Intelligent water producing and taking system and water taking method

Abstract

The invention relates to the technical field of water treatment, discloses an intelligent water making and taking system and a water taking method, and aims to overcome the defects of existing intelligent water drinking equipment in the aspects of water quality monitoring, cloud control, voice interaction and the like. The system comprises: a water quality detection module arranged at a water inlet end and a water outlet end of equipment and used for detecting water quality in real time; the cloud control module receives the water quality data and generates an equipment control instruction; the voice recognition module is integrated in a local device and provides a localized voice recognition function; and the mobile terminal application module provides a remote monitoring and control interface. By adopting the technical scheme, early warning of water quality abnormity of a regional water source, accurate life prediction of filter element performance, remote intelligent configuration and scheduling of equipment operation parameters and non-sensitive equipment operation in a scene of weak network signals or interruption can be realized, and the drinking water safety and the user experience are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of water treatment technology, specifically relating to an intelligent water production and intake system and water intake method. Background Technology

[0002] With the increasing popularity of smart drinking water equipment, users have raised higher requirements for the safety, intelligence, and ease of operation of the water purification and dispensing process. While current drinking water equipment has gradually incorporated technologies such as the Internet of Things and mobile terminal control, it still has significant shortcomings in areas such as dynamic water quality monitoring, deep cloud-based collaboration, and human-computer interaction, making it difficult to meet the growing demand for high-quality drinking water.

[0003] Among them, the intelligent water purification and intake system, as a key link connecting the water source and the user terminal, has core functions covering multiple dimensions such as source water quality assessment, water purification process monitoring, effluent quality assurance, and remote interactive control. An ideal system should be able to sense changes in the raw water quality at the inlet in real time, dynamically adjust the purification strategy, continuously monitor the filter efficiency at the outlet, and support users to achieve seamless operation and remote management through voice or mobile terminals.

[0004] Existing technologies still face multiple challenges in the aforementioned aspects: First, although some systems have basic water quality testing capabilities, they are limited to local monitoring at the water outlet or during the water purification process, lacking a comprehensive understanding of the water source in the area where the equipment is deployed, making it difficult to provide early warning of regional water quality anomalies. Secondly, cloud-based control functions are mostly limited to data uploading and simple status queries, failing to achieve remote dynamic configuration and intelligent scheduling of device operating parameters, thus limiting the system's adaptive capabilities. Furthermore, voice interaction modules are generally missing or rely on external network environments, and cannot provide localized voice recognition and response in weak network or offline states, which seriously affects the continuity of use and user experience.

[0005] The aforementioned issues collectively restrict the widespread application of smart drinking water equipment in homes, offices, and public settings, necessitating a solution from an integrated, end-to-end, and highly robust smart water production and dispensing equipment and system architecture. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent water production and intake system and its water intake method, which can effectively solve the problems in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: On one hand, an intelligent water purification and extraction device and system, comprising the following components: a water quality detection module, deployed at the inlet and outlet of the device, for real-time detection of the raw water quality in the area where the device is used, and continuous monitoring of the operating efficiency of the purified water filter cartridge; a cloud control module, communicatively connected to the water quality detection module and the device actuator, for receiving water quality data and generating device control commands to achieve remote dynamic configuration and intelligent scheduling of operating parameters; a voice recognition module, integrated locally in the device, for receiving and processing user voice commands, providing localized voice recognition and response functions in weak network or offline states; and a mobile terminal application module, existing in the form of software and mini-programs, interacting with the cloud control module to provide a remote control interface for the device; The equipment control module is used to control the various functions of the machine itself; the smart screen control module is used to accurately push advertising content to the advertising machine with the smart screen; the AI ​​artificial intelligence module is used to create a "Little Health Water Butler" to output healthy drinking water knowledge and provide voice interaction learning based on the user's physical condition and drinking habits, and to customize healthy drinking habits for the user.

