Method for creating and optimizing customized data tables, customer portal product, and non-transitory computer-readable recording medium

Abstract

The present invention relates to a method for creating and optimizing a customized data table for a predefined instrument type, the method comprising: using a measuring device and controlled by a controller to determine the amount of performance metrics of a plurality of different instrument types; storing the performance metrics together with corresponding information of the instrument type and performance scenario in a database; providing a human-machine interface coupled to the database; receiving a user request via an input terminal of the human-machine interface, wherein the user request comprises a first request for a selected instrument or instrument type from a plurality of instrument types and a second request for a selected subset of performance scenarios from a plurality of different performance scenarios of the selected instrument or instrument type; automatically generating a customized data table based on the user request by extracting information of the selected instrument type and the selected subset of performance scenarios and corresponding performance metrics from a database. The present invention also relates to a customer portal product and a non-transitory computer-readable recording medium.

Description

Technical Field

[0001] The present invention relates to a method for creating and optimizing a customized data table. The present invention also relates to a customer portal product and a non-transitory computer-readable recording medium. Background Art

[0002] A data sheet, data sheet or specification sheet is a document that summarizes the performance and other technical characteristics of an electronic device (or its components, subsystems, machines, software, etc.) in sufficient detail to allow design engineers to understand the role of the component in the entire system. In the data sheet, for each device type, the electrical characteristics to be published are represented by numerical values ​​or corresponding graphs. In addition, regarding electrical characteristics, the values ​​of characteristic parameters and graphs of characteristic curves are described for each of the individual items.

[0003] Currently, in order to generate a data sheet, engineers use some measuring instruments and measure the data sheet values ​​for a limited number of performance scenarios. This must be repeated for each new hardware and software version of the electronic device. For each new electronic device type, a new device-specific data sheet is then created. For example, for a spectrum analyzer, the amplitude flatness in dB for a certain signal power, the attenuation in the instrument, the bandwidth, and the frequency are stated. Each parameter depends on many different variables, but only a few of them are specified in the data sheet. In addition, in practice, only a few measuring devices are used to measure these parameters, so these parameters may not be representative. Moreover, users may be interested in the data sheet values ​​for different performance scenarios. In this way, the generated data sheet is a non-personalized comprehensive data sheet, which usually does not contain specific values ​​and / or performance scenarios related to a specific user of this specific electronic device.

[0004] US2016 / 0320432A1 discloses generating a data table for a device under test (DUT). A user who is not familiar with how to properly use a measuring instrument to measure a DUT is assisted to select performance parameters of a data table, and then the measurement is automatically performed. This also aggregates each metric value in a database. However, the generation of the data table is based on the measurement, but a brief data table is not generated for the user of the measuring instrument based on the details of the user's planned measurement. Summary of the invention

[0005] In this context, there is a need to provide custom data table generation.

[0006] Therefore, according to aspects of the present invention, the present invention provides a method of creating a customized data table, a customer portal product for creating a customized data table, and a non-transitory computer-readable recording medium having the features of the independent claims.

[0007] According to a first aspect of the present invention, a method for creating and optimizing a customized data table for a predefined instrument type is provided, the method comprising the following steps: using a measuring device and controlled by a controller, determining the amount of performance metrics of a plurality of different instrument types, wherein each set of performance metrics of a particular instrument type is based on a plurality of different performance scenarios; storing the performance metrics together with corresponding information of the instrument type and the performance scenario in a database; providing a human-machine interface coupled to the database, wherein the human-machine interface is configured to reliably assist the user in executing a user request by adopting a guided human-machine interaction process; receiving the user request via an input terminal of the human-machine interface, wherein the user request comprises a first request for a selected instrument or instrument type from the plurality of instrument types and a second request for a selected subset of performance scenarios from the plurality of different performance scenarios of the selected instrument or instrument type; and automatically generating a customized data table based on the user request by extracting information of the selected instrument type and the selected subset of performance scenarios and corresponding performance metrics from the database.

