Method for determining a property of a bulk material

Abstract

The present invention relates to a method for determining at least one specific piece of information relating to a bulk material. The invention further relates to a method for generating a data record including at least one piece of information relating to a bulk material, a method for obtaining a piece of information relating to a bulk material and a database, devices and computer-implemented data structures.

Description

[0001] Title of the invention

[0002] Method for determining a property of a bulk material

[0003] field of technology

[0004] The present invention relates to a method for determining at least one specific piece of information about a bulk material. The invention also relates to a method for generating a data set with at least one piece of information about a bulk material, a method for obtaining information about a bulk material, and a database, devices, a computer program product, a signal sequence, and computer-implemented data structures.

[0005] State of the art

[0006] Dosing devices for dosing bulk materials consist of a multitude of components that must be suitably selected and coordinated depending on the specific bulk material to be dosed. To design a dosing device, it was previously often necessary to examine the physical properties of the bulk material to be dosed in the laboratory and to test its interaction with various designs of mechanical components and with different operating parameter settings in experimental setups. However, reproducible and therefore reliable results from such investigations are only possible with time-consuming and costly measures that also require the deployment of specially trained personnel.

[0007] Summary of the invention

[0008] It is therefore an object of the present invention to overcome the described disadvantages of the prior art and in particular to provide means with which, against the background of a bulk material to be dosed, information on the bulk material can be determined in a simple yet reliable manner, so that the use of the bulk material in a dosing device can be reliably planned and prepared.

[0009] The object is achieved by the invention according to a first aspect by a method having the features of patent claim 1.

[0010] A method for determining specific information about a bulk material is proposed; the method comprises obtaining a first image of a sample of the bulk material, determining first bulk material information based on the data of the first image by means of a first evaluation module, and / or obtaining second bulk material information via a user input, in particular via a human-machine interface, wherein the second bulk material information is a haptic property of the bulk material. Based on the data of the first and second bulk material information, the specific information about the bulk material is then determined by means of a processing module.

[0011] The invention is therefore based on the surprising discovery that, based on a sample of a bulk material to be dosed, one or more pieces of information about the bulk material, particularly concerning its properties, can be derived at least implicitly, which can be used to assess the dosing behavior of the bulk material. Supported by additional user input regarding further bulk material information, complex investigations of the bulk material with regard to its physical properties can thus be eliminated or at least reduced.

[0012] The information obtained with the method thus provides a solid foundation for making further preparations for the use of the bulk material not only more quickly than before, but above all, more reliably. For example, the information obtained can be used to select a suitable dosing device. This can include the selection of components and / or operating parameters.

[0013] Surprisingly, it has been shown that the method can provide information about the bulk material both for previously known bulk materials and for bulk materials that are being used for the first time, for example, in a production environment.

[0014] In this context, it has been particularly surprising to find that the information obtained with the proposed method can sometimes even be superior to the data obtained by an expert in the laboratory. Apparently, the bulk material can sometimes be described even better with the information obtained from the images than with conventional parameters, which have previously been determined through complex sample tests on complicated test rigs, for example, regarding physical parameters of the bulk material.

[0015] The process also makes it particularly advantageous to operate an existing dosing device alternately with different bulk materials without great effort. This is because no material-specific background knowledge is required to obtain information about the respective bulk material. This can increase the utilization of the dosing device and thus its cost-effectiveness as well as operational reliability.

[0016] The invention is therefore based on the surprising discovery that, based on a sample of a bulk material to be dosed, one or more pieces of information about the bulk material, particularly concerning its properties, can be derived at least implicitly, which can be used to assess the dosing behavior of the bulk material. Supported by additional user input regarding further bulk material information, complex investigations of the bulk material with regard to its physical properties can thus be eliminated or at least reduced.

[0017] The information obtained with the method thus provides a solid foundation for making further preparations for the use of the bulk material not only more quickly than before, but above all, more reliably. For example, the information obtained can be used to select a suitable dosing device. This can include the selection of components and / or operating parameters.

[0018] Surprisingly, it has been shown that the method can provide information about the bulk material both for previously known bulk materials and for bulk materials that are being used for the first time, for example, in a production environment.

[0019] In this context, it has been particularly surprising to find that the information obtained with the proposed method can sometimes even be superior to the data obtained by an expert in the laboratory. Apparently, the bulk material can sometimes be described even better with the information obtained from the images than with conventional parameters, which have previously been determined through complex sample tests on complicated test rigs, for example, regarding physical parameters of the bulk material.

[0020] The process also makes it particularly advantageous to operate an existing dosing device alternately with different bulk materials without great effort. This is because no material-specific background knowledge is required to obtain information about the respective bulk material. This can increase the utilization of the dosing device and thus its cost-effectiveness as well as operational reliability.

[0021] Above all, the proposed method makes it possible for even a non-technical person to determine reliable information on a bulk material to be dosed in order to then, for example, select components of a dosing device based on this information and / or replace components of an existing dosing device as well as to adjust the operating parameters of the dosing device accordingly.

[0022] Examples of advantageous bulk materials are rock, building materials, in particular topsoil, sand, gravel and / or cement, raw materials, in particular ore, coal, clay and / or road salt, foodstuffs, in particular grain, sugar, salt, coffee and / or flour, and / or powdered goods, in particular pigments, fillers, granules and / or pellets.

[0023] The type of bulk material can be, for example, one of the following bulk material types: dust, powder, flour, grains, granules, shot, lumps, pellets and / or other.

[0024] The method is advantageously computer-implemented.

[0025] The method can advantageously be provided as a cloud service. This eliminates the need for extensive data processing at the location of the bulk material for which the information is to be determined.

[0026] In one embodiment, the method comprises storing the at least one specific piece of information as a data record in a database, in particular in a material database for bulk material.

[0027] Obtaining a recording advantageously comprises obtaining data, in particular image data, of the recording. For example, the data represents pixel values ​​of the recording. This data can be received in raw format, in a preprocessed image format, and / or from a suitable light-sensitive sensor.

[0028] When, in the present application, information is determined "based on" certain data, this advantageously means that the information would not be ascertainable without these certain data. These certain data (which can be referred to as source data) can be subjected to one or more data processing steps to obtain processed data. Within these data processing steps, the certain data can be evaluated, further processed, and / or merged with other data. With the help of the certain data, further information, in particular from other sources such as databases, can also be obtained and made available as intermediate results. Within data processing steps, the further information can be subjected to further data processing steps alongside or instead of the certain data and / or processed data, including processing with other certain data.The latter means, more generally speaking, that several different data sets, "from" which the information is determined, can be present, whereby these can each be processed individually and, at a specific processing step, two or more such different source data sets, possibly already in processed form, can be combined and optionally used as input data for subsequent data processing.

[0029] Alternatively or additionally, it can also be provided that the method comprises that the at least one specific piece of information is determined by means of a processing module, that the data of the first recording are included in the determination of the specific information by means of the processing module and / or that a first piece of information about the bulk material is determined based on the data of the first recording by means of a first evaluation module, and wherein the first piece of bulk material information is included in the determination of the at least one specific piece of information by means of the processing module and / or the first piece of information is the at least one specific piece of information.

[0030] Preferably, several first pieces of bulk material information are determined and optionally included in the determination of the specific information. In one embodiment, the (at least one) first piece of bulk material information is determined using image processing and analysis methods based on the first image. These are, for example, machine learning methods. Preferably, the first piece of bulk material information is determined using the first analysis module and / or the first piece of bulk material information (along with optionally further data) is used by the processing module to determine the specific information.

[0031] The first bulk material information can, for example, be a first bulk material property, such as a material property of the bulk material.

