SENSOR FOR GENERATING POWER MANAGEMENT DATA

Abstract

Sensor having a control unit, configured to analyze data available to the sensor, in particular measurement data of the sensor, for generating power management data, the power management data being configured to for select a power saving mode from a plurality of available power saving modes of a wireless module (103) of the sensor and/or for controlling the times of measurement intervals of the sensor and/or for power management of the sensor.

Claims

1. A sensor, comprising: a control unit configured to analyze data available to the sensor, in particular measurement data of the sensor, to generate power management data; and a wireless module configured to transmit measurement data, wherein the power management data is configured to select a power saving mode of the wireless module from a plurality of available power saving modes and/or to control the times of measurement intervals of the sensor and/or to power manage the sensor.

2. The sensor according to claim 1, wherein the plurality of available power saving modes include a Power Saving Mode (PSM).

3. The sensor according to claim 1, wherein the plurality of available power saving modes include an extended discontinuous reception (eDRX) mode.

4. The sensor according to claim 1, wherein the plurality of available power saving modes include disabling the wireless module.

5. The sensor according to claim 1, wherein the control unit, when analyzing the data available from the sensor and selecting the power saving mode from the plurality of available power saving modes, takes into account the energy required when operating the power saving mode.

6. The sensor according to claim 1, wherein the control unit, when analyzing the data available from the sensor and selecting the power saving mode from the plurality of available power saving modes, takes into account the maximum allowable duration in the power saving mode until communication must occur again.

7. The sensor according to claim 1, wherein the control unit, when analyzing the data available from the sensor and selecting the power saving mode from the plurality of available power saving modes, takes into account the power required for re-registration or re-dialing into the communication network.

8. The sensor according to claim 1, wherein the control unit, when analyzing the data available from the sensor and when selecting the power saving mode from the plurality of available power saving modes, takes into account external influences, such as temperature, utilization of a radio channel, or a movement of the sensor.

9. The sensor according to claim 1, wherein the control unit, when analyzing the data available from the sensor and selecting the power saving mode from the plurality of available power saving modes, takes into account the frequency of the current measurements.

10. A control unit for a sensor, configured to analyze data available to the sensor, in particular measurement data of the sensor, for generating power management data, wherein the power management data is configured to select a power saving mode from a plurality of available power saving modes of a wireless module of the sensor and/or to control the times of measurement intervals of the sensor and/or to power manage the sensor.

11. A control unit according to claim 10, wherein the control unit is arranged remotely from the sensor.

12. A measuring system configured to autonomously generate power management data for controlling measuring intervals and for power management of sensors, comprising: the sensor according to claim 1; and the control unit according to claim 10 and/or a computing unit, each configured to store the power management data and to forward the stored power management data to a second sensor of the measuring system.

13. A method for scheduling measurement intervals and power management of a sensor, the method comprising: analyzing data available to the sensor, in particular measurement data from the sensor, to generate power management data, wherein the power management data is configured to select a power saving mode from a plurality of available power saving modes of a wireless module of the sensor and/or to control the times of measurement intervals of the sensor and/or to power manage the sensor.

14. A program element that, when executed on a control unit or a computing unit of a sensor, instructs the control unit or the computing unit to perform the following step: analyzing data available to the sensor, in particular measurement data from the sensor, to generate power management data, wherein the power management data is configured to select a power saving mode from a plurality of available power saving modes of a wireless module of the sensor and/or to control the times of measurement intervals of the sensor and/or to power manage the sensor.

15. (canceled)

16. (canceled)

Description

BRIEF DESCRIPTION OF THE FIGURES

[0040] FIG. 1 shows a time diagram of measurement intervals of two sensors.

[0041] FIG. 2 shows another time diagram of measurement intervals.

[0042] FIG. 3 shows an overview with examples of possible data sources for self-learning sensors.

[0043] FIG. 4 shows a measuring system according to an embodiment.

[0044] FIG. 5 shows a flow diagram of a process according to an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

[0045] FIG. 1 shows a time diagram of the measurement intervals 105 of a sensor before the self-learning process, as well as the measurement intervals 106 of a, for example self-learning, sensor during or after a self-learning process with intelligent power management described above.

[0046] The measurement curve 107 shows the course of the measurement data recorded by the sensor (for example a level, a pressure or a flow) as a function of time. Over the weekdays Monday to Sunday, the level decreases, remaining constant from Friday afternoon to Monday morning.