[0008] According to a second aspect of the present invention, a customer portal product for creating and optimizing customized data tables for predefined instrument types is provided, the customer portal product comprising: a database configured to store performance metrics of instrument types together with corresponding information of the instrument types and performance scenarios; a human-machine interface coupled to the database, wherein the human-machine interface comprises an input terminal for receiving user requests, and wherein the human-machine interface is configured to adopt a guided human-machine interaction process by receiving from the user a first request for a selected instrument or instrument type from the multiple instrument types and a second request for a selected performance scenario subset of the multiple different performance scenarios of the selected instrument or instrument type, thereby reliably assisting the user in executing the user request, and is configured to automatically generate a customized data table based on the user request by extracting information of the selected instrument type and the selected performance scenario subset and corresponding performance metrics from the database.

[0009] According to a third aspect of the present invention, a non-transitory computer-readable recording medium is provided, a customer portal product. The non-transitory computer-readable recording medium is configured to store instructions executable by a computer processor, and causes the computer processor to perform a method for creating and optimizing a custom data table for a predefined instrument type, the method comprising: determining the amount of performance metrics of a plurality of different instrument types, wherein each set of performance metrics of a particular instrument type is based on a plurality of different performance scenarios; storing the performance metrics together with the corresponding information of the instrument type and the performance scenario in a database; receiving the user request via an input terminal of a human-machine interface, the human-machine interface being configured to credibly assist the user in executing the user request by adopting a guided human-machine interaction process, wherein the user request comprises a first request for a selected instrument or instrument type from the plurality of instrument types and a second request for a selected performance scenario subset of the plurality of different performance scenarios of the selected instrument or instrument type; automatically generating a custom data table based on the user request by extracting information of the selected instrument type and the selected performance scenario subset and corresponding performance metrics from the database.

[0010] The invention is based on the recognition that the excitation current flowing through the excitation device is influenced not only by the periodic signal but also by the current flowing through the supply line.

[0011] The present invention is based on the idea of ​​automatically measuring all conceivable scenarios of a plurality of instruments, instrument types or components thereof and storing this information appropriately in a database. Upon request of a user, a customized data table can then be generated. The customized data table includes only those customized information requested and / or required by the user, such as performance metrics and performance scenarios. Since the data table generated in this way no longer contains information that is not relevant to the user, a higher quality is achieved using this data table generation technique, which is moreover cheaper than known non-customized techniques.

[0012] Furthermore, the quality of the performance metrics (eg, different values) in the data table may, for example, increase from "nominal" (ie, desired by the designer) to "typical" (ie, actually measured).

[0013] Furthermore, based on the number of database entries, statistical information such as mean, standard deviation, minimum and maximum values ​​can be generated.

[0014] Advantageous configurations and developments emerge from the further dependent claims and from the description with reference to the drawings.

[0015] According to a particularly preferred aspect, the method further comprises the following steps: operating the selected instrument based on the selected subset of performance scenarios so as to obtain information of the performance metric under customized operating conditions, and feeding back the obtained information of the performance metric under customized operating conditions to the measuring device and the controller for updating the quantity of determining the performance metric of multiple different instrument types. It is particularly advantageous that the manufacturer of the corresponding instrument receives information about how the customer preferably or typically uses the corresponding instrument under operating conditions, i.e. when, how and which performance scenarios and settings are preferred and less preferred. In addition, the manufacturer receives information about which settings the customer uses during operation, for example, at which frequencies operating point measurements are preferred. The feedback of this information provides the manufacturer with a qualitative assessment of the application profile of the instrument used by the customer. Based on this information, the manufacturer can then direct its development resources so that future instruments are better adapted to the corresponding customer wishes, requirements and application profiles. The manufacturer's knowledge of what the various customers of the instrument want means that the corresponding instrument can be adjusted accordingly. This leads to an improvement in the quality of the corresponding device, and devices with improved and / or additional functions can be provided.

[0016] The selected instrument is connected to the measuring device and the controller via a communication link. The communication link is preferably an Internet connection, however, it may also be any other wired or wireless connection. In a preferred aspect, the acquired information is then immediately and dynamically forwarded via the communication link. However, it is also sufficient to forward the information after a predefined interval (e.g. after each measurement).

[0017] Typically, the maintenance or servicing of the selected instrument is performed regularly, preferably by the manufacturer, more preferably on site at the manufacturer. Typical maintenance intervals are one to two years. This maintenance is highly recommended for complex instruments such as oscilloscopes, spectrum analyzers, network analyzers, measuring instruments, etc. A specific and important task during maintenance is especially the calibration of the instrument and / or its components. In an alternative or additional aspect, the acquired information is read out during the maintenance or servicing performed and then forwarded to the measuring device, the controller and / or the database.

