Fluid composition sensor

Abstract

One or more computing devices, systems, and / or procedures are provided. In one example, a procedure includes measuring a first charge generated by a first excitation signal and transferred to a first electrode attached to a container holding a test fluid during a first time interval; measuring a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval; and determining a parameter of the test fluid based on a first charge transfer curve generated based on the first and second charges.

Description

[0001] Various sensing techniques can be used to detect the level or property of a fluid in a container. There is a growing market for combined fluid level and fluid characterization in the automotive, Internet of Things (IoT), and consumer sectors. For example, some applications for fluid level and fluid property sensing include systems for windshield washer fluid, fuel, water hardness levels for coffee machines, smart sensing in refrigerators, and / or similar devices. Many of these applications require non-contact sensing, where there is no electrical contact between the sensor and the fluid.

[0002] One task is to improve on known approaches.

[0003] This problem is solved according to the features of the independent claims. Preferred embodiments can be found in particular in the dependent claims.

[0004] The examples proposed herein can be based on at least one of the following solutions. In particular, combinations of the following features can be used to achieve a desired result. The features of claims in one category (e.g., method, device, system, storage medium, etc.) can be combined with any features of a claim in one of the other categories.

[0005] A procedure is proposed that includes the following steps: - Measuring a first charge generated by a first excitation signal and transferred during a first time interval to a first electrode attached to a container holding a test fluid; - Measuring a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval; - Determining a parameter of the test fluid based on a first charge transfer curve generated based on the first charge and the second charge.

[0006] It is a further development that determining the parameter of the test fluid involves determining the parameter using a fluid classification model based on the first charge transfer curve.

[0007] It is a further education course that - the fluid classification model includes a neural network model, - the procedure includes training the neural network model using a first reference charge transfer curve for a reference fluid with a first mass state and a second reference charge transfer curve for the reference fluid with a second mass state that is different from the first mass state.

[0008] It is a further education course that - the fluid classification model includes a neural network model, - the procedure includes training the neural network model using a first reference charge transfer curve associated with a first reference fluid with a first known property, and a second reference charge transfer curve associated with a second reference fluid with a second known property that is different from the first known property.

[0009] It is further information that the procedure also includes the following steps: - Placing the first reference fluid in the container; - Generating the first reference charge transfer curve by: - Measuring a third charge, generated by a third excitation signal and transferred to the first electrode during a third time interval; - Measuring a fourth charge, generated by a fourth excitation signal and transferred to the first electrode during a fourth time interval; - Generating the first reference charge transfer curve based on the third charge and the fourth charge.

[0010] It is a further development that measuring the first charge involves integrating a current supplied to the first electrode during the first time interval.

[0011] It is further information that the procedure includes the following steps: - Measuring a third charge generated by the first excitation signal and transferred to a second electrode attached to the container during the first time interval; - Measuring a fourth charge, which is generated by the second excitation signal and transferred to the second electrode during the second time interval; - Generating a second charge transfer curve based on the third charge and the fourth charge, wherein: - Determining the parameter of the test fluid based on the first charge transfer curve includes determining the parameter of the test fluid based on the first charge transfer curve and the second charge transfer curve.

[0012] It is a further development requirement that determining the parameter of the test fluid includes determining at least one of the following: - a conductivity, - a permittivity, - an ethanol concentration, - a diesel exhaust treatment fluid concentration, - water hardness.

[0013] It is a further education course that includes the following procedure: - Controlling a device based on the parameter of the test fluid.

[0014] It is a further education course that includes the following procedure: - Determine that an upper surface of the test fluid in the container is located at a higher height than a second electrode attached to the container; - Connecting the first electrode in series with the second electrode.

[0015] A system is also proposed that includes the following: - a container; - a first electrode attached to the container; - a signal generator configured to apply a first excitation signal to the first electrode during a first time interval and to apply a second excitation signal to the first electrode during a second time interval; - a first device configured to measure a first charge generated by the first excitation signal and transferred to the first electrode during the first time interval, and to measure a second charge generated by the second excitation signal and transferred to the first electrode during the second time interval; - a processor that is set up to determine a parameter of a test fluid in the container based on a first charge transfer curve generated based on the first charge and the second charge.

[0016] It is a further development that the processor is set up to determine the parameter using a fluid classification model based on the first charge transfer curve.

[0017] It is a further development that the fluid classification model includes a neural network model.

[0018] It is a further development that the first device includes a current integrator.

[0019] It is a further education that the system encompasses: - a second electrode attached to the container, wherein - the first device is set up to measure a third charge generated by the first excitation signal and transferred to a second electrode attached to the container during the first time interval, to measure a fourth charge generated by the second excitation signal and transferred to the second electrode during the second time interval, and to generate a second charge transfer curve based on the third charge and the fourth charge; - the processor is set up to determine the parameter of the test fluid in the container based on the first charge transfer curve and the second charge transfer curve.

[0020] It is further training that the parameter of the test fluid is at least one of the following: - a conductivity, - a permittivity, - an ethanol concentration, - a diesel exhaust treatment fluid concentration, - a water hardness.

