Method for determining a drying process and humidity measuring device

Abstract

A computer-implemented method for determining a time-dependent drying profile x(t) of an object to be examined (10) is proposed, the method comprising at least the following method steps: providing measurement data, which relate to at least a depth d of the object to be examined (10); i At least one time-dependent humidity distribution φ i (t); - using the time-dependent humidity profile φ i (t), determining at least one time-dependent reduced drying course τ of the examination object (10) i (t); - using the at least one time-dependent reduced drying variation τ i The method further comprises calibrating a material model relating to the object to be examined (10) in the presence of a calibrated material model; determining, in particular simulating, a moisture value and / or a moisture distribution and / or a time-dependent drying process τ(t) of the object to be examined using the calibrated material model. A moisture measuring device (16) for carrying out the method is also proposed.

Description

Background Art

[0001] Reliable determination of material and building moisture levels is an important aspect of modern construction. Commercially available methods and moisture measuring devices based on dielectric measurement are currently used to measure the moisture level of the material to be checked non-destructively on site. With the aid of these methods and moisture measuring devices, the current moisture value can be determined or a continuous output of the moisture value can be achieved.

[0002] Furthermore, DE 10 2014 218 375 A1 discloses an NMR measuring device having a sensor system, wherein the sensor system has at least one nuclear magnetic resonance sensor (NMR sensor) which is provided for determining a moisture value of a material to be examined.

[0003] The invention relates to a computer-implemented method for determining a time-dependent drying profile τ(t) of an examination object and to a moisture measuring device for carrying out the method. Summary of the invention

[0004] A computer-implemented method for determining a time-dependent drying course τ(t) of an object to be examined is proposed. The method according to the invention is characterized by at least the following method steps:

[0005] • Providing measurement data which relate at least to the depth d of the object under examination i At least one time-dependent humidity distribution φ i (t);

[0006] • Using at least one time-dependent humidity profile φ i (t), determining at least one time-dependent reduced drying course τ of the examination object i (t), in particular at least to a depth d i at least one time-dependent reduced drying process up to the present invention;

[0007] • using the at least one time-dependent reduced drying profile τ i (t), calibrating the material model related to the inspection object;

[0008] • Using a calibrated material model, a moisture value and / or a moisture distribution and / or a time-dependent drying profile τ(t) of the examination object is determined, in particular simulated.

[0009] "Determining the time-dependent drying process τ(t) of the inspection object" should be understood as: determining an association or allocation rule, such as a function, a calibration rule, a table, etc., which enables: at a given time point t, an absolute or relative humidity value and / or an absolute or relative dryness value to be specified. The terms humidity and dryness or humidity process and drying process are used synonymously in this article. Here, the dryness describes the moisture content of the inspection object in the case of a drying process. Similarly, the drying process describes the humidity process during the drying process. The humidity value specifies the humidity still present in the inspection object and can be described, for example, by the concentration of evaporable water (as distinguished from chemically firmly bound crystal water or hydration water) in the inspection object. The association or allocation rule of the time-dependent drying process τ(t) can especially enable: the description is also for a prediction of a future time point t, especially for an extrapolation of the prediction. According to the present invention, a time-dependent drying process τ(t) that is usually not directly obtainable in terms of measurement technology is determined, especially simulated, based on the measurement data provided for the inspection object on which it is based.

[0010] An "inspection object" is to be understood as a material that is to be inspected for moisture contained in it or moisture no longer contained in it, that is to say dryness. For example and not exclusively, the material may be a wall, a floor, a ceiling, a plaster, an organic structure (in particular a body part) and / or a part of a terrain. The inspection object may consist, for example, of wood, glass, plastic, concrete, plaster, stone, brick, plaster of Paris, metal, organic material, etc.

[0011] "Computer-implemented" is to be understood to mean, in particular, that the method is implemented in the form of software or a mixture of software and hardware. In this case, the method is executed by means of a processor device, in particular a control device and / or an evaluation device, in particular a control device and / or an evaluation device of a humidity measuring device. To execute the method, the processor device, in particular the control device and / or the evaluation device, can also have at least one storage device in which the method is stored as a machine-readable computer program.