[0018] According to a particularly preferred aspect, the method comprises a step of post-processing the performance metric and storing the post-processed performance metric in the database together with the corresponding information of the instrument type and the performance scenario. The post-processing can be performed based on a predefined template and by using a controller. In a particularly preferred embodiment, the post-processed performance metric is obtained by interpolating the measurement results of previous measurements. Depending on the specific purpose, the data table can also provide different types of values, such as average values, typical values, typical ranges of values, maximum values, minimum values, engineering tolerances and / or nominal values. These different types of values ​​can also be obtained via a post-processing step.

[0019] According to another preferred aspect, the extracted information for generating the customized data table is additionally displayed to the user on a display. In this way, the user obtains feedback on which information will actually be shown in the generated data table.

[0020] In a preferred development, the display of information is performed on the display before the step of generating the customized data sheet via the human-machine interface is performed. This has the advantage that the user has access to this information before completing the data sheet, so that he can choose to modify or adapt it, such as deleting some information or adding additional information.

[0021] The measurement scenario includes at least one of the following parameters: input power, measurement bandwidth, carrier frequency of the input signal, signal type, operating temperature, settings for compensation for a specific standard and / or internal settings. Other measurement scenarios and / or parameters may also be applied. Optionally, the data table also contains specific performance of the instrument and worst-case device performance for each performance metric.

[0022] In another preferred configuration, the generating step further comprises the step of providing a template comprising a first field for the selected subset of performance scenarios, a second field for the corresponding performance metrics and a third field for information of the selected instrument type, and wherein the customized data table is generated by inserting information in the corresponding fields. It goes without saying that alternative or additional fields may also be present.

[0023] In a particularly preferred aspect, the step of determining the amount of the performance measure comprises the step of providing said performance measure by the instrument itself. This may for example be done each time a user sets up a particular measurement.

[0024] In a development, the method further comprises the steps of recording the user request from the human-machine interface and adding additional performance scenarios to the requested performance scenarios.

[0025] In another preferred aspect, the method includes the step of predicting the estimated performance of the device on an untested performance scenario by modeling the actual performance of the tested measurement scenario. This is a rather intelligent technique that is particularly advantageous for those applications where there are a large number of different instrument types, which nevertheless display similar technical characteristics and behavior. In particular, by modeling the parameters, additional performance scenarios can be provided to the user (e.g., for each Ghz RF, the uncertainty in amplitude flatness increases by 1 dB).

[0026] In another aspect, the generated data sheet is a spreadsheet. A spreadsheet specifies characteristics in a formal structure that allows information to be processed by a computer or machine. Such a machine-readable description can facilitate information retrieval, display, design, testing, interfacing, verification, and system discovery.

[0027] According to a preferred development, the human-machine interface comprises a web service, and the guided human-machine interaction process is a web-based service. This is particularly advantageous because the data table can then be generated from anywhere (e.g. in a factory facility) when the user needs it, making it possible to generate a data table on site.

[0028] In a further preferred aspect, the method comprises the step of using artificial intelligence techniques (e.g. trained or trainable neural networks and / or machine learning techniques) to infer the performance metric. This reduces the overall measurement effort, reduces database requirements and additionally provides very reliable results.

[0029] In one aspect, the method comprises the step of storing a compact representation of the performance metric for all measurement scenarios on the instrument under test itself.

[0030] According to a typical and preferred configuration, the method is applicable to automatic data table generation of at least one of the following instrument types: measuring instrument; test equipment; spectrum analyzer; network analyzer; signal generator; oscilloscope. It goes without saying that the method can also be effectively applied to other electrical and electronic equipment, components, systems, etc.

[0031] Where appropriate, the above configurations and developments may be combined in any manner. Other possible configurations, developments and implementations of the present invention also include combinations of features of the present invention (not explicitly mentioned), which have been described above or described below with reference to the embodiments. In particular, in this case, those skilled in the art will also add various aspects as improvements or supplements to the basic form of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The invention is described in more detail below on the basis of an embodiment shown in the schematic diagram of the accompanying drawings, in which:

[0033] Figure 1 is a flow chart illustrating a method of creating and optimizing a custom data table according to one aspect of the present invention;

[0034] Figure 2 is a flow chart illustrating a method of creating and optimizing a custom data table according to another aspect of the present invention;

[0035] Figure 3 is a block diagram illustrating a customer portal product for creating and optimizing custom data tables according to one aspect of the present invention.

