Multi-functional smart home control system based on internet of things

Abstract

This invention discloses a multifunctional smart home control system based on the Internet of Things (IoT), relating to the field of intelligent control technology. It includes an array scanning module, an acoustic wave detection module, a micro-electrical detection module, a water accumulation analysis module, and a risk control module. The array scanning module transmits short-period pulse signals as excitation waves. The acoustic wave detection module collects echo signals from each transceiver channel, analyzes their degree of influence by watermarks, and generates relevant watermark echo influence signals. The micro-electrical detection module collects measured capacitance values ​​from each target detection area and, combined with equivalent dielectric properties, generates relevant watermark electrical response signals. The water accumulation analysis module performs water accumulation prediction analysis on the received relevant watermark echo influence signals and relevant watermark electrical response signals, generating relevant water accumulation risk intervention signals. The risk control module executes corresponding intervention actions based on the received relevant water accumulation risk intervention signals, achieving intelligent identification and intervention of water accumulation status in kitchens and bathrooms.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, in particular to a multifunctional smart home control system based on the Internet of Things. Background Art

[0002] As smart home scenarios extend from lighting and security to micro-environment monitoring and adaptive control, traditional home controllers have gradually evolved into composite terminals integrating multi-modal perception, edge processing and linked response. Especially in high-humidity and high-frequency use areas such as kitchens and bathrooms, the control demand for intelligent identification of floor water accumulation status and risk intervention is increasingly urgent.

[0003] At present, the monitoring and intervention of water accumulation status in household kitchen and bathroom areas generally rely on single-type sensors. Most of these devices only trigger an alarm when the accumulated water reaches a certain height or the liquid directly touches the sensor, resulting in significant response delay. At the same time, they lack the ability to identify early signals caused by water marks and structural leakage, making it difficult to achieve pre-warning. In addition, existing systems are mostly independent devices, which makes it difficult to realize data fusion and intelligent judgment for various detection methods, resulting in low identification accuracy, being easily affected by false alarms or missed alarms, and lacking systematic linked control strategies. Therefore, the existing technologies have prominent problems of slow response, insufficient identification accuracy and non-integrated system in the identification of water accumulation status and automatic intervention control in kitchens and bathrooms. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a multifunctional smart home control system based on the Internet of Things, which solves the problems in the above-mentioned background art.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions: the multifunctional smart home control system based on the Internet of Things includes an array scanning module, an acoustic wave detection module, a micro-electric detection module, a water accumulation analysis module and a risk control module; The array scanning module is configured to deploy a MEMS ultrasonic array at a position close to the kitchen and bathroom floor, and transmit signal excitation waves in the form of short-period pulses to the kitchen and bathroom floor area through its built-in transmitting elements; The acoustic wave detection module is configured to capture echo signals of each transceiving channel based on the signal excitation waves emitted by the transmitting elements, extract element echo data, and analyze the influence degree of floor water marks on the echo signals of each transceiving channel, so as to generate relevant water mark echo influence signals; The micro-electric detection module is configured to collect measured capacitance values of each target detection area according to a high-resistance weak electrode array deployed in the kitchen and bathroom floor area, and generate relevant water trace electrical response signals based on the influence of floor water accumulation on the equivalent dielectric characteristics of the target detection area; The water accumulation analysis module is used to perform water accumulation prediction analysis on the received relevant water mark echo influence signals and relevant water trace electrical response signals, and generate relevant water accumulation risk intervention signals; The risk control module is used to execute corresponding intervention actions based on the received relevant water accumulation risk intervention signals.

[0006] Preferably, the array scanning module includes a first deployment unit and a signal excitation unit; The first deployment unit is used to deploy a MEMS ultrasonic array close to the kitchen and bathroom floor, specifically: The MEMS ultrasonic array is installed on the low edge wall, under the kitchen cabinet and under the bathroom cabinet. The array is deployed horizontally close to the kitchen and bathroom floor. The MEMS ultrasonic array includes transmitting array elements, receiving array elements, edge signal amplifiers, distance sensors and signal acquisition chips. The signal acquisition chip is used for waveform generation, echo analysis and signal scheduling. The signal excitation unit is used to transmit short-period pulse signal excitation waves to the ground through the transmitting array elements. Its specific waveform function is set as follows: In the formula, S(t) represents the waveform function of the signal excitation wave, f represents the ultrasonic center frequency, and A represents the excitation amplitude. The coefficient of exponential decay is represented by t, where t represents time. The phase expression for a standard sinusoidal excitation wave.

[0007] Preferably, the acoustic wave detection module includes a time analysis unit, an energy analysis unit, a phase analysis unit, and a first determination unit; The time analysis unit is used to form several transceiver channels based on the symmetrical relationship between each transmitting element in the transmitting array and the corresponding receiving element in the receiving array, and to determine the distance between the reflection points of each transceiver channel by measuring the distance of the signal excitation wave from the transmitting element to the ground reflection point through the distance sensor. The difference between the actual transmission time and the actual reception time of the signal excitation wave of each transceiver channel is processed to determine the actual flight time of the signal of each transceiver channel. Based on the round-trip distance of each transceiver channel and the propagation velocity of the signal excitation wave, the signal flight time offset of each transceiver channel is determined, specifically as follows: In the formula, This represents the signal flight time offset of the corresponding transceiver channel. This indicates the actual flight time of the signal for the corresponding transceiver channel. The distance between the reflection points of the corresponding transceiver channel is represented by , and v represents the propagation speed of the signal excitation wave.

[0008] Preferably, the energy analysis unit is used to determine the abnormal attenuation of echo energy in each transceiver channel based on the spatial propagation attenuation characteristics of the signal excitation wave and in conjunction with the echo signals actually captured by the receiving array element in each transceiver channel. Specifically, In the formula, This represents the abnormal attenuation coefficient of the echo energy of the corresponding transmit / receive channel. This represents the actual echo energy value of the corresponding transmit / receive channel. This represents the standard echo energy value under conditions without an aqueous film.

[0009] Preferably, the phase analysis unit is used to obtain the shock wave transmission phase of each transceiver channel by using the waveform function S(t) based on the signal excitation wave and recording the initial reference phase of the signal excitation wave emitted by each transmitting unit in the transmitting array in real time through the waveform synthesizer. After receiving the echoes reflected from the ground reflection point, the echoes are orthogonally mixed with the corresponding transmitted signal excitation waves, and the DC component is extracted using a low-pass filter to determine the echo reception phase of each transceiver channel; specifically: In the formula, This indicates the echo reception phase of the corresponding transmit / receive channel. This represents the DC quadrature component of the corresponding transceiver channel. This represents the DC in-phase component of the corresponding transmit / receive channel. This represents the forward and reverse tangent operators; based on the influence of watermarks at ground reflection points on the phase delay of the signal excitation wave, the shock wave transmission phase of each transceiver channel is correlated with the echo reception phase of the corresponding transceiver channel, and the transceiver phase offset coefficient of each transceiver channel is obtained after phase difference calculation.

