Enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system

Abstract

The application discloses an enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system, belonging to the field of intelligent metering equipment and Internet of Things technology. Through the intelligent diagnosis system, real-time analysis of water meter operation data is performed, potential faults and performance decline trends are identified, early warning and intelligent diagnosis are realized, automatic reminders are given to users or service providers for maintenance, users can set the working parameters and reminders of the water meter according to their own needs and preferences, voice and text interaction functions are provided to improve the user interaction experience, a tool for evaluating the potential environmental impact of water use is integrated, and improvement suggestions are provided, modular components and standardized interfaces improve the adaptability and flexibility of the product, low-power consumption technology and optimization algorithms are adopted to prolong the battery life and reduce energy consumption, and the water meter can work normally in various environments. All independent components and technologies are integrated into a coordinated and consistent system to ensure optimal overall performance.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent metering equipment and Internet of Things, and particularly relates to an enhanced intelligent multi-modal data exchange water meter and a smart diagnosis system. BACKGROUND

[0002] As a key component in the smart city and smart home ecosystem, smart water meters have rapidly developed with the integration of innovative technologies to enhance performance and user experience. Here is an overview of the background technology in the field of smart water meters:

[0003] Traditional mechanical water meters rely on mechanical components such as impellers or propellers to measure water volume, with the advantages of simple structure and low cost, but with lower accuracy and susceptibility to external factors such as water quality, water temperature, and magnetic field.

[0004] Existing smart water meters use electronic sensors and microcontrollers and other electronic components to improve measurement accuracy and resistance to interference, supporting remote meter reading, water leakage alarms, and tiered water pricing billing functions.

[0005] MEMS technology: Microelectromechanical Systems (MEMS) technology has been applied in smart water meters due to its high sensitivity, small size, and low power consumption, making water flow detection more precise and sensitive.

[0006] Blockchain technology: With its decentralized, tamper-proof, and traceable features, blockchain technology provides a new solution for data security in smart water meters.

[0007] RFID / Bluetooth / NFC technology: These communication technologies enable non-contact reading and writing of smart water meter data, enhancing the versatility and flexibility of water meters.

[0008] Smart home systems: The popularity of smart home systems requires smart water meters to seamlessly integrate with them to achieve intelligent management of household water usage.

[0009] Artificial intelligence and machine learning: With the development of AI and machine learning technologies, smart water meters begin to integrate these technologies to achieve more advanced data processing, predictive analysis, and automated decision-making.

[0010] Big data analysis: Big data analysis technology is used to analyze and interpret the large amount of water usage data collected by smart water meters to provide in-depth insights and optimization recommendations.

[0011] Internet of Things (IoT) technology: IoT technology enables smart water meters to interconnect with other smart devices, enabling more extensive smart monitoring and management functions.

[0012] Despite the progress made in the field of smart water meters, there are still some challenges such as data security issues, insufficient functionality expansion, limited user interaction methods, device maintenance complexity, and energy efficiency. The present invention aims to solve these problems by integrating various advanced technologies to provide a more advanced, secure, reliable, and user-friendly smart water meter solution. SUMMARY

[0013] The technical problem to be solved by the present invention is to provide an enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system to address the shortcomings of the background art. The present invention analyzes the operation data of the water meter in real time, identifies potential faults and performance decline trends, and realizes early warning and intelligent diagnosis.

[0014] To solve the above technical problems, the present invention adopts the following technical solutions:

[0015] An enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system, comprising an intelligent diagnosis module, a predictive maintenance module, an energy management module, a user customization service interface module, an AI interaction assistant module, an adaptive metering module, a data security module, an environmental impact assessment module, a modular interface module, a low-power consumption module, an environmental adaptability module, a system integration module;

[0016] The intelligent diagnosis module is used to analyze the operation data of the water meter in real time through machine learning algorithms to identify and predict potential faults and performance decline trends.

[0017] The predictive maintenance module is used to predict future maintenance needs of the device by analyzing historical maintenance data and real-time operation status.

[0018] The energy management module is used to optimize water usage patterns, reduce energy waste, and provide energy-saving suggestions by analyzing water usage data.

[0019] The user customization service interface module is used to provide a flexible and intuitive interface that allows users to set the working parameters and reminders of the water meter according to their own needs and preferences.

[0020] The AI interaction assistant module is used to provide voice and text interaction functions through the integration of natural language processing (NLP) technology, making the operation of the water meter more intuitive and user-friendly.

[0021] The adaptive metering module is used to automatically adjust the metering parameters by analyzing water usage data in real time to adapt to different water usage environments and needs.

[0022] The data security module is used to ensure the security of water meter data during transmission and storage.

[0023] An environmental impact assessment module to assess the potential impact of water usage behavior on the environment and provide improvement suggestions;

[0024] A modular interface module to simplify the maintenance and upgrade process of the water meter by creating components that are easy to upgrade and replace;

[0025] A low-power module to extend the service life of the water meter battery and reduce overall energy consumption by using advanced low-power technology and optimization algorithms;

[0026] An environmental adaptability module to ensure the stability and reliability of the water meter under different environmental conditions, including temperature changes, humidity, pressure, and corrosive environments;

[0027] A system integration module to integrate all independent components and technologies into a coordinated system to ensure optimal overall performance.

[0028] As a further preferred embodiment of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system, the intelligent diagnosis module achieves intelligent diagnosis through the following steps:

[0029] Step A1, Data Collection: Collect the operating parameters of the water meter, including but not limited to flow, pressure, temperature, and vibration;

[0030] Step A2, Data Preprocessing: Clean, standardize, and extract features from the collected data to facilitate processing by machine learning models;

[0031] Step A3, Feature Selection: Select the most helpful features for diagnosis from the preprocessed data;

[0032] Step A4, Model Training: Train machine learning models using historical fault data and normal operation data, including support vector machines (SVM), random forests, or neural networks;

[0033] Step A5, Real-time Monitoring and Diagnosis: Apply the trained model to real-time data to monitor the operating status of the water meter and identify abnormal patterns;

[0034] Step A6, Fault Prediction and Warning: Predict potential faults and issue warnings based on the diagnosis results of the model;

[0035] Machine learning theory: Supervised learning, unsupervised learning, and reinforcement learning algorithms; Random forest algorithm, decision tree construction formula:

[0036] h t+1 (c)=h t (a)+λ·I(y∈R t(c) )

[0037] where ht+1 (c) is the prediction result for node c at time t+1; h t (a) is the prediction result for node a at time t; λ is the learning rate or step size, controlling the magnitude of each update; I(y∈R t(c) ) is the indicator function, which takes the value 1 if the sample y belongs to the region R t(c ) of node c at time t, and 0 otherwise;

[0038] Signal processing theory: involves Fourier transform, wavelet transform, etc. for analyzing the time-frequency characteristics of water meter operation data; Fourier transform formula:

[0039]

[0040] 其中 , X(f) 为信号 x(t) 在频率 f 处的傅里叶变换结果 ; r(t) 为时间域中的原始信号 ; f 为频率 , usually in units of Hertz (Hz); j is the imaginary unit, satisfying j 2 =-1; π is the circular constant, approximately equal to 3.14159;

[0041] Statistical analysis theory: used to evaluate model performance and data distribution characteristics; confusion matrix is used to evaluate the performance of classification models;

[0042] Confusion matrix ConfusionMatrix is a table used to describe the performance of a classification model; contains the following four basic elements: true class TP: the number of samples correctly predicted as positive class by the model; false positive class FP: the number of negative class samples incorrectly predicted as positive class by the model, i.e. the first type of error; true negative class TN: the number of samples correctly predicted as negative class by the model; false negative class FN: the number of positive class samples incorrectly predicted as negative class by the model, i.e. the second type of error;

[0043] Based on the confusion matrix, the following performance indicators are calculated in detail;

[0044]

[0045] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system of the present application, the working process of the predictive maintenance module includes the following key steps:

[0046] Step B1, data collection: collect the operation data and historical maintenance records of the equipment;

[0047] Step B2, data preprocessing: clean, standardize and handle missing values;

[0048] Step B3, Feature Engineering: Extract features from raw data that help with prediction;

[0049] Step B4, Model Selection: Choose appropriate prediction model, including time series analysis, machine learning, or deep learning models;

[0050] Time Series Analysis: Used to analyze trends, seasonality in time series data; Autoregressive Integrated Moving Average (ARIMA) model:

[0051] X t = c + φ1X t-1 +... + φ p X t-p - θ1X t-1 -... - θ q X t-q + ε t ;

[0052] where X t is the observation at time t; c is the constant term, the bias of the model; φ1…φ p are the parameters of the autoregressive (AR) part, representing the relationship between the current value and previous values; θ1…θ p are the parameters of the differencing part, used to make the data stationary; ε t is the error term, usually assumed to be white noise;

[0053] Machine Learning Theory: Involves algorithms for classification, regression, etc.; Linear regression model: y = β0 + β1x1 + … β n x n + ε; where y is the dependent variable, the result predicted by the model; β0 is the intercept term, the baseline value of the model; β1…β n are the coefficients, representing the effect of the independent variables x1…x n on y; x1…x n are the independent variables; ε is the error term, the residual of the model's prediction;

[0054] Deep Learning Theory: Uses neural networks for complex pattern recognition; Recurrent Neural Network (RNN) unit: where h t is the hidden state at time t; W n is the weight matrix from hidden layer to hidden layer; is the transpose of the weight matrix from input to hidden layer; x t is the input at time t; b h is the bias term for the hidden layer;

[0055] Long Short-Term Memory (LSTM) unit: t = σ(W ii x t + W hih t-1 +b i );f t =σ(W if x t +W hf h t-1 +b f ); o t =σ(W io x t +W ho h t-1 +b o );h t =o t *tanh(C t );

[0056] where i t is the input gate at time t; f t is the forget gate at time t; C t is the cell state at time t; O t is the output gate at time t; W z , W n , W c are the weight matrices for input, hidden layer, and cell state, respectively; b i , b j , b c are the bias terms for input gate, forget gate, and cell state, respectively; σ is the Sigmoid function, which produces values between 0 and 1 in the gating mechanism; * denotes element-wise multiplication;

[0057] Step B5, model training: use historical data to train the prediction model;

[0058] Step B6, predictive analysis: apply the trained model to real-time data to predict equipment status and maintenance needs;

[0059] Step B7, maintenance decision: based on the prediction results, develop a maintenance plan and strategy;

[0060] Step B8, reminder mechanism: when the prediction result shows that maintenance is needed soon, automatically remind the user or service provider.

[0061] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system of the present application, the working process of the energy management module is as follows:

[0062] Step C1, data collection: real-time collection of water meter water usage data, including flow, time, or pressure;

[0063] Step C2, data storage: store the collected data in the database for further analysis;

[0064] Step C3, Data Analysis: Analyze water usage patterns and energy consumption data using statistical and machine learning algorithms;

[0065] Where, Energy Efficiency Analysis: Evaluate the energy usage efficiency of a system or device; Energy Efficiency Formula: For analyzing the distribution, trends, and outliers of water data; Mean Formula: Where, μ is the mean, which is the average value of the data set; C i is the i-th observation in the data set; n is the total number of observations in the data set; Machine Learning, for identifying water usage patterns and predicting energy consumption; Clustering Algorithm for categorizing user water usage patterns:

[0066]

[0067] Where, J is the objective function of the K-means algorithm, representing the sum of squared distances of all data points to their cluster centers;

[0068] K is the number of clusters; x is a data point in the data set; C is the set of all data points in the k-th cluster; Centroidk is the cluster center of the k-th cluster; ||x-Centroidk|| represents the Euclidean distance between data point x and cluster center Centroidk;

[0069] Step C4, Energy Saving Suggestions Generation: Generate energy saving suggestions based on the analysis results, including adjusting water usage time and reducing leakage;

[0070] Step C5, Water Usage Pattern Optimization: Provide users with customized water usage patterns to reduce energy consumption;

[0071] Step C6, Real-time Monitoring and Feedback: Real-time monitoring of water usage and adjusting energy saving strategies according to user feedback;

[0072] Step C7, Report Generation: Generate energy saving reports regularly to show energy saving effects and further improvement suggestions.

