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

Abstract

The invention discloses an enhanced intelligent multi-modal data exchange water meter and an intelligent diagnosis system, belongs to the technical field of intelligent metering equipment and the Internet of Things, and aims to analyze water meter operation data in real time, identify potential faults and performance decline trends and realize early warning and intelligent diagnosis through the intelligent diagnosis system. A user or a service provider is automatically reminded of maintenance; a user can set working parameters and prompts of the water meter according to own requirements and preferences; a voice and text interaction function is provided, and the user interaction experience is improved; tools for evaluating the potential influence of water on the environment are integrated, and improvement suggestions, modular components and standardized interfaces are provided, so that the adaptability and flexibility of the product are improved; by adopting a low-power-consumption technology and an optimization algorithm, the service life of the battery is prolonged, and the energy consumption is reduced; the water meter can work normally in various environments; all independent components and technologies are integrated into a coordinated and consistent system, and the overall performance is ensured to be optimal.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent metering equipment and the Internet of Things technology, and in particular relates to an enhanced intelligent multimodal data exchange water meter and an intelligent diagnosis system. Background Art

[0002] As a key component of the smart city and smart home ecosystem, smart water meter technology is rapidly evolving, constantly incorporating innovative technologies to enhance performance and user experience. The following is an overview of the background technologies in the smart water meter field:

[0003] Traditional mechanical water meter: Traditional mechanical water meter relies on mechanical components such as impellers or propellers to measure water flow volume. It has the advantages of simple structure and low cost, but has low accuracy and is easily affected by external factors such as water quality, water temperature, and magnetic field.

[0004] Existing smart water meters: Existing smart water meters use electronic components such as electronic sensors and microcontrollers to improve measurement accuracy and anti-interference capabilities, and support functions such as remote meter reading, water leakage alarm, and tiered water price billing.

[0005] MEMS technology: Micro-electromechanical system (MEMS) technology has been applied in smart water meters due to its advantages such as high sensitivity, small size and low power consumption, making water flow detection more precise and sensitive.

[0006] Blockchain technology: Blockchain technology, with its decentralized, tamper-proof and traceable characteristics, provides a new solution for the data security of smart water meters.

[0007] RFID / Bluetooth / NFC technologies: These communication technologies enable contactless reading and writing of data in smart water meters, enhancing the versatility and flexibility of water meters.

[0008] Smart home system: The popularization of smart home system requires that smart water meters can be seamlessly connected with it to realize intelligent management of household water use.

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

[0010] Big data analytics: Big data analytics 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 intelligent monitoring and management functions.

[0012] While existing technologies have made some progress in the field of smart water meters, challenges remain, such as data security, limited functional scalability, limited user interaction, complex device maintenance, and energy efficiency. This invention aims to address these issues by integrating multiple advanced technologies to provide a more advanced, secure, reliable, and user-friendly smart water meter solution. Summary of the Invention

[0013] The technical problem to be solved by the present invention is to provide an enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system to address the shortcomings of the background technology; it analyzes the operating data of the water meter in real time, identifies potential faults and performance degradation trends, and realizes early warning and intelligent diagnosis.

[0014] The present invention adopts the following technical solutions to solve the above technical problems:

[0015] An enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system, including an intelligent diagnosis module, a predictive maintenance module, an energy management module, a user-customized 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;

[0016] Among them, the intelligent diagnosis module is used to analyze the operating data of the water meter in real time through machine learning algorithms to identify and predict potential failures and performance degradation trends;

[0017] Predictive maintenance module, which is used to predict future maintenance needs of equipment by analyzing historical maintenance data and real-time operating status;

[0018] Energy management module, which optimizes water use patterns, reduces energy waste, and provides energy-saving recommendations by analyzing water use data;

[0019] User-customized service interface module, which provides a flexible and intuitive interface, allowing users to set water meter operating parameters and reminders according to their needs and preferences;

[0020] 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 water meters more intuitive and user-friendly;

[0021] Adaptive metering module, which analyzes water usage data in real time and automatically adjusts metering parameters to suit different water usage environments and needs;

[0022] Data security module, used to ensure the security of water meter data during transmission and storage;

[0023] Environmental impact assessment module, used to evaluate the potential impact of water use on the environment and provide improvement recommendations;

[0024] Modular interface modules to simplify water meter maintenance and upgrades by creating components that are easily upgraded and replaced;

[0025] Low-power module, used to extend the life of water meter batteries and reduce overall energy consumption by adopting advanced low-power technology and optimization algorithms;

[0026] Environmental adaptability module, used to ensure that the water meter maintains stability and reliability under different environmental conditions including temperature changes, humidity, pressure, and corrosive environment;

[0027] System integration modules are used to combine all individual components and technologies into a coordinated system, ensuring optimal overall performance.

[0028] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system of the present invention, the intelligent diagnosis module implements intelligent diagnosis through the following steps:

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

[0030] Step A2, data preprocessing: cleaning, standardizing and feature extraction of the collected data to facilitate processing by the machine learning model;

[0031] Step A3, feature selection: select the most helpful features for diagnosis from the preprocessed data;

[0032] Step A4, model training: Use historical fault data and normal operation data to train a machine learning model, including support vector machine (SVM), random forest, or neural network;

[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 early warning: Based on the model’s diagnostic results, predict potential faults and issue early warnings;

[0035] 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:

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

[0037] Among them, 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, which controls the amplitude of each update; I(y∈R t(c) ) is the indicator function, if the sample y belongs to the region R of the node c at time t t(c ), the value is 1, otherwise it is 0;

[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 Hertz (Hz); j is an imaginary unit, satisfying j 2 = -1; π, approximately equal to 3.14159;

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

[0042] The confusion matrix is ​​a table used to describe the performance of a classification model. It contains the following four basic elements: True Positives (TP): the number of samples correctly predicted as positive by the model; False Positives (FP): the number of negative samples incorrectly predicted as positive by the model, also known as Type I errors; True Negatives (TN): the number of samples correctly predicted as negative by the model; False Negatives (FN): the number of positive samples incorrectly predicted as negative by the model, also known as Type II errors.

[0043] Based on the confusion matrix, the following performance indicators are calculated:

[0044]

[0045] As a further preferred embodiment of the enhanced intelligent multimodal data exchange water meter and intelligent diagnostic system of the present invention, the workflow of the predictive maintenance module includes the following key steps:

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

[0047] Step B2, data preprocessing: cleaning and standardizing the data, and handling missing values;

[0048] Step B3, feature engineering: extracting features from the raw data that are helpful for prediction;

[0049] Step B4, model selection: Select an appropriate forecasting model, including time series analysis, machine learning, or deep learning models;

[0050] Time series analysis: used to analyze the trend and seasonality of 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] Among them, X t is the observation value at time t; c is the constant term, the bias of the model; φ1…φ p is the parameter of the autoregressive (AR) part, which represents the relationship between the current value and the previous value; θ1…θ p is the parameter of the difference part, used to make the data stable; ε t is the error term, which is usually assumed to be white noise;

[0053] Machine learning theory: involves classification, regression and other 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 is the coefficient, representing the independent variable x1…x n Impact on y; x1…x n is the independent variable; ε is the error term, the residual of the model prediction;

[0054] Deep learning theory: using neural networks for complex pattern recognition; recurrent neural network RNN ​​unit: Among them, 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 input to the hidden layer; x t is the input at time t; b h is the bias term of the hidden layer;

[0055] Long Short-Term Memory Network LSTM Unit:i 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] Among them, 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 Represents the weight matrices of input, hidden layer and unit state respectively; b i ,b j ,b c Represent the bias terms of the input gate, forget gate, and unit state respectively; σ is the Sigmoid function, which is used to generate values ​​between 0 and 1 in the gating mechanism; * represents element-by-element 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: formulate maintenance plans and strategies based on the prediction results;

[0060] Step B8, reminder mechanism: When the prediction result shows that maintenance is about to be required, the user or service provider is automatically reminded.

[0061] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system of the present invention, the workflow 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: storing the collected data in a database for further analysis;

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

[0065] Energy efficiency analysis: evaluates the energy efficiency of a system or equipment; energy efficiency formula: Used to analyze the distribution, trends, and outliers of water data; mean formula: Among them, μ is the mean, that is, the average value of the data set; C i is the i-th observation in the dataset; n is the total number of observations in the dataset; machine learning is used to identify water use patterns and predict energy consumption; clustering algorithms are used to classify user water use patterns:

[0066]

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

[0068] 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 the data point x to the cluster center Centroid;

[0069] Step C4, generating energy-saving suggestions: Based on the analysis results, generating energy-saving suggestions, including adjusting water use time and reducing leakage;

[0070] Step C5, water usage mode optimization: providing users with customized water usage modes to reduce energy consumption;

[0071] Step C6, real-time monitoring and feedback: real-time monitoring of water usage and adjustment of energy-saving strategies based on user feedback;

[0072] Step C7, report generation: Generate energy-saving reports regularly to demonstrate energy-saving effects and provide further improvement suggestions.

