METHOD FOR OPERATING A DOMESTIC COOKING APPLIANCE AND DOMESTIC COOKING APPLIANCE

Abstract

A domestic cooking appliance having a cooking space, a food treatment device for treating food located in the cooking space with several parameter configurations, and a sensor determining a measured value distributions of a surface property of the food. The food is cooked locally differently using at least two different parameter configurations, wherein the food treatment device is operated for a predetermined period of time with one parameter configuration, and a measured value distribution of the surface property is determined with the sensor after expiry of the period of time. A quality value is determined from the measured value distribution and, when the quality value, as determined by comparing at least two different scalar variables calculated from the same measured value distribution, does not meet a specified quality criterion, the food treatment device is operated with another parameter configuration.

Claims

1-17. (canceled)

18. A method for operating a household cooking appliance, said method comprising: operating a food treatment apparatus of the household cooking appliance for a predetermined time period with one of the at least two parameter configurations, treating food located in a cooking chamber of the food treatment apparatus locally differently by means of the at least two parameter configurations, following an expiration of a time period, determining a measured-value distributions of a surface property of the food with a sensor directed into the cooking chamber, determining a quality value by comparing at least two different scalar variables calculated from the measured-value distribution, and, when the quality value does not meet a predetermined quality criterion, subsequently operating the food treatment apparatus with another of the at least two parameter configurations.

19. The method of claim 18, wherein the at least two different scalar variables are different mathematical average values.

20. The method of claim 19, wherein the at least two different scalar variables comprise an arithmetic mean and a median value.

21. The method of claim 18, wherein the quality value comprises a difference of the at least two different scalar variables.

22. The method of claim 21, wherein the quality value comprises an absolute value of the difference of the at least two different scalar variables.

23. The method of claim 22, wherein the predetermined quality criterion comprises reaching or falling below a predetermined quality threshold value.

24. The method of claim 18, wherein the sensor comprises a sensor directed into the cooking chamber, and further comprising determining a temperature distribution on the food pixel-by-pixel, and calculating the at least two different scalar variables from individual pixels of the measured-value distribution.

25. The method of claim 18, further comprising terminating the method when the quality value reaches a predetermined abort criterion, or when the measured-value distribution reaches a predetermined target value.

26. The method of claim 25, wherein the food has reached the predetermined target value when max (<V.sub.p>)≥V.sub.target or min (<V.sub.p>)≥V.sub.target, with (<V.sub.p>) being the measured-value distribution and V.sub.target being the predetermined target value.

27. The method of claim 18, wherein the food treatment apparatus comprises a microwave apparatus for introducing microwaves into the cooking chamber, and the at least two parameter configurations comprise different field distributions of the microwaves in the cooking chamber.

28. The method of claim 27, wherein the parameter configurations comprise each a value of an operating parameter of the microwave apparatus selected from the group an angle of rotation of a rotatable antenna; a height position of a rotatable antenna; a spatial position of a microwave reflector; a microwave frequency; relative phases between different microwave generators.

29. The method of claim 18, wherein the method proceeds iteratively by treating the food located in a cooking chamber in a p-th iteration step (p≥1) for the predetermined time period with a q-th parameter configuration (q≤p), following the expiration of the time period, determining a p-th measured-value distribution of the surface property of the food with the sensor, determining the quality value for the p-th measured-value distribution, when the quality value meets the predetermined quality criterion, operating the food treatment apparatus a subsequent (p+1)-th iteration step with an unchanged q-th parameter configuration, and when the quality value fails to meet the predetermined quality criterion, setting another of the at least two parameter configurations, and operating the food treatment apparatus in the subsequent (p+1)-th iteration step with the other of the at least two parameter configurations.

