SYSTEM AND METHOD FOR DETECTING ANOMALIES

Description

System and method for detecting anomalies DESCRIPTION Technical scope The present invention relates to a home automation system and a method 5 to detect anomalies. Technological background The invention finds particular, though not exclusive, application in technical sector relevant to home automation. Nowadays there is an ever-increasing need to monitor the use 10 of electrical and electronic devices installed in a home automation system with the aim of responding to various needs such as, for example, energy saving, simplification of life at home and personal safety. There is also a need to monitor the habitual behaviour of 15 users of home automation systems so as to be able to detect any deviations which could represent possible dangerous situations for the user and / or to improve the management of the user's life. An example of monitoring system through ad hoc sensors is described in U.S. Patent US 10,609,342 B1. 20 However, the Applicant noted that the current technical solutions aimed at the monitoring described above make the home automation system a dwelling, or, more generally, a building, architecturally more complex. Furthermore, such solutions inevitably increase costs for the installation and maintenance of the home automation system. 25 The Applicant therefore understood the opportunity and the necessity of detect anomalies in the use of a home automation system through devices that are relatively inexpensive to make. Furthermore, the Applicant understood the usefulness of detecting anomalies without excessively aggravate the complexity of the home automation system. 5 The technical problem underlying this invention is therefore that to provide a system and method for detecting anomalies structurally and functionally designed to at least partially overcome to one or more of the inconveniences complained of with reference to the technique cited note. 10 In the context of this problem, it is an object of the present invention to to provide a system and method for detecting anomalies relatively inexpensive to implement. A further object of the present invention is to provide a system and a method for detecting anomalies that does not make 15 more complex a home automation system already installed in a building, especially from a construction point of view. This problem is solved and at least one of these purposes are at least partially achieved by the invention through a system and a method for detecting anomalies according to one or more of the respective units 20 claims. In this description, as well as in the claims thereto enclosed, some terms and expressions are considered to be, unless of different explicit indications, the meaning expressed in the definitions which follow. 25 The term “electrical appliance” means one or more devices electrical and / or one or more electronic devices. Preferably, said one or multiple electrical devices and / or one or more electronic devices are present in a home or, more generally, in a building, and are designed for perform a particular function. 5 The term “anomaly” means a change or deviation of the data indicative of an operating state assumed by a device electric from a predetermined set of data. The term “objects” preferably refers to human beings or animal beings. 10 Summary of the invention In its first aspect, the present invention is directed to a system for detect anomalies, particularly in the use of a home automation system. The system comprises at least one connected electronic device operationally to a communication network and to a device 15 electrical of a home automation system, the electrical appliance being configured to assume a plurality of predefined operating states. Preferably, the system also includes the home automation system. The home automation system can be installed in a domestic building or not. Said at least one electronic device is configured to transmit 20 in the communication network data signals including indicative data of an operating state assumed by the electrical appliance between the above plurality of predefined operating states. Preferably, the data signal includes additional information in addition to the data indicating the operating state assumed by the device 25 electric. In particular, further information may include data representing the moment in time (e.g. date and / or time) at which the electrical appliance assumes this operating state. More preferably, the electrical appliance can take at least two predefined operating states, i.e. a first operating state (e.g. 5 on, open or active) and a second operating state (e.g. off, closed or inactive). Preferably, the electrical appliance is configured to transmit to at least one electronic device information about its status operational (for example through the aforementioned communication network or 10 beyond the network) and / or at least one electronic device is configured to read the operating status of the electrical appliance (e.g. through the aforementioned communication network or beyond the network). Preferably, said at least one electronic device comprises a control device configured to control the operation 15 of the electrical appliance (in particular for at least one of: activate, operate, deactivate, turn on, turn off and adjust the appliance electric), more preferably through the communication network. The system comprises a connected data processing device operationally to the electronic device through the network 20 communication and configured to receive data signals transmitted by the electronic device. The data processing device comprises a configured module to detect an anomaly in the data signals received from the at least one electronic device and to generate an indicative output signal 25 of the presence of said anomaly on the basis of said detection. Thanks to these features the system allows to detect anomalies in the use of the home automation system by a user (intended as a or more people), especially when using an electrical appliance, and, consequently, to recognize a significant deviation of the 5 user behavior compared to a usual trend. Furthermore, the use of data indicative of an operational status of the electrical appliance allows you to advantageously exploit the information normally circulating in the home automation system for the detect anomalies without the burden of using specific sensors. 10 This results in a relatively simple system according to the invention and economical to implement. Furthermore, if said at least one electronic device includes the control device you would get a single device capable of both to control the electrical appliance and detect its status. This avoids 15 advantageously to install excessive physical devices in the plant home automation. In its second aspect, the present invention is directed to a method to detect anomalies. The method includes: 20 • operationally connect an electronic device to a network communication of a home automation system, • operationally connect the electronic device to a electrical appliance of the home automation system, the appliance electric being configured to assume a plurality of states 25 predefined operations and said electronic device being configured to transmit signals in the communication network data including data indicative of an assumed operational state from the electrical appliance between said plurality of operating states predefined, 5 • operationally connect a data processing device to the electronic device through the communication network, in which the data processing device comprises a module configured to detect an anomaly in the data signals received from the electronic device, 10 • transmit data signals in the communication network using the electronic device, • receive in the module the data signals transmitted by the device electronic, • detect via the module if an anomaly is present in the signals 15 data received, • generate an output signal indicative of the module presence of said anomaly on the basis of said detection. In particular, the method may include an evaluation phase of the module based on the received data signals. The evaluation phase of the 20 the above mentioned module may include the phase of detecting by means of the module if an anomaly is present in the received data signals. The above mentioned evaluation phase preferably includes performing the module based on the data signals transmitted by the electronic device. The method formulated above therefore allows us to detect anomalies 25 in the use of the home automation system. In at least one of the above aspects, the present invention may further exhibit at least one of the preferred characteristics set out above followed. In at least one embodiment of the present invention, the Module 5 includes one of: • a machine learning model trained for detect said anomaly in the data signals received from said at least an electronic device, and • a probabilistic algorithm configured to detect said 10 anomaly in the data signals received from said at least one device electronic based on at least one probability comparison that the said electrical appliance assumes the operating state represented in said data signals with a predetermined threshold. The machine learning model is preferably configured 15 to generate an output signal indicative of the presence of said anomaly based on said detection. Preferably, said anomaly is detected by the machine learning model if the data signals received from the at least one electronic device differ from those predicted by the machine learning model based on 20 of his learning, preferably in a period of time default. The probabilistic algorithm is preferably configured to generate an output signal indicating the presence of said anomaly on the basis of said detection. 