[0032] The processing module can be implemented, for example, in software, hardware, or a combination of both. Alternatively or additionally, the processing module can comprise a memory, a processor, a receiving device (for example, to receive the recording), a transmitting device (for example, to send a generated signal to another entity), or any combination thereof. Alternatively or additionally, the processing module can provide and / or make available and / or comprise everything it comprises, such as, in particular, all the resources necessary for this purpose, for example, in the form of software and / or hardware resources.

[0033] The first evaluation module can be implemented, for example, in software, hardware, or a combination of both. Alternatively or additionally, the first evaluation module can comprise a memory, a processor, a receiving device (for example, to receive the recording), a transmitting device (for example, to send a generated signal to another entity), or any combination thereof. Alternatively or additionally, the first evaluation module can provide and / or make available and / or comprise everything it comprises, such as, in particular, all the resources necessary for this purpose, for example, in the form of software and / or hardware resources.

[0034] In an advantageous embodiment, the processing module comprises the first evaluation module. The first evaluation module and the processing module can preferably also be identical and thus form a common module.

[0035] In one embodiment, the two modules are operated spatially separate from one another. This is advantageous if the two modules have different requirements for hardware and / or software resources, allowing the module with more computationally intensive operations to be operated in an environment with sufficient hardware and / or software resources. In this case, the modules can exchange data with one another, for example via a data connection. In one embodiment, at least the data from the first recording represents input data and / or at least the first bulk material information represents output data of the first evaluation module. Further input and / or output data, in particular those described elsewhere in the application, are possible.

[0036] In one embodiment, at least the data of the first recording and / or at least parts of the output data of the first evaluation module represent input data and / or at least the specific information determined represents output data of the respective processing module. Further input and / or output data, in particular those described elsewhere in the application, are possible.

[0037] Preferably, the output data of a module are obtained by the respective module at least in part by processing at least the input data of the respective module, i.e. evaluating, analyzing and / or converting them into new data.

[0038] Alternatively or additionally, it can also be provided that the method comprises obtaining at least a second image of the sample of the bulk material, and wherein (i) the specific information on the bulk material is determined on the basis of the data of the first and second images by means of the processing module and / or (ii) the data of the second image are included in the determination of the specific information by means of the processing module, wherein preferably (i) the first information on the bulk material is determined on the basis of the data of the first and second images by means of the first evaluation module, and / or (ii) a second information on the bulk material is determined on the basis of the data of the first and / or second images by means of a second evaluation module and is included in the determination of the at least one specific information item partly by means of the processing module.

[0039] The data from the second recording can be used in addition to the data from the first recording to determine the specific information.

[0040] Preferably, the data from the first recording is not used to determine the second information. For example, the second information can then be determined based on the data from the second recording by feeding at least the data from the second recording but not the data from the first recording to the data input of the respective evaluation module. Preferably, additional input data can be provided, which are not the data from the first recording.

[0041] It is also advantageously possible to determine the first bulk material information and / or the second bulk material information using the data from the first and second recordings. For this purpose, the data from the first and second recordings can advantageously be fed to the data input of the respective evaluation module. Preferably, additional input data can be provided.

[0042] The second evaluation module can be implemented in software, hardware, or a combination of both. Alternatively or additionally, the second evaluation module can comprise a memory, a processor, a receiving device (for example, to receive the recording), a transmitting device (for example, to send a generated signal to another entity), or any combination thereof. Alternatively or additionally, the second evaluation module can provide and / or make available and / or comprise everything it comprises, such as, in particular, all necessary resources, for example, in the form of software and / or hardware resources. In an advantageous embodiment, the processing module comprises the second evaluation module. The second evaluation module and the processing module can preferably also be identical and thus form a common module.

[0043] In one embodiment, the two modules are operated spatially separated from each other. This is advantageous if the two modules have different requirements for hardware and / or software resources, allowing the module with more computationally intensive operations to be operated in an environment with sufficient hardware and / or software resources. In this case, the modules can exchange data with each other, for example, via a data connection.

[0044] It is also possible that the first evaluation module, the second evaluation module and the processing module are identical and / or that the first evaluation module and the second evaluation module are identical.

[0045] In one embodiment, alternatively or additionally, at least the data of the second recording represent input data of the first evaluation module. Further input and / or output data, in particular those described elsewhere in the application, are possible.

[0046] In one embodiment, the data of the first and / or second recording represent input data and / or the second information represents output data of the second evaluation module. Further input and / or output data, in particular those described elsewhere in the application, are possible.

[0047] In one embodiment, the data from the second recording and / or the second information alternatively or additionally represent input data of the processing module. Further input and / or output data, particularly those described elsewhere in the application, are possible. Thus, the processing module can advantageously determine the specific information by incorporating the respective data.

[0048] Alternatively or additionally, it can also be provided that the method comprises evaluating and / or analyzing the data of the first and / or second recording using methods of digital image analysis, in particular at least partially in the field of machine learning, in particular in order to determine the first information, the second information and / or the at least one specific information.

[0049] Advantageously, digital image analysis is performed using machine learning methods. For this purpose, a machine learning model can be trained on training data that is of the type of input data expected later, each with associated expected output data. Using the trained model, output data can be calculated from the respective input data, representing the respective specific information or other information.

[0050] Therefore, for example, the processing module can execute corresponding methods, in particular on at least the data of the first and / or second recording and / or the first and / or second information on the bulk material, in order to at least partially determine the specific information.

[0051] Therefore, for example, the first evaluation module can execute corresponding methods, in particular on at least the data from the first and / or second recording, in order to at least partially determine the first piece of information about the bulk material. Therefore, for example, the second evaluation module can execute corresponding methods, in particular on at least the data from the first and / or second recording, in order to at least partially determine the second piece of information about the bulk material.

[0052] Alternatively or additionally, it can also be provided that the determination of the at least one specific information item comprises the determination of a classification, in particular with regard to the type of bulk material, a, preferably average, particle size of the bulk material and / or a particle shape, and / or an identifier of the bulk material in each case, and wherein preferably a result of the classification and / or the identifier is the specific information item.

[0053] A classification can, for example, refer to the bulk material type. Within the scope of the classification, the bulk material can therefore be classified into at least one of the following types: dust, powder, flour, grains, granules, shot, lumps, pellets, and / or other. Alternatively or additionally, a classification can also refer to a particle size, particularly the average particle size, of the bulk material. Thus, the classification can advantageously have the bulk material type and / or a particle size as a result.

[0054] The identifier can, for example, be a unique identifier (e.g., an alphanumeric character string) of the bulk material. Advantageously, the identifier can be used, particularly with the involvement of a database, to determine further information, such as pre-stored information, about the bulk material represented by the identifier. Information about the bulk material, such as material specifications, can then be retrieved from a database using the identifier. This will be discussed in more detail below.

[0055] Alternatively or additionally, it can also be provided that the method comprises that, at least partially based on the classification and / or the identifier, information, in particular comprising a first piece of bulk material information, is retrieved from a database about a bulk material assigned to the classification and / or the identifier and is included in the determination of the at least one specific piece of information, in particular at least partially by means of the processing module, and / or is used as the specific information.

[0056] The additional information allows for even more reliable and accurate determination of the specific information. This allows for the inclusion of information about the bulk material that cannot be directly derived from the recorded data but can be obtained from other sources (such as the database). The identifier can be used as a link (relation) for this purpose. This allows the specific information to be obtained from the recorded data and with the further use of additional resources (such as databases, etc.).

[0057] The retrieved information may, for example, be or include information about the bulk material, such as first bulk material information of the bulk material.

[0058] The information is advantageously obtained using the identifier, for example, retrieved from the database.

