Artificial intelligence water affair equipment and digital collaborative optimization method

Abstract

The invention discloses artificial intelligence water affair equipment and a digital collaborative optimization method, and relates to the technical field of intelligent water affair. The system adopts a multi-level architecture design and comprises an intelligent sensing layer, a data processing and analyzing layer and an application service layer, the intelligent sensing layer comprises a data acquisition module which integrates various sensors including a pressure sensor, a flow sensor and a water quality monitor and collects data in a water affair system in real time; and the data transmission module adopts a wireless communication technology to ensure that the data is stably and quickly transmitted to a data processing center. According to the invention, through a multi-level architecture design, the intelligent sensing layer, the data processing and analysis layer and the application service layer are included, so that comprehensive monitoring and management of the water affair system are realized; historical and real-time data are deeply mined, potential rules and trends are found, and the operation trend and potential risks of the water affair system are predicted. Therefore, a water affair management department is helped to formulate countermeasures in advance, and emergencies are reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent water technology, and in particular to an artificial intelligence water equipment and a digital collaborative optimization method. Background Art

[0002] Traditional water management methods often rely on manual inspections and regular maintenance, which is not only inefficient but also difficult to detect and deal with problems in a timely manner. At the same time, due to the lack of effective data support and decision-making tools, traditional water management is unable to cope with complex and changing environments; with the acceleration of urbanization and population growth, the complexity and management difficulty of water systems are increasing. Therefore, how to improve the management efficiency and service quality of water systems has become an urgent problem to be solved.

[0003] Although there are some smart water products and solutions on the market, these technologies and products often have problems such as single functions, poor compatibility, and high costs. For example, some systems can only achieve simple data collection and monitoring functions, but lack the ability to deeply analyze and mine data.

[0004] After searching, the application scheme of Chinese patent application number CN202310487807.1 discloses a water safety monitoring and management system and method based on the Internet of Things and cloud computing, including: a data acquisition module for collecting operating parameters and environmental data of water equipment; a data transmission module for sending the digital signals of operating parameters and environmental data collected by the data acquisition module to the cloud platform module; a cloud platform module for receiving the data sent by the data transmission module, and processing and storing it; a terminal application module for communicating with the cloud platform module to remotely monitor, manage and optimize water equipment. The water safety monitoring and management system in the above-mentioned documents has the following deficiencies: there are deficiencies in specific data processing, early warning mechanism, optimization scheduling, etc., and the data is processed and stored through the cloud platform module, but specific optimization strategies and operation instructions cannot be generated based on these data, and it cannot cope with emergencies well, and there is still room for improvement. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an artificial intelligence water equipment and a digital collaborative optimization method.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An artificial intelligence water equipment adopts a multi-level architecture design, including: intelligent perception layer, data processing and analysis layer, application service layer;

[0008] The intelligent perception layer includes:

[0009] Data acquisition module: Integrates multiple sensors, including pressure sensors, flow sensors, and water quality monitors, to collect data from the water system in real time;

[0010] Data transmission module: using wireless communication technology to ensure stable and fast data transmission to the data processing center;

[0011] Preprocessing unit: preliminarily cleans and screens the collected data to remove noise and outliers;

[0012] The data processing and analysis layer includes:

[0013] Data storage module: uses distributed database technology for storage and management;

[0014] Data analysis module: analyzes the data collected by the data acquisition module;

[0015] The application service layer includes:

[0016] Intelligent monitoring module: real-time monitoring of the operating status of the water system, including equipment operation, water quality changes, and water consumption;

[0017] Early warning and emergency response module: establish an early warning mechanism to automatically trigger an early warning and initiate an emergency plan when potential risks or anomalies are detected;

[0018] Optimization and scheduling module: Based on the results of real-time data analysis, dynamically adjust water resource allocation and scheduling strategies to achieve supply and demand balance and maximize energy efficiency.

