Method for operating measuring device, method for training artificial intelligence, computing unit and computer program product

Abstract

The invention relates to a computer-implemented method (200) for operating a measuring device (100), in particular a wall diagnostic device, comprising: receiving (201) radar data (103) of a radar sensor unit (101) of the measuring device (100); carrying out (203) a wall diagnosis by carrying out an analysis on the radar data (103) by means of a diagnosis module (107) of the measuring device (100) and providing a diagnosis result (109); the feedback information (112) is ascertained (209). The invention also relates to a method for training artificial intelligence of a measuring device for wall diagnosis, a computing unit and a computer program product.

Description

Technical Field

[0001] The invention relates to a method for operating a measuring device, in particular a wall diagnostic device. Background Art

[0002] Diagnostic devices for diagnosing walls and for detecting objects formed in walls are known from the prior art. Summary of the Invention

[0003] One object of the present invention is to provide an improved method for operating a measuring device, in particular a wall diagnostic device. Another object is to provide an improved method for training an artificial intelligence of a measuring device.

[0004] This object is achieved by a computer-implemented method for operating a measuring device, in particular a wall diagnostic device, a method for training an artificial intelligence of a measuring device for wall diagnostics, a computing unit, and a computer program product. Advantageous embodiments are described below.

[0005] According to one aspect, a computer-implemented method for operating a measuring device, in particular a wall diagnostic device, is provided, the method comprising:

[0006] receiving radar data from a radar sensor unit of a measuring device, wherein the radar data maps a wall to be diagnosed;

[0007] The diagnostic module of the measuring device performs wall diagnosis by analyzing the radar data and provides a diagnostic result, wherein the wall diagnosis includes:

[0008] Performing wall type classification and determining the wall type of the wall by a diagnosis module, wherein the diagnosis result at least includes the wall type of the wall; and / or

[0009] Performing object recognition on an object arranged in the wall by a diagnostic module, wherein the object recognition includes object detection and object classification, and determining the object position and object type of the object in the wall, wherein a diagnostic result includes at least the object position and / or the object type;

[0010] Feedback information is obtained, wherein the feedback information describes the consistency between the diagnosis result and the current state of the wall.

[0011] This achieves the technical advantage of providing an improved method for operating a measuring device, particularly a wall diagnostic device. To this end, radar data from a radar sensor unit of the measuring device, which maps a wall to be diagnosed, is first received. Subsequently, based on this radar data, a diagnostic module of the measuring device performs wall diagnostics on the wall to be diagnosed.

[0012] Wall diagnosis includes implementing wall type classification and determining the wall type of the wall by the diagnosis module and / or implementing object recognition for objects arranged in the wall. Here, object recognition includes object detection with determining the object position and object classification with determining the object type.

[0013] Furthermore, feedback information is obtained, wherein the feedback information describes the consistency between the diagnosis results generated during the wall diagnosis and the current state of the wall.

[0014] A correspondingly configured diagnostic module makes it possible to diagnose walls, in particular to determine the wall type and / or object type, without requiring additional sensor information beyond the radar data. The wall type and / or object type can be determined solely based on radar data from the radar sensor unit. Consequently, the measuring device does not need to be equipped with additional sensors.

[0015] Automatically determining the wall type during the wall diagnosis process avoids the need for the user of the measuring device to manually enter the existing wall type of the wall to be inspected. Furthermore, the automatic determination of the wall type by the diagnostic module allows the correspondingly determined wall type to be used in further wall diagnosis, for example, as a background correction for object detection. This further improves the quality of the wall diagnosis.

[0016] By determining the feedback information, the quality of the performed wall diagnosis can be directly checked. The feedback information can be used in particular to improve the wall diagnosis, for example by updating the diagnosis module.

[0017] According to one embodiment, the method further comprises:

[0018] The feedback information is provided to an external server unit so that the feedback information is taken into account in the updating of the diagnostic module.

[0019] This provides the technical advantage of providing feedback information to an external server unit, allowing the corresponding feedback information to be used to improve the performance of the diagnostic module. The feedback information can, for example, come from multiple operating measuring devices. Using the comprehensive feedback information collected in this manner, the server unit can verify the performance of the diagnostic module built into the measuring device. This enables targeted updates to the diagnostic module software, with the updates intended to improve aspects of the diagnostic module's performance that are deficient based on the feedback information.

[0020] According to one embodiment, in addition to the feedback information, corresponding radar data are also provided to an external server unit.

[0021] This achieves the technical advantage that, by providing radar data from the measuring device to an external server unit in addition to feedback information, a comprehensive description of the situation in which the measuring device generated the diagnostic result that led to the feedback information can be provided. This further improves the updating or improvement of the diagnostic module.

[0022] According to one embodiment, performing wall diagnosis further includes:

[0023] performing object depth determination by the diagnostic module and determining the object depth of the object in the wall, wherein the object depth is defined as the distance from the object to the surface of the wall; and / or

[0024] An object extension determination is carried out by the diagnosis module and the object extension of the object along a predefined direction is determined, wherein the diagnosis result also includes at least the object depth and / or the object extension of the object.

[0025] This provides the technical advantage of enabling a comprehensive wall diagnosis. To this end, the wall diagnosis includes determining the object depth and / or the object's extension, in addition to determining the wall type and the object's position and / or object type. By taking into account the object's depth and / or object extension, a detailed description of the object detected by the measuring device can be provided.

[0026] According to one embodiment, in object recognition, multiple possible object positions and / or multiple possible object types of an object are determined as independent diagnostic results, and / or, in wall type classification, multiple possible wall types of a wall are determined as independent diagnostic results, and / or, in object depth determination, multiple possible object depths are determined as independent diagnostic results, and / or, in object extension scale determination, multiple possible object extension scales of an object are determined as independent diagnostic results, wherein each of the multiple diagnostic results is displayed on a display unit of the measuring device.

[0027] This achieves the technical advantage of enabling a detailed wall diagnosis. By determining multiple object positions and / or multiple object types and / or multiple object depths and / or multiple object extensions and / or multiple wall types during the wall diagnosis and displaying them on the display unit of the measuring device, a comprehensive image of the wall to be diagnosed can be provided to the user.

[0028] According to one embodiment, the method further comprises:

[0029] A first selection function and / or a second selection function are provided, wherein, by implementing the first selection function, a user of the measuring device can select at least one of the displayed wall types and / or one of the displayed object positions and / or object types and / or object depths and / or object extensions, and / or wherein, by implementing the second selection function, a user of the measuring device can deactivate the automatic determination of the wall type during the wall diagnosis and / or the display of the automatically determined wall type in the display unit, and the wall type can be selected manually by the user.