[0021] It is a further development that the processor is set up to control a second device based on the parameter of the test fluid.

[0022] Furthermore, a non-volatile, computer-readable medium is proposed that stores instructions which, when executed, support the performance of work processes that include the following: - Receiving an initial charge generated by an initial excitation signal and transferred during an initial time interval to a first electrode attached to a container holding a test fluid; - Receiving a second charge, generated by a second excitation signal and transferred to the first electrode during a second time interval; - Determining a parameter of the test fluid based on a charge transfer curve generated based on the first charge and the second charge.

[0023] It is a further training course that covers the following work processes: - Determining the parameter of the test fluid using a fluid classification model based on the charge transfer curve.

[0024] It is a further training course that includes determining the parameter of the test fluid: - Determine at least one of the following: conductivity, permittivity, ethanol concentration, diesel exhaust treatment fluid concentration, or water hardness.

[0025] The following overview is provided to present a selection of concepts in a simplified form, which are further described in detail below. This summary is not intended to identify any principal factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of protection of the claimed subject matter.

[0026] In one embodiment of the techniques presented here, a method comprises measuring a first charge generated by a first excitation signal and transferred to a first electrode attached to a container holding a test fluid during a first time interval, measuring a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval, and determining a parameter of the test fluid based on a first charge transfer curve generated based on the first charge and the second charge.

[0027] In one embodiment of the techniques presented here, a system comprises means for measuring a first charge generated by a first excitation signal and transferred to a first electrode attached to a container holding a test fluid during a first time interval, means for measuring a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval, and means for determining a parameter of the test fluid based on a first charge transfer curve generated based on the first charge and the second charge.

[0028] In one embodiment of the techniques presented here, a system comprises a container, a first electrode attached to the container, a signal generator configured to apply a first excitation signal to the first electrode during a first time interval and a second excitation signal to the first electrode during a second time interval, a first device configured to measure a first charge generated by the first excitation signal and transferred to the first electrode during the first time interval, and to measure a second charge generated by the second excitation signal and transferred to the first electrode during the second time interval, and a processor configured to determine a parameter of a test fluid in the container based on a first charge transfer curve generated based on the first charge and the second charge.

[0029] In one embodiment of the techniques presented here, a non-volatile, computer-readable medium stores instructions which, when executed, support the performance of operations that include receiving a first charge generated by a first excitation signal and transferred to a first electrode attached to a container holding a test fluid during a first time interval, receiving a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval, and determining a parameter of the test fluid based on a charge transfer curve generated based on the first charge and the second charge.

[0030] To achieve the foregoing and related objectives, the following description and accompanying drawings present certain illustrative aspects and implementations. These indicate just a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following description when considered in conjunction with the accompanying drawings. Fig. Figure 1 is a diagram illustrating an example system for fluid characterization according to some embodiments. Fig. Figure 2 is a diagram of an equivalent circuit for a container detection arrangement according to some embodiments. Fig. Figure 3 is a diagram of a charge transfer curve according to some embodiments. Fig. Figure 4 is a diagram of a neural network for fluid characterization according to some embodiments. Fig. Figure 5 is a diagram of a processing unit according to some embodiments. Fig. Figure 6 is a diagram illustrating a method for characterizing a fluid according to some embodiments. Fig. Figure 7 illustrates an embodiment of a computer-readable medium according to some embodiments.

[0031] The claimed subject matter is now described with reference to the drawings, using the same reference numerals to consistently refer to the same elements. Numerous specific details are set forth in the following description for explanatory purposes, in order to provide a thorough understanding of the claimed subject matter. However, it may be obvious that the claimed subject matter can be exercised without these specific details. In other cases, well-known structures and devices are shown in block diagram form to facilitate a description of the claimed subject matter.

[0032] It is understood that the following description of the embodiments is not to be understood in a limiting sense. The scope of protection of the present disclosure is not to be limited by the embodiments described below or by the drawings, which are to be understood as illustrative only. The drawings are to be regarded as schematic representations, and the elements illustrated in the drawings are not necessarily drawn to scale. Rather, the various elements are depicted in such a way that their function and general purpose are apparent to a person skilled in the art.

[0033] All numerical values ​​in the detailed description and claims herein are modified by "about" or "approximately" to reflect the stated value and account for experimental errors and variations that average professionals would expect.

[0034] Fluid characterization is used in automotive, consumer, and industrial sectors. In automotive applications, containers or reservoirs may be used for windshield washer fluid, fuel, or diesel exhaust aftertreatment fluid (AdBlue). In consumer applications, containers may be used for appliances such as refrigerators or coffee machines. In industrial applications, containers may include fermentation tanks, milk storage tanks, containers for technical fluids (e.g., oil or hydraulic fluid), process fluid tanks, or similar applications. Some applications use level sensing, some use fluid characterization, and some use both. In some applications, the container is not used for fluid storage but rather as a chamber for characterizing a fluid supplied to a device or tool.Examples of fluid characterization parameters include water hardness, ethanol concentration in wine, beer, or spirits, fat content in milk, or fluid contamination levels for oil or hydraulic fluid. Knowledge of fluid composition is advantageous for preventing waste of consumables, extending machine lifespan, controlling industrial processes, preventing the clogging of heating elements by solid carbonates, determining contamination levels and detergent concentrations to control detergent and water consumption, controlling fermentation processes, determining the quality of technical fluids to ensure optimal fluid change intervals, and similar applications.

