Method for controlling a water supply system

Abstract

The present application provides a method for controlling a water supply system. The water supply system includes at least one pump assembly, the method including: receiving a user indication indicating user-perceived acoustic noise-induced discomfort felt at least at a first location; measuring at least a first sound signal at the first location; determining one or more adjusted control parameters of the water supply system based on the measured first sound signal and based on at least one of the received user indication of the degree of user-perceived acoustic noise-induced discomfort and a received first operating parameter of the pump assembly, the first operating parameter representing an operating condition of the pump assembly when the first sound signal was measured; and controlling the water supply system based on the determined adjusted control parameters.

Description

Technical Field

[0001] The present disclosure relates to a computer-implemented method for controlling a water supply system. Background Art

[0002] Water supply systems are installed in many structures, such as in buildings. Water supply systems may include a variety of components, including, for example, pipes, valves, boilers, pumps, etc. During normal operation, at least some of the components of the water supply system may generate acoustic noise. Acoustic vibrations may be further carried and amplified by various components of the water supply system and various components of the building structure to which these components are mechanically coupled. This may be particularly evident when the acoustic noise causes resonance of components of the water supply system or components of surrounding structures. The acoustic noise may cause discomfort or interference to people near the water supply system, such as discomfort or interference to residents of a residential building where the water supply system is installed or to staff working in an office building where the water supply system is installed.

[0003] Many parameters may affect the severity of the discomfort, such as the acoustic properties of the room in which the person experiencing the discomfort, the auditory system of the person experiencing the discomfort, the constructed environment to which the components of the water system are mechanically coupled, the presence of other sound sources, etc. Therefore, addressing the discomfort caused by acoustic noise in an efficient manner has proven difficult. Summary of the invention

[0004] According to one aspect, an embodiment of a computer-implemented method for controlling one or more components of a water supply system, the water supply system including at least one pump assembly, is disclosed herein. Embodiments of the method include:

[0005] - receiving a user indication indicating user-perceived acoustic noise-induced discomfort felt at least at a first location,

[0006] - measuring at least a first sound signal at said first location.

[0007] - determining one or more adjusted control parameters of the water supply system based on the measured first sound signal and based on at least one of a received user indication of a degree of discomfort caused by the acoustic noise perceived by the user and a received first operating parameter of the pump assembly, the first operating parameter being representative of an operating condition of the pump assembly at the time of said measuring of the first sound signal, and

[0008] - Controlling the water supply system according to the determined adjusted control parameters.

[0009] Thus, embodiments of the methods described herein control the water supply system in response to a user indication of perceived noise discomfort during operation of the water supply system, rather than attempting to address all potential acoustic noise related issues during, for example, manufacture of the pump assembly. In addition, embodiments of the methods described herein control the water supply system based on a combination of a sound signal measured at the location where the user has felt discomfort and a user indication of the degree of noise discomfort and / or a combination of obtained operating parameters of the pump assembly of the water supply system. This allows acoustic noise to be reduced during the operating time of the system and in a manner that is specific to the operating conditions of the system and / or specific to the degree of noise felt.

[0010] In particular, some embodiments, the method can determine a correlation between the actual operating condition of the pump assembly and the objectively measured sound. This allows the method to verify whether the pump assembly and / or another component of the water supply system may be the source of the acoustic noise, and determine the control of the pump assembly or the adjustment of another component of the water supply system that is suitable for reducing the acoustic noise and thus possibly reducing the perceived noise discomfort.

[0011] The process can receive user input in many ways. For example, an embodiment of the process can be performed by a mobile device that is appropriately programmed or otherwise configured, or by another data processing system that is appropriately programmed to perform the process. Therefore, the process can receive user input by means of a suitable program that is executed on a mobile device or on another data processing system. The program can be a noise reporting program, a water supply system control program or a similar program. The mobile device or other data processing systems that perform the process can provide a suitable user interface that allows the user to enter relevant information about the perceived acoustic noise, such as the degree of the perceived noise, the location of the perceived noise, the time of the perceived noise, the type of the perceived noise and / or similar information. The degree of the perceived noise can be another parameter of the degree of the perceived loudness, the perceived sharp degree or the degree of the attribute of the perceived noise. The position can be identified as a position in a building (such as by specifying a specific floor, room, etc.), and / or by specifying a position relative to the water supply system (such as by specifying the distance from the components of the system, such as the distance from the pump assembly). The time at which the noise is perceived can be specified as the current date and time, the time of the day, the day of the week, and / or the like. In some embodiments, some or all of the information can be manually entered by a user. In some embodiments, at least some of the information can be automatically determined, such as by a mobile device or other data processing system for entering the information. For example, the time at which the noise is perceived can be recorded by a mobile device or other data processing system as the time at which a process has been called or the time at which an input has been received by the process. Similarly, the location can be determined by the mobile device as the location of the mobile device at the time of reporting, such as using a suitable positioning system.

[0012] In some embodiments, receiving a user indication includes receiving a descriptive characterization of noise discomfort perceived by the user, and determining that the received descriptive characterization is based on the received descriptive characterization. For example, the process may provide a user interface that allows a user to enter a descriptive characterization, such as free text input, spoken input, or the like. In such an embodiment, the process may include a natural language processing module that is configured to analyze the descriptive characterization in order to extract one or more attributes of the perceived noise discomfort, in particular the degree of the noise, such as the degree of its attributes. In other embodiments, the process may provide a user interface that allows a user to select one or more of a plurality of predetermined attributes, such as by checking the selected attribute in a list of selectable attributes. The attribute may, for example, include an attribute indicating the type of acoustic noise felt, such as by classifying the noise as "buzzing", "hammering", "sharp" and / or similar sounds. Alternatively or additionally, the attribute may include an attribute indicating the degree of discomfort, such as on a predetermined scale that can range from "no discomfort" via "acceptable" to "very severe". Similarly, the degree of other attributes of perceived noise discomfort, such as the degree of loudness, sharpness, etc., may be input in a similar manner. The process may then use the selected attributes directly as features of perceived noise discomfort, or the process may convert the selected combination of attributes into features of perceived noise discomfort, such as using a lookup table or a more complex set of conversion rules.

[0013] In some embodiments, receiving a user indication may include receiving a user indication and information about a single occurrence of perceived noise discomfort. Alternatively, receiving a user indication may include receiving a user indication and information about multiple occurrences of perceived noise discomfort, such as occurrences at different locations and / or at different times (e.g., different times of the day). Accordingly, the process may measure multiple sound signals based on the received information about the multiple occurrences, such as multiple sound signals at various locations and / or at various times. The measured locations and / or times are preferably based on the times and / or locations indicated by the received information about the multiple occurrences, such as matching the times and / or locations indicated by the received information about the multiple occurrences. Additionally or alternatively, the process may obtain information about operating parameters of multiple pump assemblies from a water supply system (e.g., pump assemblies located at different locations within a building) based on the received information about the multiple occurrences, and / or information indicating operating parameters of one or more pump assemblies at different times. To this end, the process may establish communication with multiple pump assemblies to read out their current operating parameters, and optionally, record log information about historical operating parameters. The process may even establish communication with one or more pump components at different times to read out information about current operating parameters at different times.