[0073] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system, the working process of the user customization service interface module is as follows:

[0074] Step D1, User Demand Analysis: Collect and analyze user demands for water meter functions and operation interfaces;

[0075] Step D2, Interface Design: Design an easy-to-use and feature-rich user interface;

[0076] Adopt Nielsen's usability principles: including learnability, efficiency, memorability, error prevention, user control and satisfaction;

[0077] Step D3, parameter setting function: allow users to customize the working parameters of the water meter, including water consumption limit, water time;

[0078] Step D4, reminder setting function: allow users to set water reminders, such as water leakage alarm and water consumption exceeding reminder;

[0079] Step D5, data interaction: ensure that the parameters and reminders set by the user can interact seamlessly with the hardware and software system of the water meter;

[0080] Fitts' law: used to predict the time required for a user to move to a target location, where D is the target distance and W is the target width;

[0081] Step D6, feedback mechanism: provide user feedback channels to continuously optimize the interface and functions;

[0082] Step D7, security and privacy protection: ensure the security and privacy of user data; through AES encryption, ensure the security of data transmission; through adding noise to protect the privacy of user data.

[0083] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system of the present application, the workflow of the AI interaction assistant module is as follows:

[0084] Step E1, speech recognition: convert user's voice input into text;

[0085] Speech recognition: Hidden Markov Model (HMM) is used to model the statistical characteristics in speech recognition; formula: where O is the observation sequence and S is the state sequence;

[0086] Step E2, natural language understanding: analyze user's intention and demand;

[0087] Natural language understanding: semantic role labeling (SRL) is used to identify actions and participants in sentences; formula: SRL = {(v, ARG O ),(v,ARG1),...}, where v is the verb and ARG is the corresponding argument;

[0088] Step E3, Dialogue Management: Maintain dialogue state, understand context; Dialogue Management: Dialogue state tracking (DST) is used to maintain the current state of the dialogue; Formula: DST = {slot1:value1, slot2:value2,...}; Wherein, slot represents the slot in the dialogue, value represents the value of the slot;

[0089] Step E4, Decision Making: According to the user's intention, decide the response action;

[0090] Step E5, Response Generation: Generate appropriate response, text or voice;

[0091] Step E6, Speech Synthesis: Convert text response into voice output;

[0092] Speech Synthesis: Deep learning model such as WaveNet is used to generate natural sounding voice; Formula: WaveNet = N(WaveNetParams, Input); Wherein, N represents the network model, WaveNetParams is the parameter of WaveNet model, Input is the input data;

[0093] Step E7, User Feedback Learning: Optimize interactive experience through user feedback.

[0094] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system, the working process of the adaptive metering module is as follows:

[0095] Step F1, Data Collection: Real-time collection of water meter water consumption data, including flow, pressure, temperature;

[0096] Step F2, Data Preprocessing: Clean, standardize and denoise the collected data;

[0097] Step F3, Environmental Analysis: Analyze the changes of water consumption environment, including seasonal changes and water consumption mode changes;

[0098] Step F4, Parameter Adaptive Adjustment: Automatically adjust the metering parameters according to the environmental analysis results, including the sensitivity of the flowmeter;

[0099] Step F5, Metering Accuracy Optimization: Optimize the metering algorithm to improve the metering accuracy and response speed;

[0100] Step F6, Real-time Monitoring and Feedback: Real-time monitoring of metering accuracy, dynamic adjustment according to feedback;

[0101] Step F7, Data Storage and Analysis: Store the adjusted metering data and perform long-term trend analysis;

[0102] 其中, Control Theory: PID controller is used to adjust the measurement parameter, formula: 其中 , u(t) 是控 Input, e(t) is error, K p , K i , K d Proportion, integral, differential gain, respectively;

[0103] Signal Processing Theory: Kalman filter is used to estimate the system state; formula: Where, is the estimated state, y k is the observation value, K k is the Kalman gain;

[0104] Machine Learning Theory: Regression analysis is used to predict the measurement parameter, formula: y = β0 + β1x1 +... + β n x n ; Where, y is the target variable, x1 is the characteristic variable, β n is the regression coefficient.

[0105] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system of the application, the working process of the data security module is as follows:

[0106] Step G1, data encryption: before data transmission and storage, use advanced encryption algorithm to encrypt data;

[0107] Symmetric encryption: Advanced Encryption Standard AES is a commonly used symmetric encryption algorithm, formula: C = E(K, P); Where, C is ciphertext, K is key, P is plaintext;

[0108] Asymmetric encryption: RSA encryption algorithm, formula: C = M e modn; Where, C is ciphertext, M is plaintext, e is public key exponent, n is modulus;

[0109] Step G2, key management: securely generate, store and distribute encryption keys;

[0110] Step G3. Data anonymization: when storing and analyzing data, use anonymization technology to protect user privacy;

[0111] Step G4. Access control: implement strict access control policies to ensure that only authorized users can access sensitive data;

[0112] Step G5. Data integrity verification: verify the integrity of data through hash function and digital signature;

[0113] Hash function: SHA-256 is used to generate the hash value of data, formula: H = SHA-256 (P); where H is the hash value, P is the plaintext; Digital signature: formula: σ = D -1 (H(M)); where σ is the digital signature, D is the private key, H(M) is the hash value of the message; Data anonymization: differential privacy technology, formula: Output = Original Data + Noise; where Noise is the noise drawn from the Laplace distribution;

[0114] Step G6. Security audit: record and monitor all data access and operations for security audit; Access control: role-based access control RBAC model, formula: Access = UserRole ∩ Resource Permission;

[0115] Where Access is the access permission, indicating whether the user has the right to access a specific resource; UserRole is the user role, representing the identity or role of the user in the system; ResourcePermission is the resource permission, representing the allowed operation or access level of a specific resource; N is the intersection operation of the set, used to determine the common part of the user role and resource permission;

[0116] Step G7. Anomaly detection: real-time monitoring of data access patterns, detecting and responding to abnormal behavior.

[0117] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system of the application, the working process of the environmental impact assessment module is as follows:

[0118] Step H1, data collection: collect water consumption data and environment-related data, including water consumption, water consumption time, and water quality parameters;

[0119] Step H2, environmental impact index definition: define key indicators for assessing environmental impact, including water resource consumption and water pollution;

[0120] Step H3, impact assessment model development: develop a model to assess the impact of water consumption behavior on the environment;

[0121] Where water resource consumption assessment: water resource consumption index: water resource utilization coefficient Water pollution assessment: water quality pollution index: Where w i is the weight of the i-th pollutant, p i is the concentration of the i-th pollutant; Ecological impact assessment: ecological footprint calculation: Statistical analysis: correlation analysis: where r is the correlation coefficient, x i and y i are data points, and are the mean values;

[0122] Step H4, improvement suggestion generation: based on the evaluation results, generate improvement suggestions, including water-saving measures and pollution control;

[0123] Step H5, user interaction: display evaluation results and improvement suggestions through the user interface, and interact with the user;

[0124] Step H6, data storage and analysis: store evaluation data and conduct long-term trend analysis and prediction;

[0125] Step H7, policy compliance check: check whether the water use behavior complies with relevant environmental policies and regulations.

[0126] As a further preferred scheme of the enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system of the present application, the working process of the modular interface module is as follows:

[0127] Step I1, requirement analysis: determine the functional requirements and performance targets of the upgraded interface;

[0128] Step I2, modular design: design modular components to ensure the independence and interoperability of each component;

[0129] Step I3, interface standardization: define standardized interfaces to ensure compatibility between different modules;

[0130] Step I4, component development: develop independent modular components, including sensors, communication modules, and data processing units;

[0131] Step I5, system integration: integrate modular components into the water meter system to ensure overall performance;

[0132] Step I6, testing and verification: test the modular components and interfaces to ensure their stability and reliability;

[0133] Step I7, user documentation: prepare detailed user documentation to guide users in upgrading and maintenance;

[0134] Step I8, market promotion and training: promote the modular upgrade interface and train relevant personnel;

[0135] Step I9, feedback collection and optimization: collect user feedback and continuously optimize the modular design and interface.

[0136] As a further preferred embodiment of the enhanced intelligent multimodal data exchange water meter and intelligent diagnostic system of the present invention, the working principle of the low-power module is as follows:

[0137] Energy efficiency analysis: Analyze the energy consumption of each component of the water meter to determine the main sources of energy consumption; details are as follows:

[0138] Energy Efficiency Ratio Formula: Energy Efficiency Ratio Sleep mode design: Design low-power sleep modes for components that do not operate frequently; specifically as follows: Sleep mode energy consumption formula: P sleep =P active •DutyCycle; where P sleep This refers to the energy consumption in sleep mode, P. active This refers to energy consumption in active mode; DutyCycle represents the proportion of active time. Dynamic Power Management (DVFS) adjusts voltage and frequency dynamically based on operating status to reduce energy consumption. Specifically: Dynamic Voltage and Frequency Adjustment (DVFS): P = C·V 2 ·f; where P is power, C is capacitance, V is voltage, and f is frequency; Communication optimization: optimize the working cycle of the communication module and reduce the communication frequency; specifically as follows: Communication energy consumption formula: P comm =P tx ·t tx +P rx ·t rx Among them, P comm It is the energy consumption of communication, P tx It is the transmission power, t tx It is the sending time, P rx It is the received power, t rx This refers to the reception time; Sensor power-saving design: The sensor is designed to save power and reduce standby power consumption; specifically as follows: Sensor energy consumption formula: P sensor =P1+P2; where P sensor P1 represents the total power consumption of the sensor; P2 represents the power consumption of the sensor in operating mode; P3 represents the power consumption of the sensor in standby mode; Battery capacity optimization: Based on the reduction in power consumption, reassess the battery capacity requirements; specifically as follows: Battery capacity requirement formula: Among them, C new It's a new battery capacity, C old It's the old battery capacity, E old It's the old energy consumption, E new It's a new energy consumption;

[0139] Software optimization: Optimize software algorithms to reduce energy consumption for computing and processing; Hardware selection: Select low-power hardware components, such as microcontrollers and sensors; System monitoring: Monitor system energy consumption in real time and automatically adjust the working mode.

[0140] Compared with the prior art, the application has the following technical effects:

[0141] The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system can monitor the running state in real time, discover and warn potential faults and performance problems in time, predict maintenance needs according to historical and real-time data, reduce unexpected downtime, improve the reliability of equipment, provide more personalized user experience, make user operation more convenient and intuitive, help users understand the impact of water use behavior on the environment and provide energy-saving suggestions, simplify the upgrade and maintenance process, reduce long-term operating costs, reduce the energy consumption of the water meter, prolong the battery life, realize energy saving and emission reduction, consider various environmental factors in the design of the water meter, have good environmental adaptability, ensure stable operation under different conditions, ensure the consistency of the components and technologies of the water meter, improve the overall performance and stability, ensure the security and privacy of user data through advanced data encryption and security authentication mechanisms, and provide automatic meter reading, water leakage detection and other advanced intelligent services through the integration of smart contracts and linkage operation functions. BRIEF DESCRIPTION OF DRAWINGS

[0142] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0143] Intelligent diagnosis module, predictive maintenance module, energy management module, user customization service interface module, AI interaction assistant module, adaptive metering module, data security module, environmental impact assessment module, modular interface module, low-power module, environmental adaptability module, system integration module

[0144] Figure 1 is an implementation schematic diagram of the intelligent diagnosis module of the present application;

[0145] Figure 2 is an implementation schematic diagram of the predictive maintenance module of the present application;

[0146] Figure 3 is an implementation schematic diagram of the energy management module of the present application;

[0147] Figure 4is an implementation schematic diagram of the user customization service interface module of the present application;

[0148] Figure 5 is an implementation schematic diagram of the AI interactive assistant module of the present application;

[0149] Figure 6 is an implementation schematic diagram of the adaptive metering module of the present application;

[0150] Figure 7 is an implementation schematic diagram of the data security module of the present application;

[0151] Figure 8 is an implementation schematic diagram of the environmental impact assessment module of the present application;

[0152] Figure 9 is an implementation schematic diagram of the modular interface module of the present application;

[0153] Figure 10 is an implementation schematic diagram of the low-power module of the present application;

[0154] Figure 11 is an implementation schematic diagram of the environmental adaptability module of the present application;

[0155] Figure 12 is an implementation schematic diagram of the system integration module of the present application. DETAILED DESCRIPTION

[0156] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings:

[0157] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. The purpose and effect of the present application will become more apparent below according to the drawings and preferred embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and do not limit the present application.