[0035] In some embodiments, a fluid characterization system measures charge transfer over a plurality of acquisition intervals to generate a charge transfer curve. For a given test fluid, the charge transfer curve is unique for certain fluid properties such as conductivity and permittivity. Therefore, the charge transfer curve can be used for fluid characterization, for example, to determine the hardness of water, the ethanol concentration, the degree of impurities, etc. In some embodiments, the test fluid is characterized using a neural network classifier that receives the conductivity curve as an input and produces a fluid characterization parameter as an output.

[0036] Fig. Figure 1 is a block diagram of a fluid characterization system 100 for non-contact fluid detection for characterizing a test fluid 102 in a container 104 according to some embodiments. Electrical properties of a test fluid 102 are represented by a distributed network of impedances. In some embodiments, the system 100 comprises analog processing blocks 106 and digital processing blocks 108. The analog processing blocks 106 are coupled to first electrodes 110 and second electrodes 112, the first electrodes 110 being located at first points on an outer surface of the container 104 containing the test fluid 102, and the second electrodes 104 being located at second points on the outer surface of the container 104, such as opposite the first points.In some embodiments, the first electrodes 110 and the second electrodes 112 are arranged in pairs at different heights on the container 104 to facilitate level detection. In other embodiments, the first electrodes 110 and the second electrodes 112 are arranged on the same side of the container 104 to facilitate sensor mounting. The container 104 has an ambient ground 114, which represents the grounding to surrounding objects, and wall impedances 116, 118, which represent electrical properties of the container 104, each characterized by a characteristic resistance and a characteristic capacitance.

[0037] The analog processing blocks 106 define a single-port network designed to couple a signal generator 120 and a current integrator 122, or one or more of the first electrodes 110, to determine a set of charge transfer measurements (e.g., a charge transfer curve 300) associated with the container 104 and the test fluid 102. The signal generator 120 produces an excitation signal, such as a square wave, and the current integrator 122 integrates the current supplied to one or more of the first electrodes 110 to determine a charge transferred over a selected time interval.

[0038] The digital processing blocks 108 comprise a controller 124, which controls the signal generator 120 to produce the excitation signal and receives the current integration measurements from the current integrator 122, and a fluid classifier 126, which characterizes the test fluid 102 based on the charge transfer curve. In some embodiments, the fluid classifier 126 uses a fluid classification model 128, which receives the charge transfer curve as an input and produces one or more fluid characterization parameters as an output. In some embodiments, the fluid classification model 128 comprises a neural network model trained using reference charge transfer curves of fluids with known properties. The controller 124 iterates the excitation and current measurement over a set of time intervals of varying lengths to generate the charge transfer curve.In some embodiments, only the lowest first electrode 110 is used for charge transfer measurements for fluid characterization, while all pairs of first electrode 110 and second electrode 112 are used for fluid level detection. In some embodiments, a charge transfer measurement is also used for level detection. In another embodiment, the pairs of first electrode 110 and second electrode 112 that are covered by the test fluid 102 (i.e., the upper surface of the test fluid 102 is higher than the pairs of first electrode 110 and second electrode 112) are connected in parallel by routing circuits 124 to increase the effective area available for detection.

[0039] The current integrator 122 uses a self-capacitance measurement principle to determine the charge transferred to one or more first electrodes 110. In some embodiments, a second current integrator 123 is provided in series with the second electrodes to apply counter-capacitance in order to determine the charge transferred from the first electrodes 110 to the second electrodes 112. A first charge transfer curve can be generated based on data from the first integrator 122, and a second charge transfer curve can be generated based on data from the second integrator 123. The self-capacitance and counter-capacitance charge transfer curves can be used to characterize test fluid 102. The first charge transfer curve and the second charge transfer curve can be provided as inputs to the fluid classifier 126 and the fluid classification model 128.

[0040] In some embodiments, the fluid characterization system 100 comprises one or more sensors 130 that measure a fluid property, such as density, or an environmental property, such as temperature. The fluid classifier 126 can use information from the one or more sensors 130 for fluid characterization.

[0041] Fig. Figure 2 is a diagram of an equivalent circuit 200 for the container 104 and the test fluid 102 according to some embodiments. The resistance and capacitance of the test fluid 102 are represented by a resistor 202 (Rw) and a capacitor 204 (Cw). The wall capacitance is represented by a capacitor 206 (Cp), and the switch resistance is represented by a resistor 208 (Rsw). The signal generator 120 produces a rectangular excitation signal. The properties of the test fluid 102 influence the values ​​of the resistor 202 and the capacitor 204, which changes the response of the circuit 200 to the excitation signal.

