Method and apparatus for processing data associated with a product

Abstract

A method, for example a computer-implemented method, for processing data associated with a product, comprising: determining first data from at least one data source associated with the product, determining, based on the first data, second data that characterize at least one statistical parameter of the first data, for example, representing, checking whether the determination of the first data and / or the determination of the second data has been carried out for a predefinable first time interval, and, if the check shows that the determination of the first data and / or the determination of the second data has been carried out for the predefinable first time interval, storing at least the second data.

Description

[0001] The disclosure relates to a method for processing data associated with a product.

[0002] The disclosure further relates to an apparatus for processing data associated with a product.

[0003] Exemplary embodiments relate to a method, for example a computer-implemented method, for processing data associated with a product, comprising: determining first data from at least one data source associated with the product, determining, based on the first data, second data that characterize, for example represent, at least one statistical parameter of the first data, checking whether the determination of the first data and / or the determination of the second data has been carried out for a predefinable first time interval, and, if the check shows that the determination of the first data and / or the determination of the second data has been carried out for the predefinable first time interval, storing at least the second data.

[0004] In further exemplary embodiments, alternatively or additionally to checking whether the determination of the first data and / or the determination of the second data has been carried out for a predefinable first time interval and, if the check shows that the determination of the first data and / or the determination of the second data has been carried out for the predefinable first time interval, storing at least the second data, a check can be carried out to determine whether the determination of the first data and / or the determination of the second data has been carried out for a predefinable quantity or number of respective data values, e.g. of the first data, and, if the check for the quantity or number shows that the predefinable quantity or number has been reached, storing at least the second data.

[0005] In further exemplary embodiments, the product is designed to influence a fluid flow. For example, the product is a valve or a valve block. For reasons of clarity, the following description of exemplary embodiments primarily refers to at least one product designed as a valve. In further exemplary embodiments, however, the principle according to the embodiments can also be applied to products other than the valves mentioned as examples, without limiting the generality.

[0006] In further exemplary embodiments, it is provided that the at least one data source associated with the product is integrated into the product or arranged on the product, for example attached.

[0007] In further exemplary embodiments, it is also conceivable that the at least one data source associated with the product is located remotely from, for example, not directly on, the product, but is, for example, in data communication with the product or a device of the product. For example, the device can be provided for executing aspects according to the embodiments.

[0008] In further exemplary embodiments, the device is integrated into the product or attached to the product.

[0009] In further exemplary embodiments, it is provided that the at least one data source associated with the product is a sensor, for example a sensor assigned to the product, for example, integrated into the product (or attached to it). For example, in further embodiments, a plurality, e.g., a multiplicity (e.g., more than ten), of sensors, for example of different types, can be assigned to the product.

[0010] In further exemplary embodiments, the data source is configured as a sensor and is configured, for example, to provide sensor values, for example in the form of one or more scalar values, for example repeatedly, for example periodically. For example, the first data according to exemplary embodiments thus characterize, for example, sensor data that can be interpreted, for example, as a time series of scalars.

[0011] In further exemplary embodiments, one or more of the following variables can be detected by at least one sensor assigned to the product: a) ambient temperature, b) temperature of a medium, for example a fluid, the fluid flow of which can be influenced by the product, for example a valve, c) ambient humidity, d) humidity of the medium, e) vibration, for example characterizable by a root mean square, RMS, f) acceleration, g) orientation in space, h) valve position, i) electrical supply voltage, for example for at least one electrical or electronic component, j) air pressure, for example of a pneumatic supply, k) electrical current consumption of at least one electrical or electronic component (for example device for carrying out aspects of the method according to the embodiments, electric motor of a valve drive), 1) upper end position, m) lower end position, n) at least one characteristic value of a valve membrane, o) force, for example actuating force.

[0012] In further exemplary embodiments, for example, one or more operating parameters of at least one component of the product or other data belonging to the product in the sense of, for example, as first data can be determined, for example a parameter of a, for example, electronic, controller for the product, parameters of a frequency analysis (for example using a Fast Fourier Transform, FFT), etc.

[0013] In further exemplary embodiments, for example, one or more virtual sensors can be used as a data source.

[0014] In further exemplary embodiments, combinations of one or more real sensors and one or more virtual sensors and / or other components or sources of information may be used as the data source.

[0015] In further exemplary embodiments, raw values ​​or raw data provided by the data source may be used as first data.

[0016] In further exemplary embodiments, raw values ​​or raw data supplied by the data source can be processed, for example preprocessed (for example to remove unwanted peaks, e.g. outliers), and the processed or preprocessed data obtained therefrom are used as first data.

[0017] In further exemplary embodiments, if the check reveals that the determination of the first data and / or the determination of the second data has not been carried out for the predeterminable first time interval, the method comprises: continuing the determination of the first data and / or the determination of the second data. In further exemplary embodiments, this can ensure, for example, that a sufficient amount of first data is determined, e.g., to be able to determine sufficiently meaningful second data.

[0018] In further exemplary embodiments, it is provided that a) the determination of the first data comprises a repeated, for example periodic, determination of the first data at a predeterminable rate, and / or that b) the determination of the second data comprises a repeated, for example periodic, determination of the second data, for example at the predeterminable rate.

[0019] In further exemplary embodiments, different rates for determining the first data and determining the second data are also conceivable.

[0020] In further exemplary embodiments, it is provided that storing at least the second data comprises storing at least the second data in a, for example, internal, for example non-volatile, memory of the product, and, optionally, storing time information associated with the second data, wherein, for example, the time information associated with the second data characterizes a time range associated with the second data.

[0021] In further exemplary embodiments, it is provided that the at least one statistical parameter comprises at least one of the following elements: a) minimum, b) maximum, c) mean, d) at least one percentile, e.g., a fiftieth percentile, e.g., median. In further exemplary embodiments, one or more additional statistical parameters, such as a variance or standard deviation, etc., can also be used.

[0022] In further exemplary embodiments, it is provided that the method comprises at least one of the following elements: a) initializing at least one of the following elements: a1) first counter, for example a counter associated with the predefinable first time interval, a2) current minimum value, a3) current maximum value, a4) a current first with one ora quantity associated with the mean value, b) acquiring a current value associated with the first data, c) performing filtering, for example debouncing, d) updating the first counter and / or the current first quantity, e) checking whether the current value, for example a filtered value obtained by means of the filtering, is less than the current minimum value and, if so, updating the current minimum value, f) checking whether the current value, for example a filtered value obtained by means of the filtering, is greater than the current maximum value and, if so, updating the current maximum value.

[0023] In further exemplary embodiments, the method comprises: determining the second data (e.g., minimum, maximum, mean value) for the predefinable first time interval; and storing the second data for the predefinable first time interval together with time information characterizing the first time interval, for example, a beginning and an end of the first time interval (e.g., start time of the first time interval and end time of the first time interval). In further exemplary embodiments, the said data can, for example, represent a first report or be regarded as a first report containing the said statistical parameters for the period characterized by the first time interval.

[0024] In further exemplary embodiments, the method comprises: determining the second data for a predeterminable second time interval, which, for example, follows (not necessarily directly) the first time interval, and storing the second data for the predeterminable second time interval together with time information characterizing the second time interval, for example a start and an end of the second time interval. In further exemplary embodiments, the said data can, for example, represent a second report or be viewed as a second report containing the said statistical parameters for the period characterized by the second time interval.

[0025] In further exemplary embodiments, the method comprises: providing a first data structure comprising an information element for a start time or start time of a time range associated with the second data, an information element for an end time or end time of a time range associated with the second data, and at least one information element for one or more statistical parameters associated with the first data source, and, optionally, using the first data structure, for example for storing the second data.

[0026] The following shows an example of an organization of, among other things, the second data described above in tabular form according to further exemplary embodiments. The first column Sp1 contains a start time SZ, for example, of the first time interval, the second column Sp2 contains an end time of the first time interval, the third column Sp3 contains a minimum value MinW of the first data DAT-1 of a first data source from the first time interval, the fourth column Sp4 contains a maximum value MaxW of the first data DAT-1 of the first data source from the first time interval, and the fifth column Sp5 contains an average value Avg of the first data DAT-1 of the first data source from the first time interval. Optionally, further columns Sp6, Sp7, Sp8 can be provided, which contain, for example, corresponding statistical parameters of a second data source for the first time interval, as well as possibly further columns, which is indicated by the three dots "...". Sp1 Sp2 Sp3 Sp4 Sp5 Sp6 Sp7 Sp8 ... SZ EZ MinW MaxW Avg

[0027] In further exemplary embodiments, the second data from the relevant data sources can also be processed, for example, stored, for at least one further time interval, e.g., the aforementioned second time interval, in or analogous to the tabularly organized form described above by way of example, for example, using the first data structure described above by way of example. Depending on the number of data sources, further table columns or information elements can be used for the first data structure in further exemplary embodiments.

