Method for operating measuring device

Abstract

To provide an improved method that operates a wall diagnostic device.SOLUTION: The present invention relates to a computer-implemented method (200) for operating a measuring device (100), in particular, a wall diagnostic device, and the computer-implemented method includes: receiving (201) radar data (103) on a radar sensor unit (101) of the measuring device (100); carrying out (203) a wall diagnosis by implementing an analysis of the radar data (103) by a diagnostic module (107) of the measuring device (100); providing a diagnostic result (109); providing (207) a display unit (111) of the measuring device (100) with the diagnostic result (109) by the diagnostic module (107); and displaying (209) the diagnostic result (109) on the display unit (111).SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a method for operating a measurement device, in particular a wall diagnostic device. [Background technology]

[0002] From the prior art, 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 method for operating a measuring device, in particular a wall diagnostic device. [Means for solving the problem]

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

[0005] In one aspect, a computer-implemented method of operating a measurement device, in particular a wall diagnostic device, is provided, the method comprising: receiving radar data from a radar sensor unit of the measuring device, the radar data reflecting a wall to be diagnosed; and performing a wall diagnosis by performing an analysis of the radar data by a diagnostic module of the measurement device, and providing a diagnostic result, wherein the wall diagnosis includes: performing wall type classification by a diagnostic module and determining a wall type of the wall, the diagnostic result including at least the wall type of the wall; providing a diagnostic result to a display unit of the measurement device by the diagnostic module; and The diagnostic results are displayed on a display unit.

[0006] This provides the technical advantage of providing an improved method for operating a measuring device, in particular a wall diagnosis device. To this end, radar data from a sensor unit of the measuring device is first received. The radar data reflects the wall to be diagnosed. A diagnosis module then performs a wall diagnosis based on the radar data and provides a diagnosis result. The wall diagnosis includes at least determining the wall type of the wall to be diagnosed, and the determined wall type is displayed as the diagnosis result on the display unit of the measuring device. This method allows an automatic determination of the wall type of the wall to be diagnosed based solely on radar data from the radar sensor unit. The automatic wall type determination avoids the need for a male / female user of the measuring device to manually select the wall type of the wall to be diagnosed. Furthermore, the wall type determined in the automatic wall type classification or automatic wall type determination can be further used in subsequent steps of the wall diagnosis, for example, as background correction for object recognition. This further improves the quality of the wall diagnosis.

[0007] In one embodiment, the wall diagnostics include: The method further includes: determining an uncertainty value of the diagnosis result by the diagnosis module, the uncertainty value of the diagnosis result representing a probability value of agreement between the actual state of the wall to be diagnosed and the diagnosis result, and including at least one probability value of the wall type of the wall; the diagnostic module provides the uncertainty value together with the diagnostic result to the display unit; and The uncertainty value is displayed on the display unit together with the diagnostic result.

[0008] This provides a technical advantage in that additional information about the wall diagnosis is provided by determining an uncertainty value for the diagnosis result, in particular for the wall type determined in the wall diagnosis, and by providing and displaying the uncertainty value to the user of the measuring device together with the diagnosis result on the display unit. For example, the user can interpret the diagnosis result in light of the uncertainty value and thus better adapt subsequent strategies for wall processing to the performed wall diagnosis. If a diagnosis result with a high uncertainty value is displayed, the user can assume with high probability that the determined diagnosis result corresponds to the actual state of the wall to be inspected. In contrast, if the uncertainty value is low, the user can assume with only a low probability that the determined diagnosis result reflects the actual state of the wall. In this way, the user can adapt subsequent strategies or subsequent wall processing to the diagnosis result of the wall diagnosis taking the uncertainty value into account. According to the present invention, the uncertainty value represents the probability that the provided diagnosis result is valid.

[0009] In one embodiment, in the wall type classification, a plurality of possible wall types of the wall are determined as independent diagnostic results, a probability value is determined for each of the plurality of wall types of the wall, and the plurality of wall types of the wall are displayed on the display unit as diagnostic results and the plurality of probability values ​​of the various wall types as corresponding uncertainty values ​​of the diagnostic results.

[0010] This provides a technical advantage that the quality of wall diagnosis can be further improved. For this purpose, in the wall type classification, a plurality of possible wall types of a wall are determined as independent diagnosis results. The corresponding various wall types are assigned uncertainty values ​​that represent the probability that the respective wall types correspond to the actual wall type of the wall to be inspected. The male / female user can evaluate the displayed plurality of wall types accordingly and decide for themselves which of the provided wall types corresponds to the actual wall type of the wall to be inspected.