[0042] Fig. Figure 3 is a diagram of a set of charge transfer curves 300 for fluids with different properties according to some embodiments. The charge transfer curves 300 are distinguished by fluid parameters such as conductivity and / or permittivity, which influence the values ​​of Rw and Cw for a specific wall capacitance, Cp, and a specific switch resistance, Rsw. It should be noted that the curves are distinguishable for different fluid parameters. Each data point of the charge transfer curve 300 represents the charge transferred through the electrodes 110, 112, as measured by the current integrator 122 (and optionally the current integrator 123) for a specific time interval. In some embodiments, the time intervals are defined between a minimum time interval value, t int_min , and a maximum time interval value, t int_max , varies. The maximum charge Q max depends on the wall capacitance Cp.

[0043] In some embodiments, the fluid classifier 126 receives charge transfer data points from the controller 124 for the plurality of time intervals and accumulates the charge transfer data points to generate the charge transfer curve 300 for a test fluid 102 with unknown properties. The fluid classifier 126 uses the fluid classification model 128 to produce an output that specifies a fluid parameter such as conductivity, permittivity, ethanol concentration, diesel exhaust treatment fluid concentration, water hardness, a combination of output parameters such as both conductivity and permittivity, or another parameter or combination of parameters.

[0044] In some embodiments, the fluid classification model 128 is a neural network model trained using reference charge transfer curves of reference fluids with known parameter values, such as known conductivity and / or permittivity parameters. The fluid classification model 128 can also be trained using different mass states for the reference fluid or the container 104. For example, a reference charge transfer curve for a reference fluid with a known conductivity or permittivity value can be generated for each of a set of mass states, such as ungrounded, weakly grounded, strongly grounded, fluid-grounded, etc. The mass state influences the shape of the charge transfer curve. Training the fluid classification model 128 using different mass states allows the fluid classification model 128 to determine fluid parameters independently of the grounding state.Training charge transfer curves for known fluids can be generated using theoretical calculations or by using the Fluid Characterization System 100 to create a reference charge transfer curve for a fluid with known parameters. The training set can vary depending on the desired outputs. For example, if the desired output is only one parameter, the training set includes reference charge transfer curves with known values ​​of the output parameter. If the desired output is more than one parameter, the training set includes reference charge transfer curves with known values ​​for all of the output parameters, or multiple sets of reference charge transfer curves, each set having known values ​​for one of the output parameters.

[0045] Fig. Figure 4 is a diagram of a classifier 400 of a neural network according to some embodiments. At 402, the classifier 400 of the neural network receives an input 402, such as a charge transfer curve. The classifier 400 of the neural network comprises an input layer 404, a convolution layer 406, a rectified linear unit (ReLU) layer 408, a flatten layer 410, a dense layer 412, and a softmax layer 414, which generates an output 416 from the input 402. In some embodiments, the input layer 404 receives the data points of the charge transfer curve for the test fluid 102 as an input vector. In some embodiments, the convolution layer 406 extracts features of the charge transfer curve using a kernel and bias. In some embodiments, the convolution layer 406 implements a Conv2D function with a kernel of 6x2x1x4 and a bias of 4, but other configurations can be used.In some embodiments, the ReLU layer 408 replaces negative numbers in the filtered data with zeros. In some embodiments, the flatten layer 410 is used to convert all resulting 2-dimensional arrays from the convolution layer 406 into a continuous linear vector. In some embodiments, the dense layer 412 is a layer of neurons in which each neuron receives inputs from all neurons in the previous layer and is used to classify the charge transfer curve based on outputs from the convolution layer 406. In some embodiments, the softmax layer 414 is used as an activation function in the output layer that predicts a multinomial probability distribution.Output 416 represents one or more characterization parameters of the test fluid 102, such as conductivity, permittivity, ethanol concentration, diesel exhaust treatment fluid concentration, water hardness, a combination of output parameters such as both conductivity and permittivity, or another parameter or combination of parameters.

[0046] In some embodiments, the fluid classification model 128 is trained for a specific application, such as ethanol concentration determination. It is known that the conductivity and permittivity of an ethanol-water mixture depend on the weight percentage of ethanol. After the fluid classification model 128 has been trained using charge transfer curves of fluids with different ethanol concentrations and mass states, the fluid characterization system 100 can be used to detect the ethanol concentration of the test fluid 102 and output the percentage of ethanol. The ethanol percentage can be used to automatically control a fermentation system or to automatically generate messages when ethanol percentage thresholds are reached.

[0047] Similarly, the Fluid Classification Model 128 can be trained to detect water hardness for use by appliances such as coffee makers, washing machines, dishwashers, wet vacuums, water softeners, or other devices. The appliance may have an internal water softener or use settings that depend on the water hardness. The Fluid Characterization System 100 can be integrated into the appliance so that the appliance can activate or deactivate an internal water softener or select a setting based on the water hardness measured by the Fluid Characterization System 100. For example, if the Fluid Characterization System 100 detects that the incoming water is sufficiently soft (an in-house water softener is installed, providing soft water to the appliance), the appliance can deactivate the internal water softener.If the device uses a water softener, a recharge can be initiated in response to the Fluid Characterization System 100 detecting water hardness exceeding a threshold. Initiating recharging based on the measured water hardness reduces waste from unnecessary recharges.