[0008] Preferably, the water quality detection module includes an inlet detection unit and an outlet detection unit. The inlet detection unit is equipped with a turbidity sensor, The unit comprises an array of sensors, including a conductivity sensor and a total dissolved solids sensor array, with a sampling frequency of no less than once per minute. This unit continuously collects multi-dimensional physicochemical indicators of the raw water to construct a dynamic baseline of the raw water quality in the deployment area. This baseline records the statistical characteristics and trends of water quality parameters in time-series format, used to identify early signs of regional water quality anomalies. The effluent detection unit employs a high-precision residual chlorine sensor and an organic matter concentration sensor, achieving a detection accuracy of 0.01%. The model is based on a nonlinear regression algorithm. By monitoring changes in specific indicators of purified water quality and combining them with the cumulative water flow data of the filter cartridge, a filter cartridge performance decay model is established. The input parameters include the deviation of the effluent water quality indicators, the cumulative operating time of the filter cartridge, and the total water flow. The output is the predicted value of the remaining life of the filter cartridge and the performance status evaluation result. The evaluation result is expressed as a percentage to represent the degree of decay of the current performance of the filter cartridge relative to the initial performance.

[0009] Furthermore, the cloud-based control module incorporates an adaptive control algorithm, the core of which is described by the following formula:

[0010] in, This represents the equipment control command issued at time t. This represents the deviation between the measured water quality data at time t and the preset target value. The module dynamically adjusts the proportional, integral, and derivative coefficients based on the influent water quality. According to water quality anomalies reported by the influent detection unit, it automatically adjusts the speed setpoint of the booster pump and the reverse osmosis membrane flushing cycle in the water purification equipment. The speed setpoint adjustment is based on real-time measurements of influent turbidity and total dissolved solids content, with the corresponding proportional coefficient correction determined by a lookup table. The reverse osmosis membrane flushing cycle adjustment is based on the rate of change of conductivity sensor readings; a forced flushing command is triggered when the rate of change exceeds a threshold of 0.5 μS / cm·min. The adjusted operating parameter set is sent to the equipment actuator via a secure link using AES-256 encryption.

[0011] Furthermore, the speech recognition module employs an offline speech recognition engine. Its acoustic model is built upon a deep neural network, containing five hidden layers with 256, 512, 512, 256, and 128 neurons per layer, respectively. The activation function is ReLU, and the vocabulary covers at least 500 drinking-related control commands, encompassing operations such as water dispensing, temperature setting, water volume setting, and device status query. This module preloads the acoustic and language models into local storage upon device startup. When the network signal strength is detected to be below -85dBm, it automatically switches to offline recognition mode, performing endpoint detection, feature extraction, and decoding of user voice commands. Feature extraction utilizes Mel-frequency cepstral coefficients to extract a 39-dimensional feature vector. The decoding process uses the Viterbi algorithm to perform path search within a predefined grammar network, generating corresponding device control commands and directly sending them to the device's main controller for execution, ensuring the continuity and real-time nature of voice interaction.

[0012] Preferably, the mobile terminal application module establishes a secure connection with the cloud control module through an application programming interface (API), which uses the OAuth 2.0 protocol for authentication and authorization. This module provides a real-time device status display interface, displaying information including but not limited to current inlet / outlet water quality parameters, filter cartridge remaining lifespan percentage, device operating mode, and historical water consumption statistics charts. The historical water consumption statistics charts support displaying water consumption trends by day, week, and month, and detailed data for any point in time can be viewed by sliding the timeline. Users can manually set the water intake volume, water temperature, and water intake mode through this interface, or schedule the device to start and stop during specific time periods. All user operation commands are uploaded to the cloud control module via the mobile internet, and then verified and forwarded to the corresponding device by the cloud control module.

[0013] Furthermore, the device control module is specifically used to perform multi-segment temperature control, quantitative water dispensing, child lock switch, and water dispensing switch functions. The multi-segment temperature control supports setting at least 12 temperature levels in 5-degree intervals within the range of 45°C to 100°C. The quantitative water dispensing supports setting the water volume in 50-degree increments within the range of 100mL to 2000mL. The child lock switch status is triggered by the device's main controller based on the time interval between two consecutive press operations being less than 1 second. The smart screen control module selects matching advertising content from the cloud advertising library based on the device's geographical location information, the current time period, and the user's historical operation records, and pushes it to the smart screen for carousel display via HTTP protocol. The advertising content update cycle is 24 hours.

[0014] The filtering algorithm of the smart screen control module is as follows: weight coefficients are assigned to geographical location, time period, and historical operation records respectively. , , Calculate the matching score between each candidate ad content and the current context. Where Sim is the similarity function and ClickRate is the historical click-through rate; select the N ads with the highest score S to push.