[0012] "Providing measurement data" should be understood in particular as providing these measurement data for further processing to a device that performs the method, in particular a processor device, a control device, an evaluation device, etc. In one embodiment of the method, the corresponding information can be provided by an operator. Here, the provision can be realized in particular by inputting or selecting by an operator, for example, by means of an input device or by means of a menu selection, etc. In an alternative or additional embodiment of the method, the provision can be realized by reading these measurement data from a file, for example, by reading these measurement data stored on a data server. In another alternative or additional embodiment of the method, these measurement data can be determined or measured and then provided using a sensor device, in particular a sensor device of a humidity measuring device that implements the method. Here, the input device is usually also a corresponding sensor device. The sensor device should be understood as a device by means of which the corresponding measurement data of the inspection object can be detected. Such a sensor device can have one or more sensors in particular. In one embodiment, the sensor device can be implemented as a depth-resolved humidity sensor, in particular a depth-resolved NMR sensor, a depth-resolved humidity sensor array (including a plurality of humidity sensors embedded in the inspection object), a depth-resolved radar, etc., or can be implemented as a combination thereof. Other depth-resolved humidity sensors are known to those skilled in the art. By providing the measurement data with the aid of at least one sensor device, a particularly accurate and reliable determination of these measurement data can be achieved. In particular, measurement data that match the inspection object can be provided for performing the method. In addition, erroneous inputs, in particular due to deviations from the actually existing measurement data, can be avoided.

[0013] The provided measurement data relate at least to the depth d of the object under examination. i At least one time-dependent humidity distribution φ i (t). Humidity distribution φ i (t) can be determined and provided in particular as a time-dependent vertical moisture distribution or as a moisture-depth distribution in the examination object, that is to say into the examination object. Such measurement data can be measured and thus determined and provided in particular when using the above-described depth-resolving moisture sensor. Advantageously, this can describe a simplified method, in which it is not necessary to determine the moisture distribution completely through the examination object (that is to say up to a maximum depth corresponding to the thickness of the examination object). As a result, the computing power of a processor device for carrying out the method and / or the requirements on the moisture sensor to be used can be significantly reduced.

[0014] Then, using the time-dependent humidity distribution φ i(t), determining at least one time-dependent reduced drying course τ of the examination object i (t), in particular at least to a depth d i In one embodiment, the drying process can be reduced by performing at least one time-dependent drying process up to a depth d i Humidity distribution φ i (t) is integrated to calculate the time-dependent reduced drying process τ i (t):

[0015]

[0016] Alternatively, the humidity distribution φ i (t) is averaged to calculate the reduced drying process τ i (t):

[0017]

[0018] The time-dependent drying process describes the drying process in which the surface of the substrate has a thickness d i The (average) humidity in the layer of thickness d i Corresponds to the total thickness of the inspection object (d ges ), the time-dependent reduced drying process τ i (t) is the same as the time-dependent drying process τ(t).

[0019] Then, the time-dependent reduced drying process τ i (t) is used to calibrate a material model related to the inspection object. The material model includes in particular physical and / or chemical parameters and influences that are important for the time-dependent drying process τ(t). In one embodiment of the method according to the invention, the material model takes into account at least the liquid water conductivity, vapor conductivity and water vapor surface exchange of the inspection object. "Calibration" of the material model is to be understood as: configuring the material model with initial values ​​so that the material model describes the inspection object to be characterized as closely as possible to reality. In particular, the calibration is performed by adapting the material model so that the material model describes the at least one time-dependent reduced drying process τ as well as possible. i (t)—for example, by combining the material model with the at least one time-dependent reduced drying process τ i (t) Fitting.