[0014] It will be understood that the user instructions may include additional or alternative information, such as information about the water supply system (such as the type and / or location of the pump assembly), information such as a serial number identifying the pump assembly, information about the building or other mechanical structure in which the water supply system is installed, user information, etc.

[0015] Measuring at least the first sound signal at the first position can be performed by the same device that receives the user's indication, such as by the mobile device, or by a dedicated sound measurement device that is communicatively coupled to the data processing system. The sound signal can be measured by a suitable microphone, such as by an internal microphone of the mobile device or by a separate microphone that can be connected to the mobile device or another data processing system. The sound signal can represent the measurement results within a suitable time period, such as 1 second or more seconds or even one minute or more. It will be understood that the appropriate duration of the measurement may depend on the nature of the noise. The duration can be predetermined, automatically selected or user selected. The automatic selection can be dynamically selected based on the user input received about the characteristics of the perceived noise discomfort and / or based on the real-time analysis of the measured sound signal.

[0016] In some embodiments, the process may include measuring a plurality of sound signals, such as measuring a plurality of sound signals at respective times and / or at respective locations and / or under different operating conditions of the pump assembly. Thus, the process may determine correlations between sound signals measured at different times and / or between sound signals measured at different locations within a building and / or other types of correlations, such as correlations between sound signals measured under different operating conditions.

[0017] In particular, in some embodiments, the method comprises:

[0018] - after said measuring of the first sound signal, measuring at least a second sound signal at said first location,

[0019] - optionally obtaining at least one second operating parameter of the pump assembly, which is representative of an operating condition of the pump assembly at the time of said measuring of the second sound signal.

[0020] Wherein, determining one or more adjusted control parameters of the water supply system is also based on the measured second sound signal and at least one first operating parameter and / or the obtained at least one second operating parameter. For example, in some embodiments, determining can include processing the measured second sound signal and the obtained at least one first operating parameter and / or the second operating parameter to determine the correlation between the operating condition and the measured second sound signal. The at least one second operating parameter can be the same (one or more) parameters as the at least one first operating parameter, but represents the operating condition of the pump assembly at different times. Alternatively, the at least one second operating parameter can include only some of the at least one first operating parameter and / or include one or more additional or alternative parameters. Therefore, the process can determine the correlation between the sound signal measured at different times and the operating condition. It will be understood that when the first sound signal and the second sound signal are measured within a sufficiently short period of time (during which the operating condition of the pump assembly does not change significantly), it is sufficient to obtain only the at least one first operating parameter.

[0021] Alternatively or additionally, some embodiments of the method may include:

[0022] - measuring at least one additional sound signal at a second location different from the first location,

[0023] - optionally obtaining at least one supplementary operating parameter of the pump assembly, which is representative of the operating condition of the pump assembly during said measuring of the at least one additional sound signal.

[0024] Wherein, determining one or more adjusted control parameters of the water supply system is also based on at least one additional sound signal measured at the second location, and optionally based on at least one obtained supplementary operating parameter. For example, in some embodiments, determining may include processing at least one additional sound signal measured at the second location and the obtained first operating parameter and / or the obtained supplementary operating parameter to determine the correlation between the operating condition and the at least one additional sound signal measured at the second location. The at least one supplementary operating parameter may be the same (one or more) parameters as the at least one first operating parameter, but indicates the operating condition at a different time. Alternatively, the at least one supplementary operating parameter may include only some of the at least one first operating parameter and / or include one or more additional or alternative parameters. Thus, the process may determine the correlation between the operating condition and the sound signals measured at different locations. It will be understood that when the first sound signal and the additional sound signal are measured within a sufficiently short period of time (during which the operating condition of the pump assembly does not change significantly), it is sufficient to obtain only the at least one first operating parameter.

[0025] Thus, measuring multiple sound signals at different times and / or locations allows the process to determine correlations between the measured sound signals and other information, in particular correlations between the measured sound signals and the operating conditions of the pump components of the water supply system, and use the determined correlations to determine appropriate parameters for reducing acoustic noise. Measuring multiple sound signals at different times and / or locations may be particularly useful in situations where the exact source of the acoustic noise is unknown or uncertain, for example, when the water supply system includes multiple components (e.g., multiple pump components) that may be the source of perceived noise discomfort. Multiple measurements can assist the process in determining which pump component is used to determine the adjusted control parameters. Similarly, multiple measurements can allow the process to determine multiple alternative sets of adjusted parameters that are applied at different times, such as at different times of the day or when users are staying at different locations within a building.

[0026] In some embodiments, the method comprises:

[0027] - measuring a first acoustic signal while the pump assembly is operating based on a set of initial operating parameters,

[0028] - controlling the pump assembly to operate based on a set of initially modified operating parameters, and

[0029] - measuring at least one additional sound signal while the pump assembly is operating based on the preliminary changed operating parameters, and wherein determining one or more adjusted control parameters of the water supply system is also based on the at least one additional sound signal and the set of preliminary changed operating parameters.

[0030] In particular, controlling the pump assembly may include preliminarily changing one or more control parameters of the pump assembly so as to cause the pump assembly to operate based on the preliminarily changed operating parameters.

[0031] Thus, the determination of the resulting adjustment of the control parameter to be affected may be based on the first sound signal and the at least one additional sound signal, and the respective initial operating parameter and the preliminary modified operating parameter.

[0032] It will be appreciated that the process may comprise measurement of yet further acoustic signals based on yet further preliminary changed operating parameters.The preliminary changes in operating parameters may be selected in a deterministic or random manner, in particular so as to allow exploration of the state space of the pump assembly.

[0033] The first sound signal and the additional sound signal may be measured as discrete measurements or as a single continuous measurement of a combined sound signal, in which case the first sound signal constitutes an initial part of the combined sound signal and the additional sound signal constitutes a subsequent part of the combined sound signal. For example, during the continuous measurement, the operating parameter may be changed in discrete steps or in a gradual manner, such as by gradually changing the operating parameter during the continuous measurement. For example, the process may include operating the pump assembly at different speeds, such as gradually or discretely changing the speed of the measured sound signal while the pump assembly is operating at different speeds.

[0034] The process can obtain at least the first operating parameter by the user entering the parameter value of the first operating parameter. To this end, the user can read the parameter value on the display of the pump assembly and manually enter the read parameter value into the mobile device or other data processing system. Alternatively, at least the first operating parameter can be received from the pump assembly, for example, by wired or wireless communication with the pump assembly. To this end, the mobile device or other data processing system executing the method can establish a direct or indirect communication link with the pump assembly and receive one or more operating parameters of the pump assembly. Therefore, the mobile device or other data processing system executing the embodiment of the method described herein may include a data communication interface, such as a wired or wireless communication interface. Examples of wireless communication interfaces include radio frequency communication interfaces, such as Bluetooth interfaces, Wifi interfaces, etc. Other examples include near-field communication interfaces or another type of contactless communication interface. Therefore, the pump assembly may also include a corresponding data communication interface and is configured to transmit one or more operating parameters to a mobile device or other data processing system. The communication of operating parameters may be direct or indirect communication from the pump assembly to the mobile device. For example, the pump assembly may communicate the operating parameters to a remote host system, and the mobile device may receive the operating parameters from the remote host system.