[0158] An enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system, comprising an intelligent diagnosis module, a predictive maintenance module, an energy management module, a user customization service interface module, an AI interactive assistant module, an adaptive metering module, a data security module, an environmental impact assessment module, a modular interface module, a low-power module, an environmental adaptability module, and a system integration module;

[0159] Among them, the intelligent diagnosis module is used for real-time analysis of the running data of the water meter through machine learning algorithm to identify and predict potential faults and performance decline trends.

[0160] The predictive maintenance module is used to predict future maintenance needs of the device by analyzing historical maintenance data and real-time operating status.

[0161] The energy management module is used to optimize water usage patterns, reduce energy waste, and provide energy-saving suggestions by analyzing water usage data.

[0162] The user customization service interface module is used to provide a flexible and intuitive interface that allows users to set the working parameters and reminders of the water meter according to their own needs and preferences.

[0163] The AI interaction assistant module is used to provide voice and text interaction functions through the integration of natural language processing (NLP) technology, making the operation of the water meter more intuitive and user-friendly.

[0164] The adaptive metering module is used to adjust metering parameters automatically by real-time analysis of water usage data to adapt to different water usage environments and needs.

[0165] The data security module is used to ensure the security of water meter data during transmission and storage.

[0166] The environmental impact assessment module is used to assess the potential impact of water usage behavior on the environment and provide improvement suggestions.

[0167] The modular interface module is used to simplify the maintenance and upgrade process of the water meter by creating components that are easy to upgrade and replace.

[0168] The low-power module is used to extend the service life of the water meter battery and reduce overall energy consumption by using advanced low-power technology and optimization algorithms.

[0169] The environmental adaptability module is used to ensure the stability and reliability of the water meter under different environmental conditions, including temperature changes, humidity, pressure, and corrosive environments.

[0170] The system integration module is used to integrate all independent components and technologies into a coordinated system to ensure optimal overall performance.

[0171] Intelligent diagnosis system: integrates machine learning algorithms to analyze water meter running data in real time, identify potential faults and performance decline trends, and achieve early warning and intelligent diagnosis.

[0172] The core of the intelligent diagnosis system is to use advanced machine learning algorithms to analyze the running data of the water meter in real time to identify and predict potential faults and performance decline trends.

[0173] The intelligent diagnostic module achieves intelligent diagnosis through the following steps:

[0174] Step A1, Data Acquisition: Collect the operating parameters of the water meter, including but not limited to flow rate, pressure, temperature, and vibration;

[0175] Step A2, Data Preprocessing: Clean, standardize, and extract features from the collected data to facilitate processing by machine learning models;

[0176] Step A3, Feature Selection: Select the features most helpful for diagnosis from the preprocessed data;

[0177] Step A4, Model Training: Train a machine learning model using historical failure data and normal operation data, including support vector machines (SVM), random forests, or neural networks;

[0178] Step A5, Real-time Monitoring and Diagnosis: Apply the trained model to real-time data to monitor the water meter's operating status and identify abnormal patterns;

[0179] Step A6, Fault Prediction and Early Warning: Based on the model's diagnostic results, predict potential faults and issue early warnings;

[0180] Among them, machine learning theory involves algorithms such as supervised learning, unsupervised learning, and reinforcement learning; in the random forest algorithm, the decision tree construction formula is: h t+1 (c)=h t (a)+λ·I(y∈R t(c) Among them, h t+1 (c) represents the prediction result for node c at time t+1; h t (a) represents the prediction result for node a at time t; λ is the learning rate or step size, controlling the magnitude of each update; I(y∈R) t(c) Let y be the indicator function, and let y be the region R of node c at time t. t(c) If the value is 1, the value is 1; otherwise, it is 0. Signal processing theory involves Fourier transform, wavelet transform, etc., used to analyze the time-frequency characteristics of water meter operating data. Fourier transform formula: Where X(f) is the Fourier transform of signal x(t) at frequency f; r(t) is the original signal in the time domain; f is the frequency, usually expressed in Hertz (Hz). j is the imaginary unit, satisfying j 2 =-1;

[0181] π (pi) is approximately equal to 3.14159; statistical analysis theory: used to evaluate model performance and data distribution characteristics; confusion matrix is ​​used to evaluate the performance of classification models;

[0182] Confusion Matrix is a table used to describe the performance of a classification model; it contains the following four basic elements: True Positive (TP): the number of samples correctly predicted as positive class by the model; False Positive (FP): the number of negative class samples incorrectly predicted as positive class by the model, i.e. the first type of error; True Negative (TN): the number of samples correctly predicted as negative class by the model; False Negative (FN): the number of positive class samples incorrectly predicted as negative class by the model, i.e. the second type of error;

[0183] Based on the Confusion Matrix, the following performance indicators are calculated in detail;

[0184]

[0185] The technical implementation is as shown in Figure 1 .

[0186] Requirement analysis: determine system functions and performance indicators; data collection: design data collection system, collect water meter operation data; data management: establish database, store and manage collected data; model development: develop machine learning model, perform feature selection and model training; system integration: integrate model into water meter monitoring system, realize real-time monitoring and diagnosis; test and optimization: test system, optimize model and system performance according to feedback; deployment and maintenance: deploy system to production environment, regularly maintain and update model.

[0187] Key content description: requirement analysis: clarify the goals and performance requirements of intelligent diagnosis system, including fault detection accuracy and response time. Data collection: design sensor layout and data collection frequency to ensure data integrity and accuracy. Data management: use database technology to store and index large amounts of operation data for fast query and analysis. Model development: select appropriate machine learning algorithm, perform feature engineering and model training. System integration: integrate machine learning model into existing water meter monitoring system for seamless integration. Test and optimization: test system performance through simulated faults and actual operation data, continuously optimize model parameters. Deployment and maintenance: deploy system in actual environment, regularly check and update model to adapt to new data and fault patterns.

[0188] Predictive maintenance algorithm: use collected historical maintenance data and real-time operation status to develop algorithm to predict device maintenance needs, automatically remind users or service providers to perform maintenance. The predictive maintenance algorithm predicts future maintenance needs of the device by analyzing historical maintenance data and real-time operation status. The workflow of the predictive maintenance module includes the following key steps:

[0189] Step B1, data collection: collect operation data and historical maintenance records of the device;

[0190] Step B2, data preprocessing: clean, standardize data, and handle missing values;

[0191] Step B3, Feature Engineering: Extracting features from the raw data that are helpful for prediction;

[0192] Step B4, Model Selection: Select a suitable prediction model, including time series analysis, machine learning, or deep learning models;

[0193] Time series analysis: used to analyze trends and seasonality in time series data; Autoregressive Integral Moving Average (ARIMA) model:

[0194] X t =c+φ1X t-1 +...φ p X t-p -θ1X t-1 -...-θ q X t-q +ε t ;

[0195] Among them, X t φ1...φ2 represents the observed values ​​at time t; c is a constant term, the bias of the model; φ1...φ2...φ3... p For the autoregressive (AR) part, θ1…θ2 represents the relationship between the current value and previous values. p ε is a parameter for the difference part, used to make the data stationary; t The error term is usually assumed to be white noise; machine learning theory involves algorithms such as classification and regression; linear regression model: y = β0 + β1x1 + ... + β n x n +ε; where y is the dependent variable, the result predicted by the model; β0 is the intercept term, the baseline value of the model; β1…β n The coefficients represent the independent variables x1…x n right y The impact; x1…x n ε is the independent variable; ε is the error term, the residual of the model prediction; Deep learning theory: using neural networks for complex pattern recognition; Recurrent Neural Network (RNN) unit: Among them, h t W represents the hidden state at time t. n This is the weight matrix from hidden layer to hidden layer; x is the transpose of the weight matrix input to the hidden layer; t For time t, input; b h Bias terms for hidden layers; Long Short-Term Memory (LSTM) unit: i t =σ(W ii x t +W hi h t-1 +b i );ft = σ(W if x t +W hf h t-1 +b f ) ; o t = σ(W io x t +W ho h t-1 +b o ) ; h t = o t *tanh(C t ) ;

[0196] where i t is the input gate at time t; f t is the forget gate at time t; C t is the cell state at time t; O t is the output gate at time t; W z , W n , and W c are the weight matrices for input, hidden layer, and cell state, respectively; b i , b j , and b c are the bias terms for input gate, forget gate, and cell state, respectively; σ is the Sigmoid function, which produces values between 0 and 1 for the gating mechanism; and * denotes element-wise multiplication.

[0197] Step B5, Model Training: Use historical data to train the prediction model.

[0198] Step B6, Predictive Analysis: Apply the trained model to real-time data to predict equipment status and maintenance needs.

[0199] Step B7, Maintenance Decision: Based on the prediction results, develop a maintenance plan and strategy.

[0200] Step B8, Reminder Mechanism: When the prediction results show that maintenance is imminent, automatically remind the user or service provider.

[0201] Technical implementation, such as Figure 2As shown. Requirements Analysis: Determine the goals and requirements of predictive maintenance. Data Collection: Design a data acquisition system to collect equipment operation and maintenance data; Data Management: Establish a database to store and manage data; Feature Engineering: Analyze data and extract features that are helpful for prediction; Model Development: Select a suitable model, perform feature selection and model training; Predictive Analytics: Apply the model to perform predictive analytics and develop maintenance plans; Maintenance Decision Support: Provide maintenance decision support based on prediction results; Reminder Mechanism Implementation: Develop a reminder mechanism to automatically notify relevant personnel; System Integration: Integrate the predictive maintenance algorithm into the existing equipment management system; Testing and Evaluation: Conduct system testing to evaluate prediction accuracy and maintenance effectiveness; Deployment and Monitoring: Deploy the algorithm to the production environment, monitor its performance and make adjustments; Continuous Optimization: Continuously optimize the model and reminder mechanism based on feedback.

[0202] Key Content Descriptions: Data Management: Includes data cleaning, standardization, and missing value handling to ensure data quality. Model Development: Involves feature selection and model training, choosing appropriate algorithms for prediction. Predictive Analytics: Includes state prediction and maintenance requirement prediction, providing a basis for maintenance decisions. Maintenance Decision Support: Develops and optimizes maintenance plans and strategies to improve maintenance efficiency. System Integration: Integrates algorithms into the equipment management system to achieve automation and intelligence. Testing and Evaluation: Evaluates predictive accuracy and maintenance effectiveness to ensure algorithm effectiveness. Continuous Optimization: Continuously optimizes the algorithm based on feedback from practical applications. Energy Management Module: Analyzes water usage data, provides energy-saving suggestions, optimizes water usage patterns, reduces energy waste, and supports green energy conservation. The purpose of the energy management module is to optimize water usage patterns, reduce energy waste, and provide energy-saving suggestions through the analysis of water usage data.