[0059] Thus, in one embodiment, additional information about the bulk material (in particular based on the identifier from a database) can be determined and included in the determination of the specific information (in particular at least partially by means of the processing module).

[0060] Preferably, the information relates to the material properties of the bulk material. Based on the material properties, specific information can advantageously be assessed, for example, regarding the suitability of the bulk material for use in a particular type of dosing device, a dosing device with specific equipment, for example, with regard to mechanical components and / or the setting of operating parameters. Therefore, it is advantageous if, based on the data from the first recording and with the aid of the identifier, one or more material properties are obtained and included in the determination of the specific information.

[0061] The retrieved information can advantageously be provided to an entity and / or to a user, for example, on a user interface such as a screen. This allows this additional information to be provided in addition to the specific information.

[0062] In one embodiment, alternatively or additionally, at least the retrieved information represents input data of the processing module. Further input and / or output data, in particular those described elsewhere in the application, are possible.

[0063] Alternatively or additionally, it can also be provided that the method comprises that at least one data set, which data set represents at least one second piece of information about the bulk material, preferably implicitly or explicitly, is obtained, wherein (i) at least partly based on the data set obtained, the at least one specific piece of information is determined, in particular at least partly by means of the processing module, and / or (ii) the data set obtained is included in the determination of the at least one specific piece of information, in particular at least partly by means of the processing module.

[0064] The obtained data set is therefore used in addition to at least the data from the first and / or second recording in order to determine the specific information. The data from the first and / or second recording and the data set can, for example, be processed together (for example as input data for the first evaluation module) in order to obtain at least one common intermediate value, which in turn is further processed (for example as input data of the processing module) in order to determine the specific information. Or at least one first intermediate value is obtained from the image data and at least one second intermediate value is obtained from the data set, and the first and second intermediate values ​​are further processed (for example by means of the processing module) in order to determine the specific information.

[0065] The data set can, for example, represent and / or encode one or more pieces of information about the bulk material. Alternatively or additionally, the data set could also represent a preferably binary character sequence, with each position representing information about the bulk material. An example of such a character sequence is the binary sequence 0101011101, where, in this sequence, the presence ("1") or absence ("0") of the respective property for the bulk material is encoded for ten bulk material properties (e.g., "sticky," "electrostatic," "granular," etc.).

[0066] The data set obtained can therefore be used in addition to at least the data from the first and / or second recording to determine the specific information.

[0067] The information represented by the data set obtained advantageously represents different information than the first and / or second information mentioned elsewhere.

[0068] In one embodiment, alternatively or additionally, at least the received data volume represents input data of the processing module. Further input and / or output data, in particular those described elsewhere in the application, are possible.

[0069] Alternatively or additionally, it can also be provided that the dataset is obtained by user input, in particular via a human-machine interface. Alternatively or additionally, it can also be provided that (i) the first recording and the second recording are carried out, preferably simultaneously or sequentially, from different perspectives and / or (ii) the bulk material sample is in a first sample configuration during the first recording and the bulk material sample is in a second sample configuration during the second recording, wherein the bulk material sample is preferably transferred from the first sample configuration to the second sample configuration between the two recordings.

[0070] For example, the first and second images can be taken with one and the same recording device and the position and / or orientation of the recording device can be changed between the images in order to record the two images one after the other from different perspectives.

[0071] For example, the first and second images can be captured using different recording devices, with the recording devices having different positions and / or orientations. The two images can thus be captured simultaneously from different perspectives, particularly advantageously.

[0072] In one embodiment, the perspectives of the two shots are identical.

[0073] A sample configuration is preferably understood to mean the type and manner of a spatial arrangement of the bulk material of the sample, in particular under existing boundary conditions, such as existing environmental conditions, and / or the conditions of the formation of material accumulations (e.g. clumping) within the arrangement.

[0074] For example, a bulk material sample can be piled onto a flat support from a feed pipe at a certain height. The way in which the bulk material sample forms a pile can then be understood as a sample configuration. This sample configuration can differ from a different sample configuration obtained if the bulk material sample is piled onto the flat support from a different height. Instead of the height, the angle of inclination of the support could also be changed.

[0075] Alternatively or additionally, it can also be provided that the first and second sample configurations are two differently formed heaps of the bulk material sample and / or at least one parameter of the ambient conditions, such as ambient temperature, air humidity and / or ambient pressure, of the bulk material sample is changed between the first and second sample configuration.

[0076] Preferably, the bulk material in both sample configurations is in a state of equilibrium with the respective existing physical environmental conditions.

[0077] For example, a heap can be formed from the sample at two different ambient temperatures (preferably using the same procedure in each case). The different formation of the heap in the two cases represents the different sample configurations. The bulk material of the sample is at the respective ambient temperature, thus in an equilibrium state.

[0078] Another possible environmental condition is the inclination of the base (also mentioned above) on which the bulk material sample is piled up.

[0079] Alternatively or additionally, it can also be provided that a control signal representing the determined specific information is generated and preferably supplied to an entity and / or output to a user on a human-machine interface, such as a screen, and / or wherein a dosing device is influenced, configured and / or controlled at least partially based on the determined specific information.

[0080] The control signal advantageously makes it possible to supply an entity with the specific information. For example, the entity can be a higher-level dosing device monitoring system and / or a dosing device management system. Alternatively or additionally, the control signal can be supplied to the entity to initiate monitoring of the dosing device and / or for documentation purposes. In this way, current information on the respective bulk material can advantageously be continuously obtained even during ongoing operation of the dosing device.

[0081] The entity can be the dosing device with which the bulk material is or is to be dosed, or parts thereof, such as a motor or its motor control. The entity can also be a device different from the dosing device, for example.

[0082] In one embodiment, the control signal can be an analog or digital signal. Alternatively or additionally, the control signal can also be a command within a software application.

[0083] Alternatively or additionally, it can also be provided that at least one reference object is at least partially recognizable in the first recording and / or the second recording, and wherein the reference object preferably has defined dimensions and / or patterns and / or is used to determine a distance, in particular a particle size of the bulk material, in the first and / or second recording.

[0084] The reference object can be advantageously used to determine dimensions of structures contained in the first and / or second image if the dimensions of the reference object are known.

[0085] The reference object can represent a base or parts of it on which bulk material can be piled. For example, the reference object has a pattern, such as a checkerboard pattern. This is advantageous because a length reference is obtained using the reference object even if parts of the reference object (e.g., by bulk material) are obscured.

[0086] The reference object can, for example, have a rectangular main side and preferably be a flat and / or plate-shaped element. Such a reference object can be placed or appropriately positioned next to the bulk material sample during the acquisition of the image.

[0087] The reference object can also advantageously provide information for an ML model, discussed in detail below, which ML model is used to evaluate the first and / or second image. In this case, the ML model can, for example, learn a relationship between the reference object and a particle size of the bulk material. Therefore, the reference object can advantageously also be included in the images used to train the ML model.

[0088] Alternatively or additionally, it can also be provided that the first recording is an image recording, preferably in the visible, infrared or ultraviolet spectral range, retrieved from a memory, received by a sensor, in particular an optical sensor, and / or received via a data connection.

[0089] The first image can advantageously be captured with a camera. This camera can, for example, have a sensor sensitive to the visible, infrared, and / or ultraviolet spectral range. By selecting the sensor type, i.e., which spectral components of the light are captured for the image, various features can advantageously be determined directly based on the data from the first image.

[0090] For example, using an infrared camera, the temperature of the bulk material can be determined and the specific information of this bulk material can be determined based at least partly on the temperature.

[0091] Furthermore, the first image can also be taken using a radar sensor and / or an X-ray device.