[0019] Preferably, the data acquisition module includes a detection tube assembly, and the detection tube assembly includes:

[0020] A first main delivery pipe, a plurality of first auxiliary delivery pipes are arranged on one side of the first main delivery pipe, and a first control valve is arranged on the first auxiliary delivery pipe;

[0021] A second main delivery pipe, a plurality of second auxiliary delivery pipes are arranged on one side of the second main delivery pipe, and a second control valve is arranged on the second auxiliary delivery pipe;

[0022] The detection tube has two ends which are detachably installed between the first auxiliary delivery pipe and the second auxiliary delivery pipe through flanges, and the pressure sensor, flow sensor and water quality detector are installed in the detection tube.

[0023] Preferably, a display instrument is provided on the outer wall of one side of the detection tube, and the display instrument is connected to the pressure sensor and the flow sensor signal.

[0024] Preferably, the digital collaborative optimization method of artificial intelligence water equipment comprises the following steps:

[0025] Real-time monitoring and data collection: The sensors deployed in the water system monitor the operating status of the water system in real time and collect data;

[0026] Data analysis and prediction: Analyze and mine the collected data based on data processing technology and intelligent algorithms; predict the operation trend and potential risks of the water system through the analysis of historical data; timely discover abnormal situations and trigger early warning mechanisms through the analysis of real-time data;

[0027] Optimize decision-making and execution: Based on data analysis and prediction results, a collaborative optimization algorithm is used to globally optimize the operation of the entire water system, and the optimal decision-making plan is generated according to the current system status and future prediction results. The generated optimization instructions or suggestions are sent to the relevant actuators or equipment to perform corresponding operations, realizing real-time monitoring and dynamic adjustment of the water system.

[0028] Preferably, the data analysis and prediction is performed by an anomaly detection and fault diagnosis module, and the specific method is as follows:

[0029] S11: Obtain various types of real-time monitoring data from the intelligent perception layer and perform preprocessing, including denoising, missing value filling, and standardization operations;

[0030] S12: Analyze the processed data using statistical methods and machine learning algorithms to identify data points or patterns that deviate significantly from the normal range;

[0031] S13: Identify different types of faults based on classification algorithms or clustering algorithms;

[0032] S14: When an abnormal situation is detected, the system automatically triggers the early warning mechanism and sends an alarm message to relevant personnel.

[0033] Preferably, in S12, an isolation forest algorithm is used for anomaly detection, specifically:

[0034]

[0035] Where s(x, n) is the isolation score, E(h(x)) is the average depth of the tree, and c(n) is the expected depth given the number of nodes n;

[0036] Among them, E(h(x)) calculates the average path length, and c(n) is a function of the sample size, which represents the expected depth of the complete binary tree.

[0037] Preferably, in S13, identifying different types of faults based on a classification algorithm or a clustering algorithm specifically includes:

[0038] The tree is constructed by recursively selecting the best attribute split through the decision tree as follows:

[0039]

[0040] Among them, Gini(D) is the Gini impurity, p i is the probability of category i;

[0041] In S13, the data is divided into K clusters based on K-means, and each cluster is represented by a centroid, as follows:

[0042]

[0043] Where J is the objective function of K-means, r ik is an indicator variable, if the data point x i If it belongs to cluster k, it is 1, otherwise it is 0, μ k is the centroid of cluster k.

[0044] Preferably, the optimization decision and execution is performed through an intelligent prediction and decision support module, based on data analysis and prediction results, using machine learning algorithms and optimization algorithms to perform intelligent prediction and decision support on the operation of the water system, specifically in the following manner:

[0045] S21: Data collection: Obtain various types of real-time monitoring data from the intelligent perception layer, including water quality parameters, water quantity information, and equipment status;

[0046] S22: Feature extraction and processing: preprocessing the collected data, including denoising, missing value filling, and standardization operations;

[0047] S23: Model training: Use supervised learning algorithms or unsupervised learning algorithms to build a prediction model based on historical data; use cross-validation methods to evaluate model performance, and adjust and optimize based on the evaluation results;

[0048] S24: Forecasting and decision support: Input the newly collected data into the trained model to obtain the forecast results of water consumption and water quality change trends in the future.

