Measuring device

Abstract

To correspondingly adapt the scheduled processing of the wall by taking the displayed object type into account.SOLUTION: A measuring device (100), in particular a diagnostic device for walls, includes: at least one radar sensor unit (101) for providing radar data of a wall (105) to be diagnosed; a diagnostic module (107) for performing wall diagnosis based on the radar data and for generating a diagnostic result (109); and a display unit (111) for displaying the diagnostic result (109) to a user of the measuring device (100). The diagnostic module (107) is configured to recognize an object (113) formed in the wall (105) based on the radar data, and object recognition includes determination of an object position and a classification of an object type of the object (113).SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a measuring device, in particular to a wall diagnostic device. [Background technology]

[0002] From the prior art, wall diagnostic devices are known for diagnosing walls and for detecting objects arranged in walls. Summary of the Invention [Problem to be solved by the invention]

[0003] SUMMARY OF THE INVENTION It is an object of the present invention to provide an improved measuring device, in particular a wall diagnostic device. [Means for solving the problem]

[0004] This problem is solved by the measuring device of claim 1. Preferred embodiments are the subject of the dependent claims.

[0005] In one aspect, a measurement device, in particular a wall diagnostic device, is provided, comprising at least one radar sensor unit for providing radar data of a wall to be diagnosed, a diagnostic module for performing wall diagnosis based on the radar data and for generating a diagnostic result, and a display unit for displaying the diagnostic result to a user of the measurement device, wherein the diagnostic module is set up to recognize objects configured in the wall based on the radar data, and the object recognition comprises determining an object position and classifying the object into an object type.

[0006] This provides the technical advantage of providing an improved measuring device, in particular a wall diagnostic device. The measuring device is preferably configured as a wall diagnostic device capable of inspecting a wall to be inspected. In particular, the wall diagnostic device is set up to recognize objects located in the wall. To this end, the measuring device includes at least one radar sensor unit, which emits a radar signal in the direction of the wall to be inspected and receives a radar signal reflected in front of the wall. Furthermore, the measuring device includes a diagnostic module that operates based on radar data from the radar sensor unit and is set up to perform wall diagnostics on this basis.

[0007] To this end, the diagnostic module is set up to perform object recognition of objects arranged in the wall to be inspected based on the radar data of the radar sensor unit, the object recognition including at least object detection and object classification.

[0008] Object detection involves, on the one hand, detecting an object located within the wall and determining the object position of the object within the wall. Object classification involves determining an object class to which the detected object is assigned. By means of object classification, the object type of the detected object can be unambiguously determined. Furthermore, the measuring device includes a display unit. On the display unit, which may be configured as a display, for example, the diagnosis results of the wall diagnosis, i.e., the object position and / or object type of the detected and classified object located within the wall, can be displayed to the user.

[0009] The measuring device according to the invention then has the advantage that an unambiguous classification of the detected objects, i.e., an unambiguous assignment of the object type of the detected objects, can be carried out solely on the basis of radar data from the radar sensor unit of the measuring device, and no additional information, such as measurements of additional measured quantities of additional sensor elements, is required for object recognition within the meaning of the invention.

[0010] In one embodiment, the diagnostic module is further set up to determine an object depth of an object within the wall based on the radar data, the object depth being defined by a distance from an object configured within the wall to a surface of the wall.

[0011] This provides the technical advantage that, in addition to object recognition, the measuring device can also determine the object depth within the wall based on the radar data of the radar sensor unit. The object depth here refers to the distance from the object to the wall surface. With a correspondingly configured diagnostic module, the object depth can be determined solely based on the radar data of the radar sensor unit.

[0012] In one embodiment, the diagnostic module is further set up to determine an object extension of the object in a predefined direction based on the radar data.

[0013] This provides the technical advantage that, based on the radar data of the radar sensor unit, it is also possible to determine the object extension of a detected object within the wall, where the object extension represents the object extension in at least one spatial direction, preferably in two spatial directions, particularly preferably in three spatial directions, which allows one-dimensional, preferably two-dimensional, particularly preferably three-dimensional extension information of an object within the wall.

[0014] In one embodiment, the diagnostic module is further set up to classify the wall type of the wall based on the radar data.

[0015] This provides the technical advantage that, in addition to object recognition, wall type classification can also be performed solely based on the radar data of the radar sensor unit. In wall type classification, the various wall types of the wall to be inspected can be determined or classified by a correspondingly configured diagnostic module. Automatic wall type classification eliminates the need for the user to manually input the respective wall type of the wall to be inspected as additional information for wall diagnosis, as is known from the prior art. Instead, the wall type is automatically determined by the diagnostic module based on the radar data of the radar sensor unit.

[0016] The wall type can be incorporated as additional information into the wall diagnosis and object recognition, e.g., for background correction. Alternatively or additionally, the detected wall type can be displayed to the user as additional information on the display unit as a diagnostic result.