[0015] Furthermore, the AI ​​module collects data on users' physical condition, drinking habits, and work and living environment to construct a user health profile. This module combines a rule-based expert system with a machine learning model. The expert system contains over 200 rules on healthy drinking habits, while the machine learning model uses a gradient boosting decision tree algorithm. Input features include user age, weight, daily activity intensity, and ambient temperature and humidity. The output is personalized healthy drinking habit suggestions for the user, including suitable temperature, water volume, and water quality requirements. The module continuously records users' actual drinking data and feedback, and updates model parameters using an online learning mechanism, achieving data-driven, intelligent, and timely self-learning evolution of healthy drinking habits.

[0016] In the AI ​​module, a rule-based expert system and a machine learning model work collaboratively in a cascaded manner: First, the expert system matches a preset set of drinking water rules based on the user's input of their constitution (e.g., 'Yin deficiency', 'damp heat') to initially determine the range of drinking water temperature and volume. Then, the gradient boosting decision tree model uses this range as a constraint, combined with the user's real-time drinking data (e.g., actual water intake and time), as feedback to fine-tune the suggested values. An example of the expert system's rules is: IF User constitution = 'Yin deficiency' AND Ambient humidity < 40% THEN Suggested water temperature = 45℃ ± 2℃, Suggested daily water intake = weight (kg) * 35ml.

[0017] On the other hand, a smart water production and intake device and water intake system, the specific steps of which are as follows: Step S110: Real-time water quality data of raw water and purified water are collected by sensor arrays deployed at the inlet and outlet ends, and a dynamic baseline of raw water quality and filter efficiency decay model of the equipment deployment area are constructed based on the collected data. Step S120: Receive water quality data using the cloud control module, dynamically calculate equipment control instructions through an adaptive control algorithm, and adjust the operating parameters of the water purification equipment based on abnormal influent water quality events. Step S130: The user's voice commands are processed by the voice recognition module integrated locally in the device in the weak network or offline state, and the device control commands are generated and sent to the main controller. Step S140: A remote operation interface is provided through the mobile terminal application module to receive the water intake parameters or reservation instructions set by the user, and forward them to the device after verification by the cloud. Step S150: The equipment control module performs multi-segment temperature control, quantitative water dispensing, child lock switch and water dispensing switch functions. Step S160: Push precise advertising content to the smart screen based on device context information through the smart screen control module; Step S170: The AI ​​artificial intelligence module constructs a health profile based on the user's physical condition and habit data, generates personalized healthy drinking water suggestions, and achieves self-learning and evolution.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. Through end-to-end water quality monitoring and dynamic baseline construction, early warning of regional water quality anomalies and accurate assessment of filter cartridge performance are achieved, significantly improving drinking water safety and equipment maintenance efficiency.

[0019] 2. By adopting a cloud-based adaptive control and dynamic parameter scheduling mechanism, the problem of rigid control in existing systems is overcome, and the equipment's adaptability to different water quality conditions and operational efficiency are enhanced.

[0020] 3. Integrating a local offline speech recognition engine ensures that users can still operate devices seamlessly via voice even in scenarios with weak or interrupted network signals, greatly improving the robustness of human-computer interaction and the continuity of user experience.

[0021] 4. Through deep collaboration between mobile terminals and AI modules, convenient remote control and personalized healthy drinking water management functions are provided, realizing intelligent and customized drinking water services. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall technical solution architecture of the intelligent water production and intake system and water intake method proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the adaptive control algorithm in this invention; Figure 3 This is a logical flowchart of water quality testing and filter cartridge performance monitoring in this invention; Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the terminal and the cloud in this invention; Figure 5 This is a logical flow diagram of offline speech recognition and local control execution in this invention. Detailed Implementation

[0023] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific embodiments according to the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. Example

[0024] In the application scenario of direct drinking water systems in large commercial office buildings, intelligent water production and intake systems achieve safe and efficient drinking water services through end-to-end water quality monitoring and intelligent control mechanisms. (See also...) Figure 1 The overall system architecture includes the coordinated operation of a water quality testing module, a cloud control module, a voice recognition module, a mobile terminal application module, an equipment control module, a smart screen control module, and an AI artificial intelligence module.