[0020] According to the invention, a method can be described as follows, which is used to determine or calibrate material properties, especially water transport properties, incorporated in a material model based on previously provided, especially measured measurement data about the examination object. In particular, the material model allows taking into account material properties that are important for drying, with the aid of which the examination object can be particularly well characterized and described with respect to its drying properties. The material model in particular describes at least the moisture transport phenomena in the examination object. In one embodiment, the material model can be implemented as a so-called pore model, as is known for capillary materials, for example, see the Burdine model (Burdine, 1953, "Relative Permeability Calculations From Pore Size Distribution Data") or the Mualem model (Mualem, 1976, "A new model for predicting the hydraulic conductivity of unsaturated porous media") known from the prior art. The material model can include in particular a so-called continuum approach in which the physical effects underlying the moisture transport process can be described by transport equations (see: "Coupled heat air and moisture transfer in building structures" by P. Häupl, J. Grunewald, H. Fechner, H. Stopp). By means of calibration of the material model, the material model can be adapted to the material properties of the object under examination and thus allows particularly accurate conclusions to be drawn when using the material model.

[0021] Finally, using the calibrated material model, a time-dependent drying profile τ(t) of the entire examination object is determined, in particular simulated. The time-dependent drying profile τ(t) determined in this way is based on information about the moisture transport properties of the examination object (including boundary conditions such as surface dimensions, etc.). i In particular, with the aid of the now determined time-dependent drying course τ(t), it is possible to draw conclusions about the time-dependent moisture or time-dependent dryness of the entire examination object - based on previously available measurement data which ultimately correspond to information about only a part of the examination object.

[0022] The method according to the invention makes it possible to carry out a particularly rapid calibration of a material model describing the object under examination, using which the moisture transport in the object under examination can be evaluated, in particular calculated, and in particular extrapolated at different points in time. The calibrated material model allows conclusions to be drawn about the moisture or dryness of the entire object under examination. The material model also allows conclusions to be drawn about various aspects, for example, regarding the energy evaluation and / or mold risk of the object under examination, including the characterization of the water transport properties of the object under examination. Compared to methods known from the prior art, in which the characterization of the object under examination and the calibration of the material model are tasks that last for several months, conclusions about the time-dependent drying process can be obtained in a significantly shorter period of time when the method according to the invention is used.

[0023] In one embodiment of the method according to the invention, the drying time t0 is determined, in particular extrapolated, as a function of the time-dependent drying profile τ(t) of the object under examination. For the drying time t0, it is applicable that the value τ(t0) of the time-dependent drying profile falls below a defined, in particular predeterminable, threshold τ0. In this way, it is possible to reliably estimate when the object under examination can be considered “dry”, in particular with regard to the required degree of dryness of the object under examination during subsequent processing of the object under examination. In particular in the case of tasks in the construction industry, such as in the case of further processing of poured concrete or a troweled surface, early determination and knowledge of this time point is particularly advantageous.

[0024] In one embodiment of the method according to the invention, in the last method step and / or in another method step, drying parameters that influence the time-dependent drying course τ(t) of the object to be examined are modeled or simulated and / or changed. In this way, in particular drying parameters such as temperature, heating, air exchange rate, air humidity, radiation influences (e.g. solar radiation intensity, solar radiation duration) and the like can be included in the material model and can therefore be taken into account in principle. These drying parameters are in particular the framework conditions for drying the object to be examined. In one embodiment of the method according to the invention, optimized drying parameters are calculated, which in the modeling or simulation lead to a faster time-dependent drying course τ of the object to be examined. opt (t). Here, "faster" can be understood in particular as "shortening" or "speeding up". This calculation can be carried out, for example, by changing the drying parameters and calculating the time-dependent drying process τ opt (t) or t0 (the time to reach the limit value τ0). If the drying process τ opt(t), the optimized drying parameters can be output as operating instructions to a user of the method in one embodiment of the method according to the invention. Operating instructions should be understood here, for example, as suggestions on how the user may change the drying parameters in order to achieve a faster drying process of the inspection object. Such operating instructions may, for example, include instructions such as "ventilate" or "open the window" or "increase the temperature" and the like. In this way, the user of the method can specifically influence the conditions for drying the inspection object so that the inspection object is dried as quickly as possible.