[0028] In further exemplary embodiments, it is provided that the method comprises: summarizing second data of different, for example mutually adjacent, time ranges or time intervals, wherein, for example, summarized second data are obtained, and, optionally, storing the summarized second data.

[0029] In further exemplary embodiments, it is provided that the summarizing comprises at least one of the following elements: a) determining a minimum for the summarized second data as a minimum of the different time ranges, b) determining a maximum for the summarized second data as a maximum of the different time ranges, c) determining an average value for the summarized second data as a weighted average value of the respective average values ​​of the different time ranges, wherein, for example, a respective size or duration of the respective time range can be used as weighting factors.

[0030] In further exemplary embodiments in which, for example, more than the three statistical parameters described above by way of example are used for the second data (for example, additionally a variance), the summarizing for these further statistical parameters can be carried out in an analogous manner.

[0031] In further exemplary embodiments, the second data of two, for example adjacent, time intervals can be combined during the combining, e.g. in the manner described above.

[0032] In further exemplary embodiments, the second data from more than two, for example mutually adjacent, time intervals can be summarized during the summarization, e.g. analogously to the manner described above.

[0033] In further exemplary embodiments, it is provided that the method comprises: storing third data that characterize at least one event associated with the product, and, optionally, providing and / or using a second data structure for storing the third data, wherein the second data structure comprises at least: an information element for a time of occurrence of the event, an information element (e.g., characterizing an integer variable) for information characterizing the event.

[0034] In further exemplary embodiments, instead of the time at which the event occurred, a time range, e.g., characterized by a start time and an end time, may also be provided to describe temporal information about the occurrence of an event. In further exemplary embodiments, this may, for example, simplify a summary of the third data according to further exemplary embodiments, as described in more detail below.

[0035] In further exemplary embodiments, an event associated with the product can be, for example, at least one of the following events: a) error, e.g. of a component of the product, b) warning, e.g. of a component of the product, c) information, e.g. of a component of the product, d) initialization has taken place, e) maintenance has taken place, e) motor start-up and / or start of movement in the open direction, f) motor start-up and / or start of movement in the closed direction, g) (e.g. lower) end position reached, e.g. sealing process, h) e.g. upper end position reached, i) predefinable length unit (e.g. 1 millimeter or decimeter) traveled, j) predefinable time unit traveled, k) drive activity, e.g. for a predefinable period of time, 1) configuration change takes place, m) measuring device outside a range.

[0036] In further exemplary embodiments, it is provided that the method comprises: summarizing third data of different, for example mutually adjacent, points in time or time ranges or time intervals, wherein, for example, summarized third data are obtained, and, optionally, storing the summarized third data.

[0037] In further exemplary embodiments, it is provided that the summarizing of third data of different, for example mutually adjacent, points in time and / or time ranges or time intervals comprises: assigning the third data of the different points in time or time ranges to an aggregated time range, wherein, for example, the aggregated time range comprises the different points in time and / or time ranges, and, optionally, aggregating a respective piece of information characterizing an event.

[0038] In further exemplary embodiments, the aggregating may include, for example: providing a counter for each event that occurred, the counter indicating how often the respective event occurred within the aggregated time range.

[0039] In further exemplary embodiments, the summarizing of the second data and / or the summarizing of the third data can, for example, be carried out repeatedly, for example until the summarized data obtained thereby meet a target criterion, for example fall below a predefinable maximum data volume.

[0040] In further exemplary embodiments, the method comprises: determining a first variable which characterizes a data volume of second data and / or third data stored in the memory, optionally comparing the first variable with a predefinable first threshold value, optionally based on the comparison, summarizing at least one of the following elements: a) second data stored in the memory, b) third data stored in the memory. In further exemplary embodiments, this can advantageously prevent the memory from no longer having enough free storage space, for example for storing future data, after a certain operating period. In further exemplary embodiments, for example, data on the basis of which the summarizing was carried out can be at least partially overwritten by the summarized data.

[0041] In further exemplary embodiments, it may be provided to delete from the memory such data on the basis of which a summarization has been carried out.

[0042] In further exemplary embodiments, it is provided that the summarizing is carried out, for example, exclusively, for second and / or third data that are older than a predefinable second threshold value, wherein, for example, the method comprises: omitting the summarization of second and / or third data stored in the memory that are younger than the second threshold value. In this way, in further exemplary embodiments, it can be ensured that comparatively recent data are retained unchanged, at least for a certain period of time, e.g., with respect to the second threshold value.

[0043] In further exemplary embodiments, it is provided that the method comprises: determining, for example specifying or receiving, a or the second threshold value, wherein the second threshold value characterizes a point in time such that second and / or third data stored in the memory that are more recent than the second threshold value may not be combined and / or deleted, and, optionally, determining, based on the second threshold value, whether second data and / or third data stored in the memory may be combined.

[0044] In further exemplary embodiments, it is provided that the method comprises: determining a second variable that characterizes a data volume of second data and / or third data stored in the memory that is more recent than the second threshold value, and, optionally, using the second variable, for example for controlling an operation of at least one component of the product.

[0045] In further exemplary embodiments, it is provided that the method comprises: comparing the second variable with the first variable, and, based on the comparison, carrying out at least one of the following elements: a) changing the second threshold value, and / or b) changing the predefinable first time interval, c) providing and / or changing a first parameter for the summarizing.

[0046] In further exemplary embodiments, it is provided that the comparison comprises: determining a quotient of the second variable and the first variable and comparing whether the quotient is greater than a predefinable third threshold value.

[0047] In further exemplary embodiments, for example, if the quotient is greater than the predefinable third threshold value, it can be concluded that a proportion of comparatively young, e.g. non-overwritable or non-erasable, data is comparatively large, relative to the data volume of the entire second data and / or third data stored in the memory, and / or, for example, the second threshold value can then be reduced.

[0048] In further exemplary embodiments, for example, the summarizing of second data stored in the memory and / or of third data stored in the memory may be performed based on the quotient.

[0049] In further exemplary embodiments, it is provided that the method comprises: providing first information for controlling the summarizing, wherein the first information describes, for example, a target interval size as a function of at least one operating time of the product, wherein, for example, the first information can be characterized by means of a characteristic curve, and, optionally, using the first information for the summarizing.

[0050] In further exemplary embodiments, the target interval size characterizes, for example, a, for example maximum, time period for which a report or summarized data characterizing the report is formed. In other words, the target interval size indicates, for example, the maximum time period for which second data such as a minimum and / or maximum and / or mean value are formed, for example by summarizing according to the embodiments, whereby in further exemplary embodiments a temporal resolution of the second data can be controlled. For example, a target interval size of 10 seconds indicates that - for example even after summarizing second data - statistical parameters such as the minimum and / or maximum and / or mean value are maximally associated with the stated time period of 10 seconds, i.e. represent the respective statistical properties of the underlying first data from the period of the stated 10 seconds.

[0051] In further exemplary embodiments, it is conceivable, for example, to select comparatively small values ​​for the target interval size for data that is comparatively recent, i.e., data that was determined relatively recently. In further exemplary embodiments, it is conceivable, for example, to select comparatively large values ​​for the target interval size for data that is comparatively old, i.e., data that was determined relatively long ago.

[0052] In further exemplary embodiments, the target interval size can be specified, for example, using the aforementioned characteristic curve, e.g., for different operating periods. In further exemplary embodiments, the characteristic curve can, for example, have steps or jumps.

[0053] In further exemplary embodiments, it is provided that the method comprises at least one of the following elements: a) storing, for example at least temporarily storing, at least a portion of the first data and / or the second data and / or the third data in a volatile memory (which may, for example, be integrated into the product), b) sending at least a portion of the first data and / or the second data and / or the third data to at least one further, for example external, unit, for example a mobile or stationary device for data acquisition. In further exemplary embodiments, the sending can be carried out using a wireless data connection and / or a wired data connection.

[0054] Further exemplary embodiments relate to a device for carrying out the method according to the embodiments. For example, in further exemplary embodiments, the device can be integrated into the product, e.g., integrated into a housing of the product, or attached to the product.

[0055] Further exemplary embodiments relate to a product, for example a valve or valve block, comprising at least one device according to the embodiments.

[0056] Further exemplary embodiments relate to a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to the embodiments.

[0057] Further exemplary embodiments relate to a computer program comprising instructions which, when executed by a computer, cause the computer to carry out the method according to the embodiments.

[0058] Further exemplary embodiments relate to a data carrier signal that transmits and / or characterizes the computer program according to the embodiments.