[0203] The workflow of the energy management module is as follows:

[0204] Step C1, Data Acquisition: Collect water usage data from the water meter in real time, including flow rate, time, or pressure;

[0205] Step C2, Data Storage: Store the collected data in a database for further analysis;

[0206] Step C3, Data Analysis: Analyze water usage patterns and energy consumption data using statistical and machine learning algorithms;

[0207] Energy efficiency analysis includes: evaluating the energy efficiency of a system or equipment; energy efficiency formula: Used for analyzing the distribution, trends, and outliers of water data; mean formula:

[0208] Where μ is the mean, i.e., the average value of the dataset; C ifor the i-th observation in the dataset; n is the total number of observations in the dataset; machine learning is used to identify water usage patterns and predict energy consumption; clustering algorithms are used for categorization of user water usage patterns: where J is the objective function of the K-means algorithm, represents the sum of squared distances of all data points to their cluster centers; K is the number of clusters; x is a data point in the dataset;

[0209] C is the set of all data points in the k-th cluster; Centroidk is the cluster center of the k-th cluster; ||x - Centroidk|| represents the Euclidean distance of data point x to the cluster center Centroidk;

[0210] Step C4, Energy Saving Proposal Generation: Based on the analysis results, generate energy saving proposals, including adjusting water usage time and reducing leakage;

[0211] Step C5, Water Usage Pattern Optimization: Provide users with customized water usage patterns to reduce energy consumption;

[0212] Step C6, Real-time Monitoring and Feedback: Real-time monitoring of water usage and adjusting energy saving strategies according to user feedback;

[0213] Step C7, Report Generation: Regularly generate energy saving reports to show energy saving effects and further improvement suggestions.

[0214] Technical implementation, such as Figure 3 is shown.

[0215] Requirement analysis: Determine the goals and user requirements of the module. Data collection system design: Design the system to collect the required water usage data. Data storage scheme: Choose a suitable database system to store data. Data analysis model development: Develop algorithms to analyze water usage data and energy consumption. Energy saving suggestion algorithm: Develop algorithms to generate energy saving suggestions. Water usage pattern optimization strategy: Develop strategies to optimize user's water usage patterns. User interface design: Design user interface to display energy saving suggestions and monitoring data. System integration: Integrate the energy management module into the smart water meter system. Testing and verification: Test the functionality and performance of the module, and verify the energy saving effect. User feedback collection: Collect user feedback to improve energy saving suggestions. Continuous optimization: Continuously optimize the module based on feedback and data analysis results. Report system: Develop a report system to regularly report energy saving results to users.

[0216] Key Content Description: Data Collection System Design: Design the system to ensure accurate and comprehensive collection of water usage data. Data Analysis Model Development: Develop efficient algorithms to analyze water usage data and identify energy-saving opportunities. Energy-Saving Recommendation Algorithm: Use data analysis results to generate personalized energy-saving recommendations. Water Usage Pattern Optimization Strategy: Develop strategies to help users optimize their water usage patterns and reduce energy consumption. User Interface Design: Design an intuitive user interface that allows users to easily access energy-saving information and monitor data. System Integration: Seamlessly integrate the energy management module into existing systems to ensure compatibility and stability. Testing and Validation: Test and validate the module's performance and energy-saving effects through rigorous testing. Continuous Optimization: Continuously improve the module based on user feedback and ongoing data analysis results.

[0217] User Customization Service Interface: Develop a customizable user interface that allows users to set water meter operating parameters and reminders according to their needs and preferences.

[0218] The user customization service interface aims to provide a flexible and intuitive interface that allows users to set the operating parameters and reminders of the water meter according to their needs and preferences.

[0219] The workflow of the user customization service interface module is as follows:

[0220] Step D1, User Requirement Analysis: Collect and analyze user requirements for water meter functions and interface design;

[0221] Step D2, Interface Design: Design an easy-to-use and feature-rich user interface;

[0222] Adopt Nielsen's usability principles: including learnability, efficiency, memorability, error prevention, user control and satisfaction;

[0223] Step D3, Parameter Setting Function: Allow users to customize water meter operating parameters, including water usage limits and water usage times;

[0224] Step D4, Reminder Setting Function: Allow users to set water usage reminders such as leak alerts and water usage limit reminders;

[0225] Step D5, Data Interaction: Ensure that user-set parameters and reminders can seamlessly interact with the water meter's hardware and software systems;

[0226] Fitts' Law: used to predict the time it takes for a user to move to a target location, where D is the target distance and W is the target width;

[0227] Step D6, Feedback Mechanism: Provide user feedback channels to continuously optimize the interface and functionality;

[0228] Step D7, Security and Privacy Protection: Ensure the security and privacy of user data; ensure the security of data transmission through AES encryption; protect the privacy of user data by adding noise.

[0229] Technical implementation, such as Figure 4 As shown, requirement analysis: determine user needs for customized services. Interface design: design user interface to ensure its intuitiveness and ease of use. Function development: develop parameter setting and reminder setting functions. Data interaction: ensure that user settings can interact with water meter system. Security measures: implement data encryption and privacy protection measures. Testing: conduct comprehensive testing to ensure functionality and security. User feedback: collect user feedback to optimize interface and functionality. Deployment: deploy the customized service interface to the production environment. Maintenance and updates: regularly maintain and update the interface to adapt to changes in user needs.

[0230] Key content description:

[0231] Requirement analysis: in-depth understanding of user needs to ensure that the customized service interface meets user expectations. Interface design: design an intuitive and easy-to-use user interface to improve user experience. Function development: develop core functions such as parameter setting and reminder setting to ensure users can easily customize the water meter's working mode. Data interaction: ensure that user settings can seamlessly interact with the water meter system to realize the practical application of functions. Security measures: implement data encryption and privacy protection measures to ensure user data security. Testing: conduct comprehensive testing to ensure all functions work properly and there are no security risks. User feedback: collect user feedback to continuously optimize interface and functionality. Deployment and maintenance: deploy the customized service interface to the production environment and perform regular maintenance and updates.

[0232] Interactive assistant: integrate natural language processing technology to provide voice and text interaction functions, making the water meter more user-friendly.

[0233] The AI interaction assistant integrates natural language processing (NLP) technology to provide voice and text interaction functions, making the water meter operation more intuitive and user-friendly.

[0234] The workflow of the AI interaction assistant module is as follows:

[0235] Step E1, Speech Recognition: Convert user voice input into text; Speech recognition: Hidden Markov Model (HMM) is used to model statistical properties in speech recognition; Formula: Where O is the observation sequence and S is the state sequence.

[0236] Step E2, Natural Language Understanding: Parse user's intent and requirements; Natural Language Understanding: Semantic Role Labeling is used to identify actions and participants in a sentence; Formula: SRL = {(v, ARG O ),(v, ARG1),...}, where v is a verb and ARG is the corresponding argument;

[0237] Step E3, Dialog Management: Maintain dialog state, understand context; Dialog Management: Dialog State Tracking (DST) is used to maintain the current state of the dialog; Formula: DST = {slot1:value1, slot2:value2,...}; where slot represents a slot in the dialog and value represents the value of the slot;

[0238] Step E4, Decision Making: Decide the response action based on user's intent;

[0239] Step E5, Response Generation: Generate appropriate response, either text or speech;

[0240] Step E6, Speech Synthesis: Convert text response to speech output; Speech Synthesis: Deep learning models like WaveNet are used to generate natural-sounding speech; Formula: WaveNet = N(WaveNetParams, Input); where N represents the network model, WaveNetParams are the parameters of the WaveNet model, and Input is the input data;

[0241] Step E7, User Feedback Learning: Optimize the interactive experience through user feedback.

[0242] Technical implementation, as shown in Figure 5

[0243] Requirement analysis: Determine the functional requirements of the AI assistant and user interaction scenarios. Technology selection: Choose appropriate NLP and machine learning technologies. Data preparation: Collect and process voice and text data for training. Model training: Train speech recognition, natural language understanding, and speech synthesis models. Dialog system development: Develop dialog management modules to maintain dialog state. System integration: Integrate the AI assistant into the water meter system. User interface design: Design user interaction interface, support voice and text input. Testing and optimization: Conduct comprehensive testing, optimize models and user experience. Deployment: Deploy the AI assistant to the production environment. User feedback collection: Collect user feedback, continuously optimize assistant performance. Maintenance and update: Regularly maintain and update the AI assistant to adapt to new requirements.

[0244] Key content description:

[0245] ​Requirement Analysis: In-depth understanding of user needs to ensure that the AI assistant can effectively assist users in operating water meters. Technology Selection: Select appropriate NLP and machine learning technologies, such as deep learning models and HMM. Data Preparation: Collect high-quality training data to ensure the effectiveness of model training. Model Training: Train key AI models such as speech recognition and natural language understanding models. Dialog System Development: Develop a dialog system that can understand and respond to user instructions. System Integration: Seamlessly integrate the AI assistant into the water meter system to ensure compatibility with existing systems. User Interface Design: Design an intuitive user interface that supports voice and text input. Testing and Optimization: Conduct comprehensive testing and optimize model performance and user experience. Deployment and Maintenance: Deploy the AI assistant to the production environment and perform regular maintenance and updates.

[0246] Adaptive Metering Algorithm: Develop an algorithm that can automatically adjust the metering accuracy based on real-time data to adapt to different water usage environments and needs.

[0247] The core goal of the adaptive metering algorithm is to improve the accuracy of water metering by analyzing real-time water usage data and automatically adjusting metering parameters to adapt to different water usage environments and needs.

[0248] The workflow of the adaptive metering module is as follows:

[0249] Step F1, Data Collection: Collect real-time water usage data from the water meter, including flow rate, pressure, and temperature.

[0250] Step F2, Data Preprocessing: Clean, standardize, and denoise the collected data.

[0251] Step F3, Environment Analysis: Analyze changes in the water usage environment, including seasonal changes and changes in water usage patterns.

[0252] Step F4, Parameter Adaptive Adjustment: Automatically adjust metering parameters based on environmental analysis results, including flow meter sensitivity.

[0253] Step F5, Metering Accuracy Optimization: Optimize the metering algorithm to improve metering accuracy and response speed.

[0254] Step F6, Real-time Monitoring and Feedback: Monitor metering accuracy in real time and make dynamic adjustments based on feedback.

[0255] Step F7, Data Storage and Analysis: Store adjusted metering data and perform long-term trend analysis.

[0256] Where, Control Theory: PID controller is used to adjust metering parameters, formula: Where, u(t) is the control input, e(t) is the error, K p , K i , Kd P, I, D are the proportional, integral, and derivative gains, respectively; Signal Processing Theory: Kalman Filter is used to estimate the system state; Formula: where, is the estimated state, y k is the observation, K k is the Kalman gain; Machine Learning Theory: Regression Analysis is used to predict the metrological parameters, Formula: y = β0 + β1x1 +... + β n x n ; where, y is the target variable, x1 is the feature variable, β n is the regression coefficient.

[0257] Technical Implementation, as shown in Figure 6 , includes:

[0258] Requirement Analysis: Determine the objectives and performance indicators of the algorithm; Data Collection System Design: Design a system to collect the required water usage data; Data Preprocessing: Develop data cleaning and standardization algorithms; Environmental Analysis Model Development: Develop models to analyze changes in the water usage environment; Parameter Self-Adaptive Adjustment Algorithm: Develop an algorithm to automatically adjust the metrological parameters; Metrological Precision Optimization Algorithm: Develop an algorithm to optimize metrological precision; Real-Time Monitoring System: Develop a system to monitor metrological precision in real-time; Data Storage and Analysis System: Design a system to store and analyze adjusted metrological data; System Integration: Integrate the algorithm into the water meter system; Testing and Verification: Test the performance and accuracy of the algorithm; User Feedback Collection: Collect user feedback to improve the algorithm; Maintenance and Updates: Regularly maintain and update the algorithm.

[0259] Key Content Description: Requirement Analysis: Clearly define the objectives and performance indicators of the algorithm to ensure it meets user needs; Data Collection System Design: Design a system to ensure accurate and comprehensive collection of water usage data; Data Preprocessing: Develop efficient data cleaning and standardization algorithms to improve data quality; Environmental Analysis Model Development: Develop models to identify changes in the water usage environment, providing a basis for parameter adjustment; Parameter Self-Adaptive Adjustment Algorithm: Develop an algorithm to automatically adjust metrological parameters based on environmental changes; Metrological Precision Optimization Algorithm: Develop an algorithm to optimize metrological precision, improving the accuracy of water meters; Real-Time Monitoring System: Develop a system to monitor metrological precision in real-time, ensuring the effectiveness of the algorithm; Data Storage and Analysis System: Design a system to store and analyze adjusted metrological data, supporting long-term optimization. System Integration: Seamlessly integrate the algorithm into the water meter system, ensuring compatibility with existing systems. Testing and Verification: Conduct comprehensive testing to verify the performance and accuracy of the algorithm. Maintenance and Updates: Regularly maintain and update the algorithm to adapt to new requirements and environmental changes.