[0073] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system of the present invention, the workflow of the user customized service interface module is as follows:

[0074] Step D1, user demand analysis: collect and analyze user requirements for water meter functions and operation interface;

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

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

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

[0078] Step D4, reminder setting function: allows users to set water reminders, such as water leak alarms and water consumption exceeding the limit reminders;

[0079] Step D5, data interaction: ensuring that the parameters and reminders set by the user can seamlessly interact with the water meter's hardware and software systems;

[0080] Fitts's 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;

[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; ensure the security of data transmission through AES encryption; protect the privacy of user data by adding noise.

[0083] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system of the present invention, the workflow of the AI ​​interactive assistant module is as follows:

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

[0085] Speech recognition: Hidden Markov model HMM is used to model the statistical characteristics of speech recognition; formula: Among them, O is the observation sequence and S is the state sequence;

[0086] Step E2, natural language understanding: analyzing user intentions and needs;

[0087] 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 a verb and ARG is the corresponding argument;

[0088] Step E3, dialogue management: maintaining dialogue state and understanding context; dialogue management: dialogue state tracking DST is used to maintain the current state of the dialogue; formula: DST = {slot1: value1, slot2: value2, ...}; where slot represents the slot in the dialogue and value represents the value of the slot;

[0089] Step E4, decision making: Determine the response action based on the user's intention;

[0090] Step E5, response generation: generating an appropriate response, which can be text or voice;

[0091] Step E6, speech synthesis: converting the text response into speech output;

[0092] Speech synthesis: Deep learning models such as WaveNet are used to generate natural-sounding speech. The formula is: WaveNet = N(WaveNetParams, Input); where N is the network model, WaveNetParams is the parameters of the WaveNet model, and Input is the input data.

[0093] Step E7, user feedback learning: optimize the interactive experience through user feedback.

[0094] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnostic system of the present invention, the workflow of the adaptive metering module is as follows:

[0095] Step F1, data collection: real-time collection of water meter water usage data, including flow, pressure, and temperature;

[0096] Step F2, data preprocessing: cleaning, standardizing and denoising the collected data;

[0097] Step F3, environmental analysis: analyzing changes in the water use environment, including seasonal changes and changes in water use patterns;

[0098] Step F4, parameter adaptive adjustment: automatically adjust the metering parameters, including the sensitivity of the flow meter, based on the environmental analysis results;

[0099] Step F5, metering accuracy optimization: optimize the metering algorithm to improve metering accuracy and response speed;

[0100] Step F6, real-time monitoring and feedback: real-time monitoring of measurement accuracy and dynamic adjustment based on feedback;

[0101] Step F7, data storage and analysis: store the adjusted measurement data and conduct long-term trend analysis;

[0102] Among them, control theory: PID controller is used to adjust the metering parameters, formula: Where u(t) is the control input, e(t) is the error, and K p , K i , K d They are proportional, integral, and differential gains respectively;

[0103] Signal processing theory: Kalman filter is used to estimate the system state; formula: in, is the estimated state, y k is the observed value, K k is the Kalman gain;

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

[0105] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnostic system of the present invention, the workflow of the data security module is as follows:

[0106] Step G1, data encryption: encrypt the data using an advanced encryption algorithm before transmission and storage;

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

[0108] 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;

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

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

[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 the data through hash functions and digital signatures;

[0113] Hash function: SHA-256 is used to generate the hash value of 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 the noise extracted from the Laplace distribution;

[0114] Step G6. Security Audit: Record and monitor all data access and operations for security auditing; Access Control: Use the role-based access control (RBAC) model, formula: Access = User Role ∩ Resource Permission;

[0115] Among them, Access is the access right, 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 operation or access level allowed for a specific resource; N is the intersection operation of the set, which is used to determine the common part of the user role and resource permission;

[0116] Step G7. Anomaly Detection: Monitor data access patterns in real time to detect and respond to abnormal behavior.

[0117] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system of the present invention, the workflow of the environmental impact assessment module is as follows:

[0118] Step H1, data collection: collecting water usage data and environmental data, including water consumption, water usage time, and water quality parameters;

[0119] Step H2, definition of environmental impact indicators: 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 use on the environment;

[0121] Among them, water resource consumption assessment: water resource consumption index: water resource utilization coefficient Water pollution assessment: Water pollution index: Among them, 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 iand y i is a data point, and is the mean;

[0122] Step H4, improvement suggestion generation: based on the evaluation results, improvement suggestions are generated, including water conservation measures and pollution control;

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

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

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

[0126] As a further preferred solution of the enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system of the present invention, the workflow of the modular interface module is as follows:

[0127] Step I1, requirements analysis: determine the functional requirements and performance targets of the upgrade 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: developing 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: testing modular components and interfaces to ensure their stability and reliability;

[0133] Step I7, User Documentation Preparation: Prepare detailed user documentation to guide users in upgrades and maintenance;

[0134] Step I8, Marketing and Training: Promote the modular upgrade interface and train relevant personnel;

[0135] Step I9, feedback collection and optimization: Collect user feedback and continuously optimize modular design and interfaces.

[0136] As a further preferred solution 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 and determine the main sources of energy consumption; the details are as follows:

[0138] Energy efficiency ratio formula: Energy efficiency ratio Sleep mode design: Design low-power sleep mode for components that do not work frequently; the details are as follows: Sleep mode energy consumption formula: P sleep =P active DutyCycle; where P sleep is the energy consumption in sleep mode, P active DutyCycle is the energy consumption in active mode, and Dynamic Power Management (DVFS) is the ratio of active time to active time. Dynamic power management: Dynamically adjusts voltage and frequency according to the working state to reduce energy consumption. 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; The details are as follows: Communication energy consumption formula: P comm =P tx ·t tx +P rx ·t rx Among them, P comm is the communication energy consumption, P tx is the transmit power, t tx is the sending time, P rx is the received power, t rx Is the receiving time; sensor power saving design: the sensor is designed to save power and reduce standby power consumption; the details are 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 the working state; P2 is the energy consumption of the sensor in the standby state; Battery capacity optimization: re-evaluate the battery capacity requirement based on the energy consumption reduction; the details are as follows: Battery capacity requirement formula: Among them, C new is the new battery capacity, C old is the old battery capacity, E old is the old energy consumption, E new It is the new energy consumption;

[0139] Software optimization: Optimize software algorithms to reduce computing and processing energy consumption; 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 present invention adopts the above technical solution and has the following technical effects:

[0141] The present invention provides an enhanced intelligent multimodal data exchange water meter and intelligent diagnostic system. Through the integrated intelligent diagnostic system, the water meter can monitor the operating status in real time, and promptly detect and warn of potential faults and performance problems; using predictive maintenance algorithms, the system can predict maintenance needs based on historical and real-time data, reduce unexpected downtime, and improve equipment reliability; the user-customized service interface allows users to set water meter parameters according to personal preferences, providing a more personalized user experience; the introduction of an AI interactive assistant makes user operation more convenient and intuitive through voice and text interaction; the environmental impact assessment tool helps users understand the impact of water use behavior on the environment and provides energy-saving suggestions The modular design and standardized interface simplify the upgrade and maintenance process, reducing long-term operating costs. The low-power design reduces the energy consumption of the water meter, extends the battery life, and achieves energy conservation and emission reduction. The design of the water meter takes into account various environmental factors and has good environmental adaptability, ensuring stable operation under different conditions. The system integration design ensures the coordination and consistency of the various components and technologies of the water meter, improving the overall performance and stability. Advanced data encryption and security authentication mechanisms ensure the security and privacy of user data. The integration of smart contracts and linkage operation functions provides advanced intelligent services such as automatic meter reading and water leak detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0142] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0143] Intelligent diagnosis 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, system integration module

[0144] Figure 1 is a schematic diagram of an implementation of the intelligent diagnostic module of the present invention;

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

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

[0147] Figure 4 It is a schematic diagram of the implementation of the user customized service interface module of the present invention;

[0148] Figure 5This is a schematic diagram of the implementation of the AI ​​interactive assistant module of the present invention;

