METHOD FOR OPERATING A DOMESTIC COOKING APPLIANCE AND DOMESTIC COOKING APPLIANCE

Abstract

In a method for treating food in a food handling device with a parameter configuration a measured value distribution of a surface property of the food is determined after expiration of the period of time by means of a sensor. A pattern of change is calculated from a comparison of the p-th measured value distribution with a measured value distribution determined previously. An assessment value Bq which represents a difference between a deviation of a target distribution from the measured value distribution and a deviation of the target distribution from a prediction pattern is calculated for all patterns of change stored hitherto. The prediction pattern is formed by superimposing the measured value distribution with the respective pattern of change, and the parameter configuration is set for which the assessment value Bq meets at least one predetermined criterion.

Claims

1-14. (canceled)

15. A method for operating a household cooking appliance, comprising: a) operating a food handling device for treating food located in a cooking chamber of the household cooking appliance, wherein the food is able to be treated locally differently by at least two parameter configurations, in a p-th iteration step, where p≥1, for a predetermined time period with a q-th parameter configuration, where q≤p, b) when the predetermined time period has expired, determining with a sensor directed into the cooking chamber a p-th measured value distribution of a surface property of the food, c) comparing the p-th measured value distribution with a (p−1)-th measured value distribution recorded before step a), and calculating therefrom a pattern of change and storing the pattern of change, d) calculating for each stored patterns of change a respective assessment value which represents a difference between a deviation of a target distribution from the measured value distribution and a deviation of the target distribution from a prediction pattern, with the prediction pattern representing a superimposition of the measured value distribution with the respective pattern of change, e) setting a parameter configuration of the at least two parameter configurations having a corresponding assessment value that satisfies at least one predetermined criterion, f) calculating for the p-th measured value distribution a quality value that specifies a deviation of the p-th measured value distribution from a target measured value distribution, and g) when the quality value has a smaller deviation from the target measured value distribution than a (p−1)-th quality value, returning to step a) while maintaining a current parameter configuration, and h) when the quality value has a larger deviation from the target measured value distribution than the (p−1)-th quality value, setting a new parameter configuration, and thereafter returning to step a).

16. The method of claim 15, wherein the measured value distribution and the target measured value distribution are temperature distributions.

17. The method of claim 16, wherein the food handling device comprises a microwave device for introducing microwaves into the cooking chamber to treat the food in the cooking chamber, wherein the at least two parameter configurations of the microwave device comprise different field distributions of the microwaves in the cooking chamber, the sensor comprises an infrared sensor, the measured value is a temperature, and the surface property is a surface temperature of the food.

18. The method of claim 15, wherein the measured value distribution, the target measured value distribution and the pattern of change are each segmented distributions having k segments.

19. The method of claim 15, wherein the method is terminated when the quality value reaches a predetermined criterion, or when the food reaches a predetermined target value.

20. The method as claimed in claim 19, wherein the predetermined criterion is a target quality value.

21. The method of claim 19, wherein the predetermined target value is reached when the predetermined target is less than a maximum of the measured value distribution or when the predetermined target is less than a minimum of the measured value distribution.

22. The method of claim 18, wherein the pattern of change <E(S.sub.q)> in each segment is calculated as the difference between the p-th measured value distribution <V.sub.p> and the (p−1)-th measured value distribution <V.sub.p−1> for that segment according to
<E(S.sub.q)>=<V.sub.p>−<V.sub.p−1>.

23. The method of claim 18, wherein the assessment value B.sub.q is calculated according to
B.sub.q=Σ(|<Z*>−<V.sub.p>|.sup.d−|<Z*>−<V′.sub.p>|.sup.d) with <Z*> representing the target distribution, <V.sub.p> representing the p-th measured value distribution, and <V′.sub.p> representing the prediction pattern <V′.sub.p>=<V.sub.p>+<E(S.sub.q)>, with representing the pattern of change <E(S.sub.q)> in each segment, and with d being an exponential factor.