[0048] In another embodiment, the fluid classification model 128 can be trained to identify an unknown fluid. In addition to the charge transfer curve, an additional fluid property, such as density, can be provided to the fluid classification model 128 as an input. After the fluid classification model 128 has been trained using charge transfer curves of fluids with different densities, the fluid characterization system 100 can be used to identify unknown fluids using a charge transfer curve and density information from the sensor.

[0049] Fig. Figure 5 is a diagram of a device 500 for implementing the fluid characterization system 100 according to some embodiments. In some embodiments, the device 500 comprises a bus 502, a processor 504, a memory 506 that stores software instructions or operations, an input device 508, an output device 510, a communication interface 512, and a power source 514, such as a battery. The processor 504 receives data from the current integrator 122 (and optionally the current integrator 123) and implements a software application that implements the controller 124 and the fluid classifier 126. The device 500 may have fewer components, additional components, different components, and / or a different arrangement of components than that shown in Figure 5. Fig. 5 illustrated examples are included.

[0050] According to some embodiments, the bus 502 includes a path that allows communication between the components of the device 500. The bus 502 may, for example, include a system bus, an address bus, a data bus, and / or a control bus. The bus 502 may also include bus drivers, bus arbitrators, bus interfaces, clock generators, etc. The processor 504 includes one or more processors, microprocessors, data processors, coprocessors, application-specific integrated circuits (ASICs), controllers, programmable logic devices, chipsets, field-programmable gate arrays (FPGAs), application-specific instruction set processors (ASIPs), system-on-chips (SoCs), central processing units (CPUs) (e.g., one or more cores), microcontrollers, and / or another type of component that interprets and / or executes instructions and / or data. The processor 504 may be hardware (e.g., a microprocessor, etc.).), a combination of hardware and software (e.g., a SoC, an ASIC, etc.) may be implemented, may include one or more memories (e.g., cache, etc.), etc.

[0051] In some embodiments, the processor 504 controls the overall operation or a part of the operation(s) performed by the controller 124 and the fluid classifier 126. The processor 504 performs one or more operations based on an operating system and / or various applications or computer programs (e.g., software). The processor 504 accesses instructions from the memory 506, from other components of the device 500, and / or from a source outside the device 500 (e.g., a network, another device, etc.). The processor 504 can perform an operation and / or a process based on various techniques, including, for example, multithreading, parallel processing, pipelining, nesting, etc.

[0052] In some embodiments, the Memory 506 includes one or more memory devices and / or one or more other types of storage media. For example, the Memory 506 may include one or more types of memory, such as random access memory (RAM), dynamic random access memory (DRAM), cache, read-only memory (ROM), programmable read-only memory (ROM), static random access memory (SRAM), single inline memory module (SIMM), dual inline memory module (DIMM), flash memory, and / or another suitable type of memory. The Memory 506 may include a hard disk, a magnetic disk, an optical disk, a magneto-optical disk, a solid-state disk, a storage medium based on a microelectromechanical system (MEMS), a nanotechnology-based storage medium, and / or another suitable disk. The Memory 506 may include drives for reading from and writing to the storage medium.The memory 506 can be located outside the device 500 and / or be removable from it, such as a Universal Serial Bus (USB) flash drive, a dongle, a hard drive, a mass storage device, offline storage, or another type of storage medium (e.g., a Compact Disc (CD), a Digital Versatile Disc (DVD), a Blu-ray Disc (BD), etc.). The memory 506 can store data, software, and / or applications related to the operation of the Activity Classifier 100.

[0053] The communication interface 512 allows the device 500 to communicate in a network with other devices, networks, systems, sensors, and / or the like. The communication interface 512 may include one or more wireless and / or wired interfaces. For example, the communication interface 512 may include one or more transmitters and receivers or transceivers. The communication interface 512 may operate according to a protocol stack and a communication standard. In some embodiments, the communication interface 512 includes an antenna. The communication interface 512 may include various processing logic or circuits (e.g., multiplexing / demultiplexing, filtering, amplification, conversion, error correction, etc.).In some embodiments, the 512 communication interface operates using a long-range wireless protocol, such as a cellular protocol or a WiFi protocol, a short-range protocol, such as BLUETOOTH™, or a wired protocol, such as Ethernet.

[0054] In some embodiments, the input device 508 allows input into the device 500. For example, the input device 508 may comprise a keyboard, a mouse, a display, a touchscreen, a non-touchscreen, a button, a switch, an input port, speech recognition logic, and / or another type of suitable visual, acoustic, or tactile input component. The input device 510 allows output from the device 500. For example, the output device 510 may comprise a loudspeaker, a display, a touchscreen, a non-touchscreen, a projected display, a light, an output port, and / or another type of suitable visual, acoustic, or tactile output component. In some embodiments, the output device 518 may be remote and communicate with the processor 504 using the communication interface 512.