[0149] Figure 6 Schematic diagram of the implementation of the adaptive metering module of the present invention;

[0150] Figure 7 It is a schematic diagram of the implementation of the data security module of the present invention;

[0151] Figure 8 It is a schematic diagram of the implementation of the environmental impact assessment module of the present invention;

[0152] Figure 9 Schematic diagram of the implementation of the modular interface module of the present invention;

[0153] Figure 10 Schematic diagram of the implementation of the low power consumption module of the present invention;

[0154] Figure 11 Schematic diagram of the implementation of the environmental adaptability module of the present invention;

[0155] Figure 12 It is a schematic diagram of the implementation of the system integration module of the present invention. DETAILED DESCRIPTION

[0156] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings:

[0157] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments. The purpose and effect of the present invention will become more clear. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0158] An enhanced intelligent multimodal data exchange water meter and intelligent diagnosis system, including an intelligent diagnosis module, a predictive maintenance module, an energy management module, a user-customized 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 to analyze the operating data of the water meter in real time through machine learning algorithms to identify and predict potential failures and performance degradation trends;

[0160] Predictive maintenance module, which is used to predict future maintenance needs of equipment by analyzing historical maintenance data and real-time operating status;

[0161] Energy management module, which optimizes water use patterns, reduces energy waste, and provides energy-saving recommendations by analyzing water use data;

[0162] User-customized service interface module, which provides a flexible and intuitive interface, allowing users to set water meter operating parameters and reminders according to their needs and preferences;

[0163] 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 water meters more intuitive and user-friendly;

[0164] Adaptive metering module, which analyzes water usage data in real time and automatically adjusts metering parameters to suit different water usage environments and needs;

[0165] Data security module, used to ensure the security of water meter data during transmission and storage;

[0166] Environmental impact assessment module, used to evaluate the potential impact of water use on the environment and provide improvement recommendations;

[0167] Modular interface modules to simplify water meter maintenance and upgrades by creating components that are easily upgraded and replaced;

[0168] Low-power module, used to extend the life of water meter batteries and reduce overall energy consumption by adopting advanced low-power technology and optimization algorithms;

[0169] Environmental adaptability module, used to ensure that the water meter maintains stability and reliability under different environmental conditions including temperature changes, humidity, pressure, and corrosive environment;

[0170] System integration modules are used to combine all individual components and technologies into a coordinated system, ensuring optimal overall performance.

[0171] Intelligent diagnostic system: Integrates machine learning algorithms to analyze water meter operating data in real time, identify potential failures and performance degradation trends, and achieve early warning and intelligent diagnosis.

[0172] The core of the intelligent diagnostic system lies in the use of advanced machine learning algorithms to analyze water meter operating data in real time to identify and predict potential failures and performance degradation trends.

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

[0174] Step A1, data collection: collecting operating parameters of the water meter, including but not limited to flow, pressure, temperature, and vibration;

[0175] Step A2, data preprocessing: cleaning, standardizing and feature extraction of the collected data to facilitate processing by the machine learning model;

[0176] Step A3, feature selection: select the most helpful features for diagnosis from the preprocessed data;

[0177] Step A4, model training: Use historical fault data and normal operation data to train a machine learning model, including support vector machine (SVM), random forest, or neural network;

[0178] 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;

[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 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) ) where 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 amplitude of each update; I(y∈R t(c) ) is the indicator function, if the sample y belongs to the region R of the node c at time t t(c) , then the value is 1, otherwise it is 0; Signal processing theory: involving Fourier transform, wavelet transform, etc. for analyzing the time-frequency characteristics of water meter operation data; Fourier transform formula: Where X(f) is the Fourier transform result of the signal x(t) at frequency f; r(t) is the original signal in the time domain; f is the frequency, usually in Hertz (Hz). j is the imaginary unit, satisfying j 2 =-1;

[0181] π is approximately equal to 3.14159; Statistical analysis theory: used to evaluate model performance and data distribution characteristics; confusion matrix is ​​used to evaluate classification model performance;

[0182] The confusion matrix is ​​a table used to describe the performance of a classification model. It contains the following four basic elements: True Positives (TP): the number of samples correctly predicted as positive by the model; False Positives (FP): the number of negative samples incorrectly predicted as positive by the model, also known as Type I errors; True Negatives (TN): the number of samples correctly predicted as negative by the model; and False Negatives (FN): the number of positive samples incorrectly predicted as negative by the model, also known as Type II errors.

[0183] Based on the confusion matrix, the following performance indicators are calculated:

[0184]

[0185] Technical implementation, such as Figure 1 shown.

[0186] Requirements analysis: Determine system functions and performance indicators; Data collection: Design a data collection system to collect water meter operation data; Data management: Establish a database to store and manage the collected data; Model development: Develop a machine learning model, perform feature selection and model training; System integration: Integrate the model into the water meter monitoring system to achieve real-time monitoring and diagnosis; Testing and optimization: Conduct system testing and optimize model and system performance based on feedback; Deployment and maintenance: Deploy the system to the production environment and regularly maintain and update the model.

[0187] Key content description: Requirements analysis: Clarify the goals and performance requirements of the intelligent diagnostic system, including the accuracy and response time of fault detection. Data collection: Design the sensor layout and data collection frequency to ensure the integrity and accuracy of the data. Data management: Use database technology to store and index large amounts of operating data for rapid query and analysis. Model development: Select appropriate machine learning algorithms, perform feature engineering and model training. System integration: Integrate the machine learning model into the existing water meter monitoring system for seamless docking. Testing and optimization: Test system performance through simulated faults and actual operating data, and continuously optimize model parameters. Deployment and maintenance: Deploy the system in a real environment, and regularly check and update the model to adapt to new data and failure modes.

[0188] Predictive maintenance algorithms: Leveraging collected historical maintenance data and real-time operating status, algorithms are developed to predict equipment maintenance needs and automatically alert users or service providers to perform maintenance. Predictive maintenance algorithms analyze historical maintenance data and real-time operating status to predict future equipment maintenance needs. The workflow of the predictive maintenance module includes the following key steps:

[0189] Step B1, data collection: collect equipment operating data and historical maintenance records;

[0190] Step B2, data preprocessing: cleaning and standardizing the data, and handling missing values;

[0191] Step B3, feature engineering: extracting features from the raw data that are helpful for prediction;

[0192] Step B4, model selection: Select an appropriate forecasting model, including time series analysis, machine learning, or deep learning models;

[0193] Time series analysis: used to analyze the trend and seasonality of time series data; Autoregressive Integrated 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 is the observation value at time t; c is a constant term, the bias of the model; φ1...φ p is the parameter of the autoregressive (AR) part, which represents the relationship between the current value and the previous value; θ1…θ p is the parameter of the difference part, used to make the data stable; ε t is the error term, usually assumed to be white noise; machine learning theory: involves classification, regression and other 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 is the coefficient, representing the independent variable x1…x n right y The influence of 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 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 input to the hidden layer; x t is the input at time t; b h is the bias term of the hidden layer; Long Short-Term Memory Network 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] Among them, 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 Represents the weight matrices of input, hidden layer and unit state respectively; b i ,b j ,b c Represent the bias terms of the input gate, forget gate, and unit state respectively; σ is the Sigmoid function, which is used to generate values ​​between 0 and 1 in the gating mechanism; * represents element-by-element 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: formulate maintenance plans and strategies based on the prediction results;

[0200] Step B8, reminder mechanism: When the prediction result shows that maintenance is about to be required, the user or service provider is automatically reminded.

[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 aid in prediction. Model development: Select an appropriate model, perform feature selection, and train the model. Predictive analysis: Apply the model for predictive analysis and formulate maintenance plans. Maintenance decision support: Provide maintenance decision support based on the 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] Description of key content: Data management: including data cleaning, standardization and missing value processing to ensure data quality. Model development: involves feature selection and model training, and selects appropriate algorithms for prediction. Predictive analysis: including status prediction and maintenance demand prediction, providing a basis for maintenance decisions. Maintenance decision support: formulate and optimize maintenance plans and strategies to improve maintenance efficiency. System integration: integrate the algorithm into the equipment management system to achieve automation and intelligence. Testing and evaluation: evaluate the prediction accuracy and maintenance effect to ensure the effectiveness of the algorithm. Continuous optimization: continuously optimize the algorithm based on feedback from actual applications. Energy management module: analyze water use data, provide energy-saving suggestions, optimize water use patterns, reduce energy waste, and support green energy conservation. The purpose of the energy management module is to optimize water use patterns, reduce energy waste, and provide energy-saving suggestions by analyzing water use data.