[0011] In one embodiment, the method comprises: The method further includes providing a first selection function, wherein the first selection function can be executed by a male / female user of the measuring device to select at least one of the displayed wall types.

[0012] This provides a technical advantage that the selection function allows the male / female user to select at least one of the displayed wall types as the actual wall type of the wall to be inspected. In this way, the male / female user can incorporate their own knowledge about the wall to be inspected into the wall diagnosis. This can further improve the wall diagnosis.

[0013] In one embodiment, the method comprises: The method further includes providing a second selection function, wherein the second selection function is executed by the male / female user of the measuring device to disable automatic determination of the wall type during wall diagnosis and / or display of the wall type on the display unit, and the wall type can be manually selected by the male / female user.

[0014] This provides the technical advantage that the second selection function allows the male / female user to disable the automatic determination of the wall type and manually select the wall type. In this way, the male / female user can manually input the actual wall type based on knowledge of the actual wall type, particularly when the automatic wall type determination does not determine the actual wall type of the wall to be inspected and therefore provides an insufficient result, and thus further improve the wall diagnosis in this regard. In this case, the wall type input by the male / female user can also be applied to subsequent steps of the wall diagnosis, thereby further improving the quality of the wall diagnosis.

[0015] In one embodiment, the method comprises: The method further includes displaying a plurality of possible wall types using a second selection function, and allowing a male user / female user to select at least one of the displayed possible wall types using the second selection function.

[0016] This provides the technical advantage of further improving wall diagnosis, by allowing the male / female user to select at least one of a number of wall types provided, thereby improving the correspondence between the automatically determined wall type and the actually existing wall type.

[0017] In one embodiment, the wall diagnostics further comprises: and / or wherein object recognition of an object located in the wall is performed by a diagnostic module, the object recognition including object detection and object classification, and the diagnostic result includes at least an object position in the wall and / or an object type of the object. an object depth determination is performed by the diagnostic module, and an object depth of the object in the wall is determined, the object depth being defined as the distance from the object to the surface of the wall; and / or The object extension determination is performed by the diagnostic module along a predefined direction, and the object extension of the object is determined, wherein the diagnostic result further includes the object position within the wall of the object and / or the object type and / or the object depth and / or the object extension, and the uncertainty value further includes at least a probability value of the object position of the object and / or a probability value of the object type and / or a probability value of the object depth and / or a probability value of the object extension.

[0018] This provides the technical advantage of further improving wall diagnosis. To this end, in addition to determining the wall type, object recognition is performed, including object detection with object position determination and object classification with object type determination. Furthermore, the object depth and object extension of an object located in the wall can be determined and provided as corresponding diagnostic results, which can be displayed on a display unit.

[0019] In one embodiment, the method further comprises: receiving a male / female user selection command, wherein the selection command selects a wall type by the male / female user using a first or second selection function; determining, by the diagnostic module, a probability value of the selected wall type based on the radar data; and and / or, if the probability value of the wall type selected by the male / female user is below a predetermined threshold, displaying the wall type with the highest probability value determined based on the radar data; and / or, if the probability value of the selected wall type reaches or exceeds a predefined threshold value, the wall type selected by the male / female user in the second selection function is taken into consideration for object recognition and / or object depth determination and / or object extension determination; The wall type with the greatest probability value, which is automatically determined by the diagnostic module based on the radar data, is taken into consideration for object recognition and / or object depth determination and / or object extension determination.

[0020] This provides a technical advantage of enabling further improvements in wall diagnosis. When the male / female user activates the second selection function, thereby deactivating the automatic determination of the wall type, and the male / female user manually selects a wall type, the wall type manually input by the male / female user is used for subsequent wall diagnosis only if the input wall type has a probability value that meets or exceeds a predefined limit value. Otherwise, the wall type determined by the automatic wall type determination is used for subsequent wall diagnosis. This prevents subsequent wall diagnosis from being negatively affected by an incorrect manual input of the wall type by the male / female user.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] In one embodiment, the object classification of the object type of the 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 the wall type classification of the wall type of the wall includes concrete wall, lightweight / dry construction wall, masonry wall and / or stacked wall stones, underfloor heating, wall heating.

[0026] This allows for the technical advantage of being able to recognize and classify different types of objects and walls.