[0035] For example, a mobile device or other data processing system may be configured to, upon receiving a user indication of perceived discomfort caused by acoustic noise, instruct the user to move the microphone of the mobile device or other data processing system to a location where the noise discomfort is perceived. The mobile device or other data processing system may then initiate measurement of the sound signal. The mobile device or other data processing system may further establish communication with the pump assembly and receive operating parameters. To this end, the mobile device may instruct the user to move the mobile device into the communication range of the pump assembly, or otherwise establish a wireless or wired connection with the pump assembly. Preferably, the operating parameters are obtained simultaneously with the measurement results, or at least obtained at a time close to the time of measuring the first sound signal, and at the same time as the noise discomfort is perceived, to ensure that the measured first sound signal and the obtained operating parameters are representative of the operating conditions of the pump assembly when the first sound signal is measured and when the noise discomfort is perceived. Alternatively, if the pump assembly includes a storage device for recording the operating parameters in a log, information about the operating parameters when the first sound signal is measured can be obtained from the log at a later point in time. Yet alternatively, if the pump assembly communicates operating parameters to a remote host system, and the remote host system includes a storage device for recording the operating parameters in a log, information about the operating parameters at the time of measurement of the first sound signal may be obtained from the log at a later point in time.

[0036] Examples of operating parameters of a pump component useful for determining a correlation between the operating condition and the measured first sound signal and for determining one or more adjusted control parameters of the water supply system include: pump speed, valve setting, voltage and / or frequency of the supplied power, switching frequency, modulation pattern and / or similar parameters.

[0037] Embodiments of the methods disclosed herein determine one or more adjusted control parameters of a water supply system based on a measured first sound signal and the first operating parameter obtained and / or a user indication of the degree of perceived noise. To this end, the process may include one or more sound signal processing steps, such as filtering, frequency analysis, more advanced signal analysis, and / or similar steps. Sound signal processing may, for example, result in one or more attributes representing the measured sound signal. In some embodiments, the attributes include one or more of the following: one or more sound levels, one or more dominant frequency bands, one or more harmonic families, one or more sideband effects, and the like. In some embodiments, the attributes include additional or alternative attributes, such as one or more sound quality indicators. Therefore, in some embodiments, signal processing includes calculating one or more sound quality indicators, such as loudness, sharpness, roughness, fluctuation intensity, tone-to-noise ratio, and the like, to quantify the characteristics of the measured first sound signal. Therefore, signal processing may include a feature extraction process for extracting and quantifying detailed acoustic characteristics of the measured first sound signal.

[0038] In some embodiments, determining one or more adjusted control parameters of the water supply system includes processing the measured first sound signal and the obtained first operating parameter and / or the received user indication of the degree of perceived noise to determine the correlation between the operating condition of the pump component and the measured first sound signal and / or the correlation between the perceived noise discomfort and the measured first sound signal, and determining one or more adjusted control parameters of the water supply system based on the processing. The determination of the correlation between the operating condition and the measured sound signal may include determining corresponding parameters, such as determining the speed of the pump and the dominant frequency of the measured sound signal. The correlation may also include the correlation between the change of the measured sound signal over time and the corresponding change of the operating parameter and / or the perceived noise level over time.

[0039] In some embodiments, determining one or more adjusted control parameters of the water supply system includes processing the measured first sound signal to extract one or more features and / or signatures of the first sound signal. In particular, the processing may include applying one or more feature extraction algorithms to the measured first sound signal. The processing may further include mapping the extracted one or more features and / or signatures to one or more noise categories. In one embodiment, the categories of noise may include hydraulic noise, motor noise, resonance noise, and / or similar noise. Accordingly, the categories of noise may involve classification of noise by noise source and / or by noise type. The process may further map the one or more noise categories to which the extracted features and / or signatures have been mapped to one or more candidate control parameters to be adjusted in order to reduce noise. Optionally, the mapping may further include determining an estimated degree of adjustment for (one or more) candidate control parameters. The mapping to one or more control parameters and / or the determination of the estimated degree of adjustment may further depend on the first operating parameter obtained, i.e., on the current operating condition of the pump assembly.

[0040] Each or both of the above mappings can be performed with the aid of a lookup table or a suitable mathematical model, such as a rule-based model or a machine learning model. For example, a machine learning model can be trained to map sound signals to features and / or to map features to noise categories and / or to candidate parameters to be adjusted. In some embodiments, the above mappings can be performed by a multi-layer neural network or another suitable machine learning model.

[0041] In some embodiments, determining one or more adjusted control parameters can be based on additional information, such as information input by a user, information about the location where the discomfort is perceived, or the like. The information input by the user can include a characterization of the acoustic noise as described herein. The location where the discomfort is received can be represented as a distance to the pump assembly or to another component of the water supply system or represented in a different manner. For example, the additional information can include information about the location where the discomfort is perceived, such as a location relative to the pump assembly, such as a distance relative to the pump assembly. The additional information can be used, for example, during the classification of noise based on the characteristics of the extracted sound signal. For example, when the distance to the pump assembly is relatively short, the noise may be airborne, while the noise perceived at a greater distance from the pump assembly or other possible noise sources may be structure-borne or fluid-borne.

[0042] In some embodiments, determining one or more adjusted control parameters can be based on one or more parameter adjustment rules or models, such as rule-based models, decision trees, expert systems, and / or machine learning models (such as neural networks and / or another type of artificial intelligence system). For example, an artificial intelligence system can receive a representation of a measured sound signal and an indication of the operating parameters and / or perceived noise level, and optionally additional information as input, and provide a set of control parameter values ​​as output. For example, a machine learning process can be trained to determine control parameters based on a set of training examples. Each training example can include a measured sound signal, a corresponding operating parameter of a pump assembly, and optionally additional information. Each training example can additionally include a set of control parameters that have been determined to provide a reduction in the discomfort caused by the perceived acoustic noise under the conditions represented by the measured sound signal and the operating parameters of the corresponding training example. The operating parameters can be determined from feedback received by the process from a user, such as in the form of an indication of a change in the level of discomfort perceived after the adjustment is implemented. The above data can have been collected, for example, in a real-life device during a previous effort to reduce acoustic noise. The data can be used to train a machine learning model, such as a neural network, using a training algorithm as known in the art.