[0030] This provides the technical advantage of enabling the user to select the appropriate wall type for the wall to be inspected from a plurality of displayed wall types using the first selection function. Knowing the wall type of the wall to be inspected, the user can select the most suitable possible wall type provided by the measuring device. The selected wall type can then be used for further wall diagnosis, while unselected wall types remain disregarded. This further improves the quality of the wall diagnosis.

[0031] The second selection function allows the user to deactivate the automatic determination of the wall type and manually select the appropriate wall type. This can be particularly advantageous in diagnostic situations where the user knows the wall type of the existing wall and the possible wall types provided by the measuring device do not fully reflect the actual wall type. This can further improve the quality of the wall diagnosis.

[0032] According to one embodiment, the method further comprises:

[0033] A feedback function is provided, wherein, when implementing the feedback function, the user can determine whether the displayed wall type and / or the displayed object position and / or the displayed object type and / or the displayed object depth and / or the displayed object extension scale are consistent with the actual wall type and / or the actual object position and / or the actual object type and / or the actual object depth and / or the actual object extension scale.

[0034] This provides the technical advantage that, by providing a feedback function, clear feedback can be provided regarding the consistency between the displayed wall type and the actual wall type and / or the displayed object position and / or the displayed object type and / or the displayed object depth and / or the displayed object extension and the actual object position, actual object type, actual object depth, and / or actual object extension. Thus, by operating the feedback function, feedback information regarding the quality of the performed wall diagnosis can be proactively provided. This enables the acquisition of detailed and appropriate feedback information. The feedback function may, for example, include an input function by which a user of the measuring device can manually enter feedback information.

[0035] According to one embodiment, obtaining feedback information includes:

[0036] Receive selection instructions of the first and / or second selection functions and / or feedback instructions of the feedback function, and obtain feedback information based on the selection instructions and / or feedback instructions, wherein a corresponding selection is made in the selection instruction according to the first and / or second selection function, and wherein the feedback instruction includes corresponding feedback information provided by the user.

[0037] This achieves the technical advantage that feedback information can be determined based on the received selection instructions of the first and second selection functions and / or the feedback instructions of the feedback function. Detailed feedback information about the quality of the performed wall diagnosis can thus be provided.

[0038] According to one embodiment, obtaining feedback information includes:

[0039] determining, by the diagnostic module, whether there is a deviation in the diagnostic result obtained in the wall diagnosis when the diagnosis is repeated at the same position of the measuring device relative to the wall;

[0040] displaying the deviation in a display unit;

[0041] A feedback instruction from a user regarding the deviation is received, wherein the displayed deviation is confirmed or refuted in the feedback instruction.

[0042] This achieves the technical advantage of further improving the wall diagnosis. The diagnostic module determines the deviations in the diagnostic results of multiple wall diagnoses performed using the measuring device at the same position relative to the wall. The deviations can be displayed on a display unit. A user can provide feedback regarding the deviations via a feedback function, confirming or refuting the deviations. This allows the stability of the wall diagnosis to be verified and demonstrated. The corresponding feedback information can then be used to improve the diagnostic module.

[0043] According to one embodiment, the measuring device further comprises at least one inductive sensor and / or eddy current sensor and / or capacitive sensor and / or AC sensor and / or nuclear magnetic resonance (NMR) sensor and / or ultrasonic sensor for providing additional sensor data, wherein the diagnostic module is configured to perform the wall diagnosis taking into account the additional sensor data.

[0044] This provides the technical advantage that additional information, in addition to the information from the radar data of the radar sensor unit, can be incorporated into the wall diagnosis via further sensor data from additional sensors, each of which is configured to detect a different physical measurement variable. This additional information, which is preferably complementary to the information from the radar data of the radar sensor unit, enables further refinement of the wall diagnosis or object identification.

[0045] According to one embodiment, the diagnostic module includes at least one correspondingly trained artificial intelligence, which is configured to perform object recognition and / or wall classification and / or object depth determination and / or object extension determination based on the radar data and / or the additional sensor data.

[0046] This achieves the technical advantage that by configuring the diagnostic module as a correspondingly trained artificial intelligence (which is trained to perform object recognition and / or wall classification and / or object depth determination and / or object extension determination based on radar data or, if necessary, taking into account information from additional sensors), a reliable and high-performance diagnostic module can be provided. The use of artificial intelligence technology allows for precise wall diagnosis.

[0047] According to one embodiment, the object category of the object type of the object includes: metal / non-metallic objects, cables for low voltage, magnetic / non-magnetic objects, cables with single-phase AC signals, cables with multi-phase AC signals, wooden beams, metal beams, plastic pipes, water-filled plastic pipes, such as water supply pipes, non-water-filled plastic pipes, such as drainage pipes, and / or wherein the wall type category of the wall type of the wall includes: concrete wall, lightweight structure / dry structure wall, brick wall, brick blocks of the wall, and / or wall with indicated heating (such as floor heating or wall heating).

[0048] This allows for the technical advantage of being able to identify and classify a wide variety of objects and walls. For example, metal objects can include metal pipes, metal rods, metal beams, metal cables, and all other metal objects commonly found in wall construction.

[0049] According to one aspect, a method for training artificial intelligence of a measurement device for wall diagnosis is provided, the method comprising:

[0050] providing a training data set for training the artificial intelligence, wherein the training data set includes radar data mapping a wall and objects constructed in the wall and feedback information provided according to a method for operating a measurement device;

[0051] The artificial intelligence is trained based on the training data set and taking into account the feedback information to perform object recognition of objects formed in the wall, wherein the object recognition includes at least object detection and object classification.

[0052] This achieves the technical advantage that an improved training of the artificial intelligence of the wall diagnosis device is possible, in particular subsequent training which simultaneously takes into account feedback from users of the wall diagnosis device.

[0053] According to one aspect, a computing unit is provided, which is configured to carry out a method for operating a measuring device and / or a method for training an artificial intelligence according to one of the above embodiments.