24. The method of claim 15, wherein the quality value Q.sub.p is calculated according to $Q_p=\sqrt{\frac{1}{k}\underset{i=1}{\overset{k}{.Math.}}{\left(D.Math.<Z>-<V_p>\right)}^2}$ $\mathrm{where}D=\varnothing \left(<V_k>\right)$

25. The method of claim 17, wherein the parameter configuration comprises at least one operating parameter of the microwave device selected from the group consisting of a rotational angle of a rotatable antenna; a vertical position of the rotatable antenna; a spatial position of a microwave reflector; a microwave frequency; and relative phases between different microwave generators.

26. The method of claim 17, wherein the infrared sensor comprises a thermal imaging camera recording a pixel-based thermal image.

27. The method of claim 15, wherein the measured value distribution is determined from an image from the cooking chamber recorded by the sensor.

28. A household cooking appliance comprising a cooking chamber, a food handling device for handling food located in the cooking chamber with a plurality of parameter configurations, wherein the food is able to be treated locally differently by at least two parameter configurations, a sensor which is directed into the cooking chamber for determining distributions of a surface property of the food, and a data processing device for operating the household cooking appliance by: a) operating the food handling device in a p-th iteration step, where p≥1, for a predetermined period of time with a q-th parameter configuration, where q≤p, b) when the period of time has expired, determining with a sensor directed into the cooking chamber a p-th measured value distribution of a surface property of the food, c) comparing the p-th measured value distribution with a (p−1)-th measured value distribution recorded before step a), and calculating therefrom a pattern of change and storing the pattern of change, d) calculating for each stored patterns of change a respective assessment value which represents a difference between a deviation of a target distribution from the measured value distribution and a deviation of the target distribution from a prediction pattern, with the prediction pattern representing a superimposition of the measured value distribution with the respective pattern of change, e) setting the parameter configuration having a corresponding assessment value that satisfies at least one predetermined criterion, f) calculating for the p-th measured value distribution a quality value that specifies a deviation of the measured value distribution from the target measured value distribution, and g) when the quality value has a smaller deviation from the target measured value distribution than the (p−1)-th quality value, returning to step a) while maintaining a current parameter configuration, and h) when the quality value has a larger deviation from the target measured value distribution than the (p−1)-th quality value, setting a new parameter configuration, and thereafter returning to step a).

29. The household cooking appliance of claim 28, wherein the measured value distribution and the target measured value distribution are temperature distributions.

30. The household cooking appliance of claim 28, wherein the food handling device comprises a microwave device for introducing microwaves into the cooking chamber, wherein the at least two parameter configurations of the microwave device comprise different field distributions of the microwaves in the cooking chamber, the sensor comprises an infrared sensor, the measured value is a temperature, and the surface property is a surface temperature of the food.

Description

[0101] The above-described properties, features and advantages of this invention and the manner in which they are achieved are understood more clearly and explicitly in connection with the following schematic description of an exemplary embodiment which is described in more detail in connection with the drawings.

[0102] FIG. 1 shows a simplified sketch of a household cooking appliance which is designed for carrying out the above-described method; and

[0103] FIG. 2 shows different steps of the above-described method.

[0104] FIG. 1 shows as a sectional side view a sketch of a household cooking appliance in the form of a microwave appliance 1 which is designed for the execution of the method described in more detail in FIG. 2. The microwave appliance 1 has a cooking chamber 2 with a front-side loading opening 3 which is closable by means of a door 4. Food G is arranged on a food support 5 in the cooking chamber 2.

[0105] The household cooking appliance 1 also has at least one food handling unit in the form of a microwave generating device 6. The microwave generating device 6, for example, may be 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 may have infrared radiant elements (not shown), for example a bottom heat-heating element, a top heat-heating element and/or a grill heating element.

[0106] The microwave generating device 6 is activated by means of a control unit 8. In particular, the microwave generating device 6 may be set to at least two parameter configurations S.sub.q with different field distributions in the cooking chamber 2. Different parameter configurations may correspond, for example, to different rotational angles of the rotating antenna 7. The rotational angle thus corresponds to a field-varying setting or operating parameter of the microwave appliance 1 with at least two setting values in the form of rotational angle values.

[0107] The control unit 8 is additionally 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 may record a pixel-based thermal image of the food G. As a result, the thermal imaging camera 9 may be used for recording or determining a temperature distribution <V> on the surface of the food G.