[0260] Enhanced Data Security Measures: Introduce more advanced data encryption standards and anonymization techniques to strengthen data security during transmission and storage.

[0261] Enhanced data security measures aim to ensure the security of water meter data during transmission and storage by introducing more advanced data encryption standards and anonymization techniques.

[0262] The workflow of the data security module is as follows:

[0263] Step G1, Data Encryption: Before data transmission and storage, use advanced encryption algorithms to encrypt data; symmetric encryption: Advanced Encryption Standard (AES) is a commonly used symmetric encryption algorithm, formula: C = E (K, P); where C is the ciphertext, K is the key, and P is the plaintext; asymmetric encryption: RSA encryption algorithm, formula: C = M e modn; where C is the ciphertext, M is the plaintext, e is the public key exponent, and n is the modulus;

[0264] Step G2, Key Management: Securely generate, store, and distribute encryption keys;

[0265] Step G3. Data Anonymization: Use anonymization techniques to protect user privacy when storing and analyzing data;

[0266] Step G4. Access Control: Implement strict access control policies to ensure that only authorized users can access sensitive data;

[0267] Step G5. Data Integrity Verification: Verify data integrity through hash functions and digital signatures;

[0268] Hash function: SHA-256 is used to generate the hash value of the data, formula: H = SHA-256 (P); where H is the hash value and P is the plaintext; Digital signature: formula: σ = D -1 (H(M)); where σ is the digital signature, D is the private key, and H(M) is the hash value of the message; Data anonymization: Differential privacy technique, formula: Output = Original Data + Noise; where Noise is the noise drawn from the Laplace distribution;

[0269] Step G6. Security Audit: Record and monitor all data access and operations for security audit;

[0270] Access Control: Role-Based Access Control (RBAC) model, formula: Access = UserRole ∩ ResourcePermission; where Access is the access permission, indicating whether the user has the right to access a specific resource; UserRole is the user role, representing the user's identity or role in the system; ResourcePermission is the resource permission, representing the allowed operations or access level of a specific resource; N is the intersection operation of sets, used to determine the common part of user roles and resource permissions.

[0271] Technical Implementation, such as Figure 7 as shown,

[0272] Requirement Analysis: Determine the needs and goals of data security. Encryption Strategy Design: Design data encryption strategies and select appropriate encryption algorithms. Key Management System Development: Develop a secure key management system. Data Anonymization Technology Implementation: Implement data anonymization technology to protect user privacy. Access Control Policy Formulation: Develop strict access control policies. Data Integrity Verification Mechanism: Implement data integrity verification mechanisms. Security Audit System Development: Develop a security audit system to record and monitor data access. Abnormal Detection System Development: Develop an abnormal detection system to monitor data access patterns in real time. System Integration: Integrate security measures into the water meter system. Testing and Evaluation: Test the effectiveness of security measures and evaluate their performance. Deployment and Monitoring: Deploy security measures to the production environment and conduct continuous monitoring. Maintenance and Update: Regularly maintain and update security measures to address new security threats.

[0273] Key Content Description:

[0274] Requirement Analysis: Clearly define the needs and goals of data security to ensure that security measures meet actual needs. Encryption Strategy Design: Design data encryption strategies and select appropriate encryption algorithms to ensure data security during transmission and storage. Key Management System Development: Develop a secure key management system to ensure the security of keys. Data Anonymization Technology Implementation: Implement data anonymization technology to protect user privacy and prevent sensitive information leakage. Access Control Policy Formulation: Develop strict access control policies to ensure that only authorized users can access sensitive data. Data Integrity Verification Mechanism: Implement data integrity verification mechanisms to ensure that data is not tampered with during transmission. Security Audit System Development: Develop a security audit system to record and monitor data access, facilitating post-audit and analysis. Abnormal Detection System Development: Develop an abnormal detection system to monitor data access patterns in real time, promptly detecting and responding to abnormal behavior. System Integration: Integrate security measures into the water meter system to ensure compatibility with existing systems. Testing and Evaluation: Test the effectiveness of security measures and evaluate their performance to ensure they meet security requirements.

[0275] Deployment and Monitoring: Deploy security measures to the production environment and continuously monitor to ensure their ongoing effectiveness. Maintenance and Updates: Regularly maintain and update security measures to address new security threats and maintain the security of the system.

[0276] Environmental Impact Assessment Tool: An integrated tool assesses the potential environmental impact of water usage and provides recommendations for improvement. The purpose of the Environmental Impact Assessment Tool is to assess the potential environmental impact of water usage behavior and provide recommendations for improvement.

[0277] The workflow of the environmental impact assessment module is as follows:

[0278] Step H1, Data Collection: Collect water usage data and environment-related data, including water usage volume, water usage time, and water quality parameters.

[0279] Step H2, Environmental Impact Indicator Definition: Define key indicators for assessing environmental impact, including water resource consumption and water pollution.

[0280] Step H3, Impact Assessment Model Development: Develop a model to assess the impact of water usage behavior on the environment.

[0281] Water resource consumption assessment: Water resource consumption index: water resource utilization coefficient Water pollution assessment: Water quality pollution index: where w i is the weight of the i-th pollutant, p i is the concentration of the i-th pollutant.Ecological impact assessment: Ecological footprint calculation: Statistical analysis: Correlation analysis: where r is the correlation coefficient, x i and y i are data points, and are the mean values.

[0282] Step H4, Improvement Suggestions Generation: Generate improvement suggestions based on the assessment results, including water-saving measures and pollution control.

[0283] Step H5, User Interaction: Display assessment results and improvement suggestions through a user interface and interact with users.

[0284] Step H6, Data Storage and Analysis: Store assessment data and conduct long-term trend analysis and prediction.

[0285] Step H7, Policy Compliance Check: Check whether water usage behavior complies with relevant environmental policies and regulations.

[0286] Technology implementation, as shown in Figure 8

[0287] Requirement analysis: Determine the objectives and functional requirements of the assessment tool. Data collection system design: Design the system to collect the required water usage and environmental data. Environmental impact indicator definition: Define key indicators to assess the environmental impact. Assessment model development: Develop a model to assess the impact of water usage behavior on the environment. Improvement suggestion algorithm: Develop an algorithm to generate improvement suggestions. User interface design: Design the user interface to display assessment results and improvement suggestions. Data storage and analysis system: Design a system to store and analyze assessment data. Policy compliance checking module: Develop a module to check the compliance of water usage behavior. System integration: Integrate the assessment tool into the water meter system. Testing and verification: Test the performance and accuracy of the assessment tool. User feedback collection: Collect user feedback to improve the assessment tool. Maintenance and updates: Regularly maintain and update the assessment tool.

[0288] Key content description:

[0289] Requirement analysis: Clearly define the objectives and functional requirements of the assessment tool to ensure it meets environmental management and user needs. Data collection system design: Design the system to ensure accurate and comprehensive collection of water usage and environmental data. Environmental impact indicator definition: Define key indicators to assess environmental impact, providing a basis for the assessment model. Assessment model development: Develop a model to assess the impact of water usage behavior on the environment, ensuring scientific and accurate assessment results. Improvement suggestion algorithm: Develop an algorithm to generate improvement suggestions, helping users reduce the environmental impact of water usage. User interface design: Design an intuitive and user-friendly user interface to display assessment results and improvement suggestions, improving user experience. Data storage and analysis system: Design a system to store and analyze assessment data, supporting long-term trend analysis and prediction. Policy compliance checking module: Develop a module to check the compliance of water usage behavior, ensuring users comply with relevant environmental policies and regulations. System integration: Seamlessly integrate the assessment tool into the water meter system, ensuring compatibility with existing systems. Testing and verification: Conduct comprehensive testing to verify the performance and accuracy of the assessment tool, ensuring its reliability. Maintenance and updates: Regularly maintain and update the assessment tool to adapt to new environmental policies and user needs.

[0290] Modular upgrade interface: Design a modular component that is easy to upgrade, simplifying maintenance and upgrade processes, and improving product adaptability and flexibility.

[0291] The design of the modular upgrade interface aims to simplify the maintenance and upgrade process of the water meter by creating components that are easy to upgrade and replace.

[0292] The workflow of the modular interface module is as follows:

[0293] Step I1, requirement analysis: Determine the functional requirements and performance objectives of the upgrade interface;

[0294] Step I2, Modular Design: Design modular components, ensuring independence and interoperability of each component.

[0295] Step I3, Interface Standardization: Define standardized interfaces to ensure compatibility between different modules.

[0296] Step I4, Component Development: Develop independent modular components, including sensors, communication modules, and data processing units.

[0297] Step I5, System Integration: Integrate modular components into the water meter system, ensuring overall performance.

[0298] Step I6, Testing and Validation: Test modular components and interfaces to ensure stability and reliability.

[0299] Step I7, User Documentation: Prepare detailed user documentation to guide users in upgrading and maintenance.

[0300] Step I8, Market Promotion and Training: Promote modular upgrade interfaces and train relevant personnel.

[0301] Step I9, Feedback Collection and Optimization: Collect user feedback and continuously optimize modular design and interfaces.

[0302] Theories involved in the modular upgrade interface include system engineering, interface design, reliability engineering, and user engineering. Modular Design Theory: Modular design improves system flexibility and maintainability by breaking down the system into independent, replaceable modules. Modular design is a system design approach that breaks down complex systems into a series of modules with independent functions. Each module is responsible for a specific task and interacts with other modules through well-defined interfaces. This design approach provides the following advantages: Flexibility: Modular design allows the system to easily adapt to changes, as modifying or replacing individual modules usually does not affect other modules. Maintainability: When the system has problems, you can quickly locate and fix the problem module without having to redesign the entire system. Scalability: New modules can be added to existing systems to introduce new features or enhance existing features. Reusability: Well-designed modules can be reused in different projects, reducing development time and cost.

[0303] The key to modular design is to define clear interface specifications to ensure interoperability between modules while maintaining the independence and closure of modules. Modular design usually follows the following principles:

[0304] Decoupling: Dependencies between modules are minimized, and each module should function independently of other modules. Encapsulation: Each module should encapsulate its internal implementation details and only interact with other modules through interfaces. Abstraction: Module interfaces should provide an abstraction layer, hiding complexity and simplifying communication between modules. Information hiding: Modules should only expose necessary information and hide unnecessary details. Interface design theory: Standardized interface design theories, such as ISO / IEC standards, ensure compatibility and interoperability between different modules.

[0305] Interface design is an important part of modular design, as it defines how modules communicate and interact with each other. Good interface design is crucial for achieving modularity, as it ensures flexibility and maintainability of the system. Here are some key concepts of interface design:

[0306] Standardization: Interfaces should follow industry standards, such as ISO / IEC standards, to ensure compatibility between different modules and systems.

[0307] Consistency: Interface design should be consistent throughout the system, using the same naming conventions, data formats, and interaction patterns.

[0308] Simplicity: Interfaces should be simple and straightforward, containing only the necessary operations and data to implement functionality.

[0309] Documentation: Interfaces should have detailed documentation, including the purpose of each operation, parameters, return values, and possible errors.

[0310] Interface design theory also involves the following aspects:

[0311] API design: Application Programming Interface (API) is an interface for interaction between software systems, which defines a set of functions, protocols, and tools for building software applications.

[0312] Service-Oriented Architecture (SOA): A design pattern that modularizes business functions into services, interacting through well-defined interfaces.

[0313] Microservices architecture: An architectural style that develops applications as a set of small services, each implementing specific business functionality, and interacting through lightweight communication mechanisms (usually HTTP RESTful APIs).

[0314] Reliability engineering:

[0315] 可靠性公式 : R(t) = e -λt ; where R(t) 是系统在时间 is the reliability of t, and λ is the failure rate.