25 In at least one embodiment of the present invention, the machine learning model is subjected to a phase of training aimed at training the model to detect said anomaly. Preferably, the data sent as input to the model for the training correspond to a set of data signals transmitted by at least 5 an electronic device within a first time interval pre-established, where the set of data signals includes data indicative of the operating states assumed by the electrical appliance in the first interval pre-established time. In at least one embodiment of the present invention, the 10 machine learning model is trained to detect an anomaly in the data signals received from at least one device electronically within a second predefined time interval. Preferably, the second default time interval is smaller of the first default interval. 15 In at least one embodiment of the present invention, the machine learning model is configured to generate said exit signal indicating the presence of an anomaly inside of the second default time interval if: • this anomaly is detected by this learning model 20 automatic in one of the data signals received in the second interval default time, or • this anomaly is detected by this learning model automatic in all data signals received in the second interval default time, or 25 • this anomaly is detected by this learning model automatic in at least a predetermined percentage of signals data received in the second predefined time interval (e.g. example in at least 50% + 1 of the data signals received in the second predefined time interval). 5 In at least one embodiment of the present invention, the machine learning model includes at least one of the following: following machine learning algorithms: Isolation Forest, Gaussian Mixture Model, One-Class SVM and Elliptic Envelope. In addition to the machine learning model, the module can 10 preferably implement statistical techniques, such as at least one of mean and variance, to process the information produced by the machine learning model so as to generate the above mentioned exit signal. In at least one embodiment of the present invention, the 15 module including the probabilistic algorithm is subjected to a training phase. Preferably, the data sent as input to the module for the training phase correspond to a set of data signals transmitted by at least one electronic device within a first time interval 20 pre-established. The data signal set includes data indicative of the operating states taken by the electrical appliance in the first time interval pre-established. For each operating state that the electrical appliance can assume, the phase 25 training allows the probabilistic algorithm to determine the probability that this operating state will be assumed by the device electric at respective moments in time included in the first interval pre-set time (for example for each minute of each day belonging to the first pre-established time interval) and, 5 preferably, further probability that such operating state is taken by the electrical appliance at times including one or more different sub-intervals of the first interval respectively pre-established time. A sub-interval can correspond to one between a season of the year 10 (spring, summer, autumn or winter), a month of the year (January, February, …, November and December), the Mondays of the year, the Tuesdays of the year, Wednesday of the year, Thursday of the year, Friday of the year, Saturday of the year Sunday of the year, weekdays of the year and days holidays of the year. 15 In at least one embodiment of the present invention, the probabilistic algorithm is configured to compare at least one probability that the said electrical appliance assumes the operating state represented in a data signal received from the at least one device electronic with a predetermined threshold. 20 Preferably, at least one such probability corresponds to the probability determined by the probabilistic algorithm in relation to the first predetermined time interval. Alternatively, at least one such probability may correspond to a plurality of probabilities determined by the probabilistic algorithm 25 respectively in relation to the first pre-established time interval and to one or more different sub-intervals of the first time interval pre-established. Preferably, the probabilistic algorithm is configured to detect an anomaly in the received data signal if a predefined percentage of 5 said at least one probability is less than, or equal to, the threshold pre-established. More preferably, the probabilistic algorithm is configured to detect an anomaly if the majority of said at least one probability is less than or equal to the pre-established threshold. 10 In at least one embodiment of the present invention, the probabilistic algorithm is configured to generate said signal exit indicating the presence of an anomaly within a second default time interval if: • this anomaly is detected by this probabilistic algorithm in 15 one of the data signals received within said second interval default time, or • this anomaly is detected by this probabilistic algorithm in all data signals received in the second time slot default, or 20 • said anomaly is detected by said probabilistic algorithm in at least a predetermined percentage of the data signals received in the second predefined time interval (for example in at least 50% + 1 of the data signals received in the second interval default time). 25 In addition to the probabilistic algorithm, the module can implement preferably statistical techniques, such as average or variance, to process the information produced by the algorithm probabilistic so as to generate the aforementioned output signal. Preferably, the module is configured to detect such anomaly in 5 relation to a specific operating state of the electrical appliance. More preferably, the module is configured to generate a signal output indicating the presence of said anomaly if the state operational represented in at least one of the received data signals differs from the operating state expected by the module. 10 The term “operational state expected by the module” means the operating state predicted by the machine learning model or it means the operating state to which the probabilistic algorithm has associated a probability greater than, or equal to, a threshold pre-established. 15 Preferably, the specific operating state of the electrical appliance can correspond to one of on, off, open, closed, active and inactive. These features prove to be particularly advantageous in order to optimize the functioning of the module, in particular to optimize 20 the detection of an anomaly in relation to specific operating states of the electrical appliance. In addition or as an alternative to the above features, the module is configured to generate an output signal indicative of the absence of this anomaly occurs if the received data signals correspond to those expected 25 from the form. In at least one embodiment of the present invention, the electrical appliance includes at least one of: • at least one lighting device that represents preferably a first type of electrical appliance, 5 • at least one shutter which preferably represents a second type of electrical appliance, • at least one contact device (preferably contact magnetic) to signal the opening or closing of a window which preferably represents a third type of device 10 electric, • at least one electrical outlet which preferably represents a fourth type of electrical appliance, • at least one heating device and, preferably, cooking which preferably represents a fifth type of 15 electrical appliance, • a thermal conditioning system designed for condition (directly or indirectly) the air temperature within one or more rooms of a building that represents preferably a sixth type of electrical appliance, 20 • at least one fan coil unit which preferably represents a seventh type of electrical appliance, • at least one thermostatic valve which preferably represents an eighth type of electrical appliance, • at least one actuator for controlling valves of a system 25 air conditioning which preferably represents a ninth type of electrical appliance, • a device for measuring energy production or consumption which preferably represents a tenth type of appliance electric, and 5 • a smart card reader device equipped with NFC / RFID that preferably represents an eleventh type of device electric. In at least one embodiment of the present invention, the electronic device is operationally connected to at least one 10 lighting device and is configured to generate a signal data including data indicative of an assumed operational state from at least one lighting device, in particular of a first operating state or a second operating state of the at least one lighting device. 