[0025] Water quality monitoring modules are deployed at both the inlet and outlet of the equipment, forming a complete water quality monitoring system. (See also...) Figure 2 The inlet detection unit is equipped with a turbidity sensor. The unit employs an array of sensors, including a conductivity sensor and a total dissolved solids sensor, with a sampling frequency set to once per minute. This unit continuously collects multi-dimensional physicochemical indicators of the raw water to construct a dynamic baseline of the raw water quality in the deployment area. This baseline records the statistical characteristics and trends of water quality parameters in time-series format, specifically including the 24-hour fluctuation range of turbidity values. The daily average variation curve of the turbidity value, the statistical analysis of the standard deviation of conductivity, and the peak distribution characteristics of total dissolved solids content were analyzed. When the turbidity value exceeded 5 for three consecutive samplings... and When the value deviates from the range of 7.0 ± 0.5, the system identifies it as an early sign of a regional water quality anomaly and immediately activates the early warning mechanism. The effluent detection unit uses a high-precision residual chlorine sensor and an organic matter concentration sensor, achieving a detection accuracy of 0.01. Scale. This unit monitors changes in specific water quality indicators after purification and, combined with cumulative water flow data from the filter cartridge, establishes a filter cartridge performance degradation model. The model is based on a nonlinear regression algorithm. Input parameters include deviations in effluent water quality indicators, cumulative filter cartridge operating time, and total water flow. Outputs a predicted remaining lifespan of the filter cartridge and a performance status assessment. The assessment results are expressed as a percentage representing the degree of degradation of the filter cartridge's current performance relative to its initial performance. A filter cartridge replacement reminder is triggered when the degradation exceeds 80%.

[0026] The cloud-based control module establishes a communication connection with the water quality detection module and equipment actuators, enabling remote dynamic configuration and intelligent scheduling of operating parameters. (See also...) Figure 3 This module has a built-in adaptive control algorithm, and its core control law is described by the following formula:

[0027] Where u(t) represents the equipment control command issued at time t. This represents the deviation between the measured water quality data at time t and the preset target value. , , This module dynamically adjusts the proportional, integral, and derivative coefficients based on the influent water quality. It automatically adjusts the speed setpoint of the booster pump and the reverse osmosis membrane flushing cycle in the water purification equipment based on water quality anomalies reported by the influent detection unit. The speed setpoint adjustment is based on real-time measurements of influent turbidity and total dissolved solids content, with the corresponding proportional coefficient correction determined using a lookup table method. In specific implementation, when the influent turbidity increases from 1... Rise to 3 When, the proportionality coefficient The integral coefficient was adjusted from the initial value of 1.2 to 1.5. The value was adjusted from 0.8 to 1.0. The adjustment of the reverse osmosis membrane flushing cycle is based on the rate of change of the conductivity sensor reading. When the rate of change exceeds the threshold of 0.5 microSiemens per centimeter per minute, a forced flushing command is triggered. The adjusted set of operating parameters is sent to the device actuators through a secure link using the AES-256 encryption algorithm, ensuring the integrity and confidentiality of data transmission.

[0028] The voice recognition module is integrated locally on the device, and an offline voice recognition engine ensures continuous interaction even in weak network environments. See also Figure 4 The acoustic model of this module is built based on a deep neural network, which contains five hidden layers with 256, 512, 512, 256, and 128 neurons in each layer, respectively, and uses ReLU as the activation function. The vocabulary covers 500 drinking water-related control commands, including operations such as water intake, temperature setting, water volume setting, and equipment status query.

[0029] Upon device startup, the acoustic and language models are pre-loaded into local storage, occupying approximately 150 megabytes of storage space. When a network signal strength is detected to be below -85 dBmW, the system automatically switches to offline recognition mode. In this mode, endpoint detection, feature extraction, and decoding are performed on user voice commands. Feature extraction uses Mel-frequency cepstral coefficients to extract a 39-dimensional feature vector, including 12-dimensional Mel-frequency cepstral coefficients, 12-dimensional first-order difference coefficients, and 12-dimensional second-order difference coefficients. The decoding process uses the Viterbi algorithm to perform path search in a predefined grammar network, generating corresponding device control commands and sending them directly to the device's main controller for execution. Actual testing shows that in environments with a signal-to-noise ratio greater than 15 dB, the offline recognition accuracy reaches over 95%.

[0030] The mobile terminal application module establishes a secure connection with the cloud control module through an application programming interface (API), which uses the OAuth 2.0 protocol for authentication and authorization. This module provides a real-time device status display interface, showing current influent and effluent water quality parameters, filter cartridge remaining lifespan percentage, device operating mode, and historical water consumption statistics charts. The historical water consumption statistics charts support displaying water consumption trends by day, week, and month, and detailed data for any point in time can be viewed by sliding the timeline.