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

[0204] Step C1, data collection: real-time collection of water meter water usage data, including flow, time, or pressure;

[0205] Step C2, data storage: storing the collected data in a database for further analysis;

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

[0207] Energy efficiency analysis: evaluates the energy efficiency of a system or equipment; energy efficiency formula: Used to analyze the distribution, trends, and outliers of water data; mean formula:

[0208] Among them, μ is the mean, that is, the average value of the data set; C iis the i-th observation in the dataset; n is the total number of observations in the dataset; machine learning is used to identify water use patterns and predict energy consumption; clustering algorithms are used to classify user water use patterns: Where J is the objective function of the K-means algorithm, which represents the sum of the 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 kth cluster; Centroidp is the cluster center of the kth cluster; x-Centroidk|| represents the Euclidean distance from the data point x to the cluster center Centroid;

[0210] Step C4, generating energy-saving suggestions: Based on the analysis results, generating energy-saving suggestions, including adjusting water use time and reducing leakage;

[0211] Step C5, water usage mode optimization: providing users with customized water usage modes to reduce energy consumption;

[0212] Step C6, real-time monitoring and feedback: real-time monitoring of water usage and adjustment of energy-saving strategies based on user feedback;

[0213] Step C7, report generation: Generate energy-saving reports regularly to demonstrate energy-saving effects and provide further improvement suggestions.

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

[0215] Requirements analysis: Determine the module's objectives and user needs. Data acquisition system design: Design a system to collect the required water use data. Data storage solution: Select a suitable database system to store data. Data analysis model development: Develop an algorithm to analyze water use data and energy consumption. Energy-saving suggestion algorithm: Develop an algorithm to generate energy-saving suggestions. Water use pattern optimization strategy: Develop a strategy to optimize users' water use patterns. User interface design: Design a 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 module's functionality and performance 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. Reporting system: Develop a reporting system to regularly report energy-saving results to users.

[0216] Key content description: Data collection system design: Design a system to ensure accurate and comprehensive collection of water use data. Data analysis model development: Develop an efficient algorithm to analyze water use data and identify energy-saving opportunities. Energy-saving recommendation algorithm: Use data analysis results to generate personalized energy-saving recommendations. Water use pattern optimization strategy: Develop strategies to help users optimize water use patterns and reduce energy consumption. User interface design: Design an intuitive user interface so that users can easily obtain energy-saving information and monitoring data. System integration: Seamlessly integrate the energy management module into the existing system to ensure compatibility and stability. Testing and verification: Verify the performance and energy-saving effects of the module through rigorous testing. Continuous optimization: Continuously improve the module based on user feedback and ongoing data analysis results.

[0217] User-customized service interface: Develop a customizable user interface that allows users to set the water meter's operating parameters and reminders based on their needs and preferences.

[0218] The user-customized service interface is designed to provide a flexible and intuitive interface that allows users to set the water meter's operating parameters and reminders according to their needs and preferences.

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

[0220] Step D1, user demand analysis: collect and analyze user requirements for water meter functions and operation interface;

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

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

[0223] Step D3, parameter setting function: allows users to customize the working parameters of the water meter, including water consumption limit and water use time;

[0224] Step D4, reminder setting function: allows users to set water reminders, such as water leak alarms and water consumption exceeding the limit reminders;

[0225] Step D5, data interaction: ensuring that the parameters and reminders set by the user can seamlessly interact with the water meter's hardware and software systems;

[0226] Fitts's 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 functions;

[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 in the figure, demand analysis: determine the user's demand for customized services. Interface design: design the 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 the 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 and optimize the interface and functions. Deployment: deploy the customized service interface to the production environment. Maintenance and update: regularly maintain and update the interface to adapt to changes in user needs.

[0230] Key content description:

[0231] Demand analysis: Deeply understand user needs and ensure that the customized service interface meets user expectations. Interface design: Design an intuitive and easy-to-use user interface to enhance the user experience. Function development: Develop core functions, such as parameter settings and reminder settings, to ensure that users can easily customize how the water meter works. Data interaction: Ensure that user settings can seamlessly interact with the water meter system to achieve practical application of functions. Security measures: Implement data encryption and privacy protection measures to ensure the security of user data. Testing: Conduct comprehensive testing to ensure that all functions work properly and there are no security risks. User feedback: Collect user feedback and continuously optimize the interface and functions. Deployment and maintenance: Deploy the customized service interface to the production environment and perform regular maintenance and updates.

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

[0233] The AI ​​interactive assistant provides voice and text interaction functions by integrating natural language processing (NLP) technology, making the operation of the water meter more intuitive and user-friendly.

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

[0235] Step E1, speech recognition: convert the user's speech input into text; speech recognition: Hidden Markov model HMM is used to model the statistical characteristics of speech recognition; formula: Among them, O is the observation sequence and S is the state sequence;

[0236] Step E2, natural language understanding: parse the user's intentions and needs; natural language understanding: Semantic role labeling is used to identify actions and participants in the sentence; formula: SRL = {(v, ARG O ),(v,ARG1),…}, where v is a verb and ARG is the corresponding argument;

[0237] Step E3, dialogue management: maintaining dialogue state and understanding context; dialogue management: dialogue state tracking DST is used to maintain the current state of the dialogue; formula: DST = {slot1: value1, slot2: value2, ...}; where slot represents the slot in the dialogue and value represents the value of the slot;

[0238] Step E4, decision making: Determine the response action based on the user's intention;

[0239] Step E5, response generation: generating an appropriate response, which can be text or voice;

[0240] Step E6, speech synthesis: convert the text response into speech output; speech synthesis: a deep learning model such as WaveNet is 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, such as Figure 5 As shown,

[0243] Requirements analysis: Determine the functional requirements and user interaction scenarios of the AI ​​assistant. Technology selection: Select 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. Dialogue system development: Develop a dialogue management module to maintain dialogue status. System integration: Integrate the AI ​​assistant into the water meter system. User interface design: Design a user interaction interface that supports voice and text input. Testing and optimization: Conduct comprehensive testing to optimize the model and user experience. Deployment: Deploy the AI ​​assistant to the production environment. User feedback collection: Collect user feedback and continuously optimize the assistant's performance. Maintenance and update: Regularly maintain and update the AI ​​assistant to adapt to new needs.

[0244] Key content description:

[0245] Demand analysis: Deeply understand 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. Dialogue system development: Develop a dialogue system that can understand and respond to user instructions. System integration: Seamlessly integrate the AI ​​assistant into the water meter system to ensure its functional compatibility with the existing system. User interface design: Design an intuitive user interface that supports voice and text input. Testing and optimization: Conduct comprehensive testing to 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 algorithms: Develop algorithms that can automatically adjust metering accuracy based on real-time data to adapt to different water use environments and needs.

[0247] The core goal of the adaptive metering algorithm is to improve the metering accuracy of water meters by analyzing water usage data in real time 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: real-time collection of water meter water usage data, including flow, pressure, and temperature;

[0250] Step F2, data preprocessing: cleaning, standardizing and denoising the collected data;

[0251] Step F3, environmental analysis: analyzing changes in the water use environment, including seasonal changes and changes in water use patterns;

[0252] Step F4, parameter adaptive adjustment: automatically adjust the metering parameters, including the sensitivity of the flow meter, based on the environmental analysis results;

[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: real-time monitoring of measurement accuracy and dynamic adjustment based on feedback;

[0255] Step F7, data storage and analysis: store the adjusted measurement data and conduct long-term trend analysis;

[0256] Among them, control theory: PID controller is used to adjust the metering parameters, formula: Where u(t) is the control input, e(t) is the error, and K p , K i , Kd They are proportional, integral, and differential gains respectively; Signal processing theory: Kalman filter is used to estimate the system state; Formula: in, is the estimated state, y k is the observed value, K k is the Kalman gain; Machine learning theory: regression analysis is used to predict measurement parameters, formula: y = β0 + β1x1 + ... + β n x n ; Among them, y is the target variable, x1 is the feature variable, β n is the regression coefficient.

[0257] Technical implementation, such as Figure 6 As shown,

[0258] Demand analysis: Determine the algorithm's objectives and performance indicators; Data acquisition system design: Design a system to collect the required water use data; Data preprocessing: Develop data cleaning and standardization algorithms; Environmental analysis model development: Develop a model to analyze changes in the water use environment; Parameter adaptive adjustment algorithm: Develop an algorithm to automatically adjust metering parameters; Metering accuracy optimization algorithm: Develop an algorithm to optimize metering accuracy; Real-time monitoring system: Develop a system to monitor metering accuracy in real time; Data storage and analysis system: Design a system to store and analyze adjusted metering 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 update: Regularly maintain and update the algorithm.