[0059] Further exemplary embodiments relate to a use of the method according to the embodiments and / or the device according to the embodiments and / or the product according to the embodiments and / or the computer-readable storage medium according to the embodiments and / or the computer program according to the embodiments and / or the data carrier signal according to the embodiments for at least one of the following elements: a) processing data of a product located in the field, b) at least temporarily storing data locally, for example data characterizing statistical parameters, in the product, c) compressing data, d) reducing data, e) increasing efficiency with regard to, for example, longer-term storage of data, for example the second data ordata derived from the second data, f) creating reports relating to at least the second data, g) adapting, for example dynamically (during operation, for example of the product or of a device executing aspects according to the embodiments), variables influencing storage of at least the second data, h) specifying different temporal granularities for storing and / or summarizing data, for example the second and / or the third data, i) distinguishing between, for example, data that is not currently to be summarized or deleted and data that can be summarized and / or deleted, for example currently, j) providing free storage space, for example in the, for example internal, for example non-volatile, memory of the product, k) summarizing data to a predefinable size, 1) conditionally retaining data.

[0060] Further features, possible applications, and advantages of the invention will become apparent from the following description of exemplary embodiments of the invention, which are illustrated in the figures of the drawing. All described or illustrated features, individually or in any combination, constitute the subject matter of the invention, regardless of their summary in the claims or their references, as well as regardless of their wording or representation in the description or in the drawing.

[0061] The drawing shows: Fig. 1 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 2 schematically shows a simplified block diagram according to exemplary embodiments, Fig. 3 schematically shows a simplified block diagram according to exemplary embodiments, Fig. 4 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 5 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 6 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 7 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 8 schematically shows a data structure according to exemplary embodiments, Fig. 9 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 10 schematically shows a simplified timing diagram according to exemplary embodiments, Fig. 11 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 12 schematically shows a data structure according to exemplary embodiments, Fig. 13 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 14 schematically shows a simplified timing diagram according to exemplary embodiments, Fig. 15 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 16 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 17 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 18 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 19 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 20 schematically shows a simplified flow diagram according to exemplary embodiments, Fig. 21 schematically shows a simplified block diagram according to exemplary embodiments, Fig. 22 schematically shows a simplified timing diagram according to exemplary embodiments, Fig. 23 schematically shows a simplified block diagram according to exemplary embodiments, Fig. 24 schematically illustrates aspects of uses according to exemplary embodiments.

[0062] For exemplary embodiments, see Fig. 1, Fig. 2, relate to a method, for example a computer-implemented method, for processing a product 10 ( Fig. 2) associated data, comprising: Determining 100 ( Fig. 1) from first data DAT-1 of at least one data source DQ-1 associated with the product 10, determining 102, based on the first data DAT-1, second data DAT-2 which has at least one statistical parameter SP (see Fig. 3) characterize, for example represent, the first data DAT-1, checking 104 whether the determination 100 of the first data DAT-1 and / or the determination 102 of the second data DAT-2 has been carried out for a predefinable first time interval T-INT-1, and, if the check 104 shows that the determination 100 of the first data and / or the determination 102 of the second data has been carried out for the predefinable first time interval T-INT-1, storing 106 at least the second data DAT-2.

[0063] In further exemplary embodiments, Fig. 2, it is provided that the product 10 is designed to influence a fluid flow. For example, the product 10 is a valve or a valve block. For reasons of clarity, the following description of exemplary embodiments refers primarily to at least one product 10 designed as a valve. In further exemplary embodiments, however, the principle according to the embodiments can also be applied to products other than the valves mentioned as examples, without limiting the generality.

[0064] In further exemplary embodiments, Fig. 2, it is provided that the at least one data source DQ-1 associated with the product 10 is integrated into the product or arranged on the product, for example attached, e.g. the block DQ from Fig. 2. Optionally, additional data sources DQ-2, DQ-3, ... can also be provided.

[0065] In further exemplary embodiments, it is also conceivable that the at least one data source DQ-1 associated with the product 10 is arranged remotely from, for example not directly on, the product 10, but is, for example, in data communication with the product 10 or a device 200 for the product 10. For example, the device 200 can be provided for executing aspects according to the embodiments, for example for executing aspects of the process according to Fig. 1.

[0066] In further exemplary embodiments, Fig. 2, the device is integrated into the product, see block 200', or attached to the product 10, see block 200.

[0067] In further exemplary embodiments, Fig. 2, the product 10 has at least one memory 12, 12a for at least temporary storage of, for example, the first data DAT-1 and / or the second data DAT-2 and / or further data.

[0068] In further exemplary embodiments, Fig. 2, the product has 10 additional components, e.g. components of a valve, which for reasons of clarity are shown in Fig. 2 are collectively symbolized by block 14.

[0069] In further exemplary embodiments, Fig. 2, it is provided that the at least one data source DQ, DQ-1, DQ-2, DQ-3, ... associated with the product 10 is a sensor, for example a sensor assigned to the product 10, for example, integrated into the product 10 (or attached thereto). By way of example, in further embodiments, a plurality, e.g., a multiplicity (e.g., more than ten), of sensors, for example of different types, can be assigned to the product 10.

[0070] In further exemplary embodiments, the data source DQ, DQ-1, DQ-2, DQ-3, ... is designed as a sensor and is designed, for example, to provide sensor values, for example in the form of one or more scalar values, for example repeatedly, for example periodically.

[0071] For example, the first data DAT-1 according to exemplary embodiments thus characterizes, for example, sensor data that can be interpreted as a time series of scalars. Block B1 according to Fig. 2 symbolizes the determination of 100 according to Fig. 1, and block B2 symbolizes, by way of example, the determination 102 according to Fig. 1.

[0072] In further exemplary embodiments, one or more of the following variables can be detected by at least one sensor DQ-1, ... assigned to the product 10, e.g. as first data DAT-1: a) ambient temperature, b) temperature of a medium, e.g. fluid, whose fluid flow can be influenced by the product, e.g. valve, 10, c) ambient humidity, d) humidity of the medium, e) vibration, e.g. characterizable by a root mean square value, RMS, f) acceleration, g) orientation in space, h) valve position, i) electrical supply voltage, e.g. for at least one electrical or electronic component, j) air pressure, e.g. of a pneumatic supply, k) electrical current consumption of at least one electrical or electronic component (e.g.Device for carrying out aspects of the method according to the embodiments, electric motor of a valve drive), 1) upper end position, m) lower end position, n) at least one characteristic value of a valve membrane, o) force, for example actuating force.

[0073] In further exemplary embodiments, Fig. 2, one or more operating parameters of at least one component 14 of the product 10 or other data belonging to the product 10 can be determined in the sense of the first data DAT-1, e.g. as the first data DAT-1 or as part of the first data DAT-1, e.g. a parameter of a, e.g. electronic, controller for the product 10, parameters of a frequency analysis (e.g. using a Fast Fourier Transform, FFT), etc. In other words, in some exemplary embodiments, at least part of the first data DAT-1 is not necessarily information that can be measured by sensors or is measured by sensors, but rather, e.g., internal variables, e.g., of the device 200, 200' or of a component 14.

[0074] In further exemplary embodiments, Fig. 1, it is provided that if the check 104 shows that the determination 100 of the first data DAT-1 and / or the determination 102 of the second data DAT-2 has not been carried out, e.g., has not already been carried out, for the predefinable first time interval T-INT-1, the method comprises: continuing 108 the determination 100 of the first data DAT-1 and / or the determination 102 of the second data DAT-2. In this way, in further exemplary embodiments, it can be ensured, e.g., that a sufficient number of first data DAT-1 are determined, e.g., in order to be able to determine sufficiently meaningful second data DAT-2.

[0075] In further exemplary embodiments, Fig. 1, it is provided that a) the determination 100 of the first data DAT-1 comprises a repeated, for example periodic, determination 100a of the first data DAT-1 at a predeterminable rate, and / or that b) the determination 102 of the second data DAT-2 comprises a repeated, for example periodic, determination 102a of the second data DAT-2, for example at the predeterminable rate.

[0076] In further exemplary embodiments, different rates for determining 100, 100a the first data DAT-1 and determining 102, 102a the second data DAT-2 are also conceivable.

[0077] In further exemplary embodiments, Fig. 2, it is provided that the storing 106 of at least the second data DAT-2 comprises a storing 106a of at least the second data DAT-2 in a, for example, internal, for example non-volatile, memory 12 of the product 10, and, optionally, a storing 106b of time information TIM-INF-DAT-2 associated with the second data DAT-2, wherein, for example, the time information TIM-INF-DAT-2 associated with the second data DAT-2 characterizes a time range associated with the second data DAT-2, that is to say, for example, a time range or a time interval for which the second data DAT-2 have been determined based on the first data DAT-1.

[0078] In further exemplary embodiments, Fig. 3, it is provided that the at least one statistical parameter SP comprises at least one of the following elements: a) minimum MIN, b) maximum MAX, c) mean AVG. In further exemplary embodiments, one or more further statistical parameters such as a variance and / or standard deviation and / or at least one percentile, e.g., a fiftieth percentile, e.g., median, etc. (not shown) can also be used.