[0010] Preferably, the first determination unit is used to construct array element echo data based on the acquired signal flight time offset, echo energy abnormal attenuation coefficient, and transmit / receive phase offset coefficient of each transmit / receive channel; Feature identification was performed on the phase echo data. The extracted signal time-of-flight bias, echo energy anomaly attenuation coefficient, and transmit / receive phase offset coefficient of each transmit / receive channel were correlated. After dimensionless processing, the degree of influence of ground watermarks on the echo signals of each transmit / receive channel was analyzed to determine the watermark influence index of each transmit / receive channel. Specifically: This indicates the watermark impact index for the corresponding transmitting and receiving channels. This represents a function that takes the minimum value. By pre-setting an impact reference threshold and comparing and analyzing the impact reference threshold with the watermark impact index of the corresponding transceiver channel, a relevant watermark echo impact signal is generated. The specific process is as follows: If the watermark impact index of the corresponding transceiver channel is greater than the preset impact reference threshold, a "watermark echo impact signal" will be generated; if the watermark impact index of the corresponding transceiver channel is not greater than the preset impact reference threshold, a "watermark echo no impact signal" will be generated. The relevant watermark echo impact signals include "watermark echo impact signals" and "watermark echo unaffected signals", and the relevant watermark echo impact signals are sent to the water accumulation analysis module.

[0011] Preferably, the micro-electrical detection module includes a second deployment unit, an electrical analysis unit, and a second determination unit; The second deployment unit is used to deploy a high-resistivity, low-electrode array in the kitchen and bathroom floor area, specifically: The kitchen and bathroom floor area is divided into several target detection areas by grid, with the center point of the target detection area as the sampling point, and miniature sensing electrodes are deployed. Each miniature sensing electrode is connected to a capacitance measurement circuit to form a high-resistance weak electrode array. The electrical analysis unit is used to collect the measured capacitance value of each target detection area by applying an electrical signal of fixed frequency and amplitude to the sampling points in each target detection area and combining the parallel capacitance equivalent modeling method. Based on the measured capacitance values ​​of each target detection area collected, and combined with the statistical averaging algorithm, the average measured capacitance value of the kitchen and bathroom floor area is obtained. Based on the influence of surface water on the equivalent dielectric properties of the target detection area, the measured capacitance value of each target detection area is compared with the average measured capacitance value. The number of target detection areas whose measured capacitance value exceeds the average measured capacitance value is counted and recorded as the number of water trace target detection areas. The proportion of water stain area on kitchen and bathroom floors is determined by the ratio of the number of water stain target detection areas to the total number of target detection areas.

[0012] Preferably, the second determination unit is used to generate relevant water stain electrical response signals based on the proportion of water stain area in the kitchen and bathroom floor area, and the specific process is as follows; If the proportion of water stains on the kitchen and bathroom floor is not equal to zero, it means that there is a water stain target detection area in the kitchen and bathroom floor area, and a "water stain electrical response signal" is generated. If the proportion of water stains on the kitchen and bathroom floor area is equal to zero, it means that there is no water stain target detection area in the kitchen and bathroom floor area, and a "water stain electrical no response signal" is generated. The relevant water trace electrical response signals include "water trace electrical response signals" and "water trace electrical non-response signals", and the relevant water trace electrical response signals are sent to the water accumulation analysis module.

[0013] Preferably, the water accumulation analysis module is used to perform water accumulation prediction analysis on the received relevant water mark echo influence signals and relevant water trace electrical response signals, and generate relevant water accumulation risk intervention signals. The specific process is as follows: Based on the relevant watermark echo influence signals, establish a set X, label the "watermark echo influence signal" as element x1, label the "watermark echo no influence signal" as element x2, and element x1∈set X, element x2∈set X; Based on the relevant water trace electrical response signals, establish a set Y, label the "water trace electrical response signal" as element y1, label the "water trace no electrical response signal" as element y2, and element y1∈set Y, element y2∈set Y; The set X and set Y are combined. If X∪Y={x1, y1}, a "Level 1 flood risk intervention signal" is generated. If X∪Y={x1, y2} or {x2, y1}, a "Level 2 flood risk intervention signal" is generated. If X∪Y={x2, y2}, a "Level 3 flood risk intervention signal" is generated. The relevant waterlogging risk intervention signals include "Level 1 waterlogging risk intervention signal", "Level 2 waterlogging risk intervention signal" and "Level 3 waterlogging risk intervention signal", and the relevant waterlogging risk intervention signals are sent to the risk control module.

[0014] Preferably, the risk control module is used to execute corresponding intervention actions based on the received relevant water accumulation risk intervention signals, and the specific process is as follows: When a "Level 1 Water Accumulation Risk Intervention Signal" is received, it indicates that there is water accumulation in the kitchen and bathroom floor areas and there is a risk of leakage. Emergency intervention actions are executed, including: automatically shutting off the water inlet solenoid valves of the kitchen and bathroom through the central control platform; triggering the local audible and visual alarm; and remotely pushing an "emergency water leakage warning" to notify the user to intervene on-site. When a "Level 2 Water Accumulation Risk Intervention Signal" is received, it indicates that there is water accumulation in the kitchen and bathroom floor area, but there is no risk of leakage. A gradual intervention action is executed, including: the robot vacuum cleaner is activated to perform a short-cycle mopping mode in the kitchen and bathroom floor area; at the same time, a notification message is sent to the user that "there is water accumulation in the kitchen and bathroom floor area and mopping mode is being executed". When a "Level 3 Water Accumulation Risk Intervention Signal" is received, it indicates that there is currently no water accumulation in the kitchen and bathroom floor area. The system will then perform maintenance monitoring and periodic inspection actions, including: timed sampling, data log recording and risk profile updates, and maintaining low power consumption. At the same time, the system will push a "Current kitchen and bathroom floor area is in good condition" notification message to the user.

[0015] This invention provides a multifunctional smart home control system based on the Internet of Things, which has the following beneficial effects: (1) By constructing a multi-dimensional sensing mechanism that integrates acoustic echo analysis and micro-electric response detection, and combining data fusion and risk level classification, early identification and response control of water accumulation in kitchen and bathroom floor areas are realized. The controller breaks through the bottlenecks of single sensing, slow response and rough judgment in the existing technology through unified data structure and linkage logic. It significantly improves the environmental understanding, predictive ability and intervention efficiency of smart home system in kitchen and bathroom scenarios. It has the comprehensive advantages of flexible deployment, multiple sensing dimensions and automated risk response, and effectively prevents home safety risks and equipment damage caused by water accumulation.

[0016] (2) By deploying a MEMS ultrasonic array close to the kitchen and bathroom floor, and constructing multiple transceiver channels with transmitting and receiving elements, it can actively transmit short-period pulse signal excitation waves, and accurately perceive the abnormal characteristics of sound wave propagation in the ground area based on dimensions such as time difference, energy attenuation and phase shift; through time analysis unit, energy analysis unit and phase analysis unit, the influence of water marks, water film or local damp areas on signal flight time, reflection energy and waveform phase can be identified respectively, and the above changes are integrated into a water mark influence index for quantitative evaluation; compared with the existing single-channel acoustic detection method, it realizes the refined identification of echo signal in spatial distribution, has higher environmental adaptability and error tolerance, and significantly improves the sensitivity and accuracy of identifying hidden water marks.