[0259] Key Content Description: Requirements Analysis: Define the algorithm's objectives and performance indicators to ensure the algorithm meets user needs; Data Collection System Design: Design a system to ensure accurate and comprehensive collection of water use data; Data Preprocessing: Develop efficient data cleaning and standardization algorithms to improve data quality; Environmental Analysis Model Development: Develop a model to identify changes in the water use environment and provide a basis for parameter adjustment; Parameter Adaptive Adjustment Algorithm: Develop an algorithm to automatically adjust metering parameters based on environmental changes; Metering Accuracy Optimization Algorithm: Develop an algorithm to optimize metering accuracy and improve water meter accuracy; Real-Time Monitoring System: Develop a system to monitor metering accuracy in real time to ensure the algorithm's effectiveness; Data Storage and Analysis System: Design a system to store and analyze adjusted metering data to support long-term optimization. System Integration: Seamlessly integrate the algorithm into the water meter system to ensure its functional compatibility with the existing system. Testing and Validation: Conduct comprehensive testing to verify the algorithm's performance and accuracy. Maintenance and Updates: Regularly maintain and update the algorithm to adapt to new requirements and environmental changes.

[0260] Enhanced data security measures: Introducing more advanced data encryption standards and anonymization technologies to strengthen the security of data during transmission and storage.

[0261] The 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 technologies.

[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 ciphertext, K is key, and P is 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 technology 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 the integrity of the data through hash functions and digital signatures;

[0268] Hash function: SHA-256 is used to generate the hash value of 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 the noise extracted from the Laplace distribution;

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

[0270] Access control: Role-based access control (RBAC) model, formula: Access = User Role ∩ Resource Permission; where Access is the access right, 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 operations or access level allowed for a specific resource; N is the intersection operation of the set, used to determine the common part of the user role and resource permission.

[0271] Technical implementation, such as Figure 7 As shown,

[0272] Requirements analysis: Determine the needs and goals for data security. Encryption strategy design: Design a data encryption strategy and select an appropriate encryption algorithm. Key management system development: Develop a secure key management system. Data anonymization technology implementation: Implement data anonymization technology to protect user privacy. Access control strategy formulation: Develop a strict access control strategy. Data integrity verification mechanism: Implement a data integrity verification mechanism. Security audit system development: Develop a security audit system to record and monitor data access. Anomaly detection system development: Develop an anomaly 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 respond to new security threats.

[0273] Key content description:

[0274] Requirements analysis: Clarify the needs and goals of data security and ensure that security measures meet actual needs. Encryption strategy design: Design a data encryption strategy and select an appropriate encryption algorithm to ensure the security of data 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 the leakage of sensitive information. Access control policy formulation: Develop strict access control policies to ensure that only authorized users can access sensitive data. Data integrity verification mechanism: Implement a data integrity verification mechanism to ensure that data has not been tampered with during transmission. Security audit system development: Develop a security audit system to record and monitor data access for post-audit and analysis. Anomaly detection system development: Develop an anomaly detection system to monitor data access patterns in real time and promptly detect and respond to abnormal behavior. System integration: Integrate security measures into the water meter system to ensure its compatibility with existing systems. Testing and evaluation: Test the effectiveness of security measures, evaluate their performance, and ensure that they meet security requirements.

[0275] Deployment and Monitoring: Deploy security measures to the production environment and conduct continuous monitoring to ensure their continued effectiveness. Maintenance and Updates: Regularly maintain and update security measures to respond to new security threats and maintain system security.

[0276] Environmental Impact Assessment Tool: An integrated tool that assesses the potential impact of water use on the environment and provides recommendations for improvements. The Environmental Impact Assessment Tool aims to assess the potential impact of water use on the environment and provide recommendations for improvements.

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

[0278] Step H1, data collection: collecting water usage data and environmental data, including water consumption, water usage time, and water quality parameters;

[0279] Step H2, definition of environmental impact indicators: 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 use on the environment;

[0281] Among them, water resource consumption assessment: water resource consumption index: water resource utilization coefficient Water pollution assessment: Water pollution index: Among them, 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 is a data point, and is the mean;

[0282] Step H4, improvement suggestion generation: based on the evaluation results, improvement suggestions are generated, including water conservation measures and pollution control;

[0283] Step H5, user interaction: displaying evaluation results and improvement suggestions through the user interface and interacting with users;

[0284] Step H6, data storage and analysis: store the evaluation data and conduct long-term trend analysis and forecasting;

[0285] Step H7, policy compliance check: Check whether water use behavior complies with relevant environmental policies and regulations.

[0286] Technical implementation, such as Figure 8 As shown,

[0287] Requirements Analysis: Determine the objectives and functional requirements of the assessment tool. Data Collection System Design: Design a system to collect the required water use and environmental data. Environmental Impact Indicator Definition: Define key indicators for assessing environmental impacts. Assessment Model Development: Develop a model to assess the environmental impact of water use. Improvement Suggestion Algorithm: Develop an algorithm to generate improvement suggestions. User Interface Design: Design a 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 Check Module: Develop a module to check the compliance of water use behavior. System Integration: Integrate the assessment tool into the water meter system. Testing and Validation: Test the performance and accuracy of the assessment tool. User Feedback Collection: Collect user feedback to improve the assessment tool. Maintenance and Update: Regularly maintain and update the assessment tool.

[0288] Key content description:

[0289] Requirements Analysis: Define the objectives and functional requirements of the assessment tool to ensure it meets environmental management and user needs. Data Collection System Design: Design a system to ensure accurate and comprehensive collection of water use and environmental data. Environmental Impact Indicator Definition: Define key indicators for assessing environmental impacts to provide a foundation for the assessment model. Assessment Model Development: Develop a model to assess the environmental impact of water use and ensure the scientific and accurate assessment results. Improvement Suggestion Algorithm: Develop an algorithm to generate improvement suggestions to help users reduce the environmental impact of water use. User Interface Design: Design an intuitive and easy-to-use user interface to display assessment results and improvement suggestions, enhancing the user experience. Data Storage and Analysis System: Design a system to store and analyze assessment data to support long-term trend analysis and forecasting. Policy Compliance Check Module: Develop a module to check the compliance of water use behavior and ensure users adhere to relevant environmental policies and regulations. System Integration: Seamlessly integrate the assessment tool into the water meter system to ensure its compatibility with existing systems. Testing and Validation: Conduct comprehensive testing to verify the performance and accuracy of the assessment tool and ensure 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 easy-to-upgrade modular components to simplify maintenance and upgrade processes and improve product adaptability and flexibility.

[0291] The modular upgrade interface is designed to simplify the maintenance and upgrade process of water meters by creating components that are easily upgraded and replaced.

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

[0293] Step I1, requirements analysis: determine the functional requirements and performance targets of the upgrade interface;

[0294] Step I2, modular design: Design modular components to ensure the 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: developing independent modular components, including sensors, communication modules, and data processing units;

[0297] Step I5, system integration: Integrate modular components into the water meter system to ensure overall performance;

[0298] Step I6, testing and verification: testing modular components and interfaces to ensure their stability and reliability;

[0299] Step I7, User Documentation Preparation: Prepare detailed user documentation to guide users in upgrades and maintenance;

[0300] Step I8, Marketing and Training: Promote the modular upgrade interface and train relevant personnel;

[0301] Step I9, feedback collection and optimization: Collect user feedback and continuously optimize modular design and interfaces.

[0302] The theories involved in modular upgrade interfaces include system engineering, interface design, reliability engineering, and user engineering. Modular design theory: Modular design improves the flexibility and maintainability of the system by breaking down the system into independent, replaceable modules. Modular design is a system design approach that breaks down a complex system into a series of modules with independent functions. Each module is responsible for a specific part of the 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, because modifying or replacing a single module usually does not affect other modules. Maintainability: When a problem occurs in the system, the problem module can be quickly located and fixed without having to redesign the entire system. Scalability: New modules can be added to existing systems to introduce new functions or enhance existing functions. Reusability: Well-designed modules can be reused in different projects, reducing development time and costs.

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

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

[0305] Interface design is an important part of modular design. It defines how modules communicate and interact with each other. Good interface design is the key to achieving modularity, ensuring the flexibility and maintainability of the system. The following 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: The interface should be concise and clear, containing only the operations and data necessary to implement the functionality.