[0047] The sensor “without experience” is operated in a fixed time grid, with constant time intervals between the individual measuring intervals 105. This ensures intensive energy consumption and can result in regular, premature replacement of used batteries or accumulators. This requires maintenance effort or even a new purchase or reassembly of the sensors if replacement of the energy accumulators is not possible.

[0048] Through the self-learning process, if necessary, the sensor learns not to measure according to a fixed rigid time pattern, but only when a measurement appears necessary. This significantly reduces the energy consumption of the sensors.

[0049] In this way, a low-maintenance and energy-saving sensor system (measuring system) with possibly self-learning sensors can be implemented, whereby each sensor calculates or receives its own power management data, which is regularly adapted to the measuring environment.

[0050] The measurement intervals 106 show that the sensor has learned “with experience” to measure only in those time intervals during which the measurement data also change, i.e. the curve 107 has a slope and equals zero (because the level falls). No measurement is made during the plateaus.

[0051] FIG. 2 shows another example of the time distribution of the measurement intervals. Here, too, measurements are only taken in those time intervals in which the fill level drops. The lower the fill level is (especially on Friday), the more frequently measurements are taken during emptying to avoid the container running empty.

[0052] The measurements are triggered by intelligent measurement intervals and power management.

[0053] Thus, the energy consumption of the complete measuring point can be significantly reduced. On non-working days, or when a tank is in the storage of the silo filler, no measurement takes place.

[0054] The possibly self-learning measuring intervals can be generated, for example, by the following data:

[0055] Analysis of own level measurement data (day, night, pause times, tank content (fewer measurements when the tank is full), emptying process (fewer measurements when small quantities are taken));

[0056] Analysis of measurement data from a sensor network;

[0057] Analysis by internal or external sensor technology (for example, environmental, positional or location data);

[0058] Analysis by external signals from external actuators (for example pump), controllers or mobile devices;

[0059] Analysis of preset settings or calendars (for example, weekends, holidays, company vacations).

[0060] The sensor may be set up to learn the optimal times to measure independently through experience from the above data.

[0061] This can extend battery life and/or sensor life.

[0062] The sensor becomes smarter and more effective in its energy savings through its self-learning process and the increasingly long period of self-learning.

[0063] It can be provided that the sensor automatically adjusts the time and length of the measurement interval as well as the frequency of measurements in this measurement interval accordingly in case of unforeseen level changes. An example of this is temporary Saturday work. On subsequent Saturdays, a measurement is carried out until no more level changes occur on Saturdays.

[0064] In particular, it can be provided that the experience values of a sensor are transferred to other sensors of the customer. The experience values of the sensor(s) can be stored locally or decentrally in a cloud for further processing. Triggering the measurement by intelligent measurement intervals and power management can be used in stand-alone sensors with energy storage as well as in wired sensors. The sensors can be permanently installed or mobile.

[0065] The module for generating intelligent measuring intervals and power management can be permanently integrated in the sensor or used as an extension of existing measuring points.

[0066] FIG. 3 shows examples of possible data sources for a possible self-learning sensor 100.

[0067] One possible data source for the data available from the sensor that is used for the analysis is sensors own data, such as measured values, information about emptying processes and filling processes.

[0068] Another example is the data available in an external data storage, for example in a cloud. This is, for example, calendar entries, calendar data or data from other sensors.

[0069] Another example is data and signals from external actuators (for example, “pump running” or “plant control”).

[0070] Another example is environmental data, such as temperature, wind, rain, snow.

[0071] Another example is position and location data, such as information on whether the sensor is installed horizontally or vertically, or whether the container is lying or standing upright, or whether the container is on a construction site or in a warehouse.

[0072] Another example is mobile device data, such as operator presence or a trigger signal via an app.

[0073] FIG. 4 shows a measurement system with several optionally self-learning sensors 100, a new sensor 300, a control unit 101 located in one of the optionally self-learning sensors, another control unit 101 located outside the optionally self-learning sensors, and a central processing unit 200.

[0074] The computer unit is set up to receive data from all sensors and to analyze it centrally. It can also be set up to collect the data described with regard to FIG. 3 and to include it in the analysis in order to generate individual power management data for each sensor, which can then be transmitted to the sensors. In particular, the sensor 100 has a wireless module 103 that is used for transmitting measurement data.