[0025] In one embodiment of the method according to the invention, the at least one time-dependent humidity profile φ is provided for at least six different points in time, in particular for at least six different days. i In this way, it is possible to use the time-dependent humidity distribution φ at the first point in time. i The material model is first calibrated initially (and roughly) with the measured data of (t). Then, the time-dependent moisture distribution φ at other time points is involved. i The further measurement data of the material model (t) allow, in particular in parallel with the drying process of the examination object, a recalibration of the calibration of the material model, in particular a refinement, until a particularly high degree of agreement between the material model and the examination object is achieved. In this way, conclusions obtained using the material model can be further specified and in particular more precisely derived. In one embodiment of the method according to the invention, the determination of the time-dependent drying course τ(t) of the examination object is also refined, in particular continuously or quasi-continuously (that is to say repeatedly), using the further measurement data.

[0026] In one embodiment of the method according to the invention, at least two, in particular at least three, depths d of the examination object are measured. i providing a time-dependent humidity profile φ i (t) The measured data of humidity at depth d i (Until this depth, the applicable humidity distribution φ i (t)) to distinguish multiple humidity distributions φ i (t) advantageously allows: avoiding ambiguities when calibrating the material model, that is to say when numerically adapting the system of equations on which the material model is based, and thus making the material model more robust. In one embodiment, using a depth-resolving moisture sensor, in particular an NMR sensor, data relating to the at least one time-dependent moisture distribution φ are measured and provided for depths of up to 1 cm, 1.5 cm, 2 cm, 2.5 cm, 3 cm, 3.5 cm and 4 cm. i(t) measurement data.

[0027] In one embodiment of the method according to the invention, these measurement data are measured and provided using a depth-resolving moisture sensor, in particular a depth-resolving NMR sensor and / or a depth-resolving moisture sensor array and / or a depth-resolving radar.

[0028] A humidity measuring device is also proposed, which has at least one depth-resolving humidity sensor and at least one control device, in particular for controlling the humidity measuring device, wherein the control device is also set up to perform the method according to the present invention. In particular, the humidity measuring device also includes a depth-resolving humidity sensor, in particular a depth-resolving NMR sensor and / or a depth-resolving humidity sensor array and / or a depth-resolving radar, for measuring and providing measurement data. In one embodiment of the humidity measuring device, the humidity measuring device is implemented as a mobile humidity measuring device, in particular as a handheld measuring device. The control device should be understood in particular as a device having at least one control electronic device, which has: a device for communicating with other components of the measuring device, for example, a device for controlling and adjusting the humidity sensor; and / or a device for data processing. The control device is particularly configured to: set at least one operating function parameter of the measuring device. "Set" should be understood in particular as "programmed", "designed" and / or "equipped" specifically. An object "set" for a specific function should be understood in particular as: the object performs and / or implements the specific function in at least one application and / or operating state or is designed to perform the function. Advantageously, the control electronics of the control device according to the invention can be understood as a processor unit in combination with a storage unit and with an operating program stored in the storage unit, which is executed during the control process. In particular, the electronics of the control device can be arranged on a circuit board (printed circuit board), preferably in the form of a microcontroller. Furthermore, the control device can be configured in particular to control the entire measuring device and to enable the operation of the measuring device. The control device also has a stored program code in the form of a computer program, the execution of which allows the method according to the invention to be implemented.

[0029] In another aspect, a computer program is provided. The computer program is configured to implement the above method. The computer program comprises instructions that cause a processor device to implement the above method with all its steps.

[0030] A computer-readable storage medium is also proposed, on which the computer program is saved, in particular stored. Storage media are known to those skilled in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In the following description, the present invention is explained in detail based on the embodiments shown in the drawings. The drawings, the specification and the claims contain a large number of combined features. Appropriately, those skilled in the art will also consider these features individually and combine them into other reasonable combinations. The same or similar reference numerals in the drawings represent the same or similar elements.

[0032] in:

[0033] Figure 1 A flow chart showing an exemplary embodiment of the method according to the invention for determining a time-dependent drying profile τ(t) of an examination object;

[0034] Figure 2 shows a schematic perspective view of an exemplary measurement scenario when using the humidity measuring device according to the invention;

[0035] Figure 3 Shows that for Figure 2 The time-dependent reduced drying course τ that can be determined for the measurement scenario shown in i (t) and the time-dependent reduced drying course τ(t). DETAILED DESCRIPTION

[0036] exist Figure 1 1 shows a flow chart 100 which reproduces an exemplary embodiment of a computer-implemented method according to the invention for determining a time-dependent drying course τ(t) of an object to be examined 10, wherein the time-dependent drying course τ(t) specifies the functional relationship between time and the dryness (or moisture) of the object to be examined 10 (see Figure 3 ).