[0108] The control unit 8 may additionally be designed to carry out the above-described method and may also serve as an evaluation device. Alternatively, the evaluation may run on an entity which is external to the appliance, such as a network computer or the so-called “cloud” (not shown).

[0109] FIG. 2 shows different steps of the above-described method, which may be executed, for example, in the microwave appliance 1 described in FIG. 1. This method is configured as an iteration method, wherein the number of iterations is specified by the step or iteration index p.

[0110] After introducing the food G into the cooking chamber 2 the method is started and firstly a starting or initial step S0 is carried out. This initial step S0 may be assigned an iteration index p=0.

[0111] In a first partial step S0-1 of the initial step S0, a target temperature T.sub.target is set for the food G.

[0112] Subsequently in a partial step S0-2 a first parameter configuration S.sub.q=S.sub.1 for the rotating antenna 7 is set and then the food G is handled for a predetermined time period Δt (for example between 2 s and 15 s) by means of the microwaves output by the microwave generating device 6. The number of parameter configurations S.sub.q set previously during the course of the method is denoted by the index q. At the start, therefore, q=1. The first parameter configuration S.sub.1 may be predetermined or selected randomly or pseudo-randomly.

[0113] After the expiration of the time period Δt in a third partial step S0-3 an initial temperature distribution <V.sub.p=0> of the food G is determined by means of the thermal camera.

[0114] The temperature distribution <V.sub.p> of the food G is a segmented temperature distribution such that it has different sub-regions respectively with uniform temperature values. For example, the image recorded by the thermal imaging camera may be divided into image segments of a specific edge length or specific number of pixels. The value represented by a segment is a constant temperature value for this segment and may 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 for carrying out the method is a pixel-based temperature distribution. Hereinafter it should be assumed by way of example that the temperature distribution <V.sub.p> of the food G is divided into k segments V.sub.p;i where i=1, . . . , k, i.e. <V.sub.p>=<V.sub.p:l; . . . ; V.sub.p:k> applies.

[0115] In a method step S1 the microwave device is operated for the predetermined time period Δt with a q-th parameter configuration S.sub.q where q≤p in order to treat food G located in the cooking chamber with microwaves. If step S1 is carried out for the first time after the initial step S0 and/or step S1 immediately follows the initial step S0, the following applies p=q=1. Since the parameter configuration S.sub.q may be selected from a group of maximum p parameter configurations, therefore, when step S1 is carried out for the first time, initially only the parameter configuration S1 set in step S0-2 is present.

[0116] In a step S2 after the expiration of the time period Δt a p-th temperature distribution <V.sub.p> of the food G is determined by means of the thermal camera. The determination of the temperature distribution may comprise an averaging of temperature measured values of individual pixels assigned to the respective segment V.sub.p;i if the segments V.sub.p;i comprise more than one pixel.

[0117] In a simplified example where k=4 segments, the temperature distribution <V.sub.p> in the iteration step p may look as follows:

[00006]$<V_p>=\left[\begin{array}{cc}45& 48\\ 46& 45\end{array}\right]$

wherein the individual temperature values V.sub.p,i are specified in degrees Celsius.

[0118] In a step S3 a query is made as to whether the temperature distribution <V.sub.p> measured in step S2 has reached or exceeded the target temperature value T.sub.target. If yes (“Y”) the method is terminated in step S4. The condition or query in step S3 may generally be written as <V.sub.p>≥T.sub.target and in one example expressed as

max{V.sub.p,i}≥T.sub.target

i.e. the method is terminated when at least one segment V.sub.p,i of the temperature distribution <V.sub.p> has exceeded the target temperature. Alternatively, the method may be terminated, for example, when a specific number of segments V.sub.p,i a specific percentage of segments V.sub.p,i or all of the segments V.sub.p,i has or have reached or exceeded the target temperature value T.sub.target. The last condition may also be denoted as min {V.sub.p,i}≥T.sub.target.

[0119] If the condition is not fulfilled in the query carried out in step S3 (“N”) the process branches to step S5.

[0120] In step S5 the previously measured p-th temperature distribution <V.sub.p> is compared and/or combined with the previously measured temperature distribution <V.sub.p−1>, and a specific pattern of change <E(S.sub.q)> for the currently set parameter configuration S.sub.q is calculated therefrom and this pattern of change <E(S.sub.q)> is then stored. This may be carried out, in particular, such that the temperature distributions <V.sub.p−1> and <V.sub.p> are compared in segments, i.e. corresponding segments of the two temperature distributions <V.sub.p−1> and <V.sub.p> with the same index i are combined with one another.