[0017] In one embodiment, the diagnostic module is further set up to perform background correction of the object recognition based on the classified wall type.

[0018] This provides the technical advantage that the classified wall type of the wall to be inspected can be taken into account in the background correction of the radar data of the radar sensor unit, thereby resulting in more accurate object recognition. Different wall types cause different radar signals to be reflected by the wall. When taking each wall type into account in the background correction, the effect of the different wall types on the radar signal can also be taken into account. This makes it possible to avoid errors in object recognition based on wall type and enables more accurate recognition of objects located within the wall.

[0019] In one embodiment, the display unit is set up to display to the user as diagnostic results the object position of the object in the wall and the object type classification of the object, and / or the object depth, and / or the object extension of the object, and / or the wall type classification of the wall.

[0020] This offers the technical advantage that a large number of different pieces of information relating to the wall to be inspected can be provided to the user on the display of the measuring device. In this way, the user can see the main results of the wall diagnosis at a glance on the display unit and can use this as a basis to carry out the planned wall processing.

[0021] In one embodiment, the diagnostic module includes at least one pre-processing module, a wall type classification module, and an object recognition module, wherein the pre-processing module is set up to pre-process radar data of the radar sensor unit and provide input data for the wall type classification module and the object recognition module, the wall type classification module is set up to classify a wall type of the wall based on the input data provided by the pre-processing module, and the object recognition module is set up to detect objects configured in the wall and classify an object type of each of the objects based on the input data provided by the pre-processing module.

[0022] This provides the technical advantage that a correspondingly configured diagnostic module can perform an accurate wall diagnosis based on radar data from the radar sensor unit. The radar data from the radar sensor unit can be preprocessed to convert the radar data into a form required for wall diagnosis. A correspondingly configured wall type classification module can perform a corresponding wall type classification based on the preprocessed radar data to determine the respective wall type of the wall.

[0023] The object recognition module allows to perform object recognition of objects located in the wall based on the preprocessed radar data and taking into account the wall type provided by the wall type classification module, where the object recognition module is set up to perform object detection and object classification of objects located in the wall based on the preprocessed radar data and taking into account the wall type provided. The proposed architecture of the diagnosis module allows for as accurate and reliable wall diagnosis as possible of the wall to be inspected.

[0024] In one embodiment, the diagnostic module includes at least a first pre-processing module and a second pre-processing module, wherein the first pre-processing module is set up to pre-process radar data of the radar sensor unit to provide input data for the wall type classification module, the wall type classification module is set up to classify wall types of walls based on the input data provided by the first pre-processing module and provide wall type information to the second pre-processing module, the second pre-processing module is set up to pre-process radar data of the radar sensor unit to provide input data for the object recognition module after taking into account the wall type information of the wall type classification module, and the object recognition module is set up to detect objects configured in the wall and classify object types of each of the objects based on the input data provided by the second pre-processing module.

[0025] This provides the technical advantage that the first and second preprocessing modules enable more accurate preprocessing of the radar data of the radar sensor unit, i.e. the proposed architecture of the diagnostic module allows for more precise object recognition and therefore more precise wall diagnosis.

[0026] In one embodiment, the measurement device further comprises at least one inductive sensor and / or eddy current sensor and / or capacitance sensor and / or AC sensor and / or NMR sensor and / or ultrasonic sensor for providing additional sensor data, and the diagnostic module is set up to perform wall diagnostics taking into account the additional sensor data.

[0027] This provides the technical advantage that further sensor data from additional sensors, each set up to detect a different physical measurement quantity, can be added to the information on the radar data from the radar sensor unit to provide additional information for wall diagnosis, which additional information, preferably complementary to the information on the radar data from the radar sensor unit, allows for further refinement of the wall diagnosis or object recognition.

[0028] In one embodiment, the diagnostic module includes at least one appropriately trained artificial intelligence set up to perform object recognition and / or wall classification and / or object depth determination based on radar data and / or additional sensor data.

[0029] This provides the technical advantage of providing a reliable and high-performance diagnostic module, in that the diagnostic module is configured 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 on the basis of radar data and / or possibly taking into account information from additional sensors. By utilizing artificial intelligence techniques, a precise wall diagnosis can be provided.

[0030] In one embodiment, the measurement device further comprises a motion detection unit, the motion detection unit set up to detect movement of the measurement device along the surface of the wall.

[0031] This provides the technical advantage that the movement of the measuring device relative to the wall can be determined by the movement detection unit. During normal use of the wall diagnosis device, the user moves the wall diagnosis device along the wall to be diagnosed. The movement detection unit can determine the corresponding relative movement of the measuring device relative to the wall. Based on the relative movement determined in this way, wall diagnosis can be performed for various positions of the measuring device relative to the wall. This allows for planar inspection of the wall to be inspected and allows for the recognition of objects and the determination of the object's extent over an area substantially larger than the active area of ​​the radar sensor unit.