[0092] The first receptacle for the bulk material to be dosed can be created, for example, before the dosing device is filled with the bulk material to be dosed. For example, the bulk material captured by the first receptacle can be located in a preferably sealed material packaging. For example, the bulk material captured by the first receptacle can be located in the inlet of the dosing device and / or in a storage container from which the bulk material is advantageously removed and fed to the dosing device. In one embodiment, the bulk material captured by the first receptacle is located outside the dosing device, inside the dosing device, and / or in front of and / or behind the discharge device.

[0093] Alternatively or additionally, it can also be provided that the second recording is an image recording, preferably in the visible, infrared or ultraviolet spectral range, retrieved from a memory, received by a sensor, in particular an optical sensor, and / or received via a data connection.

[0094] The second image can advantageously be captured with a camera. This camera can, for example, have a sensor sensitive to the visible, infrared, and / or ultraviolet spectral range. By selecting the sensor type—that is, which spectral components of the light are captured for the image—different features can advantageously be determined directly based on the data from the second image.

[0095] For example, using an infrared camera, the temperature of the bulk material can be determined and the specific information of this bulk material can be determined based at least partly on the temperature.

[0096] Furthermore, the second image can also be taken using a radar sensor and / or an X-ray device.

[0097] The second receptacle for the bulk material to be dosed can be created, for example, before the dosing device is filled with the bulk material to be dosed. For example, the bulk material captured by the second receptacle can be located in a preferably sealed material packaging. For example, the bulk material captured by the second receptacle can be located in the inlet of the dosing device and / or in a storage container from which the bulk material is advantageously removed and fed to the dosing device. In one embodiment, the bulk material captured by the second receptacle is located outside the dosing device, inside the dosing device, and / or in front of and / or behind the discharge device.

[0098] Alternatively or additionally, it may also be provided that the processing module has an ML model that has been pre-trained and / or is included in the determination of the specific information.

[0099] Advantageously, the machine learning model (ML model) is trained with data from a large number of recordings of one or more known bulk materials. In this way, a relationship can be learned between, on the one hand, a recording (of a bulk material) and, on the other hand, a specific bulk material or information about it (such as an identifier and / or information about the bulk material) and / or specific information about the bulk material. Alternatively or additionally, in addition to the recording data, further or different information such as first and / or second bulk material information can be provided as input data, and the training can take this information into account accordingly. In this way, the learned relationship can be expanded or modified.

[0100] The ML model preferably has, at least in part, the input data and output data of the processing module as input data and output data, respectively. The ML model therefore advantageously calculates the output data based on the input data. Accordingly, the ML model is advantageously trained on input data that corresponds to the input data used later.

[0101] The ML model can also have learned a relationship between an identifier (of a bulk material) and specific information. Optionally, additional information can be provided as input data, and the training can incorporate this information accordingly. This allows the learned relationship to be expanded.

[0102] To extract information from images, convolutional neural networks (CNN), especially from the field of deep learning, have proven to be advantageous and are therefore preferred as the basis for the first ML model.

[0103] The initial ML model can also be retrained with newly acquired images. This allows the reliability and quality of the specific information obtained to be continuously improved.

[0104] Alternatively or additionally, it can also be provided that the first evaluation module has an ML model that has been pre-trained and / or is included in the determination of the first and / or second bulk material information and / or the identifier.

[0105] Alternatively or additionally, it can also be provided that the second evaluation module has an ML model that has been pre-trained and / or is included in the determination of the second bulk material information.

[0106] Advantageously, the machine learning model (ML model) of the first and / or second evaluation module is trained with data from a large number of images of one or more known bulk materials. In this way, a relationship can be learned between, on the one hand, an image (of a bulk material) and, on the other hand, a specific bulk material or information about it (such as an identifier or information about the bulk material). Optionally, the relationship can also be learned with different sample configurations.

[0107] Advantageously, an ML model can be used in each of the processing module and the first and second evaluation modules, with the ML models being independent. In this case, it is advantageous if the ML model of the processing module is at least partially trained with data that results from the output of the ML model of the first and / or second evaluation module and / or that is determined based at least partially on the output data of the ML model of the first and / or second evaluation module. In this way, the ML model of the processing module can learn a relationship between, on the one hand, the respective output data of the first and / or second evaluation module (as well as any additional information) and, on the other hand, a specific piece of information.If the ML models exist, specific information can therefore advantageously be determined with the ML model of the processing module, at least partially based on the output data of the ML model of the first and / or second evaluation module and / or the information determined based on the output data of the ML model of the first and / or second evaluation module.

[0108] In order to extract information from images, convolutional neural networks (CNN), especially from the field of deep learning, have proven to be advantageous and are therefore preferably used as the basis for the ML model of the first and / or second evaluation module.

[0109] The ML model of the first and / or second evaluation module can also be retrained with newly acquired images. This allows the reliability and quality of the specific information obtained to be continuously improved.

[0110] The ML model of the respective evaluation module preferably has, at least in part, the input data and output data of the respective evaluation module as input data and output data, respectively. The respective ML model therefore advantageously calculates the output data based on the input data. Accordingly, the ML model is advantageously trained on input data that corresponds to the input data later used.

[0111] Alternatively or additionally, it can also be provided that the first bulk material information is at least one of the following properties of the bulk material or a measure thereof: a grain shape, a grain size, a particle size, an angle of repose, an outlet angle, a physical property, a chemical property, a moisture content, a material type, a density, a flow behavior, a tendency to bridge formation, a tendency to shoot when fluidized, a lump content, an electrostatic chargeability, a chemical instability, a temperature sensitivity, suspended in air, a tendency to demix, consisting of components, a free flowability, a tendency to agglomeration, an abrasiveness, a corrosiveness, a mechanical sensitivity, a fragility, an explosiveness, a flammability, a dustiness, a moisture content, an adhesion, a consistency, a hygroscopic behavior, a temperature, a tendency to fluidize,a tendency to harden, radioactivity, toxicity, thixotropic behavior, a tendency to deteriorate, a tendency to soften, static electricity, the presence of oils and fats, flakiness and / or stickiness.

[0112] Alternatively or additionally, it can also be provided that the second bulk material information is or characterizes a haptic property of the bulk material or is at least one of the following properties of the bulk material or a measure thereof: a grain shape, a grain size, a particle size, an angle of repose, an outlet angle, a physical property, a chemical property, a moisture content, a material type, a density, a flow behavior, a tendency to bridge formation, a tendency to shoot when fluidized, a lump content, an electrostatic chargeability, a chemical instability, a temperature sensitivity, suspended in air, a tendency to demix, consisting of components, a free flowability, a tendency to agglomeration, an abrasiveness, a corrosiveness, a mechanical sensitivity, a fragility, an explosiveness, a flammability, a dustiness, a moisture content, an adhesion, a consistency,a hygroscopic behavior, a temperature, a tendency to fluidize, a tendency to harden, a radioactivity, a toxicity, a thixotropic behavior, a tendency to spoil, a tendency to soften, static electricity, the presence of oils and fats, flakiness and / or stickiness. Further information on bulk solid properties (particularly on material properties of bulk solids) can be found, for example, in the document "General Bulk Solid Properties and their Brief Representation" of the "FEDERATION EUROPEENNE DE LA MANUTENTION SECTION II", FEM 2 582, Original D, Edition D, November 1991, the document "Specific Bulk Solid Properties for Mechanical Conveying" of the "FEDERATION EUROPEENNE DE LA MANUTENTION SECTION II", FEM 2 181, Original E, Edition D, 1989, and the document "Bulk Solid Properties" of the "FEDERATION EUROPEENNE DE LA MANUTENTION SECTION II", FEM 2 581, Original D, Edition D, November 1991.