[0017] (3) A micro-electric disturbance identification model was constructed by introducing a high-resistance weak electrode array combined with a parallel capacitor modeling mechanism. By dividing the kitchen and bathroom floor into multiple grid target detection areas and deploying micro-sensing electrodes at each center point, the measured capacitance values ​​of each target detection area are periodically collected without contact with water. The average value algorithm is used to determine the area of ​​abnormal capacitance increase and to statistically analyze the proportion of water stain area, thereby achieving non-invasive moisture detection and water accumulation state prediction. Compared with the traditional water immersion sensor, which can only respond to liquid contact, the capacitance change is used to identify the process of material dampness or local water film formation, thereby achieving accurate capture of early water accumulation signs and expanding the accuracy and proactive response of smart home controllers in the identification of ground electrical environment.

[0018] (4) By constructing a joint judgment model that integrates the relevant watermark echo influence signal and the relevant watermark electrical response signal, a hierarchical water accumulation risk assessment mechanism is formed; by performing union analysis on the relevant watermark echo influence signal and the relevant watermark electrical response signal, a multi-source information correspondence set is established, and water accumulation risk intervention signals of three risk levels are automatically generated according to the combination situation; this mechanism not only realizes the joint identification of visible water accumulation and hidden watermarks, but also has the ability to predict the development trend of water accumulation, so that the water accumulation treatment strategy is transformed from passive response to active early warning; compared with the traditional system that can only trigger alarms at a single point based on a fixed threshold, this invention improves the reliability of prediction and the precision of response through multimodal signal fusion and risk level classification, and provides a more intelligent processing framework for water accumulation control in smart home environment. Attached Figure Description

[0019] Figure 1 This is a block diagram of the multifunctional smart home control system based on the Internet of Things of this invention; Figure 2 This is a schematic diagram of the MEMS ultrasonic array composition of the present invention; Figure 3 This is a flowchart illustrating the implementation of the multifunctional smart home control system based on the Internet of Things of this invention. Figure 4 This is a schematic diagram of the high-resistivity weak electrode array of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1

[0022] Please see Figure 1 and Figure 3 This invention provides a multi-functional smart home control system based on the Internet of Things, including an array scanning module, an acoustic wave detection module, a micro-electricity detection module, a water accumulation analysis module, and a risk control module; The array scanning module is used to deploy a MEMS ultrasonic array close to the kitchen and bathroom floor, and to emit short-period pulse signal excitation waves into the kitchen and bathroom floor area through its built-in transmitting array elements; The acoustic detection module captures the echo signals of each transmitting and receiving channel based on the excitation wave emitted by the transmitting array element, extracts the echo data of the array element, and analyzes the degree of influence of ground water marks on the echo signals of each transmitting and receiving channel in order to generate relevant water mark echo influence signals. The micro-electric detection module is used to collect the measured capacitance values ​​of each target detection area based on the high-resistivity weak electrode array deployed in the kitchen and bathroom floor area, and generate relevant water trace electrical response signals based on the influence of surface water on the equivalent dielectric properties of the target detection area. The water accumulation analysis module is used to perform water accumulation prediction analysis on the received relevant water mark echo influence signals and relevant water trace electrical response signals, and generate relevant water accumulation risk intervention signals; The risk control module is used to execute corresponding intervention actions based on the received relevant water accumulation risk intervention signals.

[0023] In this embodiment, by integrating an array scanning module, an acoustic wave detection module, a micro-electrical detection module, a water accumulation analysis module, and a risk control module, high-precision, multi-dimensional, and graded intelligent identification and intervention control of water accumulation on kitchen and bathroom floors are achieved. Unlike traditional solutions that rely solely on single-point contact water sensors or humidity thresholds, this invention utilizes a MEMS ultrasonic array combined with acoustic wave flight time, reflection energy, and phase characteristic analysis to effectively identify acoustic disturbances caused by watermarks on the ground. Simultaneously, it integrates capacitance change information collected by a high-resistivity weak electrode array to achieve early detection of moisture absorption and potential water film formation on ground materials. Periodic monitoring significantly enhances the ability to detect hidden water seepage. Based on this, the water accumulation analysis module correlates and judges acoustic and electrical dual-source data to construct a hierarchical risk intervention model, which can output three levels of water accumulation risk intervention signals, accurately reflecting the development trend and potential threat level of water stains in the kitchen and bathroom environment. The special advantages of this system are its forward-looking predictive identification capabilities, non-contact non-destructive detection characteristics, and flexible control strategies with graded responses. It significantly improves the security management level of smart home systems in complex and humid environments, achieving a fundamental leap from passive alarms to proactive early warning and automatic response.

[0024] Example 2

[0025] Please refer to Figure 1 and Figure 2 Specifically: the array scanning module includes a first deployment unit and a signal excitation unit; The first deployment unit is used to deploy a MEMS ultrasonic array close to the kitchen and bathroom floor, specifically: The MEMS ultrasonic array is installed on the low edge wall, under the kitchen cabinet and under the bathroom cabinet. The array is deployed horizontally close to the kitchen and bathroom floor. The MEMS ultrasonic array includes transmitting array elements, receiving array elements, edge signal amplifiers, distance sensors and signal acquisition chips. The signal acquisition chip is used for waveform generation, echo analysis and signal scheduling. The signal excitation unit is used to transmit short-period pulse signal excitation waves to the ground through the transmitting array elements. Its specific waveform function is set as follows: In the formula, S(t) represents the waveform function of the signal excitation wave, f represents the ultrasonic center frequency, and A represents the excitation amplitude. The coefficient of exponential decay is represented by t, where t represents time. The phase expression for a standard sinusoidal excitation wave.

[0026] The signal excitation waveform function shown in the signal excitation unit is a short-period pulse excitation signal used by the MEMS ultrasonic array transmitting unit. Its structural design is closely related to the shortcomings of existing kitchen and bathroom water accumulation detection technologies, such as delayed response and difficulty in identifying subtle watermark changes. This waveform function combines the periodic characteristics of a sine wave with an exponential decay form, enabling the generation of high-energy-density, rapidly decaying, and easily localized acoustic pulses in a short time. The exponential decay coefficient is used to limit the duration of the waveform and enhance the resolution of the echo. The design of this function enables the system to quickly identify the minute changes in reflection characteristics caused by water marks without relying on the contact of liquid water. This achieves highly sensitive, non-contact identification of potential water accumulation risks on the ground, effectively overcoming the shortcomings of traditional water sensor response lag. It is the key physical basis for realizing early intelligent sensing.