[0043] In some embodiments, the process may communicate the measured first sound signal and / or one or more properties of the measured first sound signal to the remote host system. For example, the process may communicate one or more results of the sound processing step performed on the measured sound signal to the remote host system. In addition, the process may communicate additional data such as user feedback and / or information about adjustments to system control to the remote host system. Still further, in embodiments where the process includes measuring additional sound signals, the process may communicate additional sound signals and / or their determined properties and / or associated additional data to the remote host system. Accordingly, the remote host system may collect data from multiple water supply systems and use the collected data as training examples for training updated machine learning models to be used when subsequently determining adjusted control parameters. Therefore, the remote host system may refine the best action (strategy) given feedback across multiple devices / systems. This refinement may be done automatically, for example using machine learning, or at least partially based on user input. The remote host system may be a suitably programmed server computer or other data processing system, such as a distributed data processing system, a virtual machine, etc. Therefore, the remote host system may receive corresponding data from multiple mobile devices and / or data associated with multiple users and / or multiple water supply systems. The remote host system can apply suitable machine learning methods, such as reinforcement learning, to train updated machine learning models given input and feedback across multiple systems and / or users. The remote host system can then transmit the updated machine learning model to the mobile device for use by the mobile device in analyzing subsequent sound signals in the future. Alternatively, in embodiments where the model is applied by the remote host system, the remote host system may not need to transmit the updated model to the mobile device, but simply applies the updated model in response to receiving a subsequent set of inputs. In some embodiments, the best actions (strategies) refined given feedback across multiple devices / systems can also be used to define default parameter settings for control parameters to be used when manufacturing or otherwise configuring the pump.

[0044] In general, the determination of the adjusted control parameters can be based on stored data indicating known correlations between operating conditions and sound signals. It will be appreciated that many different models can be employed, including hybrid models that include one or more machine learning components and other, for example, rule-based components. In some embodiments, the model may include a machine learning classification model that maps features extracted from the measured sound signals and, optionally, operating parameters of the pump system to a selected one of several predetermined control strategy categories. Each control strategy may be defined by one or more control parameters, or otherwise associated with one or more control parameters. Training of the machine learning model may be performed as supervised learning. In some embodiments, the machine learning model may be trained using a reinforcement learning approach. The reinforcement learning approach may use the degree of perceived noise as reward feedback.

[0045] In general, many adjustable control parameters of the pump assembly and / or other components of the water supply system can affect the noise generated by the water supply system. Therefore, various embodiments of the processes described herein can control different types of control parameters, including one or more control parameters for controlling the operation of the pump assembly and / or control parameters for controlling the operation of one or more other components of the water supply system. Examples of control parameters that can be adjusted by some embodiments of the processes described herein include one or more of the following: pump speed, valve settings, frequency and / or voltage of the drive current used to drive the pump motor, control mode of the pump assembly, parameters indicating a selected control strategy, and / or similar parameters.

[0046] In some embodiments, the method includes determining at least an adjusted first set of control parameters and an adjusted second set of control parameters. Thus, the control may include selecting one of the first set of control parameters and the second set of control parameters, for example, conditional on one or more of the following: user input, time of day, day of the week, operating mode of the water supply system. For example, the process may determine two or more alternative adjustments of the control parameters, and may give the user the option of selecting one of the alternatives. To this end, the process may allow the user to tentatively try each alternative adjustment, and then select the adjustment that results in the lowest perceived discomfort as the final adjustment. Alternatively, the process may automatically adjust the control parameters of the water supply system. Alternatively or in addition, the process may determine multiple sets of alternative control parameter values, and selectively control the water supply system at different times based on corresponding sets in the determined alternative groups. For example, the process may generate a set of control parameters that results in the lowest perceived discomfort in a bedroom of a building, and the process may select the set of control parameters at night when the user is likely to stay in the bedroom. At other times of the day, the process may select a different set of control parameters, such as a set of control parameters that results in less noise discomfort being perceived at other locations in the building and / or a set of control parameters that are preferred for other reasons (e.g., to improve performance of the water and electricity system, to conserve energy, and / or the like). In some embodiments, the process may create multiple user-specific operational profiles, each with a corresponding set of control parameters. The process may allow the user to manually select individual profiles and / or provide a mechanism for automatically switching between different user profiles, such as based on the time of day, day of the week, operational conditions of the water supply system, and / or the like. The manual selection may be accomplished via a user interface of the pump assembly, a remote control, a central control unit, and the like.

[0047] In some embodiments, the process receives feedback from the user, such as in response to controlling the water supply system based on the determined control parameters. The feedback may be, for example, in the form of user input, such as a user selection among two or more options. The user feedback may indicate a perceived noise level or a change in the perceived noise level. For example, after the control parameters have been adjusted, the process may request user feedback on whether the perceived noise discomfort has improved, deteriorated, or remained unchanged. Such user feedback may allow the process to derive an optimal choice of control parameters in an iterative manner, such as using reinforcement learning or another suitable adaptive strategy. To this end, according to some embodiments, the method includes: receiving a user indication indicating a change in user-perceived noise discomfort felt at least at a first location; and optionally repeating measurement, acquisition, determination, and control in response to the received user indication. Therefore, even if acoustic noise cannot be completely avoided, the process allows the noise to be at least adapted to the user's personal sound quality preferences.

[0048] Alternatively or additionally, the received user feedback can be used to create or at least supplement a set of training examples for training a machine learning model configured to determine the adjusted control parameters as described herein. In particular, the measured first sound signal, the obtained operating parameters, the determined adjusted control parameters, and the user feedback can together form training examples for training a subsequent machine learning model.

[0049] In general, since the adjustment of the control parameters is based on initial user input and / or user feedback, the adjustment of the control parameters can be based on one or more individual parameter adaptation rules (e.g., individual user metrics) that are defined for the individual user based on the input and / or feedback. In particular, the process can achieve personalized active control of the pump assembly based on the user input and / or feedback.

[0050] Embodiments of the water supply system include a pump assembly. Some embodiments of the pump assembly include a pump and a motor (e.g., an electric motor), and a control circuit for controlling the motor. Some embodiments of the water supply system also include components such as pipes, valves, reservoirs, boilers, etc. In some embodiments, the water supply system includes two or more pump assemblies; wherein the acquisition includes acquiring corresponding operating parameters from each pump assembly, and wherein determining the adjusted control parameters includes determining the adjusted control parameters of at least one (e.g., at least two) of the pump assemblies. Thus, the process can determine which of the multiple pump assemblies may be the source of the perceived noise discomfort, for example by determining the strongest correlation between the operating condition of the pump assembly and the measured (one or more) sound signals. In addition, the process can reduce acoustic noise associated with interference between the operation of multiple pump assemblies.

[0051] In some embodiments, a mobile device or other data processing system that performs an embodiment of the method described herein may communicate the determined adjusted control parameters to a control circuit for controlling a water supply system, such as via a wired or wireless data communication connection. To this end, a mobile device or other data processing system that performs the method may establish a direct or indirect communication link with a pump assembly and / or with another component of the water supply system so as to transmit one or more adjusted control parameters to the pump assembly or other components, and cause the pump assembly or other components to operate based on the transmitted control parameters. Therefore, a mobile device or other data processing system that performs an embodiment of the method described herein may include a data communication interface, such as a wired or wireless communication interface. Examples of wireless communication interfaces include radio frequency communication interfaces, such as Bluetooth interfaces, Wifi interfaces, or similar interfaces. Other examples include near field communication interfaces or another type of contactless communication interface. Therefore, a pump assembly or other component may include a corresponding data communication interface and is configured to receive the adjusted control parameters transmitted by a mobile device or other data processing system. In some embodiments, the adjusted control parameters are communicated from the mobile device to a remote host system, which in turn communicates the adjusted control parameters to other parts of the pump assembly or water supply system.