[0032] In this way, the motion detection unit makes it possible to carry out wall diagnosis during the movement of the measuring device relative to the wall, thereby enabling a larger area of ​​the wall to be inspected to be covered and wall diagnosis to be correspondingly faster.

[0033] In one embodiment, the object type of the detected object includes metal object / non-metal object, cable for low voltage, cable carrying single phase AC signal, cable carrying polyphase AC signal, wooden support, metal support, plastic pipe, water filled plastic pipe, e.g. water pipe, non water filled plastic pipe, e.g. sewer pipe, and / or wall type type of the wall includes concrete wall, light construction wall / dry construction wall, masonry wall and / or stacked wall stones, underfloor heating, wall heating.

[0034] This provides the technical advantage of being able to recognize or classify a large number of different objects, each of which has a different object type. The measuring device or diagnostic module can be trained to detect and classify objects that are typically integrated into building walls. This allows for particularly precise wall diagnostics, in which detected objects can be assigned accurately and unambiguously to the corresponding object type.

[0035] By providing the user with accurate object classification information via the display unit, the most informative possible wall diagnosis is possible. The user not only knows that an object is located inside the wall and where it is, but also knows the object type of the detected object, allowing the user to make appropriate decisions about how to carry out subsequent wall processing with respect to the detected object. Thus, providing the object type of the object classification of the detected object is a key aspect of wall diagnosis, since the user can adapt the planned wall processing accordingly based on the displayed object type.

[0036] Embodiments of the present invention will now be described with reference to the following drawings, in which: [Brief explanation of the drawings]

[0037] [Figure 1] FIG. 1 is a schematic diagram illustrating a measurement device according to one embodiment. [Figure 2] FIG. 10 is another schematic diagram showing a measurement device according to another embodiment. [Figure 3] FIG. 10 is another schematic diagram showing a measurement device according to another embodiment. [Figure 4] FIG. 1 is a schematic diagram illustrating a measurement performed by a measurement device according to one embodiment. [Figure 5] FIG. 10 is another schematic diagram showing a measurement device according to another embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0038] FIG. 1 shows a schematic diagram of a measurement device 100 according to one embodiment.

[0039] The present invention relates to a measuring device, in particular to a wall diagnostic device for inspecting a wall 105 to be processed. Wall diagnostic devices are known in the prior art that are used to detect objects located in the wall. Devices of this kind allow a user to inspect the wall to be processed for the presence of objects located therein and, based on this, to carry out planned operations, such as drilling holes in the wall, in order to avoid damaging the objects located therein.

[0040] In the illustrated embodiment, the measuring device 100 includes a housing 150 having a grip 152 for a user to hold the measuring device 100, a display unit 111 for displaying the wall diagnostic results 109, and an operating member 154 for switching the measuring device 100 into various operating modes.

[0041] According to the invention, the measuring device 100 comprises at least one radar sensor unit 101. The radar sensor unit 101 makes it possible to emit a radar signal in the direction of a wall 105 to be inspected and to receive the radar signal reflected from the wall 105.

[0042] The radar sensor unit 101 may be configured as a narrowband radar detector, for example in the frequency range from 2.4 GHz to 2.4835 GHz, or as an ultra-wideband radar detector, for example in the frequency range from 1.8 GHz to 5.8 GHz.

[0043] The measuring device 100 further comprises a diagnostic module 107 executable in the computing unit 151 of the measuring device 100 for performing a wall diagnosis. The diagnostic module 107 is set up to perform a corresponding diagnosis of the wall to be inspected on the basis of the radar data 103 of the radar sensor unit 101, which reflect the wall 105 to be inspected and possibly an object 113 arranged in the wall 105.

[0044] The wall diagnosis performed by the diagnosis module 107 then includes at least performing object recognition, where object recognition includes object detection and object classification of an object 113 located in the wall 105. The object detection then includes at least determining an object position 115, where the object position represents the positioning of the object located in the wall 105 relative to a reference system defined by the measurement device 100. The object classification of the detected object 113 includes at least determining an object type 117 of the detected object 113.

[0045] The diagnostic result of the wall diagnosis determined in this way, 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, is subsequently displayed to the user of the measuring device 100 on a display unit 111 of the measuring device 100. The display unit 111 can be configured, for example, as a suitable display and can display the diagnostic result 109 optically. In addition, the display of the diagnostic result 109 can be supported by acoustic and / or tactile signals. The tactile signals can be embodied, for example, by suitable vibration signals.

[0046] The object 113 can then be displayed on the display, for example, by a corresponding symbol. The object 113 can then be displayed on the display at a corresponding object position 115. The object extension 121 can be visualized by a corresponding size of the displayed symbol. The respective object type 117 of the object 113 can be visualized by a corresponding concept, by color highlighting of the symbol, or by a special shape of the symbol representing the object 113.