[0055] In some embodiments, the fluid characterization parameter generated by the fluid characterization system 100 is used to modify an operating parameter of a remote device 516, for example, based on communication via the communication interface 512. If the remote device 516 is a unit, the operating parameter can be a mode of the remote device 516, such as a mode in which the water softener is activated, a mode in which the water softener is deactivated, or another mode. In some embodiments, the remote device 516 can perform an action based on the fluid characterization parameter, such as initiating a charging cycle of a water softener.In some embodiments, the remote device 516 can display a hint or notification based on the fluid characterization parameter, such as a hint to stop fermentation, a hint that a fluid parameter is outside permissible limits, or another hint.

[0056] Fig. Figure 6 is a flowchart illustrating an example procedure 600 for fluid characterization. In Figure 602, a first charge, generated by a first excitation signal and transferred to a first electrode 110 attached to a container 104 containing a test fluid 102 during a first time interval, is measured. In Figure 604, a second charge, generated by a second excitation signal and transferred to the first electrode 110 during a second time interval, is measured. In Figure 606, a parameter of the test fluid is determined based on a charge transfer curve generated from the first and second charges.

[0057] Fig.Figure 7 illustrates an embodiment 700 of a computer-readable medium 702 according to some embodiments. One or more embodiments include a computer-readable medium comprising processor-executable instructions designed to implement one or more of the techniques presented herein. The embodiment 700 comprises a non-volatile computer-readable medium 702 (e.g., a CD-R, DVD-R, flash drive, disk of a hard disk drive, etc.) on which computer-readable data 704 is encoded. This computer-readable data 704, in turn, comprises a set of processor-executable computer instructions 706, which, when executed by a computing device 708 comprising a reader 710 for reading the processor-executable computer instructions 706 and a processor 712 for executing the processor-executable computer instructions 706, are designed to support operations according to one or more of the principles set forth herein.In some embodiments, the processor-executable computer instructions 706, when executed, are designed to support the execution of a method 714, such as at least part of the aforementioned method(s). In some embodiments, the processor-executable computer instructions 706, when executed, are designed to enable the implementation of a system, such as at least part of one or more of the aforementioned systems. Those skilled in the art can design many such computer-readable media that are configured to operate according to the techniques presented here.

[0058] In one embodiment of the techniques presented here, a method comprises measuring a first charge generated by a first excitation signal and transferred to a first electrode attached to a container holding a test fluid during a first time interval, measuring a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval, and determining a parameter of the test fluid based on a first charge transfer curve generated based on the first charge and the second charge.

[0059] In one embodiment of the techniques presented here, determining the parameter of the test fluid includes determining the parameter using a fluid classification model based on the first charge transfer curve.

[0060] In one embodiment of the techniques presented here, the fluid classification model comprises a neural network model, and the method includes training the neural network model using a first reference charge transfer curve for a reference fluid with a first mass state and a second reference charge transfer curve for the reference fluid with a second mass state that is different from the first mass state.

[0061] In one embodiment of the techniques presented here, the fluid classification model comprises a neural network model, and the method comprises training the neural network model using a first reference charge transfer curve associated with a first reference fluid having a first known property, and a second reference charge transfer curve associated with a second reference fluid having a second known property that is different from the first known property.

[0062] In one embodiment of the techniques presented here, the method comprises placing the first reference fluid in the container, generating the first reference charge transfer curve by measuring a third charge generated by a third excitation signal and transferred to the first electrode during a third time interval, measuring a fourth charge generated by a fourth excitation signal and transferred to the first electrode during a fourth time interval, and generating the first reference charge transfer curve based on the third charge and the fourth charge.

[0063] In one embodiment of the techniques presented here, measuring the first charge includes integrating a current supplied to the first electrode during the first time interval.

[0064] In one embodiment of the techniques presented here, the method comprises measuring a third charge generated by the first excitation signal and transferred to a second electrode attached to the container during the first time interval, measuring a fourth charge generated by the second excitation signal and transferred to the second electrode during the second time interval, and generating a second charge transfer curve based on the third charge and the fourth charge, wherein determining the parameter of the test fluid based on the first charge transfer curve and the second charge transfer curve.

[0065] In one embodiment of the techniques presented here, determining the parameter of the test fluid includes determining at least one of a conductivity, permittivity, ethanol concentration, diesel exhaust treatment fluid concentration or water hardness.

[0066] In one embodiment of the techniques presented here, the method comprises controlling a device based on the parameter of the test fluid.

[0067] In one embodiment of the techniques presented here, the method comprises determining that an upper surface of the test fluid in the container is located at a higher height than a second electrode attached to the container, and connecting the first electrode in series with the second electrode.