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

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

[0311] API design: Application programming interface (API) is an interface for interaction between software systems. It 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 that interact through well-defined interfaces.

[0313] Microservices architecture: An architectural style that develops an application as a set of small services, each of which implements a specific business function and interacts through lightweight communication mechanisms (usually HTTP RESTful APIs).

[0314] Reliability Engineering:

[0315] Reliability formula: R(t)=e -λt ; where R(t) is the reliability of the system at time t and λ is the failure rate.

[0316] User Engineering: The User Experience Formula: Among them, UX is user experience, which measures the ratio of user satisfaction to user effort.

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

[0318] Requirements analysis: Determine the functional requirements and performance targets for the upgraded interface. Modular design: Design modular components to ensure the 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 upgrades and maintenance.

[0319] Marketing 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 description: Demand analysis: Deeply understand 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 and simplify upgrades and replacements.

[0321] Component development: Develop independent modular components, such as sensors, communication modules, data processing units, etc., to ensure the high performance and reliability of the 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 that they meet design requirements. User documentation: Prepare detailed user documentation to guide users in upgrades and maintenance, and improve user experience. Marketing and training: Promote modular upgrade interfaces, train relevant personnel, and improve market acceptance. Feedback collection and optimization: Collect user feedback, continuously optimize modular design and interfaces, and improve product adaptability and flexibility.

[0322] Energy efficiency analysis: Analyze the energy consumption of each component of the water meter and determine the main sources of energy consumption; the details are as follows: Energy efficiency ratio formula: Energy efficiency ratio Sleep mode design: Design low-power sleep mode for components that do not work frequently; the details are as follows: Sleep mode energy consumption formula: P sleep =P active DutyCycle; where P sleep is the energy consumption in sleep mode, P activeDutyCycle is the energy consumption in active mode, and Dynamic Power Management (DVFS) is the ratio of active time to active time. Dynamic power management: Dynamically adjusts voltage and frequency according to the working state to reduce energy consumption. 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; The details are as follows: Communication energy consumption formula: P comm =P tx ·t tx +P rx ·t rx Among them, P comm is the communication energy consumption, P tx is the transmit power, t tx is the sending time, P rx is the received power, t rx Is the receiving time; sensor power saving design: the sensor is designed to save power and reduce standby power consumption; the details are 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 the working state; P2 is the energy consumption of the sensor in the standby state; Battery capacity optimization: re-evaluate the battery capacity requirements based on the energy consumption reduction; the details are as follows:

[0323] Battery capacity requirement formula: Among them, C new is the new battery capacity, C old is the old battery capacity, E old is the old energy consumption, E new It is the new energy consumption;

[0324] Software optimization: Optimize software algorithms to reduce computing and processing energy consumption;

[0325] Hardware selection: Choose low-power hardware components such as microcontrollers and sensors;

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

[0327] Technical implementation, such as Figure 10 shown.

[0328] Demand analysis: Determine the goals and performance indicators of low-power design. Energy efficiency analysis: Analyze the energy consumption of each component of the water meter and determine the main sources of energy consumption. Sleep mode design: Design a low-power sleep mode for components that do not work frequently. Dynamic power management: Implement dynamic power management and adjust the voltage and frequency according to the working status. Communication optimization: Optimize the working cycle of the communication module and reduce the communication frequency. Sensor power saving design: Design the sensor to save power and reduce standby power consumption. Battery capacity optimization: Re-evaluate the battery capacity requirements based on the energy consumption reduction. Software optimization: Optimize the software algorithm to reduce the 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 the 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 perform ongoing maintenance.

[0329] Key Content Description: Requirements Analysis: Define the low-power design goals and performance indicators to ensure the design meets actual requirements. Energy Efficiency Analysis: Thoroughly analyze the energy consumption of each water meter component, identify the main sources of energy consumption, and provide a basis for optimization. Sleep Mode Design: Design low-power sleep modes for components that operate infrequently to reduce energy consumption when not in operation. Dynamic Power Management: Implement dynamic power management to adjust voltage and frequency based on operating conditions to reduce energy consumption. Communication Optimization: Optimize the communication module's duty cycle, reduce communication frequency, and lower communication energy consumption. Sensor Power Saving Design: Implement power-saving design for sensors to reduce standby power consumption and extend battery life. Battery Capacity Optimization: Based on energy consumption reduction, reassess battery capacity requirements and may reduce battery specifications to reduce cost and size. Software Optimization: Optimize software algorithms to reduce computing and processing energy consumption and improve overall system energy efficiency. Hardware Selection: Select low-power hardware components, such as microcontrollers and sensors, to improve overall system energy efficiency. System Monitoring: Develop a system monitoring module to monitor system energy consumption in real time and automatically adjust operating modes to ensure low-power operation. Testing and Verification: Test the effectiveness of the low-power design, verify its performance, and ensure that the design meets the expected goals. Deployment and Maintenance: Deploy the low-power design to the production environment and perform ongoing maintenance 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 in various environments.

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

[0332] Here's how the design works:

[0333] Environmental factor analysis: Identify and analyze the various environmental factors that the water meter may encounter. Material selection: Select suitable materials to resist the impact of environmental factors. Sealing design: Use sealing technology to protect the internal components of the water meter from the intrusion of moisture and contaminants. Temperature compensation: Design a temperature compensation mechanism to ensure that the measurement accuracy of the water meter is not affected at different temperatures. Pressure and water hammer protection: Design a structure that can withstand high water pressure and water hammer impact. Anti-interference design: Implement electromagnetic compatibility (EMC) measures to reduce the impact of external electromagnetic interference on the electronic components of the water meter. 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 and continuously optimize the design.

[0334] The theories involved in environmentally adaptable design include materials science, thermodynamics, fluid mechanics, and electromagnetism.

[0335] Material selection theory: Corrosion resistance evaluation of materials: Among them, material resistance refers to the ability of the material to resist corrosion, and environmental corrosiveness refers to the corrosive pressure caused by the environment on the material.

[0336] Seal Design Theory: Seal Performance Evaluation: Among them, the adaptability of sealing materials refers to the ability of sealing materials to adapt to environmental conditions, and the degree of 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 x temperature coefficient. Compensated reading refers to the measurement value after temperature effects are taken into account, original reading refers to the direct measurement value without temperature effects, and temperature coefficient is the scaling factor used to adjust for temperature effects.

[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: Design should ensure that equipment can operate normally in an electromagnetic environment without interfering with other equipment. This principle does not directly correspond to a specific mathematical formula, but rather a series of design guidelines and testing standards, such as the ISO11452 series.

[0340] Technical implementation, such as Figure 11 shown.

[0341] Demand 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: Select 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 protective 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 and continuously optimize the design. 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] Requirements Analysis: 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: Select suitable materials to withstand environmental factors, such as corrosion-resistant and pressure-resistant materials. Sealing Design: Design a sealing structure to protect internal components from moisture and contaminants. Temperature Compensation: Develop a temperature compensation algorithm to ensure the water meter's measurement accuracy at different temperatures. Pressure and Water Hammer Protection Design: Design a protective 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 water meter's electronic components. Testing and Verification: Test the water meter's performance under simulated environmental conditions to ensure its reliability in various environments. User Feedback: Collect user feedback from different environments to continuously optimize the design. Deployment and Monitoring: Deploy the design to the actual environment and monitor it to ensure its 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: focuses on integrating all individual components and technologies into a coordinated system to ensure optimal overall performance.

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

[0346] Requirements analysis: Clarify the overall functional and performance requirements of the system. Component selection: Select appropriate hardware and software components to ensure that they can work together. Interface standardization: Ensure that the interfaces between all components are standardized to facilitate integration and expansion. Modular design: Use modular design methods to improve the flexibility and maintainability of the system. Data flow design: Design data flow and control flow to ensure that data flows efficiently in the system. System-level testing: Conduct comprehensive system-level testing to verify the collaborative work and overall performance of each component. User interaction design: Design user interfaces 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] The theories involved in system integration design include system engineering, modular design, interface design, and user experience design. The following are some key theories and formulas:

[0348] Systems Engineering Theory: Systems engineering emphasizes the design and management of the system as a whole, taking into account the interactions between all components and subsystems.

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

[0350] Interface design theory: Interface design ensures that different modules or components can exchange information efficiently and accurately. 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] Technical implementation, such as Figure 12 shown.

[0353] Requirements 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 processes to ensure efficient data flow. System-level testing: Conduct comprehensive testing to verify the overall performance of the system. User interaction design: Design user interfaces and interaction methods to enhance 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 a production environment and monitor it. Optimization and upgrade: Optimize and upgrade the system based on feedback and monitoring results.