[0049] Preferably, the optimization decision and execution generates an optimization strategy through an optimization scheduling module, and the specific method is as follows:

[0050] S31: Data collection and processing: Obtain various types of real-time monitoring data from the intelligent perception layer, including energy consumption data and water resource usage; pre-process the data, including denoising, missing value filling, and standardization operations;

[0051] S32: Energy consumption analysis and evaluation: Analyze and evaluate energy consumption data using statistical analysis methods and machine learning algorithms; find out the links and causes of high energy consumption by comparing energy consumption trends and equipment operating efficiency factors in different time periods;

[0052] S33: Resource optimization strategy generation: Based on the energy consumption analysis results, combined with the operation requirements and economic indicators of the water system, formulate a reasonable resource optimization strategy.

[0053] Preferably, in S32, linear discriminant analysis is used to reduce the dimension and classify the energy consumption data, specifically:

[0054] w=(W T W) -1 W T ywhere w is the discriminant vector, W is the design matrix containing all features, and y is the class label vector.

[0055] The beneficial effects of the present invention are:

[0056] 1. The present invention realizes comprehensive monitoring and management of water systems through a multi-level architecture design, including an intelligent perception layer, a data processing and analysis layer, and an application service layer; it conducts in-depth mining of historical and real-time data to discover potential laws and trends, and predict the operating trends and potential risks of water systems. This helps water management departments formulate response measures in advance and reduce the occurrence of emergencies.

[0057] 2. Based on the data analysis results, the present invention monitors the operating status of the water system in real time, and promptly detects and handles abnormal situations; the early warning and emergency response module can automatically trigger an early warning and initiate an emergency plan when potential risks or abnormalities are detected; the optimization and scheduling module dynamically adjusts the water resource allocation and scheduling strategy according to the current system status and future prediction results to achieve supply and demand balance and maximize energy efficiency.

[0058] 3. The present invention can better cope with abnormal situations, such as blockage, by setting up multiple passages, and the detection tube is detachable and easy to replace and repair. In addition, the detection situation can be intuitively observed through the display instrument, thereby improving reliability.

[0059] 4. In the present invention, when the flow sensor of a certain passage detects abnormal flow or the water quality monitor detects that the pollutants exceed the standard, the intelligent monitoring module can immediately close the first control valve or the second control valve of the passage to prevent contaminated water from entering the main delivery pipe. At the same time, the early warning and emergency response module will issue an early warning and initiate an emergency plan, such as notifying relevant personnel to conduct inspections and repairs. After the problem is solved, the control valve can be reopened according to the instructions of the optimization and scheduling module. This design allows the detection tube assembly to work closely with the digital collaborative optimization method, realizing more intelligent and automated management of the water system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0061] Figure 1 This is a structural schematic diagram of a detection tube assembly of an artificial intelligence water conservancy equipment proposed by the present invention;

[0062] Figure 2 A flowchart of abnormality detection and fault diagnosis in a digital collaborative optimization method for artificial intelligence water equipment proposed by the present invention;

[0063] Figure 3 A flowchart of intelligent prediction and decision support in a digital collaborative optimization method for artificial intelligence water equipment proposed by the present invention;

[0064] Figure 4 A flow chart for generating an optimization strategy in a digital collaborative optimization method for artificial intelligence water equipment proposed in the present invention.

[0065] In the figure:

[0066] 1-first main delivery pipe; 2-first auxiliary delivery pipe; 3-detection pipe; 4-flange; 5-second main delivery pipe; 6-second auxiliary delivery pipe; 7-second control valve; 8-display instrument; 9-first control valve. DETAILED DESCRIPTION

[0067] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0068] Embodiment 1:

[0069] An artificial intelligence water equipment adopts a multi-level architecture design, including: intelligent perception layer, data processing and analysis layer, application service layer;

[0070] The intelligent perception layer includes:

[0071] Data acquisition module: Integrates multiple sensors (such as pressure sensors, flow sensors, water quality monitors, etc.) to collect various data in the water system in real time;

[0072] Data transmission module: uses wireless communication technology (such as NB-IoT, LoRa, etc.) to ensure stable and fast data transmission to the data processing center;