[0047] Alternatively, the wall diagnosis may additionally comprise the determination of a wall type 123 in the form of a wall type classification of the wall 105 to be inspected, where the wall type 123 represents the respective type of the wall 105 to be inspected. The wall type may for example be assigned to a corresponding wall type classification, which may include: concrete wall, light / dry wall, masonry wall and / or stacked stone, underfloor heating, wall heating or similar wall types found in buildings.

[0048] In one embodiment, the diagnostic module 107 is further set up to determine an object depth 119 of the object 113 inside the wall 105 based on the radar data 103. The object depth 119 is then defined by the distance from the object configured inside the wall 105 to the surface of the wall 105. This distance may be defined on the object side, for example with respect to the object surface or with respect to the object center point. The distance to the surface of the wall 105 represents the shortest distance defined in a direction perpendicular to the surface of the wall 105.

[0049] In one embodiment, the diagnostic module 107 is further set up to determine an object extension 121 of the object 113 in at least one predefined direction based on the radar data 103. The object extension 121 of the object 113 then represents the spatial extension of the object 113 in at least one spatial direction, preferably in two spatial directions, particularly preferably in three spatial directions. The object 113 can thereby be described as a one-dimensional, two-dimensional or three-dimensional object 113.

[0050] In a typical application, the measuring device 100 is placed on the surface of a wall 105 to be inspected. A radar signal is transmitted through the radar sensor unit 101 in the direction of the wall 105, and a radar signal reflected by the wall 105 or an object 113 located behind it is received. Based on this radar data 103 from the radar sensor unit 101, the diagnostic module 107 performs the wall diagnosis described above and determines a corresponding diagnostic result 109.

[0051] The diagnosis result 109 may include, for example, an object location 115 and / or an object type 117 of an object 113 located in the wall 105. Alternatively or additionally, the diagnosis result 109 may include a wall type 123 of the wall 105, and / or an object depth 119 and / or an object extension 121 of the object 113.

[0052] The diagnostic result 109 configured in this way can subsequently be displayed to the user of the measuring device 100 on a display unit 111 of the measuring device 100. The display unit 111 can be configured, for example, as a suitable display. The diagnostic result 109 can be displayed on the display unit 111 in graphical or textual form.

[0053] In one embodiment, the measuring device 100 further includes a motion detection unit 141. The motion detection unit 141 can detect the motion of the measuring device 100 relative to the wall 105. To this end, the motion detection unit 141 can have, for example, at least one roller member. When the roller member rests on the wall surface of the wall 105, the motion of the measuring device 100 can be detected relative to the wall 105 when the measuring device 100 moves along the motion direction 153 due to the rolling of the roller member. Alternatively, the motion detection unit 141 can have any other configuration capable of detecting the relative motion of the measuring device 100 relative to the wall 105.

[0054] By moving the measuring device 100 relative to the wall 105, the radar data 103 of the radar sensor unit 101 can be recorded for a number of different positionings of the measuring device 100 relative to the wall 105. This allows for the inspection of the wall 105 over a larger spatial area than is given by the range of action of the radar sensor unit 101. This allows for the detection of objects 113 having a spatial extension greater than the range of action of the radar sensor unit 101.

[0055] During the movement of the measuring device 100 along the direction of movement 153, radar data 103 of the radar sensor unit 101 can be continuously recorded. Based on such radar data 103 during the movement of the measuring device 100 along the direction of movement 153, a wall diagnosis can be evaluated by the diagnostic module 107. This allows for a fast wall diagnosis that takes into account the positioning of the measuring device 100 relative to the wall 105.

[0056] In this embodiment, the diagnostic module 107 is configured as a correspondingly trained artificial intelligence 125, which is trained at least to perform the wall diagnostics described above based on the radar data 103 of the radar sensor unit 101 and to determine at least the object position 115 and the object type 117 of an object 113 located inside the wall 105. The determination of the object classification or object type 117 includes assigning the detected object 113 to a predefined object classification.

[0057] The object types may include: metal / non-metallic objects, cables for low voltage, cables carrying single-phase AC signals, cables carrying polyphase AC signals, wooden supports, metal supports, plastic pipes, water-filled plastic pipes, e.g., water pipes, non-water-filled plastic pipes, e.g., sewer pipes, or other components normally attached to building walls.

[0058] Furthermore, the artificial intelligence 125 may be trained to determine the wall type 123 of the wall 105 to be inspected based at least on the radar data 103 of the radar sensor unit 101. The possible wall types 123 may include: concrete walls, light / dry construction walls, masonry walls and / or individual stones in masonry walls, underfloor heating, wall heating, or any other wall type typically installed in buildings.

[0059] In one embodiment, the measurement device 100 may include additional sensors in addition to the radar sensor unit 101, which may detect additional physical quantities. For example, the measurement device 100 may include an inductive sensor and / or an eddy current sensor and / or a capacitance sensor and / or an AC sensor and / or an NMR sensor and / or an ultrasonic sensor, or any other sensor typically mounted in a wall diagnostic device.