[0052] It is noted that the features of the various embodiments of the computer-implemented methods described above and below may be implemented at least in part in software or firmware by the execution of program code components (such as computer executable instructions) and executed on a data processing system or other processing components. Here and hereinafter, the term processing component includes any circuit and / or device that is suitably adapted to perform the above functions. In particular, the above terms include general or special programmable microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable logic arrays (PLAs), field programmable gate arrays (FPGAs), graphics processing units (GPUs), dedicated electronic circuits, etc., or combinations thereof.

[0053] The present disclosure is directed to different aspects, including the above-mentioned method and the methods, systems, devices and product parts further described below, each of which produces one or more of the advantages and advantages described in relation to one or more of the other aspects, and each has one or more embodiments corresponding to the embodiments described in relation to one or more of the other aspects described herein and / or disclosed in the appended claims.

[0054] In particular, another aspect disclosed herein relates to an embodiment of a data processing system configured to perform the operations of the methods described herein. To this end, the data processing system may have a program code stored thereon, which is configured to cause the data processing system to perform the operations of the methods described herein when executed by the data processing system. In some embodiments, the data processing system may include a mobile device, in particular a portable data processing device, such as a tablet computer or a mobile phone. The mobile device may include a microphone for measuring sound signals and / or a data communication interface for communicating with other components of the pump assembly and / or the water supply system. The mobile device may be appropriately programmed, such as by an application or other form of software, to perform an embodiment of the process described herein as a stand-alone device alone or as part of a distributed data processing system (e.g., as a client terminal of a client-server system or as a client terminal of a cloud-based architecture).

[0055] Another aspect disclosed herein relates to an embodiment of a computer program, which is configured to cause a data processing system to perform the steps of the computer-implemented method described above and below. The computer program may include a program code component, which is adapted to cause the data processing system to perform the steps of the computer-implemented method disclosed above and below when the program code component is executed on the data processing system. The computer program may be stored on a computer-readable storage medium (particularly stored in a non-transient storage medium) or embodied as a data signal. The non-transient storage medium may include any suitable circuit or device for storing data, such as RAM, ROM, EPROM, EEPROM, flash memory, magnetic or optical storage device, such as CD ROM, DVD, hard disk and / or similar device. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and other aspects will be apparent and elucidated from the embodiments described below with reference to the accompanying drawings, in which:

[0057] Figure 1 A water supply system and a mobile device communicatively coupled to the water supply system and configured to control the water supply system are schematically shown.

[0058] Figure 2 A block diagram of a water supply system and a mobile device communicatively coupled to the water supply system and configured to control the water supply system is schematically shown.

[0059] Figure 3 The process of controlling a water supply system is shown.

[0060] Figure 4 A process for determining parameter adjustments based on measured sound signals is shown.

[0061] Figure 5 Another process for controlling a water supply system is shown.

[0062] Figure 6 Yet another process of controlling a water supply system is shown. DETAILED DESCRIPTION

[0063] Figure 1 A water supply system, generally designated by reference numeral 100, and a mobile device 30 communicatively coupled to the water supply system via a communication link 60 and configured to adjust the control of the water supply system are schematically shown. The water supply system 100 includes a pump assembly 20 and other components, illustrated in this example as a water boiler 10. Although for ease of illustration, the water supply system 100 is illustrated in FIG. Figure 1 Although not explicitly shown, it will be understood that embodiments of the water supply system typically include numerous alternative or additional components, such as valves, meters, additional pump assemblies, piping, filtration systems, and / or the like.

[0064] The mobile device 30 may be operated, for example, by a technician 40 or by a user of the water supply system. Although the mobile device 30 is shown as a mobile phone, it will be understood that embodiments of the water supply system may be controlled by other types of data processing systems, such as other types of mobile devices, such as appropriately programmed tablet computers or other types of portable data processing devices. It will also be understood that the water supply system may be controlled by more than one mobile device or other data processing system. For example, a mobile device may be provided for each service technician or even each user. It will also be understood that the mobile device 30 need not be operable to control only a single water supply system, but may instead be operable to control different water supply systems based on indications of discomfort caused by perceived acoustic noise and based on measured sound signals as described herein.

[0065] The mobile device 30 is configured to receive an indication of perceived discomfort caused by acoustic noise, for example, including an indication of the degree of perceived noise discomfort, for example, via a user interface of the mobile device. The mobile device 30 is also configured to measure a sound signal 50 at a location where the discomfort caused by the acoustic noise has been perceived, in particular a sound signal representing acoustic noise originating from the water supply system 100. In addition, the mobile device 30 is configured to establish a communication link 60 with the pump assembly 20 so as to receive at least one operating parameter of the pump assembly from the pump assembly via the communication link 60, which represents the operating condition of the pump assembly when the sound signal is measured. Examples of operating parameters include pump speed, frequency and / or voltage of the drive current, operating mode and / or other settings, etc. The mobile device 30 is also configured to determine one or more adjusted control parameters of the water supply system based on the measured sound signal and based on the received operating parameters and / or the indication of the degree of perceived noise. The mobile device 30 is also configured to control the pump assembly based on the determined adjusted control parameters, for example, by transmitting the adjusted control parameters to the pump assembly via the communication link 60, so as to cause the pump assembly to operate based on the adjusted control parameters. It will be appreciated that the mobile device may be configured to control other components of the water supply system, for example, by determining adjusted control parameters for these other components and by transmitting the adjusted control parameters to the other components. To this end, the mobile device may establish corresponding communication links with the other components of the water supply system. Alternatively, the mobile device may communicate the adjusted control parameters to the pump assembly 20 or a central control unit (not shown) of the water supply system (which may then forward the control parameters to the relevant components to which the adjusted control parameters belong), or control other components based on the adjusted control parameters.

[0066] Figure 2 A water supply system and a mobile device (eg, a mobile device) communicatively coupled to the water supply system and configured to control the water supply system are schematically shown. Figure 1The water supply system 100 includes a pump assembly 20 and additional components 10, such as valves, meters, additional pump assemblies, pipes, boilers, filtration systems, and / or the like. The pump assembly includes a motor 21 and a pump 24 driven by the motor. The pump assembly also includes a motor controller 23 for controlling the operation of the motor 21. The motor controller may, for example, include a suitably programmed microprocessor and / or other control circuit. The motor controller includes a memory 231 or other suitable circuit that stores or otherwise represents one or more control parameters, wherein the motor is controlled based on the one or more control parameters. The control parameters may include one or more of the following: control mode, speed of the motor, one or more control voltages, frequencies, and / or the like of the control current / signal, and / or the like. The pump assembly also includes a communication interface 22, which may be a wired interface or a wireless interface. For example, the wired interface may include an electrical connector, such as a USB port for establishing a wired communication interface with an external device (e.g., with a mobile device 30). Examples of wireless communication interfaces may include a radio transceiver (e.g., a Bluetooth transceiver), a Wifi adapter, or the like. Other examples of wireless communication interfaces include infrared interfaces, near field communication interfaces, and / or the like.