[0054] According to one aspect, a computer program product is provided, which includes instructions which, when the program is executed by a data processing unit, cause the data processing unit to carry out a method for operating a measuring device and / or a method for training an artificial intelligence according to one embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Embodiments of the present invention will be described with reference to the following drawings. These drawings show:

[0056] Figure 1 A schematic illustration of a measuring device according to one embodiment;

[0057] Figure 2 Another schematic illustration of a measuring device according to another embodiment;

[0058] Figure 3 Another schematic illustration of a measuring device according to another embodiment;

[0059] Figure 4 Schematic illustration of a measurement of a measuring device according to one embodiment,

[0060] Figure 5 Another schematic illustration of a measuring device according to another embodiment;

[0061] Figure 6 Schematic illustration of a system for operating a measuring device according to one embodiment,

[0062] Figure 7 A flow chart of a method for operating a measuring device according to one embodiment,

[0063] Figure 8 Another flow chart of a method for operating a measuring device according to another embodiment,

[0064] Figure 9 Another flow chart of a method for operating a measuring device according to another embodiment,

[0065] Figure 10 Flowchart of a method for training an artificial intelligence of a measuring device according to one embodiment,

[0066] Figure 11 Schematic illustration of a computer program product. DETAILED DESCRIPTION

[0067] Figure 1 A schematic illustration of a measuring device 100 according to one specific embodiment is shown.

[0068] The present invention relates to a measuring device, and in particular to a wall diagnostic device for inspecting a wall 105 to be processed. Wall diagnostic devices for detecting objects arranged in a wall are known in the prior art. Such devices allow a user to inspect the wall to be processed based on the objects arranged in the wall, so that planned work, such as drilling a hole in the wall, can be carried out based on this information, in a manner that avoids damage to the objects arranged in the wall.

[0069] In the embodiment shown, the measuring device 100 includes a housing 150 having a handle 152 configured for a user to hold the measuring device 100 , a display unit 111 configured to display a diagnosis result 109 of a wall diagnosis, and operating elements 154 configured to switch the measuring device 100 into different operating modes.

[0070] According to the present invention, measuring device 100 comprises at least one radar sensor unit 101. Radar sensor unit 101 can transmit radar signals in the direction of a wall 105 to be inspected and can receive radar signals reflected by wall 105.

[0071] For example, radar sensor unit 101 may be designed as a narrowband radar detector device in the frequency range of 2.4 GHz to 2.4835 GHz.

[0072] Measuring device 100 also includes a diagnostic module 107, which can be executed on computing unit 151 of measuring device 100, for performing wall diagnostics. Diagnostic module 107 is configured to perform corresponding diagnostics of the wall to be inspected based on radar data 103 from radar sensor unit 101. Radar data 103 from radar sensor unit 101 represents wall 105 to be inspected and objects 113 that may be located within wall 105.

[0073] The wall diagnosis performed by diagnostic module 107 includes at least the implementation of object recognition. Object recognition includes object detection and object classification of objects 113 located in wall 105. Object detection includes at least the determination of object position 115. The object position describes the position of the object located in wall 105 relative to the coordinate system defined by measuring device 100. Object classification of detected objects 113 includes at least the determination of object type 117 of detected objects 113.

[0074] The diagnostic results of the wall diagnosis determined in this manner, i.e., at least the determined object position 115 and / or the determined object type 117 of the object 113 arranged in the wall 105, are then displayed to the user of the measuring device 100 on the display unit 111 of the measuring device 100. The display unit 111 can be configured as a corresponding display screen, for example, and the diagnostic results 109 can be displayed visually. In addition, the display of the diagnostic results 109 can be assisted by acoustic and / or tactile signals. For example, the tactile signals can be implemented by corresponding vibration signals.

[0075] In this case, object 113 can be displayed, for example, by a corresponding symbol in a display screen. Object 113 can be displayed in a corresponding object position 115 in the display screen. Object extent 121 can be visualized by the corresponding size of the displayed symbol. The corresponding object type 117 of object 113 can be visualized by a corresponding term, a colored background of the symbol, or a specific shape of the symbol representing object 113.

[0076] Alternatively, the wall diagnosis can additionally include determining a wall type 123 in the form of a wall type classification of the wall 105 to be inspected. Wall type 123 describes the respective type of wall 105 to be inspected. For example, the wall type can be assigned to a respective wall type category, which can include: concrete wall, lightweight / drywall wall, brick wall and / or wall block, floor heating, wall heating, or wall types found in similar buildings.

[0077] According to one embodiment, diagnostic module 107 is further configured to determine object depth 119 of object 113 within wall 105 based on radar data 103. Object depth 119 is defined as the distance of an object embedded in wall 105 from the surface of wall 105. This distance can be defined, for example, relative to the object surface or relative to the object's center point. The distance to the surface of wall 105 describes the shortest distance defined by a direction perpendicular to the surface of wall 105.

[0078] According to one embodiment, diagnostic module 107 is further configured to determine object extent 121 of object 113 in at least one predefined direction based on radar data 103. Object extent 121 of object 113 describes the spatial extent of object 113 in at least one spatial direction, preferably in two spatial directions, and particularly preferably in three spatial directions. Thus, object 113 can be described as a one-dimensional, two-dimensional, or three-dimensional object 113.

[0079] In typical use, measuring device 100 is placed on the wall surface of a wall 105 to be inspected. Radar signals are transmitted toward wall 105 via radar sensor unit 101, and radar signals reflected by wall 105 or objects 113 located behind it are received. Based on radar data 103 from radar sensor unit 101, diagnostic module 107 performs the aforementioned wall diagnosis and determines corresponding diagnostic results 109.

[0080] Diagnosis result 109 may include, for example, object position 115 and / or object type 117 of object 113 arranged in wall 105. Alternatively or additionally, diagnosis result 109 may include wall type 123 of wall 105 and / or object depth 119 and / or object extent 121 of object 113.

[0081] The diagnosis result 109 formed in this way can then be displayed to the user of the measuring device 100 on the display unit 111 of the measuring device 100. The display unit 111 can be designed as a corresponding display screen, for example. The diagnosis result 109 can be displayed in a graphical form or in a textual form on the display unit 111.

[0082] According to one embodiment, measuring device 100 further includes a motion detection unit 141. Motion detection unit 141 can be used to detect the movement of measuring device 100 relative to wall 105. To this end, motion detection unit 141 can include, for example, at least one rolling element. When the rolling element is supported on the wall surface of wall 105, the movement of measuring device 100 relative to wall 105 can be detected when measuring device 100 moves along motion direction 153 due to the rolling of the rolling element. Alternatively, motion detection unit 141 can have other configurations that can detect the relative movement of measuring device 100 relative to wall 105.

[0083] By moving measuring device 100 relative to wall 105, radar data 103 of radar sensor unit 101 can be recorded for a plurality of different positionings of measuring device 100 relative to wall 105. This allows for an inspection of wall 105 over a larger spatial area than that given by the range of action of radar sensor unit 101. This allows for the detection of objects 113 having a larger spatial extent than the range of action of radar sensor unit 101.

[0084] While measuring device 100 is moving along movement device 153, radar data 103 from radar sensor unit 101 can be continuously recorded. Based on these radar data 103, wall diagnostics can be evaluated by diagnostic module 107 while measuring device 100 is moving along movement direction 153. This allows for accelerated wall diagnostics that take into account the positioning of measuring device 100 relative to wall 105.