[0121] Specifically the pattern of change <E(S.sub.q)> may be calculated as a difference between the two temperature distributions <V.sub.p−1> and <V.sub.p>, i.e. <E(S.sub.q)>=<V.sub.p>−<V.sub.p−1> is determined. The pattern of change <E(S.sub.q)> is thus also divided into k segments E (S.sub.q). In this case, in particular, segments V.sub.p;i and V.sub.p−1;i with the same index i are subtracted from one another, i.e. for all segments E.sub.i, (S.sub.q) the operation is calculated as

E.sub.i(S.sub.q)=V.sub.p−V.sub.p−1

[0122] The pattern of change <E(S.sub.q)> corresponds to a segmented distribution of the temperature differences between the two chronologically following temperature distributions <V.sub.p−1> and <V.sub.p> and thus substantially to an effect on the food G produced by this set parameter configuration S.sub.q.

[0123] Relative to the above example, when

[00007]$<V_{p-1}>=\left[\begin{array}{cc}44& 42\\ 44& 43\end{array}\right]$

applies, for example, this results in a pattern of change E.sub.q=<E(S.sub.q)> according to

[00008]$E_q=\left[\begin{array}{cc}45& 48\\ 46& 45\end{array}\right]-\left[\begin{array}{cc}44& 42\\ 44& 43\end{array}\right]=\left[\begin{array}{cc}1& 6\\ 2& 2\end{array}\right]$

[0124] The pattern of change <E(S.sub.q)> may also be specified, for example, as the temperature increase per time unit, apart from the temperature difference. The physical unit may in this case be specified, for example, as ° C./s.

[0125] In a step S6 one respective assessment value B(S.sub.q) is calculated for all patterns of change <E(S)>={<E(S.sub.q)>} stored hitherto. When step S5 is first carried out, only the pattern of change <E(S.sub.1)> is present, so that only one assessment value B(S.sub.1) is then calculated.

[0126] The assessment value B(S.sub.q) is based in this case on a respective combination of the temperature distribution <V.sub.p> and a prediction pattern <V′.sub.p> with a target pattern <Z> for the food G. In this case the prediction pattern <V′.sub.p> corresponds to a segmented temperature distribution which corresponds to a temperature distribution which approximates or is closer to the next iteration step, if the parameter configuration S.sub.q were to be used.

[0127] The prediction pattern <V′.sub.p> may be calculated for a specific pattern of change <E(S.sub.q)>, for example segmented, according to

<V′.sub.p>=<V.sub.p>+<E(S.sub.q)>

[0128] In the above example in this case

[00009]$<V_p^{\prime }>=\left[\begin{array}{cc}46& 54\\ 48& 47\end{array}\right]$

would result.

[0129] The assessment value B(S.sub.q) represents a quality or an amount of a likely deviation of the prediction pattern <V′.sub.p> to a target pattern <Z> for the food G. The “best” calculation value B(S.sub.q) specifies that when the microwave device is set to the parameter configuration S.sub.q associated therewith, this is likely to be closer to the target pattern <Z> than with other parameter configurations S.sub.q already set or tested. The assessment value B.sub.q=B(S.sub.q) may also be denoted as the “prediction quality”.

[0130] Specifically the assessment value B(S.sub.q) may be calculated according to

B.sub.q=Σ(|<Z*>−<V.sub.p>|.sup.d−|<Z*>−<V′.sub.p>|.sup.d)

which corresponds in the segmented representation of the calculation

[00010]$B_q=\underset{i=1}{\overset{k}{.Math.}}\left({.Math.Z_i^{*}-V_{p,i}.Math.}^d-{.Math.Z_i^{*}-V_{p,i}^{\prime }.Math.}^d\right)$

where k corresponds to the number of segments i. In this case the closer it is to the target distribution <Z>, the greater the value of B.sub.q.