[0079] In further exemplary embodiments, Fig. 4, it is provided that the method comprises at least one of the following elements: a) Initializing 110 at least one of the following elements: a1) first counter sc_tim, for example a counter associated with the predefinable first time interval T-INT-1, a2) current minimum value sc_min, a3) current maximum value sc_max, a4) a current first variable sc_avg associated with a mean value, b) detecting 111 a current value val associated with the first data DAT-1 (e.g.Sensor data value of the first data source DQ-1), c) carrying out 112 a filtering, for example for debouncing, whereby a filtered value val' is obtained, d) updating 113 the first counter sc_tim and / or the current first variable sc_avg, e) checking 114 whether the current value val, for example the filtered value val' obtained by means of filtering 112, is less than the current minimum value sc_min, and, if so, updating 114a the current minimum value sc_min, f) checking 115 whether the current value val, for example the filtered value val' obtained by means of filtering 112, is greater than the current maximum value sc_max, and, if so, updating 115a the current maximum value sc_max.

[0080] The exemplary, simplified flowchart from Fig. 5 clarifies further aspects of the with reference to Fig. 4 according to further exemplary embodiments. Element E1 symbolizes a start, element E2 symbolizes the initialization 110 according to Fig. 4, Element E3 symbolizes the acquisition 111 of the current value val, Element E4 symbolizes the optional filtering 112, e.g., for debouncing. Element E5 according to Fig. 5 symbolizes the update 113 according to Fig. 4, the elements E6, E7, E8, E9 according to Fig. 5 correspond to blocks 114, 114a, 115, 115a according to Fig. 4.

[0081] For example, initializing the first counter sc_tim can comprise setting the first counter sc_tim to zero. For example, initializing the current minimum value sc_min can comprise setting the current minimum value to a value of "plus infinity" or a corresponding maximum value of a data type used for the current minimum value sc_min. For example, initializing the current maximum value sc_max can comprise setting the current maximum value to a value of "minus infinity" or a corresponding minimum value of a data type used for the current maximum value sc_min. For example, updating 113 of the first counter sc_tim can comprise incrementing, for example by an increment that corresponds to a reciprocal of a predefinable acquisition or sampling rate.For example, updating 113 the current first mean-associated quantity sc_avg may comprise incrementing the quantity sc_avg by the value val or the filtered value val'.

[0082] In further exemplary embodiments, Fig. 5, for example, the expiration of the predeterminable first time interval T-INT-1 can be concluded when the first counter sc_tim reaches or exceeds a threshold value corresponding to the predeterminable first time interval T-INT-1, which is done, for example, in block E10 according to Fig. 5 can be determined. In this case, for example, the current first value sc_avg associated with the mean value can be divided by a product of the value of the first counter sc_tim with the value of the acquisition or sampling rate, which leads to the time average of the value val or the filtered value val' for the predeterminable first time interval T-INT-1, see block E11. This illustrates Fig. 5 shows, by way of example, a determination of the second data DAT-2 in terms of a minimum, a maximum, and an average value for a predetermined time interval according to further exemplary embodiments. In further exemplary embodiments, the second data DAT-2 determined in this way can be used as already described above with reference to Fig. 1 described.

[0083] In further exemplary embodiments, Fig. 6, it is provided that the method comprises: determining 120a the second data DAT-2 (e.g. comprising or characterizing minimum MIN, maximum MAX, mean value AVG) for the predefinable first time interval T-INT-1 and storing 120b the second data DAT-2 for the predefinable first time interval T-INT-1 together with time information INF-T-INT-1 characterizing the first time interval T-INT-1, for example a beginning and an end of the first time interval (e.g. start time of the first time interval and end time of the first time interval). In further exemplary embodiments, the said data can, for example, represent a first report BER-1 or can be regarded as a first report BER-1 which contains the said statistical parameters SP for the period characterized by the first time interval T-INT-1.

[0084] In further exemplary embodiments, Fig. 6, it is provided that the method comprises: determining 122a the second data DAT-2 for a predefinable second time interval, which e.g. (not necessarily directly) follows the first time interval T-INT-1, and storing 122b the second data DAT-2 for the predefinable second time interval together with time information INF-T-INT-2 characterizing the second time interval, e.g. a start and an end of the second time interval. In further exemplary embodiments, the said data can e.g. represent a second report BER-2 or can be regarded as a second report BER-2 which contains the said statistical parameters for the period characterized by the second time interval.

[0085] The optional block 124 according to Fig. 6 symbolizes an optional storage of second DAT-2, which are associated with further time intervals or time ranges than the above-mentioned first and second time intervals, wherein the storage 124, for example, again takes place in the internal non-volatile memory 12a ( Fig. 2) is carried out.

[0086] In further exemplary embodiments, Fig. 7, Fig. 8, it is provided that the method comprises: providing 125 a first data structure DS-1 comprising an information element E20 for a start time or a starting time of a time range associated with the second data DAT-2, an information element E21 for an end time or an end time of the time range associated with the second data DAT-2, and at least one information element E22 for one or more statistical parameters associated with the first data source DQ-1, and, optionally, using 127 the first data structure DS-1, for example, for storing the second data DAT-2. Optionally, the first data structure DS-1, Fig. 8, may also comprise further information elements E23, ..., e.g. for statistical parameters associated with at least one further data source DQ2, ...

[0087] The following illustrates an organization of, among other things, the second data DAT-2 described above in tabular form according to further exemplary embodiments. The first column Sp1 contains a start time SZ, for example, of the first time interval; the second column Sp2 contains an end time EZ of the first time interval; the third column Sp3 contains a minimum value MinW of the first data DAT-1 of a first data source DQ-1 from the first time interval; the fourth column Sp4 contains a maximum value MaxW of the first data DAT-1 of the first data source DQ-1 from the first time interval; the fifth column Sp5 contains an average value Avg of the first data DAT-1 of the first data source DQ-1 from the first time interval. Optionally, further columns Sp6, Sp7, Sp8 can be provided, which contain, for example, corresponding statistical parameters, for example, of at least one second data source for the first time interval; as well as possibly further columns, which are indicated by the three dots "..." is indicated. Sp1 Sp2 Sp3 Sp4 Sp5 Sp6 Sp7 Sp8 ... SZ EZ MinW MaxW Avg

[0088] In further exemplary embodiments, the second data from the relevant data sources can also be processed, for example stored, for at least one further time interval, e.g., the aforementioned second time interval, in or analogous to the tabularly organized form described above, for example, using the first data structure DS-1 described above as an example. Depending on the number of data sources, further table columns or information elements can be used for the first data structure DS-1 in further exemplary embodiments.

[0089] In further exemplary embodiments, Fig. 9, it is provided that the method comprises: summarizing 130 second data of different, for example mutually adjacent, time ranges or time intervals, whereby, for example, summarized second data is obtained, and, optionally, storing 132 the summarized second data.

[0090] Fig. 10 schematically shows a time diagram in which second data E30 for a first time period T12 between the start time t1 and the end time t2 are symbolized. For example, the second data E30 for the first time period T12 are based on a sequence according to Fig. 4 and / or Fig. 5. In a comparable manner, further second data E31, E32, E33 were determined for further time periods T23 (between times t2, t3), T34 (between times t3, t4), and T45 (between times t4, t5). It should be noted that the different time periods T12, T23, T34, T45 may, for example, each have different lengths or durations in further exemplary embodiments.

[0091] In further exemplary embodiments, for example, the second data E30, E31 of the time ranges T12, T23 can be summarized, whereby summarized second data E34 for the time range T13 is obtained.

[0092] In further exemplary embodiments, for example, the (already summarized) second data E34 and the second data E32 of the time ranges T13, T34 can be summarized, whereby (eg further) summarized second data E35 are obtained for the time range T14.

[0093] For example, the second data E34 characterizes statistical parameters such as minimum MIN, maximum MAX and mean AVG ( Fig. 3) for the aggregated time period T13. For example, the second data E35 characterizes statistical parameters such as minimum, maximum, and mean for the aggregated time period T14.

[0094] In further exemplary embodiments, for example, a repeated summarization of second data that may have already been summarized or not yet summarized is also possible, for example also according to an iterative or recursive method.

[0095] In further exemplary embodiments, Fig. 9, it is provided that the summarizing 130 comprises at least one of the following elements: a) determining 130a a minimum for the summarized second data E34 ( Fig. 10) as the minimum of the different time ranges T12, T23 or the minima of the second data E30, E31 belonging to the different time ranges T12, T23, b) Determine 130b ( Fig. 9) a maximum for the combined second data E34 as the maximum of the different time ranges T12, T23, or the maxima of the second data E30, E31 belonging to the different time ranges T12, T23, c) determining 130c a mean value for the combined second data E34 as a weighted mean value of the respective mean values ​​of the different time ranges T12, T23 or the mean values ​​of the second data E30, E31 belonging to the different time ranges T12, T23, wherein, for example, a respective size or duration of the respective time range T12, T23 can be used as weighting factors.