[0037] In method step 102, measurement data are provided, in particular provided to a processor device that carries out the method, which measurement data relate to at least the depth d of the object under examination 10. i (See also Figure 2 ) to at least one time-dependent humidity distribution φ i In the illustrated embodiment of the method, six time-dependent humidity profiles φ are provided. 1 (t),φ 2 (t),φ 3 (t),φ 4 (t),φ 5 (t),φ 6 (t), where these time-dependent humidity distributions correspond to six different depths d 1 d 2 d 3,d 4 ,d 5 ,d 6 The six depths can be, for example, up to 1 cm, up to 1.5 cm, up to 2 cm, up to 2.5 cm, up to 3 cm and up to 3.5 cm (see Figure 2 ). Optionally, these measurement data can be measured in method step 102a and then provided to the processor device. For example, these measurement data can be detected and provided using a depth-resolving moisture sensor 12, in particular a depth-resolving NMR sensor 14, of a moisture measuring device 16, as in Figure 2 As schematically shown in .

[0038] In method step 104, the provided measurement data are processed, in particular prepared for evaluation, for example by smoothing, filtering, etc. Furthermore, at least one time-dependent reduced drying profile τ of the examination object 10 is determined, in particular calculated, based on the measurement data prepared in this way. i (t). In particular, in this embodiment, when using these six time-dependent humidity distributions φ 1 (t),φ 2 (t),φ 3 (t),φ 4 (t),φ 5 (t),φ 6 (t) to determine six time-dependent reduced drying processes τ 1 (t), τ 2 (t), τ 3 (t), τ 4 (t), τ 5 (t), τ 6 (t). In this embodiment, by i The corresponding humidity distribution φ i (t) is integrated to calculate the corresponding time-dependent reduced drying process τ i (t):

[0039] .

[0040] exist Figure 3 The time-dependent reduced drying process τ is shown in i (t) example.

[0041] In method step 106, using the at least one time-dependent reduced drying profile τ i (t), that is, in this exemplary embodiment, using these six time-dependent reduced drying processes τ1 (t), τ 2 (t), τ 3 (t), τ 4 (t), τ 5 (t), τ 6 (t) is used to calibrate a material model of the object under examination 10. The material model includes the physical and / or chemical parameters and influences that are important for the time-dependent drying process τ(t) of the entire object under examination 10. The material model describes at least the moisture transport phenomena in the object under examination 10 and takes into account at least the liquid water conductivity, the vapor conductivity and the water vapor surface exchange of the object under examination 10. In this exemplary embodiment, the material model is based on models known from the prior art, such as the DELPHIN model (see: "Coupled heat air and moisture transfer in building structures" by P. Häupl, J. Grunewald, H. Fechner, H. Stopp). The material model is calibrated by the six time-dependent reduced drying processes τ 1 (t), τ 2 (t), τ 3 (t), τ 4 (t), τ 5 (t), τ 6 (t) The fitting is initially calibrated, wherein the fitting parameters are optimized (for example by applying the method of least squares (least-square fit)) and thus a material model of the examination object 10 is obtained which describes it as well as possible.

[0042] In method step 108, a time-dependent drying course τ(t) of the examination object 10 is determined, in particular simulated, using a calibrated material model. Depending on the material model and on the simulated time-dependent drying course τ(t), different conclusions can then be drawn in method step 110 by evaluating the material model and / or the time-dependent drying course τ(t). In particular, conclusions can be drawn about the time-dependent moisture content or the time-dependent dryness of the entire examination object 10. In particular, in method step 110, the drying time t0 at which the value τ(t0) of the time-dependent drying course τ(t) of the examination object 10 falls below a defined threshold τ0, in particular below a threshold τ0 that can be adapted to the application scenario, is extrapolated.