[0027] In this embodiment, a miniaturized ultrasonic detection system highly integrated with the kitchen and bathroom floor is constructed by combining a first deployment unit and a signal excitation unit. By deploying MEMS ultrasonic arrays at key locations such as the lower edge of kitchen cabinets, under bathroom cabinets, and low-lying edge walls in a horizontal configuration, the ability to detect shallow water marks on the floor is significantly improved. The array includes transmitting elements, receiving elements, edge signal amplifiers, distance sensors, and an integrated signal acquisition chip, providing precise waveform control and accurate echo acquisition capabilities. High temporal resolution and precise detection of the ground condition are achieved through short-period pulse excitation waves emitted by the signal excitation unit. High spatial consistency scanning significantly outperforms traditional single-point water sensors in terms of detection range and response speed. In particular, the use of a parameter-adjustable excitation wavefunction, whose center frequency, amplitude, and exponential decay coefficient can be flexibly set according to the application scenario, effectively adapts to different floor materials, spatial layouts, and environmental noise interference conditions, achieving low-interference and high-sensitivity signal injection and response recognition in complex areas of kitchens and bathrooms. This array-type, adjustable, and ground-mounted design not only expands the system's detection coverage but also significantly enhances the ability to perceive early, weak water accumulation and abnormal watermark conditions, providing the entire system with a highly efficient, low-power, and environmentally adaptable basic sensing platform.

[0028] Example 3

[0029] Please refer to Figure 1 Specifically: the acoustic wave detection module includes a time analysis unit, an energy analysis unit, a phase analysis unit, and a first determination unit; The time analysis unit is used to form several transceiver channels based on the symmetrical relationship between each transmitting element in the transmitting array and the corresponding receiving element in the receiving array, and to determine the distance between the reflection points of each transceiver channel by measuring the distance of the signal excitation wave from the transmitting element to the ground reflection point through the distance sensor. The transceiver channel refers to the ultrasonic propagation path formed between a transmitting unit in the transmitting array and a receiving unit in the receiving array that is symmetrically arranged with it. Each pair of transmitting and receiving units constitutes an independent signal channel. The reflection point spacing refers to the straight-line distance from the transmitting element to the ground reflection point in the spatial path of the signal from the transmitting element, after reflection at the ground reflection point, to the receiving element. Its function is to provide a geometric reference for subsequent calculation of the signal flight time bias, reflecting the actual physical path on which the lower wave propagation depends in each channel. This distance is calculated by the high-precision distance sensor built into the system in conjunction with the array layout geometry. It does not rely on manual calibration and can achieve automatic correction, which helps to build an accurate and stable acoustic wave propagation model, thereby improving the reliability and resolution of the time analysis dimension in watermark judgment. The difference between the actual transmission time and the actual reception time of the signal excitation wave of each transceiver channel is processed to determine the actual flight time of the signal of each transceiver channel. The actual flight time of a signal refers to the actual propagation time of an ultrasonic signal in each transceiver channel, from its emission from the transmitting element, reflection at the ground reflection point, to its final reception by the corresponding receiving element. Its function is to reflect the propagation delay of sound waves along a specific path and is a fundamental parameter for assessing the differences in propagation characteristics caused by changes in the state of the ground medium. This time is calculated by recording the difference between the actual emission time of the signal excitation wave and the time when it is first detected by the receiving element. The time analysis unit, in conjunction with a high-precision timing sampling mechanism, automatically processes this time difference. As a temporal dimension reflecting the propagation behavior of sound waves in the medium, the flight time can be used to subsequently derive flight time biases, thereby identifying changes in sound speed caused by surface water. Based on the round-trip distance of each transceiver channel and the propagation velocity of the signal excitation wave, the signal flight time offset of each transceiver channel is determined, specifically as follows: This represents the signal flight time offset of the corresponding transceiver channel. This indicates the actual flight time of the signal for the corresponding transceiver channel. The distance between the reflection points of the corresponding transceiver channels is represented by , and v represents the propagation speed of the signal excitation wave. The signal flight time deviation reflects the difference between the sound wave propagation time and the theoretical propagation time, and is an important time characteristic indicator for determining whether watermarks exist on the ground. The function of this formula is to reveal that when the ground medium changes, the sound wave propagation speed and path will change, causing the actual flight time to deviate from the theoretical value, thus forming a quantifiable signal flight time deviation. This effectively compensates for the shortcomings of existing technologies in identifying early water seepage and only triggering obvious water accumulation. This value not only enhances the system's sensitivity to the state of watermarks on the ground, but also provides a structured basis in the time domain for watermark judgment, improving the overall accuracy and predictive ability of identification. Specifically, the energy analysis unit is used to determine the abnormal attenuation of echo energy in each transceiver channel based on the spatial propagation attenuation characteristics of the signal excitation wave and in conjunction with the echo signals actually captured by the receiving array elements. Specifically: In the formula, This represents the abnormal attenuation coefficient of the echo energy of the corresponding transmit / receive channel. Ao represents the actual echo energy value of the corresponding transceiver channel, while Ao represents the standard echo energy value under the condition of no water film.

[0030] The formula in the energy analysis unit is used to calculate the echo energy anomaly attenuation coefficient of each transceiver channel. This coefficient is used to quantify the degree of energy attenuation of the echo signal in a certain area, reflecting the energy absorption, scattering, and reduced reflectivity caused by ground water marks during sound wave propagation. In contrast to the problem in the background technology that it is difficult to identify hidden water accumulation and is only triggered by the formation of liquid water, this coefficient can achieve early warning judgment through slight changes in energy in the early stage without obvious water accumulation. It is an effective supplement to the traditional time threshold-based judgment method. Its significance is that the larger the echo energy anomaly attenuation coefficient, the more serious the echo energy attenuation, and the more likely there are abnormal ground absorption characteristics. This allows the extraction of the physical characteristics behind the abnormal sound wave propagation path, improving the accuracy and coverage of wet condition identification, thereby achieving non-contact, low-latency, and early identification sound wave sensing effect. Specifically, the phase analysis unit is used to obtain the shock wave transmission phase of each transceiver channel by using the waveform function S(t) based on the signal excitation wave and recording the initial reference phase of the signal excitation wave emitted by each transmitting unit in the transmitting array in real time through the waveform synthesizer. The shock wave emission phase refers to the sinusoidal phase angle of the waveform at the initial moment when each transmitting unit emits a signal excitation wave, i.e., the starting phase position of the waveform function relative to the standard sine wave. Its function is to provide an accurate reference benchmark for subsequent phase analysis. By comparing it with the echo reception phase, it can be determined whether the sound wave experiences phase delay during propagation due to abnormal media such as watermarks on the ground. The shock wave emission phase is automatically calculated and recorded by the waveform synthesizer embedded in the system at the moment of signal emission based on the preset excitation waveform function, ensuring that the phase tracking of each transceiver channel has an initial reference consistency. The accurate acquisition of the shock wave emission phase is a prerequisite for constructing a transceiver phase offset model, thereby achieving high-sensitivity identification of waveform structure disturbances caused by weak medium changes, which is particularly suitable for non-contact, sub-water accumulation level intelligent detection scenarios. After receiving the echoes reflected from the ground reflection point, the echoes are orthogonally mixed with the corresponding transmitted signal excitation waves, and the DC component is extracted using a low-pass filter to determine the echo reception phase of each transceiver channel; specifically: ; This indicates the echo reception phase of the corresponding transmit / receive channel. This represents the DC quadrature component of the corresponding transceiver channel. This represents the DC in-phase component of the corresponding transmit / receive channel. Represents the forward and reverse tangent operators; These represent the DC quadrature component and the in-phase component of the echo signal after quadrature demodulation, respectively. The two phase parameters are based on the reference starting phase generated by the excitation waveform function S(t), and are combined with the waveform synthesizer for quadrature mixing and low-pass filtering extraction to achieve high-precision determination of the phase delay of the ground reflection signal. Compared with the shortcomings of the background technology that only relies on time or energy changes and is difficult to capture the weak disturbances caused by water marks, this phase analysis mechanism can identify the sound wave propagation phase shift caused by water marks at the ground reflection point without changing the sound speed and energy amplitude. The change in the echo reception phase is usually the first signal feature that appears in the early stage of water accumulation but is difficult to detect. The acquisition of the echo reception phase enables the system to identify the hidden wet state from the waveform structure dimension. Based on the effect of watermarks at ground reflection points on the phase delay of the signal excitation wave, the shock wave transmission phase of each transceiver channel is correlated with the echo reception phase of the corresponding transceiver channel. After calculating the phase difference, the transceiver phase offset coefficient of each transceiver channel is obtained.