[0131] The value of the exponent d is a preset value which determines how much consideration is given to the deviations from the target distribution <Z>. Where d>1 it follows that the assessment value B prefers such patterns of change <E(S.sub.q)> which compensate for large differences between the current temperature distribution <V.sub.p> and the target distribution <Z>.

[0132] In the above example, if a uniform temperature distribution where T.sub.Target=80° C. were to be desired as a (standardized) target distribution <Z>, i.e. the following would apply

[00011]$<Z>=\left[\begin{array}{cc}1& 1\\ 1& 1\end{array}\right]$

where d=1 and with an average value D of Ø (<V.sub.p>) where

[00012]$D=\frac{\left(45+48+46+45\right)}{4}°C.=46°C.<Z^{*}>=\left[\begin{array}{cc}46& 46\\ 46& 46\end{array}\right]°C.$

follows and an assessment value

[00013]$E_j=\left[\begin{array}{cc}3& 1\\ 1& 2\end{array}\right]$$B\left(S_j\right)=\left(.Math.1*46-45.Math.-.Math.1*46-48.Math.\right)+\left(.Math.1*46-48.Math.-.Math.1*46-49.Math.\right)+\left(.Math.1*46-46.Math.-.Math.1*46-47.Math.\right)+\left(.Math.1-46-45.Math.-.Math.1*46-47.Math.\right)=\left(1-2\right)+\left(2-3\right)+\left(0-1\right)+\left(1-1\right)=1-1-1+0=-3$

results therefrom.

[0133] For comparison, therefore, the assessment value B.sub.3 of a further older heating pattern <E.sub.j> where j<q, is determined:

[00014]$B\left(S_q\right)=\left(.Math.1*46-45.Math.-.Math.1*46-46.Math.\right)+\left(.Math.1*46-48.Math.-.Math.1*46-54.Math.\right)+\left(.Math.1*46-46.Math.-.Math.1*46-48.Math.\right)+\left(.Math.1-46-45.Math.-.Math.1*46-47.Math.\right)=\left(1-0\right)+\left(2-8\right)+\left(0-2\right)+\left(1-1\right)=1-6-2+0=7$

[0134] As a result the pattern of change <Ej>≡<E(S.sub.j)> would be selected, since B(S.sub.j)>B(S.sub.q) applies. The comparison of the patterns <V′.sub.p(E.sub.q)>, which is produced by applying <E.sub.q>≡<E(S.sub.q)>, and <V′.sub.p (E.sub.j)>, which is produced by applying <E(S.sub.3)>, shows that the result <V′.sub.p(E.sub.j)> is more uniform:

[00015]$<V_p^{\prime }\left(E_q\right)>=\left[\begin{array}{cc}46& 54\\ 48& 47\end{array}\right]<V_p^{\prime }\left(E_j\right)>=\left[\begin{array}{cc}48& 49\\ 48& 47\end{array}\right]$

[0135] In a variant of the method, an average value D′ may be used instead of

[00016]$D=\varnothing \left(<V_k>\right)=\frac{1}{k}\underset{i=1}{\overset{k}{.Math.}}{V}_{p,i}$

said average value already taking into consideration the heating to be anticipated when applying a pattern of change <E(S.sub.q)>, which may be represented in the formula

[00017]$D^{\prime }=\varnothing \left(<\mathrm{Vk}>+<E\left(S_q\right)>\right)=\frac{1}{k}\underset{i=1}{\overset{k}{.Math.}}\left(V_{p,i}+E_i\left(S_q\right)\right)$

D and D′ may be specified in ° C.

[0136] In a further variant, the average heating of a pattern of change <E(S.sub.q)> may also be considered, in particular, in comparison with the average heating of all of the patterns of change.

[0137] In one development, patterns of change which do not have a certain minimum threshold value in their average heating are excluded. Thus the method may be prevented from being incorrectly controlled, since in the borderline case <E(Sq)>=<0> where

V.sub.p,i=V′.sub.p,i and for B.sub.q=Σ.sub.i=1.sup.k(|Z*.sub.i−V.sub.p,i|.sup.d−|Z*.sub.i−V′.sub.p,i|.sup.d) thus B.sub.q=0.

applies.