15 The lighting device is in the first operating state if it is turned on, that is, if it is in function to emit light (e.g. constant, dimmed or intermittent), while it is in the second operating state if it is worn out. In particular, if the electronic device is connected to a plurality 20 of lighting devices then the electronic device is configured to generate a data signal comprising indicative data of a first operating state if at least one lighting device of the plurality of lighting devices is turned on, or a signal data including data indicative of a second operational state if all 25 lighting devices of the plurality of lighting devices they are off. In at least one embodiment of the present invention, the electronic device is operationally connected to at least one shutter and is configured to generate a data signal comprising 5 data indicative of an operating state assumed by at least one shutter, in particular of a first operational state or a second state operating at least one shutter. A shutter is in the first operating state if it is open (partially or fully open) while it is in the second operating state if it is closed. 10 In particular, if the electronic device is connected to a plurality of shutters then the electronic device is configured to generate a data signal comprising data indicative of a first state operational if at least one of the shutters is open, or a data signal comprising data indicative of one second 15 operating state if all the shutters of the plurality of shutters are closed. In at least one embodiment of the present invention, the electronic device is operationally connected to the system thermal conditioning and is configured to generate a data signal 20 including data indicative of an operating state assumed by the system of thermal conditioning, in particular of a first operational state, a second operational state, a third operational state or a fourth operating status of the thermal air conditioning system. The thermal conditioning system is in the first operational state if it is 25 in operation and is set to heat the air temperature inside one or more rooms of a building, it is in the second state operational if it is in operation and is set to cool the air temperature inside one or more rooms of a building, is in third operational state if it is not in operation and is set for 5 heat the air temperature inside one or more rooms of a building, while it is in the fourth operational state if it is not in operation and is designed to cool the air temperature inside one or multiple rooms in a building. In at least one embodiment of the present invention, the 10 electronic device includes a thermostat set up for detect the air temperature of a room. Additionally or alternatively, the thermostat can be set up for detect the temperature of an element delimiting the environment, such as for example a structural element of a room, in particular the 15 room screed. Preferably, the electronic device comprising the thermostat is operationally connected to the thermal conditioning system and is configured to generate a data signal comprising both data indicative of the operating state assumed by the air conditioning system 20 thermal which is indicative of the temperature detected by the thermostat. In at least one embodiment of the present invention, the system comprises a plurality of connected electronic devices operationally to the data processing device and to respective electrical appliances of the home automation system. 25 In at least one embodiment of the present invention, the electrical appliances connected to a plurality of electronic devices can assume at least a first operational state (for example on, open or active) and a second operating state (e.g. off, closed or inactive). 5 Electrical appliances connected to a plurality of electronic devices define a set of electrical appliances. The set of electrical appliances can assume a plurality of states predefined operations. Preferably, the set of electrical appliances assumes a first state 10 operational if at least one of the electrical appliances that make up said together it is in the first operational state, while it assumes a second state operational if all the electrical appliances of the said assembly are in the second operational state. Preferably, the module is configured to detect an anomaly in the 15 data signals received from the plurality of electronic devices and is configured to generate an output signal indicative of presence of said anomaly on the basis of said finding. This forecast therefore allows us to detect anomalies associated with a set made up of several electrical appliances. 20 Preferably, this anomaly is detected by the module if the signals The data received differs from that expected by the form. Preferably, this module is configured to detect this anomaly. in relation to a specific operating state of the set of devices electrical. In particular, the module is configured to generate a signal 25 output indicating the presence of said anomaly if the state operation of the set of electrical appliances differs from that provided for by the form. In addition to or as an alternative to the plurality of electronic devices, the system preferably comprises at least one sensor device 5 operationally connected to the data processing device through the communication network. The at least one sensor device comprises a sensor and is configured to transmit data signals in the communication network including data indicative of a physical quantity detected by the 10 sensor. Preferably, the sensor is included in the home automation system. In at least one embodiment of the present invention, the system comprises a plurality of sensor devices. This allows you to take advantage of the information provided by one or more sensors to detect anomalies. 15 In at least one embodiment of the present invention, the sensor device includes an object detection sensor in an environment, such sensor device being configured to generate data signals including data indicative of absence or presence of objects in that environment. 20 In at least one embodiment of the present invention, the sensor device includes a sensor for the motion of objects in an environment, such sensor device being configured to generate data signals including data indicative of movement in that environment. In at least one embodiment of the present invention, the 25 sensor device comprises an air temperature sensor, such sensor device being configured to generate a data signal including indicative data of the air temperature detected in an environment. In at least one embodiment of the present invention, the 5 data processing device comprises a plurality of modules. Preferably, each of said modules comprises a related machine learning model or probabilistic algorithm. Each module of the plurality of modules is configured to detect an anomaly in the data signals received from a related group of devices 10 consisting of at least one electronic device and / or at least one sensor device, and is configured to generate a corresponding signal output indicative of the presence of said anomaly on the basis of said detection. The provision of a plurality of modules advantageously allows for 15 identify different anomalies, each associated with a relative group of devices. Preferably, said anomaly is detected by said module if the data signals received from it differ from those expected by that module. 20 Preferably, electrical appliances belonging to the same group of devices are installed in the same room of a building, for example example in the same room. Preferably, electrical appliances belonging to the same group of devices are of the same type, that is, for example, they are all 25 lighting devices or all shutters. The fact that a group of devices consists of electrical appliances of the same type advantageously allows you to define a group homogeneous of devices and therefore to obtain a module of machine learning specialized in detecting anomalies in 5 relation to this homogeneous group of devices. Preferably, the plurality of device groups comprises at least a first group of devices and a second group of devices, the type of electrical appliances belonging to the first group being different from the type of electrical appliances belonging to the second group. 10 In at least one embodiment of the present invention, at least two modules of the aforementioned plurality of modules are configured to detect an anomaly in the data signals received from the same group of devices but in relation to different operating states of the electrical appliances that belong to this group of devices. 