[0031] The transmit / receive phase offset coefficient refers to the phase difference between the shock wave transmission phase and the echo reception phase in each transmit / receive channel. It is used to quantify the degree of phase delay or disturbance caused by changes in the ground medium during the propagation and reflection of the sound wave. Its function is to reveal the waveform structure changes caused by changes in dielectric properties or propagation impedance when the sound wave passes through ground materials in different states. It is an important waveform characteristic indicator for determining whether there are water marks on the ground. The transmit / receive phase offset coefficient is obtained by the system by recording the difference between the initial reference phase of the shock wave and the echo reception phase extracted by the receiving array element, and then forming a standardized index through phase difference normalization. Compared with time and energy parameters, the transmit / receive phase offset coefficient is more sensitive to minute changes in the state of the material and is an indispensable important parameter for building a highly sensitive water mark detection model. Specifically, the first determination unit is used to construct array element echo data based on the acquired signal flight time offset, echo energy abnormal attenuation coefficient, and transmit / receive phase offset coefficient of each transmit / receive channel. Feature identification was performed on the phase echo data. The extracted signal time-of-flight bias, echo energy anomaly attenuation coefficient, and transmit / receive phase offset coefficient of each transmit / receive channel were correlated. After dimensionless processing, the degree of influence of ground watermarks on the echo signals of each transmit / receive channel was analyzed to determine the watermark influence index of each transmit / receive channel. Specifically: This indicates the watermark impact index for the corresponding transmitting and receiving channels. This represents a function that takes the minimum value. The formula shown in the first judgment unit is used to calculate the watermark impact index of each transceiver channel, comprehensively considering three key parameters: signal flight time deviation, echo energy anomaly attenuation coefficient, and transceiver phase offset coefficient. This index constructs a robust quantitative indicator of the impact of ground watermarks by weighted fusion of abnormal signals in three dimensions and introducing a minimum value function to avoid amplifying a single anomaly. It effectively solves the defects of the background technology that relies only on a single physical feature, has low identification accuracy, and is prone to false alarms and false negatives. Through the correlation analysis of time, energy, and phase signals, it achieves multi-angle identification of watermark status. The higher the watermark impact index, the more obvious the signal of the channel is affected by ground watermarks, which is an important criterion for assessing whether there is water accumulation in the kitchen and bathroom floor area. Its core function is to construct a fusion identification model of multi-source abnormal signals, improve the overall system's response sensitivity and judgment accuracy under complex humidity change conditions, and provide a decision-making basis for subsequent risk level classification and intervention control. By pre-setting an impact reference threshold and comparing and analyzing the impact reference threshold with the watermark impact index of the corresponding transceiver channel, a relevant watermark echo impact signal is generated. The specific process is as follows: If the watermark impact index of the corresponding transceiver channel is greater than the preset impact reference threshold, a "watermark echo impact signal" will be generated; if the watermark impact index of the corresponding transceiver channel is not greater than the preset impact reference threshold, a "watermark echo no impact signal" will be generated. The relevant watermark echo impact signals include "watermark echo impact signals" and "watermark echo unaffected signals", and the relevant watermark echo impact signals are sent to the water accumulation analysis module.

[0032] In this embodiment, a highly sensitive acoustic sensing mechanism for accurate identification of ground watermark conditions is constructed by integrating a time analysis unit, an energy analysis unit, a phase analysis unit, and a first determination unit. By constructing the transmitting and receiving array elements into several symmetrical transmit and receive channels, and combining a distance sensor to accurately measure the distance between echo reflection points of each channel, the system can effectively capture multi-dimensional acoustic characteristics of signal flight time, energy attenuation, and phase shift, achieving multi-level modeling and dynamic monitoring of ground conditions. The time analysis unit calculates the signal flight time based on the time difference between transmission and reception, and, combined with the propagation distance and sound speed, infers the flight time deviation, enabling the identification of the sound speed reduction phenomenon caused by the formation of ground watermarks. The energy analysis unit further evaluates the abnormal attenuation degree between the echo energy of each channel and the echo energy under standard dry conditions, thereby revealing the influence of watermarks on the intensity of sound wave reflection. The phase analysis unit records the emission of the signal excitation wave in real time. The system transmits a reference phase and performs orthogonal mixing and low-pass filtering with the echo signal to extract the received phase, thereby obtaining the offset between the transmitted and received phases. This achieves accurate quantification of the phase hysteresis phenomenon caused by watermarks. The first judgment unit then integrates and analyzes the above three parameters, constructs a watermark influence index after dimensionless processing, and compares it with a preset reference threshold to generate clear "watermark echo influence signal" and "watermark echo no influence signal" for the water accumulation analysis module to make decision-making judgments. The significant advantage of this acoustic detection module lies in its ability to achieve time-energy-phase three-dimensional linkage recognition of acoustic feature changes caused by trace watermarks. It has outstanding features of non-contact, high resolution, strong contrast, and low false alarm rate. It can effectively solve the problem that traditional water sensing devices only respond after liquid accumulation triggers, truly realizing intelligent and proactive recognition of water accumulation precursor states, and providing a solid and reliable acoustic data foundation for subsequent water accumulation prediction analysis and risk control.