30. The method of claim 18, further comprising: a) treating the food located in a cooking chamber in a p-th iteration step (p≥1) for the predetermined time period with a q-th parameter configuration (q≤p), b) following the expiration of the time period, determining a p-th measured-value distribution of the surface property of the food with the sensor, c) calculating a change pattern from a comparison of the p-th measured-value distribution with a (p−1)-th measured-value distribution recorded before step a) and saving the change pattern, d) calculating an evaluation value for all previously saved change patterns, 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 an overlay of the measured-value distribution with an associated change pattern, e) setting the parameter configuration that has an evaluation value meeting a predetermined criterion, f) calculating the quality value for the p-th measured-value distribution, g) when the quality value meets the predetermined quality criterion, branching iteratively to step a) while retaining the current parameter configuration, and h) when the quality value fails to meet the predetermined quality criterion, setting the other of the at least two parameter configurations, and then branching iteratively to step a).

31. The method of claim 30, wherein the food treatment apparatus comprises a microwave apparatus for introducing microwaves into the cooking chamber, with the at least two parameter configurations generating different field distributions of the microwaves in the cooking chamber, the surface property is a surface temperature of the food, and the sensor comprises an infrared sensor or a thermal imaging camera directed into the cooking chamber.

32. The method of claim 30, wherein the change pattern (<E(S.sub.q)>) is calculated pixel-by-pixel as the difference between the p-th measured-value distribution (<V.sub.p>) and the preceding (p−1)-th distribution (<V.sub.p−1>) according to
<E(S.sub.q)>=<V.sub.p>−<V.sub.p−1>.

33. The method of claim 30, wherein the evaluation value (B.sub.q) is calculated according to
B.sub.q=Σ(|<Z*>−<V.sub.p>|.sub.d−|<Z*>−<V′.sub.p>|.sup.d), wherein <Z*> is the target distribution, <V.sub.p> is the p-th measured-value distribution, <V′.sub.p> is the prediction pattern <V′.sub.p>=<V.sub.p>+<E(S.sub.q)> with (<E(S.sub.q)>) representing the change pattern, and d is an exponential factor.

34. The method of claim 18, further comprising determining the measured-value distribution of the food by isolating in an image recorded from the cooking chamber with the sensor.

35. A household cooking appliance, comprising a cooking chamber; a food treatment apparatus having at least two parameter configurations for treating food located in the cooking chamber; a sensor directed into the cooking chamber to determine measured-value distributions of a surface property of the food; and a control unit configured to: operate the food treatment apparatus for a predetermined time period with one of the at least two parameter configurations, treat the food located in a cooking chamber of the food treatment apparatus locally differently by means of the at least two parameter configurations, following an expiration of a time period, determine a measured-value distributions of a surface property of the food with the sensor directed into the cooking chamber, determine a quality value by comparing at least two different scalar variables calculated from the measured-value distribution, and, when the quality value does not meet a predetermined quality criterion, subsequently operate the food treatment apparatus with another of the at least two parameter configurations.

Description

[0138] The above-described properties, features and advantages of this invention and the manner in which they are achieved can be more clearly understood with reference to the schematic description below of an exemplary embodiment which is explained in more detail with reference to the drawings.

[0139] FIG. 1 shows a simplified outline of a household cooking appliance which is configured to perform the above-described method; and

[0140] FIG. 2 shows various process steps of the above-described method.

[0141] FIG. 1 shows a sectional side view of an outline of a household cooking appliance in the form of a microwave appliance 1, which is configured to perform the method described in more detail in FIG. 2. The microwave appliance 1 has a cooking chamber 2 with a loading opening 3 on the front, which can be closed by means of a door 4. In the cooking chamber 2, food G is arranged on a food support 5.

[0142] The household cooking appliance 1 also has at least one food treatment unit in the form of a microwave generating apparatus 6. The microwave generating apparatus 6 can, for example, have an inverter-controlled microwave generator, a rotationally adjustable and/or height-adjustable rotary antenna 7 and/or a rotationally adjustable and/or height-adjustable wobbler (not shown). In addition, the microwave appliance 1 can have infrared radiant heating elements (not shown), for example a bottom-heat heating element, a top-heat heating element and/or a grill heating element.