[0027] In one aspect, a method for training artificial intelligence of a measurement device for wall diagnostics is provided, the method comprising: providing a training data set for training the artificial intelligence, wherein the training data set includes radar data reflecting walls and objects configured within the walls, and feedback information provided based on a method of operating the measurement device; The method includes training an artificial intelligence to perform object recognition of objects configured within the wall based on the training dataset and taking into account the feedback information, the object recognition including at least object detection and object classification.

[0028] This provides a technical advantage in that improved training, particularly post-training, of the artificial intelligence of the wall diagnostic device is possible, whereby feedback from both male and female users of the wall diagnostic device is taken into consideration.

[0029] In one aspect, there is provided a computing unit set up to perform a method for operating a measurement device according to one of the above embodiments.

[0030] In one aspect, a computer program product is provided that includes instructions that, when executed by a data processing unit, direct it to perform a method of operating a measurement device according to one embodiment.

[0031] Embodiments of the present invention will now be described with reference to the following drawings, in which: [Brief explanation of the drawings]

[0032] [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. [Figure 6] FIG. 1 is a schematic diagram illustrating a system for operating a measurement device, according to one embodiment. [Figure 7] 1 is a flowchart of a method for operating a measurement device according to one embodiment. [Figure 8] 10 is another flowchart of a method for operating a measurement device, according to another embodiment. [Figure 9] 10 is another flowchart of a method for operating a measurement device, according to another embodiment. [Figure 10] 10 is another flowchart of a method for operating a measurement device, according to another embodiment. [Figure 11] FIG. 1 is a schematic diagram illustrating a computer program product. DETAILED DESCRIPTION OF THE INVENTION

[0033] FIG. 1 shows a schematic diagram of a measurement device 100 according to one embodiment.

[0034] The present invention relates to a measuring device, in particular to a wall diagnostic device for the inspection of a wall 105 to be processed. In the prior art, wall diagnostic devices are known which are used to detect objects placed in the wall. This type of device allows a male / female user to inspect the wall to be processed for the presence of objects placed in the wall and, based on this, to carry out a planned operation, such as drilling holes in the wall, in order to avoid damaging the objects placed in the wall.

[0035] In the illustrated embodiment, the measuring device 100 includes a housing 150 having a grip 152 for a male / female user to hold the measuring device 100, a display unit 111 for displaying the wall diagnosis results 109, and an operating member 154 for switching the measuring device 100 into various operating modes.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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 male / female 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] The diagnostic result 109 thus configured can then be displayed to the male / female 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] FIG. 2 shows another schematic diagram of a measurement device 100 according to another embodiment.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] FIG. 3 shows another schematic diagram of a measurement device 100 according to another embodiment.

[0068] 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.

[0069] 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.

[0070] Alternatively or additionally, different processing paths 102 may perform different aspects of wall diagnostics based on the same sensor information.

[0071] 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.

[0072] 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 .

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] FIG. 4 shows a measurement schematic diagram of the measurement device 100 according to one embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The next temporal or spatial window 167 can then be provided as soon as one or more sampling points become available.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] FIG. 5 shows another schematic diagram of a measurement device 100 according to another embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] FIG. 6 shows a schematic diagram of a system 600 for operating the measurement device 100, according to one embodiment.

[0103] In the illustrated embodiment, the system 600 includes at least a measurement device 100 that includes a diagnostic module 107 .

[0104] To perform the wall diagnosis, the diagnosis module 107, according to the present invention, performs at least a wall type classification based on the radar data 103, and determines at least the wall type 123 of the wall 105. The respective wall type 123 is then provided as a diagnosis result 109 to the display unit 111 and displayed there.

[0105] In addition to the wall type 123, the object position 115 and / or the object type 117 and / or the object depth 119 and / or the object extension 121 can also be determined as corresponding diagnosis results 109 in the wall diagnosis.

[0106] Furthermore, in the illustrated embodiment, for each determined diagnostic result 109, an uncertainty value 110 is determined by the diagnostic module 107. The uncertainty value is a probability value that the respective diagnostic result 109 reflects the actual state of the wall 105 to be inspected. That is, in the illustrated embodiment, the uncertainty value 110 defines a probability value that the displayed wall type 123 corresponds to the actual wall type 123 of the wall 105 to be inspected.

[0107] In the illustrated embodiment, during wall diagnosis, the diagnostic module 107 determines a number of possible wall types 123. The determined wall types 123 are displayed on the display unit 111 as corresponding independent diagnostic results 109. Each determined possible wall type is assigned a corresponding uncertainty value 110.