[0033] Example 4 Please refer to Figure 1 and Figure 4 Specifically: the micro-electrical detection module includes a second deployment unit, an electrical analysis unit, and a second judgment unit; The second deployment unit is used to deploy a high-resistivity, low-electrode array in the kitchen and bathroom floor area, specifically: The kitchen and bathroom floor area is divided into several target detection areas by grid, with the center point of the target detection area as the sampling point, and miniature sensing electrodes are deployed. Each miniature sensing electrode is connected to a capacitance measurement circuit to form a high-resistance weak electrode array. The electrical analysis unit is used to collect the measured capacitance value of each target detection area by applying an electrical signal of fixed frequency and amplitude to the sampling points in each target detection area and combining the parallel capacitance equivalent modeling method. In the electrical detection process of a high-resistivity weak electrode array, a fixed frequency and amplitude electrical signal is applied to the sampling points in each target detection area. Combined with the parallel capacitance equivalent modeling method, the measured capacitance values ​​of each target detection area are collected. This involves injecting a stable sinusoidal voltage signal through a miniature inductive electrode at a preset sampling point and measuring the equivalent response current to ground at that point. Using the parallel capacitance model formed between the electrode and the ground, the equivalent capacitance value corresponding to that point is deduced through the phase relationship and amplitude ratio between voltage and current. The measured capacitance value here represents the electric field response result formed under the actual ground conditions of the target detection area, objectively reflecting the dielectric constant and the presence of water in the target detection area. When there is water accumulation on the ground, its dielectric constant increases, leading to an increase in the measured capacitance value; conversely, it decreases. By acquiring the measured capacitance values ​​of each target detection area and performing spatial statistical analysis, a regional water distribution map can be constructed, which is an important electrical parameter for identifying hidden water accumulation. Based on the measured capacitance values ​​of each target detection area collected, and combined with the statistical averaging algorithm, the average measured capacitance value of the kitchen and bathroom floor area is obtained. Based on the influence of surface water on the equivalent dielectric properties of the target detection area, the measured capacitance value of each target detection area is compared with the average measured capacitance value. The number of target detection areas whose measured capacitance value exceeds the average measured capacitance value is counted and recorded as the number of water trace target detection areas. The impact of surface water on the equivalent dielectric properties of the target detection area refers to the following: Surface water significantly alters the dielectric properties of ground materials, especially the equivalent dielectric constant. When the ground is dry, surface materials such as tiles and cement have low dielectric constants. However, when water accumulates in the target detection area, the equivalent capacitance value of the entire detection area increases significantly due to the high dielectric constant of water itself. For example, if most of the measured capacitance values ​​in several target detection areas are 0.5 nF, but one target detection area reaches 0.9 nF, it can be determined that there is water accumulation in these target detection areas. This comparison essentially uses the statistical mean as a dry background baseline to identify target detection areas with abnormally high capacitance. The number of these deviation points is used to assess the distribution of water stains, thereby reflecting the actual size and distribution trend of the area affected by water accumulation. The proportion of water stain area on kitchen and bathroom floors is determined by the ratio of the number of water stain target detection areas to the total number of target detection areas.

[0034] Specifically, the second determination unit is used to generate relevant electrical response signals for water stains based on the proportion of water stain area in the kitchen and bathroom floor areas. The specific process is as follows: If the proportion of water stains on the kitchen and bathroom floor is not equal to zero, it means that there is a water stain target detection area in the kitchen and bathroom floor area, and a "water stain electrical response signal" is generated. If the proportion of water stains on the kitchen and bathroom floor area is equal to zero, it means that there is no water stain target detection area in the kitchen and bathroom floor area, and a "water stain electrical no response signal" is generated. The relevant water trace electrical response signals include "water trace electrical response signals" and "water trace electrical non-response signals", and the relevant water trace electrical response signals are sent to the water accumulation analysis module.

[0035] In this embodiment, a non-contact electrical detection system based on a capacitance response mechanism is constructed through the collaborative design of a second deployment unit, an electrical analysis unit, and a second judgment unit to identify the distribution of water stains on kitchen and bathroom floors. This module divides the kitchen and bathroom floor into several target detection areas and deploys miniature sensing electrodes at the center of each area to form a high-resistivity, low-frequency electrode array. A low-amplitude, fixed-frequency electrical signal is applied to each target detection area, and the measured capacitance value is obtained through parallel capacitance modeling. Unlike traditional water sensors that rely on liquid contact for conduction, this module identifies the capacitance increase caused by water penetration or wet adhesion by sensing changes in the material's equivalent dielectric constant. This is particularly suitable for identifying early-stage water stains that have not yet formed significant water accumulation. Furthermore, a statistical mean algorithm is used to calculate... The average capacitance level of the entire ground area is calculated, and the measured values ​​of each detection area are compared with it to accurately count the number of areas with abnormally high capacitance, thereby deducing the distribution ratio of water stains in the space. Based on this, the second judgment unit automatically generates a "water stain electrical response signal" or a "water stain electrical non-response signal", realizing water stain recognition and structured information output in the electrical dimension. The special advantage of this module is that it has the characteristics of being non-contact, not requiring visible water accumulation, and being highly sensitive to potential damp conditions. It is particularly suitable for deployment at the bottom of cabinets, corner areas, or near the ground on walls. It breaks through the limitation of traditional water sensors that can only recognize water-triggered events, significantly improving the system's spatial recognition ability and early warning ability for water stain coverage, and providing solid electrical data support for subsequent water accumulation trend modeling and risk level judgment.

[0036] Example 5 Please refer to Figure 1 Specifically: The water accumulation analysis module is used to perform water accumulation prediction analysis on the received relevant water mark echo influence signals and relevant water trace electrical response signals, and generate relevant water accumulation risk intervention signals. The specific process is as follows: Based on the relevant watermark echo influence signals, establish a set X, label the "watermark echo influence signal" as element x1, label the "watermark echo no influence signal" as element x2, and element x1∈set X, element x2∈set X; Based on the relevant water trace electrical response signals, establish a set Y, label the "water trace electrical response signal" as element y1, label the "water trace no electrical response signal" as element y2, and element y1∈set Y, element y2∈set Y; The set X and set Y are combined. If X∪Y={x1, y1}, a "Level 1 flood risk intervention signal" is generated. If X∪Y={x1, y2} or {x2, y1}, a "Level 2 flood risk intervention signal" is generated. If X∪Y={x2, y2}, a "Level 3 flood risk intervention signal" is generated. The relevant waterlogging risk intervention signals include "Level 1 waterlogging risk intervention signal", "Level 2 waterlogging risk intervention signal" and "Level 3 waterlogging risk intervention signal", and the relevant waterlogging risk intervention signals are sent to the risk control module.

[0037] In this embodiment, a logically clear and structurally rigorous water accumulation risk prediction model is constructed by fusing the "water mark echo influence signal" generated by the acoustic wave detection module and the "water mark electrical response signal" generated by the micro-electrical detection module. The water accumulation analysis module standardizes the two types of heterogeneous signals using set theory. By establishing sets X and Y, the acoustic anomaly and electrical anomaly states are calibrated, and the multi-level identification of the current water accumulation state in the kitchen and bathroom is achieved through the union of X∪Y. Specifically, when the system simultaneously identifies acoustic echo anomalies and electrical response anomalies, i.e., {x1, y1}, it indicates that the water accumulation state has obvious manifestations and structural leakage characteristics, and a "Level 1 water accumulation risk intervention signal" is generated immediately. When only a single one occurs... When a signal is abnormal, it is judged as a potentially low-risk area, generating a "Level 2 water accumulation risk intervention signal." When all signals are normal, the system generates a "Level 3 water accumulation risk intervention signal," indicating a stable state. Its unique advantage lies in its integration of acoustic and electrical detection results, overcoming the limitations of false alarms or missed alarms from single signals. It achieves joint judgment and graded response to water accumulation trends, exhibiting high robustness and environmental adaptability. Through the hierarchical prediction mechanism, it not only significantly improves the accuracy of water accumulation identification but also provides clear and executable intervention level instructions for subsequent risk control modules, thereby constructing a closed-loop control link from perception to response. This truly realizes the intelligent control goals of prediction first, appropriate intervention, and safety priority.