[0143] The microwave generating apparatus 6 is controlled by means of a control unit 8. In particular, the microwave generating apparatus 6 can be set to at least two parameter configurations S.sub.q, S.sub.q+1 with different field distributions in the cooking chamber 2. Different parameter configurations S.sub.q, S.sub.q+1 can correspond, for example, to different angles of rotation of the rotary antenna 7. The angle of rotation thus corresponds to a field-varying setting or operating parameter of the microwave appliance 1 with at least two settings in the form of angle-of-rotation values.

[0144] 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-type thermal image of the food G. As a result, the thermal imaging camera 9 can be used to record or determine a temperature distribution <V> on the surface of the food G.

[0145] 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 be performed on an external instance such as a network computer or the so-called “cloud” (not shown).

[0146] FIG. 2 shows various process steps of the above-described method, which can run, for example, in the microwave appliance 1 described in FIG. 1. This method is designed as an iteration method, the number of iterations being indicated by the step or iteration index p.

[0147] After the food G has been introduced into the cooking chamber 2, the method is started, and an initial or starting step S0 is first performed for this purpose. An iteration index p=0 can be assigned to this starting step S0.

[0148] In a first sub-step S0-1 of the starting step S0, a target temperature T.sub.target is set for the food G.

[0149] In a sub-step S0-2, a first parameter configuration S.sub.q=S.sub.1 is subsequently set for the rotary antenna 7, and the food G is then treated for a predetermined time Δt (for example, between 2 s and 15 s) by means of microwaves emitted by the microwave generating apparatus 6. The number of parameter configurations S.sub.q previously set within the scope of the method is designated by the index q. Initially, therefore, q=1. The first parameter configuration S.sub.1 can be predetermined or can be chosen randomly or pseudorandomly.

[0150] After the time period Δt has elapsed, an initial temperature distribution <V.sub.p=0> of the food G is determined in a third sub-step S0-3 by means of the thermal camera.

[0151] The temperature distribution <V.sub.p> of the food G is a segmental temperature distribution in that it has different sub-areas, each with uniform temperature values. For example, the image recorded by the thermal imaging camera can be divided into image segments of a certain edge length or a certain 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 perform the method is a pixel-by-pixel temperature distribution. In the following it is assumed as an 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;1; . . . ; V.sub.p;k> applies.

[0152] In a method step S1, the microwave apparatus is operated for the predetermined time period Δt with a q.sup.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 run through for the first time after the starting step S0 or if step S1 immediately follows the starting step S0, then p=q=1. Since the parameter configuration S.sub.q can be selected from a group of no more than p parameter configurations, then when step S1 is run through for the first time, initially only the parameter configuration S1 set in step S0-2 is available.

[0153] In a step S2, after the time period Δt has elapsed, a p.sup.th temperature distribution <V.sub.p> of the food G is determined by means of the thermal camera. The determination of the temperature distribution can comprise averaging of the temperature measurement values of individual pixels assigned to the respective segments V.sub.p;i, if the segments V.sub.p;i comprise more than one pixel.

[0154] In a simplified example with k=4 segments, the temperature distribution <V.sub.p> in iteration step p can appear as follows:

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

[0155] wherein the individual temperature values V.sub.p,i are given in degrees Celsius.

[0156] 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 a step S4. The condition or query in step S3 can generally be written as <V.sub.p>≥T.sub.target and in one example embodied as

max {V.sub.p,i}≥T.sub.target

[0157] i.e. the method is terminated if at least one segment V.sub.p,i of the temperature distribution <V.sub.p> has exceeded the target temperature. Alternatively, the method can be terminated, for example, if a certain number of segments V.sub.p,i, a certain percentage of the segments V.sub.p,i or all the segments V.sub.p,i have reached or exceeded the target temperature value T.sub.target. The latter condition can also be denoted as min {V.sub.p,i}≥T.sub.target.

[0158] If in the query performed in step S3 the condition is not met (“N”), the method branches to step S5.