[0060] The diagnostic module 107, in particular a correspondingly trained artificial intelligence 125, can then be set up to perform the wall diagnostics described above based on the radar data 103 of the radar sensor unit 101 and taking into account additional sensor information from other sensors. For this purpose, additional information from the aforementioned additional sensors can be used, in particular for object recognition of objects 113 arranged in the wall 105. Through the additional sensor information, an improved detection of the objects 113 and possibly an improved classification of the objects 113 can possibly be achieved.

[0061] In particular, illustratively, the classification of the material of the object 113, for example as a metallic or non-metallic material, can be improved by utilizing additional sensor information.

[0062] FIG. 2 shows another schematic diagram of a measurement device 100 according to another embodiment.

[0063] In the illustrated embodiment, the measuring device 100 includes a pre-processing module 127 in addition to the diagnostic module 107. For wall diagnosis, the measuring device 100 first receives radar data 103 from the radar sensor unit 101. Pre-processing of the received radar data 103 is carried out via the pre-processing module 127. Through pre-processing by the pre-processing module 127, the radar data can be arranged, for example, into a corresponding data structure required for wall diagnosis by the diagnostic module 107.

[0064] As explained above, during the wall diagnosis, the diagnostic module 107 generates the above-mentioned diagnostic result 109. The diagnostic result 109 may then include, for example, the object position 115 and / or object type 117 and / or object depth 119 and / or object extension 121 of an object 113 arranged in the wall 105 to be inspected, and / or the wall type 123 of the wall 105 to be inspected. The correspondingly generated diagnostic result 109 can then be displayed on the display unit 111 of the measuring device 100.

[0065] In one embodiment, in addition to the radar data 103 of the radar sensor unit 101, the above-described additional sensor information of the additional sensors can be taken into account in the wall diagnosis of the diagnostic module 107. A corresponding pre-processing of the additional sensor information can be performed accordingly by the pre-processing module 127.

[0066] In the illustrated embodiment, the diagnostic module 107 includes a wall-type classification module 129 and an object recognition module 131. The preprocessing module 127 includes a first preprocessing module 135 and a second preprocessing module 137. The first preprocessing module 135 includes an S-matrix reduction 155. The second preprocessing module 137 includes a background correction 157, an inverse fast Fourier transform 159, and focusing and migration 161. The preprocessing of the radar data 103 by the preprocessing module 127 begins with the preprocessing of the radar data 103 by the first preprocessing module 135 and the S-matrix reduction 155 included therein.

[0067] The first pre-processing module 135 then generates input data 133 based on the radar data 103. The input data 133 serves as input data for the wall type classification module 129. The wall type classification module 129 then performs wall type classification of the wall 105 to be inspected based on the input data 133 to generate wall type information 139. The wall type information 139 includes the wall type 123 of the wall 105 to be inspected determined in the wall type classification.

[0068] Subsequently, a second pre-processing module 137 performs pre-processing based on the radar data 103 and the wall type information 139. At this time, a background correction 157 of the radar data 103 is performed taking into account the wall type 123 contained in the wall type information 139. Depending on the wall type 123 of the wall 105 to be inspected, different effects on the radar data 103 may occur.

[0069] A background correction 157 can correct such effects that may affect object recognition, which mainly depend on the respective wall type 123. After the background correction has been performed, another preprocessing can be performed, such as an inverse fast Fourier transform 159 or focusing and migration 161, to prepare new input data 133 for the object recognition module 131. Based on the input data 133 provided by the second preprocessing module 137, the object recognition module 131 performs object recognition of objects 113 located in the wall 105 to be inspected, and determines at least the object position 115 and the object type 117 of each object 113. In addition, the object recognition module 131 can determine the object depth 119 and the object extension 121.

[0070] In one embodiment, the diagnostic module is further set up to determine an object depth of an object inside the wall based on the radar data, the object depth being defined as a distance from an object configured within the wall to a surface of the wall.

[0071] Preprocessing is optional here. Depending on the algorithm applied to the diagnostic module 107, completely unprocessed radar echoes of various frequencies can be used as radar data 103 and as input data for the diagnostic module 107. Alternatively, radar data 103 that has been processed in advance through several steps can be used. These preprocessing steps include, for example, transforming the signal from frequency space to time or distance space, background removal, denoising and normalizing the signal, etc. For radar data 103 that exists in the form of complex numbers, only absolute values ​​can be processed. Alternatively or additionally, phase information can be considered.

[0072] FIG. 3 shows another schematic diagram of a measurement device 100 according to another embodiment.

[0073] In the illustrated embodiment, the diagnostic module 107 includes multiple processing paths 102 running in parallel. Each processing path 102 includes a diagnostic module 107 including a pre-processing module 127, e.g., a wall-type classification module 129 and / or an object recognition module 131 according to the embodiment of FIG. 2, and a post-processing module 163.