[0075] Stand-alone sensors 100 with mobile wireless modules (mobile radio modems, mobile radio chips for, for example, “NBIoT-”, “LTEM1-”, etc.) must dial in to the network operator before sending data for the first time. This involves a data connection and registration with the corresponding network operator, via a cell tower. As long as the device is registered in the network, there is no need to dial in again, which saves energy. The wireless module 103 must be permanently supplied with power in order to be able to communicate regularly with the radio mast.

[0076] If no data communication is required for a longer period of time, the wireless module can be set to a power saving mode (e.g. eDRX, PSM, . . . ), which can reduce the required current of the wireless module from milliamps to a few microamps. These modes also eliminate the need for new dial-up in the cellular network.

[0077] For stand-alone sensors, this significantly increases the battery life. With mains-powered (230 V) sensors or sensors supplied by an interface (4-20 mA), the power consumption is reduced.

[0078] If the sensor 100 is not needed for a very long time, it is advantageous to completely deactivate the wireless module 103 in order to save the power consumption of a few microamperes in power saving mode. However, this makes it necessary to re-register with the cellular network before sending data.

[0079] Stand-alone sensors usually only send data via mobile radio at specified times. For example, every two hours from 8:00 a.m. to 4:00 p.m. on weekdays. At night and on weekends, however, not at all or only every eight hours. This can make it advantageous to switch off or deactivate the mobile wireless module for a long idle period.

[0080] One or more of the following factors can be included in deciding which of the power saving modes to use or turn off. Not only the current value of a factor, but also the historical/old value(s), as well as the values that can be expected in the future, can be taken into account:

[0081] 1. Power saving modes available (eDRX, PSM or . . . )

[0082] 2. Required energy in the respective power saving mode; this can be measured/measured during operation or be an expected value (default).

[0083] 3. Mobile technology used (NB-IoT, LTE-M1, . . . ).

[0084] 4. Desired/scheduled time in power saving mode.

[0085] 5. Maximum permissible duration in the respective power saving mode until communication has to take place again (determined by the network operator).

[0086] 6. Band used for communication (is the band to be used known? Number of bands tested for dial-up; Different power requirements for different bands).

[0087] 7. Energy required for a new registration/dial-in (measured/measured during operation; expected value (default); in simplified form, the time required for a dial-in process can be used here (measured or specified)).

[0088] 8. Transmission power during a dial-up.

[0089] 9. Reception quality of the mobile radio link.

[0090] 10. Which power saving mode is more advantageous for the power supply? Is the power supply designed or efficient (efficiency) for the low current demand in power saving mode? For example, can passivation of a lithium thionyl chloride battery be prevented?

[0091] 11. External influences, such as temperature, utilization of the radio channel, movement of the sensor and an associated radio cell change. Is the sensor moving right now? Is the sensor likely or certain to move?

[0092] The factors listed above may also change the selection of the power saving mode to be used (eDRX, PSM, etc.).

[0093] Thus, a method is provided for decision making of selecting power saving modes or disabling cellular modules to optimize the runtime of stand-alone sensors or to reduce power consumption for continuously powered sensors.

[0094] For example, the control unit 101 is programmed as follows: Data is sent by cellular radio every two hours on weekdays from 8:00 am to 4:00 pm. On weekdays from 4:00 p.m. to 8:00 a.m., data is sent every four hours. On weekends and holidays, the rhythm between sending data is eight hours.

[0095] By using several of the above factors, it was calculated by the sensor or the control unit that the power saving mode PSM in a two-hour rhythm provides an advantage in terms of energy. At a rhythm of four hours, the device is operated in power saving mode eDRX. From a transmission pause of six hours, the wireless module is deactivated to save the quiescent current of several microamperes.

[0096] FIG. 5 shows a flowchart of a method according to one embodiment. In step 501, a large amount of data is collected. In step 502, this data is analyzed centrally (or it is analyzed by a sensor) and in step 503, power management data is generated therefrom. This power management data has commands for selecting a power saving mode of the wireless module from a plurality of available power saving modes and/or for controlling the times of measurement intervals of the sensors and/or for power management of the sensors.

[0097] Supplementally, it should be noted that “comprising” and “having” do not exclude other elements or steps, and the indefinite articles “a” or “an” do not exclude a plurality. It should further be noted that features or steps that have been described with reference to any of the above embodiments may also be used in combination with other features or steps of other embodiments described above. Reference signs in the claims are not to be regarded as limitations.