[0138] In a step S7 the parameter configuration S.sub.q which is likely to be closest to the target distribution <Z> is set from the available group of parameter configurations {S.sub.q} already previously set at least once. This may be, in particular, the parameter configuration S.sub.q which corresponds to the largest assessment value B(S.sub.q).

[0139] In a step S8 an associated (p-th) scalar quality value Q.sub.p<V.sub.p>, <Z>) is also calculated for the p-th temperature distribution <V.sub.p>, said quality value measuring a deviation of the current measured p-th temperature distribution <V.sub.p> from the target distribution <Z> or a degree of similarity of the currently measured p-th temperature distribution <V.sub.p> to the target distribution <Z>. For example the quality value Q.sub.p may be calculated according to

[00018]$Q_p=\sqrt{\frac{1}{k}\underset{i=1}{\overset{k}{.Math.}}{\left(D.Math.<Z>-<V_p>\right)}^2}=\sqrt{\frac{1}{k}\underset{i=1}{\overset{k}{.Math.}}{\left(D.Math.Z_i-V_{p,i}\right)}^2}$

wherein D corresponds to the average value of all segments V.sub.p,i which may be calculated, for example, according to

[00019]$D=\varnothing \left(<V_k>\right)=\frac{1}{k}\underset{i=1}{\overset{k}{.Math.}}{V}_{p,i}$

[0140] In this case D is in a value range 0≤D≤1. The smaller the value of Q.sub.p, the closer <V.sub.p> is to <Z>. Similarly Q.sub.p,norm may also be used instead of Q.sub.p.

[0141] In the case of a uniform target temperature distribution (which may be expressed, for example, as <Z>=constant) Q.sub.p corresponds to the standard deviation. Q.sub.p may also be denoted, therefore, in the above practical embodiment as the “modified standard deviation”

[0142] In a variant, in this calculation step the temperature distribution <V*.sub.p> which is standardized to the maximum temperature value V.sub.p,max of the segments V.sub.p,i is advantageously used with its segments V*.sub.p,i=e.g. V.sub.p,i/V.sub.p,max instead of the temperature distribution <V.sub.p>, and at the same time the average value D is also calculated from the standardized segments V*.sub.p,i.

[0143] In step S9, which may also be optional, it is monitored whether Q.sub.p≤Q.sub.Ziel applies, i.e. whether the quality value Q.sub.p has reached a predetermined target value Q.sub.target, i.e. whether the target distribution <Z> or <Z*> has been reached in a sufficiently accurate manner. If yes (“Y”) the process branches back to step S1.

[0144] If the quality value Q.sub.p has not reached the at least one criterion (“N”), the process branches to step S10.

[0145] In step S10 a query is made as to whether the quality value Q.sub.p is better or worse than the quality value Q.sub.p−1 (<V.sub.p−1>, <Z>) calculated for the previous (p−1)-th step, which is symbolized by the expression “Q.sub.pQ.sub.p−1?”. If yes (“Y”) whilst maintaining the current parameter configuration S.sub.q, the process branches back to step S1. In this case the iteration index p is incremented by the value of one according to p:=p+1.

[0146] If in step S10 the quality value Q.sub.p is worse than the quality value Q.sub.p−1 (“N”) (i.e. the correspondence with the target distribution <Z> for the p-th execution is worse than in the previous (p−1)-th execution), in a step S11 a new parameter configuration S.sub.q+1 is set and then the process branches back to step S1. In this case the iteration index p is incremented by one according to p:=p+1 (“iterative branching back”). The new parameter configuration S.sub.q+1 has hitherto not yet been set during the course of the method. It may be predetermined or randomly or pseudo-randomly selected. As a result, the number of group members of the group {S.sub.q} of parameter configurations S.sub.q increases by one.

[0147] The above-described method permits a targeted control of a heating distribution of food when using microwaves and/or HF radiation, with the assistance of data from a thermal imaging camera. Thus it is possible to implement with little effort an intelligent control of a microwave cooking appliance which is able to achieve the best possible cooking result dynamically and only relative to the current moment. Thus targeted temperature patterns and distributions may be set even in conventional microwave appliances, which hitherto was virtually excluded—and namely merely with the assistance of a single thermal camera and a stepper motor for the rotating antenna.

[0148] Naturally the present invention is not limited to the exemplary embodiment shown.