[0074] 3, radar data 103 is primarily shown as input data for wall diagnosis. However, in addition to the illustrated radar data, additional information from additional sensors can also serve as input data for wall diagnosis. In this case, 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 wall diagnosis is completed, the individual partial analysis results can be integrated through an integration module and combined into a diagnosis result 109 for wall diagnosis.

[0075] Alternatively or additionally, different processing paths 102 may perform different aspects of wall diagnostics based on the same sensor information.

[0076] The individual processing paths 102 can then process different radar data 103, which have been recorded for different positions of the measuring device 100 relative to the wall 105, for example during the movement of the measuring device 100 relative to the wall 105. The radar data 103, which have been recorded successively in time during the movement of the measuring device 100 relative to the wall 105 and which reflect different areas of the wall 105, can then be processed in the various processing paths 102 by the modules shown.

[0077] The various processing paths then perform independent wall diagnostics, which include at least determining the object location 115 and / or object type 117 of an object 113 located within the wall 105 .

[0078] The integration module 165 allows the integration of the partial results of the independent wall diagnosis of different areas of the wall 105 provided in the individual processing paths 102 into an associated diagnostic result 109, which then describes the wall diagnosis of the relevant spatial area reflected by the corresponding radar data 103 passed through and recorded during the movement of the measuring device 100 relative to the wall 105. This parallel processing of the radar data 103 or additional sensor information 104 of additional sensor elements in the various processing paths 102 allows for accelerated wall diagnosis.

[0079] Alternatively, different functions of the wall diagnostics may be performed in different processing paths 102. For example, a processing path 102 may perform wall type classification of a wall 105 to be inspected and determination of the wall type 123. Another processing path 102 may perform object recognition of an object 113 located in the wall. Object detection together with determination of object location 115 and object classification together with determination of object type 117 may then be performed in one processing path 102.

[0080] Alternatively, object detection and object classification can be performed in two separate processing paths 102. The separate processing paths 102 can each trigger an object depth determination, i.e., a determination of the object depth 119 and / or a determination of the object extension 121. In an integration module 165, various partial results of the wall diagnosis can be integrated into a corresponding diagnosis result 109.

[0081] The diagnostic module 107 may then be divided into different artificial intelligences 125, as already shown in the embodiment of Fig. 2. The diagnostic module 107 may then include, for example, a wall type classification module 129 and an object recognition module 131. The object recognition module may be further divided into an object detection module and an object classification module. The diagnostic module 107 may further include an object depth determination module and an object extension module, which are set up to determine the object depth 119 and the object extension 121, respectively.

[0082] Each corresponding module may be configured as an independent artificial intelligence 125, for example as a neural network, or alternatively, the various modules may 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.

[0083] FIG. 4 shows a measurement schematic diagram of the measurement device 100 according to one embodiment.

[0084] For preprocessing, the radar data 103 or the additional sensor information 104 of other sensors can be normalized, in particular for numerical stabilization of the subsequent steps performed by the diagnostic module 107 during wall diagnosis. For this purpose, for example, amplitude and / or offset compensation can be performed. Furthermore, the radar data 103 can be filtered to reduce disturbances, and the corresponding sensor data can be downsampled to reduce the data rate. Furthermore, the radar data 103 or the additional sensor information 104 can be transformed into the required frequency domain or time domain, respectively. For this purpose, methods known from the prior art can be applied.

[0085] Furthermore, the recorded radar data 103 or additional sensor information 104 can be divided into temporal or spatial windows 167. The temporal windows 167 can be generated by recording the radar data 103 or additional sensor information or preprocessed radar data 103 over a fixed time interval, whereas the spatial windows 167 can be generated by assigning the radar data 103 or additional sensor information 104 to the position of the measuring device 100 relative to the wall 105 along the direction of movement 153.

[0086] Diagram a) of Figure 4 shows such a data matrix resulting from the steps described above. The data matrix in window 167 shown in diagram a) shows multiple 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 or a space / time axis 169.

[0087] The width of the time window 167 can then be selected to compensate for the different sampling rates of the sensors and to provide new windows 167 sufficiently frequently, so that the display of the wall diagnostic results 109 on the display unit 111 can be performed without excessive time delay while the measurement is being carried out or immediately after the measurement of the measuring device 100 has been completed.

[0088] For this purpose, a rate of 2 to 20 windows per second of data recording of sensor data may be preferred. For spatial windows, the spatial sampling rate can be selected to achieve the desired position accuracy. A sampling rate of 1 mm to 1 cm may then be preferred. This means that for every 1 mm to 1 cm of movement of the measuring device 100 along the direction of movement 153, the corresponding sensor data is recorded.

[0089] The width of the spatial window 167 can be selected so that the window contains information related to the object 113. A width of 1 cm to 20 cm for each spatial window 167 may then be preferred, resulting in 4 to 100 measurements per window 167. This allows for more efficient algorithmic processing of the correspondingly recorded radar data 103 or additional sensor information by the diagnostic module 107.