[0085] Depending on its embodiment, diagnostic module 107 is designed as a correspondingly trained artificial intelligence 125. Artificial intelligence 125 is trained to perform the aforementioned wall diagnosis based on radar data 103 from radar sensor unit 101 and to determine at least object position 115 and object type 117 of object 113 located in wall 105. Object classification or determination of object type 117 includes assigning detected object 113 to a predefined object class.

[0086] Object categories here can include: metallic / non-metallic objects, cables for low voltage, cables with single-phase AC signals, cables with multi-phase AC signals, wooden beams, metal beams, plastic pipes, water-filled plastic pipes, such as water supply pipes, non-water-filled plastic pipes, such as drainage pipes, or other common elements built into building walls.

[0087] Furthermore, artificial intelligence 125 may be trained to determine wall type 123 of wall 105 to be inspected based at least on radar data 103 of radar sensor unit 101. Possible wall types 123 may include: concrete walls, lightweight / drywall walls, brickwork and / or brickwork walls, floor heating, wall heating, or other common wall types constructed in buildings.

[0088] According to one embodiment, in addition to radar sensor unit 101, measuring device 100 may also include other additional sensors, by means of which additional physical variables can be detected. For example, measuring device 100 may include an inductive sensor, an eddy current sensor, a capacitive sensor, an AC sensor, a nuclear magnetic resonance sensor, an ultrasonic sensor, or other sensors commonly used in wall diagnostic equipment.

[0089] Diagnostic module 107, in particular a correspondingly trained artificial intelligence 125, can be configured to perform the aforementioned wall diagnosis based on radar data 103 from radar sensor unit 101 and taking into account additional sensor information from other sensors. The additional information from these additional sensors can be used, in particular, to identify objects 113 located in wall 105. The additional sensor information can optionally enable improved detection of objects 113 and, if necessary, improved classification of objects 113.

[0090] In particular, the material of the object 113, such as a metallic material or a non-metallic material, may be refined and classified, for example, by using additional sensor information.

[0091] Figure 2 A further schematic illustration of a measuring device 100 according to another specific embodiment is shown.

[0092] In the illustrated embodiment, in addition to diagnostic module 107, measuring device 100 also includes a preprocessing module 127. To perform wall diagnosis, measuring device 100 first receives radar data 103 from radar sensor unit 101. Received radar data 103 is preprocessed by preprocessing module 127. Preprocessing by preprocessing module 127 can, for example, convert the radar data into a corresponding data structure required for wall diagnosis by diagnostic module 107.

[0093] As described above, the diagnostic module 107 generates the aforementioned diagnostic result 109 during the wall diagnostic process. Diagnostic result 109 may include, for example, an object position 115 of an object 113 located within the wall 105 to be inspected, an object type 117, an object depth 119, an object extension 121, and / or a wall type 123 of the wall 105 to be inspected. The correspondingly generated diagnostic result 109 may then be displayed on the display unit 111 of the measuring device 100.

[0094] According to one embodiment, in addition to radar data 103 from radar sensor unit 101, additional sensor information from the aforementioned additional sensors may also be considered in the wall diagnosis of diagnosis module 107. Preprocessing module 127 may accordingly perform corresponding preprocessing of the additional sensor information.

[0095] In the illustrated embodiment, diagnosis module 107 includes a wall type classification module 129 and an object recognition module 131. Preprocessing module 127 includes a first preprocessing module 135 and a second preprocessing module 137. First preprocessing module 135 includes S-matrix reduction 155. Second preprocessing module 137 includes background correction 157, inverse fast Fourier transform 159, and focusing and migration 161. When radar data 103 is preprocessed by preprocessing module 127, radar data 103 is first preprocessed by first preprocessing module 135 and S-matrix reduction 155 included therein.

[0096] Here, first preprocessing module 135 generates input data 133 based on radar data 103. Input data 133 serves as input data for wall type classification module 129. Wall type classification module 129 classifies the wall 105 to be inspected into a certain type based on input data 133 and generates wall type information 139. Wall type information 139 contains the wall type 123 of the wall 105 to be inspected determined in the wall type classification.

[0097] Second preprocessing module 137 then performs preprocessing based on radar data 103 and wall type information 139. Background correction 157 is performed on radar data 103, taking into account wall type 123 ascertained in wall type information 139. Depending on wall type 123 of wall 105 to be inspected, different effects on radar data 103 may occur.

[0098] These effects, which are primarily due to the respective wall type 123 and may affect object recognition, can be corrected by background correction 157. After background correction, further preprocessing can be performed by performing an inverse fast Fourier transform 159 or focusing and shifting 161, and new input data 133 can be generated for the object recognition module 131. Based on the input data 133 provided by the second preprocessing module 137, the object recognition module 133 recognizes an object 113 located in the wall 105 to be inspected and determines at least the object position 115 and the object type 117 of the respective object 113. Additionally, the object recognition module 131 can determine the object depth 119 and the object extent 121.

[0099] According to one specific embodiment, the diagnostic module is further configured to determine an object depth of an object within the wall based on the radar data, wherein the object depth is defined by the distance of the object formed in the wall from the surface of the wall.

[0100] Preprocessing is optional. Depending on the algorithm used for diagnostic module 107, completely unprocessed radar echoes of different frequencies can be used as radar data 103 and as input data for diagnostic module 107. Alternatively, radar data 103 processed in multiple steps can be used. Preprocessing steps include, for example, converting the signal from the frequency domain to the time domain or range space, background removal, denoising, and signal normalization. In the case of radar data 103 in complex form, only the absolute values ​​can be processed. Alternatively or additionally, phase information can be taken into account.

[0101] Figure 3 A further schematic illustration of a measuring device 100 according to another specific embodiment is shown.

[0102] In the embodiment shown, the diagnostic module 107 includes a plurality of processing paths 102 extending in parallel. In each processing path 102 there is a pre-processing module 127, a diagnostic module 107 (eg, including a Figure 2 The wall type classification module 129 and / or object recognition module 131) and the post-processing module 163 of the embodiment.

[0103] exist Figure 3 In the example, radar data 103 is primarily presented as input data for wall diagnosis. However, in addition to the radar data shown, additional information from additional sensors can also be used as input data for wall diagnosis. Different information from different sensor types can be processed in different parallel processing paths 102, and corresponding wall diagnosis can be performed separately based on the different sensor information. After the wall diagnosis is completed, the summary module can combine the analysis results of each component into a wall diagnosis result 109.