[0108] The diagnostic module 107 is then set up to calculate a respective probability value for each possible wall type 123 determined based on the wall diagnostics performed.

[0109] In the embodiment shown in diagram a), the measurement device 100 further provides a first selection function 116 and a second selection function 118. The first selection function 116 allows the male / female user to select a diagnostic result 109 to be considered for subsequent wall diagnosis from among the displayed multiple diagnostic results 109, for example, from among the displayed multiple wall types 123. Therefore, the diagnostic result 109 that is not selected will not be considered for subsequent wall diagnosis. Thus, in the illustrated embodiment, the male / female user can select one of the displayed multiple possible wall types 123 for subsequent wall diagnosis through the first selection function 116.

[0110] Alternatively, through a second selection function 118, the male / female user can deactivate the automatic wall type determination. Additionally, the male / female user can manually input a wall type 123 on which subsequent wall diagnostics should be performed.

[0111] In one embodiment, in the case where the wall type 123 is manually input by the male / female user through the second selection function 118, the diagnostic module 107 is set up to determine an uncertainty value 110 for the wall type 123 based on the wall diagnosis. Furthermore, if the determined uncertainty value 110 reaches or exceeds a predefined limit value, the diagnostic module 107 is set up to consider the wall type 123 manually input by the male / female user for subsequent wall diagnosis based on the determined uncertainty value 110. On the other hand, if the determined uncertainty value 110 of the wall type 123 selected by the male / female user does not reach the predefined limit value, this can be displayed to the male / female user on the display unit 111. For example, in this case, one of the wall types 123 automatically determined during the diagnosis can be displayed to the male / female user as a possible wall type. Alternatively, multiple determined wall types 123 can be displayed to the male / female user. For subsequent wall diagnostics, one of the automatically determined wall types 123, preferably having the highest uncertainty value 110, can be considered.

[0112] Diagram b) shows another embodiment of the operation of the second selection function 118. When the automatic wall type determination is deactivated by the operation of the second selection function 118, a plurality of pre-stored wall types 123 can be displayed to the male / female user as an alternative or in addition to the option of the male / female user manually entering the wall type 123. In this way, the male / female user can select one of the possible wall types 123 stored in, for example, the data bank 120.

[0113] FIG. 7 shows a flow chart of a method 200 of operating the measurement device 100 according to one embodiment.

[0114] To operate the measuring device 100, firstly, radar data 103 of the radar sensor unit 101 of the measuring device 100 is received in method step 201. The radar data 103 then reflects a wall 105 to be diagnosed.

[0115] In a next method step 203 , a wall diagnosis is performed by the diagnostic module 107 based on the radar data 103 and a diagnosis result 109 is provided.

[0116] To that end, in method step 205 a wall type classification is performed in which the wall type 123 of the wall 105 is determined by the diagnostic module 107 .

[0117] In the next method step 207 , the diagnostic results are provided by the diagnostic module 107 to the display unit 111 .

[0118] In method step 209 , the diagnostic result 109 is displayed on the display unit 111 .

[0119] FIG. 8 shows another flow chart of a method 200 of operating a measurement device 100 according to another embodiment.

[0120] The embodiment of FIG. 8 is based on the embodiment of FIG. 7 and includes all of the method steps described therein.

[0121] In the illustrated embodiment, during wall diagnosis, in method step 211, an uncertainty value 110 of the diagnosis result 109 is determined by the diagnosis module 107. The uncertainty value 110 then represents a probability value of agreement between the diagnosis result 109 and the actual state of the wall 105 to be inspected.

[0122] In method steps 207 and 209, the uncertainty values ​​determined in method step 211 together with the corresponding diagnostic results 109 are provided to a display unit 111 and displayed there.

[0123] FIG. 9 shows another flow chart of a method 200 of operating the measurement device 100 according to another embodiment.

[0124] The embodiment of FIG. 9 is based on the embodiment of FIG. 8 and includes all method steps described therein.

[0125] In the illustrated embodiment, during the wall diagnosis in method step 219, object recognition of at least one object 113 located in the wall 105 is performed by object detection, which includes determining the object position 115, and object classification, which includes determining the object type 117.

[0126] In the next method step 221, an object depth determination is performed, in which the object depth 119 of the object 113 is determined.

[0127] In method step 223 an object extension determination is performed in which the object extension 121 of the object 113 is determined.