[0031] Users can manually set the water volume, temperature, and dispensing mode through this interface. The water volume setting ranges from 100 ml to 2000 ml, in 50 ml increments. The water temperature setting supports 12 temperature levels within the range of 45 degrees Celsius to 100 degrees Celsius. The scheduling function allows users to set the device to start and stop during specific time periods. All user operation commands are uploaded to the cloud control module via the mobile internet. The cloud control module verifies the command format and permissions, and forwards them to the corresponding device after matching with the device identifier. The command verification process includes three levels: command syntax checking, user permission verification, and device status compatibility detection.

[0032] The equipment control module specifically executes multi-segment temperature control, quantitative water dispensing, child lock switch, and water dispensing switch functions. The multi-segment temperature control mechanism uses a temperature sensor to monitor the output water temperature of the heating unit in real time. When the deviation between the set temperature and the measured temperature exceeds ±2 degrees Celsius, a proportional-integral-derivative (PID) control algorithm is activated to adjust the heating power. The quantitative water dispensing function accurately measures the output water volume using a flow meter. When the accumulated water volume reaches 95% of the set value, the flow rate begins to decrease, and the solenoid valve completely closes when the set value is reached. The child lock switch status is triggered by the main controller based on a time interval of less than 1 second between two consecutive presses, accompanied by audible and visual prompts. The smart screen control module filters matching advertising content from a cloud-based advertising library based on the device's geographical location information, the current time period, and user historical operation records. The filtering algorithm is based on collaborative filtering principles, analyzing the click behavior of other users in similar geographical locations and time periods to calculate the relevance score of the advertising content. Advertising content with a relevance score exceeding 0.8 is pushed to the smart screen via HTTP protocol for carousel display, with the advertising content update cycle strictly controlled to 24 hours.

[0033] The AI ​​module collects data on users' physical condition, drinking habits, and work / living environment to construct a user health profile. This module combines a rule-based expert system with a machine learning model. The expert system contains 200 rules on healthy drinking habits, covering dimensions such as suitable water temperature, recommended daily water intake, and drinking time distribution for different body types. The machine learning model uses a gradient boosting decision tree algorithm, taking into account user age, weight, daily activity intensity, and environmental temperature and humidity, and outputting personalized healthy drinking habit suggestions. Specifically, for a 35-year-old male user weighing 70 kg and working in an office, the model suggests a daily water intake of 2000 ml, a 2-hour interval between water intakes, and a suitable water temperature of 45 degrees Celsius. The module continuously records users' actual drinking data and feedback, updating model parameters through an online learning mechanism. The online learning uses a mini-batch gradient descent method, updating model parameters after accumulating 100 user feedback data points, achieving data-driven, intelligent, and real-time self-learning evolution for healthy drinking habits.

[0034] The execution process of the intelligent water production and intake method strictly follows the preset sequence of steps.

[0035] Step S110 involves real-time acquisition of raw water and purified water quality data using sensor arrays deployed at the inlet and outlet ends. The sampling interval is 60 seconds, and each sampling lasts for 5 seconds. Based on the acquired data, a dynamic baseline of raw water quality and a filter cartridge performance degradation model for the equipment deployment area are constructed. The baseline is updated once per hour, and the model is retrained once per week.

[0036] Step S120 utilizes the cloud control module to receive water quality data and dynamically calculates equipment control commands through an adaptive control algorithm. The algorithm executes every 10 seconds, and the control commands are immediately sent via an encrypted link after generation. Based on abnormal influent water quality events, the operating parameters of the water purification equipment are adjusted. The adjustment range is graded according to the severity of the abnormality: minor abnormalities result in an adjustment range of no more than 20%, while severe abnormalities can result in an adjustment range of up to 50%.

[0037] Step S130 processes user voice commands in weak network or offline conditions using a voice recognition module integrated locally on the device. The voice signal sampling rate is 16 kHz, the quantization bit depth is 16 bits, and endpoint detection uses a dual-threshold comparison method. A silence period exceeding 300 milliseconds is considered the end of the voice command. Device control commands are generated and sent to the main controller, with command transmission latency controlled within 100 milliseconds.

[0038] Step S140 provides a remote operation interface through the mobile terminal application module to receive user-set water dispensing parameters or reservation instructions. User operation data is transmitted after being encapsulated in JSON format, with the data packet size not exceeding 1 kilobyte. After verification by the cloud, it is forwarded to the device. The verification process includes digital signature verification and timestamp validity check.