[0104] Alternatively or additionally, different partial aspects of the wall diagnosis can also be performed by different processing paths 102 based on the same sensor information.

[0105] For example, the various processing paths 102 can process different radar data 103 that were recorded for different positions of measuring device 100 relative to wall 105 during the movement of measuring device 100 relative to wall 105. Radar data 103 that thus map different areas of wall 105 and were recorded temporally sequentially during the movement of measuring device 100 relative to wall 105 can then be processed in the various processing paths 102 by the modules shown.

[0106] The different processing paths here carry out independent wall diagnoses, which include at least determining an object position 115 and / or an object type 117 of an object 113 arranged in the wall 105 .

[0107] The summarization module 165 can summarize the partial results of independent wall diagnosis of different areas of the wall 105 provided in each processing path 102 into a coherent Diagnosis result 109. In this case, a coherent diagnosis result describes a wall diagnosis for a coherent spatial region scanned during the movement of measuring device 100 relative to wall 105 and mapped by the correspondingly recorded radar data 103. Therefore, parallel processing of radar data 103 or additional sensor information 104 of additional sensor elements in different processing paths 102 enables accelerated wall diagnosis.

[0108] Alternatively, different functions of the wall diagnosis can also be implemented in different processing paths 102. Thus, for example, wall type classification can be implemented in one processing path 102, and the wall type 123 of the wall 105 to be inspected can be determined. In another processing path 102, object recognition of objects 113 arranged in the wall can be implemented. In this case, object detection with determination of the object position 115 and object classification with determination of the object type 113 can be implemented in one processing path 102.

[0109] Alternatively, object detection and object classification can also be performed in two separate processing paths 102. In the other processing path 102, object depth determination (i.e., determination of object depth 119) and / or determination of object extension 121 can be performed separately. In the aggregation module 165, the different partial results of the wall diagnosis can be aggregated into the corresponding diagnostic result 109.

[0110] The diagnostic module 107 can be divided into different artificial intelligences 125, as already described. Figure 2 As presented in the embodiment of FIG. , the diagnosis module 107 may include, for example, a wall type classification module 129 and an object recognition module 131. The object recognition module can be further divided into an object detection module and an object classification module. The diagnosis module 107 may also include an object depth determination module and an object extension scale module, which are configured to determine an object depth 119 and an object extension scale 121, respectively.

[0111] The corresponding modules can each be designed as an independent artificial intelligence 125, such as a neural network. Alternatively, the different modules can form parts of an overall artificial neural network, which are connected to form the overall neural network according to structures known from the prior art.

[0112] Figure 4 A schematic illustration of a measurement of a measuring device 100 according to one specific embodiment is shown.

[0113] For preprocessing, radar data 103 or additional sensor information 104 from other sensors can be normalized, in particular, to ensure numerical stability in the subsequent steps performed by diagnostic module 107 during wall diagnosis. For example, amplitude and / or offset compensation can be performed for this purpose. Furthermore, radar data 103 can be filtered to reduce interfering elements, and the corresponding sensor data can be downsampled to reduce the data rate. Furthermore, radar data 103 or additional sensor information 104 can be converted to the desired frequency range or time range. Methods known from the prior art can be used for this purpose.

[0114] Furthermore, recorded radar data 103 or additional sensor information 104 can be divided into time windows or spatial windows 167. Time windows 167 can be generated by recording radar data 103 or additional sensor information or preprocessed radar data 103 at fixed time intervals. Conversely, spatial windows 167 can be generated by assigning radar data 103 or additional sensor information 104 to the position of measuring device 100 relative to wall 105 along direction of movement 153.

[0115] Figure 4 Graph a) shows such a data matrix resulting from the above steps. The data matrix in window 167 shown in graph a) shows a plurality of sensor data, which may include, for example, radar data 103 or additional sensor information 104 from other sensors, plotted along a frequency channel axis 171 and along a space / time axis 169.

[0116] The width of the time window 167 can be selected in such a way that different sampling rates of the sensors can be compensated and a new window 167 can be provided frequently enough so that the diagnosis result 109 of the wall diagnosis can be displayed on the display unit 111 without a significant time delay during the measurement or shortly after the end of the measurement of the measuring device 100.

[0117] For this purpose, a data recording rate of 2 to 20 windows per second of sensor data may be advantageous. For the spatial windows, the spatial sampling rate may be selected such that the desired position accuracy is achieved. A sampling rate of 1 mm to 1 cm may be advantageous. This means that for every 1 mm to 1 cm of movement of measuring device 100 along direction of motion 153 , corresponding sensor data is recorded.

[0118] The width of spatial window 167 can be selected such that a window contains coherent information about an object 113. A width of 1 to 20 centimeters for the respective spatial window 167 can be advantageous. This results in 4 to 100 measured values ​​per window 167. This enables efficient further algorithmic processing of the correspondingly recorded radar data 103 or additional sensor information by diagnostic module 107.

[0119] Here, once one or more sampling points are available, another time window 167 or spatial window 167 may be provided.

[0120] The diagnosis module 107 can be configured such that as input data, for example also as Figure 3 The input data of each processing path 102 in the embodiment of the present invention is recorded in a matrix corresponding to the window size of the corresponding spatial or temporal window 167. Figure 2 In this embodiment, the corresponding input data may include respectively preprocessed sensor data, namely radar data 103 and additional sensor information 104 of additional sensors.

[0121] As described above, wall diagnosis can be performed by diagnostic module 107 based on a correspondingly trained artificial intelligence. Alternatively, different processing paths can also be calculated using rule-based algorithms. It is also possible to combine artificial intelligence and rule-based algorithms within a single processing path 102, either in parallel or in series.

[0122] The diagnostic results 109 of the wall diagnosis can be clearly expressed as numerical values, vectors, or matrices. Furthermore, for object detection, the probability of detection can be given, or for wall type classification or object classification, the probability of the specified object class or wall type class can be given. The same applies to position and / or depth determination, for which corresponding probability values ​​can also be given.

[0123] If, in addition to radar data 103 , additional sensor information from other sensor types is processed in a processing path 102 , this sensor information can be combined either within artificial intelligence 125 or by rule-based combination.

[0124] exist Figure 3 In the embodiment of the present invention, in the post-processing of each processing path 102, multiple algorithm results based on multiple windows 167 can be aggregated by an aggregation module 165. This aggregation can be achieved in particular by majority voting, summing or multiplication of consecutive probability values.

[0125] Furthermore, by clustering a plurality of results, for example, clustering a plurality of objects detected close to one another, it is possible to identify which objects are the same object, so that these objects are not mistakenly identified multiple times.