[0128] The object position 115 and / or object type 117 and / or object depth 119 and / or object extension 121 are provided to the display unit 111 and displayed there as corresponding diagnostic results 109. Corresponding uncertainty values ​​110 are calculated for the object position 115 and / or object type 117 and / or object depth 119 and / or object extension 121 and displayed on the display unit 111.

[0129] FIG. 10 shows another flow chart of a method 200 of operating a measurement device 100 according to another embodiment.

[0130] The embodiment of FIG. 10 is based on the embodiment of FIG. 8 and includes all of the method steps described therein.

[0131] In the illustrated embodiment, the wall diagnosis provides multiple possible alternative results for the diagnosis result 109 by the diagnostic module 107. For example, multiple different possible wall types 123 can be determined and displayed on the display unit 111. The multiple wall types 123 are assigned corresponding uncertainty values ​​110. The same applies to the object position 115, object type 117, object depth 119, and object extension 121, for which multiple possible alternative values ​​can also be determined, including the uncertainty values ​​110.

[0132] In the next method step 213, a first selection function 116 is provided. The first selection function 116 is activated to select at least one of the multiple possible wall types 123 displayed.

[0133] Further, in method step 215, a second selection function 118 is provided. Activation of the second selection function 118 can deactivate the automatic determination of the wall type 123. Furthermore, the wall type 123 can be manually selected by the male / female user.

[0134] In a next method step 217, a plurality of possible wall types 123 are displayed in a second selection function 118. The male / female user can select at least one of the displayed wall types 123 by activating the second selection function 118.

[0135] In a next method step 225, a male / female user selection command is received by the measurement device 100, and the wall type 123 is selected by the selection command.

[0136] In a next method step 227, for the selected wall type 123, a corresponding uncertainty value in the form of a probability value can be determined or determined.

[0137] In the next method step 229, if the probability value of the wall type 123 selected by the male / female user is below a predetermined limit value, the wall type 123 with a high probability value determined based on the radar data 103 is displayed on the display unit.

[0138] In the next method step 235, the probability value of the determined wall type 123 is checked.

[0139] In another method step 231, if the probability value of the selected wall type 123 reaches or exceeds a predefined limit value, the wall type 123 selected by the male / female user in the second selection function 118 is considered for further wall diagnosis.

[0140] If the probability value of the selected wall type does not reach the predetermined limit value, in another method step 233, the wall type 123 having a high uncertainty value 110, as determined in the automatic determination of the wall type 123, is considered for further wall diagnosis.

[0141] FIG. 11 shows a schematic diagram of a computer program product 500 comprising commands which, when executed by a data processing unit, direct the measurement device 100 to perform the method 200 for operating the device.

[0142] The computer program product 500 is stored in the illustrated embodiment on a storage medium 501, which may be any storage medium known in the art. [Explanation of symbols]

[0143] 100 Measuring Device 101 Radar sensor unit 103 Radar Data 105 Wall 107 Diagnostic Module 109 Diagnosis Results 110 Uncertainty Values 111 Display unit 113 Object 115 Object position 116 First Selection Function 117 Object Types 118 Second Selection Function 119 Object Depth 121 Object extension 123 Wall Type 125 Artificial Intelligence 151 computing units 200 ways 201 Radar data reception 203 Performing wall diagnostics 205 Performing Wall Type Classification 207 Providing diagnostic results 209 Displaying diagnostic results 211 Determination of Uncertainty Values 213 Providing the first selection function 215 Providing a second selection function 217 Display of wall type 219 Performing Object Recognition 221 Performing Object Depth Determination 223 Implementing Object Extension Decisions 225 Receiving Selection Orders 227 Determining Probability Values 229 Wall Type Display 231 Considering wall types 233 Considering wall types 500 Computer Program Products

Claims

1. A computer-implemented method (200) for operating a measurement 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), the radar data (103) reflecting a wall (105) to be diagnosed; The wall diagnosis is performed (203) by a diagnostic module (107) of the measurement device (100) by performing an analysis of the radar data (103) and providing a diagnostic result (109), wherein the wall diagnosis includes: a wall type classification is performed (205) by the diagnostic module (107) and a wall type (123) of the wall (105) is determined, and the diagnostic result (109) includes at least the wall type (123) of the wall (105); providing (207) a diagnostic result (109) to a display unit (111) of the measuring device (100) by the diagnostic module (107); and The diagnostic result (109) is displayed (209) on the display unit (111).