[0113] The object is achieved by the invention according to a second aspect in that a method for generating a data set with at least one piece of information about a bulk material, in particular with regard to at least one property of the bulk material, the method comprising offering a screen output on a user interface to a user, determining an interaction of the user with the screen output on the user interface in response to the offering of the screen output, and generating, based on the determined interaction of the user, at least one characteristic value that can be stored in a database for the at least one piece of information about the bulk material, in particular the at least one property of the bulk material, as a data set.

[0114] The invention is therefore based on the surprising finding that the manual recording of measured values ​​or the like and that the carrying out of complex physical investigations on bulk material samples prior to this procedure is unnecessary if the information on the bulk material, such as in particular bulk material properties, is queried with the aid of screen interactions that are easy to carry out by a user.

[0115] This eliminates the need for complex material tests in the laboratory (e.g., shear tests, etc.) or at least reduces their scope. Furthermore, bulk material information can be easily determined and generated as a data set, even by laypersons.

[0116] Such data sets can advantageously be used to create, build, and / or supplement a knowledge database on the material properties of bulk materials. In one embodiment, the data sets are therefore stored in a knowledge database on bulk materials, in particular on the material properties of bulk materials.

[0117] For example, the process is carried out using a smartphone, enabling mobile data generation.

[0118] Preferably, the method is computer-implemented.

[0119] Alternatively or additionally, it can also be provided that the user's interaction is a swipe gesture on the user interface, which is designed in particular as a touch-sensitive screen, and / or a selection of at least one option from a selection menu offered on the screen output.

[0120] For example, with respect to a specific bulk material property, a swipe gesture on the user interface in a first direction can be a first value of the bulk material property and / or a swipe gesture on the user interface in a second direction can be a second value of the bulk material property. For example, the bulk material property can be stickiness, and the first value can be "sticky" and the second value can be "non-sticky." The possible directions of a swipe gesture can be indicated to the user by at least temporarily displaying an auxiliary symbol. This can make the user's operation safer and support the correct generation of the data set.

[0121] Alternatively or additionally, it can also be provided that a plurality of screen outputs are offered to the user on the user interface, in particular one after the other, and for each screen output, an interaction of the user with the screen output on the user interface is determined in response to the offering of the respective screen output and, based on the determined interactions of the user, one or more than one characteristic value storable in a database for two or more than two items of information about the bulk material, in particular for two or more than two properties of the bulk material, is generated as a data record.

[0122] This makes it easy to carry out more extensive characterizations of the bulk material.

[0123] Alternatively or additionally, it can also be provided that specific information is a haptic property of the bulk material or a measure thereof.

[0124] For example, at least one data set can be created for each of several bulk materials. Alternatively or additionally, a plurality of users can each generate a data set containing at least one piece of information relating to at least one piece of information about a bulk material.

[0125] Alternatively or additionally, it can also be provided that several such data sets are generated, in particular through interactions of several users, and wherein preferably the several data sets are transferred into a common data set in which the information on the bulk material is constructed from the information of the individual data sets.

[0126] It is advantageous if several data sets generated for one and the same bulk material (in particular if they were generated by more than one user) are replaced by a common data set, wherein the information from the several data sets can be consolidated in the common data set, for example by averaging, in particular by means of a preferably user-dependent weighting of the individual data sets.

[0127] Alternatively or additionally, it can also be provided that the data record or data records are or are each associated with an assignment to the bulk material and / or are or are stored in at least one database, in particular for the purpose of building, supplementing and / or updating a knowledge database on bulk material information.

[0128] The dataset can also be the shared dataset.

[0129] This database can advantageously be the aforementioned knowledge database.

[0130] Alternatively or additionally, it can also be provided that the generated data set is provided in a method according to the first aspect of the invention and is retained there as a data set, in particular representing a second bulk material information.

[0131] Alternatively or additionally, it can also be provided that, in a method according to the first aspect of the invention, the ML model of the first evaluation module, the second evaluation module, and / or the processing module is at least partially trained with the generated data sets and / or the data stored in the database. The object is achieved by the invention according to a third aspect in that a database is proposed that is designed to store data sets from a method according to the second aspect of the invention.

[0132] All advantages described with respect to the method according to the second aspect of the invention also apply correspondingly to the database according to the third aspect of the invention. Therefore, reference can be made to the previous explanations at this point.

[0133] The object is achieved by the invention according to a fourth aspect in that a method for obtaining information about a bulk material, comprising that at least a first image of at least one sample of the bulk material is taken, in particular in a first sample configuration and / or with a camera, and / or at least a second image of the sample of the bulk material is taken, in particular in a second sample configuration and / or with a camera, and is provided in a method according to the first aspect of the invention and is obtained there as a first image and / or second image, is proposed.

[0134] In particular, the method by which the recordings are provided can also be executed at a remote location, such as on a cloud computer. Thus, only a camera and a corresponding data connection need to be provided on the client side in order to be able to use a method according to the fourth aspect of the invention.

[0135] For example, the process is carried out using a smartphone. This allows operators to obtain information about a bulk material on the go.

[0136] Preferably, the method is computer-implemented.

[0137] All advantages described with respect to the method according to the first aspect of the invention also apply accordingly to the method according to the fourth aspect of the invention. Therefore, reference can be made to the previous explanations at this point.

[0138] Alternatively or additionally, it can also be provided that the method comprises (i) that the specific information determined in the method according to the first aspect of the invention is obtained and / or that the control signal generated in the method according to the first aspect of the invention is obtained and / or (ii) that a property, in particular a material property, for example a haptic material property, of the bulk material is determined, in particular manually, and provided in the method according to the first aspect of the invention and is obtained there as a data set representing a second bulk material information.

[0139] The object is achieved by the invention according to a fifth aspect in that a device for data processing is proposed, comprising means which are designed to carry out a method according to the first aspect of the invention and / or according to the second aspect of the invention.

[0140] The data processing device can comprise the processing module, the first evaluation module and / or the second evaluation module and / or be operatively connected thereto.

[0141] All advantages and options explained with regard to the method according to the first and / or second aspect of the invention also apply accordingly to a data processing device according to the fifth aspect of the invention. Therefore, reference can be made to the previous explanations in this regard.

[0142] For example, the data processing device is a cloud computing system. The data processing device can comprise or represent a distributed system. The object is achieved by the invention according to a sixth aspect in that a dosing device is proposed that comprises a data processing device according to the fifth aspect of the invention and / or is operatively connected thereto.

[0143] Such a dosing device makes it particularly advantageous to continuously obtain current bulk material information during operation of the dosing device and, for example, to abort an ongoing dosing process and / or not to start a new dosing process depending on the bulk material information.

[0144] All advantages and options explained with regard to the method according to the first and / or second aspect of the invention, as well as with regard to the data processing device according to the fifth aspect of the invention, also apply accordingly to a dosing device according to the sixth aspect of the invention. Therefore, reference can be made to the previous explanations in this regard.

[0145] The object is achieved by the invention according to a seventh aspect in that a device for data processing, in particular a smartphone, with a camera and further means which are designed to provide images taken with the camera in a method according to the first aspect of the invention in such a way that these are obtained there as a first recording and / or as a second recording is proposed.

[0146] The smartphone can have a screen for displaying specific information. The data processing device can be used to carry out a method according to the second aspect of the invention.

[0147] The data processing device may optionally receive the specific information determined in the method according to the first aspect of the invention.

[0148] All advantages and options explained with regard to the method according to the second aspect of the invention also apply accordingly to the data processing device according to the seventh aspect of the invention. Therefore, reference can be made to the previous explanations in this regard.