[0159] In step S5, the previously measured p.sup.th temperature distribution <V.sub.p> is compared or linked to the previously measured temperature distribution <V.sub.p−1> and from this a specific change pattern <E(S.sub.q)> for the currently set parameter configuration S.sub.q is calculated, and this change pattern <E(S.sub.q)> is then saved. This can in particular be performed in such a way that the temperature distributions <V.sub.p−1> and <V.sub.p> are compared segment by segment, that is to say corresponding segments of the two temperature distributions <V.sub.p−1> and <V.sub.p> are linked to one another with the same index i.

[0160] Specifically, the change pattern <E(S.sub.q)> can be calculated as the 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 change pattern <E(S.sub.q)> is therefore also divided into k segments E.sub.i(S.sub.q). In particular, segments V.sub.p;i and V.sub.p−1;i are subtracted from one another with the same index i, i.e. for all segments E.sub.i(S.sub.q), the link

E.sub.i(S.sub.q)=V.sub.p−V.sub.p−1 [0161] a. is calculated. The change pattern <E(S.sub.q)> corresponds to a segment-by-segment distribution of the temperature differences between the two temporally consecutive temperature distributions <V.sub.p−1> and <V.sub.p> and thus substantively to an effect on the food G caused by this set parameter configuration S.sub.q.

[0162] Based on the example above, for example if

[00007]$.Math.V_{p-1}.Math.=\left[\begin{array}{cc}44& 42\\ 44& 43\end{array}\right]$ [0163] b. applies, a change pattern E.sub.q=<E(S.sub.q)> is then given by

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

[0164] The change pattern <E(S.sub.q)> can be specified not only as a temperature difference, but also for example as a temperature increase per unit of time. In this case, the physical unit can be specified, for example, as ° C./s.

[0165] In a step S6, for all previously stored change patterns <E(S)>={<E(S.sub.q)>}, a respective evaluation value B(S.sub.q) is calculated. When step S5 is run through for the first time, only the change pattern <E(S.sub.1)> is available, so that only one evaluation value B(S.sub.1) is then calculated.

[0166] The evaluation value B(S.sub.q) is based here on a respective linking of the temperature distribution <V.sub.p> and a prediction pattern <V′.sub.p> to a target pattern <Z> for the food G. The prediction pattern <V′.sub.p> corresponds to a segment-type temperature distribution, which corresponds to a temperature distribution approximated for the next iteration step, if the parameter configuration S.sub.q were applied.

[0167] The prediction pattern <V′.sub.p> can be calculated for a certain change pattern <E(S.sub.q)>, for example, segment by segment according to

<V′.sub.p>=<V.sub.p>+<E(S.sub.q)> [0168] c. In the above example, the result would be

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

[0169] The evaluation value B(S.sub.q) represents a degree or a measure of a probable deviation of the prediction pattern <V′.sub.p> from a target pattern <Z> for the food G. The “best” calculation value B(S.sub.q) indicates that if the microwave apparatus is set to the associated parameter configuration S.sub.q, the target pattern <Z> is expected to be better approximated than with other previously set or trialed parameter configurations S.sub.q. The evaluation value B.sub.q=B(S.sub.q) can also be referred to as “prediction quality”.

[0170] Specifically, the evaluation value B(S.sub.q) can be calculated according to

B.sub.q=Σ(|<Z*>−<V.sub.p>|.sup.d−|Z*>−<V′.sub.p>|.sup.d) [0171] d. which corresponds in segment-by-segment representation to the calculation

B.sub.q=Σ.sub.i=1.sup.k(|Z*.sub.i−V.sub.p−1|.sup.d−|Z*.sub.i−V′.sub.p,i|.sup.d) [0172] e. where k is the number of segments i. In this case, the greater the value of B.sub.q, the better the target distribution <Z> is approximated.

[0173] The value of the exponent d is a preset value that determines how strongly deviations from the target distribution <Z> are taken into account. For d>1, it follows that the evaluation value B prefers change patterns <E(S.sub.q)> which compensate for large differences between the current temperature distribution <V.sub.p> and the target distribution <Z>.