[0039] Step S150 executes multi-segment temperature control, quantitative water dispensing, child lock switch, and water dispensing switch functions through the equipment control module. The temperature control accuracy is ±1 degree Celsius, and the water volume control error does not exceed ±3% of the set value.

[0040] Step S160 involves pushing precise advertising content to the smart screen via the smart screen control module based on device context information. Context information includes the device's geographical location (latitude and longitude), the day's time period divided into 6 time slots, and statistics of the user's operation records for the past 7 days.

[0041] Step S170 uses the AI ​​module to construct a health profile based on the user's physical condition and habits data. Data is collected daily, and the profile is updated weekly. Personalized healthy drinking suggestions are generated and the system learns and evolves. Suggestions are output daily during breakfast time, and model parameter updates are performed automatically during system idle periods.

[0042] In the application scenario of direct drinking water systems in large commercial office buildings, intelligent water production and intake systems achieve safe and efficient drinking water services through end-to-end water quality monitoring and intelligent control mechanisms. (See also...) Figure 1 The overall system architecture includes the coordinated operation of a water quality testing module, a cloud control module, a voice recognition module, a mobile terminal application module, an equipment control module, a smart screen control module, and an AI artificial intelligence module. Example

[0043] In high-end residential community applications, the intelligent water production and intake system is specifically optimized for the personalized needs of household users. The water quality testing module adds a heavy metal ion detection unit, using electrochemical sensors to detect the concentration of harmful metal ions such as lead, cadmium, and mercury, with a detection limit of 0.001 mg / L. Seasonal variation factors are incorporated into the construction of the raw water quality dynamic baseline, establishing different baseline standards for water quality differences between rainy and dry seasons. The filter cartridge performance degradation model incorporates a water temperature influence parameter; when the inlet water temperature is below 10 degrees Celsius, the model automatically adjusts the degradation coefficient calculation method.

[0044] The cloud-based control module enhances the robustness of the adaptive control algorithm by optimizing the adjustment strategy of proportional-integral-derivative coefficients to address the water quality fluctuation characteristics of the community's secondary water supply system. When the pH value fluctuates by more than one unit within a short period, the algorithm automatically increases the weight of the derivative coefficient to suppress system overshoot. The speech recognition module expands its dialect recognition capabilities, adding acoustic models for Cantonese, Minnan, and other dialects, and expanding the vocabulary to 800 commands to cover more interactive needs in home scenarios.

[0045] The mobile application module adds a family member management function, supporting up to six independent accounts for family members, each of which can save personalized water usage preferences. The device control module enhances the child lock safety mechanism, adds a fingerprint recognition unlocking option, and upgrades the accidental touch protection level to IP54 standard. The smart screen control module adapts to the home environment, adding categories such as children's education and family health to the advertising content filtering, and adjusting the carousel interval to once every 15 minutes.

[0046] The AI ​​module deepens the construction of health profiles, increasing the collection of health indicators such as sleep quality and exercise volume. The expert system rules have been expanded to 300, with the addition of drinking water guidance rules for special groups such as pregnant women and the elderly. The machine learning model introduces a transfer learning mechanism, using drinking water data from users with similar physical conditions within the community to accelerate model convergence, reducing the time for generating personalized suggestions from 24 hours to 12 hours.

[0047] During the execution of steps S110 to S170, the system optimizes parameter configuration for home use scenarios.

[0048] The water quality data collection interval in step S110 is adjusted to 30 minutes each time to reduce equipment energy consumption.

[0049] The generation cycle of equipment control commands in step S120 is extended to 30 seconds each time to balance control accuracy and system load.

[0050] The voice recognition in step S130 adds a wake-up word detection function, and the standby power consumption is controlled within 3 watts.

[0051] The remote operation interface in step S140 simplifies the operation process, and commonly used water dispensing modes can be set with one click.

[0052] Step S150 adds an energy-saving mode option to the equipment control, which automatically reduces the standby temperature of the heating unit during nighttime.

[0053] The advertising push in step S160 adds parental control functions and supports content rating management.