[0316] User engineering: User experience formula: Where UX is the user experience, measuring the ratio of user satisfaction to user effort.

[0317] Technical implementation, such as Figure 9 is shown.

[0318] Requirement analysis: Determine the functional requirements and performance targets of the upgrade interface. Modular design: Design modular components to ensure independence and interoperability of each component. Interface standardization: Define standardized interfaces to ensure compatibility between different modules. Component development: Develop independent modular components such as sensors, communication modules, data processing units, etc. System integration: Integrate modular components into the water meter system to ensure overall performance. Testing and verification: Test modular components and interfaces to ensure their stability and reliability. User documentation: Prepare detailed user documentation to guide users in upgrading and maintenance.

[0319] Market promotion and training: Promote modular upgrade interfaces and train relevant personnel. Feedback collection and optimization: Collect user feedback and continuously optimize modular design and interfaces.

[0320] Key content explanation: Requirement analysis: In-depth understanding of user and market needs to ensure that modular design meets actual application scenarios. Modular design: Design modular components to ensure their independence and interoperability, improving system flexibility and maintainability. Interface standardization: Define standardized interfaces to ensure compatibility between different modules, simplifying the upgrade and replacement process.

[0321] Component development: Develop independent modular components such as sensors, communication modules, data processing units, etc. to ensure high performance and reliability of components. System integration: Integrate modular components into the water meter system to ensure overall performance and stability. Testing and verification: Conduct comprehensive testing to verify the stability and reliability of modular components and interfaces to ensure they meet design requirements. User documentation: Prepare detailed user documentation to guide users in upgrading and maintenance, improving user experience. Market promotion and training: Promote modular upgrade interfaces and train relevant personnel to improve market acceptance. Feedback collection and optimization: Collect user feedback and continuously optimize modular design and interfaces to improve product adaptability and flexibility.

[0322] Energy efficiency analysis: Analyze the energy consumption of each component of the water meter to determine the main energy consumption sources; as follows: Energy efficiency ratio formula: Energy efficiency ratio Sleep mode design: Design low-power sleep mode for components that do not work frequently; as follows: Sleep mode energy consumption formula: P sleep = P active · DutyCycle; where P sleep is the energy consumption in sleep mode, P activeis the energy consumption in active mode, DutyCycle is the active time proportion; Dynamic power management: dynamically adjust voltage and frequency according to working state, reduce energy consumption; as follows: Dynamic voltage and frequency scaling DVFS: P = C V 2 ; where P is power, C is capacitance, V is voltage, f is frequency; Communication optimization: optimize the working cycle of communication module, reduce communication frequency; as follows: Communication energy consumption formula: P comm = P tx · t tx + P rx · t rx ; where P comm is communication energy consumption, P tx is sending power, t tx is sending time, P rx is receiving power, t rx is receiving time; Sensor power saving design: power saving design for sensors, reduce standby power consumption; as follows: Sensor energy consumption formula: P sensor = P1 + P2 where P sensor is the total energy consumption of the sensor; P1 is the energy consumption of the sensor in working state; P2 is the energy consumption of the sensor in standby state; Battery capacity optimization: re-evaluate battery capacity demand according to energy consumption reduction; as follows:

[0323] Battery capacity demand formula: where C new is the new battery capacity, C old is the old battery capacity, E old is the old energy consumption, E new is the new energy consumption;

[0324] Software optimization: optimize software algorithm, reduce the energy consumption of calculation and processing;

[0325] Hardware selection: select low-power hardware components, such as microcontroller and sensor;

[0326] System monitoring: real-time monitoring of system energy consumption, automatic adjustment of working mode.

[0327] Technology implementation, as shown in Figure 10 .

[0328] Requirement analysis: Determine the goals and performance indicators of the low-power design. Energy efficiency analysis: Analyze the energy consumption of each component of the water meter and identify the main sources of energy consumption. Sleep mode design: Design low-power sleep modes for components that do not work frequently. Dynamic power management: Implement dynamic power management to adjust voltage and frequency based on working status. Communication optimization: Optimize the working cycle of the communication module to reduce communication frequency. Sensor power saving design: Optimize the power consumption of sensors to reduce standby power consumption. Battery capacity optimization: Re-evaluate the battery capacity requirement based on energy consumption reduction. Software optimization: Optimize software algorithms to reduce energy consumption of calculation and processing. Hardware selection: Select low-power hardware components such as microcontrollers and sensors. System monitoring: Develop a system monitoring module to monitor system energy consumption in real time. Testing and verification: Test the effectiveness of the low-power design and verify its performance. Deployment and maintenance: Deploy the low-power design to the production environment and maintain it continuously.

[0329] Key content description: Requirement analysis: Clearly define the goals and performance indicators of the low-power design to ensure that the design meets actual needs. Energy efficiency analysis: In-depth analysis of the energy consumption of each component of the water meter, determine the main sources of energy consumption, provide basis for optimization. Sleep mode design: Design low-power sleep mode for components that do not work frequently, reduce energy consumption in non-working state. Dynamic power management: Implement dynamic power management, adjust voltage and frequency according to working status, reduce energy consumption. Communication optimization: Optimize the working cycle of the communication module, reduce communication frequency, reduce communication energy consumption. Sensor power saving design: Optimize the power consumption of sensors to reduce standby power consumption and prolong battery life. Battery capacity optimization: Re-evaluate the battery capacity requirement based on energy consumption reduction, possibly reduce battery size to reduce cost and volume. Software optimization: Optimize software algorithms to reduce energy consumption of calculation and processing, improve overall energy efficiency of the system. Hardware selection: Select low-power hardware components such as microcontrollers and sensors to improve overall energy efficiency of the system. System monitoring: Develop a system monitoring module to monitor system energy consumption in real time, automatically adjust working mode to ensure low-power operation. Testing and verification: Test the effectiveness of the low-power design and verify its performance to ensure that the design meets the expected goals. Deployment and maintenance: Deploy the low-power design to the production environment and maintain it continuously to ensure long-term stable operation.

[0330] Environmental adaptability design: Consider the stability and reliability of the water meter under different environmental conditions to ensure that the water meter can work normally under various environments.

[0331] The goal of environmental adaptability design is to ensure the stability and reliability of the water meter under different environmental conditions (such as temperature changes, humidity, pressure, corrosive environment, etc.).

[0332] The working principle of this design is as follows:

[0333] Environmental factor analysis: Identify and analyze various environmental factors that the water meter may encounter. Material selection: Choose appropriate materials to resist the effects of environmental factors. Sealing design: Use sealing techniques to protect internal components from moisture and contaminants. Temperature compensation: Design temperature compensation mechanisms to ensure measurement accuracy at different temperatures. Pressure and water hammer protection: Design structures capable of withstanding high water pressure and water hammer impacts. Anti-interference design: Implement electromagnetic compatibility (EMC) measures to reduce external electromagnetic interference on electronic components. Testing and verification: Test the performance and reliability of the water meter under various simulated environmental conditions. User feedback: Collect user feedback in different environments for continuous design optimization.

[0334] Theories involved in environmental adaptability design include material science, thermodynamics, fluid mechanics, and electromagnetism.

[0335] Material selection theory: Evaluation of material corrosion resistance: Where material resistance refers to the ability of a material to resist corrosion, and environmental aggressiveness refers to the corrosion pressure exerted on the material by the environment.

[0336] Sealing design theory: Sealing performance evaluation: Where sealing material adaptability refers to the ability of sealing materials to adapt to environmental conditions, and environmental exposure refers to the severity of the environment in which the seal is located.

[0337] Temperature compensation theory: Temperature compensation formula: Compensated reading = original reading * temperature coefficient; where compensated reading refers to the measurement value considering temperature influence, original reading refers to the direct measurement value without considering temperature influence, and temperature coefficient is the proportional factor used to adjust temperature influence.

[0338] Pressure and water hammer protection theory: Pressure vessel design: Where P is the pressure inside the pressure vessel, σ is the yield strength of the material, L is the length of the vessel, and r is the radius of the vessel.

[0339] Electromagnetic compatibility (EMC) theory: EMC design principles: The design should ensure that the device can work normally in an electromagnetic environment and does not interfere with other devices. This principle does not directly correspond to specific mathematical formulas, but rather a series of design guidelines and test standards, such as the ISO11452 series of standards.

[0340] Implementation of technology, such as Figure 11 as shown.

[0341] Requirement Analysis: Determine the performance requirements of the water meter in different environments. Environmental Factor Analysis: Identify and analyze environmental factors that may affect the performance of the water meter. Material Selection: Choose suitable materials to resist environmental factors. Sealing Design: Design a sealing structure to protect internal components. Temperature Compensation Mechanism: Develop a temperature compensation algorithm to ensure measurement accuracy. Pressure and Water Hammer Protection Design: Design a protection structure to withstand high water pressure and water hammer impact. Anti-interference Design: Implement EMC measures to reduce electromagnetic interference. Testing and Verification: Test the performance of the water meter under simulated environmental conditions. User Feedback: Collect user feedback for continuous design optimization. Deployment and Monitoring: Deploy the design to the actual environment and monitor it. Maintenance and Upgrade: Regularly maintain and upgrade the design to adapt to new environmental conditions

[0342] Key Content Description:

[0343] Requirement Analysis: Clearly define the performance requirements of the water meter in different environments to ensure the design meets actual needs. Environmental Factor Analysis: Identify and analyze environmental factors that may affect the performance of the water meter to provide a basis for design. Material Selection: Choose suitable materials to resist environmental factors, such as corrosion-resistant materials and pressure-resistant materials. Sealing Design: Design a sealing structure to protect internal components from moisture and contaminants. Temperature Compensation Mechanism: Develop a temperature compensation algorithm to ensure the measurement accuracy of the water meter at different temperatures. Pressure and Water Hammer Protection Design: Design a protection structure to withstand high water pressure and water hammer impact, ensuring the mechanical stability of the water meter. Anti-interference Design: Implement EMC measures to reduce the impact of external electromagnetic interference on the electronic components of the water meter. Testing and Verification: Test the performance of the water meter under simulated environmental conditions to ensure its reliability in various environments. User Feedback: Collect user feedback in different environments for continuous design optimization. Deployment and Monitoring: Deploy the design to the actual environment and monitor it to ensure long-term stable operation. Maintenance and Upgrade: Regularly maintain and upgrade the design to adapt to new environmental conditions and user needs.

[0344] System Integration Design: Focus on integrating all independent components and technologies into a coordinated and consistent system to ensure optimal overall performance.

[0345] The core of system integration design is to integrate all independent components and technologies into a coordinated and consistent system to ensure optimal overall performance. Here's how the design works:

[0346] Requirement analysis: Clearly define the overall function and performance requirements of the system. Component selection: Select appropriate hardware and software components to ensure they work together. Interface standardization: Ensure the standardization of interfaces between all components to facilitate integration and expansion. Modular design: Adopt modular design methods to improve system flexibility and maintainability. Data flow design: Design data flow and control flow to ensure efficient data flow in the system. System-level testing: Conduct comprehensive system-level testing to verify the cooperative work of components and overall performance. User interaction design: Design user interface and interaction methods to improve user experience. Security policy integration: Integrate security policies to ensure the security of system data and operations. Maintenance strategy design: Design system maintenance and upgrade strategies to ensure long-term stable operation.

[0347] System integration design involves theories such as system engineering, modular design, interface design, and user experience design. Here are some key theories and formulas:

[0348] System engineering theory: System engineering emphasizes the design and management of the system as a whole, considering the interaction between all components and subsystems.