[0174] In the above example, if an even temperature distribution with T.sub.target=80° C. is desired as the (normalized) target distribution <Z>, i.e.

[00010]$.Math.Z.Math.=\left[\begin{array}{cc}1& 1\\ 1& 1\end{array}\right]$ [0175] f. applies, such that with d=1 and an arithmetic mean X.sub.arithm (<V.sub.p>) where

[00011]$x_{\mathrm{arithm}}=\frac{\left(45+48+46+45\right)}{4}°C.=46°C..Math.Z^{*}.Math.=\left[\begin{array}{cc}46& 46\\ 46& 46\end{array}\right]°C.$ [0176] g. follows, and this results in an evaluation value

B(Sq)=(|1*46−45|−|1*46−46|)+(|1*46−48|—|1*46−54|)+(|1*46−46|−|1*46−48|)+(|1*46−45|−11*46−47|)=(1−0)+(2−8)+(0−2)+(1−1)=1−6−2+0=−7

[0177] For comparison, the evaluation value B.sub.j of another, older heating pattern <E.sub.j> is now determined with j<q:

[00012]$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$

[0178] As a result, the change pattern <E.sub.j>≡=<E(S.sub.j)> would be selected, since B(S.sub.j)>B(S.sub.q) holds. The comparison of the patterns <V′.sub.p(E.sub.q)>, which results from applying <E.sub.q>≡=<E(S.sub.q)>, and <V′.sub.p(E.sub.j)>, which results from applying <E(S.sub.j)>, shows that the result <V′.sub.p(E.sub.j)> is more even:

[00013]$.Math.V_p^{\prime }\left(E_q\right).Math.=\left[\begin{array}{cc}46& 54\\ 48& 47\end{array}\right].Math.V_p^{\prime }\left(E_j\right).Math.=\left[\begin{array}{cc}48& 49\\ 48& 47\end{array}\right]$ [0179] h. In a variant of the method, instead of

[00014]$x_{\mathrm{arithm}}\left(.Math.V_p.Math.\right)=\frac{1}{k}{.Math.}_{i=1}^k{v}_{p,i}$ [0180] i. an average value x′.sub.arithm can be used, which already takes into consideration the expected heating when a change pattern <E(S.sub.q)> is applied, which can be represented in the form

[00015]$x_{\mathrm{arithm}}^{\prime }\left(.Math.V_p.Math.+.Math.E\left(S_q\right).Math.\right)=\frac{1}{k}{.Math.}_{i=1}^k\left(v_{p,i}+E_i\left(S_q\right)\right)$

[0181] x.sub.arithm and x′.sub.arithm can be given in ° C.

[0182] In another variant, the average heating of a change pattern <E(S.sub.q)> can also be taken into consideration, especially in comparison to the average heating of the totality of all change patterns.

[0183] It is a development to exclude change patterns that do not have a certain minimum threshold in their average heating. This can prevent incorrect control of the method, since in the limit case <E(S.sub.q)>=<0> with [0184] j.

[00016]$V_{p,i}=V_{p,i}^{\prime }\mathrm{and}\mathrm{for}$$B_q={.Math.}_{i=1}^k\left({.Math.Z_i^{*}-V_{p,i}.Math.}^d-{.Math.Z_i^{*}-V_{p,i}^{\prime }.Math.}^d\right)$$\mathrm{consequently}{B}_q=0\mathrm{holds}.$

[0185] In a step S7, the parameter configuration S.sub.q from the available group of parameter configurations {S.sub.q} which have already been set at least once is set, which is likely to best approximate the target distribution <Z>. In particular, this can be the parameter configuration S.sub.q that corresponds to the greatest evaluation value B(S.sub.q).