15 In other words, the plurality of modules may comprise a first module configured to detect an anomaly in the data signals received from a group of devices and in relation to an initial operating state of the electrical appliances belonging to this group of devices, and a second module configured to detect an anomaly in the received data signals 20 from the same group of devices and in relation to a second state operation of the above-mentioned electrical appliances. As an example, the first module can be configured to detect an anomaly in relation to a device power-on state of lighting pertaining to a first group of devices while the 25 second module can be configured to detect an anomaly in relation to a state of off lighting devices above mentioned. In at least one embodiment of the present invention, the data processing device is physically distinct from the device 5 electronic system, or from each electronic device if the system includes a plurality of electronic devices. Preferably, the data processing device is physically separate also from said at least one sensor device if the system understand the latter. 10 This feature allows you to advantageously use electronic devices (in particular control devices) electrical appliances) and sensor devices with reduced sensing capacity calculation, therefore economical to carry out, since the detection of anomalies is delegated to a separate entity, i.e. the device 15 data processing. In at least one embodiment of the present invention, the home automation system includes the data processing device. Therefore, in this case, the data processing device is preferably installed in a building equipped with a home automation system. 20 Alternatively, in at least one embodiment hereof invention, the data processing device is included in a cloud computing platform. Therefore, in this case, the data processing device is remote from the home automation system and the electrical device(s) included in the system 25 to detect anomalies. This feature proves to be particularly advantageous in terms of lightening the hardware capacity of electronic devices and / or of electrical appliances. In addition, the forecast of the platform of cloud computing can help you gain benefits in terms of 5 scalability, flexibility and / or applicability to existing electrical appliances in the home automation system. Preferably, the system includes a gateway device configured to connect the plant's communication network home automation to the network used by the cloud computing platform. 10 In at least one embodiment of the present invention, the network home automation system communication includes at least one of a wired network, a wireless network, a Bluetooth mesh network, and a KNX. In at least one embodiment of the present invention, the 15 system, especially the data processing device, is configured to generate an alarm signal if the output signal generated by the data processing unit module (or at least one module if (the data processing unit comprises a plurality of modules) indicates the presence of an anomaly in the data signals received by it. 20 Preferably, the alarm signal corresponds to one of a signal sound, a visual signal and a message transmitted through a wireless communication, or a combination thereof. In at least one embodiment of the present invention, the system includes an interface intended to be used by a 25 home automation system users. The interface is operationally connected. to the data processing device and configured to receive the signal of the aforementioned alarm. Preferably, the interface is included in a mobile device. With particular reference to the method for detecting anomalies, such 5 method preferably includes: • operationally connect a plurality of electronic devices to the data processing device and related electrical appliances of the home automation system, and / or operationally connect at least one sensor device to the data processing device, at least 10 a sensor device comprising a sensor and being configured to transmit signals in the communication network data including data indicative of a detected physical quantity from said sensor, • define a plurality of groups of devices each consisting of 15 at least one electronic device and / or from at least one device sensor, wherein the data processing device comprises a plurality of modules, each module being configured for detect an anomaly in the data signals received from a relative device group of the plurality of device groups, 20 • transmit data signals in the communication network using the plurality of groups of devices, • receive in the modules the data signals transmitted by the respective groups of electronic devices, • detect via the modules if an anomaly is present in the signals 25 data received, • generate indicative output signals via the respective modules of the presence of respective anomalies based on the findings. Preferably, an anomaly is detected by one module of the plurality of modules if the data signals received from that module differ from those 5 provided for by it. In at least one embodiment of the present invention, the method involves retraining at least one learning model automatic or re-determine the probability that the electrical appliance assume an operational state at respective points in time based on 10 of data signals transmitted by a related group of electronic devices within a pre-established first time interval. Preferably, at least one machine learning model is retrained at a predetermined initial frequency. In at least one embodiment of the present invention, the 15 method includes evaluating the machine learning model at a predetermined execution frequency preferably greater than said first frequency. This is particularly advantageous for correctly detecting anomalies in particular periods of time such as, for example, in periods 20 prolonged absence by a system user from the building that incorporates the home automation system. Brief description of the drawings The features and advantages of the present invention better will result from the detailed description of his favorite examples of 25 realization, illustrated for indicative and non-limiting purposes with reference to the attached drawings, in which: • Figure 1 is a schematic view of a system for detecting anomalies according to an embodiment of the invention, • Figure 2 is a schematic view of a further system for 5 detecting anomalies according to one embodiment of the invention, the system comprising a plurality of electronic devices and electrical appliances arranged in two distinct rooms of a building, and • Figure 3 is a flowchart representing a method 10 for detecting anomalies according to one embodiment of the invention. Description of embodiments of the invention With reference to the enclosed figures, with 100 or 100' it is overall indicated a system to detect anomalies (hereinafter also identified 15 with “system” for brevity). With particular reference to figure 1, system 100 comprises preferably a home automation system 1 comprising a network of communication 2 and an electrical appliance 3. The electrical appliance 3 It is configured to assume a plurality of predefined operating states. 20 The system 100 also comprises an electronic device 4 connected operationally to the communication network 2 and to the electrical appliance 3. The electronic device 4 is configured to transmit in the network of communication 2 data signals S including data indicative of one operating state assumed by the electrical appliance 3 among the plurality of 25 predefined operating states. In detail, the electronic device 4 comprises a control device 5 configured to control the operation of the electrical appliance 3 through the mains communication 2. System 100 further comprises a data processing device 5 6 operationally connected to the electronic device 4 through the communication network 2 and configured to receive data signals S transmitted by the electronic device 4. The processing device data 6 includes a module, in particular corresponding to a machine learning model 7, the learning model 10 automatic 7 being trained to detect an anomaly in the signals data S received from the electronic device 4, in particular within a predetermined time interval, for example at least 10 minutes. The machine learning model 7 (hereinafter referred to as also “model” for short) is also configured to generate a 15 output signal O indicating the presence of said anomaly on the basis of said detection. Figure 2 shows a further example of a system schematically. to detect anomalies, indicated compressively with 100'. The 100' system differs from the 100 system described above in that 20 include a plurality of electronic devices, appliances electrical and machine learning models. The plurality of electronic devices and electrical appliances are arranged in two distinct rooms A,B of a building E. In detail, in room A there is a first device 25 electric, i.e. a first lighting device 3a, a second electrical appliance, i.e. a first shutter 3b, and a third electrical appliance, i.e. a second shutter 3c. A first electronic device 4a, a second electronic device 4b, a third electronic device 4c and a fourth device 5 4d electronics are arranged in room A. The first electronic device 4a is operationally connected to the lighting device 3a and is configured to generate a signal S1 data including data indicative of a first operational state or a second operating state assumed by the first device 10 lighting 3a, wherein the first lighting device 3a is in the first operating state if it is on while it is in the second operating state if it is off. Preferably, the data signal S1 also includes data representing the time point in time when the first device electric is in the first operating state or in the second operating state. 