[0067] The mobile device 30 includes a communication interface 32, such as a wired or wireless communication interface configured to establish a communication link 60 with the communication interface 22 of the pump assembly. Thus, the wired interface may include an electrical connector, such as a USB port for establishing a wired communication interface with the communication interface 22 of the pump assembly. Examples of wireless communication interfaces may include radio transceivers, such as Bluetooth transceivers. Other examples of wireless communication interfaces include infrared interfaces, near field communication interfaces, and / or similar interfaces. The communication link 60 may be a direct communication link or an indirect communication link, such as via a local network. The mobile device 30 also includes a microphone 31 or other suitable transducer for measuring sound signals. The microphone may be a built-in microphone of the mobile device. Alternatively, the microphone may be an external microphone that can be connected to the mobile device. The mobile device also includes a user interface 35 (e.g., a touch screen or another type of display) and / or other types of devices for receiving user input, such as physical buttons, pointing devices (e.g., a mouse), a touchpad, a camera for capturing user gestures, and / or the like. The mobile device also includes a processing unit 33, such as a properly programmed microprocessor, a CPU, and / or the like. The processing unit is communicatively coupled to the microphone, the communication interface, and the user interface. The processing unit 33 is programmed to perform some or all steps of the embodiments of the process described herein. To this end, the mobile device includes a memory 34 for storing program code (e.g., one or more applications), and for storing data (e.g., for storing recorded sound signals and / or received operating parameters).

[0068] In some embodiments, the mobile device 30 can be further configured to communicate with the host system 70, for example via a cellular telecommunications network or via a suitable computer network, such as the Internet. To this end, the mobile device may include a suitable radio interface 36. The mobile device 30 can communicate the measured sound signals and / or the received operating parameters and / or user feedback information to the remote host system 70. The remote host system can be a suitably programmed server computer or other data processing system, such as a distributed data processing system, a virtual machine, etc. The remote host system 70 can analyze the information received from the mobile device 30 and return the adjusted control parameters to the mobile device, which can then forward the adjusted control parameters to the pump assembly 20 via the communication link 60. In other embodiments, the mobile device 30 can operate in a standalone working mode, wherein the mobile device itself performs analysis of the measured sound signals, operating parameters, and user feedback, and determines the adjusted control parameters by itself.

[0069] Regardless of whether the data analysis and determination of the adjusted control parameters are performed by the mobile device 30 or the remote host system 70, the mobile device 30 can still be configured to communicate with the host system 70 and convey the obtained data to the remote host system for use by the host system to create an improved data analysis model, such as by implementing a machine learning process.

[0070] Figure 3 shows the process of controlling a water supply system, for example, by Figure 1 or Figure 2 Mobile devices execute to control Figure 1 or Figure 2 process of the water supply system.

[0071] In an initial step S1, the process receives a user indication indicating user-perceived acoustic noise-induced discomfort felt at least at a first location. For example, a user of a mobile device may invoke an application on the mobile device. The process may receive additional information about the discomfort caused by the acoustic noise, such as indicating the severity or other degree of perceived discomfort, the type of acoustic noise, the location where the noise was perceived, the time when the noise occurred, and / or similar additional information.

[0072] In step S2, the process requests the user to place the mobile device at a location where discomfort caused by acoustic noise is perceived. When the user has placed the mobile device at the location, the user can confirm the location, for example, by pressing a button on the mobile device.

[0073] In step S3, the mobile device establishes communication with the pump assembly of the water supply system and receives operating parameters from the pump assembly. To this end, the mobile device can determine whether the pump assembly is within the communication range of the mobile device and establish a connection with the pump assembly. If multiple pump assemblies are within the communication range, the mobile device can establish communication with all of them and receive operating parameters from all pump assemblies within the communication range. If no pump assembly is within the communication range (or if the pump assembly suspected of causing noise discomfort is not within the communication range), the mobile device can instruct the user to initially move the mobile device to a position within the communication range of the pump assembly so that the operating parameters of the pump assembly can be received before returning to the position where the sound measurement is to be performed.

[0074] In step S4, the mobile device records the audio for a period of time, such as a predetermined period of time or a user-controllable period of time, such as a few seconds or even a few minutes. In some embodiments, one or more of steps S2 to S4 can be repeated, such as at different times of the day or at different locations where the mobile device is placed when measuring the sound signal, or in order to receive operating parameters from multiple pump assemblies placed at different locations. Thus, the process obtains one or more sets of data, which include corresponding sound signals and associated operating conditions and additional information, such as user input, location information, time information, etc.

[0075] In step S5, the mobile device may request the user to provide user input representing the characteristics of the discomfort caused by the perceived acoustic noise, including, for example, the degree. The user input may be in a variety of forms. For example, the mobile device may guide the user through one or more questions of an electronic questionnaire or through the steps of a wizard process. For example, the user may be requested to rate the level of discomfort and / or indicate the characteristics of the discomfort or noise on a predetermined scale. The user may even be asked to provide a free text description of the discomfort. If one or more of the measurement steps S2 to S4 are performed at different times and / or locations, the process may also receive user feedback indicating the noise perceived at the different times and / or locations.

[0076] If the received user input indicates that the level of discomfort is satisfactory, the process may terminate; otherwise, the process continues at step S6.

[0077] At step S6, the process analyzes the measured (one or more) sound signals, the operating parameters received from the one or more pump assemblies, and the user feedback to determine one or more adjusted control parameters to control the one or more pump assemblies. If the process determines that the noise is unlikely to be caused by the water supply system or cannot be affected by controlling the pump assembly or other components of the water supply system, the process can notify the user accordingly, for example, via a display of a mobile device. For example, the process can perform a correlation analysis on the operating parameters and the measured sound signal, for example, to determine whether the dominant spectral content of the sound signal is related to the pump assembly or one of the multiple pump assemblies. When the dominant frequency of the measured sound signal is independent of, for example, the rotation speed of one / multiple pump assemblies, the process can determine that the acoustic noise is independent of the water supply system. Similarly, the process can perform a feature extraction and noise classification process to determine the category of the measured noise. When the determined noise category is independent of, for example, the operating condition of the water supply system, the process can determine that the acoustic noise is independent of the water supply system.

[0078] The determination of the adjusted control parameter (e.g., correlation analysis) can be based at least in part on the location of the mobile device relative to the pump assembly, e.g., as defined by the distance to the pump assembly. The location information can be manually input by a user, or it can be automatically determined, e.g., through location tracking of the mobile device, signal strength of a communication link to the pump, and / or the like.

[0079] The following will refer to Figure 4 Describe an example of a possible process for analyzing a sound signal.