[0186] In a step S8, for the p.sup.th temperature distribution <V.sub.p> an associated (p.sup.th) scalar quality value Q.sub.p<V.sub.p>, <Z>) is also calculated, which measures a deviation of the currently measured p.sup.th temperature distribution <V.sub.p> from the target distribution <Z> or represents a measure of the similarity of the currently measured p.sup.th temperature distribution <V.sub.p> to the target distribution <Z>. In the case here, for example, an even or homogeneous target distribution <Z> has been selected with <Z>=const., and the quality value Q.sub.p is a difference of the two scalar variables arithmetic mean x.sub.arithm and median value x.sub.med, in particular an amount of the difference. This enables a particularly easy calculation and results in a quality value which can approximate a desired target distribution of the surface property of the food particularly closely and effectively. The quality value Q can consequently be calculated in particular according to

Q.sub.p=|x.sub.arithm(<V.sub.p>)−x.sub.med(<V.sub.p>)| [0187] k. where

[00017]$Q_p=.Math.x_{\mathrm{arithm}}\left(<V_p>\right)-x_{\mathrm{med}}\left(<V_p>\right).Math.$$k.\mathrm{where}$$x_{\mathrm{arithm}}\left(<V_p>\right)=\frac{1}{k}\underset{i=1}{\overset{k}{.Math.}}{V}_{p,i}$$l.\mathrm{and}\mathrm{where}$

[00018]$x_{med}\left(.Math.V_p.Math.\right)=\{\frac{V_{p,\left(\frac{k}{2}\right)}\mathrm{for}k\mathrm{uneven}}{\frac{1}{2}\left(V_{p,\left(\frac{k}{2}\right)}+V_{p,\left(\frac{k}{2}+1\right)}\right)\mathrm{for}k\mathrm{even}}$

[0188] The smaller Q.sub.p, the closer x.sub.arithm generally is to x.sub.med and thus <V.sub.p> is to <Z>. Analogously, the normalized quality value Q.sub.p,norm can also be used instead of Q.sub.p.

[0189] In this calculation step, in one variant, instead of the temperature distribution <V.sub.p>, the temperature distribution <V*.sub.p>, normalized to the maximum temperature value V.sub.p,max of the segments V.sub.p,i, with its segments V*.sub.p,i=e.g. V.sub.p,i/V.sub.p,max, is used.

[0190] In step S9, which can also be optional, it is checked whether Q.sub.p<Q.sub.target 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 achieved sufficiently precisely. If yes (“Y”), the method branches back to step S1.

[0191] If the quality value Q.sub.p has not reached the quality value Q.sub.target (“N”), the method branches to step S10.

[0192] 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 calculated for the previous (p−1).sup.th step, which is symbolized by the expression “Q.sub.pQ.sub.p−1?”. In particular, if the calculation rule

Q.sub.p=|x.sub.arithm(<V.sub.p>)−x.sub.med(<V.sub.p>)| [0193] m. is used, where

[00019]$x_{\mathrm{arithm}}\left(.Math.V_p.Math.\right)=\frac{1}{k}{.Math.}_{i=1}^k{V}_{p,i}$ [0194] n. and

[00020]$x_{\mathrm{med}}\left(<V_p>\right)=\{\frac{v_{p,\left(\frac{k+1}{2}\right)}\mathrm{for}k\mathrm{uneven}}{\frac{1}{2}\left(V_{p,\left(\frac{k}{2}\right)}+V_{p,\left(\frac{k}{2}+1\right)}\right)\mathrm{for}k\mathrm{even}}$ [0195] o. the expression “Q.sub.pQ.sub.p−1?” can be replaced by

Q.sub.p<a.Math.Q.sub.p−1? [0196] p. where a.Math.Q.sub.p−1 corresponds to the quality threshold value and a≤1. In this way, it can in particular be achieved that the improvement in the quality value Q.sub.p compared to the quality value Q.sub.p−1 of the previous iteration must reach or exceed a certain minimum, in particular if a<1, e.g. where a=0.995. This can advantageously prevent quasi-static states occurring in which only an infinitesimal cooking progress occurs. The minimum a can be chosen randomly, but then it can be fixed, or it can be adjusted dynamically. If the quality value Q.sub.p is better than the quality value Q.sub.p−1 (“Y”), i.e. if in particular the condition Q.sub.p<a.Math.Q.sub.p−1 is met, the method branches back to step S1, with the current parameter configuration S.sub.q being maintained. In this case, the iteration index p is incremented by the value one according to p:=p+1. If, however, the condition is not met (“N”), the quality value Q.sub.p is therefore not better or is even worse than the quality value Q.sub.p−1, the method branches to step S11.