[0126] It is also possible to apply a weighting function 177 in a multiplicative manner when aggregating the results from a plurality of windows 167. Advantageously, the diagnostic partial results 175 corresponding to the respective data points in space can be weighted with reference to the positioning of the diagnostic partial results 175 relative to the center point of the respective window 167. This is illustrated by way of example in diagram b), in which the individual diagnostic partial results 175 are weighted with reference to the center point of the illustrated window 167 according to the illustrated weighting function 177.

[0127] According to one embodiment, the result of one processing path 102 can influence the expansion of another processing path 102 after post-processing 163. In this case, weighting parameters can be adjusted, which can be related to the corresponding result from the processing path 102 for each window.

[0128] For example, the result of the object classification, in which object types 117 of objects arranged in wall 105 are defined, can be used to increase the weight of the wall type classification in which wall type 123 of the corresponding wall 105 is determined at locations where there are no objects 113 in post-processing, because the corresponding radar data 103 at these locations are less affected by reflections from objects 113.

[0129] Figure 5 A further schematic illustration of a measuring device 100 according to another specific embodiment is shown.

[0130] Figure 5 Graphs a) and b) of FIG. 1 show two different alternatives for the joint data processing of radar data 103 and additional sensor information 104 by diagnostic module 107 .

[0131] Diagram b) shows the joint processing of radar data 103 and additional sensor information 104 from an additional sensor by diagnostic module 107. To this end, radar data 103 and additional sensor information 104 serve as input data for diagnostic module 107, which is designed as an artificial intelligence (particularly, as an artificial neural network). Diagnostic module 107 includes a plurality of convolutional layers 108 and a plurality of dense layers 106. Radar data 103 and additional sensor information 104 are processed together as input data via convolutional layers 108 and dense layers 106. The diagnostic result 109, already mentioned, is generated as output data from diagnostic module 107 based on this data.

[0132] In contrast, in diagram b), radar data 103 and additional sensor information 104 serve as independent input data for diagnostic module 107. Diagnostic module 107 is divided into a plurality of processing paths 102. Each processing path 102 includes a plurality of convolutional layers 108 and at least one dense layer 106. In different processing paths 102, diagnostic module 107 generates a wall diagnosis based on radar data 103 or additional sensor information 104 separately from one another.

[0133] In an additional connection layer 148, the partial results of the partial diagnosis of the different processing paths 102 are combined and fed to the last dense layer 106. The output data of the diagnosis module 107 correspond to the diagnosis result 109 described above.

[0134] The correspondingly designed diagnostic module 107 is configured to carry out the above-described wall diagnosis with the above-described features based on the radar data 103 and the additional sensor information 104 .

[0135] In the embodiment shown, the diagnosis module 107 is designed as an artificial neural network, in particular as a convolutional neural network. A corresponding network architecture with convolutional layers 108, dense layers 106 and connection layers 148 is known from the prior art.

[0136] Figure 6 A schematic illustration of a system 600 for operating a measuring device 100 according to one specific embodiment is shown.

[0137] In the embodiment shown, the system 600 for operating the measuring device 100 also includes an external server unit 114 in addition to the measuring device 100 .

[0138] According to the invention, measuring device 100 is configured to perform a wall diagnosis of wall 105 to be inspected (including objects 113 arranged therein) via diagnostic module 107 based on radar data 103 of radar sensor unit 101 and, if necessary, taking into account additional sensor information 104 .

[0139] According to the present invention, wall diagnosis includes at least determining wall type 123 of wall 105 and / or determining object position 115 and / or object type 117 of at least one object 113 arranged in the wall. Therefore, the correspondingly provided diagnostic result 109 includes at least the determined wall type 123 and / or the determined object position 115 and object type 117.

[0140] Alternatively, the wall diagnosis may also include the determination of the object depth 119 and / or the object extension 121 .

[0141] According to the present invention, feedback information 112 is determined by measuring device 100. Feedback information 112 describes the consistency of diagnosis result 109 with the current state of wall 105. The current state of wall 105 involves the actual wall type 123 and / or the actual object position 115 as well as the actual object type 117 of at least one object 113 arranged in the wall.

[0142] In the embodiment shown, feedback information 112 from the measuring device 100 is provided to an external server unit 114. The external server unit 114 is configured to use the feedback information 112 to improve the software of the diagnostic module 107, for example, by retraining the diagnostic module 107. In particular, the external server unit 114 is configured to generate a training data set 143 for retraining the diagnostic module 107, taking into account the feedback information 112.

[0143] According to one embodiment, in addition to the feedback information 112 , the radar data 103 can be provided to an external server unit 114 by the measuring device 100 , wall diagnosis has been performed based on the radar data by the diagnostic module 107 , and the feedback information 112 has been provided by the user of the measuring device regarding the radar data.

[0144] In the embodiment shown, the measuring device 100 also provides a first selection function 116 and a second selection function 118 .

[0145] By means of the first selection function 116, the user of the measuring device 100 can select a wall type 123, an object position 115, an object type 117, an object depth 119, and / or an object extension 121 from a plurality of possible wall types 123, a plurality of possible object positions 115, a plurality of possible object types 117, a plurality of possible object depths 119, and / or a plurality of possible object extensions 121 provided by the measuring device 100 (the wall types, object positions, object depths, and / or object extensions are provided as the diagnosis results 109 by the diagnosis module 107 during the wall diagnosis). Thus, by operating the first selection function 116, the user can select the diagnosis result 109 that he / she believes best reflects the actual condition of the wall 105.

[0146] The user can deactivate the automatic wall type determination by means of a second selection function 118. In addition, the user can manually input an existing wall type 123.

[0147] In the embodiment shown, the measuring device 100 also provides a feedback function 122 . Via the feedback function 122 , the user can input direct feedback on the consistency of the wall diagnosis with the actual state of the wall 105 .

[0148] To this end, the diagnosis module 107 receives a selection instruction 120 input by the user for the first and second selection functions 116 and 118. The selection instruction 120 describes the selection made by the user by operating the first selection function 116 and / or the second selection function 118 of the diagnosis result 109 provided by the diagnosis module 107, or describes the termination of the automatic execution of the wall type determination.

[0149] In addition, the corresponding feedback instruction 124 of the feedback function 122 may be received by the diagnosis module 107 . Here, the feedback instruction 124 includes the feedback information 112 provided by the user by operating the feedback function 122 .

[0150] In addition to feedback instructions 124, measuring device 100 is configured to determine feedback information 112 based on selection instructions 120 for first and second selection functions 116 and 118. For example, when a plurality of possible wall types 123 are provided, by selecting one of wall types 123, the selected wall type 123 is assigned positive feedback, while unselected wall types 123 are correspondingly assigned negative feedback. Accordingly, when second selection function 118, which deactivates automatic wall type determination, is operated, negative feedback regarding the wall type determination is recorded. A similar situation applies to the selection of other diagnostic results 109.