[0149] The object is achieved by the invention according to an eighth aspect in that a computer program product comprising instructions which, when the program is executed by a data processing device, in particular a device according to the fifth aspect of the invention, cause the device to carry out a method according to the first and / or second aspect of the invention.

[0150] The object is achieved by the invention according to a ninth aspect in that a signal sequence representing instructions which, when executed on a data processing device, in particular a device according to the fifth aspect of the invention, cause the device to carry out a method according to the first and / or second aspect of the invention, is proposed.

[0151] The object is achieved by the invention according to a tenth aspect in that a computer-implemented data structure which has instructions in one area of ​​the data structure for calculating a machine learning data model stored in another area of ​​the data structure, so that the data model, when it is calculated according to the instructions by a data processing device on training data stored in a further area of ​​the data structure, which in particular (i) comprise images of bulk material samples with respective associated bulk material information, (ii) were at least partially obtained from a method according to the second aspect of the invention and / or (iii) were at least partially retrieved from a database according to the third aspect of the invention, is trained in such a way thatthat by means of the trained data model, at least one piece of information about the bulk material can be determined based on at least one image recording of at least one sample of a bulk material.

[0152] The data structure advantageously makes it possible to provide a bundle of a program product and a data model as well as training data as a single unit.

[0153] Preferably, the data structure is stored on a data storage medium.

[0154] The object is achieved by the invention according to an eleventh aspect in that a computer-implemented data structure which has instructions in one area of ​​the data structure for calculating a trained data model of machine learning stored in another area of ​​the data structure, so that by means of the data model, when it is calculated at least on data of one or more image recordings of at least one sample of a bulk material according to the instructions by a device for data processing, at least one piece of information about the bulk material is determined, in particular by analyzing the one or more image recordings and classifying the bulk material by means of the trained data model.

[0155] The data structure advantageously makes it possible to provide a bundle of a program product and a data model as a single unit.

[0156] Preferably, the data structure is stored on a data storage medium.

[0157] Short description of the drawings

[0158] Further features and advantages of the invention will become apparent from the following description, in which preferred embodiments of the invention are explained with reference to schematic drawings.

[0159] Showing:

[0160] Fig. 1 is a schematic representation of a prior art device

[0161] Dosing device;

[0162] Fig. 2a a first image of a sample of a bulk material in a first

[0163] Sample configuration;

[0164] Fig. 2b a second image of the bulk material sample from Fig. 2a in a second

[0165] Sample configuration;

[0166] Fig. 3 is a flowchart of a method according to the first aspect of the invention in a first embodiment;

[0167] Fig. 4 is a flowchart of a method according to the first aspect of the invention in a second embodiment;

[0168] Fig. 5 is a flowchart of a method according to the first aspect of the invention in a third embodiment;

[0169] Fig. 6 is a flowchart of a method according to the second aspect of the invention;

[0170] Fig. 7 shows a screen output; Fig. 8 shows a database according to the third aspect of the invention;

[0171] Fig. 9 is a flowchart of a method according to the fourth aspect of the invention;

[0172] Fig. 10 is a schematic representation of a data processing device according to the fifth aspect of the invention;

[0173] Fig. 11 is a schematic representation of a dosing device according to the sixth aspect of the invention; and

[0174] Fig. 12 is a schematic representation of a data processing device according to the seventh aspect of the invention.

[0175] Description of the embodiments

[0176] Fig. 1 shows a schematic representation of a previously known dosing device 1.

[0177] A bulk material 3 to be dosed is fed to the dosing device 1 from a storage container 5. From the storage container 5, the bulk material passes via a selectively openable and closable connecting section 7 into a receiving container 9 of the dosing device 1, where it is present as a bulk material quantity with a surface 11. By opening the connecting section 7, bulk material can be transferred from the storage container 5 into the receiving container 9. By means of a discharge element 13, the bulk material 3 is then discharged from the dosing device 1, i.e. from the receiving container 9, in a manner known per se and leaves the latter via a vertical discharge 15. The discharge element 13 is coupled to a motor 17 and can be rotated at an adjustable, variable speed, controlled by a motor controller of the motor 17.During the material discharge, a change in the weight of a system of the dosing device 1 weighed by a load cell 19 is used to control the speed of the discharge element 13.

[0178] The dosing device 1 must be suitably configured for the bulk material 3 to be dosed. Therefore, it may be of particular interest, for example, to obtain information about the bulk material before a first dosing process in order to properly prepare the dosing device 1. For this purpose, a method according to the first aspect of the invention, with which specific information about the bulk material can be determined, can be advantageously used.

[0179] Fig. 2a shows a first image 21a of a sample 23 of a bulk material (which, for example, is to be dosed with the dosing device 1), in which the sample 23 is in a first sample configuration. To set the first sample configuration, the bulk material sample 23 was piled onto a base 25 from a defined height (e.g., 40 cm).

[0180] Fig. 2b shows a second image 21b of the bulk material sample 23, in which the sample 23 is in a second sample configuration. The bulk material sample 23 was transferred from the first to the second sample configuration by piling the bulk material sample 23 onto the base 25 again from a defined height (e.g., 80 cm).

[0181] Both images were taken from the side with an identical perspective. The sample configurations shown are two differently formed heaps 27a, 27b of the same bulk material sample 23. Due to properties, in particular material properties, of the bulk material of sample 23, the two differently formed heaps 27a, 27b with different heights H1 and H2 and different widths Bl and B2 result for the two different drop heights, which are illustrated in the two images by labeled double-headed arrows. A reference object 29 can also be seen in both images 21a, 21b. The reference object 29 is plate-shaped and attached to a holder 31 such that the main side of the reference object 29 is captured frontally in images 21a, 21b. The dimensions of the reference object 29 and of the checkerboard pattern on the reference object 29 are known.Thus, the reference object 29 can be used to determine a distance, for example a dimension of a structure of the bulk material, in the respective receptacle 21a, 21b.

[0182] Fig. 3 shows a flowchart 100 of a method according to the first aspect of the invention in a first embodiment.

[0183] At 101, the first image 21a is obtained, i.e., the image 21a of the sample 23 of the bulk material to be dosed with the dosing device 1 in a first sample configuration. For this purpose, the data from the image 21a is received, for example, via a data line or retrieved from a memory.

[0184] In 103, based on the data from the first image 21a, specific information about the bulk material of the sample 23 is determined. For this purpose, the image data is processed using a processing module. Strictly speaking, the processing module is a machine learning (ML) model that, during training, has learned specific information about bulk materials based on numerous images of different bulk materials with associated specific information. (The images used for training each show a sample of the bulk material in a sample configuration as described for Fig. 2a, including the correspondingly positioned reference object.) The obtained image data is therefore fed to the processing module and thus to the ML model, and the ML model is calculated based on this image data. The ML model then produces output data representing the specific information about the bulk material.

[0185] In 105, the determined specific information is output to a user on a user interface, such as a screen, and / or stored in a database.

[0186] This allows the specific information about the bulk material to be determined based on the data from the first recording 21a.

[0187] Fig. 4 shows a flowchart 200 of a method according to the first aspect of the invention in a second embodiment.

[0188] In 201, the first image 21a is obtained, i.e., the image 21a of the sample 23 of the bulk material to be dosed with the dosing device 1 in a first sample configuration. For this purpose, the data from the image 21a is received, for example, via a data line or retrieved from a memory.

[0189] In 203, the second image 21b is obtained, i.e., the image 21b of the sample 23 of the bulk material to be dosed with the dosing device in a second sample configuration. For this purpose, the data from the image 21b is received, for example, via a data line or retrieved from a memory.