[0073] Preprocessing unit: performs preliminary cleaning and screening of the collected data to remove noise and outliers in preparation for subsequent analysis;

[0074] The data processing and analysis layer includes:

[0075] Data storage module: uses distributed database technology for storage and management;

[0076] Data analysis module: analyzes the data collected by the data acquisition module;

[0077] The application service layer includes:

[0078] Intelligent monitoring module: real-time monitoring of the operating status of the water system, including equipment operation, water quality changes, water consumption, etc., to promptly detect and handle abnormal situations;

[0079] Early warning and emergency response module: establish an early warning mechanism to automatically trigger an early warning and initiate an emergency plan when potential risks or anomalies are detected;

[0080] Optimization and scheduling module: Based on the results of real-time data analysis, dynamically adjust water resource allocation and scheduling strategies to achieve supply and demand balance and maximize energy efficiency.

[0081] Wherein, the data acquisition module includes a detection tube assembly, and the detection tube assembly includes:

[0082] A first main delivery pipe 1, a plurality of first auxiliary delivery pipes 2 are arranged on one side of the first main delivery pipe 1, and a first control valve 9 is arranged on the first auxiliary delivery pipe 2;

[0083] A second main delivery pipe 5, a plurality of second auxiliary delivery pipes 6 are arranged on one side of the second main delivery pipe 5, and a second control valve 7 is arranged on the second auxiliary delivery pipe 6;

[0084] The detection tube 3 has two ends which are detachably installed between the first auxiliary delivery pipe 2 and the second auxiliary delivery pipe 6 through flanges 4 . The pressure sensor, the flow sensor and the water quality detector are installed in the detection tube 3 .

[0085] A display instrument 8 is disposed on the outer wall of one side of the detection tube 3, and the display instrument 8 is connected to the pressure sensor and the flow sensor signals.

[0086] By setting up multiple passages, it is possible to better cope with abnormal situations, such as blockage, and the detection tube 3 is detachable and easy to replace and repair. In addition, the detection situation can be intuitively observed through the display instrument 8, which improves reliability.

[0087] Embodiment 2:

[0088] A digital collaborative optimization method for artificial intelligence water equipment includes the following steps:

[0089] Real-time monitoring and data collection: Through the sensors deployed in the water system, the operating status of the water system is monitored in real time and various data are collected, such as water quality parameters, water quantity information, equipment status, etc.

[0090] Data analysis and prediction: In-depth analysis and mining of collected data based on data processing technology and intelligent algorithms; predicting the operation trend and potential risks of the water system through analysis of historical data; timely discovering abnormal situations and triggering early warning mechanisms through analysis of real-time data;

[0091] Optimize decision-making and execution: Based on data analysis and prediction results, a collaborative optimization algorithm is used to globally optimize the operation of the entire water system, and the optimal decision-making plan is generated according to the current system status and future prediction results. The generated optimization instructions or suggestions are sent to the relevant actuators or equipment to perform corresponding operations, realizing real-time monitoring and dynamic adjustment of the water system.

[0092] The data analysis and prediction is performed through anomaly detection and fault diagnosis modules, and the specific methods are as follows:

[0093] S11: Obtain various types of real-time monitoring data from the intelligent perception layer and perform preprocessing, including denoising, missing value filling, standardization and other operations;

[0094] S12: Analyze the processed data using statistical methods and machine learning algorithms to identify data points or patterns that deviate significantly from the normal range;

[0095] S13: Identify different types of faults based on classification algorithms (such as decision trees, random forests) or clustering algorithms (such as K-means);

[0096] S14: When an abnormal situation is detected, the system will automatically trigger the early warning mechanism and send an alarm message to relevant personnel. At the same time, according to the fault diagnosis results, corresponding solutions or suggestions are provided to help operation and maintenance personnel quickly locate the problem and take measures to repair it.