[0038] Example 6 Please refer to Figure 1Specifically: The risk control module is used to execute corresponding intervention actions based on the received relevant water accumulation risk intervention signals. The specific process is as follows: When a "Level 1 Water Accumulation Risk Intervention Signal" is received, it indicates that there is water accumulation in the kitchen and bathroom floor areas and there is a risk of leakage. Emergency intervention actions are executed, including: automatically shutting off the water inlet solenoid valves of the kitchen and bathroom through the central control platform; triggering the local audible and visual alarm; and remotely pushing an "emergency water leakage warning" to notify the user to intervene on-site. When a "Level 2 Water Accumulation Risk Intervention Signal" is received, it indicates that there is water accumulation in the kitchen and bathroom floor area, but there is no risk of leakage. A gradual intervention action is executed, including: the robot vacuum cleaner is activated to perform a short-cycle mopping mode in the kitchen and bathroom floor area; at the same time, a notification message is sent to the user that "there is water accumulation in the kitchen and bathroom floor area and mopping mode is being executed". When a "Level 3 Water Accumulation Risk Intervention Signal" is received, it indicates that there is currently no water accumulation in the kitchen and bathroom floor area. The system will then perform maintenance monitoring and periodic inspection actions, including: timed sampling, data log recording and risk profile updates, and maintaining low power consumption. At the same time, the system will push a "Current kitchen and bathroom floor area is in good condition" notification message to the user.

[0039] In this embodiment, a closed-loop response mechanism from active identification to intelligent intervention is realized based on the generated intervention signals of different levels. The risk control module accurately analyzes the corresponding levels of water accumulation risk intervention signals and matches appropriate control strategies, significantly improving the automated processing capability of the smart home system in complex water environment scenarios. In particular, when a "Level 1 water accumulation risk intervention signal" is received, the system automatically closes the water inlet solenoid valves in the kitchen and bathroom areas through the central control platform to prevent water spread and leakage accidents. At the same time, it activates the local audible and visual alarm device and remotely pushes emergency warning information to the user, minimizing property damage and safety hazards caused by water damage. When a "Level 2 water accumulation risk intervention signal" is received... When water accumulation is present, the system can proactively schedule robot vacuums to perform short-cycle mopping and simultaneously prompt users for relevant intervention actions, achieving both automatic handling of minor water accumulation and user awareness assurance. In a risk-free state, the system maintains low-power operation, periodically performing inspections and data updates to ensure continuous system stability. The risk control module's unique advantage lies in its adaptive response mechanism with corresponding levels, matching actions, and intelligent execution. It truly achieves a closed-loop linkage throughout the entire process from water accumulation perception and risk identification to intervention control, enhancing the smart home system's ability to respond instantly to sudden water events, maintain the daily environment, and proactively engage users. It is a key hub for achieving intelligent and safe management of the home environment.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multifunctional smart home control system based on the Internet of Things, characterized in that: It includes an array scanning module, an acoustic wave detection module, a micro-electrical detection module, a water accumulation analysis module, and a risk control module; The array scanning module is used to deploy a MEMS ultrasonic array close to the kitchen and bathroom floor, and to emit short-period pulse signal excitation waves into the kitchen and bathroom floor area through its built-in transmitting array elements; The acoustic detection module captures the echo signals of each transmitting and receiving channel based on the excitation wave emitted by the transmitting array element, extracts the echo data of the array element, and analyzes the degree of influence of ground water marks on the echo signals of each transmitting and receiving channel in order to generate relevant water mark echo influence signals. The micro-electric detection module is used to collect the measured capacitance values ​​of each target detection area based on the high-resistivity weak electrode array deployed in the kitchen and bathroom floor area, and generate relevant water trace electrical response signals based on the influence of surface water on the equivalent dielectric properties of the target detection area. The water accumulation analysis module is used to perform water accumulation prediction analysis on the received relevant water mark echo influence signals and relevant water trace electrical response signals, and generate relevant water accumulation risk intervention signals; The risk control module is used to execute corresponding intervention actions based on the received relevant water accumulation risk intervention signals.

2. The multifunctional smart home control system based on the Internet of Things according to claim 1, characterized in that: The array scanning module includes a first deployment unit and a signal excitation unit; The first deployment unit is used to deploy a MEMS ultrasonic array close to the kitchen and bathroom floor, specifically: The MEMS ultrasonic array is installed on the low edge wall, under the kitchen cabinet and under the bathroom cabinet. The array is deployed horizontally close to the kitchen and bathroom floor. The MEMS ultrasonic array includes transmitting array elements, receiving array elements, edge signal amplifiers, distance sensors and signal acquisition chips. The signal acquisition chip is used for waveform generation, echo analysis and signal scheduling. The signal excitation unit is used to transmit short-period pulse signal excitation waves to the ground through the transmitting array elements. Its specific waveform function is set as follows: d; where, The waveform function represents the excitation wave of the signal, where f represents the center frequency of the ultrasound, and A represents the excitation amplitude. The coefficient of exponential decay is represented by t, where t represents time. The phase expression for a standard sinusoidal excitation wave.

3. The multifunctional smart home control system based on the Internet of Things according to claim 2, characterized in that: The acoustic wave detection module includes a time analysis unit, an energy analysis unit, a phase analysis unit, and a first determination unit; The time analysis unit is used to construct several transceiver channels based on the symmetrical relationship between each transmitting element in the transmitting array and the corresponding receiving element in the receiving array. It determines the distance between the reflection points of each transceiver channel by measuring the distance of the signal excitation wave from the transmitting element to the ground reflection point using a distance sensor. It then performs difference processing on the actual transmission and reception times of the signal excitation wave for each transceiver channel to determine the actual flight time of the signal for each channel. Based on the round-trip distance of each transceiver channel and the propagation speed of the signal excitation wave, it determines the signal flight time offset for each transceiver channel, specifically: In the formula, This represents the signal flight time offset of the corresponding transceiver channel. This indicates the actual flight time of the signal for the corresponding transceiver channel. The distance between the reflection points of the corresponding transceiver channel is represented by , and v represents the propagation speed of the signal excitation wave.

4. The multifunctional smart home control system based on the Internet of Things according to claim 3, characterized in that: The energy analysis unit is used to determine the abnormal attenuation of echo energy in each transceiver channel based on the spatial propagation attenuation characteristics of the signal excitation wave and in conjunction with the echo signals actually captured by the receiving array elements. Specifically: In the formula, This represents the abnormal attenuation coefficient of the echo energy of the corresponding transmit / receive channel. This represents the actual echo energy value of the corresponding transmit / receive channel. This represents the standard echo energy value under conditions without an aqueous film.