[0096] In further exemplary embodiments, Fig. 10, the second data E30 for the time period T12 can, for example, be interpreted as a report and / or referred to as □(T12). Similarly, the second data E31 for the time period T23 can, for example, be interpreted as a report and / or referred to as □(T23).

[0097] In further exemplary embodiments, the summarizing 130 ( Fig. 9) can generally be understood as a mapping, for example a function, e.g. according to □(T13)= □(□(T12), □(T23)), where summarized second data □(T13) for the time range T13 are based, for example, on the second data □(T12), □(T23) of the time ranges T12, T23 considered for the summarizing 130, for example according to the above-described rules 130a, 130b, 130c according to Fig. 9.

[0098] In further exemplary embodiments, in which, for example, more than the three statistical parameters MIN, MAX, AVG ( Fig. 3) are used for the second data (e.g. additionally a variance), the summarization for these further statistical parameters can be carried out in an analogous manner.

[0099] In further exemplary embodiments, for example, fewer than the three statistical parameters MIN, MAX, AVG ( Fig. 3) are used for the second data, e.g. only the mean AVG or the maximum MAX and the mean, etc. In these further exemplary embodiments, the summarizing for the relevant statistical parameters can also be carried out in an analogous manner.

[0100] In further exemplary embodiments, Fig. 10, the second data E30, E31 of two, for example mutually adjacent, time intervals T12, T23 can be combined during the summarization, e.g. in the manner described above.

[0101] In further exemplary embodiments, Fig. 10, when summarizing, the second data of more than two, for example mutually adjacent, time intervals can be summarized, e.g. analogously to the manner described above.

[0102] For example, in further exemplary embodiments, the second data E30, E31, E32 can be summarized, which leads, for example, to the summarized second data E35. When using the statistical parameters MIN, MAX, AVG, for example, the respective resulting values ​​for the summarized second data E35 can be determined analogously to the procedure described above with reference to summarizing the two elements E30, E31. For example, the minimum MIN for the second data E35 summarized based on the three elements E30, E31, E32 results from the minimum of the respective minima of the three elements E30, E31, E32. The rules for summarizing 130 described above apply accordingly to the maximum and the mean value.

[0103] In further exemplary embodiments, Fig. 11, it is provided that the method comprises: storing 140 third data DAT-3, which at least one with the product 10 ( Fig. 2) characterize the associated event, and, optionally, providing 142 and / or using a second data structure DS-2 ( Fig. 12) for storing 140 the third data DAT-3, wherein the second data structure DS-2 comprises at least: an information element E40a for a time of occurrence of the event, an information element E41 (e.g. characterizing an integer variable or a bit field) for information characterizing the event.

[0104] In further exemplary embodiments, the storage 140 of the third DAT-3 may take place at least temporarily in the memory 12 or 12a.

[0105] In further exemplary embodiments, Fig. 12, instead of the time for the occurrence of the event (see information element E40a), a time range, e.g., characterized by a start time (see information element E40a) and an end time (see optional information element E40b), can also be provided to describe temporal information about the occurrence of an event. In further exemplary embodiments, this can, for example, simplify a summary of the third data DAT-3, described in more detail below, according to further exemplary embodiments.

[0106] In further exemplary embodiments, an event associated with the product 10 can be, for example, at least one of the following events: a) error, e.g. of a component of the product, b) warning, e.g. of a component of the product, c) information, e.g. of a component of the product, d) initialization has taken place, e) maintenance has taken place, e) motor start-up and / or start of movement in the open direction, f) motor start-up and / or start of movement in the closed direction, g) (e.g. lower) end position reached, e.g. sealing process, h) e.g. upper end position reached, i) predefinable length unit (e.g. 1 millimeter or decimetre) traveled, j) predefinable time unit traveled, k) drive activity, e.g. for a predefinable period of time, 1) configuration change takes place, m) measuring device outside a range.

[0107] In further exemplary embodiments, Fig. 13, it is provided that the method comprises: summarizing 145 third data of different, for example mutually adjacent, points in time or time ranges or time intervals, whereby, for example, summarized third data are obtained, and, optionally, storing 147 the summarized third data.

[0108] Fig. Figure 14 shows a time diagram in which element E45 symbolizes third data for a time range T12, element E46 symbolizes third data for a time range T23, and element E47 symbolizes the summarized third data, which characterize, for example, events for the aggregated time range T13. It should be noted that the times t1, t2, t3 according to Fig. 14 are chosen as examples and in some exemplary embodiments, for example, do not correspond to the times t1, t2, t3 according to Fig. 10. However, in other exemplary embodiments, the times t1, t2, t3 may be Fig. 14 e.g. the times t1, t2, t3 according to Fig. 10 correspond.

[0109] In further exemplary embodiments, Fig. 13, it is provided that the summarizing 145 of third data of different, for example mutually adjacent, times and / or time ranges or time intervals comprises: assigning 145a the third data E45, E46 of the different times or time ranges T12, T23 to an aggregated time range T13, wherein, for example, the aggregated time range T13 has the different times and / or time ranges T12, T23, for example corresponds to a combination of the different time ranges T12, T23, and, optionally, aggregating 145b a respective piece of information characterizing an event.

[0110] In further exemplary embodiments, the aggregating 145b may include, for example: providing a counter for each event that occurred, the counter indicating how often the respective event occurred within the aggregated time range T13.

[0111] In further exemplary embodiments, the summarizing 130 ( Fig. 9) the second data DAT-2 and / or summarizing 145 ( Fig. 13) of the third data DAT-3, for example, are executed repeatedly, for example until the summarized (second or third) data obtained thereby meet a target criterion, for example fall below a predeterminable maximum data volume.

[0112] In further exemplary embodiments, the summarizing may also be performed selectively, for example, only for at least some of the second data DAT-2, but not, for example, for the third data DAT-3. In further exemplary embodiments, the summarizing may also be performed selectively, for example, only for at least some of the third data DAT-3, but not, for example, for the second data DAT-2.

[0113] In this way, the respective second or third data can be reduced in further exemplary embodiments, e.g. such that they require less storage space, e.g. in the memory 12, without too much information of the respective data being lost, e.g. for many application purposes.

[0114] For example, when summarizing 130 second data according to exemplary embodiments, information about a temporal occurrence, e.g. of a maximum MAX, is reduced to a certain extent, which correlates e.g. with a time duration of a time range of the summarized second data, which, however, is acceptable for many, e.g. important use cases in further exemplary embodiments, in view of a possible saving of storage space that results thereby.

[0115] For example, combining second data for ten time periods can reduce the memory requirements for storing associated statistical parameters such as MIN, MAX, AVG to one-tenth, with the temporal resolution, for example, for determining when the maximum occurred—assuming, for example, that the ten time periods are of equal length—being reduced to one-tenth. This is acceptable in other exemplary embodiments, for example, with relatively old data, or does not represent a limitation for many significant applications. Comparable statements apply in other exemplary embodiments for a possible combining of at least some parts of the third data DAT-3.

[0116] In further exemplary embodiments, Fig. 15, it is provided that the method comprises: determining 150 a first size G1, which is a data quantity of in the memory 12 ( Fig. 2) characterized by stored second data DAT-2 and / or third data DAT-3, optionally comparing 152 the first variable G1 with a predefinable first threshold value SW1, optionally based on the comparison 152, summarizing 154 at least one of the following elements: a) second data DAT-2 stored in the memory 12, b) third data DAT-3 stored in the memory 12.

[0117] As a result, in further exemplary embodiments it can be advantageously avoided that, for example after a certain operating period, the memory 12 ( Fig. 2) no longer has enough free storage space, for example, for storing future data. In further exemplary embodiments, for example, the data on the basis of which the summarizing 130, 145, 154 was performed may be at least partially overwritten by the summarized data.

[0118] In further exemplary embodiments, it may be provided to delete from the memory 12 such data on the basis of which a summarization 130, 145, 154 has been carried out.

[0119] In further exemplary embodiments, Fig. 15, it is provided that the summarizing 154 is carried out, for example exclusively, for second and / or third data, see block 154a, which are older than a predefinable second threshold value SW2, wherein, for example, the method comprises: omitting 154b a summarizing of second and / or third data stored in the memory 12 which are more recent than the second threshold value SW2. In this way, in further exemplary embodiments, it can be ensured that comparatively young data are retained unchanged, at least for a certain period of time, e.g. with respect to the second threshold value, which, for example, enables the maximum temporal resolution to be maintained.

[0120] In further exemplary embodiments, Fig. 16, it is provided that the method comprises: determining 155, for example specifying 155a (e.g. locally in the product 10, for example by the device 200, 200') or receiving 155b (e.g. from a further unit 20), a or the second threshold value SW2, wherein the second threshold value SW2 characterizes a time such that in the memory 12 ( Fig. 2) stored second and / or third data that are more recent than the second threshold SW2 may not be aggregated and / or deleted, and, optionally, determining 157 ( Fig. 16), based on the second threshold value SW2, whether second data and / or third data stored in the memory 12 may be combined.