[0090] The next temporal or spatial window 167 can then be provided as soon as one or more sampling points become available.

[0091] The diagnostic module 107 may be configured to record as input data for each processing path 102, for example in the embodiment of Fig. 3, a matrix corresponding to the window size of the respective spatial or temporal window 167. In this case, the corresponding input data may comprise preprocessed sensor data, i.e. radar data 103 and additional sensor information 104 of the additional sensor, in accordance with the embodiment of Fig. 2.

[0092] As explained above, the wall diagnosis by the diagnostic module 107 can be performed on the basis of an appropriately trained artificial intelligence. Alternatively, the various processing paths can also be calculated by rule-based algorithms. Within the processing path 102, a combination of artificial intelligence and rule-based algorithms is also possible in the form of a parallel circuit or a concatenation.

[0093] The wall diagnosis result 109 can be expressed as a numerical value, a vector, or a matrix. Furthermore, for object detection, the probability of detection can be displayed, or for wall type classification or object classification, the probability of the displayed object type or wall type type. The same can also be applied to the position determination and / or depth determination, which can also display corresponding probability values.

[0094] If, in addition to radar data 103, additional sensor information from another sensor type is also processed in processing path 102, these can be integrated within artificial intelligence 125 or combined by rule-based combination.

[0095] In the post-processing of each processing path 102 of the embodiment of Figure 3, the results of multiple algorithms based on multiple windows 167 can be combined by a combination module 165. Such combination can be realized by, among other things, majority formation, sum formation, or multiplication of successive probability values.

[0096] Furthermore, clustering of multiple results, for example of multiple objects detected close to each other, allows recognition of which objects are the same object, so that they are not mistakenly recognized multiple times.

[0097] Similarly, a weighting function 177 can be applied by multiplication when combining results from multiple windows 167. Preferably, the diagnostic sub-results 175 corresponding to corresponding data points in space can then be weighted with respect to the positioning of the diagnostic sub-results 175 relative to the center point of the respective window 167. This is shown by way of example in diagram b), where each diagnostic sub-result 175 is weighted with respect to the center point of the respective window 167 according to the weighting function 177 shown.

[0098] In one embodiment, the results of one processing path 102 can influence the expansion of other processing paths 102 after post-processing 163. Weighting parameters can then be adapted and for each window the weighting parameters can be made dependent on the respective results coming from the processing paths 102.

[0099] For example, using the results of object classification that defines the object type 117 of an object located inside a wall 105, the weight of wall type classification that determines the wall type 123 of each wall 105 can be increased in post-processing in areas where there are no objects 113. This is because the radar data 103 in such areas is less affected by reflections from the objects 113.

[0100] FIG. 5 shows another schematic diagram of a measurement device 100 according to another embodiment.

[0101] Diagrams a) and b) of FIG. 5 show two different options for joint data processing of radar data 103 and additional sensor information 104 by the diagnostic module 107.

[0102] Diagram a) shows the joint processing of radar data 103 and additional sensor information 104 from an additional sensor by a diagnostic module 107. To this end, the radar data 103 and the additional sensor information 104 are jointly used as input data for a diagnostic module 107 configured as an artificial intelligence, in particular as an artificial neural network. The diagnostic module 107 includes a number of folding layers 108 and a number of tightly coupled layers 106. The radar data 103 and the additional sensor information 104 are jointly processed as input data via the folding layers 108 and the tightly coupled layers 106. The output data of the diagnostic module 107, on the basis of which the above-described diagnostic result 109 is generated.

[0103] In contrast, in diagram b), radar data 103 and additional sensor information 104 are used as independent input data for a diagnostic module 107. The diagnostic module 107 is made up of multiple processing paths 102, each of which includes multiple folding layers 108 and at least one tightly coupled layer 106. In the various processing paths 102, wall diagnoses are generated by the diagnostic module 107 independently of each other, based on the radar data 103 or the additional sensor information 104.

[0104] In an additional concatenation layer 148, the partial results of the partial diagnoses of the various processing paths 102 are integrated and fed to the final tightly coupled layer 106. The output data of the diagnosis module 107 corresponds to the diagnosis result 109 described above.

[0105] A correspondingly configured diagnostic module 107 is set up to perform the wall diagnostics described above based on the radar data 103 and additional sensor information 104 and with the configuration requirements described above.