[0151] According to one embodiment, the diagnostic module 107 is further configured to determine, when repeatedly performing a wall diagnosis (the wall diagnosis is respectively performed at the same position of the measuring device 100 relative to the wall 105), a deviation between the repeatedly performed wall diagnoses, or to determine a deviation between the diagnostic results 109 during the wall diagnosis.

[0152] Furthermore, the measuring device 100 is configured to display the corresponding deviations on the display unit 111 . Via the operating feedback function 122 , the user can confirm or refute these deviations using corresponding feedback instructions 124 .

[0153] Selection instructions 120 and / or feedback instructions 124 can be input, for example, via operating element 154. Alternatively, display unit 111 can be designed, for example, as a touch screen, via which a user can input selection instructions 120 and / or feedback instructions 124.

[0154] The deviation of the diagnostic results 109 obtained from different wall diagnoses at the same position of the measuring device 100 relative to the wall 105 can be determined by the diagnostic module 107, for example, by comparing the diagnostic results 109 obtained from different wall diagnoses at different points in time. To this end, the diagnostic module 107 can include a correspondingly configured comparison module.

[0155] Figure 7 A flow chart of a method 200 for operating a measuring device 100 according to one specific embodiment is shown.

[0156] To operate measuring device 100 , radar data 103 of radar sensor unit 101 of measuring device 100 that are mapped to wall 105 to be diagnosed are first received in method step 201 .

[0157] In a further method step 203 , a wall diagnosis is carried out by diagnostic module 107 based on radar data 103 and a diagnostic result 109 is provided.

[0158] For this purpose, in method step 205 , a wall type classification is carried out by diagnosis module 107 and the wall type 123 of wall 105 is ascertained.

[0159] In a further method step 207, object recognition is performed by the diagnostic module 107 on at least one object 113 arranged in the wall 105. Object recognition includes object detection with determination of the object position 115 and object classification with determination of the object type 117. The diagnostic result 109 provided by the diagnostic module 107 includes at least the object position 115 and / or the object type 117 as well as the wall type 123.

[0160] Furthermore, feedback information 112 is determined in method step 209 . In this case, feedback information 112 describes the consistency of diagnosis result 109 with the current state of wall 105 .

[0161] Figure 8 A further flow chart of a method 200 for operating a measuring device 100 according to another specific embodiment is shown.

[0162] Figure 8 The embodiment shown in Figure 7 and includes all method steps described there.

[0163] In the embodiment shown, the wall diagnosis furthermore comprises carrying out an object depth determination in method step 213 and determining object depth 119 of object 113 .

[0164] Furthermore, in method step 215 , an object extension determination is carried out and object extension 121 of object 113 is determined.

[0165] Furthermore, in method step 211 , feedback information 112 is provided to external server unit 114 in order to take feedback information 112 into account in the updating of diagnostic module 107 .

[0166] Figure 9A further flow chart of a method 200 for operating a measuring device 100 according to another specific embodiment is shown.

[0167] Figure 9 The implementation in Figure 8 and includes all method steps listed therein.

[0168] In the specific embodiment shown, first selection function 116 and / or second selection function 118 is provided in method step 217 .

[0169] By implementing the first selection function 116, a suitable wall type 123 and / or a suitable object position 115 and / or a suitable object type 117 and / or a suitable object depth 119 and / or a suitable object extension scale 121 can be selected from a plurality of possible wall types 123 and / or possible object positions 115 and / or possible object types 117 and / or possible object depths 119 and / or possible object extension scales 121 provided during the wall diagnosis process.

[0170] By implementing the second selection function 118 , the automatic wall type determination can be deactivated and the wall type 123 can be selected manually.

[0171] Furthermore, a feedback function 122 is provided in a further method step 219. When the feedback function 122 is operated, a user can provide feedback information 112. The feedback information 112 can be used to provide feedback on the consistency of the provided diagnosis result 109 with the actual state of the wall 105.

[0172] In the embodiment shown, ascertaining 209 feedback information 112 also includes receiving, in method step 221 , selection instructions 120 of first and / or second selection functions 116 , 118 and / or feedback instructions 124 of feedback function 122 and ascertaining feedback information 112 based on selection instructions 120 and / or feedback instructions 124 .

[0173] Furthermore, in method step 223 , the diagnosis module 107 determines whether there are deviations in the diagnosis result 109 determined in the wall diagnosis when the wall diagnosis is repeated for the same position of the measuring device 100 relative to the wall 105 .

[0174] In a further method step 225 , the deviation is displayed on the display unit 111 .

[0175] In a further method step 227 , a feedback command 124 is received regarding the deviation, wherein the displayed deviation is confirmed or refuted in the feedback command 124 .

[0176] According to one embodiment, in addition to the feedback information 112, the external server unit 114 also provides corresponding radar data 103, based on which the wall diagnosis was previously performed. The feedback information 112 can be provided to the external server unit 114 by multiple measuring devices 100 in use by users.

[0177] Figure 10 A flow chart of a method 400 for training artificial intelligence 125 of measuring device 100 according to one specific embodiment is shown.

[0178] In order to train the artificial intelligence, in a first method step 401, a training data set 143 generated according to method 200 is first provided, wherein the training data set 143 includes radar data 103 mapping the wall 105 to be diagnosed and at least one object 113 arranged in the wall 105, as well as feedback information 112 provided in accordance with the method 200 for operating the measuring device 100 according to any of the above-mentioned embodiments.

[0179] In a further method step 403, artificial intelligence 125 is trained for object recognition of objects 113 formed in wall 105 based on training data set 143 and taking into account feedback information 112. The training is carried out in such a way that feedback information 112 is taken into account when performing a wall diagnosis by the correspondingly trained artificial intelligence.

[0180] Figure 11 A schematic illustration of a computer program product 500 is shown, which includes instructions which, when executed by a data processing unit, cause the data processing unit to carry out the method 200 for operating the measuring device 100 and / or the method 400 for training the artificial intelligence 125 .

[0181] In the embodiment shown, the computer program product 500 is stored on a storage medium 501. In this case, the storage medium 501 may be any storage medium known from the prior art.