[0354] Key content description:

[0355] Requirements Analysis: Gain a thorough understanding of the system's overall functional and performance requirements to ensure the design meets these objectives. Component Selection: Select appropriate hardware and software components to ensure they work together and meet performance and compatibility requirements. Interface Standardization: Define standardized interfaces to ensure interoperability between components and system scalability. Modular Design: Employ a modular design approach to improve system flexibility and maintainability. Data Flow Design: Design data flows and control processes to ensure efficient data flow within the system and improve system responsiveness. System-Level Testing: Conduct comprehensive system-level testing to verify the interoperability and overall performance of each component, ensuring system stability and reliability. User Interaction Design: Design intuitive and easy-to-use user interfaces and interaction methods to enhance the 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 a system maintenance and upgrade strategy to ensure long-term stable operation. Deployment and Monitoring: Deploy the system to a production environment and monitor it to ensure normal operation. Optimization and Upgrade: Based on user feedback and monitoring results, continuously optimize and upgrade the system to improve system performance and user experience.

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

[0357] Background: A residential complex installed enhanced smart multimodal 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 within the complex, ensuring that each meter's MEMS sensor, communication module, and intelligent diagnostic system are correctly configured. Data Collection: The water meters begin collecting real-time data such as flow, pressure, and temperature.

[0360] Smart Diagnosis: The smart diagnostic system analyzes data using machine learning algorithms and detects an abnormal flow pattern on a resident's water meter. The system automatically flags and records the event. Fault Warning: The system identifies a possible incipient water leak and immediately sends an early warning notification to the property management office and residents through the community's smart management system. Maintenance Action: Upon receiving the notification, the property management office quickly dispatches a maintenance crew to inspect and resolve the issue before the leak causes serious damage. Results: Through the smart diagnostic system, the community has achieved early detection and timely maintenance of potential water leaks, reducing water waste, avoiding potential property losses, and improving resident satisfaction.

[0361] Example 2: Implementation of a user-customized service interface

[0362] Background: In order to provide more personalized water management services, a smart building company decided to install enhanced intelligent multimodal data exchange water meters in the commercial buildings it manages.

[0363] Implementation steps:

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

[0365] User interface customization: The building administrator sets water usage patterns for weekdays and weekends through a user-customized service interface, including water consumption limits and disabling during specific time periods. Intelligent interaction: Tenants in the building use the AI ​​interactive assistant to query current water consumption and adjust water use plans as needed. Energy-saving optimization: The system automatically optimizes water use patterns based on tenants' water use habits and set parameters, such as using water storage equipment to supply water during off-peak hours. Data feedback: At the end of the month, the system generates a detailed water use report that displays energy-saving effects and user water use behavior analysis for reference by tenants and administrators. Effect: Through the user-customized service interface, the building has successfully achieved personalized water management, improved water resource utilization efficiency, reduced energy consumption, and enhanced tenants' participation and satisfaction with smart water management.

[0366] This invention has developed an intelligent diagnostic system integrating machine learning algorithms to analyze water meter operating data in real time, identify potential failures and performance degradation trends, and provide early warning and intelligent diagnosis. Leveraging historical maintenance data and real-time operating status, an algorithm has been developed to predict device maintenance needs and automatically alert users or service providers to maintenance. A customizable user interface has been designed, allowing users to set water meter operating parameters and reminders based on their needs and preferences. An AI interactive assistant, integrated with natural language processing technology, provides voice and text interaction, enhancing the user experience. Tools have been integrated to assess the potential environmental impact of water use and provide improvement recommendations, supporting green energy conservation. Easily upgradeable modular components and standardized interfaces have been designed to simplify maintenance and upgrade processes, increasing product adaptability and flexibility. Low-power technologies and optimized algorithms have been adopted to extend battery life and reduce energy consumption. The stability and reliability of the water meter under varying environmental conditions have been considered to ensure its proper operation in a variety of environments. A focus has been placed on integrating all independent components and technologies into a coordinated system to ensure optimal overall performance.

[0367] Those skilled in the art will understand that the above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will still be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the invention shall be included within the scope of protection of the invention. All technical features in this embodiment may be freely combined according to actual needs.

[0368] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system, characterized by: It includes intelligent diagnosis 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 diagnosis module is used to analyze the operating data of the water meter in real time through machine learning algorithms to identify and predict potential failures and performance degradation trends; Predictive maintenance module, which is used to predict future maintenance needs of equipment by analyzing historical maintenance data and real-time operating status; Energy management module, which is used to optimize water use patterns, reduce energy waste, and provide energy-saving suggestions by analyzing water use data; User-customized service interface module, which provides a flexible and intuitive interface, allowing users to set the working parameters and reminders of the water meter according to their needs and preferences; AI interactive assistant module, which is used to provide voice and text interactive functions by integrating natural language processing (NLP) technology, making the operation of water meters more intuitive and user-friendly; Adaptive metering module, which analyzes water usage data in real time and automatically adjusts metering parameters to adapt to different water usage environments and needs; Data security module, used to ensure the security of water meter data during transmission and storage; Environmental impact assessment module, which is used to evaluate the potential impact of water use on the environment and provide improvement suggestions; Modular interface modules to simplify the maintenance and upgrade process of water meters by creating components that are easily upgraded and replaced; Low-power module, used to extend the life of water meter batteries and reduce overall energy consumption by adopting advanced low-power technology and optimization algorithms; Environmental adaptability module, used to ensure that the water meter maintains stability and reliability under different environmental conditions including temperature changes, humidity, pressure, and corrosive environment; System integration modules are used to combine all individual components and technologies into a coordinated system to ensure optimal overall performance.

2. The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The intelligent diagnosis module implements intelligent diagnosis through the following steps: Step A1, data collection: collecting operating parameters of the water meter, including but not limited to flow, pressure, temperature, and vibration; Step A2, data preprocessing: cleaning, standardizing and feature extraction of the collected data to facilitate processing by the machine learning model; Step A3, feature selection: select the most helpful features for diagnosis from the preprocessed data; Step A4, model training: Use historical fault data and normal operation data to train a machine learning model, including support vector machine SVM, random forest or neural network; 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; Step A6, fault prediction and early warning: predict potential faults and issue early warnings based on the model’s diagnostic results; 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) ) is the indicator function. If sample y belongs to region R of node c at time t, t(c ), the value is 1, otherwise it is 0; Signal processing theory: Involving Fourier transform, wavelet transform, etc. 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 measured in Hertz (Hz); j is an imaginary unit, satisfying j 2 = -1; π is the ratio of a circle to a circle, which is approximately 3.14159; Statistical analysis theory: used to evaluate model performance and data distribution characteristics; confusion matrix is ​​used to evaluate classification model performance; The confusion matrix is ​​a table used to describe the performance of a classification model; it contains the following four basic elements: True class TP: the number of samples correctly predicted by the model as positive class; False positive FP: The number of negative samples that the model mistakenly predicts as positive is the first type of error; True negative class TN: the number of samples correctly predicted by the model as negative; False negative FN: The number of positive samples that the model mistakenly predicts as negative is the second type of error; Based on the confusion matrix, the following performance indicators are calculated:

3. The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The workflow of the predictive maintenance module includes the following key steps: Step B1, data collection: collect equipment operation data and historical maintenance records; Step B2, data preprocessing: cleaning and standardizing the data, and handling missing values; Step B3, feature engineering: extracting features from raw data that are helpful for prediction; Step B4, model selection: select a suitable forecasting model, including time series analysis, machine learning or deep learning models; Time series analysis: used to analyze trends and seasonality of time series data; Autoregressive Integrated 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 is the observed value at time t; c is a constant term, the bias of the model; φ1…φ p is the parameter of the autoregressive (AR) part, which indicates the relationship between the current value and the previous value; θ1…θ p is the parameter of the difference part, which is used to make the data stable; ε t is the error term, which is usually assumed to be white noise; Machine learning theory: involving classification, regression and other algorithms; Linear regression model: y = β0 + β1x1 + ... β n x n +ε; Among them, 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 is the coefficient, representing the independent variable x1…x n Impact 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 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 input to the hidden layer; x t is the input at time t; b h is the bias term of the hidden layer; Long Short-Term Memory Network 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 =o t *tanh(C t ); Among them, 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 The weight matrices representing the input, hidden layer, and unit state, respectively; b i ,b j ,b c Represent the bias terms of input gate, forget gate and unit state respectively; σ is the Sigmoid function, which is used in the gating mechanism to generate values ​​between 0 and 1; * represents element-by-element multiplication; Step B5, model training: using historical data to train the prediction model; Step B6, predictive analysis: Apply the trained model to real-time data to predict equipment status and maintenance needs; Step B7, maintenance decision: formulate maintenance plans and strategies based on the prediction results; Step B8, reminder mechanism: when the prediction result shows that maintenance is about to be required, the user or service provider is automatically reminded.