[0036] The accompanying drawings are intended to provide a further understanding of the embodiments of the present invention. The accompanying drawings illustrate the embodiments and, in conjunction with the description, assist in explaining the principles and concepts of the present invention. Other embodiments and many advantages mentioned become apparent in view of the accompanying drawings. The elements in the drawings are not necessarily shown to scale. DETAILED DESCRIPTION

[0037] Figure 1 A flow chart showing a method of creating a custom data table according to one aspect of the present invention.

[0038] In a first step S1, quantities of performance metrics of a plurality of different instrument types are determined. The performance metrics, e.g., measured values, are provided by the manufacturer of the instrument and / or measured using suitable measuring equipment. Since, depending on the specific purpose, the data table may provide different types of values, e.g., average values, typical values, typical ranges of values, engineering tolerances and / or nominal values, this additional information is preferably obtained by employing suitable post-processing steps ( Figure 1 In addition, each set of performance metrics for a particular instrument type is based on a plurality of different performance scenarios. The determination of the performance metrics is typically controlled by a controller, which may be part of the measuring device and / or the instrument under test itself.

[0039] In a subsequent step S2, the determined performance metrics are stored in a database together with corresponding information of the instrument type tested and the applied performance scenario.

[0040] In the next step S3, a human-machine interface coupled to the database is provided. According to the basic functions of the human-machine interface, the human-machine interface is configured to appropriately assist the user in executing the user request, for example by applying request-response communication. This is accomplished according to the present invention by adopting a guided human-machine interaction process.

[0041] In the next step S4, the user starts a guided human-machine interaction process with the human-machine interface by using an input terminal of the human-machine interface. The user inputs a request received by the human-machine interface. The user request includes a first request for a selected instrument or instrument type from a plurality of instrument types. After receiving the first user request, the human-machine interface provides the user with a selection, such as an appropriate set of performance scenarios for the instrument or instrument type. Then, the user selects a subset of performance scenarios from a plurality of different performance scenarios for the selected instrument or instrument type via a second request. It goes without saying that the order of the first and second requests can also be reversed.

[0042] In the final step S5, the human-machine interface automatically generates a customized data table based on the user request (i.e., based on the information obtained via the first and second user requests). This is done by extracting information of the selected instrument type and the selected performance scenario subset from the database and by merging them with the corresponding performance metrics.

[0043] It goes without saying that for such a presentation of the data sheet, there are a number of different options, such as presenting the information on a paper data sheet, on a display (such as on a smartphone or a measuring device), in the form of a spreadsheet, etc. Furthermore, the data values ​​can be inserted into predefined fields of an appropriate template and / or in the form of a graphical representation.

[0044] Figure 2 A flow chart showing a method of creating and optimizing a custom data table according to another aspect of the present invention.

[0045] Figure 2 The method shown in basically includes Figure 1 Steps S1-S5 of the method shown. In addition, Figure 2 The method further comprises steps S6 and S7 forming a feedback loop.

[0046] In step S6 following step S5, the selected instrument is operated based on the selected subset of performance scenarios.During the operating mode, information of performance metrics under customized operating conditions is acquired.

[0047] In a subsequent step S7 forming a feedback loop, the information of the acquired performance metric is then fed back to the measuring device in step S1 and fed back to the controller or fed back to the database in step S2 ( Figure 2 In this way, updated determination of quantities for performance metrics of a plurality of different instrument types may be provided.

[0048] Figure 3 A block diagram illustrating a customer portal product for creating and optimizing custom data tables according to one aspect of the present invention.

[0049] The customer portal product is indicated by reference numeral 10. The customer portal product 10 comprises a database 12, a controller 13 and a human-machine interface 14. Furthermore, the customer portal product 10 can be connected to a measuring device 15.

[0050] The measuring device 15 may be a rack or a set of measuring instruments. The measuring device 15 may also include all equipment and devices required for test and / or measurement purposes, such as power meters, signal generators, computers for IQ output, markers, etc. ( Figure 3 An external or internal computer controls the rack of measuring instruments and runs various measuring scenarios.