[0054] Step S170, which builds a health profile, adds a data privacy protection mechanism, where all personal health data is encrypted locally before being uploaded.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An intelligent water production and intake system, characterized in that, The system includes the following components: The water quality testing module is deployed at the inlet and outlet of the equipment to perform real-time testing of the raw water quality in the area where the equipment is used, and to continuously monitor the operating efficiency of the pure water purification filter cartridge. The cloud control module communicates with the water quality detection module and equipment actuators to receive water quality data and generate equipment control commands, enabling remote dynamic configuration and intelligent scheduling of operating parameters. The voice recognition module, integrated locally on the device, is used to receive and process user voice commands, providing local voice recognition and response functions in weak network or offline conditions; The mobile terminal application module exists in the form of software and mini-programs, interacts with the cloud control module, and provides a remote control interface for the device. The equipment control module is used to control the various functions of the machine itself; The smart screen control module is used to accurately push advertising content to advertising machines equipped with smart screens; The AI ​​module is used to collect data on the user's physical condition, drinking habits, and environment. Through a cascaded expert system and machine learning model, it generates and outputs personalized healthy drinking water suggestions for the user, including appropriate drinking water temperature, water volume, and time.

2. The intelligent water production and intake system according to claim 1, characterized in that, The inlet water quality detection unit of the water quality detection module is equipped with a turbidity sensor, a pH sensor, a conductivity sensor, and a total dissolved solids sensor array. Its sampling frequency is no less than once per minute. This unit continuously collects multi-dimensional physicochemical indicators of the raw water to construct a dynamic baseline of the raw water quality in the equipment deployment area. This baseline records the statistical characteristics and changing trends of water quality parameters in the form of a time series, which is used to identify early signs of regional water quality anomalies. The outlet water quality detection unit adopts a high-precision residual chlorine sensor and an organic matter concentration sensor, with a detection accuracy of 0.01 mg / L. This unit monitors the changes in specific indicators of the purified water quality and, combined with the cumulative water flow data of the filter cartridge, establishes a filter cartridge performance decay model. This model is based on a nonlinear regression algorithm. The input parameters include the deviation of the effluent water quality indicators, the cumulative operating time of the filter cartridge, and the total water flow. The output is the predicted value of the remaining life of the filter cartridge and the performance status evaluation result. The evaluation result is expressed as a percentage to represent the degree of decay of the current performance of the filter cartridge relative to its initial performance.

3. The intelligent water production and intake system according to claim 1, characterized in that, The cloud-based control module has a built-in adaptive control algorithm, and its core control law is described by the following formula: in, This represents the equipment control command issued at time t. This represents the deviation between the measured water quality data at time t and the preset target value. To dynamically adjust the proportional, integral, and derivative coefficients based on the influent water quality, this module automatically adjusts the speed setpoint of the booster pump and the reverse osmosis membrane flushing cycle in the water purification equipment according to the water quality anomaly events reported by the influent end detection unit. The speed setpoint is adjusted based on the real-time measured values ​​of influent turbidity and total dissolved solids content, and the corresponding proportional coefficient correction is determined by looking up a table. The reverse osmosis membrane flushing cycle is adjusted based on the rate of change of the conductivity sensor reading. When the rate of change exceeds the threshold of 0.5 μS / cm·min, a forced flushing command is triggered. The adjusted set of operating parameters is sent to the equipment actuator through a secure link using the AES-256 encryption algorithm.

4. The intelligent water production and intake system according to claim 1, characterized in that, The speech recognition module employs an offline speech recognition engine. Its acoustic model is built upon a deep neural network, which contains five hidden layers with 256, 512, 512, 256, and 128 neurons per layer, respectively. The activation function is ReLU, and the vocabulary covers no fewer than 500 drinking-related control commands, encompassing operations such as water intake, temperature setting, water volume setting, and device status query. Upon device startup, the acoustic and language models are preloaded into local storage. When the network signal strength is detected to be below -85dBm, the system automatically switches to offline recognition mode. In this mode, endpoint detection, feature extraction, and decoding are performed on the user's voice commands. Feature extraction utilizes Mel-frequency cepstral coefficients to extract a 39-dimensional feature vector. The decoding process uses the Viterbi algorithm to perform path search within a predefined grammar network, generating corresponding device control commands which are then directly sent to the device's main controller for execution.