[0106] In the illustrated embodiment, the diagnostic module 107 is configured as an artificial neural network, in particular as a folding network. Corresponding network architectures with a folding layer 108, a densely connected layer 106, and a connection layer 148 are well known in the prior art. [Explanation of symbols]

[0107] 100 Measuring Device 101 Radar sensor unit 103 Radar Data 105 Wall 107 Diagnostic Module 109 Diagnosis Results 111 Display unit 113 Object 115 Object position 117 Object Types 119 Object Depth 121 Object extension 123 Wall Type 125 Artificial Intelligence 127 Pre-processing module 129 Wall Type Classification Module 131 Object Recognition Module 133 Input Data 135 First Pre-processing Module 137 Second Pre-processing Module 139 Wall Type Information 141 Motion Detection Unit

Claims

1. 1. A measuring device (100), in particular a wall diagnostic device, comprising: at least one radar sensor unit (101) for providing radar data (103) of a wall (105) to be diagnosed; a diagnostic module (107) for performing wall diagnosis based on the radar data (103) and for generating a diagnostic result (109); and a display unit (111) for displaying the diagnostic result (109) to a user of the measuring device (100), wherein the diagnostic module (107) is set up to recognize an object (113) arranged in the wall (105) based on the radar data (103), the object recognition including determining an object position (115) and classifying the object (113) into an object type (117).

2. 2. The measuring device (100) of claim 1, wherein the diagnostic module (107) is further set up to determine an object depth (119) of an object (113) inside the wall (105) based on the radar data (103), the object depth (119) being defined by a distance from the object (113) configured in the wall (105) to the surface of the wall (105).

3. 3. The measuring device (100) of claim 1 or 2, further comprising a diagnostic module (107) configured to determine an object extension (121) of an object (113) in a predefined direction based on radar data (103).

4. 4. The measuring device (100) of claim 1, wherein the diagnostic module (107) is further set up to classify a wall type (123) of the wall (105) based on the radar data (103).

5. 5. The measuring device (100) of claim 1, wherein the diagnostic module (107) is further set up to perform background correction of object recognition on the basis of the classified wall type (123).

6. The measuring device (100) of any one of claims 1 to 5, wherein the display unit (111) is set up to display to a user as diagnostic results (109) the object position (115) of an object (113) in a wall (105), the type classification of the object type (117) of the object (113), and / or the object depth (119), and / or the object extension (121) of the object (113), and / or the type classification of the wall type (123) of the wall (105).

7. The diagnostic module (107) includes at least one pre-processing module (127), a wall type classification module (129), and an object recognition module (131), the pre-processing module (127) being set up to pre-process radar data (103) of the radar sensor unit (101) and provide input data (133) for the wall type classification module (129) and the object recognition module (131), and the wall type classification module (129) being set up to pre-process radar data (103) of the radar sensor unit (101) and provide input data (133) for the wall type classification module (129).

7. The measuring device (100) of claim 1, wherein the object recognition module (131) is set up to classify a wall type (123) of a wall (105) based on input data (133) provided by the pre-processing module (127), and the object recognition module (131) is set up to detect objects (113) arranged in the wall (105) and classify the object type (117) of each of the objects (113) based on the input data (133) provided by the pre-processing module (127).

8. The diagnostic module (107) includes at least a first pre-processing module (135) and a second pre-processing module (137), wherein the first pre-processing module (135) is set up to pre-process radar data (103) of the radar sensor unit (101) and provide input data (133) for the wall-type classification module (129), and the wall-type classification module (129) is set up to classify a wall type (123) of a wall (105) based on the input data (133) provided by the first pre-processing module (135) and provide wall-type information (139) to the second pre-processing module (137).

8. The measuring device (100) of claim 7, wherein the second pre-processing module (137) is set up to pre-process the radar data (103) of the radar sensor unit (101) to provide input data (133) for the object recognition module (131) taking into account wall type information (139) of the wall type classification module (129), and the object recognition module (131) is set up to detect objects (113) arranged in the wall (105) and classify the object types (117) of the respective objects (113) based on the input data (133) provided by the second pre-processing module (137).

9. 9. The measuring device (100) according to claim 1, further comprising at least one inductive sensor and / or eddy current sensor and / or capacitance sensor and / or AC sensor and / or NMR sensor and / or ultrasonic sensor for providing additional sensor data, and the diagnostic module (107) is set up to perform wall diagnostics taking into account the additional sensor data.

10. 10. The measuring device (100) according to any one of claims 1 to 9, wherein the diagnostic module (107) comprises at least one appropriately trained artificial intelligence (125) set up to perform object recognition and / or wall classification and / or object depth determination based on radar data (103) and / or additional sensor data.

11. 11. The measuring device (100) of claim 1, further comprising a motion detection unit (141), the motion detection unit (141) being set up to detect movement of the measuring device (100) along a surface of a wall (105).

12. 12. The measuring device (100) according to any one of claims 1 to 11, wherein the object types of the detected objects (113) include metal objects / non-metal objects, cables for low voltage, cables carrying single-phase AC signals, cables carrying polyphase AC signals, wooden supports, metal supports, plastic pipes, water-filled plastic pipes, e.g., water pipes, non-water-filled plastic pipes, e.g., sewer pipes, and / or the wall type types of the walls (105) include concrete walls, lightweight / dry-construction walls, masonry walls and / or stacked stones, underfloor heating, wall heating.

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