[0097] Among them, in S12, anomaly detection is performed using an isolation forest algorithm, specifically:

[0098]

[0099] Where s(x,n) is the isolation score, E(h(x)) is the average depth of the tree, and c(n) is the expected depth given the number of nodes n;

[0100] Among them, E(h(x)) calculates the average value of the path length, and c(n) is a function of the sample size, which represents the expected depth of the complete binary tree;

[0101] The isolation forest algorithm is used for anomaly detection and identifies outliers by calculating the degree of isolation between samples. It is specifically used for real-time monitoring of pressure, flow and water quality data in water systems. When abnormal data is detected, the system will automatically trigger an early warning mechanism and send an alarm message to relevant personnel.

[0102] In S13, different types of faults are identified based on a classification algorithm or a clustering algorithm, specifically including:

[0103] The tree is constructed by recursively selecting the best attribute split through the decision tree as follows:

[0104]

[0105] Among them, Gini(D) is the Gini impurity, p i is the probability of class i.

[0106] In S13, the data is divided into K clusters based on K-means, and each cluster is represented by a centroid, as follows:

[0107]

[0108] Where J is the objective function of K-means, r ik is an indicator variable, if the data point x i If it belongs to cluster k, it is 1, otherwise it is 0, μ k is the centroid of cluster k;

[0109] The K-means clustering algorithm is used to classify different types of faults. It identifies possible problem areas by clustering data such as equipment operating status and water quality changes. The algorithm recursively selects the best attribute split through a decision tree to build a tree model to achieve intelligent prediction and decision support for the water system.

[0110] The optimization decision and execution is carried out through the intelligent prediction and decision support module, based on data analysis and prediction results, using machine learning algorithms and optimization algorithms to make intelligent predictions and decision support for the operation of the water system. The specific methods are as follows:

[0111] S21: Data collection: Obtain various types of real-time monitoring data from the intelligent perception layer, including water quality parameters, water quantity information, equipment status, etc.;

[0112] S22: Feature extraction and processing: pre-process the collected data, including denoising, missing value filling, standardization and other operations to improve data quality;

[0113] S23: Model training: Use supervised learning algorithms (such as regression analysis, classification algorithms) or unsupervised learning algorithms (such as clustering algorithms) to build a prediction model based on historical data; use methods such as cross-validation to evaluate model performance, and adjust and optimize based on the evaluation results;

[0114] S24: Forecasting and decision support: Input the newly collected data into the trained model to obtain the forecast results of water consumption and water quality trends in the future; according to the forecast results, combined with actual conditions and experience and knowledge, formulate corresponding decision plans, such as adjusting water resource allocation strategies, launching emergency plans, etc.

[0115] The optimization decision and execution generate the optimization strategy through the optimization scheduling module, and the specific method is as follows:

[0116] S31: Data collection and processing: Obtain various types of real-time monitoring data from the intelligent perception layer, including energy consumption data (such as electricity consumption), water resource usage, etc.; pre-process the data, including denoising, missing value filling, standardization and other operations;

[0117] S32: Energy consumption analysis and evaluation: Analyze and evaluate energy consumption data using statistical analysis methods and machine learning algorithms; find out the links and causes of high energy consumption by comparing energy consumption trends in different time periods, equipment operating efficiency and other factors;

[0118] S33: Resource optimization strategy generation: Based on the energy consumption analysis results, combined with the operation requirements and economic indicators of the water system, formulate reasonable resource optimization strategies; for example, adjust the water pump operation frequency, optimize the water supply network layout and other measures to reduce energy consumption and improve the energy efficiency of the system.

[0119] Among them, in S32, linear discriminant analysis is used to reduce the dimension and classify the energy consumption data, specifically:

[0120] w=(W T W) -1 W T y

[0121] Where w is the discriminant vector, W is the design matrix containing all features, and y is the class label vector;

[0122] Linear discriminant analysis is used for dimensionality reduction and classification of energy consumption data. By analyzing factors such as energy consumption trends in different time periods and equipment operating efficiency, the links and causes of high energy consumption can be found. This helps the optimization scheduling module generate reasonable resource optimization strategies, such as adjusting the operating frequency of water pumps and optimizing the layout of water supply pipe networks.