2. Wall diagnosis is The method further comprises: determining (211) an uncertainty value (110) of the diagnostic result (109) by the diagnostic module (107), the uncertainty value (110) of the diagnostic result representing a probability value of a match between the actual state of the wall (105) to be diagnosed and the diagnostic result, and comprising at least one probability value of a wall type (123) of the wall (105); the diagnostic module (107) provides (207) the display unit (111) with an uncertainty value (110) together with the diagnostic result (109); and 2. The method (200) of claim 1, comprising displaying (209) an uncertainty value (110) together with a diagnostic result (109) on the display unit (111).

3. 3. The method (200) according to claim 1 or 2, wherein in the wall type classification, a plurality of possible wall types (123) of the wall (105) are determined as independent diagnostic results (109), a probability value is determined for each of the plurality of wall types (123) of the wall (105), and the plurality of wall types (123) of the wall (105) are displayed on the display unit (111) as diagnostic results (109) and the plurality of probability values ​​of the various wall types (123) as corresponding uncertainty values ​​(110) of the diagnostic result (109).

4. 4. The method (200) of claim 3, further comprising providing (213) a first selection function (116), wherein the first selection function (116) can be executed by a male / female user of the measuring device (100) to select at least one of the displayed wall types (123).

5. 4. The method (200) of claim 3, further comprising providing (215) a second selection function (118), wherein execution of the second selection function (118) by a male / female user of the measuring device (100) can deactivate automatic determination of a wall type (123) during wall diagnosis and / or display of the wall type (123) on the display unit (111), and the wall type (123) can be manually selected by the male / female user.

6. 6. The method (200) of claim 5, further comprising displaying (217) a plurality of possible wall types (123) in the second selection function (118), and allowing a male / female user to select at least one of the displayed possible wall types (123) in the second selection function (118).

7. Wall diagnosis further and / or wherein object recognition of an object (113) located within the wall (105) is performed (219) by the diagnostic module (107), the object recognition including object detection and object classification, and the diagnostic result (109) includes at least an object position (115) within the wall (105) and / or an object type (117) of the object (113). An object depth determination is performed (221) by the diagnostic module (107), and includes determining an object depth (119) of the object (113) in the wall (105), the object depth (119) being defined as the distance from the object (113) to the surface of the wall (105); and / or A method (200) according to any one of claims 1 to 6, comprising: an object extension determination being performed (223) by the diagnostic module (107) along a predefined direction; and determining an object extension (121) of the object (113), wherein the diagnostic result (109) further comprises an object position (115) within a wall (105) of the object (113) and / or an object type (117) and / or an object depth (119) and / or an object extension (121), and wherein the uncertainty value (110) further comprises at least a probability value of the object position (115) of the object (113) and / or a probability value of the object type (117) and / or a probability value of the object depth (119) and / or a probability value of the object extension (121).

8. moreover, receiving (225) a male / female user selection command, wherein the selection command selects a wall type (123) by the male / female user using the first or second selection function; determining (227) a probability value for the selected wall type (123) based on the radar data (103) by the diagnostic module (107); and and / or, if the probability value of the wall type (123) selected by the male / female user is below a predetermined limit, displaying (229) the wall type (123) with the highest probability value determined based on the radar data; and / or, the wall type (123) selected by the male / female user in the second selection function is taken into consideration (231) for object recognition and / or object depth determination and / or object extension determination if the probability value of the selected wall type (123) reaches or exceeds a predefined limit value; The method (200) according to claim 5 or 6 and 7, comprising taking into account (233) the wall type (123) with the greatest probability value, determined automatically by the diagnostic module (107) based on the radar data (103), for object recognition and / or object depth determination and / or object extension determination.

9. 9. The method (200) according to any one of claims 1 to 8, wherein the object classification of the object type (117) of the object (113) comprises 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 the wall type classification of the wall type (123) of the wall (105) comprises concrete wall, lightweight / dry wall, masonry wall and / or stacked wall stones, underfloor heating, wall heating.

10. 10. The method (200) according to any one of claims 1 to 9, wherein the measuring device (100) 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 (107) is set up to perform wall diagnostics taking into account the additional sensor data.

11. The method (200) according to any one of claims 1 to 10, 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 and / or object extension determination based on radar data (103) and / or additional sensor data.

12. A computing unit (151) set up to perform a method (200) for operating a measuring device (100) according to any one of claims 1 to 11.

13. 12. A computer program product (500) comprising instructions which, when executed by a data processing unit, instruct it to perform a method (200) for operating a measuring device (100) according to any one of claims 1 to 11.

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