15 The second electronic device 4b is operationally connected to the first shutter 3b and is configured to generate a data signal S2 including data indicative of an initial operational state or of a second operating state assumed by the first shutter 3b, in which the first shutter 3b is in the first operating state if it is open while it is in the 20 seconds operating state if closed. Preferably, the S2 data signal It also includes data representing the time point in time at which the second electrical appliance is in the first operating state or in the second operational status. The third electronic device 4c is operationally connected to the 25 second shutter 3c and is configured to generate an S3 data signal including data indicative of an initial operational state or of a second operating state assumed by the second shutter 3c, in which the second shutter 3c is in the first operating state if it is open while it is in the second operating state if it is closed. Preferably, the data signal 5 S3 also includes data representing the time point in time at which the third electrical appliance is in the first or second operating state operational status. The first shutter 3b and the second shutter 3c form a first set of electrical appliances. The first set of electrical appliances 10 assumes a first operational state if at least one of the first shutters 3b and the second shutter 3c is open, and a second operating state if the first shutter 3b and the second shutter 3c are closed. The electronic devices 4a, 4b and 4c include respective control devices command 5 configured to control the operation of the respective 15 electrical appliances 3a, 3b and 3c via communication network 2. The fourth electronic device 4d includes a thermostat T1 designed to detect an air temperature in room A and is operationally connected to a 3D thermal conditioning system, the latter representing a fourth electrical appliance of the 20 system 100'. The 3D thermal conditioning system is designed to condition the air temperature inside the room A of the E building. The 3d thermal conditioning system can assume a first state operational, a second operational state, a third operational state, or a 25th operational state. The 3D thermal conditioning system is in the first operational state if it is in operation and is set to heat the air temperature within environment A, it is in the second operational state if it is in function and is designed to cool the air temperature 5 inside environment A, is in the third operational state if it is not in function and is designed to heat the air temperature inside environment A, while it is in the fourth operational state if not it is in operation and is set to cool the air temperature within environment A. 10 The fourth electronic device 4d is also configured to generate an S4 data signal comprising both status-indicating data operational assumed by the 3d thermal conditioning system, which data indicative of the temperature detected by thermostat T1. Preferably, The S4 data signal also includes data representing the moment 15 time in which the fourth electrical appliance is in the first state operational, in the second operational state, in the third operational state or in the how much operational status and data representing the time moment in which thermostat T1 detects the temperature. As for room B, there is a fifth device in it 20 electric 3e1+3e2, consisting of a second lighting device 3e1 and a third lighting device 3e2, a sixth device electric, i.e. a third 3f shutter, and a seventh appliance electric, i.e. a fourth 3g shutter. A fifth electronic device 4e, a sixth electronic device 4f, 25 a seventh 4G electronic device and an eighth device electronic 4h are arranged in room B. The fifth electronic device 4e is operationally connected to the second 3e1 lighting device and the third lighting device 3e2 illumination and is configured to generate an S5 data signal 5 including data indicative of a first operational state or of a second operating state assumed by the fifth electrical appliance 3e1+3e2, where the fifth electrical appliance is in the first state operational if at least one of the second lighting device 3e1 and the third lighting device 3e2 is on while it is in the second 10 operating state if both lighting devices 3e1 and 3e2 are off. Preferably, the S5 data signal also includes data representing the time point in time when the fifth device electric is in the first operating state or in the second operating state. The sixth electronic device 4f is operationally connected to the third 15 3f shutter and is configured to generate an S6 data signal including data indicative of an initial operational state or of a second operating state assumed by the third shutter 3f, in which the third 3f shutter is in the first operating state if it is open while it is in the second operating state if it is closed. Preferably, the S6 data signal 20 also includes data representing the time point in time in which the sixth electrical appliance is in the first operating state or in the second operational status. The seventh 4G electronic device is operationally connected to the fourth 3g shutter and is configured to generate an S7 data signal 25 including data indicative of a first operational state or of a second operating state assumed by the fourth shutter 3g, in which in which the fourth shutter 3g is in the first operating state if it is open while It is in the second operating state if it is closed. Preferably, the signal S7 data also includes data representing the time point in time 5 where the seventh electrical appliance is in the first operating state or in the second operational state. The third shutter 3f and the fourth shutter 3g form a second set of electrical appliances. The second set of electrical appliances assumes a first operational state if at least one of the third shutters 10 3f and the fourth shutter 3g is open, and a second operating state if the third shutter 3f and fourth shutter 3g are closed. The 4e, 4f and 4g electronic devices include respective command 5 configured to control the operation of the respective 3e1+3e2, 3f, and 3g electrical appliances through the network 15 communication 2. The eighth 4h electronic device includes a T2 thermostat set up to detect an air temperature in room B and is configured to generate an S8 data signal comprising data indicative of the temperature detected by thermostat T2. Preferably, 20 the S8 data signal also includes data representing the moment time in which thermostat T2 detects the temperature. The 100' system also includes a DS1 sensor device which includes an object detection sensor. The sensor device DS1 is placed in environment B and is configured to generate a 25 S9 data signal including data indicative of absence or presence of objects in the environment B. Preferably, the 100' system comprises seven groups of devices defined as follows: a first group of devices corresponding to the first electronic device 4a, a second group of devices that 5 is formed by the second electronic device 4b and the third device electronic 4c, a third group of devices corresponding to the fourth 4d electronic device, a fourth group of devices corresponding to the fifth electronic device 4e, a fifth group of devices which is formed by the sixth electronic device 4f and the 10 seventh 4G electronic device, a sixth group of devices corresponding to the eighth 4h electronic device, and a seventh device group corresponding to the DS1 sensor device. The data processing device 6 of the system 100' comprises a plurality of modules corresponding to respective learning models 15 automatic devices trained to detect an anomaly in received data signals from respective device groups. In particular, the 100' system includes: • a first model, called first virtual sensor 7a, trained to detect an anomaly in the S1 data signals received from the first group 20 of devices, in relation to the first operating state of the first 3a lighting device, • a second model, called second virtual sensor 7b, trained to detect an anomaly in the S1 data signals