[0051] The measuring device 15 is configured to perform tests of different instruments and / or instrument types, such as a network analyzer, a spectrum analyzer, an oscilloscope or any other electrical or electronic device. Figure 3 In FIG. 1 , different instrument types tested by the measuring device 15 are indicated by different reference numerals 16 , 17 , 18 , and the testing of these instruments 16 , 17 , 18 is indicated by arrows 16 a , 17 a , 18 a .

[0052] The measuring device 15 is configured to perform at least one of the following measurements:

[0053] - Amplitude flatness (+-dB), deviation from linear phase (+-°), and / or level uncertainty (+-dB) (all for certain bandwidths (80MHz, 320MHz, 500MHz, 2GHz) and radio frequencies (0-8GHz, <26.5GHz, <85GHz));

[0054] - average noise level displayed;

[0055] - Phase noise, e.g. decibels relative to the carrier (dBc) at 1kHz offset or 10kHz offset;

[0056] - Electromagnetic compatibility (EMC) such as defined in IEEE802.11 ac@5GHz;

[0057] - intermodulation, such as third-order intermodulation or second-order intermodulation;

[0058] -Residual false responses.

[0059] The measurement device 15 is configured to determine a specific amount of performance metrics for each of the instruments 16, 17, 18 (or instrument types). Each set of performance metrics for a specific instrument type is based on one or more performance scenarios specific to the corresponding instrument type.

[0060] For measurement purposes, the measurement device 15 may employ specific software that performs measurements, for example, based on the parameters listed above. The software may be part of a separate, specially designed measurement computer that is part of the measurement device 15 rather than part of the measuring instrumentation devices 16, 17, 18. The software may be configured to derive performance values ​​(e.g., amplitude flatness, noise floor, etc.) from raw measurement data (e.g., IQ samples, quantizer data, etc.).

[0061] The performance metric X determined by the measuring device 15 is then stored in the database 12 of the customer portal product 10. Typically, but not necessarily, corresponding information of the tested instrument type and the performance scenario is also stored in the database 12.

[0062] Preferably, but not necessarily, the information collected during the measurement is post-processed using the controller 13, for example to provide average values, minimum and / or maximum values, to extrapolate and / or interpolate previously measured values, etc.

[0063] A human-machine interface 14 coupled to the database 12 includes an input terminal 11. The input terminal 11 is configured to receive a user request. The human-machine interface 14 is configured to assist a user (such as a customer) in executing a user request received via the input terminal 11. In particular, the human-machine interface 14 is configured to assist the user by means of a guided human-machine interaction process. If the human-machine interface 14 receives a first request from the user for a selected instrument or instrument type from a plurality of available instrument types, the human-machine interface 14 provides the user with a selection of a selected subset of performance scenarios (i.e., operating modes) from a plurality of different performance scenarios according to the instrument or instrument type selected by the user. This selection of the selected subset of performance scenarios, also extracted from the database, is assigned to a specific instrument or instrument type. In particular, for example, the user can state certain measurement settings of the measuring device 15. These measurement settings can include amplitude flatness for 10 dB attenuation and 200 MHz bandwidth for FSW-67@50 GHz.

[0064] After the user has made his selection, i.e., after having selected a user-defined performance scenario, a customized data table 20 is automatically generated within the customer portal product 10 and in the output module 19 connected to the human-machine interface 14. This customized data table generation is performed by extracting information of the selected instrument type and the selected performance scenario subset and the corresponding performance metrics from the database 12.

[0065] The human-machine interface 14 may include, for example, one or more drop-down menus for selecting different performance scenarios, drill-down selections, etc. The user may select, for example, input carrier frequency, signal type (OFDM wideband or narrowband sinusoidal), various operating temperatures, settings for compensation for a particular standard (e.g., the amount of acceptable phase noise compensation for the measuring instrument), internal settings (e.g., IF attenuation or amplifiers in the processing chain), among various input powers, various measurement bandwidths.

[0066] The information extracted from the database 12 may be collected by estimating, interpolating and / or predicting data or information that may be obtained from an appropriate database. The estimation, interpolation and / or prediction of information from the database may be done by storing values ​​for the center frequencies of 50 GHz and 52 GHz and then by performing a preferably linear interpolation of the value of 51 GHz. The database may preferably be a lookup database, but may also be any type of storage device or cloud-based storage.

[0067] Specific software can be used for the task of information collection and / or for creating statistics if multiple measurements are available. Appropriate software can also be employed to create a well-formatted document with the required values.