[0190] At 205, the specific information about the bulk material of the sample 23 is determined based on the data from the first and second images 21a, 21b. For this purpose, the image data is processed using a processing module. Strictly speaking, the processing module is a machine learning (ML) model that, during training, has learned specific information about bulk materials based on numerous pairs of first and second images of different bulk materials (wherein the respective images of the respective sample of the bulk material are in the first and second sample configurations described above) with associated specific information. (The images used for training also show the correspondingly positioned reference object 29). The obtained image data is therefore fed to the processing module and thus to the ML model, and the ML model is calculated based on this image data.The ML model then produces output data that represents the specific information about the bulk material.

[0191] In 207, the determined specific information is output to a user on a user interface, such as a screen, and / or stored in a database.

[0192] This allows the specific information about the bulk material to be determined based on the data from the first and second recordings 21a, 21b.

[0193] Fig. 5 shows a flowchart 300 of a method according to the first aspect of the invention in a third embodiment.

[0194] In 301, the first image 21a is obtained, which is again the image 21a of the sample 23 of the bulk material to be dosed with the dosing device 1 in a first sample configuration. For this purpose, the data from the image 21a is received, for example, via a data line or retrieved from a memory.

[0195] At 303, the image data is processed by a first evaluation module. Strictly speaking, the first evaluation module is a machine learning model (ML model) that, as part of a training session, has learned bulk material properties based on numerous images of different bulk materials with assigned properties. (The images used for training each show a sample of the bulk material in a sample configuration as described in Fig. 2a, including the correspondingly positioned reference object 29.) The obtained image data are therefore fed to the first evaluation module and thus to the ML model, and the ML model is calculated based on this image data. The ML model then produces output data representing a first property of the bulk material (i.e., a first piece of bulk material information). This is a material property of the bulk material.

[0196] At 305, a data set representing a second property of the bulk material (i.e., a second piece of bulk material information) is obtained via user input. This is another material property of the bulk material.

[0197] In 307, the first property and the second property are processed by a processing module to determine the specific information about the bulk material of the sample. Strictly speaking, the processing module is a machine learning (ML) model that, during training, has learned specific information about bulk materials based on numerous different combinations of the first property and the second property with associated specific bulk material information. The data (i.e., the first property and the second property) are therefore fed to the processing module and thus to the ML model, and the ML model is calculated based on this data. The ML model then produces output data representing the specific information about the bulk material. Additional data can also be provided as input data to the ML model, although this is not necessary in this case.

[0198] At 309, the determined specific information is output to a user on a user interface, such as a screen, and / or stored in a database. For example, the second property is a material property of the bulk material that was not determined from the recording or possibly cannot be determined at all.

[0199] This allows the specific information about the bulk material to be determined based on the data from the first recording 21a and the data set. The data from the first recording 21a is not directly processed with the data set; instead, the first bulk material property determined from the data from the first recording 21a is then processed together with the second bulk material property by feeding this data to the ML model of the processing module as input data.

[0200] In each of the above-explained embodiments of the method according to the first aspect of the invention, the determined specific information can be, for example, a result of a classification and / or an identifier of the bulk material. For this purpose, an ML model can be provided in the processing module, which processes at least the respectively mentioned input data with the ML model and delivers the classification result and / or the identifier as output data. Determining the classification and / or determining the identifier then each represents at least part of the determination of the specific information. Classification can advantageously be carried out with regard to the bulk material type and / or a (particularly average) particle size of the bulk material.It goes without saying that in the previous explanations, the first bulk material property represents a first piece of bulk material information and the second bulk material property represents a second piece of bulk material information.

[0201] It should be noted that the reference object 29 also allows the ML model to inherently learn and exploit a relationship between bulk material parts and the reference object, which can lead to better specific information. However, in alternative embodiments, it is possible for no reference object to be placed in the images (both those obtained in the method and those used to train the respective ML model).

[0202] Fig. 6 shows a flowchart 400 of a method according to the second aspect of the invention. The method can be used to generate a data set containing information about a bulk material. This data set can be provided, for example, as a data set in a method according to the first aspect of the invention. It is also possible to build a knowledge database containing information about bulk materials in this way. The information can, for example, relate to the material properties of the bulk material.

[0203] In 401, a screen output on a touch-sensitive screen is offered to a user.

[0204] Fig. 7 shows an example screen output 33. In an area 35 of the screen output 33, a question ("Lorem?") is displayed regarding information about a given bulk material. In areas 37, two possible answers ("Ipsum" and "Dolor") to the question are displayed. In addition, two symbols 39 are shown in the screen output 33. These symbols 39 instruct the user, by interacting with the screen output 33, to move the screen output 33 either to the left or to the right by performing a corresponding swipe gesture on the touch-sensitive screen. This depends on which of the two answers the user considers to be correct. The symbols 39 can be used to make the user's interaction with the screen output safer and more reliable. This also makes it possible to generate the data set more reliably.As indicated by the six circular areas in a lower area 41 of the screen output 33, the screen output 33 in Fig. 7 is the third of a total of six screen outputs that are offered to the user one after the other for different bulk material information.

[0205] In 403 (see Fig. 6), a user interaction with the screen output 33 in the form of a swipe gesture is detected.

[0206] In 405, depending on whether the interaction is detected as a swipe gesture to the left or as a swipe gesture to the right, a characteristic value is generated for the requested information of the bulk material, for example either "0" (if a swipe gesture to the left, i.e. the question "Lorem?" is answered with "Ipsum") or "1" (if a swipe gesture to the right, i.e. the question "Lorem?" is answered with "Dolor").

[0207] By offering six such screen outputs with six different pieces of information about the bulk material (i.e., with different questions and answer options) and detecting a user interaction with the respective screen output, a characteristic value for this information is generated in 407, for example, "110011," where each digit is the characteristic value of the individual screen outputs generated in 405. The characteristic value is then available as a generated data set.

[0208] In 409, the data set is saved in a database. A knowledge base containing information about bulk materials can be created in this database by saving the generated data set with an assignment to the bulk material, which is optionally also the case here.

[0209] Fig. 8 shows a database 43 according to the third aspect of the invention, which is designed to store data records from a method according to the second aspect of the invention. For example, the database mentioned at 407 in the method according to the second aspect of the invention, which was described above with reference to the flowchart 400 of Fig. 6, can be database 43.

[0210] The data within the database are also particularly advantageous for being used alternatively or additionally as training data for ML models in methods according to the first aspect of the invention.

[0211] Fig. 9 shows a flowchart 500 of a method according to the fourth aspect of the invention. This method can be used to obtain specific information about a bulk material.

[0212] In 501, a first image of a sample of the bulk material in a first sample configuration is taken with a camera (this is, for example, the first image 21a from Fig. 2a) and / or a second image of the sample of the bulk material in a second sample configuration is taken with the camera (this is, for example, the second image 21b from Fig. 2b). The reference object 29 can also be provided accordingly in each image.

[0213] At 503, the first image or the two images (in particular their image data) are provided in a method according to the first aspect of the invention and obtained there as the first image and / or the second image. For example, this could be a method as explained with reference to Figs. 3 to 5.

[0214] In 505 specific information about the bulk material is obtained.

[0215] With the help of this specific information, the dosing device 1 can be easily adjusted to suit the bulk material to be dosed. The process to which the image data is provided can advantageously be executed on a cloud server. Fig. 10 shows a schematic representation of a data processing device 45 according to the fifth aspect of the invention.

[0216] The data processing device 45 comprises means configured to carry out a method according to the first and / or second aspect of the invention.

[0217] Fig. 11 shows a schematic representation of a dosing device 47 according to the sixth aspect of the invention.