received from the first group of devices, in relation to the second operating state 25 of the first lighting device 3a, • a third model, called the third virtual sensor 7c, trained to detect an anomaly in the data signals S2 and S3 received from the second group of devices, in relation to the first operating state of the first set of electrical appliances, 5 • a fourth model, called the fourth virtual sensor 7d, trained to detect an anomaly in the received S2 and S3 data signals from the second group of devices, in relation to the second state operation of the first set of electrical appliances, • a fifth model, called the fifth virtual sensor 7e, 10 trained to detect an anomaly in the S4 data signals received from the third group of devices, in relation to the first operating state of the 3D thermal and temperature conditioning system (in (particularly in relation to the temperature trend) detected by the thermostat T1, 15 • a sixth model, called the sixth virtual sensor 7f, trained to detect an anomaly in the S4 signals received from the third group of devices, in relation to the second operating state of the system 3D thermal conditioning and temperature (in particular (based on the temperature trend) detected by thermostat T1, 20 • a seventh model, called the seventh virtual sensor 7g, trained to detect an anomaly in the S4 data signals received from the third group of devices, in relation to the third operating state of the 3D thermal and temperature conditioning system (in (particularly in relation to the temperature trend) detected by the thermostat 25 T1, • an eighth model, called the eighth virtual sensor 7h, trained to detect an anomaly in the S4 data signals received from the third group of devices, in relation to the fourth operating state of the 3D thermal and temperature conditioning system (in 5 (particularly in relation to the temperature trend) detected by the thermostat T1, • a ninth model, called the ninth virtual sensor 7i, trained to detect an anomaly in the S5 signals received from the fourth group of devices, in relation to the first operating state of the fifth 10 electrical appliance 3e1+3e2, • a tenth model, called the 7l virtual tenth sensor, trained to detect an anomaly in the S5 signals received from the fourth group of devices, in relation to the second operating state of the fifth electrical appliance 3e1+3e2, 15 • an eleventh model, called the eleventh virtual sensor 7m, trained to detect an anomaly in the S6 and S7 data signals received from the fifth group of devices, in relation to the first state operation of the second set of electrical appliances, • a twelfth model, called the twelfth virtual sensor 7n, 20 trained to detect an anomaly in the received S6 and S7 data signals from the fifth group of devices, in relation to the second state operation of the second set of electrical appliances, • a thirteenth model, called the thirteenth virtual sensor 7th, trained to detect an anomaly in received S8 data signals 25 from the sixth group of devices, in relation to the detected temperature from thermostat T2, • a fourteenth model, called the fourteenth sensor virtual 7p, trained to detect an anomaly in S9 signals received from the seventh group of devices, in relation to the absence of 5 objects in environment B, and • a fifteenth model, called the fifteenth virtual sensor 7q, trained to detect an anomaly in the S9 signals received from the seventh group of devices, in relation to the presence of objects in environment B. 10 A further model may also be envisaged, i.e. a further virtual sensor, trained to detect an anomaly in data signals received from thermostat T1 and indicative of the temperature from it detected. Furthermore, each model of the plurality of learning models 15 automatic 7a-7q is configured to generate a relative signal exit indicating the presence of said anomaly if the latter is detected by this machine learning model. Specifically, the first model 7a is configured to generate a first O1 output signal indicating the presence of an anomaly if the 20 data signals received by it represent the second operating state of the first lighting device 3a while the operating state expected by the first virtual sensor 7a corresponds to the first state operating. The second model 7b is configured to generate a second signal 25 O2 output indicative of the presence of an anomaly if the given signals received from it represent the first operational state of the first lighting device 3a while the expected operating state from second virtual sensor 7b corresponds to the second operating state. The third model 7c is configured to generate a third output signal 5 O3 indicative of the presence of an anomaly if the signals given by it received represent the second operating state of the first set of electrical appliances while the operating state expected by the third sensor virtual 7c corresponds to the first operating state. The fourth 7d model is configured to generate a fourth signal 10 output O4 indicative of the presence of an anomaly if the data signals received from it represent the first operational state of the first set of electrical appliances while the expected operating state from third virtual sensor 7d corresponds to the second operating state. The fifth model 7e is configured to generate a fifth signal 15 output O5 indicative of the presence of an anomaly if the data signals received from it represent the first operating state of the system 3D thermal conditioning and a failure to increase the temperature detected by thermostat T1 while the fifth sensor virtual 7e provides that the 3d thermal conditioning system is 20 in the first operating state and that the temperature detected by the thermostat T1 increments. The sixth model 7f is configured to generate a sixth output signal O6 indicative of the presence of an anomaly if the signals given by it received represent the second operating state of the system 25 3d thermal conditioning and a failure to decrease the temperature detected by thermostat T1 while the sixth virtual sensor 7f provides that the 3d thermal conditioning system is in the second operating state and that the temperature detected by the thermostat T1 decreases. 5 The seventh model 7g is configured to generate a seventh signal output O7 indicating the presence of an anomaly if the data signals received from it represent the third operational state of the system 3D thermal conditioning and an increase in temperature detected by thermostat T1 while the seventh virtual sensor 7g 10 provides that the 3d thermal conditioning system is in the third operating state and that the temperature detected by thermostat T1 is not increments. The 7h model is configured to generate an eighth signal output O8 indicating the presence of an anomaly if the data signals 15 received from it represent the fourth operational state of the system 3D thermal conditioning and a decrease in temperature detected by thermostat T1 while the eighth virtual sensor 7h provides that the 3d thermal conditioning system is in the fourth state operational and that the temperature detected by thermostat T1 is not 20 decreases. The ninth 7i model is configured to generate a ninth output signal O9 indicative of the presence of an anomaly if the signals given by it received represent the second operational state of the fifth electrical appliance 3e1+3e2 while the expected operating state from 25 ninth virtual sensor 7i corresponds to the first operating state. The tenth model 7l is configured to generate a tenth signal output O10 indicating the presence of an anomaly if the data signals received from it represent the first operational state of the fifth electrical appliance 3e1+3e2 while the expected operating state from 5 tenth virtual sensor 7l corresponds to the second operating state. The eleventh 7m model is configured to generate an eleventh output signal O11 indicating the presence of an anomaly if the data signals received from it represent the second operating state of the second set of electrical appliances while the operating state 10 expected by the eleventh virtual sensor 7m corresponds to the first state operating. The twelfth 7n model is configured to generate a twelfth O12 output signal indicating the presence of an anomaly if the data signals received from it represent the first operating state of the 15 second set of electrical appliances while the expected operating state from the twelfth virtual sensor 7n corresponds to the second state operating. The thirteenth model 7o is configured to generate a thirteenth output signal O13 indicating the presence of an anomaly if the 20 temperature detected by thermostat T2 differs from the temperature predicted by the thirteenth virtual sensor 7o. The fourteenth 7p model is configured to generate a fourteenth output signal O14 indicating the presence of a anomaly if the data signals received from it indicate the presence of 25 objects in environment B while the fourteenth model 7p provides the absence of objects in the environment B. The fifteenth 7q model is configured to generate a fifteenth Output signal O15 indicating the presence of an anomaly if the data signals received from it indicate the absence of objects in the environment 5 B while the fifteenth model 7q provides for the presence of objects in environment B. The data processing device 6 of the system 100' is physically distinct from the 4a-4h electronic devices and the DS1 sensor device because it is included in a cloud computing platform 8. 