[0068] According to certain preferred aspects, this information (e.g., directly measured values, estimated / interpolated or predicted values, statistical information, etc.) is communicated directly to the user. The user may also query for additional information, such as requesting the spectral flatness of a predefined device. This user request may then be transmitted in the form of a feedback loop to the application engineer responsible for the task and / or included directly into a list of measurements to be performed, such as those outlined above.

[0069] In an additional or alternative aspect of the invention, all data table information and values ​​required for the data table are (additionally or alternatively) stored in the auditable database 12 of the tested measuring instrument 15. Thus, a compact representation of the measurements for all conceivable scenarios is already stored on the measuring instrument 15. This has the additional advantage that, for example, if the user has selected a specific measurement scenario, the measuring instrument 15 directly shows the expected measurement uncertainty.

[0070] For Figure 3 In this additional or alternative aspect shown in dashed lines in FIG. 1 , the technical implementation may include at least one of the following:

[0071] The measuring instrument 15 comprises a database 24. The database 24 comprises a compact representation of some or all of the measurement results. The database 24 may be part of the database 12 or may also be a separate storage device.

[0072] The measuring instrument 15 further comprises an intelligent interface 21. The intelligent interface 21 is configured to be aware of the status of all relevant configurations of the measuring instrument 15, such as the status of hardware switches, settings, parameters and the like.

[0073] The measuring instrument 15 also includes a software module 22. The software module 22 is configured to derive from measurement information from an ongoing measurement currently being performed by a user, such as which uncertainty parameters are relevant to the user and which are not. The software module 22 is also configured to estimate, interpolate and / or predict the uncertainty of the measurement based on hardware settings, information from a database, customer measurement scenarios, etc.

[0074] The measuring instrument 15 also includes a screen or display 23. The display 23 is configured to show predefined information, which can also be requested remotely. Specifically, the display 23 can output uncertainty information shown on the screen:

[0075] - Error bars in the spectrum for amplitude flatness or in the level display for level uncertainty;

[0076] - A thick grey and / or opaque line behind the spectrum. The thickness of the line can describe the measurement uncertainty. As an alternative, this can also be shown with a changing color or transparency of the measurement points with greater / less probability.

[0077] - textual information, for example in an appropriate table.

[0078] Although the present invention has been described above by preferred embodiments, the present invention is not limited thereto but can be modified in many ways. In particular, the present invention can be changed or modified in various ways without departing from the core of the present invention.

[0079] Reference numerals list

[0080] 10 Customer Portal Products

[0081] 11 Input terminals

[0082] 12 Database

[0083] 13 Controller

[0084] 14 Human-Machine Interface

[0085] 15. Measuring equipment

[0086] 16 Instruments

[0087] 16a Test

[0088] 17 Instruments

[0089] 17a Test

[0090] 18 Instruments

[0091] 18a Test

[0092] 19 Output Module

[0093] 20 Data Sheets

[0094] 21 (Intelligent) Interface

[0095] 22 Software Modules

[0096] 23 Screen or display

[0097] 24 Database

[0098] Steps S1-S7

[0099] X Performance Metrics

Claims

1. A method for creating and optimizing a custom data table for a predefined instrument type, the method comprising the following steps: Using the measurement device and controlled by the controller, determining quantities of performance metrics for a plurality of different instrument types, wherein each set of performance metrics for a particular instrument type is based on a plurality of different performance scenarios; storing the determined performance metric together with corresponding information of the instrument type and performance scenario in a database; providing a human-machine interface coupled to the database, wherein the human-machine interface is configured to credibly assist a user in executing a user request by employing a guided human-machine interaction process; receiving the user request via an input terminal of the human-machine interface, wherein the user request comprises a first request for a selected instrument or instrument type from among the plurality of different instrument types and a second request for a selected subset of performance scenarios from among the plurality of different performance scenarios for the selected instrument or instrument type; Through the human-machine interface, a customized data table is automatically generated based on the user request by extracting information of the selected instrument type and the selected performance scenario subset and corresponding performance metrics from the database.

2. The method according to claim 1, further comprising: operating the selected instrument based on the selected subset of performance scenarios to obtain information on the performance metric under customized operating conditions, and The acquired information of the performance metric under the customized operating condition is fed back to at least one of the measurement device, the controller or the database to update the quantity determining the performance metric of the plurality of different instrument types.