[0043] In method step 112, it is checked whether these calculations are physically plausible and / or mathematically unambiguous and / or convergent, so that reliable conclusions can be drawn from this evaluation. In particular, the previous method steps 104-112 can be repeated in the next process cycle, wherein in method step 114 further measurement data are now provided, in particular relating to the six time-dependent humidity profiles φ for at least one further point in time, for example for another day. 1 (t),φ 2 (t),φ 3 (t),φ 4 (t),φ 5 (t),φ 6 It is also conceivable that in method step 114, a time-dependent humidity distribution φ is provided. i (t), the time-dependent moisture distribution relates to a further depth d of the examination object i Likewise, these other measurement data can also be measured directly at the examination object 10 in method step 114a and provided to the processor device. In this way, the time-dependent humidity distribution φ can be used in conjunction with the first time point or up to the first time point. i (t), here in particular the six time-dependent humidity distributions φ 1 (t),φ 2 (t),φ 3 (t),φ 4 (t),φ 5 (t),φ 6 The material model is first calibrated using the measured data of (t) and then by means of the time-dependent moisture distribution φ at other points in time i (t), here are the six time-dependent humidity distributions φ 1 (t),φ 2 (t),φ 3 (t),φ 4 (t),φ 5 (t),φ 6The calibration of the material model can be recalibrated or refined, in particular in parallel with the drying process of the object to be examined 10, by using other measurement data of the material model (t). In this way, the time-dependent drying course τ(t) of the object to be examined 10 determined in method step 108 can also be refined or updated. In method step 112, the convergence or improved quality of the material model, in particular the time-dependent drying course τ(t) determined in method step 108 and / or the convergence of the drying time t0 extrapolated in method step 110, can be checked again. The repetition cycle is only left when a defined quality of the material model is present, for example, with regard to the convergence of the extrapolated drying time t0, as measured.

[0044] In this exemplary embodiment, the material model takes into account drying parameters that influence the drying course τ(t) of the object 10 to be examined and thus allows these drying parameters to be included in the simulation. The drying parameters in this exemplary embodiment make it possible to simulate at least the influence of ventilation (e.g. air exchange rate), temperature changes (e.g. heating), air humidity changes on the drying course τ(t) of the object 10 to be examined. In method step 116, these drying parameters are varied, wherein optimized drying parameters are calculated, which in the simulation lead to a faster or shorter time-dependent drying course τ of the object 10 to be examined compared to the current standard framework conditions. opt (t).

[0045] In method step 118 , the drying time t0 extrapolated in method step 110 and the optimized drying parameters obtained are output as operating instructions to a user of the method.

[0046] exist Figure 2 , a schematic perspective view of an exemplary measurement scenario using an exemplary embodiment of a humidity measuring device 16 according to the present invention is shown. The humidity measuring device 16 described as an example has at least one depth-resolving humidity sensor 12 and a control device (not shown in further detail here) for controlling the humidity measuring device 16. The control device is set up to carry out the method according to the present invention. The control device includes in particular a processor device, a storage unit and a computer program stored in the storage unit for implementing the method according to the present invention. The humidity measuring device 16 is implemented as a handheld NMR measuring device, which has a depth-resolving NMR sensor 14 as a humidity sensor. The NMR humidity sensor 14 is capable of: depth-resolved measurement of at least a maximum depth d up to the object under examination 16. max Humidity distribution φ i The measurement data, in Figure 2 The maximum depth d is shown in FIG. 1 by the variable magnetic field 18. In this embodiment, the maximum depth d max4 cm. The humidity measuring device 16 also comprises an input device in the housing, which is suitable for switching on and off, for starting and configuring the measuring process and for inputting operating parameters. An output device in the form of a screen is also provided in the housing for outputting the determined information and for outputting the operating parameters. In order to supply the humidity measuring device 16 with energy, the humidity measuring device 16 has a receptacle for accommodating an energy store that is independent of the power grid.

[0047] exist Figure 3 The figure shows the Figure 2 The curves determined by the measurement scenario shown in FIG. 1 reproduce the reduced time-dependent drying process τ i (t) and the reduced drying course τ(t) as a function of time. The x-axis 20 is scaled with time, for example in days, and the y-axis 22 is scaled with humidity (for example in volume %, kg / kg, m3 / m3, kg / m3, kg, ...). The reduced drying course τ as a function of time is represented by i (t) can be interpreted as the thickness d during drying. i The drying profile τ(t) corresponds to the time-dependent drying profile of the entire examination object 10 , ie, over the entire depth or thickness.