[0123] When the flow sensor of a certain passage detects abnormal flow or the water quality monitor detects that the pollutants exceed the standard, the intelligent monitoring module can immediately close the first control valve or the second control valve of the passage to prevent contaminated water from entering the main delivery pipe. At the same time, the early warning and emergency response module will issue an early warning and initiate an emergency plan, such as notifying relevant personnel to conduct inspections and repairs. After the problem is solved, the control valve can be reopened according to the instructions of the optimization and scheduling module. This design allows the detection pipe assembly to work closely with the digital collaborative optimization method, realizing more intelligent and automated management of the water system.

[0124] Specifically, an identity recognition module is provided on the detection tube assembly, and the information recognition module is used in conjunction with a portable information storage module. After inspection and maintenance by relevant personnel, information transmission is realized based on the interaction between the portable information storage module and the information recognition module, and the optimization scheduling module controls the control valve to be reopened.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An artificial intelligence water conservancy equipment, characterized in that: Adopt multi-layer architecture design, including: intelligent perception layer, data processing and analysis layer, application service layer; The intelligent perception layer includes: Data acquisition module: Integrates multiple sensors, including pressure sensors, flow sensors, and water quality monitors, to collect data from the water system in real time; Data transmission module: using wireless communication technology to ensure stable and fast data transmission to the data processing center; Preprocessing unit: preliminarily cleans and screens the collected data to remove noise and outliers; The data processing and analysis layer includes: Data storage module: uses distributed database technology for storage and management; Data analysis module: analyzes the data collected by the data acquisition module; The application service layer includes: Intelligent monitoring module: real-time monitoring of the operating status of the water system, including equipment operation, water quality changes, and water consumption; Early warning and emergency response module: establish an early warning mechanism to automatically trigger an early warning and initiate an emergency plan when potential risks or anomalies are detected; Optimization and scheduling module: Based on the results of real-time data analysis, dynamically adjust water resource allocation and scheduling strategies to achieve supply and demand balance and maximize energy efficiency.

2. The artificial intelligence water conservancy equipment according to claim 1, characterized in that: The data acquisition module includes a detection tube assembly, and the detection tube assembly includes: A first main delivery pipe (1), a plurality of first auxiliary delivery pipes (2) being arranged on one side of the first main delivery pipe (1), and a first control valve (9) being arranged on the first auxiliary delivery pipe (2); A second main delivery pipe (5), a plurality of second auxiliary delivery pipes (6) are arranged on one side of the second main delivery pipe (5), and a second control valve (7) is arranged on the second auxiliary delivery pipe (6); A detection tube (3), both ends of which are detachably mounted between the first auxiliary delivery tube (2) and the second auxiliary delivery tube (6) via flanges (4); a pressure sensor, a flow sensor, and a water quality detector are mounted in the detection tube (3).

3. The artificial intelligence water conservancy equipment according to claim 2, characterized in that: A display instrument (8) is provided on the outer wall of one side of the detection tube (3), and the display instrument (8) is connected to the pressure sensor and the flow sensor signals.

4. A digital collaborative optimization method for artificial intelligence water equipment, characterized in that: The artificial intelligence water conservancy equipment according to any one of claims 1 to 3 is implemented, comprising the following steps: Real-time monitoring and data collection: The sensors deployed in the water system monitor the operating status of the water system in real time and collect data; Data analysis and prediction: Analyze and mine the collected data based on data processing technology and intelligent algorithms; predict the operation trend and potential risks of the water system through the analysis of historical data; timely discover abnormal situations and trigger early warning mechanisms through the analysis of real-time data; Optimize decision-making and execution: Based on data analysis and prediction results, a collaborative optimization algorithm is used to globally optimize the operation of the entire water system, and the optimal decision-making plan is generated according to the current system status and future prediction results. The generated optimization instructions or suggestions are sent to the relevant actuators or equipment to perform corresponding operations, realizing real-time monitoring and dynamic adjustment of the water system.