[0149] Thus the above method steps may also be carried out in different sequences or optionally also in parallel. For example, the sequence of steps S5 to S7 and S8 to S10 may be reversed, the steps S3 and S3 may be carried out directly before step S8 or afterward, etc.

[0150] The steps S7 and S8 may also be already carried out for the step p=1, if a quality value Q.sub.0 is present, for example, since it has been calculated during the course of the initial step S0.

[0151] In step S10 it may additionally be required that the improvement of the quality value Q.sub.p relative to the quality value Q.sub.p−1 of the previous iteration has to reach or exceed a specific minimum amount a, for example in the form of the condition Q.sub.p≤Q.sub.p−1.Math.a where a<1, e.g. a=0.995, if a smaller value of Q means a better correspondence. The minimum amount a may be selected in an arbitrary manner but then fixed, or it may be dynamically adapted. Thus it may be advantageously prevented that quasi-static states occur in which the cooking progress is merely infinitesimal. If the condition is not fulfilled, the process branches to step S11. Step S10 may thus be configured such that the process only branches back directly to step S1 when the condition Q.sub.p<Q.sub.p−1 and also the condition Q.sub.p<Q.sub.p−1.Math.a where a<1 are fulfilled.

[0152] If a different definition of Q.sub.p is used, this optionally requires an expedient adaptation. For example, the condition for a quality value Q.sub.p, which is defined in the formula, is that its 870 numerical functional value rises when it is closer to the target distribution <Z>, corresponding to Q.sub.p≥Q.sub.p−1.Math.a, wherein a>1.

[0153] In a further, also generally useful, modification, step 10 may be carried out directly after step S7 (i.e. steps S8 and S9 are dispensed with). The quality assessment Q may thus be carried out predictively in the formula Q=Q.sub.p (<V.sub.p>+<E(S.sub.q)>, <Z>) even before the parameter configuration S.sub.q is actually set. If the quality value Q.sub.p is smaller than the quality value Q.sub.p−1, the parameter configuration S.sub.q is not used but a new parameter configuration S.sub.q+1 is sought and then the process branches back to step S1. This has the advantage that a parameter configuration S.sub.q is not set since it would not improve the overall result, although it represents the best of the currently available options, based on the results of the assessment function B.sub.q.

[0154] It may also be considered that due to the variability of the food and the overall system it is possible that patterns of change <E(S.sub.q)> which were determined in the past are no longer valid. Generally it may then be advantageous if patterns of change <E(S.sub.q)> no longer used over a longer period of time (for example after one minute) are dynamically updated and/or sporadically monitored for their validity. This may be carried out, for example, by an intermediate step in which the microwave appliance 1 is set to the associated parameter configuration S.sub.q and then after the food is handled with this parameter configuration S.sub.q the associated pattern of change <E(S.sub.q)> is calculated and is stored instead of the old pattern of change <E(S.sub.q)>.

[0155] Moreover the step sequence S3, S4 may be exchanged for the step sequence S1, S2. Then the process branches back to S3 instead of step S1.

[0156] Generally the method may be carried out with standardized or non-standardized values and distributions.

[0157] Generally, “one” etc. may be understood as a singular or a plurality, in particular in the sense of “at least one” or “one or more”, etc. provided this is not explicitly excluded, for example by the expression “exactly one”, etc.

[0158] A specified number may also encompass exactly the specified number and also a general tolerance range as long as this is not explicitly excluded.

LIST OF REFERENCE CHARACTERS

[0159] 1 Microwave appliance [0160] 2 Cooking chamber [0161] 3 Loading opening [0162] 4 Door [0163] 5 Food support [0164] 6 Microwave generating device [0165] 7 Rotating antenna [0166] 8 Control unit [0167] 9 Thermal imaging camera [0168] B(S.sub.q) Assessment value [0169] <E(S.sub.q)> Pattern of change [0170] G Food [0171] p Iteration step [0172] Q.sub.p Quality value of the p-th iteration [0173] Q.sub.target Target quality value [0174] S.sub.q Parameter configuration [0175] S1-S11 Method steps [0176] T.sub.target Target temperature [0177] Δt Time period [0178] <V> Temperature distribution on surface of food [0179] <V.sub.p> Temperature distribution in the p-th iteration