5. The intelligent water production and intake system according to claim 1, characterized in that, The mobile terminal application module establishes a secure connection with the cloud control module through an application programming interface (API), which uses the OAuth 2.0 protocol for authentication and authorization. The module provides a real-time device status display interface, displaying current inlet / outlet water quality parameters, filter cartridge remaining lifespan percentage, device operating mode, and historical water consumption statistics charts. The historical water consumption statistics charts support displaying water consumption trends by day, week, and month, and detailed data for any point in time can be viewed by sliding the timeline. Users can manually set the water intake volume, water temperature, and water intake mode through this interface, or schedule the device to start and stop during specific time periods. All user operation commands are uploaded to the cloud control module via the mobile internet. The cloud control module verifies the command format and permissions, and forwards them to the corresponding device after matching with the device identifier.

6. The intelligent water production and intake system according to claim 1, characterized in that, The device control module is specifically used to perform multi-segment temperature control, quantitative water dispensing, child lock switch, and water dispensing switch functions. The multi-segment temperature control supports setting at least 12 temperature levels in 5-degree intervals within the range of 45℃ to 100℃. The quantitative water dispensing supports setting the water volume in 50-degree increments within the range of 100mL to 2000mL. The child lock switch status is triggered by the device's main controller based on the time interval between two consecutive press operations being less than 1 second. The smart screen control module selects matching advertising content from the cloud advertising library based on the device's geographical location information, the current time period, and the user's historical operation records, and pushes it to the smart screen for carousel display via HTTP protocol. The advertising content update cycle is 24 hours.

7. The intelligent water production and intake system according to claim 1, characterized in that, The AI ​​module collects data on users' physical condition, drinking habits, and work / living environment to construct a user health profile. This module combines a rule-based expert system with a machine learning model. The expert system contains over 200 rules related to healthy drinking habits, while the machine learning model uses a gradient boosting decision tree algorithm. Input features include user age, weight, daily activity intensity, and ambient temperature and humidity. Output is personalized healthy drinking habit suggestions, including suitable temperature, water volume, and water quality requirements. The module continuously records users' actual drinking data and feedback, using an online learning mechanism to update model parameters, achieving data-driven, intelligent, and real-time self-learning evolution for healthy drinking habits.

8. A smart water production and extraction method, characterized in that, The specific steps of this method are as follows: Step S110: Real-time water quality data of raw water and purified water are collected by sensor arrays deployed at the inlet and outlet ends, and a dynamic baseline of raw water quality and filter efficiency decay model of the equipment deployment area are constructed based on the collected data. Step S120: Receive water quality data using the cloud control module, dynamically calculate equipment control instructions through an adaptive control algorithm, and adjust the operating parameters of the water purification equipment based on abnormal influent water quality events. Step S130: The user's voice commands are processed by the voice recognition module integrated locally in the device in the weak network or offline state, and the device control commands are generated and sent to the main controller. Step S140: A remote operation interface is provided through the mobile terminal application module to receive the water intake parameters or reservation instructions set by the user, and forward them to the device after verification by the cloud. Step S150: The equipment control module performs multi-segment temperature control, quantitative water dispensing, child lock switch and water dispensing switch functions. Step S160: Push precise advertising content to the smart screen based on device context information through the smart screen control module; Step S170: The AI ​​artificial intelligence module constructs a health profile based on the user's physical condition and habit data, generates personalized healthy drinking water suggestions, and achieves self-learning and evolution.

9. The intelligent water production and intake method according to claim 8, characterized in that, In step S110, the sampling interval of the sensor array is 60 seconds, and each sampling lasts for 5 seconds; the update frequency of the raw water quality dynamic baseline is once per hour, and the retraining cycle of the filter cartridge efficiency decay model is once per week; in step S120, the execution cycle of the adaptive control algorithm is 10 seconds each time, and the control command is immediately sent through an encrypted link after it is generated; when adjusting the operating parameters of the water purification equipment based on abnormal influent water quality events, the parameter adjustment range is graded according to the degree of abnormality, with the adjustment range not exceeding 20% ​​for mild abnormalities and up to 50% for severe abnormalities.

10. The intelligent water production and intake method according to claim 8, characterized in that, In step S130, the voice signal sampling rate is 16kHz, the quantization bit depth is 16 bits, the endpoint detection uses a dual-threshold comparison method, and the silence duration exceeds 300 milliseconds to determine the end of the voice. The transmission delay of the generated device control command and sent to the main controller is controlled within 100 milliseconds. In step S140, the user operation data is transmitted after being encapsulated in JSON format, the data packet size does not exceed 1 kilobyte, and the verification process includes digital signature verification and timestamp validity check. In step S150, the temperature control accuracy is ±1℃, and the water volume control error does not exceed ±3% of the set value.

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