[0068] In one embodiment of the techniques presented here, a system comprises a container, a first electrode attached to the container, a signal generator configured to apply a first excitation signal to the first electrode during a first time interval and a second excitation signal to the first electrode during a second time interval, a first device configured to measure a first charge generated by the first excitation signal and transferred to the first electrode during the first time interval, and to measure a second charge generated by the second excitation signal and transferred to the first electrode during the second time interval, and a processor configured to determine a parameter of a test fluid in the container based on a first charge transfer curve generated based on the first charge and the second charge.

[0069] In one embodiment of the techniques presented here, the processor is designed to determine the parameter using a fluid classification model based on the first charge transfer curve.

[0070] In one embodiment of the techniques presented here, the fluid classification model comprises a neural network model.

[0071] In one embodiment of the techniques presented here, the first device comprises a current integrator.

[0072] In one embodiment of the techniques presented here, the system comprises a second electrode attached to the container, wherein the first device is configured to measure a third charge generated by the first excitation signal and transferred to a second electrode attached to the container during the first time interval, to measure a fourth charge generated by the second excitation signal and transferred to the second electrode during the second time interval, and to generate a second charge transfer curve based on the third charge and the fourth charge, and the processor is configured to determine the parameter of the test fluid in the container based on the first charge transfer curve and the second charge transfer curve.

[0073] In one embodiment of the techniques presented here, the parameter of the test fluid comprises at least one of a conductivity, permittivity, ethanol concentration, diesel exhaust treatment fluid concentration or water hardness.

[0074] In one embodiment of the techniques presented here, the processor is designed to control a second device based on the parameter of the test fluid.

[0075] In one embodiment of the techniques presented here, a non-volatile, computer-readable medium stores instructions which, when executed, support the performance of operations that include receiving a first charge generated by a first excitation signal and transferred to a first electrode attached to a container holding a test fluid during a first time interval, receiving a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval, and determining a parameter of the test fluid based on a charge transfer curve generated based on the first charge and the second charge.

[0076] In one embodiment of the techniques presented here, the work processes include determining the parameter of the test fluid using a fluid classification model based on the charge transfer curve.

[0077] In one embodiment of the techniques presented here, determining the parameter of the test fluid includes determining at least one of a conductivity, permittivity, ethanol concentration, diesel exhaust treatment fluid concentration or water hardness.

[0078] The term "computer-readable media" can include communication media. Communication media typically implement computer-readable instructions or other data in a "modulated data signal," such as a carrier wave or other transport mechanism, and include any information delivery medium. The term "modulated data signal" can include a signal in which one or more characteristics are set or modified in such a way as to encode information in the signal.

[0079] Any aspect or design described herein as an “example,” and / or the like, is not necessarily to be considered advantageous over other aspects or designs. Instead, the use of the word “example” is intended to present a possible aspect and / or implementation that may pertain to the techniques presented herein. Such examples are not necessary for such techniques, nor are they intended to be limiting. Different embodiments of such techniques may include such an example alone or in combination with other features, and / or may vary and / or omit the illustrated example.

[0080] Various operations of embodiments are provided herein. In one embodiment, one or more of the described operations may be computer-readable instructions, which may be stored on one or more computer-readable media. When executed by a computing device, these instructions cause the computing device to perform the described operations. The order in which some or all of the operations are described is not to be understood as implying that these operations are necessarily dependent on the order in which they are performed. Alternative orders may be implemented without deviating from the scope of the disclosure. Furthermore, it is understood that not all operations are necessarily present in every embodiment provided herein. It is also understood that not all operations are necessary in some embodiments.

[0081] Although the subject matter of the invention has been described in terms specific to structural features and / or methodological processes, it is understood that the subject matter of the invention defined in the appended claims is not necessarily limited to the specific features and processes described above. Rather, the specific features and actions described above are disclosed as exemplary ways of implementing at least some of the claims.

[0082] As used in the present application, the expression "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is to say, unless otherwise specified or it is clear from the context, "X uses A or B" is intended to mean any of the natural inclusive permutations. That is to say, if X uses A; uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the above cases. Furthermore, the articles "a" and "an", as used in the present application and in the appended claims, can generally be understood to mean "one or more", unless otherwise specified or it is clear from the context that they refer to a singular form. Also, unless otherwise specified, "first", "second", or the like are not intended to imply any temporal or spatial aspect, any order, etc.Rather, such terms are merely used as identifiers, names, etc., for characteristics, elements, objects, etc. For example, a first and a second element generally correspond to element A and element B, or to two different or two identical elements, or to the same element.

[0083] Although the disclosure has been shown and described with reference to one or more implementations, equivalent variations and modifications will also become apparent to those skilled in the art upon reading and understanding this description and the accompanying drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of protection of the following claims. In particular, with regard to the various functions performed by the components described above (e.g., elements, resources, etc.), the terms used to describe such components, unless otherwise specified, shall correspond to any component that performs the specified function of the described component (which is, for example, functionally equivalent), even though it is not structurally equivalent to the disclosed structure that performs the function in the exemplary implementations of the disclosure illustrated herein.Furthermore, although a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such a feature may be combined with one or more other features of the other implementations, as may be desirable and advantageous for any given or particular application. Moreover, to the extent that the expressions "include," "having," "featuring," "with," or variants thereof are used either in the detailed description or the claims, such expressions shall be inclusive in a manner similar to the expression "comprising."