[0080] Still refer to Figure 3 At step S7 , the process controls one or more pump components based on the adjusted control parameters, for example by transmitting the adjusted control parameters to the pump components.

[0081] The process then returns to step S3 and performs another iteration of acquiring operating parameters, sound measurements, and user feedback to determine whether the level of discomfort has been reduced. For example, when the pump is running at a certain speed (rpm), resonant noise may appear in other parts of the water supply system, or even in other structural parts of the building that are mechanically coupled to the water supply system. If the rotational speed matches the resonant frequency of the system or other structures, annoying noise may occur. Therefore, the process can adjust the control parameters of the pump system so that the pump system does not operate at a rotational speed corresponding to the dominant frequency of the measured sound signal. This process can be repeated several times until the discomfort has reached a satisfactory level, or until the process determines that no further reduction in discomfort can be achieved by adjusting the control parameters.

[0082] In some embodiments, the process may communicate some or all of the acquired and / or processed data, user feedback, and information about adjustments to system controls to a remote host system. Accordingly, the remote host system may collect data from multiple water systems and use the collected data to refine the best action (strategy) given feedback across multiple devices / systems. This refinement may be done automatically (e.g., using machine learning) or based at least in part on user input.

[0083] Figure 4 A process for determining parameter adjustments based on measured sound signals is shown.

[0084] In step S61, the process performs a feature extraction step on the measured sound signal 50. The feature extraction process can extract multiple features, also referred to as acoustic markers, from the measured sound signal. The feature extraction process can use any method known in the prior art for extracting features from acoustic signals, such as principal component analysis, etc. Feature extraction can be performed in the time domain, frequency domain and / or time-frequency domain. Feature extraction in the time domain can, for example, use the energy envelope of the signal to extract acoustic features. Alternatively or in addition, the number of zero crossings of the signal within a time interval can be used. Feature extraction in the frequency domain can use techniques such as fast Fourier transform and / or power spectral density (PSD). Suitable techniques for extracting features in the time-frequency domain include short-time Fourier transform (STFT) and wavelet transform (WT).

[0085] In step S62, the process determines the noise category based on the extracted features. For this reason, the process can adopt a suitable classifier. The classifier provides a function or rule for dividing the feature space into various regions, wherein each region belongs to a specific noise category. In general, based on the understanding of the signal distribution parameters, the classifier can be classified as a parameter classifier and a non-parameter classifier. A parameter classifier is a classifier that can be represented by a closed form, that is, some assumptions are made about the probability density function for each category, and in a non-parameter classifier, no assumptions are made about the density function. The example of a suitable classifier includes a Bayesian classifier, a support vector machine, a Gaussian mixture model, a hidden Markov model, an artificial neural network, a decision tree, a classifier based on fuzzy logic rules, or a combination thereof.

[0086] The classifier receives the extracted features, for example in the form of a feature vector. In some embodiments, the classifier may additionally receive one or more operating parameters indicating the current operating conditions of the pump system 20, such as the current pump speed, voltage, drive frequency, operating mode, etc.

[0087] Alternatively or in addition, the classifier may further receive one or more inputs from the user 40, such as one or more inputs indicating the location of the perceived noise, one or more inputs indicating a user's subjective classification or rating of the noise (e.g., an indication of the degree of the perceived noise), and / or the like.

[0088] In step S63, the process selects one or more control parameters of the pump system to be adjusted. The selection of the parameters to be adjusted can be based on the determined noise category. Optionally, the selection of the parameters to be adjusted can also be based on additional inputs, in particular on the current operating parameters of the system and / or on user input. The selection can be performed by a rule-based system, by a machine learning model (e.g., an artificial neural network), or by a combination thereof.

[0089] In step S64, the process determines the specific adjustment of the selected parameters. As in the previous step, this determination can be based on the determined noise classification, and optionally based on further inputs, such as the current operating parameters of the system and / or user input. This determination can be performed by a rule-based system, by a machine learning model (e.g., an artificial neural network), or by a combination thereof.

[0090] It will be appreciated that in some embodiments, some of the above steps may be combined into a single step or implemented by the same rule-based system and / or machine learning model. For example, a multi-layer neural network may be used in which one or more initial layers perform feature extraction and one or more subsequent layers perform classification and parameter selection tasks.

[0091] Figure 5 and Figure 6 An example of a process for controlling a water supply system is shown. In particular, Figure 5 and Figure 6 The process is shown how the mobile device 30 and the remote host system 70 (eg, as shown in FIG. Figure 1 and / or Figure 2 An example of distribution between systems described herein.

[0092] Figure 5 and Figure 6The process of the process determines the control strategy to be applied in a given situation (i.e., for a given set of inputs) so that the perceived noise discomfort is minimized. The inputs may include one or more of the following: calculated acoustic indicators and / or acoustic markers, location, building information, user information, etc. To this end, the process may utilize reinforcement learning (see, for example, Richard S. Sutton and Andrew G. Barto, 1998, Introduction to Reinforcement Learning (First Edition), MIT Press, Cambridge, MA, USA). In general, reinforcement learning seeks to learn a good strategy (i.e., learn to take an action given the current situation) in order to maximize a certain reward. In the current embodiment, the state can be defined by the calculated acoustic indicators and / or acoustic markers together with optional additional information (location, building information, user information, etc.). The set of actions that can be taken is defined by different control strategies and a reward signal as user feedback, which can be represented by a single scalar value and can represent an indication of how the noise discomfort has been improved by a given action. In a specific embodiment, Q learning can be applied. Since reinforcement learning may require relatively many interactions (trials) to learn a good policy, some embodiments may perform a reinforcement learning process across multiple users and / or across multiple systems, such as a Q-learning process, i.e., all data is collected in a single pool and then a single policy is learned, which is then applied to all users / systems. Thus, the reinforcement learning process may be advantageously implemented by a central host system, such as Figure 5 and Figure 6 shown.

[0093] Figure 5 The system is similar to the combination of Figure 2 The system described herein includes a mobile device 30 and a host system 70. The mobile device 30 is communicatively coupled to the remote host system, and all are as combined Figure 2 As described.

[0094] During operation, the mobile device 30 records the sound signal 50. The mobile device 30 also implements a process for determining the adjusted control parameters based on the recorded sound signal. For example, to this end, the mobile device may implement a process such as in combination with Figure 3 and / or Figure 4The process. In particular, the mobile device can calculate one or more indicators and / or markers of the recorded sound signal and feed the calculated indicators / markers into a computing model that implements the control strategy. Optionally, the mobile device can further feed additional information into the computing model, such as location information, user information (such as the user's age), etc. In addition, the mobile device can also feed input received from the user 40 into the computing model, wherein the input indicates a property of the perceived noise, such as indicating a degree of discomfort, perceived loudness, and / or the like. The computing model can, for example, include a classifier and a subsequent parameter selection and adjustment stage, for example, as combined Figure 4 At least a portion of the determination of the control parameter may be performed by the remote host system 70. To this end, the mobile device 30 may transmit the recorded audio and / or the extracted acoustic indicators / acoustic markers, and optionally additional information, together with the received user input to the remote host system 70. The remote host system may then return the determined control strategy for adjusting the selected control parameter.