5. The multifunctional smart home control system based on the Internet of Things according to claim 3, characterized in that: The phase analysis unit is used to obtain the shock wave transmission phase of each transceiver channel by using the waveform function S(t) based on the signal excitation wave and recording the initial reference phase of the signal excitation wave emitted by each transmitting unit in the transmitting array in real time through the waveform synthesizer. After receiving the echoes reflected from the ground reflection point, the echoes are orthogonally mixed with the corresponding transmitted signal excitation waves, and the DC component is extracted using a low-pass filter to determine the echo reception phase of each transceiver channel; specifically: In the formula, This indicates the echo reception phase of the corresponding transmit / receive channel. This represents the DC quadrature component of the corresponding transceiver channel. This represents the DC in-phase component of the corresponding transmit / receive channel. Represents the forward and reverse tangent operators; Based on the effect of watermarks at ground reflection points on the phase delay of the signal excitation wave, the shock wave transmission phase of each transceiver channel is correlated with the echo reception phase of the corresponding transceiver channel. After calculating the phase difference, the transceiver phase offset coefficient of each transceiver channel is obtained.

6. The multifunctional smart home control system based on the Internet of Things according to claim 3, characterized in that: The first determination unit is used to construct array element echo data based on the acquired signal flight time offset, echo energy abnormal attenuation coefficient, and transmit / receive phase offset coefficient of each transmit / receive channel. Feature identification was performed on the phase echo data. The extracted signal time-of-flight bias, echo energy anomaly attenuation coefficient, and transmit / receive phase offset coefficient of each transmit / receive channel were correlated. After dimensionless processing, the degree of influence of ground watermarks on the echo signals of each transmit / receive channel was analyzed to determine the watermark influence index of each transmit / receive channel. Specifically: In the formula, This indicates the watermark impact index for the corresponding transmitting and receiving channels. This represents a function that takes the minimum value. By pre-setting an impact reference threshold and comparing and analyzing the impact reference threshold with the watermark impact index of the corresponding transceiver channel, a relevant watermark echo impact signal is generated. The specific process is as follows: If the watermark impact index of the corresponding transceiver channel is greater than the preset impact reference threshold, a "watermark echo impact signal" will be generated; if the watermark impact index of the corresponding transceiver channel is not greater than the preset impact reference threshold, a "watermark echo no impact signal" will be generated. The relevant watermark echo influence signals include "watermark echo influence signals" and "watermark echo no influence signals", and the relevant watermark echo influence signals are sent to the water accumulation analysis module.

7. The multifunctional smart home control system based on the Internet of Things according to claim 1, characterized in that: The micro-electrical detection module includes a second deployment unit, an electrical analysis unit, and a second determination unit; The second deployment unit is used to deploy a high-resistivity, low-electrode array in the kitchen and bathroom floor area, specifically: The kitchen and bathroom floor area is divided into several target detection areas by grid, with the center point of the target detection area as the sampling point, and miniature sensing electrodes are deployed. Each miniature sensing electrode is connected to a capacitance measurement circuit to form a high-resistance weak electrode array. The electrical analysis unit is used to collect the measured capacitance values ​​of each target detection area by applying an electrical signal of fixed frequency and amplitude to sampling points in each target detection area, combined with the parallel capacitance equivalent modeling method. Based on the collected measured capacitance values ​​of each target detection area, and combined with a statistical averaging algorithm, the average measured capacitance value of the kitchen and bathroom floor area is obtained. According to the influence of surface water on the equivalent dielectric properties of the target detection area, the measured capacitance value of each target detection area is compared with the average measured capacitance value, and the number of target detection areas whose measured capacitance value exceeds the average measured capacitance value is counted as the number of water stain target detection areas. The proportion of water stain area in the kitchen and bathroom floor area is determined based on the ratio of the number of water stain target detection areas to the total number of target detection areas.

8. The multifunctional smart home control system based on the Internet of Things according to claim 7, characterized in that: The second determination unit is used to generate relevant electrical response signals for water stains based on the proportion of water stain area in the kitchen and bathroom floor areas. The specific process is as follows: If the proportion of water stains on the kitchen and bathroom floor is not equal to zero, it means that there is a water stain target detection area in the kitchen and bathroom floor area, and a "water stain electrical response signal" is generated. If the proportion of water stains on the kitchen and bathroom floor area is equal to zero, it means that there is no water stain target detection area in the kitchen and bathroom floor area, and a "water stain electrical no response signal" is generated. The relevant water trace electrical response signals include "water trace electrical response signals" and "water trace electrical non-response signals", and the relevant water trace electrical response signals are sent to the water accumulation analysis module.

9. The multifunctional smart home control system based on the Internet of Things according to claim 1, characterized in that: The water accumulation analysis module is used to perform water accumulation prediction analysis on the received relevant water mark echo influence signals and relevant water trace electrical response signals, and generate relevant water accumulation risk intervention signals. The specific process is as follows: Based on the relevant watermark echo influence signals, establish a set X, labeling "watermark echo influence signals" as element x1 and "watermark echo no influence signals" as element x2, where element x1 ∈ set X and element x2 ∈ set X; based on the relevant watermark electrical response signals, establish a set Y, labeling "watermark electrical response signals" as element y1 and "watermark no electrical response signals" as element y2, where element y1 ∈ set Y and element y2 ∈ set Y; The set X and set Y are combined. If X∪Y={x1, y1}, a "Level 1 flood risk intervention signal" is generated. If X∪Y={x1, y2} or {x2, y1}, a "Level 2 flood risk intervention signal" is generated. If X∪Y={x2, y2}, a "Level 3 flood risk intervention signal" is generated. The relevant waterlogging risk intervention signals include "Level 1 waterlogging risk intervention signal", "Level 2 waterlogging risk intervention signal" and "Level 3 waterlogging risk intervention signal", and the relevant waterlogging risk intervention signals are sent to the risk control module.

10. The multifunctional smart home control system based on the Internet of Things according to claim 1, characterized in that: The risk control module is used to execute corresponding intervention actions based on the received relevant water accumulation risk intervention signals. The specific process is as follows: When a "Level 1 Water Accumulation Risk Intervention Signal" is received, it indicates that there is water accumulation in the kitchen and bathroom floor areas and there is a risk of leakage. Emergency intervention actions are executed, including: automatically shutting off the water inlet solenoid valves of the kitchen and bathroom through the central control platform; triggering the local audible and visual alarms; and remotely pushing an "emergency water leakage warning" to notify the user to intervene on-site. When a "Level 2 Water Accumulation Risk Intervention Signal" is received, it indicates that there is water accumulation in the kitchen and bathroom floor area, but there is no risk of leakage. A gradual intervention action is executed, including: the robot vacuum cleaner is activated to perform a short-cycle mopping mode in the kitchen and bathroom floor area; at the same time, a notification message is sent to the user that "there is water accumulation in the kitchen and bathroom floor area and mopping mode is being executed". When a "Level 3 Water Accumulation Risk Intervention Signal" is received, it indicates that there is currently no water accumulation in the kitchen and bathroom floor area. The system will then perform maintenance monitoring and periodic inspection actions, including: timed sampling, data log recording and risk profile updates, and maintaining low power consumption. At the same time, the system will push a "Current kitchen and bathroom floor area is in good condition" notification message to the user.

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