[0121] In further exemplary embodiments, Fig. 17, it is provided that the method comprises: determining 160 a second variable G2 which characterizes a data set of second data and / or third data stored in the memory 12 which is more recent than the second threshold value SW2, and, optionally, using 162 the second variable G2, for example for controlling an operation of at least one component of the product 10, for example for a future summarization 130, 145.

[0122] In further exemplary embodiments, Fig. 18, it is provided that the method comprises: comparing 165 the second variable G2 with the first variable G1, and, based on the comparison 165, executing 167 at least one of the following elements: a) changing 167a the second threshold value SW2, and / or b) changing 167b the predefinable first time interval T-INT-1, c) providing 167c and / or changing 167d a first parameter P1 for the summarizing. For example, the first parameter P1 for the summarizing can specify a) to what extent the summarizing is carried out, and / or b) when the summarizing is carried out, and / or b) which statistical parameters SP are taken into account for the summarizing (e.g., all of the stored parameters such as MIN, MAX, AVG, or e.g., only some of them).

[0123] In further exemplary embodiments, Fig. 18, it is provided that the comparison 165 comprises: determining 165a a quotient Q-G2-G1 from the second variable G2 and the first variable G1 and comparing 165b whether the quotient Q-G2-G1 is greater than a predefinable third threshold value SW3.

[0124] In further exemplary embodiments, for example, if the quotient Q-G2-G1 is greater than the predefinable third threshold value SW3, it can be concluded that a proportion of comparatively young, e.g. non-rewritable or non-erasable, data DAT-2, DAT-3 is comparatively large, relative to the data volume of the entire second data DAT-2 and / or third data DAT-3 stored in the memory 12, and / or, for example, the second threshold value SW2 can then be reduced.

[0125] In further exemplary embodiments, for example, the summarizing 130, 145 of second data DAT-2 stored in the memory 12 and / or of third data DAT-3 stored in the memory 12 may be carried out based on the quotient Q-G2-G1.

[0126] In further exemplary embodiments, for example, a value comparable to the quotient Q-G2-G1 can be selectively determined, e.g., only for the second data DAT-2 or only for the third data DAT-3. In further exemplary embodiments, a similar situation also applies to measures derived based on the quotient, e.g., for adjusting a summary of, e.g., the second data or the third data.

[0127] In further exemplary embodiments, the changing 167b of the predeterminable first time interval T-INT-1 can be carried out, for example, according to specification or configuration, e.g. also dynamically (during operation, e.g., of the product 10 or of a device 200, 200' executing aspects according to the embodiments), e.g. event-based, which, in further exemplary embodiments, e.g., can be based on different lengths of time ranges T12, T23, T34, T45 ( Fig. 10) can lead to.

[0128] For example, in further exemplary embodiments, it can be provided that the predeterminable first time interval T-INT-1 is reduced when a predeterminable error has occurred, so that from then on, for example, the second data DAT-2 are determined with a greater temporal resolution.

[0129] For example, in further exemplary embodiments, it can be provided that the predeterminable first time interval T-INT-1 is increased, e.g. if no error has occurred for a certain operating period, so that from then on, e.g. the second data DAT-2 are determined with a greater temporal resolution.

[0130] In further exemplary embodiments, Fig. 19, it is provided that the method comprises: providing 170 first information I-1 for controlling the summarizing 130, 145, wherein the first information I-1 describes, for example, a target interval size as a function of at least one operating time of the product 10, wherein, for example, the first information I-1 can be characterized by means of a characteristic curve, and, optionally, using 172 the first information I-1 for the summarizing 130, 145.

[0131] In further exemplary embodiments, the target interval size characterizes, for example, a, for example, maximum, time period at which a report or summarized data AT-2, DAT-3 characterizing the report is formed. In other words, the target interval size indicates, for example, the maximum time period at which second data such as a minimum and / or maximum and / or average are formed, for example by summarizing 130 according to the embodiments, whereby in further exemplary embodiments a temporal resolution of the (e.g. summarized) second data DAT-2 is controllable. For example, a target interval size of 10 seconds indicates that - for example, even after summarizing second data - statistical parameters such asMinimum and / or maximum and / or mean are maximally associated with the stated period of 10 seconds, i.e. represent the respective statistical properties of the underlying first data from the period of the stated 10 seconds.

[0132] In further exemplary embodiments, it is conceivable, for example, to select comparatively small values ​​for the target interval size for data that is comparatively recent, i.e., data that was determined relatively recently. In further exemplary embodiments, it is conceivable, for example, to select comparatively large values ​​for the target interval size for data that is comparatively old, i.e., data that was determined relatively long ago.

[0133] In further exemplary embodiments, the target interval size can be specified, for example, using the aforementioned characteristic curve, e.g., for different operating times. In further exemplary embodiments, the characteristic curve can, for example, have steps or jumps. Fig. 22 shows, by way of example, possible characteristic curves K1, K2, K3 according to further exemplary embodiments, which each symbolize a target interval size SI over a time - e.g., corresponding to an age of the data DAT-2, DAT-3 considered.

[0134] In further exemplary embodiments, a possible characteristic curve (not shown) for the target interval size has comparatively small values ​​for comparatively young data and comparatively old data, and comparatively large values ​​for data with an age between the young data and the old data, at least approximately corresponding to, for example, an inverse "bathtub curve".

[0135] In further exemplary embodiments, Fig. 20, it is provided that the method comprises at least one of the following elements: a) storing 180, for example at least temporarily storing, at least a part of the first data DAT-1 and / or the second data DAT-2 and / or the third data DAT-3 in a volatile memory 12a ( Fig. 2) (which may, for example, be integrated into the product 10), b) sending 182 at least a portion of the first data DAT-1 and / or the second data DAT-2 and / or the third data DAT-3 to at least one further, for example external, unit 20, for example a mobile or stationary data acquisition device. In further exemplary embodiments, the sending 182 may be performed using a wireless data connection and / or a wired data connection.

[0136] Fig. 21 schematically shows a simplified block diagram according to exemplary embodiments. Element E50 symbolizes a plurality of data sources, for example, a sensor cluster with sensors for detecting acceleration and / or temperature and / or humidity and / or pressure and / or other parameters, wherein, for reasons of clarity, only one sensor E50a is shown. In further exemplary embodiments, the sensor cluster E50 can have several tens to several hundred or more sensors. The sensor data provided by the sensors of the sensor cluster E50 can, in further exemplary embodiments, be determined, for example, as first data DAT-1, and based on this first data DAT-1, second data DAT-2 can be formed, for example, following the principle according to the embodiments, etc.

[0137] Element E51 symbolizes further data sources E51a, ..., which are not sensors, for example, which transform at least one physical parameter into corresponding sensor data values. Rather, the further data sources E51a, ... can provide, for example, operating variables, e.g., internal variables (e.g., setpoints of a controller, voltage values, position information from drives, values ​​of variables of at least one computer program associated with the product 10) of a device 200, 200', the storage of which can be useful, for example, for documenting a proper principle of the product 10 or the device 200, 200' and / or for locating errors, etc. The data provided by the further data sources E51 can also be determined, for example, as first data DAT-1 in further exemplary embodiments, and based on this first data DAT-1, for example,following the principle according to the embodiments, second data DAT-2 are formed, etc.

[0138] Due to the possible plurality of data sources E50, E51, comparatively high data volumes per time, e.g. data rates, can occur in further exemplary embodiments, see the block arrow A1, wherein, e.g. a summary of second data DAT-2 determined from these data can be carried out according to exemplary embodiments, e.g. in order to enable efficient storage A2, e.g. in the form of reports, in a memory E53, and / or a transmission A3 of at least a part of the second data or the summarized second data, e.g. to at least one further unit 20 ( Fig. 2).

[0139] Block E52 symbolizes aspects of data processing according to further exemplary embodiments, wherein block E52a symbolizes, for example, the determination of first data DAT-1 from the data sources E50, E51 and / or second data DAT-2 based on the first data DAT-1 from the data sources E50, E51. Block E52b symbolizes a determination of events according to further exemplary embodiments, which are characterized, for example, by third data DAT-3. Block E52c symbolizes an optional summarizing 130, 145 of the second data DAT-2 and / or the third data DAT-3, for example using the principle according to the embodiments. By summarizing 130, 145, in further exemplary embodiments, for example, a comparatively small data rate or data volume can be achieved for storing A2 and / or transmitting A3 of the summarized data, for example, compared to the data rate of the data, for exampleRaw data, A1 of the data sources E50, E51, while essential information such as the respective statistical parameters or information on the frequency of events that have occurred is retained.

[0140] Further exemplary embodiments, Fig. 23, refer to a device 200 for carrying out the method according to the embodiments. For example, in further exemplary embodiments, the device 200 can be integrated into the product 10, e.g., integrated into a housing of the product (see, e.g., the block 200' according to Fig. 2), or attached to product 10.