[0095] In any case, the mobile device then controls the water supply system 100 based on the determined control parameters. The mobile device also receives input from the user 40 indicating the perceived noise or indicating a change in the perceived noise in response to a change in the implemented control parameters. Thus, the user input can be used as a reward in the reinforcement learning process.

[0096] The mobile device may transmit the received user input to the remote host system 70. If the mobile device has not already done so in the previous step, the mobile device transmits the recorded audio and / or extracted acoustic indicators / markers and optionally additional information along with the received user input to the remote host system 70.

[0097] Based on the sound signal and / or the calculated indicator / marker, and optionally as additional information representing the input of the system state, and based on the user input representing the reward, the remote host system 70 implements a reinforcement learning process. The remote host system 70 receives corresponding data from multiple mobile devices and / or data associated with multiple users and / or multiple water supply systems. Given the input and reward feedback across multiple systems and / or users, the remote host system applies reinforcement learning to learn the optimization strategy (i.e., the optimal selection and adjustment of control parameters). The remote host system can then transmit the updated model (implementing the updated control strategy) to the mobile device for use by the mobile device in the future analysis of subsequent sound signals. Alternatively, in an embodiment where the model is applied by the remote host system, the remote host system may not need to transmit the updated model to the mobile device, but only applies the updated model in response to receiving a set of subsequent inputs.

[0098] Figure 6 A similar process for controlling a water supply system is shown. This process is similar to combining Figure 5 The process described, except that in this embodiment, the additional information on which the control strategy is based also includes one or more operating parameters of the pump assembly 20 of the water supply system 100, such as pump speed, pressure measurement results, temperature measurement results and / or similar parameters. Accordingly, the mobile device also receives one or more operating parameters from the pump assembly 20.

[0099] Embodiments of the methods described herein may be implemented by means of hardware comprising several different elements, and / or at least in part by means of a suitably programmed microprocessor. In a device claim enumerating several components, several of these components may be embodied by one and the same element, component or item of hardware. The mere fact that certain measures are set forth in mutually different dependent claims or described in different embodiments does not indicate that a combination of these measures cannot be used to advantage. It should be emphasized that when used in this specification, the term "comprising / consisting of" is considered to indicate the presence of the features, elements, steps or components described, but does not exclude the presence or addition of one or more other features, elements, steps, components or groups thereof.

Claims

1. A computer-implemented method for controlling one or more components of a water supply system, the water supply system including at least one pump assembly, the method comprising: - receiving a user indication indicating user perceived acoustic noise-induced discomfort felt at least at a first location, wherein the first location is a location at which the user perceived acoustic noise-induced discomfort is located; - measuring at least a first sound signal at said first location, - determining one or more adjusted control parameters of the water supply system based on the measured first sound signal and based on a received user indication of a degree of discomfort caused by the acoustic noise as perceived by the user and a received first operating parameter of the pump assembly, the first operating parameter being indicative of an operating condition of the pump assembly at the time the first sound signal was measured, and - Controlling the water supply system based on the determined adjusted control parameters.

2. The computer-implemented method of claim 1 , comprising: A user indication is received indicating a change in user perceived noise discomfort felt at least at the first location.

3. The computer-implemented method of claim 2, further comprising: The measuring, acquiring, determining, and controlling are repeated in response to the received user instructions.

4. The computer-implemented method of claim 1 , comprising obtaining at least the first operating parameter, and wherein determining one or more adjusted control parameters of the water supply system comprises: The measured first sound signal and the acquired first operating parameter are processed to determine a correlation between the operating condition and the measured first sound signal, and one or more adjusted control parameters of the water supply system are determined based on the processing.

5. The computer-implemented method of claim 1 , comprising: - after measuring said first sound signal, measuring at least a second sound signal at said first location, Wherein, determining one or more adjusted control parameters of the water supply system is further based on the measured second sound signal.

6. The computer-implemented method of claim 5, comprising: - acquiring at least one second operating parameter of the pump assembly, which is representative of the operating condition of the pump assembly when measuring said second sound signal, Wherein, determining one or more adjusted control parameters of the water supply system is further based on the measured second sound signal and on the acquired second operating parameter.

7. The computer-implemented method of claim 1 , comprising: - measuring at least one additional sound signal at a second location different from said first location, Therein, determining one or more adjusted control parameters of the water supply system is further based on the measured additional sound signal.

8. The computer-implemented method of claim 7, comprising: - acquiring at least one additional operating parameter of the pump assembly, which is representative of the operating condition of the pump assembly when said additional sound signal is measured, Therein, determining one or more adjusted control parameters of the water supply system is further based on the measured additional sound signal and on the acquired supplementary operating parameters.

9. The computer-implemented method of claim 4, wherein: Receiving a user indication includes receiving a descriptive characterization of noise discomfort perceived by the user, and wherein at least one of said processing and said determining one or more adjusted control parameters of said water supply system is based on the received descriptive characterization.

10. The computer-implemented method of any one of claims 1-8, wherein: The determining of one or more adjusted control parameters of the water system is based on stored data indicative of known correlations between operating conditions and acoustic signals.

11. The computer-implemented method of any one of claims 1 to 8, wherein: The determining of one or more adjusted control parameters of the water supply system is based on at least one of: an expert system, a machine learning model.

12. The computer-implemented method of any one of claims 1 to 8, wherein: The determining of one or more adjusted control parameters of the water supply system is based on at least one of: a decision tree, an expert system.

13. The computer-implemented method of any one of claims 1 to 8, wherein: The first operating parameters include one or more of: pump speed, valve setting, voltage of supplied power, frequency of supplied power, switching frequency, modulation mode.

14. A computer-implemented method according to any one of claims 1-8, comprising at least determining an adjusted first set of control parameters and an adjusted second set of control parameters; and wherein the controlling comprises selecting one of the first set of control parameters and the second set of control parameters based on one or more of the following as conditions: user input, time of day, day of the week, operating conditions of the water supply system.

15. The computer-implemented method of any one of claims 1-8, wherein: The adjusted control parameters include one or more of: pump speed, valve setting, voltage used to drive the pump motor, frequency of a drive current used to drive the pump motor.

16. The computer-implemented method of any one of claims 1-8, wherein: The water supply system comprises two or more pump assemblies; wherein the method comprises obtaining respective operating parameters from each pump assembly, and wherein determining the adjusted control parameter comprises determining an adjusted control parameter for at least one of the pump assemblies.

17. The computer-implemented method of any one of claims 1-8, wherein: The water supply system comprises two or more pump assemblies; wherein the method comprises obtaining respective operating parameters from each pump assembly, and wherein determining the adjusted control parameters comprises determining the adjusted control parameters for at least two of the pump assemblies.

18. A data processing system configured to perform the operations of the method according to any one of claims 1 to 17.

19. A computer program product configured to cause a data processing system to perform the operations of the computer implemented method according to any one of claims 1 to 17.

20. A water supply system comprising a pump assembly and the data processing system according to claim 18.

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