4. The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The working process of the energy management module is as follows: Step C1, data collection: real-time collection of water meter water usage data, including flow, time, or pressure; Step C2, data storage: storing the collected data in a database for further analysis; Step C3, data analysis: using statistical and machine learning algorithms to analyze water use patterns and energy consumption data; Among them, energy efficiency analysis: evaluates the energy efficiency of a system or equipment; Energy efficiency formula: Used to analyze water data for distribution, trends, and outliers; Mean formula: Among them, μ is the mean, that 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 to identify water use patterns and predict energy consumption; Clustering algorithms are used to classify user water usage patterns: Among them, J is the objective function of the K-means algorithm, which represents the sum of the square distances of 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 kth cluster; Centroidp is the cluster center of the kth cluster; x-Centroidk|| represents the Euclidean distance from the data point x to the cluster center Centroid; Step C4, generating energy-saving suggestions: generating energy-saving suggestions based on the analysis results, including adjusting water use time and reducing leakage; Step C5, water use mode optimization: providing users with customized water use modes to reduce energy consumption; Step C6, real-time monitoring and feedback: real-time monitoring of water usage and adjustment of energy-saving strategies based on user feedback; Step C7, report generation: Generate energy-saving reports regularly to demonstrate energy-saving effects and further improvement suggestions.

5. The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The workflow of the user customized service interface module is as follows: Step D1, user demand analysis: collecting and analyzing user demands for water meter functions and operation interface; Step D2, interface design: design an easy-to-use and feature-rich user interface; Adopt Nielsen’s usability principles: learnability, efficiency, memorability, error prevention, user control, and satisfaction; Step D3, parameter setting function: allows the user to customize the working parameters of the water meter, including water consumption limit and water use time; Step D4, reminder setting function: allows the user to set water use reminders, such as water leak alarms and water consumption exceeding limit reminders; Step D5, data interaction: ensuring that the parameters and reminders set by the user can interact seamlessly with the hardware and software systems of the water meter; 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; 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 intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The workflow of the AI ​​interactive assistant module is as follows: Step E1, speech recognition: converting the user's speech input into text; Speech recognition: Hidden Markov Model (HMM) is used to model the statistical characteristics in speech recognition; formula: Among them, O is the observation sequence and S is the state sequence; Step E2, natural language understanding: analyzing user intentions 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 a verb and ARG is the corresponding argument; Step E3, dialogue management: maintaining dialogue status and understanding context; Dialogue management: Dialogue state tracking DST is used to maintain the current state of the dialogue; Formula: DST={slot1:value1,slot2:value2,...}; Among them, slot represents the slot in the conversation, and value represents the value of the slot; Step E4, decision making: determine the response action based on the user's intention; Step E5, response generation: generating an appropriate response, which may be text or voice; Step E6, speech synthesis: converting the text response into speech output; Speech synthesis: Deep learning models such as WaveNet are used to generate natural-sounding speech; Formula: WaveNet=N(WaveNetParams,Input); Where N represents the network model, WaveNetParams is 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 intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The workflow of the adaptive metering module is as follows: Step F1, data collection: real-time collection of water meter water consumption data, including flow, pressure, and temperature; Step F2, data preprocessing: cleaning, standardizing and denoising the collected data; Step F3, environmental analysis: analyzing changes in water use environment, including seasonal changes and changes in water use patterns; Step F4, parameter adaptive adjustment: automatically adjust the metering parameters, including the sensitivity of the flow meter, according to the environmental analysis results; Step F5, metering accuracy optimization: optimize metering algorithm to improve metering accuracy and response speed; Step F6, real-time monitoring and feedback: real-time monitoring of measurement accuracy, and dynamic adjustment based on feedback; Step F7, data storage and analysis: store the adjusted metering data and conduct long-term trend analysis; Among them, control theory: The PID controller is used to adjust the metering parameters, formula: Where u(t) is the control input, e(t) is the error, and K p , K i , K d They are proportional, integral, and differential gains respectively; Signal processing theory: Kalman filter is used to estimate the system state; formula: in, is the estimated state, y k is the observed value, K k is the Kalman gain; Machine Learning Theory: Regression analysis is used to predict measurement parameters, formula: y = β0 + β1x1 + ... + β n x n ; Among them, y is the target variable, x1 is the feature variable, β n is the regression coefficient.

8. The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The workflow of the data security module is as follows: Step G1, data encryption: encrypt the data using an advanced encryption algorithm before data transmission and storage; 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; Step G2, key management: securely generate, store and distribute encryption keys; Step G3. Data anonymization: Use anonymization technology to protect user privacy when storing and analyzing data; 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 through hash functions and digital signatures; Hash function: SHA-256 is used to generate the hash value of data, formula: H = SHA-256 (P); where H is the hash value and P is the plain text; 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 the noise extracted from the Laplace distribution; Step G6. Security Audit: Record and monitor all data access and operations for security auditing; Access control: Role-based access control RBAC model, formula: Access = User Role ∩ Resource Permission; Among them, Access is the access permission, indicating whether the user has the right to access a specific resource; UserRole is a user role, which represents the user's identity or role in the system; ResourcePermission is a resource permission, which represents the operation or access level allowed for a specific resource; N is the intersection operation of the set, which is 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: collecting water use data and environmental related data, including water consumption, water use 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 impact of water use behavior on the environment; Among them, water resource consumption assessment: Water consumption index: water resource utilization coefficient Water pollution assessment: Water pollution index: Among them, w i is the weight of the ith pollutant, p i is the concentration of the ith pollutant; Ecological Impact Assessment: Ecological Footprint Calculation: Ecological Footprint Statistical analysis: Correlation analysis: Among them, r is the correlation coefficient, x i and i is a data point, and is the mean; Step H4, generation of improvement suggestions: based on the evaluation results, generation of improvement suggestions, including water conservation measures and pollution control; Step H5, user interaction: display the evaluation results and improvement suggestions through the user interface and interact with the user; Step H6, data storage and analysis: store evaluation data and conduct long-term trend analysis and prediction; Step H7, policy compliance check: Check whether water use behavior complies with relevant environmental policies and regulations.

9. The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis 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: developing 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: testing modular components and interfaces to ensure their stability and reliability; Step I7, user documentation preparation: prepare detailed user documentation to guide users in upgrading and maintenance; Step I8, marketing and training: promote modular upgrade interfaces and train relevant personnel; Step I9, feedback collection and optimization: Collect user feedback and continuously optimize modular design and interfaces.

10. The enhanced intelligent multi-modal data exchange water meter and intelligent diagnosis system according to claim 1, characterized in that: The working principle of the low power consumption module is as follows: Energy efficiency analysis: Analyze the energy consumption of each component of the water meter and determine the main sources of energy consumption; the details are as follows: Energy efficiency ratio formula: Energy efficiency ratio Sleep mode design: Design low-power sleep modes for components that do not work frequently; details are as follows: Sleep mode energy consumption formula: P sleep =P active DutyCycle; where P sleep is the energy consumption in sleep mode, P active is the energy consumption in active mode, and DutyCycle is the proportion of active time; Dynamic power management: Dynamically adjust voltage and frequency according to the working status to reduce energy consumption; the details are as follows: Dynamic voltage frequency scaling 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; the details are as follows: Communication energy consumption formula: P comm =P tx ·t tx +P rx ·t rx ; Among them, P comm is the communication energy consumption, P tx is the transmit power, t tx is the sending time, P rx is the received power, t rx is the receiving time; Sensor power saving design: Design the sensor to save power and reduce standby power consumption; the details are as follows: Sensor energy consumption formula: P sensor =P1+P2 Among them, 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 mode; Battery capacity optimization: Re-evaluate battery capacity requirements based on energy consumption reduction; details are as follows: Battery capacity requirement formula: Among them, C new is the new battery capacity, C old is the old battery capacity, E old is the old energy consumption, E new It is the new energy consumption; Software optimization: optimize software algorithms to reduce computing and processing energy consumption; Hardware selection: Choose low-power hardware components such as microcontrollers and sensors; System monitoring: Real-time monitoring of system energy consumption and automatic adjustment of working mode.

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