[0141] In further exemplary embodiments, Fig. 23, the device 200 comprises: a computing device (“computer”) 202 having at least one computing core 202a, a memory device 204 assigned to the computing device 202 for at least temporarily storing at least one of the following elements: a) data DAT, b) computer program PRG, for example for carrying out the method according to the embodiments.

[0142] In further exemplary embodiments, Fig. 23, the memory device 204 can be used, for example, to realize or implement at least one memory 12, 12a ( Fig. 2) of the product 10. In other words, in exemplary embodiments, the memory device 204 of the apparatus 200 can be used to provide the memory 12 and / or the memory 12a.

[0143] In further exemplary embodiments, Fig. 23, the data DAT thus comprise, for example, at least temporarily at least one of the following elements: a) the first data DAT-1, b) the second data DAT-2, c) the third data DAT-3, d) or summarized data determined based on at least some of these data.

[0144] In further exemplary embodiments, Fig. 23, the storage device 204 has a volatile memory (e.g., random access memory (RAM)) 204a, and / or a non-volatile (NVM) memory (e.g., Flash EEPROM) 204b, or a combination thereof or with other memory types not explicitly mentioned. Although, in the present example, the data DAT is depicted as being arranged in the volatile memory 204a and the computer program PRG as being arranged in the non-volatile memory 204b, it is understood that in further exemplary embodiments a) the data DAT can be stored at least partially, e.g., also, in the non-volatile memory 204b, and / or b) the computer program PRG can be stored at least partially, e.g., also, in the volatile memory 204a, e.g., at least temporarily.

[0145] Further exemplary embodiments, Fig. 23, refer to a computer-readable storage medium SM comprising instructions PRG' which, when executed by a computer 202, cause the computer 202 to carry out the method according to the embodiments.

[0146] Further exemplary embodiments relate to a computer program PRG, PRG', comprising instructions which, when the program PRG, PRG' is executed by a computer 202, cause the computer 202 to carry out the method according to the embodiments.

[0147] Further exemplary embodiments relate to a data carrier signal DCS, which characterizes and / or transmits the computer program PRG, PRG' according to the embodiments. The data carrier signal DCS can be received, for example, via an optional data interface 206 of the device 200. Likewise, for example, the data DAT or at least some of the data DAT-1, DAT-2, DAT-3 can be transmitted (sent and / or received) via the optional data interface 206. In further exemplary embodiments, the communication or data communication with the further unit 20 ( Fig. 2) via the optional data interface 206.

[0148] Further exemplary embodiments, Fig. 2, refer to a product 10, for example a valve or valve block, comprising at least one device 200, 200' according to the embodiments.

[0149] Further exemplary embodiments, Fig. 24, relate to a use 300 of the method according to the embodiments and / or the device 200, 200' according to the embodiments and / or the product 10 according to the embodiments and / or the computer-readable storage medium SM according to the embodiments and / or the computer program PRG, PRG' according to the embodiments and / or the data carrier signal DCS according to the embodiments for at least one of the following elements: a) processing 301 of data DAT-1, DAT-2, DAT-3 of a product 10 located in the field, e.g. a valve or valve installed in a system for processing fluids.valve block, b) at least temporary local storage 302 of data, for example data DAT-2 characterizing statistical parameters SP, in the product 10, c) compressing 303 data, d) reducing 304 data, e) increasing 305 an efficiency with regard to, for example, longer-term storage of data, for example the second data DAT-2 or data derived from the second data DAT-2 (e.g., summarized second data), f) creating 306 reports BER-1, BER-2 with regard to at least the second data DAT-2, g) adapting 307, for example, dynamically adapting, variables influencing storage of at least the second data DAT-2, h) specifying 308 different temporal granularities for storing and / or summarizing data, for example the second data DAT-2 and / or the third data DAT-3, i) distinguishing 309 between, for example, momentarily, not to be summarized ordata that is not to be deleted and data that can be summarized and / or deleted, for example momentarily, j) providing 310 free storage space, for example in the, for example internal, for example non-volatile, memory 12 of the product 10, k) summarizing 311 data to a predeterminable size, 1) conditionally retaining 312 data.

[0150] In further exemplary embodiments, the principle according to the embodiments is e.g. for a targeted reduction of a data volume e.g. of existing ones, e.g. in the memory 12 ( Fig. 2) stored second data DAT-2. For example, a user (not shown) can use the mobile device 20 for data capture, see Fig.2, request a transmission of data stored in the product 10 or its memory 12, 12a to the mobile device 20, for example via a wireless data connection DV with a comparatively low bandwidth, e.g. a Bluetooth data connection. In order to obtain interesting information about previous operation of the product 10 or its components in a comparatively short time, in further exemplary embodiments, a summarization of the data stored in the product 10 or its memory 12, 12a can be carried out, for example, in such a way that, for example, stored data associated with a predefinable time range is reduced to a maximum data volume suitable for transmission to the mobile device 20, e.g. using the summarization 130, 145 according to exemplary embodiments, for example also a possibly repeated summarization.For example, the data summarized for transmission to the mobile device 20 can be temporarily stored in the volatile memory 12 or 204a and then discarded again, for example.

[0151] In further exemplary embodiments, the method according to the embodiments can thus, for example, comprise at least one of the following aspects: a) Receiving a request for the transmission of data, for example of second data DAT-2 (and / or third data DAT-3) stored in the product or data derivable therefrom, wherein, for example, the request is receivable via a wireless data connection DV, for example via the optional data interface 206 of the device 200, wherein, for example, the request has a default value characterizing a maximum data volume (e.g., "100 kilobytes") for the transmission of the requested data, wherein, for example, the request characterizes a time range for which the data is to be transmitted, wherein, for example, the request has a parameter that indicates which form of summarizing 130, 145 (e.g.,Summarizing second data DAT-2 and third data DAT-3, or summarizing second data DAT-2 but not third data DAT-3, or summarizing third data DAT-3 but not second data DAT-2), wherein, for example, the request has a parameter that specifies the minimum temporal resolution required for summarized data, b) determining the data to be transmitted, c) optionally, summarizing data for transmission, for example according to at least one piece of information in the request, d) sending the possibly summarized data.

[0152] In further exemplary embodiments, the principle according to the embodiments is scalable, both, for example, with regard to a number of data sources DQ-1, DQ-2, ... and a number of statistical parameters.