[0197] If in step S10 (“N”) (i.e. in particular Q.sub.p≥a.Math.Q.sub.p−1 applies), a new parameter configuration S.sub.q+1 is set in a step S11 and the method then branches back to step S1. 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 not yet been set within the scope of the method. It can be predetermined or chosen randomly or pseudorandomly. This increases the number of group members of the group {S.sub.q} of parameter configurations S.sub.q by one.

[0198] The above-described method enables a targeted control of a heating distribution of food when using microwave or HF radiation with the aid of data from a thermal imaging camera. Intelligent control of a microwave cooking appliance, which can achieve a best possible cooking result dynamically and only in relation to the current moment, can be implemented with little outlay. Consequently, targeted temperature patterns and distributions can also be set in conventional microwave appliances, which was previously considered almost impossible—and this can be done merely with the aid of a simple thermal camera and a stepper motor for the rotary antenna.

[0199] Of course, the present invention is not limited to the exemplary embodiment shown.

[0200] Thus, the above method steps can also be performed in different sequences or, optionally, in parallel. For example, the sequence 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.

[0201] Steps S7 and S8 can also already be performed for step p=1 if a quality value Q.sub.0 is available, for example because it was calculated as part of the starting step S0.

[0202] In a further, also generally usable, modification, step S10 can be performed directly after step S7 (i.e. steps S8 and S9 are omitted). The quality evaluation can then be performed predictively in the form Q.sub.p=Q.sub.p (<V.sub.p>+<E(S.sub.q)>, <Z>) even before the parameter configuration S.sub.q is actually set.

[0203] It can also be taken into consideration that, due to the variability of the food and the overall system, it is possible that change patterns <E(S.sub.q)> determined in the past are no longer valid. It can then be generally advantageous if change patterns <E(S.sub.q)> that have no longer been used for a prolonged period (for example, upwards of a minute) are updated dynamically or are checked sporadically for their validity. This can be done, for example, by means of an intermediate step in which the microwave appliance 1 is set to the associated parameter configuration S.sub.q and then, after treatment of the food with this parameter configuration S.sub.q, the associated change pattern <E(S.sub.q)> is calculated and is saved in place of the old change pattern <E(S.sub.q)>.

[0204] Furthermore, the step sequence S3, S4 can be swapped with the step sequence S1, S2. The method then branches back to step S3 instead of step S1.

[0205] In addition, normalized or non-normalized values and variables can be used.

[0206] In general, the method can be performed with normalized or non-normalized values and distributions.

[0207] In general, “a”, “an” etc. can be understood to mean 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.

[0208] A numerical specification can also comprise precisely the specified number as well as a customary tolerance range, unless this is explicitly excluded.

LIST OF REFERENCE CHARACTERS

[0209] 1 Microwave appliance [0210] 2 Cooking chamber [0211] 3 Loading opening [0212] 4 Door [0213] 5 Food support [0214] 6 Microwave generating apparatus [0215] 7 Rotary antenna [0216] 8 Control unit [0217] 9 Thermal imaging camera [0218] B(S.sub.q) Evaluation value [0219] <E(S.sub.q)> Change pattern [0220] G Food [0221] p Iteration step [0222] Q.sub.p Quality value of the p.sup.th iteration [0223] Q.sub.target Target quality value [0224] S.sub.q Parameter configuration [0225] S1-S11 Method steps [0226] T.sub.target Target temperature [0227] Δt Time period [0228] <V> Temperature distribution on the surface of the food [0229] <V.sub.p> Temperature distribution in the p.sup.th iteration [0230] X.sub.arith Arithmetic mean [0231] X.sub.med Median value