Claims

[1] Procedure comprising: - measuring a first charge generated by a first excitation signal and transferred to a first electrode attached to a container containing a test fluid during a first time interval; - measuring a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval; - Determining a parameter of the test fluid based on a first charge transfer curve generated based on the first charge and the second charge. [2] The method according to claim 1, wherein - determining the parameter of the test fluid comprises determining the parameter using a fluid classification model based on the first charge transfer curve. [3] Method according to claim 2, in which - the fluid classification model includes a neural network model, - the method comprises training the neural network model using a first reference charge transfer curve for a reference fluid having a first mass state and a second reference charge transfer curve for the reference fluid having a second mass state different from the first mass state. [4] Method according to claim 2, in which - the fluid classification model includes a neural network model, - the method comprises training the neural network model using a first reference charge transfer curve associated with a first reference fluid having a first known property and a second reference charge transfer curve associated with a second reference fluid having a second known property different from the first known property. [5] A method according to claim 4, comprising: - Placing the first reference fluid in the container; - Generate the first reference charge transfer curve by: - measuring a third charge generated by a third excitation signal and transferred to the first electrode during a third time interval; - measuring a fourth charge generated by a fourth excitation signal and transferred to the first electrode during a fourth time interval; - Generating the first reference charge transfer curve based on the third charge and the fourth charge. [6] Method according to one of the preceding claims, in which - measuring the first charge comprises integrating a current provided to the first electrode during the first time interval. [7] A method according to any one of the preceding claims, comprising: - measuring a third charge generated by the first excitation signal and transferred to a second electrode attached to the container during the first time interval; - measuring a fourth charge generated by the second excitation signal and transferred to the second electrode during the second time interval; - Generating a second charge transfer curve based on the third charge and the fourth charge, wherein: - determining the parameter of the test fluid based on the first charge transfer curve comprises determining the parameter of the test fluid based on the first charge transfer curve and the second charge transfer curve. [8] Method according to one of the preceding claims, in which - determining the parameter of the test fluid comprises determining at least one of the following: a conductivity, a permittivity, an ethanol concentration, a diesel exhaust treatment fluid concentration, a water hardness. [9] A method according to any one of the preceding claims, comprising: - Controlling a device based on the parameter of the test fluid. [10] A method according to any one of the preceding claims, comprising: - determining that an upper surface of the test fluid in the container is at a higher elevation than a second electrode attached to the container; - Connecting the first electrode in series with the second electrode. [11] System comprising: - a container; - a first electrode attached to the container; - a signal generator configured to apply a first excitation signal to the first electrode during a first time interval and to apply a second excitation signal to the first electrode during a second time interval; - a first device configured to measure a first charge generated by the first excitation signal and transferred to the first electrode during the first time interval, and to measure a second charge generated by the second excitation signal and transferred to the first electrode during the second time interval; - a processor configured to determine a parameter of a test fluid in the container based on a first charge transfer curve generated based on the first charge and the second charge. [12] System according to claim 11, wherein - the processor is configured to determine the parameter using a fluid classification model based on the first charge transfer curve. [13] System according to claim 12, wherein - the fluid classification model includes a neural network model. [14] System according to one of claims 11 to 13, in which - the first device comprises a current integrator. [15] A system according to any one of claims 11 to 14, comprising: - a second electrode attached to the container, wherein - the first device is configured to measure a third charge generated by the first excitation signal and transferred to a second electrode attached to the container during the first time interval, to measure a fourth charge generated by the second excitation signal and transferred to the second electrode during the second time interval, and to generate a second charge transfer curve based on the third charge and the fourth charge; - the processor is configured to determine the parameter of the test fluid in the container based on the first charge transfer curve and the second charge transfer curve. [16] System according to one of claims 11 to 15, in which - the parameter of the test fluid is at least one of the following: a conductivity, a permittivity, an ethanol concentration, a diesel exhaust treatment fluid concentration, a water hardness. [17] System according to one of claims 11 to 16, in which - the processor is configured to control a second device based on the parameter of the test fluid. [18] Non-transitory computer-readable medium that stores instructions that, when executed, support the performance of operations that include: - receiving a first charge generated by a first excitation signal and transferred during a first time interval to a first electrode attached to a container containing a test fluid; - receiving a second charge generated by a second excitation signal and transferred to the first electrode during a second time interval; - Determining a parameter of the test fluid based on a charge transfer curve generated based on the first charge and the second charge. [19] The non-transitory computer-readable medium of claim 18, wherein the operations comprise: - Determine the parameter of the test fluid using a fluid classification model based on the charge transfer curve. [20] A non-transitory computer-readable medium according to any one of claims 18 or 19, wherein determining the parameter of the test fluid comprises: - Determining at least one of the following: a conductivity, a permittivity, an ethanol concentration, a diesel exhaust treatment fluid concentration, a water hardness.

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