10 It should be noted that the virtual sensors thus obtained, residing advantageously in a cloud computing platform, they can be used by further authorised data processing systems for perform specific analyses or tasks, for example based on signals exit O1-15. 15 The systems 100 and 100', illustrated in figures 1 and 2, have been described in relation to one or more modules corresponding to respective models of machine learning. However, one or more of these modules may correspond to respective probabilistic algorithms instead of the models of machine learning, called probabilistic algorithms falling within 20 within the scope of protection defined by the invention as claimed. An example of probabilistic algorithm evaluation based on ten data signals received on February 6, 2024 are given below exposed. Table 1 shows the operating states as an example of the first 3a lighting device in relation to a period 25 [0.9] equal to nine minutes drawn from the day February 6, 2024. In period [0,9] the module comprising the probabilistic algorithm It therefore receives ten data signals: the first data signal at minute 0 and the next 9 data signals spaced one minute apart. Table 1 minute Operating status (1 = first operating state; 0 = second operating state) 0 1 1 1 2 1 3 0 4 0 5 1 6 0 7 0 8 0 9 0 5 Tables 2 and 3 below show in illustrative terms the probability that the first 3a lighting device will take the first operating state (“on”) and the second operating state (“off”) compared to the same ten-minute period. In detail, the probabilities of each column of the table were determined by the algorithm 10 probabilistic based on data signals received in time intervals different during the training phase, namely: general_tab column: probabilities determined by the algorithm probabilistic based on all the data sent as input to the module in the training phase, winter_tab column: probabilities determined by the algorithm probabilistic based on data relating to the “winter” season and sent as input to the module in the training phase, 5th column tab_February: probabilities determined by the algorithm probabilistic based on data relating to the month of "February" and sent in entry to the module in the training phase, Tuesday_tab column: probabilities determined by the algorithm probabilistic based on data relating to the days "Tuesday" and sent in 10 entry to the module in the training phase, weekday table column: probabilities determined by the algorithm probabilistic based on data relating to “weekdays” and sent in entry to the module in the training phase. The columns “tab_winter”, “tab_february”, “tab_tuesday” and 15 “tab_feriale” were extracted by the probabilistic algorithm from the set of columns relating to the season, month, day of the week and type of day (weekday / holiday) as they are relevant to the day 6 February 2024 under review. Table 2 Probability of the first operational state (between 0 and 1) minute general tab winter tab February tab Tuesday tab weekday 0 0.001 0.002 0.09 0.0011 0.0009 1 0.0012 0.0021 0.11 0.0012 0.001 2 0.0011 0.002 0.1 0.0011 0.0012 3 0.001 0.002 0.12 0.001 0.001 4 0.0012 0.0022 0.15 0.009 0.001 5 0.95 0.94 0.98 0.99 0.901 6 0.001 0.002 0.09 0.0011 0.0009 7 0.0012 0.0021 0.11 0.0012 0.001 8 0.0011 0.002 0.1 0.0011 0.0012 9 0.001 0.002 0.12 0.001 0.001 Table 3 Probability of the second operational state (between 0 and 1) minute general tab winter tab February tab Tuesday tab weekday 0 0.999 0.998 0.91 0.9989 0.9991 1 0.9988 0.9979 0.89 0.9988 0.999 2 0.9989 0.998 0.9 0.9989 0.9988 3 0.999 0.998 0.88 0.999 0.999 4 0.9988 0.9978 0.85 0.991 0.999 5 0.05 0.06 0.02 0.01 0.099 6 0.999 0.998 0.91 0.9989 0.9991 7 0.9988 0.9979 0.89 0.9988 0.999 8 0.9989 0.998 0.9 0.9989 0.9988 9 0.999 0.998 0.88 0.999 0.999 Setting the pre-established threshold equal to 0.9, the following table shows instead the detection of anomalies by the probabilistic algorithm in relation to the operating states assumed by the first device 5 illumination 3a in the period [0,9] and at the different time intervals above described, where 0 represents the presence of an anomaly while 1 represents the absence of an anomaly. Table 4 minute state Feedback anomaly from anomaly anomaly anomaly anomaly operational tab_general tab_winter tab_february tab_tuesday tab_weekday 0 1 0 0 0 0 0 1 1 0 0 0 0 0 2 1 0 0 0 0 0 3 0 1 1 0 1 1 4 0 1 1 0 1 1 5 1 1 1 1 1 1 6 0 1 1 1 1 1 7 0 1 1 0 1 1 8 0 1 1 1 1 1 9 0 1 1 0 1 1 In particular, the “minute 0” line shows the presence of an anomaly for each column “anomaly” because the probability of the first state operating at that minute was less than the threshold for each column 5 of Table 2. On the contrary, the “minute 3” row shows the presence of an anomaly for only the column “anomaly tab_febbraio” because the probability of the second operational state in that minute was less than the threshold only for the “tab_febbraio” column of Table 3. The following table represents the final detection of anomalies 10 by the probabilistic algorithm in relation to the operating states assumed by the first lighting device 3a in the period [0,9], where the 0 in the “anomaly” column represents the presence of an anomaly while 1 represents the absence of an anomaly. In this case, an anomaly is detected in reference to a specific data signal if the majority of the “anomaly” columns in Table 4 have a 0 in relation to the same data signal, i.e. in relation to the same minute of the period [0,9]. 5 Table 5 minute operating status anomaly 0 1 0 1 1 0 2 1 0 3 0 1 4 0 1 5 1 1 6 0 1 7 0 1 8 0 1 9 0 1 Finally, as an example, the probabilistic algorithm is configured to generate the output signal indicating the presence of a anomaly within the period [0,9] of the day February 6, 2024 if the anomaly is detected by the probabilistic algorithm in at least one 10 predetermined percentage of data signals received in the period [0,9] (ad (e.g. in at least 50% + 1 of the received data signals). Figure 3 shows a flowchart representing a method 200 for detecting anomalies according to one embodiment of the invention. Method 200 includes a step 201 of operatively connecting the electronic device 4 to the communication network 2 of a system home automation 1, a phase 202 of operationally connecting the device electronic 4 to the electrical appliance 3, and a phase 203 to connect 5 operationally the data processing device 6 to the device electronic 4 through the communication network 2. Method 200 further includes a step 204 of transmitting signals data S in the communication network 2 via the electronic device 4, a receiving step 205 in the processing device module 7 10 data 6 the data signals S transmitted by the electronic device 4 and detect using this module if an anomaly is present in the data signals S, and a phase 206 of generating by the learning model automatic 7 an output signal O indicative of the presence of said anomaly based on said finding. 15 With particular reference to figure 2, method 200 can also include the following phases: • operationally connect a plurality of electronic devices 4a-4h to the data processing device 6 and respective 3a-3g electrical appliances, and operationally connect the 20 sensor device DS1 to data processing device 6, • define a plurality of groups of devices each consisting of at least one electronic device and / or sensor device DS1, wherein said data processing device comprises a plurality of modules, in particular corresponding to respective 25 Machine Learning Models 7a-7q, each module of said plurality of modules being configured to detect an anomaly in the data signals received from a related group of devices of the plurality of device groups, • transmit data signals S1-S9 in communication network 2 5 through the plurality of groups of devices, • receive in modules 7a-7q the data signals transmitted by the respective groups of electronic devices and evaluate said models of machine learning based on received data signals, and • detect using said modules if an anomaly is present in the 10 data signals received, • generate the respective output signals O- using modules 7a-7q O15 indicative of the presence of respective anomalies on the basis of said findings. In case of modules corresponding to learning models 15 automatic, it can also be foreseen to retrain at least one model machine learning based on data signals transmitted by a relative group of electronic devices in a given period of time time. Preferably, at least one machine learning model 20 is retrained every 7 days. Preferably, the model of Machine learning is evaluated every 10 minutes.