[0349] Modular design theory: Modular design improves system flexibility and maintainability by breaking down the system into independent, replaceable modules. Modularization measurement formula: Modularization measurement inter-module coupling + intra-module cohesion;

[0350] Interface design theory: Interface design ensures efficient and accurate information exchange between different modules or components. Interface compatibility evaluation formula: User experience design theory: User experience design focuses on the ease of use and satisfaction of users interacting with the system. User satisfaction formula: Reliability engineering theory: Reliability engineering ensures that the system can continue to operate under specified conditions and within a specified time. System reliability formula: R(t) = e -λt ; where R(t) is the reliability of the system at time t, and λ is the failure rate. Maintainability theory: Maintainability theory focuses on the ease of maintenance and upgrade of the system during operation. Maintainability evaluation formula: Security theory: Security theory ensures that system design can prevent unauthorized access and data leakage. Security risk assessment formula: Security risk = threat × vulnerability × impact. Cost-benefit analysis: Cost-benefit analysis evaluates the economic feasibility of system design. Cost-benefit ratio formula: System performance evaluation: System performance evaluation ensures that the system meets the predetermined performance indicators.

[0351] System performance evaluation formula:

[0352] Technology implementation, such as Figure 12

[0353] Requirement analysis: Determine system functions and performance indicators. Component selection: Select hardware and software components to ensure compatibility and performance. Interface standardization: Define standardized interfaces to ensure interoperability between components. Modular design: Design a modular architecture to improve system flexibility. Data flow design: Design data flow and control flow to ensure efficient data flow. System-level testing: Conduct comprehensive testing to verify overall system performance. User interaction design: Design user interfaces and interaction methods to improve user experience. Security policy integration: Integrate security policies to ensure system security. Maintenance strategy design: Design system maintenance and upgrade strategies. Deployment and monitoring: Deploy the system to the production environment and monitor it. Optimization and upgrade: Optimize and upgrade the system based on feedback and monitoring results

[0354] Key content description:

[0355] Requirement analysis: In-depth understanding of the overall function and performance requirements of the system to ensure that the design meets the target. Component selection: Select appropriate hardware and software components to ensure they work together to meet performance and compatibility requirements. Interface standardization: Define standardized interfaces to ensure interoperability between components and system scalability. Modular design: Use modular design methods to improve system flexibility and maintainability. Data flow design: Design data flow and control flow to ensure efficient data flow in the system and improve system response speed. System-level testing: Conduct comprehensive system-level testing to verify the cooperative work of each component and overall performance to ensure system stability and reliability. User interaction design: Design intuitive and easy-to-use user interfaces and interaction methods to improve user experience. Security policy integration: Integrate security policies to ensure the security of system data and operations and protect user privacy. Maintenance strategy design: Design system maintenance and upgrade strategies to ensure long-term stable operation of the system. Deployment and monitoring: Deploy the system to the production environment and monitor it to ensure normal operation. Optimization and upgrade: Continuously optimize and upgrade the system based on user feedback and monitoring results to improve system performance and user experience.

[0356] Example 1: Application of intelligent diagnostic system;

[0357] Background: A residential community has installed enhanced intelligent multi-modal data exchange water meters to achieve more efficient water resource management and maintenance.

[0358] Implementation steps:

[0359] System installation: Install new water meters in all households in the community and ensure that the MEMS sensor, communication module and intelligent diagnostic system of each water meter are correctly configured. Data collection: The water meter starts collecting real-time data such as flow, pressure and temperature.​

[0360] Smart Diagnosis: The smart diagnostic system analyzes data through machine learning algorithms and detects abnormal flow patterns at a certain household's water meter. The system automatically flags and records the event. Early Warning: The system identifies potential early-stage water leakage and immediately sends a warning notification to the property management office and the household through the community's smart management system. Maintenance Action: Upon receiving the notification, the property management office promptly dispatches maintenance personnel for inspection and resolves the issue before the water leakage causes significant damage. Effect: Through the smart diagnostic system, the community achieves early detection and timely maintenance of potential water leakage issues, reducing water waste, avoiding potential property damage, and improving resident satisfaction.

[0361] Example 2: Implementation of User Customized Service Interface

[0362] Background: An intelligent building company decides to install enhanced smart multi-modal data exchange water meters in the commercial buildings it manages to provide more personalized water management services.

[0363] Implementation Steps:

[0364] System Deployment: Install new water meters in the building and ensure integration of AI interaction assistants and user customized service interfaces.

[0365] User Interface Customization: The building's administrator sets up water usage patterns for weekdays and weekends, including water usage limits and specific time period bans, through the user customized service interface. Intelligent Interaction: Tenants in the building query the current water usage through AI interaction assistants and adjust water usage plans as needed. Energy Optimization: The system automatically optimizes water usage patterns based on tenants' water usage habits and set parameters, such as using water storage devices for water supply during off-peak hours. Data Feedback: At the end of the month, the system generates detailed water usage reports showing energy-saving effects and user water usage behavior analysis for reference by tenants and administrators. Effect: Through the user customized service interface, the building successfully achieves personalized water management, improves water resource efficiency, reduces energy consumption, and enhances tenants' participation and satisfaction in smart water management.

[0366] The present application develops an intelligent diagnostic system integrated with machine learning algorithms for real-time analysis of water meter operation data, identification of potential faults and performance decline trends, and early warning and intelligent diagnosis. Using historical maintenance data and real-time operation status, an algorithm is developed to predict equipment maintenance needs and automatically remind users or service providers to maintain. A customizable user interface is designed to allow users to set water meter operating parameters and reminders according to their own needs and preferences. An AI interaction assistant integrated with natural language processing technology provides voice and text interaction functions to improve user interaction experience. A tool is integrated to assess the potential environmental impact of water use and provide improvement suggestions to support green energy saving. Modular components and standardized interfaces are designed for easy upgrading, simplifying maintenance and upgrading processes and improving product adaptability and flexibility. Low-power consumption technology and optimized algorithms are used to extend battery life and reduce energy consumption. The stability and reliability of the water meter under different environmental conditions are considered to ensure that the water meter can work normally under various environments. Attention is paid to integrating all independent components and technologies into a coordinated and consistent system to ensure optimal overall performance.

[0367] Those skilled in the art can understand that the above only describes the preferred examples of the application and is not intended to limit the application. Although the application has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent replacements for some technical features. Any modification, equivalent replacement, etc. within the spirit and principles of the application should be included in the protection scope of the application. All technical features in the embodiments can be freely combined according to actual needs.

[0368] Finally, it should be noted that the above only describes the preferred embodiments of the application and is not intended to limit the application. Although the application has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent replacements for some technical features. Any modification, equivalent replacement, etc. within the spirit and principles of the application should be included in the protection scope of the application.

Claims

1. An enhanced intelligent multimodal data exchange water meter and intelligent diagnostic system, characterized in that: It includes intelligent diagnostic module, predictive maintenance module, energy management module, user-customized service interface module, AI interactive assistant module, adaptive metering module, data security module, environmental impact assessment module, modular interface module, low power consumption module, environmental adaptability module, and system integration module; Among them, the intelligent diagnostic module is used to analyze the water meter's operating data in real time through machine learning algorithms in order to identify and predict potential faults and performance degradation trends; The predictive maintenance module is used to predict future maintenance needs of equipment by analyzing historical maintenance data and real-time operating status. The energy management module is used to optimize water usage patterns, reduce energy waste, and provide energy-saving suggestions by analyzing water usage data. The user-customized service interface module provides a flexible and intuitive interface that allows users to set the water meter's operating parameters and reminders according to their own needs and preferences. The AI ​​interactive assistant module is used to provide voice and text interaction functions by integrating natural language processing (NLP) technology, making the operation of the water meter more intuitive and user-friendly. The adaptive metering module is used to analyze water usage data in real time and automatically adjust metering parameters to adapt to different water usage environments and needs. The data security module is used to ensure the security of water meter data during transmission and storage. The Environmental Impact Assessment module is used to assess the potential environmental impacts of water use behavior and provide improvement recommendations. Modular interface modules are used to simplify the maintenance and upgrade process of water meters by creating components that are easy to upgrade and replace; The low-power module is designed to extend the lifespan of the water meter battery and reduce overall energy consumption by employing advanced low-power technologies and optimized algorithms. The environmental adaptability module is used to ensure that the water meter maintains stability and reliability under different environmental conditions, including temperature changes, humidity, pressure, and corrosive environments. The system integration module is used to integrate all independent components and technologies into a coordinated system to ensure optimal overall performance.

2. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The intelligent diagnostic module achieves intelligent diagnosis through the following steps: Step A1, Data Acquisition: Collect the water meter's operating parameters, including flow rate, pressure, temperature, and vibration; Step A2, Data Preprocessing: Clean, standardize, and extract features from the collected data to facilitate processing by machine learning models; Step A3, Feature Selection: Select the features most helpful for diagnosis from the preprocessed data; Step A4, Model Training: Train a machine learning model using historical failure data and normal operation data, including support vector machines (SVM), random forests, or neural networks; Step A5, Real-time Monitoring and Diagnosis: Apply the trained model to real-time data to monitor the water meter's operating status and identify abnormal patterns; Step A6, Fault Prediction and Early Warning: Based on the model's diagnostic results, predict potential faults and issue early warnings; Among them, machine learning theory involves supervised learning, unsupervised learning, and reinforcement learning algorithms; in the random forest algorithm, the decision tree construction formula is: h t+1 (c)=h t (a)+λ·I(y∈R t(c) ) Among them, h t+1 (c) is the prediction result for node c at time t+1; h t (a) is the prediction result for node a at time t; λ is the learning rate or step size, which controls the magnitude of each update; I(y∈R t(c) Let y be the indicator function, and let y be the region R of node c at time t. t(c) If the value is 1, then the value is 1; otherwise, the value is 0. Signal processing theory: Involves Fourier transform and wavelet transform for analyzing the time-frequency characteristics of water meter operation data; Fourier transform formula: Where X(f) is the Fourier transform result of signal x(t) at frequency f; r(t) is the original signal in the time domain; f is the frequency, usually expressed in Hertz (Hz); j is the imaginary unit, satisfying j 2 =-1; π (pi) is approximately equal to 3.14159. Statistical analysis theory: used to evaluate model performance and data distribution characteristics; confusion matrix is ​​used to evaluate the performance of classification models; The confusion matrix is ​​a table used to describe the performance of a classification model; it contains the following four basic elements: True positive class (TP): The number of samples that the model correctly predicts as positive. False positive (FP): The number of negative class samples that the model incorrectly predicts as positive, i.e., the Type I error; True negative class (TN): The number of samples correctly predicted as negative by the model; False negatives (FN): The number of positive class samples that the model incorrectly predicts as negative, i.e., the type II error; Based on the confusion matrix, the following performance metrics are calculated.

3. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The predictive maintenance module's workflow includes the following key steps: Step B1, Data Collection: Collect equipment operating data and historical maintenance records; Step B2, Data Preprocessing: Clean and standardize the data, and handle missing values; Step B3, Feature Engineering: Extracting features from the raw data that are helpful for prediction; Step B4, Model Selection: Select a suitable prediction model, including time series analysis, machine learning, or deep learning models; Time series analysis: used to analyze the trends and seasonality of time series data; Autoregressive Integral Moving Average (ARIMA) model: X t =c+φ1X t-1 +…φ p X t-p -θ1X t-1 -...-θ q X t-q +e t ; Among them, X t The observation value at time t; c is a constant term, representing the bias of the model; φ1…φ p The parameters for the autoregressive (AR) part represent the relationship between the current value and previous values; θ1…θ p These are the parameters for the difference part, used to make the data smooth; ε t This is the error term, which is usually assumed to be white noise; Machine learning theory: involving classification and regression algorithms; Linear regression model: y = β0 + β1x1 + ... + β n x n +ε; Where y is the dependent variable, the result predicted by the model; β0 is the intercept term, the baseline value of the model; β1…β n The coefficients represent the independent variables x1…x n The effect on y; x1…x n is the independent variable; ε is the error term, the residual of the model prediction; Deep learning theory: using neural networks for complex pattern recognition; Recurrent Neural Network (RNN) Unit: Among them, h t The hidden state at time t; W n This is the weight matrix from hidden layer to hidden layer; This is the transpose of the weight matrix input to the hidden layer; x t The input is time t; b h For the bias term of the hidden layer; Long Short-Term Memory (LSTM) unit: i t =σ(W ii x t +W hi h t-1 +b i ); f t =σ(W if x t +W hf h t-1 +b f ); o t =σ(W io x t +W ho h t-1 +b o ); h t = no t *fish(C) t ); Among them, i t It is the input gate for time t; f t Forget gate at time t; C t The state of the cell at time t; O t For time t, the output gate; W z W n W c These represent the weight matrices for the input, hidden layer, and unit state, respectively. b i ,b j ,b c These represent the bias terms for the input gate, forget gate, and cell state, respectively. σ is the Sigmoid function, used in gating mechanisms to generate values ​​between 0 and 1; * indicates element-wise multiplication; Step B5, Model Training: Train the prediction model using historical data; Step B6, Predictive Analysis: Apply the trained model to real-time data to predict equipment status and maintenance needs; Step B7, Maintenance Decision: Based on the forecast results, develop maintenance plans and strategies; Step B8, Reminder Mechanism: When the prediction results indicate that maintenance is imminent, automatically remind the user or service provider.

4. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The workflow of the energy management module is as follows: Step C1, Data Acquisition: Collect water usage data from the water meter in real time, including flow rate, time, or pressure; Step C2, Data Storage: Store the collected data in a database for further analysis; Step C3, Data Analysis: Analyze water usage patterns and energy consumption data using statistical and machine learning algorithms; Among them, energy efficiency analysis: assesses the energy efficiency of a system or equipment; Energy efficiency formula: Used to analyze the distribution, trends, and outliers of water data; Mean formula: Where μ is the mean, i.e. the average value of the dataset; C i Let i be the i-th observation in the dataset; n is the total number of observations in the dataset; Machine learning is used to identify water usage patterns and predict energy consumption. Clustering algorithms are used to classify user water usage patterns: Where J is the objective function of the K-means algorithm, representing the sum of the squared distances from all data points to their cluster centers; K is the number of clusters; x is a data point in the dataset; C is the set of all data points in the k-th cluster; Centroidp is the cluster center of the k-th cluster; x-Centroidk|| represents the Euclidean distance from data point x to cluster center Centroid; Step C4, Energy Saving Recommendation Generation: Based on the analysis results, energy saving recommendations are generated, including adjusting water usage time and reducing leaks; Step C5, Water usage pattern optimization: Provide users with customized water usage patterns to reduce energy consumption; Step C6, Real-time monitoring and feedback: Monitor water usage in real time and adjust energy-saving strategies based on user feedback; Step C7, Report Generation: Regularly generate energy-saving reports to showcase energy-saving effects and provide suggestions for further improvement.

5. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The workflow of the user-customized service interface module is as follows: Step D1, User Needs Analysis: Collect and analyze user needs for water meter functions and user interface; Step D2, Interface Design: Design an easy-to-use and feature-rich user interface; Adopting Nielsen's usability principles: including learnability, efficiency, memorability, error prevention, user control, and satisfaction; Step D3, parameter setting function: allows users to customize the water meter's operating parameters, including water consumption limits and water usage time; Step D4, Reminder Settings: Allows users to set water usage reminders, including leak alarms and reminders for exceeding water usage limits; Step D5, Data Interaction: Ensure that the parameters and reminders set by the user can interact seamlessly with the water meter's hardware and software systems; Fitts' Law: Used to predict the time required for a user to move to a target location. Where D is the target distance and W is the target width; Step D6, Feedback Mechanism: Provide user feedback channels to continuously optimize the interface and functions; Step D7, Security and Privacy Protection: Ensure the security and privacy of user data; ensure the security of data transmission through AES encryption; protect the privacy of user data by adding noise.

6. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The workflow of the AI ​​interaction assistant module is as follows: Step E1, Speech Recognition: Convert the user's voice input into text; Speech recognition: Hidden Markov Models (HMMs) are used to model the statistical properties in speech recognition; formula: Where O is the observation sequence and S is the state sequence; Step E2, Natural Language Understanding: Parsing the user's intent and needs; Natural Language Understanding: Semantic Role Labeling is used to identify actions and participants in sentences; Formula: SRL={(v,ARG O ),(v,ARG1),...}, where v is the verb and ARG is the corresponding argument; Step E3, Dialogue Management: Maintain dialogue state and understand context; Dialogue Management: Dialogue State Tracking (DST) is used to maintain the current state of a dialogue; Formula: DST={slot1:value1,slot2:value2,...}; Wherein, slot represents a slot in the dialogue, and value represents the value of the slot; Step E4, Decision Making: Determine the response action based on the user's intent; Step E5, Response Generation: Generate a text or voice response; Step E6, Speech Synthesis: Convert the text response into speech output; Speech synthesis: The deep learning model uses WaveNet to generate natural-sounding speech; Formula: WaveNet=N(WaveNetParams,Input); Where N represents the network model, WaveNetParams are the parameters of the WaveNet model, and Input is the input data; Step E7, User Feedback Learning: Optimize the interactive experience through user feedback.

7. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The workflow of the adaptive metering module is as follows: Step F1, Data Acquisition: Collect water usage data from the water meter in real time, including flow rate, pressure, and temperature; Step F2, Data Preprocessing: The collected data is cleaned, standardized, and denoised. Step F3, Environmental Analysis: Analyze changes in the water use environment, including seasonal changes and changes in water use patterns; Step F4, Parameter Adaptive Adjustment: Based on the environmental analysis results, the metering parameters, including the flow meter sensitivity, are automatically adjusted; Step F5, Measurement Accuracy Optimization: Optimize the measurement algorithm to improve measurement accuracy and response speed; Step F6, Real-time monitoring and feedback: Monitor measurement accuracy in real time and make dynamic adjustments based on feedback; Step F7, Data Storage and Analysis: Store the adjusted measurement data and perform long-term trend analysis; Among them, control theory: PID controllers are used to adjust metering parameters. The formula is: Where u(t) is the control input, e(t) is the error, and K is the control input. p K i K d These are proportional, integral, and differential gain, respectively. Signal processing theory: Kalman filters are used to estimate system states; formula: in, It is an estimated state, y k It is an observed value, K k It is the Kalman gain; Machine learning theory: Regression analysis is used to predict econometric parameters. The formula is: y = β0 + β1x1 + ... + β n x n Where y is the target variable, x1 is the feature variable, and β is the target variable. n It is the regression coefficient.

8. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The workflow of the data security module is as follows: Step G1, Data Encryption: Before data transmission and storage, the data is encrypted using advanced encryption algorithms; Symmetric encryption: Advanced Encryption Standard (AES) is a commonly used symmetric encryption algorithm, with the formula: C = E(K, P); where C is the ciphertext, K is the key, and P is the plaintext. Asymmetric encryption: RSA encryption algorithm, formula: C = M e mod n; where C is the ciphertext, M is the plaintext, e is the public key exponent, and n is the modulus; Step G2, Key Management: Securely generate, store, and distribute encryption keys; Step G3. Data Anonymization: When storing and analyzing data, use anonymization techniques to protect user privacy; Step G4. Access Control: Implement strict access control policies to ensure that only authorized users can access sensitive data; Step G5. Data Integrity Verification: Verify the integrity of the data using hash functions and digital signatures; Hash function: SHA-256 is used to generate hash values ​​for data. The formula is: H = SHA-256(P); where H is the hash value and P is the plaintext. Digital Signature: Formula: σ = D -1 (H(M)); where σ is the digital signature, D is the private key, and H(M) is the hash value of the message; Data anonymization: Differential privacy technology, formula: Output = Original Data + Noise; where Noise is noise extracted from the Laplace distribution; Step G6. Security Audit: Record and monitor all data access and operations for security audit purposes; Access control: Role-based access control (RBAC) model, formula: Access = User Role ∩ Resource Permission; Access refers to access permissions, indicating whether a user has the right to access a specific resource; UserRole is a user role, representing a user's identity or role in the system; ResourcePermission is a resource permission that represents the level of operation or access allowed for a specific resource. N is the intersection operation of sets, used to determine the common part of user roles and resource permissions; Step G7. Anomaly Detection: Monitor data access patterns in real time, detect and respond to abnormal behavior; The workflow of the environmental impact assessment module is as follows: Step H1, Data Collection: Collect water usage data and environmental data, including water consumption, water usage time, and water quality parameters; Step H2, Definition of Environmental Impact Indicators: Define key indicators for assessing environmental impact, including water resource consumption and water pollution; Step H3, Impact Assessment Model Development: Develop a model to assess the environmental impact of water use behavior; Among them, water resource consumption assessment: Water resource consumption index: water resource utilization coefficient Water pollution assessment: Water pollution index: Among them, w i It is the weight of the i-th pollutant, p i It is the concentration of the i-th pollutant; Ecological impact assessment: Ecological footprint calculation: Ecological footprint Statistical analysis: Correlation analysis: Where r is the correlation coefficient, x i and y i It's a data point. and It is the mean; Step H4, Improvement Recommendation Generation: Based on the evaluation results, improvement recommendations are generated, including water-saving measures and pollution control. Step H5, User Interaction: Display evaluation results and improvement suggestions through the user interface to interact with users; Step H6, Data Storage and Analysis: Store the evaluation data and conduct long-term trend analysis and forecasting; Step H7, Policy Compliance Check: Check whether water use behavior complies with relevant environmental policies and regulations.

9. The enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The workflow of the modular interface module is as follows: Step I1, Requirements Analysis: Determine the functional requirements and performance targets of the upgrade interface; Step I2, Modular Design: Design modular components to ensure the independence and interoperability of each component; Step I3, Interface Standardization: Define standardized interfaces to ensure compatibility between different modules; Step I4, Component Development: Develop independent modular components, including sensors, communication modules, and data processing units; Step I5, System Integration: Integrate modular components into the water meter system to ensure overall performance; Step I6, Testing and Verification: Test modular components and interfaces to ensure their stability and reliability; Step I7, User Documentation Compilation: Compile detailed user documentation to guide users in upgrading and maintaining the system; Step I8, Marketing and Training: Promote the modular upgrade interface and train relevant personnel; Step I9, Feedback Collection and Optimization: Collect user feedback and continuously optimize modular design and interfaces.

10. An enhanced smart multimodal data exchange water meter and intelligent diagnostic system according to claim 1, characterized in that: The working principle of the low-power module is as follows: Energy efficiency analysis: Analyze the energy consumption of each component of the water meter to determine the main sources of energy consumption; details are as follows: Energy Efficiency Ratio Formula: Energy Efficiency Ratio Sleep mode design: Design low-power sleep modes for components that do not operate frequently; details are as follows: Sleep mode energy consumption formula: P sleep =P active •DutyCycle; where P sleep This refers to the energy consumption in sleep mode, P. active This is the energy consumption in active mode; DutyCycle is the percentage of active time. Dynamic power management: Adjusts voltage and frequency dynamically based on operating conditions to reduce energy consumption; details are as follows: Dynamic Voltage Frequency Adjustment (DVFS): P = C·V 2 ·f; where P is power, C is capacitance, V is voltage, and f is frequency; Communication optimization: Optimize the working cycle of the communication module and reduce the communication frequency; details are as follows: Communication energy consumption formula: P comm =P tx ·t tx +P rx ·t rx Among them, P comm It is the energy consumption of communication, P tx It is the transmission power, t tx It is the sending time, P rx It is the received power, t rx It is the reception time; Sensor power-saving design: Power-saving design for sensors to reduce standby power consumption; details are as follows: Sensor energy consumption formula: P sensor =P1+P2 Among them, P sensor This represents the total power consumption of the sensor. P1 represents the energy consumption of the sensor during operation. P2 represents the power consumption of the sensor in standby mode; Battery capacity optimization: Based on the reduction in energy consumption, reassess battery capacity requirements; details are as follows: Battery capacity requirement formula: Among them, C new It's a new battery capacity, C old It's the old battery capacity, E old It's the old energy consumption, E new It's a new energy consumption; Software optimization: Optimize software algorithms to reduce energy consumption for computation and processing; Hardware selection: Choose low-power hardware components, including microcontrollers and sensors; System monitoring: Real-time monitoring of system energy consumption and automatic adjustment of operating mode.

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