3. The method according to claim 2, wherein: The selected instruments are connected to the measuring device, the controller and the database, respectively, via communication links, and wherein the acquired information is forwarded immediately or after a predefined interval via the communication links.

4. The method according to claim 2, further comprising: Perform maintenance or repair on the selected instrument, reading the acquired information during said maintenance or repair, and The read acquired information is forwarded to the measuring device, the controller and the database respectively.

5. The method according to claim 1, further comprising: post-processing the performance metric, and The post-processed performance metrics are stored in the database together with corresponding information of the instrument type and performance scenario.

6. The method according to claim 1, further comprising: The extracted information used to generate the customized data table is displayed on a display.

7. The method according to claim 6, wherein: Before executing the step of generating a customized data table through the human-machine interface, displaying the extracted information is performed.

8. The method according to claim 1, wherein: The measurement scenario includes at least one of the following: Input power; Measurement bandwidth; The carrier frequency of the input signal; Signal type; Operating temperature; Setting compensation for specific standards; Internal settings.

9. The method according to claim 1, wherein: The generation steps also include: A step of providing a template comprising a first field for the selected subset of performance scenarios, a second field for corresponding performance metrics and a third field for information of the selected instrument type, and wherein the custom data table is generated by inserting information in corresponding fields.

10. The method according to claim 1, wherein: The steps to determine the quantity of the performance metric include: The performance metrics are provided by the instrument itself.

11. The method according to claim 1, further comprising: The user request from the human-machine interface is recorded, and additional performance scenarios are added to the requested performance scenarios.

12. The method according to claim 1, further comprising: The estimated performance of the device on untested performance scenarios is predicted by modeling the actual performance of the tested measurement scenarios.

13. The method according to claim 1, wherein: The data table generated is a spreadsheet.

14. The method according to claim 1, wherein: The human-machine interface includes network services, and the guided human-machine interaction process is based on network services.

15. The method according to claim 1, further comprising: Use artificial intelligence techniques to infer performance metrics.

16. The method according to claim 1, further comprising: A compact representation of the performance metric for all measurement scenarios on the instrument is stored.

17. The method according to claim 1, wherein: The instrument type is at least one of the following: Measuring instruments; Test equipment; Spectrum analyzer; Network analyzer; Signal generator; Oscilloscope.

18. A customer portal product for creating and optimizing custom data tables for predefined instrument types, the customer portal product comprising: a database configured to store performance metrics of instrument types together with corresponding information of the instrument types and performance scenarios; A human-machine interface coupled to the database, wherein the human-machine interface comprises an input terminal for receiving a user request, and wherein the human-machine interface is configured to: credibly assisting a user to execute the user request by means of a guided human-computer interaction process by receiving from the user a first request for a selected instrument or instrument type from a plurality of instrument types and a second request for a selected subset of performance scenarios from a plurality of different performance scenarios of the selected instrument or instrument type, and A customized data table is automatically generated based on the user request by extracting information of the selected instrument type and the selected performance scenario subset and corresponding performance metrics from the database.

19. The customer portal product of claim 18, further comprising: A feedback loop, wherein the feedback loop is configured to be connectable to a selected instrument, wherein during customized operating conditions, the selected instrument is operated based on the selected subset of performance scenarios, and wherein the feedback loop is also configured to feed back information of the performance metrics acquired under the customized operating conditions to the database to additionally store the acquired information in the database.

20. A non-transitory computer readable recording medium storing instructions executable by a computer processor, the instructions causing the computer processor to perform a method for creating and optimizing a customized data table for a predefined instrument type, the method comprising: determining quantities of performance metrics for a plurality of different instrument types, wherein each set of performance metrics for a particular instrument type is based on a plurality of different performance scenarios; storing the determined performance metric together with corresponding information of the instrument type and performance scenario in a database; receiving a user request via an input terminal of a human-machine interface, the human-machine interface being configured to credibly assist a user in executing the user request by means of a guided human-machine interaction process, wherein the user request comprises a first request for a selected instrument or instrument type from the plurality of different instrument types and a second request for a selected subset of performance scenarios from the plurality of different performance scenarios for the selected instrument or instrument type; A customized data table is automatically generated based on the user request by extracting information of the selected instrument type and the selected performance scenario subset and corresponding performance metrics from the database.

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