[0218] The dosing device 47 may be the dosing device of Fig. 1 with a data processing device 49 according to the fifth aspect of the invention, such as the device 43 as described with reference to Fig. 8.

[0219] Fig. 12 shows a schematic representation of a data processing device 51 according to the seventh aspect of the invention.

[0220] The data processing device 51 is a smartphone with a camera 53. The device 51 offers the operating personnel of the dosing device 1 a flexible possibility of determining information about the bulk material 3 to be dosed, for example, before it is filled into the storage container 5.

[0221] For this purpose, the data processing device 51 has means which are designed to provide images taken with the camera 53 (for example the images 21a and / or 21b) in a method according to the first aspect of the invention in such a way that they are obtained there as a first image and / or a second image.

[0222] The features disclosed in the foregoing description, in the drawings and in the claims may be essential to the invention in its various embodiments, both individually and in any combination.

[0223] List of reference symbols

[0224] 1 dosing device

[0225] 3 Bulk goods

[0226] 5 storage containers

[0227] 7 connecting section

[0228] 9 Receptacles

[0229] 11 Surface

[0230] 13 Discharge organ

[0231] 15 Vertical drop

[0232] 17 Engine

[0233] 19 Load cell

[0234] 21a, 21b recording

[0235] 23 Bulk sample

[0236] 25 base

[0237] 27a, 27b Heap

[0238] 29 Reference object

[0239] 31 Bracket

[0240] 33 Screen output

[0241] 35 Area

[0242] 37 Area

[0243] 39 Guiding symbol

[0244] 41 Area

[0245] 43 Database

[0246] 45 Data processing device

[0247] 47 Dosing device

[0248] 49 Data processing device

[0249] 51 Data processing device

[0250] 53 Camera

[0251] 100 Flowchart

[0252] 101 Obtaining a first image of a bulk material sample 103 Determining specific information about the bulk material

[0253] 105 Output and / or storage of the specific information determined

[0254] 200 Flowchart

[0255] 201 Obtaining a first image of a bulk material sample

[0256] 203 Obtaining a second image of the bulk sample

[0257] 205 Determining specific information about the bulk material

[0258] 207 Output and / or storage of the specific information determined

[0259] 300 Flowchart

[0260] 301 Obtaining a first image of a bulk material sample

[0261] 303 Processing the data of the first recording with a first evaluation module and obtaining a first property of the bulk material

[0262] 305 Obtaining a set of data

[0263] 307 Determining specific information about the bulk material

[0264] 309 Output and / or storage of the specific information determined

[0265] 400 Flowchart

[0266] 401 Offering a screen output to a user

[0267] 403 Detecting user interaction with screen output

[0268] 405 Generating a characteristic value depending on the interaction

[0269] 407 Generating a characteristic value as a data set based on a variety of interactions with a variety of screen outputs

[0270] 409 Saving the record in a database

[0271] 500 Flowchart

[0272] 501 Picking up a first and / or second pickup of a bulk material

[0273] 503 Providing the recordings in a method according to the first aspect of the invention

[0274] 505 Obtaining information about a bulk material

[0275] Bl, B2 width

[0276] Hl, H2 height

Claims

Patent claims 1. A method for determining specific information about a bulk material, comprising the method - that a first image is obtained from a sample of the bulk material, - that a first evaluation module is used to determine a first bulk material information based on the data of the first image recording, and - a second piece of bulk material information is obtained by a user input, in particular via a human-machine interface, wherein the second piece of bulk material information is a haptic property of the bulk material, wherein the specific information on the bulk material is determined by means of a processing module based on the data of the first and second pieces of bulk material information.

2. The method according to claim 1, wherein the method comprises obtaining a second image of the sample of the bulk material, and wherein the specific information about the bulk material is determined by means of the processing module based on the data of the first and second image recordings.

3. Method according to one of the preceding claims, wherein the method comprises evaluating and / or analyzing the data of the first and / or second image recording using methods of digital image analysis in the field of machine learning, in each case in order to determine the first information, the second information and / or the one specific information.

4. Method according to one of the preceding claims, wherein determining the one specific piece of information comprises determining a classification with respect to the type of bulk material, an average particle size of the bulk material and / or a particle shape, and / or an identifier of the bulk material, and wherein a result of the classification and / or the identifier is the specific information.

5. Method according to one of the preceding claims, wherein a control signal representing the determined specific information is generated and supplied to an entity and / or output to a user on a human-machine interface and / or wherein a dosing device is influenced, configured and / or controlled based on the determined specific information.

6. Method according to one of the preceding claims, wherein (i) the first bulk material information is one of the following properties of the bulk material or a measure thereof: a grain shape, a grain size, a particle size, an angle of repose, an angle of discharge, a physical property, a chemical property, a moisture content, a material type, a density, a flow behavior, a tendency to bridge, a tendency to shoot when fluidized, a clumping content, an electrostatic chargeability, a chemical instability, a temperature sensitivity, suspended in air, a tendency to segregate, consisting of components, a free flowability, a tendency to agglomerate, an abrasiveness, a corrosiveness, a mechanical sensitivity, a fragility, an explosiveness, a flammability, a dustiness, a moisture, an adhesion, a consistency, a hygroscopic behavior, a temperature, a tendency to fluidize, a tendency to harden, a radioactivity, a toxicity, a thixotropic behavior, a tendency to spoil, a tendency to soften, a static electricity, a presence of oils and fats, a flakiness and / or a stickiness; and / or (ii) the second bulk material information is one of the following properties of the bulk material or a measure thereof: A grain shape, a grain size, a particle size, an angle of repose, an angle of discharge, a physical property, a chemical property, a moisture content, a material type, a density, a flow behavior, a tendency to bridge, a tendency to shoot when fluidized, a clumping content, an electrostatic chargeability, a chemical instability, a temperature sensitivity, suspended in air, a tendency to segregate, consisting of components, a free flow, a tendency to agglomerate, an abrasiveness, a corrosiveness, a mechanical sensitivity, a fragility, an explosiveness, a flammability, a dustiness, a moisture, an adhesion, a consistency, a hygroscopic behavior, a temperature, a tendency to fluidize, a tendency to harden, a radioactivity, a toxicity, a thixotropic behavior, a tendency to spoil, a tendency to soften,static electricity, presence of oils and greases, flakiness and / or stickiness.

7. Method according to one of the preceding claims, the method comprising offering a screen output on a user interface to a user, detecting an interaction of the user with the screen output on the user interface in response to the offering of the screen output, and generating, based on the detected interaction of the user, a characteristic value storable in a database for the one specific piece of information of the bulk material as a data record.

8. The method according to claim 7, wherein a plurality of screen outputs are offered to the user on the user interface and for each screen output an interaction of the user with the screen output on the user interface in response to the offering of the respective screen output is determined and based on the determined interactions of the user one or more than one characteristic value storable in a database for two or more than two items of information about the bulk material, in particular for two or more than two properties of the bulk material, is generated as a data record.

9. Method according to one of claims 7 to 8, wherein (i) several such data sets are generated by interactions between several users, and wherein preferably the several data sets are transferred into a common data set in which the specific information on the bulk material is constructed from the information of the individual data sets, and / or (ii) the data set or data sets are or are each associated with an assignment to the bulk material and / or stored in a database is or will be stored, in particular for the purpose of building, supplementing and / or updating a knowledge database on bulk material information.

10. Device for data processing, in particular a smartphone, with a camera and further means which are designed to provide data in a method according to one of claims 1 to 9 in such a way that images taken with the camera are received there as a first image recording and / or as a second image recording and thus as first bulk material information and haptic properties of the bulk material entered via a user input are received as second bulk material information.

Images