Claims

1. A computer-implemented method for determining a time-dependent drying profile τ(t) of an object to be examined (10), the method comprising at least the following method steps: • Providing measurement data, which relate to at least a depth d of the examination object (10) i At least one time-dependent humidity distribution φ i (t); • Using the time-dependent humidity profile φ i (t), determining at least one time-dependent reduced drying process τ i (t), wherein the time-dependent reduced drying process τ i (t) With the help of to calculate; • When using the at least one time-dependent reduced drying process τ i (t), calibrating a material model related to the inspection object (10); • determining a moisture value and / or a moisture distribution and / or a time-dependent drying profile τ(t) of the examination object (10) using a calibrated material model, In this case, based on a time-dependent drying profile τ(t) of the examination object (10), a drying time t0 is determined at which a value τ(t0) of the time-dependent drying profile falls below a threshold τ0.

2. The computer-implemented method according to claim 1, wherein determining the moisture value and / or the moisture distribution and / or the time-dependent drying process τ(t) of the inspection object (10) comprises simulating the moisture value and / or the moisture distribution and / or the time-dependent drying process τ(t) of the inspection object (10). 3 . The computer-implemented method according to claim 1 , wherein the drying time point t 0 at which the value τ(t 0 ) of the time-dependent drying profile falls below a threshold τ 0 is extrapolated.

4. The computer-implemented method according to any one of the preceding claims 1 to 3, wherein the material model takes into account at least the liquid water conductivity, the vapor conductivity and the water vapor surface exchange of the examination object (10).

5. The computer-implemented method according to claim 1, wherein in the last method step and / or in a further method step, a drying parameter which influences the drying course τ(t) of the examination object (10) is simulated and / or varied.

6. The computer-implemented method according to claim 5, wherein optimized drying parameters are calculated, which in the simulation lead to a faster time-dependent drying course τ of the examination object (10). opt (t).

7. The computer-implemented method of claim 6, wherein the optimized drying parameters are output as operating instructions to a user of the method.

8. The computer-implemented method according to any one of the preceding claims 1 to 3, wherein the at least one time-dependent humidity profile φ is provided for at least six different time points. i (t) measurement data.

9. The computer-implemented method of claim 8, wherein the at least one time-dependent humidity profile φ is provided for at least six different days. i (t) measurement data.

10. The computer-implemented method according to any one of the preceding claims 1 to 3, wherein for at least two depths d of the examination object (10) i providing a humidity profile φ related to the at least one time-dependent humidity profile i (t) measurement data.

11. The computer-implemented method according to claim 10, wherein for at least three depths d of the examination object (10) i providing a humidity profile φ related to the at least one time-dependent humidity profile i (t) measurement data. 12 . The computer-implemented method according to claim 1 , wherein the determination of the time-dependent drying profile τ(t) of the examination object ( 10 ) is refined using further measurement data.

13. The computer-implemented method according to any one of the preceding claims 1 to 3, wherein the measurement data are detected and provided using a depth-resolving moisture sensor (12).

14. The computer-implemented method of claim 13, wherein the depth-resolved humidity sensor (12) is a depth-resolved NMR sensor (14) and / or a depth-resolved humidity sensor array and / or a depth-resolved radar. 15 . A moisture measuring device ( 16 ) having at least one depth-resolving moisture sensor ( 12 ) and a control device, wherein the control device is configured to carry out the method according to claim 1 . 16 . A computer program product having a computer program, the computer program comprising instructions which, when executed by means of a processor device, carry out the method according to claim 1 . 17 . A computer-readable storage medium on which a computer program having instructions is stored which, when executed by means of a processor device, implement the method according to claim 1 .

18. A computer device having at least one processor device and a memory device, wherein instructions are stored on the memory device, which instructions, when executed by the processor device, cause the computer device to execute the method according to any one of claims 1 to 14.

19. The computer device according to claim 18, wherein the computer device is a humidity measuring device (16).

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