Claims

[1] Method, for example a computer-implemented method, for processing data associated with a product (10), comprising: determining (100) first data (DAT-1) of at least one data source (DQ-1) associated with the product (10), determining (102), based on the first data (DAT-1), second data (DAT-2) which characterize, for example represent, at least one statistical parameter (SP) of the first data (DAT-1), checking (104) whether the determination (100) of the first data (DAT-1) and / or the determination (102) of the second data (DAT-2) has been carried out for a predefinable first time interval (T-INT-1), and, if the check (104) shows that the determination (100) of the first data (DAT-1) and / or the determination (102) of the second data (DAT-2) for the predefinable first time interval (T-INT-1) has been executed, storing (106) at least the second data (DAT-2). [2] Method according to claim 1, wherein the at least one data source (DQ-1) associated with the product (10) is a sensor, for example a sensor associated with the product (10), for example integrated into the product (10). [3] Method according to at least one of the preceding claims, wherein, if the checking (104) shows that the determination (100) of the first data (DAT-1) and / or the determination (102) of the second data (DAT-2) has not been carried out for the predeterminable first time interval (T-INT-1), the method comprises: continuing (108) the determination (100) of the first data (DAT-1) and / or the determination (102) of the second data (DAT-2). [4] Method according to at least one of the preceding claims, wherein a) the determination (100) of the first data (DAT-1) comprises a repeated, for example periodic, determination (100a) of the first data (DAT-1) at a predeterminable rate, and / or wherein b) the determination (102) of the second data (DAT-2) comprises a repeated, for example periodic, determination (102a) of the second data (DAT-2), for example at the predeterminable rate. [5] Method according to at least one of the preceding claims, wherein the storing (106) of at least the second data (DAT-2) comprises storing (106a) of at least the second data (DAT-2) in a, for example, internal, for example non-volatile, memory (12) of the product (10), and, optionally, storing (106b) time information (TIM-INF-DAT-2) associated with the second data (DAT-2), wherein, for example, the time information (TIM-INF-DAT-2) associated with the second data (DAT-2) characterizes a time range associated with the second data (DAT-2). [6] Method according to at least one of the preceding claims, wherein the at least one statistical parameter (SP) comprises at least one of the following elements: a) minimum (MIN), b) maximum (MAX), c) mean (AVG), d) at least one percentile, e.g. a fiftieth percentile, e.g. median. [7] Method according to at least one of the preceding claims, comprising at least one of the following elements: a) initializing (110) at least one of the following elements: a1) first counter, for example counter (sc_tim) associated with the predefinable first time interval (T-INT-1), a2) current minimum value (sc_min), a3) current maximum value (sc_max), a4) a current first counter associated with one or more of the following elements:the mean value associated with the first data (DAT-1), b) detecting (111) a current value (val) associated with the first data (DAT-1), c) performing (112) a filtering operation, for example debouncing, d) updating (113) the first counter (sc_tim) and / or the current first value (sc_avg), e) checking (114) whether the current value (val), for example a filtered value (val') obtained by means of the filtering (112), is less than the current minimum value (sc_min), and, if so, updating (114a) the current minimum value (sc_min), f) checking (115) whether the current value (val), for example a filtered value (val') obtained by means of the filtering (112), is greater than the current maximum value (sc_max), and, if so, updating (114a) the current maximum value (sc_max). [8] Method according to at least one of the preceding claims, comprising: determining (120a) the second data (DAT-2) for the predeterminable first time interval (T-INT-1) and storing (120b) the second data (DAT-2) for the predeterminable first time interval (T-INT-1) together with time information (INF-T-INT-1) characterizing the first time interval (T-INT-1), for example a start and an end of the first time interval (T-INT-1), for example in the form of a first report (BER-1), determining (122a) the second data (DAT-2) for a predeterminable second time interval (T-INT-2) and storing (122b) the second data (DAT-2) for the predeterminable second time interval (T-INT-2) together with time information characterizing the second time interval (T-INT-2), for example a start and an end of the second time interval (T-INT-2) (INF-T-INT-2), for example in the form of a second report (BER-1). [9] Method according to at least one of the preceding claims, comprising: providing (125) a first data structure (DS-1) comprising an information element (E20) for a start time of a time range associated with the second data (DAT-2), an information element (E21) for an end time of a time range associated with the second data (DAT-2), and at least one information element (E22, E23, ...) for one or more statistical parameters associated with the first data source (DQ-1), and, optionally, using (127) the first data structure (DS-1), for example for storing (120b, 122b) the second data (DAT-2). [10] Method according to at least one of the preceding claims, comprising: summarizing (130) second data (E30, E31; E34, E32) of different, for example mutually adjacent, time ranges (T12, T23; T13, T34), for example summarised second data (E34; E35) being obtained, and, optionally, storing (132) the summarised second data (E34; E35). [11] Method according to claim 10, wherein the summarizing (130) comprises at least one of the following elements: a) determining (130a) a minimum for the summarized second data (E34; E35) as a minimum of the different time ranges (T12, T23; T13, T34), b) determining (130b) a maximum for the summarized second data (E34; E35) as a maximum of the different time ranges (T12, T23; T13, T34), c) determining (130c) a mean value for the summarized second data (E34; E35) as a weighted mean value of the respective mean values ​​of the different time ranges (T12, T23; T13, T34), wherein, for example, a respective size or duration of the respective time range (T12, T23; T13, T34) can be used as weighting factors. [12] Method according to at least one of the preceding claims, comprising: storing (140) third data (DAT-3) which characterize at least one event associated with the product (10), and, optionally, providing (141) and / or using (142) a second data structure (DS-2) for storing (140) the third data (DAT-3), wherein the second data structure (DS-2) comprises at least: an information element (E40a) for a time of occurrence of the event, an information element (E41) for information characterizing the event. [13] Method according to claim 12, comprising: summarizing (145) third data (E45, E46) of different, for example mutually adjacent, times or time ranges (T12, T23), wherein, for example, summarized third data (E47) are obtained, and, optionally, storing (147) the summarized third data (E47). [14] Method according to claim 13, wherein the summarizing (145) of third data (E45, E46) of different, for example mutually adjacent, times and / or time ranges (T12, T23) comprises: assigning (145a) the third data (E45, E46) of the different times or time ranges (T12, T23) to an aggregated time range, wherein, for example, the aggregated time range comprises the different times and / or time ranges (T12, T23), and, optionally, aggregating (145b) a respective item of information characterizing an event. [15] Method according to at least one of the preceding claims, comprising: determining (150) a first variable (G1) which characterizes a data volume of second data (DAT-2) and / or third data (DAT-3) stored in the memory (12), optionally comparing (152) the first variable (G1) with a predefinable first threshold value (SW1), optionally based on the comparison (152), summarizing (154; 130; 145) at least one of the following elements: a) second data (DAT-2) stored in the memory (12), b) third data (DAT-3) stored in the memory (12). [16] Method according to claim 15, wherein the summarizing (154; 130; 145), for example exclusively, is carried out (154a) for second and / or third data (DAT-2, DAT-3) which are older than a predeterminable second threshold value (SW2), wherein, for example, the method comprises: omitting (154b) a summarizing of second and / or third data (DAT-2, DAT-3) stored in the memory (12) which are younger than the second threshold value (SW2). [17] Method according to at least one of the preceding claims, comprising: determining (155), for example specifying (155a) or receiving (155b), a or the second threshold value (SW2), wherein the second threshold value (SW2) characterizes a point in time such that second and / or third data (DAT-2, DAT-3) stored in the memory (12) that are more recent than the second threshold value (SW2) may not be combined and / or deleted, and, optionally, determining (157), based on the second threshold value (SW2), whether second data (DAT-2) and / or third data (DAT-3) stored in the memory (12) may be combined. [18] Method according to claim 17, comprising: determining (160) a second variable (G2) which characterizes a data set of second data (DAT-2) and / or third data (DAT-3) stored in the memory (12) which are more recent than the second threshold value (SW2), and, optionally, using (162) the second variable (G2), for example for controlling an operation of at least one component of the product (10). [19] Method according to claim 18 and 17, dependent on at least one of claims 15 to 16, comprising: Comparing (165) the second variable (G2) with the first variable (G1), and, based on the comparison (165), carrying out (167) at least one of the following elements: a) changing (167a) the second threshold value (SW2), and / or b) changing (167b) the predefinable first time interval (T-INT-1), c) providing (167c) and / or changing (167d) a first parameter (P1) for the summarizing (154; 130; 145). [20] Method according to claim 19, wherein the comparing (165) comprises: determining (165a) a quotient (Q-G2-G1) from the second variable (G2) and the first variable (G1) and comparing (165b) whether the quotient (Q-G2-G1) is greater than a predeterminable third threshold value (SW3). [21] Method according to at least one of claims 10 to 20, comprising: providing (170) first information (I-1) for controlling the summarizing (130; 145), wherein the first information (I-1) describes, for example, a target interval size as a function of at least one operating time of the product (10), wherein, for example, the first information (I-1) can be characterized by means of a characteristic curve (K1; K2; K3), and, optionally, using (172) the first information (I-1) for the summarizing (130; 145). [22] Method according to at least one of the preceding claims, comprising at least one of the following elements: a) storing (180), for example at least temporarily storing (180a), at least a part of the first data (DAT-1) and / or the second data (DAT-2) and / or the third data (DAT-3) in a volatile memory (12a), b) sending (182) at least a part of the first data (DAT-1) and / or the second data (DAT-2) and / or the third data (DAT-3) to at least one further, for example external, unit (20). [23] Device (200; 200') for carrying out the method according to at least one of the preceding claims. [24] Product, for example valve, (10), comprising at least one device (200; 200') according to claim 23. [25] Computer-readable storage medium (SM) comprising instructions (PRG') which, when executed by a computer (202), cause the computer to carry out the method according to at least one of claims 1 to 22. [26] Computer program (PRG; PRG') comprising instructions which, when the program (PRG; PRG') is executed by a computer (202), cause the computer (202) to carry out the method according to at least one of claims 1 to 22. [27] Data carrier signal (DCS) which transmits and / or characterises the computer program (PRG; PRG') according to claim 26. [28] Use (300) of the method according to at least one of claims 1 to 22 and / or the device (200; 200') according to claim 23 and / or the product (10) according to claim 24 and / or the computer-readable storage medium (SM) according to claim 25 and / or the computer program (PRG; PRG') according to claim 26 and / or the data carrier signal (DCS) according to claim 27 for at least one of the following elements: a) processing (301) of data of a product (10) located in the field, b) at least temporarily storing (302) data locally, for example data characterizing statistical parameters, in the product (10), c) compressing (303) data, d) reducing (304) data, e) increasing (305) efficiency with regard to, for example, longer-term storage of data, for example the second data (DAT-2) ordata derived from the second data (DAT-2), f) creating (306) reports relating to at least the second data (DAT-2), g) adapting (307), for example dynamically adapting, variables influencing storage of at least the second data (DAT-2), h) specifying (308) different temporal granularities for storing and / or summarizing data, for example the second (DAT-2) and / or the third data (DAT-3), i) distinguishing (309) between, for example, data that is not currently to be summarized or deleted and data that can be summarized and / or deleted, for example currently, j) providing (310) free storage space, for example in the, for example, internal, for example non-volatile, memory (12) of the product (10), k) summarizing (311) data to a predefinable size, 1) conditionally retaining (312) data.

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