Claims

1. A computer-implemented method (200) for operating a measuring device (100), in particular a wall diagnostic device, comprising: Receiving (201) radar data (103) from a radar sensor unit (101) of the measuring device (100), wherein the radar data (103) maps a wall (105) to be diagnosed; The diagnostic module (107) of the measuring device (100) performs (203) wall diagnosis by analyzing the radar data (103) and provides a diagnostic result (109), wherein the wall diagnosis includes: Implementing (205) wall type classification and determining the wall type (123) of the wall (105) by the diagnosis module (107), wherein the diagnosis result (109) at least includes the wall type (123) of the wall (105); and / or performing (207) object recognition on an object (113) arranged in the wall (105) by the diagnostic module (107), wherein the object recognition includes object detection and object classification, and determining an object position (115) and an object type (117) of the object (113) in the wall (105), wherein the diagnostic result (109) includes at least the object position (115) and / or the object type (117); Feedback information (112) is obtained (209), wherein the feedback information (112) describes the consistency between the diagnosis result (109) and the current state of the wall (105).

2. The method (200) of claim 1, further comprising: The feedback information (112) is provided (211) to an external server unit (114) so ​​that the feedback information (112) is taken into account in the updating of the diagnostic module (107).

3. The method (200) according to claim 2, wherein: In addition to the feedback information (112), corresponding radar data (103) are also provided to the external server unit (114).

4. The method (200) according to any one of the preceding claims, wherein: Implementing (203) the wall diagnosis further includes: performing (213) object depth determination by the diagnostic module (107) and determining an object depth (119) of the object (113) in the wall (105), wherein the object depth (119) is defined as the distance from the object (113) to the surface of the wall (105); and / or The object extension dimension determination is implemented (215) by the diagnostic module (107) and the object extension dimension (121) of the object (113) along a predefined direction is determined, wherein the diagnostic result (109) also includes at least the object depth (119) and / or the object extension dimension (121) of the object (113).

5. The method (200) according to any one of the preceding claims, wherein: In the object recognition, a plurality of possible object positions (115) and / or a plurality of possible object types (117) of the object (113) are determined as independent diagnostic results (109), and / or wherein, in the wall type classification, a plurality of possible wall types (123) of the wall (105) are determined as independent diagnostic results (109), and / or wherein, in the object depth determination, a plurality of possible object depths (119) are determined as independent diagnostic results (109), and / or wherein, in the object extension scale determination, a plurality of possible object extension scales (121) of the object (113) are determined as independent diagnostic results (109), wherein each of the plurality of diagnostic results (109) is displayed on a display unit (111) of the measuring device (100).

6. The method (200) of claim 5, further comprising: A first selection function (116) and / or a second selection function (118) is provided (217), wherein, by implementing the first selection function (116), a user of the measuring device (100) can select at least one of the displayed wall types (123) and / or one of the displayed object positions (115) and / or object types (117) and / or object depths (119) and / or object extension dimensions (121), and / or wherein, by implementing the second selection function (118), a user of the measuring device (100) can deactivate the automatic determination of the wall type (123) during the wall diagnosis and / or the display of the automatically determined wall type (123) in the display unit (111), and can manually select a wall type (123).

7. The method (200) of claim 6, further comprising: A feedback function (122) is provided (219), wherein, when implementing the feedback function (122), the user can determine whether the displayed wall type (123) and / or the displayed object position (115) and / or the displayed object type (117) and / or the displayed object depth (119) and / or the displayed object extension scale (121) are consistent with the actual wall type (123) and / or the actual object position (115) and / or the actual object type (117) and / or the actual object depth (119) and / or the actual object extension scale (121).

8. The method (200) according to claim 6 or 7, wherein: Obtaining (209) the feedback information (112) includes: Receive (221) a selection instruction (120) of the first and / or second selection function (116, 118) and / or a feedback instruction (124) of the feedback function (122), and obtain the feedback information (112) based on the selection instruction (120) and / or the feedback instruction (124), wherein a corresponding selection is made in the selection instruction (120) according to the first and / or second selection function (116, 118), and wherein the corresponding feedback information (112) provided by the user is included in the feedback instruction (124).

9. The method (200) according to any one of the preceding claims, wherein: Obtaining (209) the feedback information (112) includes: determining (223) by the diagnostic module (107) whether there is a deviation in the diagnostic result (109) obtained in the wall diagnosis when repeatedly diagnosing the same position of the measuring device (100) relative to the wall (105); displaying (225) the deviation in the display unit (111); A feedback instruction (124) from the user regarding the deviation is received (227), wherein the displayed deviation is confirmed or refuted in the feedback instruction (124).

10. The method (200) according to any one of the preceding claims, wherein: The measuring device (100) further comprises at least one inductive sensor and / or eddy current sensor and / or capacitive sensor and / or AC sensor and / or nuclear magnetic resonance sensor and / or ultrasonic sensor for providing additional sensor data, wherein the diagnostic module (107) is configured to perform the wall diagnosis while taking into account the additional sensor data.

11. The method (200) according to any one of the preceding claims, wherein: The diagnostic module (107) comprises at least one correspondingly trained artificial intelligence (125), which is configured to perform object recognition and / or wall classification and / or object depth determination and / or object extension determination based on the radar data (103) and / or the additional sensor data.

12. The method (200) according to any one of the preceding claims, wherein: The object categories of the object type (117) of the object (113) include: metal / non-metal objects, cables for low voltage, magnetic / non-magnetic objects, cables with single-phase AC signals, cables with multi-phase AC signals, wooden beams, metal beams, plastic pipes, water-filled plastic pipes, such as water supply pipes, non-water-filled plastic pipes, such as drainage pipes, and / or wherein the wall type categories of the wall type (123) of the wall (105) include: concrete walls, lightweight / dry structure walls, brick walls and / or brick blocks of walls, floor heating, wall heating.

13. A method (400) for training an artificial intelligence (125) of a measuring device (100) for wall diagnosis, the method comprising: providing (401) a training data set (143) for training the artificial intelligence (125), wherein the training data set (143) comprises radar data (103) mapping a wall (105) and objects (113) constructed in the wall (105) and feedback information (112) provided by the method (200) for operating a measuring device (100) according to any one of claims 1 to 12; The artificial intelligence (125) is trained (403) based on the training data set (143) and taking into account the feedback information (112) for performing object recognition of an object (113) arranged in a wall (105), wherein the object recognition includes at least object detection and object classification.

14. A computing unit (151) configured to implement the method (200) for operating a measuring device (100) according to any one of claims 1 to 12 and / or the method (400) for training an artificial intelligence (125) of a measuring device (100) for wall diagnosis according to claim 13.

15. A computer program product (500), comprising instructions which, when executed by a data processing unit, cause the data processing unit to execute the method (200) for operating a measuring device (100) according to any one of claims 1 to 12 and / or the method (400) for training an artificial intelligence (125) of a measuring device (100) for wall diagnosis according to claim 13.

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