Claims

1. System (100;100') for detecting anomalies comprising: • at least one electronic device (4) operatively connected to a communication network (2) and to an electrical appliance (3) of a home automation system (1), said electrical appliance being configured to assume a plurality of predefined operating states, wherein said at least one electronic device is configured to transmit in said communication network data signals (S) comprising data indicative of an operating state assumed by the electrical appliance among said plurality of predefined operating states, and • a data processing device (6) operatively connected to said at least one electronic device through said communication network and configured to receive data signals transmitted by said at least one electronic device,wherein said data processing device comprises a module configured to detect an anomaly in the data signals (S) received from said at least one electronic device and to generate an output signal (O) indicative of the presence of said anomaly based on said detection., 2. System according to claim 1, wherein said module comprises one of: • a machine learning model (7) trained to detect said anomaly in the data signals (S) received from said at least one electronic device, and • a probabilistic algorithm configured to detect said anomaly in the data signals (S) received from said at least one electronic device based on at least one comparison of a probability that said electrical appliance assumes the operating state represented in said data signals (S) with a predetermined threshold.

3. System according to one of the preceding claims, wherein said at least one electronic device comprises at least one control device (5) configured to control the operation of said electrical appliance.

4. System according to one of the preceding claims, wherein said module is configured to detect said anomaly in relation to a specific operating state of said electrical appliance.

5. System according to one of the preceding claims, wherein said electrical appliance comprises at least one of: • at least one lighting device (3a, 3e1, 3e2), • at least one shutter (3b, 3c, 3f, 3g), • at least one contact device to signal the opening or closing of a window, • at least one electrical socket, • at least one heating and, preferably, cooking device, • a thermal conditioning system (3d) designed to condition the air temperature inside one or more rooms of a building, • at least one fan coil, • at least one thermostatic valve, • at least one actuator for controlling valves of an air conditioning system, • a device for measuring energy production or consumption, and • a smart card reader device equipped with NFC / RFID.

6. System according to one of the preceding claims, wherein said system comprises a plurality of electronic devices (4a,..., 4h) operatively connected to said data processing device and to respective electrical appliances (3a,..., 3g) of said home automation system, said module being configured to detect an anomaly in the data signals received from said plurality of electronic devices and to generate an output signal indicative of the presence of said anomaly on the basis of said detection.

7. System according to one of the preceding claims, wherein said system comprises at least one sensor device (DS1) operatively connected to said processing device via said communication network, said at least one sensor device comprising a sensor and being configured to transmit data signals in said communication network comprising data indicative of a physical quantity detected by said sensor.

8. The system of claim 7, wherein said sensor is a sensor for detecting objects in an environment, said sensor device (DS1) being configured to generate data signals comprising data indicative of the absence or presence of objects in said environment.

9. System according to one of the preceding claims, wherein said data processing device comprises a plurality of modules, each module being configured to detect an anomaly in the data signals (S1, ..., S9) received from a related group of devices consisting of at least one electronic device and / or at least one sensor device, and being configured to generate a related output signal (O1, ..., O15) indicative of the presence of said anomaly on the basis of said detection.

10. System according to one of the preceding claims, wherein said data processing device is physically distinct from each electronic device and from said at least one sensor device if said system comprises the latter.

11. System according to one of the preceding claims, wherein said data processing device is included in a cloud computing platform (8).

12. A method for detecting anomalies, the method (200) comprising: • operatively connecting an electronic device (4) to a communication network (2) of a home automation system (1), • operatively connecting said electronic device to an electrical appliance (3) of said home automation system, said electrical appliance being configured to assume a plurality of predefined operating states and said at least one electronic device being configured to transmit into the communication network data signals (S) comprising data indicative of an operating state assumed by the electrical appliance among said plurality of predefined operating states, • operatively connecting a data processing device (6) to said electronic device via said communication network, wherein said data processing device comprises a module, said module being configured to detect an anomaly in the data signals received from said electronic device,• transmit data signals in said communication network by means of said electronic device, • receive in said module the data signals transmitted by said electronic device, • detect by means of said module whether an anomaly is present in said received data signals, • generate by means of said module an output signal (O) indicative of the presence of said anomaly on the basis of said detection., 13. Method according to claim 12, comprising: • operatively connecting a plurality of electronic devices (4a,..., 4h) to said data processing device and to respective electrical appliances (3a,..., 3g) of said home automation system, and / or operatively connecting at least one sensor device (DS1) to said data processing device, said at least one sensor device comprising a sensor and being configured to transmit data signals in said communication network comprising data indicative of a physical quantity detected by said sensor, • defining a plurality of groups of devices each formed by at least one electronic device and / or by at least one sensor device, wherein said data processing device comprises a plurality of modules, each module being configured to detect an anomaly in the data signals (S1, .„, S9) received from a respective group of devices of said plurality of groups of devices, • transmit data signals in said communication network by means of said plurality of groups of devices, • receive in said modules the data signals transmitted by the respective groups of electronic devices, • detect by means of said modules whether an anomaly is present in said received data signals, • generate by means of said modules respective output signals (O1, ..., O15) indicative of the presence of respective anomalies on the basis of said detections.