5. The digital collaborative optimization method of artificial intelligence water equipment according to claim 4 is characterized in that: The data analysis and prediction is performed through anomaly detection and fault diagnosis modules, and the specific methods are as follows: S11: Obtain various types of real-time monitoring data from the intelligent perception layer and perform preprocessing, including denoising, missing value filling, and standardization operations; S12: Analyze the processed data using statistical methods and machine learning algorithms to identify data points or patterns that deviate significantly from the normal range; S13: Identify different types of faults based on classification algorithms or clustering algorithms; S14: When an abnormal situation is detected, the system automatically triggers the early warning mechanism and sends an alarm message to relevant personnel.

6. The digital collaborative optimization method of artificial intelligence water equipment according to claim 5 is characterized in that: In S12, anomaly detection is performed using an isolation forest algorithm, specifically: Where s(x, n) is the isolation score, E(h(x)) is the average depth of the tree, and c(n) is the expected depth given the number of nodes n; Among them, E(h(x)) calculates the average path length, and c(n) is a function of the sample size, which represents the expected depth of the complete binary tree.

7. The digital collaborative optimization method of artificial intelligence water equipment according to claim 6 is characterized in that: In S13, different types of faults are identified based on a classification algorithm or a clustering algorithm, specifically including: The tree is constructed by recursively selecting the best attribute split through the decision tree as follows: Among them, Gini(D) is the Gini impurity, p i is the probability of category i; In S13, the data is divided into K clusters based on K-means, and each cluster is represented by a centroid, as follows: Where J is the objective function of K-means, r ik is an indicator variable, if the data point x i If it belongs to cluster k, it is 1, otherwise it is 0, μ k is the centroid of cluster k.

8. The digital collaborative optimization method of artificial intelligence water equipment according to claim 7 is characterized in that: The optimization decision and execution, through the intelligent prediction and decision support module, based on data analysis and prediction results, using machine learning algorithms and optimization algorithms, intelligently predict and support the operation of the water system. The specific methods are as follows: S21: Data collection: Obtain various types of real-time monitoring data from the intelligent perception layer, including water quality parameters, water quantity information, and equipment status; S22: Feature extraction and processing: preprocessing the collected data, including denoising, missing value filling, and standardization operations; S23: Model training: Use supervised learning algorithms or unsupervised learning algorithms to build a prediction model based on historical data; use cross-validation methods to evaluate model performance, and adjust and optimize based on the evaluation results; S24: Forecasting and decision support: Input the newly collected data into the trained model to obtain the forecast results of water consumption and water quality change trends in the future.

9. The digital collaborative optimization method of artificial intelligence water equipment according to claim 8 is characterized in that: The optimization decision and execution generate the optimization strategy through the optimization scheduling module, and the specific method is as follows: S31: Data collection and processing: Obtain various types of real-time monitoring data from the intelligent perception layer, including energy consumption data and water resource usage; pre-process the data, including denoising, missing value filling, and standardization operations; S32: Energy consumption analysis and evaluation: Analyze and evaluate energy consumption data using statistical analysis methods and machine learning algorithms; find out the links and causes of high energy consumption by comparing energy consumption trends and equipment operating efficiency factors in different time periods; S33: Resource optimization strategy generation: Based on the energy consumption analysis results, combined with the operation requirements and economic indicators of the water system, formulate a reasonable resource optimization strategy; In S32, linear discriminant analysis is used to reduce the dimension and classify the energy consumption data, specifically: w=(W T W) -1 W T y Where w is the discriminant vector, W is the design matrix containing all features, and y is the class label vector.

10. The digital collaborative optimization method of artificial intelligence water equipment according to claim 9 is characterized in that: When the flow sensor of a certain passage of the detection tube assembly detects abnormal flow or the water quality monitor detects that the pollutants exceed the standard, the intelligent monitoring module immediately closes the first control valve or the second control valve of the passage to prevent the contaminated water from entering the main delivery pipe. At the same time, the early warning and emergency response module will issue an early warning and start the emergency plan; An identity recognition module is provided on the detection tube assembly, and the information recognition module is used in conjunction with a portable information storage module. After inspection and maintenance by relevant personnel, information transmission is realized based on the interaction between the portable information storage module and the information recognition module, and the optimization scheduling module controls the control valve to be reopened.

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