding claims, wherein the at least one sensor (9) comprises at least one sensor (9) directed into the cooking chamber (2) to determine pixel-type temperature distributions < V > on the food (G), and the average values (Xarith, Xmed) are calculated from the individual pixels of the measured-value distribution < V >.

6. Method (S1-S11) according to one of the preceding claims, in which the method is terminated if - the quality value (Q) reaches a predetermined abort criterion and / or - the measured-value distribution < V > reaches a predetermined target value (Vtarget).

7. Method according to claim 6, in which the food (G) has reached the predetermined target value (Vtarget) if max (< Vp >) ≥ Vtarget or min (< Vp >) ≥ Vtarget is met.

8. Method (S1-S11) according to one of the preceding claims, in which different field distributions of the microwaves in the cooking chamber (2) can be generated by means of at least two parameter configurations (Sq) of the microwave apparatus (6).

9. Method according to claim 8, in which a parameter configuration (Sq) comprises in each case a value of at least one operating parameter (Sq) of the microwave apparatus (6) from the group - respective angle of rotation of at least one rotatable antenna (7); - respective height position of at least one rotatable antenna (7); - spatial position of at least one microwave reflector; - microwave frequency; - relative phases between different microwave generators.

10. Method according to one of the preceding claims, in which the method proceeds iteratively, whereby - the at least one food treatment apparatus (6) is operated in a pth iteration step (p) where p ≥ 1 for the predetermined time period (Δt) with a qth parameter configuration (Sq) where q ≤ p in order to treat food (G) located in the cooking chamber (2), - following expiration of the time period (Δt), a pth measured-value distribution < Vp > of the surface property of the food (G) is determined by means of the at least one sensor (9), - for the pth measured-value distribution < Vp >, the quality value (Qp) is calculated, and - if for the quality value (Qp) the predetermined quality criterion is met, the at least one food treatment apparatus (6) is operated in a following (p+1)th iteration step with the same qth parameter configuration (Sq), and - if for the quality value (Qp) the predetermined quality criterion is not met, another parameter configuration (Sq+1) is set, and the at least one food treatment apparatus (6) is then operated in a following (p+1)th iteration step with the other parameter configuration (Sq+1).

11. Method (S1-S11) according to one of the preceding claims, in which a) the at least one food treatment apparatus (6) is operated in a pth iteration step (p) where p ≥ 1 for the predetermined time period (Δt) with a qth parameter configuration (Sq) where q ≤ p in order to treat food (G) located in the cooking chamber (2), b) following expiration of the time period (Δt), a pth measured-value distribution < Vp > of the surface property of the food (G) is determined by means of the at least one sensor (9), c) from a comparison of the pth measured-value distribution < Vp > with a (p-1)th measured-value distribution < Vp-1 > recorded before step a) a change pattern is calculated and saved, d) for all change patterns {< E(Sq) >} saved previously in the course of this method, an evaluation value Bq is calculated in each case, which represents a difference between a deviation of a target distribution < Z > relative to the measured-value distribution < Vp > and a deviation of the target distribution < Z > relative to a prediction pattern < V'p >, wherein the prediction pattern < V'p > represents an overlaying of the measured-value distribution < Vp > with the associated change pattern < E(Sq) >, e) the parameter configuration (Sq) whose evaluation value Bq meets at least one predetermined criterion is set, f) for the pth measured-value distribution < Vp > the quality value (Qp) is calculated, g) if for the quality value (Qp) the predetermined quality criterion is met, the method branches iteratively to step a) while retaining the current parameter configuration (Sq), and h) if for the quality value (Qp) the predetermined quality criterion is not met, the other parameter configuration (Sq+1) is set, and the method then branches iteratively to step a).

12. Method according to claim 11, in which - the at least one food treatment apparatus comprises a microwave apparatus (6) for introducing microwaves into the cooking chamber (G), wherein different field distributions of the microwaves in the cooking chamber (2) can be generated by means of at least two parameter configurations (Sq) of the microwave apparatus (6), - the surface property is a surface temperature of the food (G) and - the at least one sensor (9) comprises at least one infrared sensor (9), in particular thermal imaging camera, directed into the cooking chamber (2) for determining temperature distributions < V > on the food (G), wherein, in the method, a) the at least one microwave apparatus (6) is operated in a pth iteration step (p) where p ≥ 1 for a predetermined time period (Δt) with a qth parameter configuration (Sq) where q ≤ p, in order to treat food (G) located in the cooking chamber (2) with microwaves, b) following expiration of the time period (Δt), a pth measured-value distribution < Vp > of the food (G) is determined by means of the at least one infrared sensor (9), c) from a comparison of the pth temperature distribution < Vp > with a (p-1)th temperature distribution < Vp-1 > recorded before step a) a change pattern < E(Sq) > is calculated and saved, d) for all change patterns {< E(Sq) >} saved previously in the course of this method, an evaluation value Bq is calculated in each case, which represents a difference between a deviation of a target temperature distribution < Z > relative to the temperature distribution < Vp > and a deviation of the target temperature distribution < Z > relative to a prediction pattern < V'p >, wherein the prediction pattern < V'p > represents an overlaying of the temperature distribution < Vp > with the associated change pattern < E(Sq) >, e) the parameter configuration (Sq) whose evaluation value Bq meets at least one predetermined criterion is set, f) for the pth measured-value distribution < Vp >, the quality value (Qp) is calculated, g) if for the quality value (Qp) the predetermined quality criterion is met, the method branches iteratively to step a) while retaining the current parameter configuration (Sq), and h) if for the quality value (Qp) the predetermined quality criterion is not met, the other parameter configuration (Sq+1) is set and the method then branches iteratively to step a).

13. Method according to one of claims 11 to 12, in which the change pattern < E(Sq) > is calculated pixel-by-pixel as the difference between the pth measured-value distribution < Vp > and the (p-1)th distribution < Vp-1 >, in particular according to < E S q > = < V p > − < V p − 1 > .

14. Method according to one of claims 11 to 13, in which the evaluation value Bq is calculated according to B q = ∑ < Z ∗ > − < V p > d − < Z ∗ > − < V ′ p > d with the prediction pattern < V'p > = < Vp > + < E(Sq) > and an exponential factor d.

15. Method according to one of the preceding claims, in which, to determine the measured-value distribution < V > of the food (G), the measured-value distribution < V > is isolated in an image recorded from the cooking chamber (2) by means of the at least one sensor (9).

16. Household cooking appliance (1), having - a cooking chamber (2), - at least one food treatment apparatus (6) having multiple parameter configurations (Sq) for treating food (G) located in the cooking chamber (2), wherein the food (G) can be treated locally differently by means of at least two parameter configurations (Sq), - at least one sensor (9) directed into the cooking chamber (2) to determine measured-value distributions < V > of a surface property of the food (G) and - a data processing apparatus (10), characterised in that the data processing apparatus (10) is configured for performing the method (S1-S11) according to one of the preceding claims.